ABSTRACT SYSTEMS ANALYSIS AND SIMULATION STUDY OF NIGERIAN FORESTRY SECTOR: WOOD CONSUMPTION COMPONENT BY Felix Izu Nweke The current high world market prices for petroleum products and high rate of drilling for petroleum in Nigeria have generated rates of growth of income higher than anyone could have predicted a few years ago. The governments of Nigeria are investing this income in the education, agriculture, transportation, health, etc. sectors. These changes, if sustained, will result in an increased pace of modernization which will have important consequences for wood consumption. The various Nigerian governments and other public bodies who own forest lands are currently investing heavily in forest plantations in their attempts to convert some of the natural forest reserves into forest plantations. It does not seem likely that forest land can be extended beyond the present forest reserves because of increasing demand for land from other sectors particularly agriculture, industrialization, urbanization, modern road construction, etc. Under such circumstances the supply of forest products can be increased only by intensive methods. An insight into the future markets for the products is needed for decisions about heavy investments in forestry industries which Felix Izu Nweke mature with long time lags. Such an insight involves descriptive know- ledge of the time paths of wood consumption in the presence of rapidly changing income and government actions. Some of the objectives of this study were to formulate a generalized simulation model of the wood con- sumption component of the Nigerian forestry sector which can always be updated and used to estimate the annual consumption of wood products; to track annual consumption of wood products for Nigeria in the past from 1965 to 1974 and to project the same into the future from 1975 to 1990; and, to help provide a basis for prescribing actions which would lead to the attainment of the forestry sector objectives of providing the needs of the country in timber and other objectives. This thesis will hopefully contribute both descriptive knowledge for public and private investment decisions in the forestry sector as well as analyti- cal tools that may subsequently be employed in prescriptive and predictive analysis. The model of the entire forestry sector is specified in a general form but not formulated in detail. The model of the wood con- sumption component is formulated in detail and used to make projections of annual wood consumption from 1965 to 1990. The various wood products consumed in Nigeria are aggregated into unprocessed wood, processed wood, building board woodpulp, paper pulp, and fuelwood. Wood using sub- sectors are also aggregated into residential housing construction, non-residential building construction, farm construction, casket manufacture, bridge and vehicle construction, paper consumption, and fuelwood consumption subsectors. The variables which determine the consumption of those wood products in these uses are identified as rural-urban location, income, and educational attainment of individuals; Felix Izu Nweke availability and relative prices of substitutes and complements for wood products in various uses; and public investments in education, agricul- ture, and other key sectors. The Nigerian population as well as some of the wood using subsectors are disaggregated into traditional, semi- traditional, and non-traditional groups and estimates of wood consumption made separately for each group to account for the differences in wood consumption due to rural-urban location, income, and educational attain- ment of individuals. Historical projections of annual consumption of various wood products are made for the period 1965-1974. These backward projections are based on the actual recorded values of GDP, government investments, etc. Three sets of projections based on three assumed alternative rates of growth of GDP are made for annual consumption of the various wood products in the future of the period 1975-1990. Two consequences, one immediate and the other lagged, of changes in income and government spending on wood consumption are apparent from the projections. Wood consumption in residential housing construction subsector is particu- larly sensitive to the lagged consequences. The model was validated in an iterative manner on the basis of the objective tests of clarity, coherence, logical consistency, and workability of the information and concepts employed in or gained from the study. The projections generated with the model were also tested for consistency with available projections from other studies as well as with recorded experience in Nigeria and in some other parts of the world. These tests are not final as the passage of time will reveal further inconsistencies and informa- tion that we are not aware of at present. A SYSTEMS ANALYSIS AND SIMULATION STUDY OF NIGERIAN FORESTRY SECTOR: WOOD_CONSUMPTION COMPONENT BY Felix Izu Nweke A DISSERTATION Submitted to Michigan State University in partial fulfillment of the requirement for the degree of DOCTOR OF PHILOSOPHY Department of Agricultural Economics 1975 DE D I CAT I ON To Warren H. Vincent and his family and to Anita Mackie: they provided hope when I had none and turned that hope into reality. ii ACKNOWLEDGMENTS Michigan State University financially supported my graduate work; in this connection, the chairman, Department of Agricultural Economics, and his graduate committee were very helpful in every way. Professor Glenn L. Johnson was my major professor and thesis supervisor. In addition to his direction of my studies his advice and assistance in my personal problems were very valuable. As members of my guidance and thesis committees Professors Chappelle, Lieldholm, Manetsch, Shapiro, and Vincent made important contributions to this study. Enid Maitland, Judy Pardee, Edith Nosow, Janet Munn especially, and other splendid secretaries in the Department of Agricultural Eco- nomics typed the various drafts of this thesis. VTheir friendliness made graduate work more exciting. The Director, Nigerian Federal Department of Forestry, Mr. A. M. Oseni, the Food and Agricultural Organization's Highforest Development (Nigeria) Project manager Mr. Thomas Dow and his project economist Mr. Ophan-Izel Baykal were very cooperative during my six months of field work in Nigeria. My wife, Sarah Obiageli, my son, Nnake Ikemefuna (keke), and my daughter, Arizeikwunnem Adaobi (Ada) loved and waited all through the hard times. I am grateful to all these individuals and organizations and to others whom I have not mentioned. iii TABLE OF CONTENTS Page LIST OF TABLES. . . . . . . . . . . . . . . . . ix LIST OF FIGURES . . . . . . . . . . . . . . . . xiii Chapter ' I. BACKGROUND, DESCRIPTION OF STUDY, AND RELATED METHODOLOGICAL ISSUES . . . . . . . . . . . 1 Background . . . . . . . . . . . . . . . 1 General Information . . . . . . . . . . . 1 National Economy . . . . . . . . . . . . 3 Forestry in the National Economy . . . . . . . 7 Description of Study. . . . . . . . . . . . 8 The Sc0pe of the Study . . . . . . . . . . 8 Some Existing Generalized Models of Forestry . . . 9 Some Available Wood Consumption Estimates . . . . 11 The Objectives of the Study. . . . . . . . . 13 The Approach of the Study . . . . . . . . . . l4 Multidisciplinary Nature of the Approach . . . . 15 Philosophic Position of the Approach. . . . . . 17 Application of the Approach. . . . . . . . . 20 Complexity of the Approach . . . . . . . . . 25 Organization of the Thesis. . . . . . . . . . 26 II. SPECIFICATION OF A SIMULATION MODEL FOR THE FORESTRY SECTOR . . . . . . . . . . . . . 28 Introduction . . . . . . . . . . . . . . 28 Background on the Forestry Sector . . . . . . . 29 What is Included As the Forestry Sector. . . . . 29 Forest Resources of Nigeria. . . . . . . . . 29 Ownership of Forestry Industries . . . . . ‘. . 32 iv Chapter Page How the Forestry Sector Interacts with Other Sectors. . . . . . . . . . . . . . . . 37 The Components of the Forestry Sector. . . . . . 37 Interactions Among the Components and Between the Sector and Other Sectors . . . . . . . . 42 Decision-Making in the Forestry Sector . . . . . . 45 Organization and Functions of the Public Departments of Forestry. . . . . . . . . . 46 Public Decisions. . . . . . . . . . . . 46 Some Examples of Public Decisions . . . . . . . 49 Private Decisions . . . . . . . . . . . . 50 Summary of Chapter. . . . . . . . . . . . . 52 III. A MORE DETAILED DESCRIPTION OF THE WOOD CONSUMPTION COMPONENT. . . . . . . . . . . . . . . . 53 Introduction. . . . . . . . . . . . . . . 53 Nigerian Wood Products . . . . . . . . 54 Unprocessed Wood. . . . . . . . . . . . . 54 Processed Wood . . . . . . . . . . . . . 55 Building Board Woodpulp . . . . . . . . . . 55 Paper Woodpulp . . . . . . . . . . . . . 56 Fuelwood . . . . . . . . . . . . . . . 56 Units of Measurement . . . . . . . . . . . 57 Factors Which Shape the Wood Consumption Habits of Individuals . . . . . . . . . . . . . . 57 Rural-Urban Location of Individuals . . . . . . 58 Individual's Income. . . . . . . . . . . . 58 Availability of Substitutes and Complements for Wood. . . . . . . . . . . . . . . 59 Educational Attainment of Individuals. . . . . . 60 How the Determinants of Wood Consumption Habits of Individuals are Incorporated in the Model. . . 61 Wood Using Subsectors. . . . . . . . . . . . 62 Residential Housing Construction Subsector . . . . 63 Non-Residential Building Construction Subsectors . . 63 Farm Construction Subsector . . . . . . . . 64 Casket Manufacture, Vehicle Body, and Bridge Construction Subsectors. . . . . . . . . . 64 Furniture and Utensils. . . . . . . . . . . 65 Fuelwood Consumption Subsector . . . . . . . . 66 Chapter Paper Consumption Subsector. . . . . . . Other Uses of Wood. . . . . . . . . . Government Actions Which Affect Wood Consumption Government Investments . . . . . . . . Other Public Actions . . . . . . . . . IV. THE MODEL OF THE WOOD CONSUMPTION COMPONENT. . . Introduction . . . . . . . . . . . . Conceptualization of the Model . . . . . . How the Model Is Structured. . . . . . . How the Model Operates Through Time . . . . The Mathematical Model . . . . . . . . . The Computer Model . . . . . . . . . . The Computer Program . . . . . . . . . CLASS Documents Followed. . . . . . . . V. THE INPUTS OF THE MODEL OF THE WOOD CONSUMPTION COMPONENT O . O I O O O O O O O O 0 Introduction . . . . . . . . . . . . The Data Which the Model Needs to Operate. . . The Data Used to Run the Model . . . . . . Constant Inputs. . . . . . . . . . . Estimation of Regression Parameters . . . . Sources of the Data . . . . . . . . . . The National Wood Consumption Survey. . . . Sources of Time Series Data. . . . . . . VI. SUMMARY OF BASE RUN ASSUMPTIONS, ESTIMATES, AND PROJECTIONS. . . . . . . . . . . . . Introduction . . . . . . . . . . . . Assumptions About Variables . . . . . . . GDP and Government Investments. . . . .' . Other Variables. . . . . . . . . . . Projections of Intermediate Variables . . . . vi Page 66 66 67 67 69 71 71 71 71 76 83 85 85 86 88 88 88 9O 90 102 112 112 118 122 122 123 123 124 125 Chapter ‘ Page Population. . . . . . . . . . . . . . . 125 Building Construction Rate . . . . . . . . . 126 Other Intermediate Variables. . . . . . . . . 128 Projections of Annual Wood Consumption . . . . . . 128 WOod for Construction and Manufacturing . . . . . 133 Fuelwood . . . . . . . . . . . . . . . 141 Wood Consumption Projections by Wood Consumption Population Groups. . . . . . . . 144 Wood for Paper . . . . . . . . . . . . . 151 Summary of Chapter. . . . . . . . . . . . . 153 VII. MODEL VALIDATION AND VERIFICATION . . . . . . . . 158 Introduction. . . . . . . . . . . . . . . 158 Tests of Clarity and Logical Consistency . . . . . 159 Tests of Workability . . . . . . . . . . . . 160 Sensitivity Tests on Model Parameters. . . . . . 160 Summary of Model Runs Under Alternative Assumptions About GDP and Government Investments. . . . . . . . . . . . . . 163 Further Tests of Objectivity . . . . . . . . . 174 Comparison of Model Estimates with Estimates by Other People . . . . . . . . . 175 Comparison of Model Estimates with Past Consumption in Nigeria . . . . . . . . 178 Summary of Validation and Verification Tests . . . . 180 VIII. CONCLUSIONS. . . . . . . . . . . . . . . . 182 Summary . . . . . . . . . . . . . . . . 182 The Structure of the Model . . . . . . . . . 182 Data Used in Operating the Model . . . . . . . 185 Projections from the Model . . . . . . . . . 186 Prescriptions . . . . . . . . . . . . . . 189 Unfinished Work. . . . . . . . . . . . . . 196 Need for a Generalized Model of the Entire . Forestry Sector . . . . . . . . . . . . 196 Other Areas for Further Research . . . . . . . 198 vii Chapter Page APPENDICES Appendix A. Mathematical Model of the Wood Consumption Component of Nigerian Forestry Sector. . . . . . . . . . 200 8. Computer Model of the WOod Consumption Component of Nigerian Forestry Sector. . . . . . . . . . 245 C. Base Run Projections of Intermediate Variables, 1965-1990. . . . . . . . . . . . . . . . 262 D. Base Run Projections of Annual Consumption of Different Wood Products in the Aggregate and by Uses, 1965-1990. . . . . . . . . . . . . . 266 E. Base Run Projections of Annual Consumption of Various Wood Products by Wood Consumption Population Groups, 1965-1990 . . . . . . . . . 272 F. Projections of Annual Consumption of Different Wood Products by Uses Under Alternative Rates of Growth of GDP, 1975-1990. . . . . . . . . . 277 BIBLIOGMPHY O O C O O O O O O O O O O O O O O O 288 viii LIST OF TABLES Table Page 1. The Average Proportions of Buildings Per Unit. . . . . 92 2. Average Amount of Unprocessed Wood Per Building by Type of Building. . . . . . . . . . . . . . 94 2A. Average Amount of Processed Wood Per Building by Type of Building. . . . . . . . . . . . . . 95 28. Average Amount of Building Board Woodpulp Per Building by Type of Building. . . . . . . . . . 96 3. Mean Delay Periods of Final Stages of Individual Aging Process and Decay of Buildings . . . . . . . 98 4. Number of Buildings Lost During Delay Processes as Proportions of Total Numbers of Buildings in the Delay Processes by Type of Building . . . . . . . 100 5. Average Amount of Processed Wood Per Traditional Residential Building by Year(s) of Construction . . . 104 5A. Average Amount of Processed Wood Per Semi- Traditional Residential Building by Year(s) of Construction . . . . . . . . . . . . . . 104 SB. Average Amount of Processed Wood Per Non- Traditional Residential Building by Year(s) of Construction . . . . . . . . . . . . . . 105 6. Parameters of Processed Wood Consumption in the Construction of Residential Buildings as Function of Prices . . . . . . . . . . . . . 105 7. Average Amount of Fuelwood Per Adult Per Year by' Local Market Area . . . . . . . . . . . . . 107 8. Parameters of Market Supplied Fuelwood Consumption as Function of Fuelwood Price . . . . . . . . . 108 9. Estimates of Other Parameters . . . . . . . . . . 110 ix Table Page 10. Locations of Households Included in the Rural Residential Wood Consumption Sample Survey. . . . . . 116 11. Annual Values (at Five Yearly Intervals) of GDP and Government Expenditures Assumed for the Base Run (N Million/Year), 1965-1990. . . . . . . . 124 12. Summary of Some of the Population Estimates and Projections, 1965-1990 . . . . . . . . . . . . 126 13. Summary, Base Run Projections of Overall Annual Consumption of Different Wood Products, 1965-1990. . . . . . . . . . . . . . . . . 129 14. Summary, Base Run Projections of Annual Wood Consumption in Residential Housing: Absolute Amounts and as PrOportions of Aggregate Con- sumption, 1965-1990 . . . . . . . . . . . . . 134 15. Summary, Base Run Projections of Annual Wood Consumption in Non-Residential Building: Absolute Amounts and as Proportions of Over- all Consumption, 1965-1990 . . . . . . . . . . . 137 16. Summary, Base Run Projections of Annual Con- sumption of Processed Wood in Caskets, Lumber Truck: Absolute Amounts and as Proportions of Overall Processed Wood Consumption, 1965—1990. . . . . 139 17. Summary, Base Run Projections of Annual Wood Consumption in the Farm Construction: Absolute Amounts and as Proportions of Over- all Consumption, 1965-1990 . . . . . . . . . . . 141 18. Summary, Base Run Projections of Annual Fuelwood Consumption by Market and Non-Market Sources: Absolute Amounts and as Proportions of Overall Fuelwood Consumption, 1965-1990 . . . . . . . . . 142 19. Summary, Base Run Projections of Annual Con- sumption of unprocessed Wood: Absolute Amounts and as Proportions of Overall Unprocessed Wood Consumption by Wood Consumption Groups, 1965-1990 . . . . . .‘ . . . . 145 20. Summary, Base Run Projections of Annual Con- sumption of Processed Wood: Absolute Amounts and as Proportions of Overall Processed Wood Consumption by WOod Consumption Groups, 1965-1990 . . . . . . . . . . 146 Table 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. Summary, Base Run Projections of Consumption of Building Board Woodpulp: Absolute Amounts and as Proportions of Overall Consumption by Wood Consumption Groups, 1965-1990. . . . . . . . Summary, Base Run Projections of Annual Consumption of Fuelwood: Absolute Amounts and as Proportions of Overall Consumption by Wood Consumption Groups, 1965-1990. . . . . . . . . . . . Summary, Base Run Projections of Annual Consumption of Paper Woodpulp: Absolute Amounts and as Proportions of Overall Consumption by Income Group, 1965-1990 . . . . . . . . . . . Summary, Results of Sensitivity Tests with Some Parameter Estimates . . . . . . . . . Values (N Million/year) of GDP and Government Investments Under Alternative Assumptions About Rates of Growth of GDP, 1975-1990 . . . . Summary, Projections of Annual Wood Requirements for Residential Construction Under Alternative Rates of Growth of GDP, 1975-1990 . . . . . Summary, Projections of Annual Wood Requirements for Residential Construction as Proportions of Overall WOod Requirements Under Alternative Rates of Growth of GDP, 1975-1990 . .p . . . . Summary, Projections of Annual Wood Requirements for Farm Construction Under Alternative Rates of Growth of GDP, 1975-1990 . . . . . . . . . Summary, Projections of Annual Wood Requirements for Farm Construction as Proportions of Overall Wood Requirements Under Alternative Rates of Growth of GDP. 1975-1990. . . . . . . . . . . . Summary, Projections of Annual Wood Requirements for Non-Residential Housing Construction Under Alter- native Rates of Growth of GDP, 1975-1990 . . . . Summary, Projections of Annual wood Requirements for Non-Residential Housing Construction as Proportions of Overall WOod Requirements Under Alternative Rates of Growth of GDP, 1975-1990 . . . . . . xi Page 147 153 154 162 164 165 165 167 167 168 168 Table _ Page 32. Summary, Projections of Annual Processed Wood Requirements for Casket, Lumber Trucks, and Bridges Under Alternative Rates of Growth of GDP. 1975-1990. . . . . . . . . . . . . . 169 33. Summary, Projections of Annual Processed Wood Requirements for Casket, Lumber Trucks, and Bridge Construction as Proportions of Overall Requirements of Processed Wood Under Alter- native Rates of Growth of GDP, 1975-1990 . . . . . 170 34. Summary, Projections of Annual Consumption of Fuelwood From Market and Non-Market Sources: Absolute Amounts and as Proportions of Overall Consumption Under Alternative Rates of Growth of GDP. 1975-1990. . . . . . . . . . . . . 171 35. Summary. Projections of Annual Consumption of Paper Woodpulp Under Alternative Rates of Growth of GDP, 1975-1990 . . . . . . . . . . 172 36. Summary, Projections of Aggregate Annual Wood Requirements Under Alternative Rates of Growth of GDP, 1975-1990 . . . . . . . . . . 173 37. Comparison of Estimates of Consumption of Processed Wood by FAO with Model Estimates . . . . 176 38. Comparison of Estimates of Rates of Consumption of Wood by Enabor with Model Estimates. . . . . . 176 39. Population, Lumber Consumption, and National Income Trends in Japan, 1953-1963 . . . . . . . 177 40. Comparison of Model Estimates of Annual Consumption of Pulp Products with Consumption Data Derived from Statistics of Import of Pulp Products . . . . . . . . . . . . . . 179 xii a t 44 Figure l. 10. 11. 12. LIST OF FIGURES Application of Generalized Systems Analysis and Simulation Approach to Wood Consumption Model. Sources of Unprocessed Wood . . . . . . . Interaction Among the Components and Between the Forestry Sector and Other Sectors. . . . . Decision-Making in the Forestry Sector. . . . General Structure of Building Construction Sub— Models. . . . . . . . . . . . . . Time Paths of Various Population Groups as Functions of Income, (PrOportion/year) . . . Time Paths of the Rates of Deterioration of Different Groups of Buildings . . . . . Base Run Projections of Annual Growth of Traditional (A), Semi-Traditional (B), and Non-Traditional (C) Population Groups, (People/year), 1965-1990. . . . . . . . Base Run Projections of Annual Consumption of Unprocessed Wood, 1965-1990. . . . . . . Base Run Projections of Annual Consumption of Processed Wood, 1965-1990 . . . . . . . Base Run Projections of Annual Consumption of Paper Woodpulp, 1965-1990 . . . . . . . Base Run Projections of Annual Consumption of Unprocessed Wood (A), Processed Wood (B), and Building Board Woodpulp (C) in Residential Housing Construction, 1965-1990 . . . . . xiii Page 21 39 43 47 73 79 82 127 130 131 132 135 Figure 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. Base Run Projections of Annual Consumption of Unprocessed Wood (A), Processed Wood (B), and Building Board Woodpulp (C) in the Non- Residential Building Construction Subsectors, 1965-1990. . . . . . . . . . . . . . Base Run Projections of Annual Consumption of Processed Wood in Casket Manufacture (A), Lumber Truck (B), and Bridge Construction (C). 1965-1990. . . . . . . . . . . . . . Base Run Projections of Annual Consumption of Fuelwood from Non—Market (A), and Market (B) Sources, 1965-1990. . . . . . . . . Base Run Projections of Annual Consumption of Unprocessed Wood in the Traditional (A) and Semi-Traditional (B) Sectors, 1965-1990. . Base Run Projections of Annual Consumption of Processed Wood in the Traditional (A). Semi- Traditional (B), Non-Traditional (C), and in the Non-Specific (D) Sectors, 1965-1990. . Base Run Projections of Annual Consumption of Building Board Woodpulp in the Semi- Traditional (A), Non-Traditional (B). and Non-Specific (C) Sectors, 1965-1990 . . . . Base Run Projections of Annual Consumption of Fuelwood by Traditional (A) and Semi- Traditional (B) Population Groups, 1965-1990 . . Base Run Projections of Annual Consumption of Paper Woodpulp by Semi-Traditional (B), Non- Traditional (C), and Student (D) Population Groups, 1965-1990 . . . . . . . . . . . Flow Diagram of Population Subcomponent . . . . Flow Diagram of Residential Housing Construction Subcomponent. . . . . . . . . . . . . Flow Diagram of Elementary School Building Construction Subcomponent . . . . . . . . Flow Diagram of Commercial, Religious, and Public Administration Building Construction Subcomponents . . . . . . . . . . . . xiv Page 138 140 143 148 149 150 152 155 202 211 214 215 Figure Page 25. Flow Diagram of High School and College Building Construction Subcomponents. . . . . . . . . . 216 26. Flow Diagram of Hospital Construction Subcomponent . . 217 27. Flow Diagram of Farm Construction Subcomponent. . . . 228 28. Flow Diagram of Fuelwood Consumption Subcomponent. . . 229 29. Flow Diagram of Casket Manufacture and Lumber Truck Construction Subcomponent . . . . . . . . . . 234 30. Flow Diagram of Bridge Construction Subcomponent . . . 235 31. Flow Diagram of Paper Consumption Subcomponent. . . . 240 CHAPTER I BACKGROUND, DESCRIPTION OF STUDY, AND RELATED METHODOLOGICAL ISSUES Background We shall start by presenting some background information which will enhance the understanding by readers who may not be familiar with Nigeria of the model to be developed in this dissertation. This infor— mation includes size (land area), geographical location, vegetation, political divisions, population, and the culture of Nigeria. We shall also describe the national economy and the role of forestry in it. All these are important in studying wood consumption in Nigeria although not all of them are considered explicitly in this study. General Information 1. Size and Location: Nigeria has an area of 923,774 square kilometers which is unevenly divided into north, southwest, and south- east by the rivers Niger and Benue. The greatest length of Nigeria from east to west is about 1,148 kilometers, and from north to south about 1,066 kilometers (47). Bounded on the south by the Gulf of Guinea Nigeria extends to the subSahalien border on the north. It is bounded on the west and north by the Republic of Dahomey and Niger, and on the east by the 3‘... ~- ‘— Republic of Cameroun. Entirely within the tropical zone, Nigeria lies between parallels 4° and 14° north and longitudes 3° and 15° east (47). 2. vegetation: The vegetation of Nigeria can be divided into four easily recognizable belts. The southern most part consists of swamp forests which in some areas stretch up to 100 kilometers into the interior. The characteristic vegetation is mangrove trees which furnish mostly roundwood for local building construction. The rain forest lies to the north of the swamp forests, forming a belt of 150 kilometers. The rain forest provides the most valuable timber species which include mahogany (Khaya ivorensis), sapele-wood (Entandrophragina cylindricum), iroko (Chlorophora excelsa). African walnut (Lovea klaineana). guarea (Guarea thompsonii), opepe (Sarco- cephalus diderrichii), agba (Gossweilerodendron balsamiferum), obeche (Triplochiton scheroxylon), etc. North of the rain forest is the savannah which forms a belt of some 500 kilometers. The savannah is a region of wide grasslands dotted with trees. North of the savannah is the sub-Sahel zone. The vegetation of the sub-Sahel consists of dwarfed bushes (47). 3. Nigeria has a military government and is divided into twelve states. The twelve states vary from 3,577 to 272,015 square kilometers in land area, and from 1.443 million to 9.428 million in population (1963 census). Each state has an internal self-government which has powers of internal self-government. These powers are deter- mined by the Military Council which appoints the governors for each state. 4. People: In 1963 a population census showed that Nigeria had about 56 million people. In 1973 another census indicated that the population has risen to 79 million. Many people including some Nigerians believe that the 1973 census involves statistical and other errors which resulted in the inflation of the figures. Up to this time the 1973 census is controversial in Nigeria and is unacceptable to some Nigerian state governments. The Nigerian population is made of nearly 248 language groups (5). The populations of these groups vary considerably ranging from a few hundred to several million. The three major groups are Hausa, Yoruba, and Ibo accounting for 11.65, 11.32, and 9.50 percent respec- tively of the total population in 1963 (47). The Hausas are based mainly in the north, the Yorubas in the southwest and the Ibos in the southeast. In the absence of a single common language English has been adopted as the official and commercial language. The three major religious groups are Muslem, Christian, and animism accounting for 47.2, 34.5, and 18.3 percent respectively of total population in 1963 (47). Muslems are based mainly in the north among Hausas and other language groups. The number of Muslems among the Ibos and other language groups in the southeastern region is negligible, the religious adherence being shared approximately equally between Christianity and animism. Among the Yorubas and other language groups in the southwestern region, Muslem has an edge over Christianity and animism.(38). National Economy Nigeria is predominantly a primary producer of agricultural and mineral products. About 80 percent of total working males are engaged in crop production, wood production, animal husbandry, and fishing. These activities contributed some 49 percent of national income in 1969-70 (26). Food production is based on grains, pulses, sugar cane, and cattle in the northern region, and root crops (yams and cassava) and fruits in the southwestern and southeastern regions. Each of the three regions derives its wealth from one of Nigeria's agriculture export crops. These are palm oil and kernel in the southeast. cocoa in the southwest, and peanuts in the north. The country is a major world source for these crops accounting for as much as 50 percent of the world supply of palm kernels, 30 percent of palm (Hi, and 13 percent of cocoa (47). The crops are grown in small holdings by Nigerian farmers and sold through semi-governmental mar- keting boards. Mining plays an important role in Nigeria's economy. Among the minerals are tin, columbite. limestone. and petroleum. Nigeria ranks Sixth (43) in tin and has a virtual world monopoly in columbite. Uranium.bearing rocks, extensive areas of iron-ore deposits and lignite have been located. Nigeria ranks eighth in petroleum production. The relative importance of petroleum rose sharply in 1973 because of high World prices of petroleum products and increased drilling in Nigeria. In 1973 the export of crude petroleum accounted for about 90 percent of the total foreign exchange earning (6). Since then this relative impor- tance of petroleum has risen steadily. Although the Nigerian economy remains predominantly agricultural, there has been progress in industrial development. Traditional crafts have always played an important part in the life of the people with mDdern factory type industry a recent development. In the early 19503 the contribution of all manufacturing including handicrafts to total output was less than 3 percent; by 1970 this had risen to more than 8 percent (26). Some of the main industries Operating in Nigeria today are cement, cigarette, soap, oil refining, tyre, leather, shoes, sugar, textiles, aluminum products, glass, plastics, sawmilling (including plywood and veneer), canning and food processing. Major developments in industrialization are expected soon when the planned additional oil refineries (two), pulp mills (four) using Nigerian wood, an integrated iron and steel complex using Nigerian ore, limestone. coal and electric power, and three car assembly plants are set up. A wide range of indigenous raw materials is available. With a population of nearly 80 million, Nigeria offers potentially the largest single market in Africa. The Nigerian economy exemplifies a dualistic system in which poor masses coexist with fairly well-to-do few in the same economy. In a paper published in 1972, Aboyade (1) observed that 90 percent of Nigerian families earn less than N300 (N126USl.60) per family per year and 0.8 percent of families earn more than N1,750 per family per year. Educational and agricultural Opportunities are equally unevenly distri— buted. By 1971 approximately 27 percent of children of primary school age were in school (7). At the secondary level approximately 3.4 per- cent of school age youth were in school (7). In agriculture which contributed about 50 percent of gross domestic product and employed about 70 percent of the labor force in 1970, 83 percent of farming households farmed two hectares of food crop or less, or one hectare or less of tree crops per family (35). The rate of rural to urban migration has grown from 1.1 percent in 1961 to 7.5 percent in 1971. It was observed to be positively correlated with rural-urban income differential and varied from 85.5 percent among people between the ages of 11 and 20 to 0.0 percent among peOple older than 40 (33). None of the families studied reported a returning migrant implying that the rural-urban migration is one way. This trend will probably continue unless the rural—urban income gap is narrowed substantially. On the aggregate the economy has grown. We have mentioned the increasing importance of petroleum in the economy. This accounts for most of the economic growth. The gross domestic product has grown from N3,144 million in 1964-65 (26) to M24,235 million in 1974-75 (7). Public and private investments in various types of construction-~resi- dential housing, schools, hospitals, commercial centers, roads, etc.-- have grown at comparable rates. During the second national development plan which ended in March 1975, N630 million was spent on roads. The capital allocation to education was N360 million. The total enrollment in primary schools rose from 3.5 million in 1970 to 4.5 million in 1973 and university enrollment rose from 14,500 in 1970-71 to 20,000 in 1974-75 (7). In the Third Five Year Development Plan which started in April 1975, N3,400 million is allocated to road construction, and N2,000 million to education. Primary school enrollment is expected to rise to 7.5 million in 1976 and to 11.5 million by 1980. It is planned to increase university enrollment from the present 20,000 to 53,000 by 1980 by expanding the existing six universities and by building four additional ones (7). Private investment is expected to grow at similar rates. It is expected that average annual private investment in the five year period beginning April 1975 will be about N2,000 million in contrast to pre- 1975 rate of N1,000 million (7). Forestry in the National Economy» The concept of forestry as an organized sector in Nigeria was started after 1861 when Nigeria became a formal colony of England. The colonial government set aside specific natural forest areas as forest reserves. The aims were to conserve forest resources for the benefit of the local communities owning the land, to oppose the wholesale exploita- tion of those resources, to impress the native with the economic value of the forests as a source of present and continual revenue for himself and his children, to prevent destruction through indiscriminate farming operations and bushfires, and to prevent the felling of immature trees (38). There are about 96,000 square kilometers of such forest lands today in Nigeria out of 980,000 square kilometers of total surface area (21). There seems to be no potentials for expansion of forest lands beyond the present area because of increasing demands on land for agri- culture, urbanization, industrialization, and road construction. Natural forests are gradually being converted into forest plantations to increase the production of forest products by intensive techniques. The relative importance of forestry in the national economy in terms of its contribution to GDP and foreign exchange earnings seems to be declining. In 1959-60 forestry including activities related to the production, processing and distribution of wood and minor forest pro- ducts, the conservation of soil, watershed and wildlife and those related with recreational activities contributed 6.5 percent of the gross domestic preduct. by 1969-70 this proportion has fallen to 2.3 l ,. our 0— ave: u... a. be ”a... . 'y. 4. - - A». a. u on" ~ :- “ Iv. . ..‘ - ~o... c “I~ a.- .7.’. \-;.. Tat. ‘- “Q percent (26). In 1962 export of wood products contributed N17.2 million in foreign exchange; by 1972 this contribution has fallen to H6.3 million (21). The decline in the relative importance of forestry in the gross domestic product could be due to increasing importance of petroleum. The decline in the contribution of forestry in foreign exchange earning is certainly due to increased domestic consumption resulting in a cut- back on the exportation of wood products. Records of domestic consumption of wood products do not exist but one estimate (15) suggests that domestic consumption of wood including sawlongs, veneer logs, and woodpulp was 610 cubic meters (roundwood equivalent) in 1960-61 and 1,360 cubic meters in 1973-74. This suggests that with respect to domestic consumption of forest products the importance of forestry in the economy is increasing. Description of Study The chpe of the Study The forestry sector can be defined to include activities related to the production, processing and distribution of wood and minor forest products, the conservation of soil, watershed and wildlife, and those related with tourism and other recreational activities. The general model of the forestry sector will be concerned with activities related to only the production, processing. and distribution of wood products. In the present study we shall merely specify the general model of the ferestry sector such that later it can form the basis for the construc- tion of the model itself. We shall concentrate our efforts on modeling the consumption component, use it to track annual consumption of ‘I ~\-! -.I‘ different wood products in the past from 1965 to 1974 and in the future from 1975 to 1990. The construction of the general model for the entire sector including production, processing, and consumption components would involve resources that are not available to us at present. We concen- trate on a component of the sector which will logically come first and which we can formulate with the scanty resources we have at present. We shall formulate it in such a way that, eventually, it can be easily integrated with the models of the other sub-components. The consumption component comes first because to plan production investments in an industry where investments mature with long time lags one needs an insight into the future for the availability of the market for the products. Without the model of the whole sector, however, we shall not be able to make serious analysis and draw conclusions about the conse- quences of decisions and actions being taken in the forestry sector. Such analysis and conclusions should be broadly based on the information yielded by the model of the entire sector. Some Existing Generalized Models of Forestry_ A few simulation models of forestry sector are available for different countries but we will describe only two of them briefly because of their different approaches. One of these is Simulation Studies of Forest Sector Development Alternatives in West Malaysia (17) prepared by FAO in 1972. This is a simulation model used to analyze a total of twenty-eight forestry policy alternatives on the basis of major national objectives. The simulation model which is computerized 10 is used for Malaysia to make variations in data on forest resource flows, wood based industries and market information for wood products. The effects on employment, income, foreign exchange earnings, capital requirements and return on investments of different forestry management practices and of converting forest land to other uses would be evaluated on the basis of the information yielded by the model. This formed back- ground material from which the Malaysian government could choose that forest development strategy most compatible with their overall strategy for national growth and develOpment. The second general model of forestry sector which we want to describe is that developed for the forestry sector of Trinidad in 1969 by M. Gane (23). Gane defines consumption as the ultimate economic goal and treats it as if it were an interpersonally valid welfare measure. Maximum consumption is set as the sole forestry policy objec- tive and all input and performance variables of the forestry sector are reduced to consumption gains and losses. He employs "Feldstein Multi- plier" to calculate the social opportunity cost and social benefit of public investments in forestry. Net social benefit is defined as the value at the time of the decision-making of the net addition to future consumption that would result from undertaking the project. The desire to consume now as against in the future is social time preference which is assigned an arbitrary value. Consumption gains and losses based on an arbitrarily chosen social time preference is certainly not interpersonally valid because people have different time preferences. The concept of social time preference is not well understood at present. The calculation of Feld- stein Multiplier requires such parameters as marginal propensity to 11 consume, marginal propensity to consume from imported goods, marginal propensity to invest, net output per acre, etc. Underdeveloped coun- tries may not have the necessary data for the estimation of such parameters. Since forestry competes for resources with agriculture, the analysis must be done for all competing crops before the social opportunity cost and social net benefit of a resource in forestry as defined by Gane can be calculated. Such analysis will be enormous. Some Available Wood Consumption Estimates The works of FAO like European Timber Trends and Prospects (16) published in 1953, and World Demand for Paper to 1975 (19), published in 1960 and the work of Stanford Research Institute, America's Demand for Wood (54), published in 1954 are some of the works done in the general area of demand/consumption forecast for wood products in other parts of the world. The objective of European Timber Trends and Pros- peggg is to estimate what the European demand for wood and its products were likely to be in l960--a period of ten years. Two sets of assump- tions were made, one for a low and a high level of economic growth and the other for a high and low level of prices for wood products in the year 1960. Four sets of figures for demand in 1960 are presented, each of the alternative assumptions about economic development being combined with each of the alternative assumptions about relative price movements. In America's Demand for WOod the objectives include an estimate of the consumption of each of the major timber products in 1952 and a projection of these estimates for the target years 1960, 1965, 1970, and 1975 using regression equations of the form, 12 Y = a+bx where Y = dependent variable a,b = constants x = independent variable In all cases economic estimates are made for all the factors that influence demand for wood products such as population, gross national products and in some cases economic trends. FAO in World Demand for Paper to 1975 used the log normal dis- tribution function of the form: Y = scoff... 1/5 exPOI‘ent-tZ/z dt where Y = dependent variable t = (1n x - m)/g Sm = Saturation value in kg/capita x = independent variable m and g = constants and cross-sectional data of national income and paper consumption from different countries to estimate a demand curve for paper. This form is realistically used when the dependent variable will approach a satura- tion value as the independent variable increases and when the dependent variable will rise according to the sigmoid represented by the integral of the log normal distribution. The elasticity coefficient decreases as the independent variable increases. The saturation value for the dependent variable is not a fixed value but may change with time. It is simply a tool for the design of a suitable mathematical formula to fit the statistical data. The log normal demand function offers little advantage over the straight line function when making projections over a ..- o ..~~ ., .5 w.- "mu, - oh... Iv‘. u... ‘v .. '. I 1" ~ s ‘ 13 limited range in the independent variable. Its usefulness lies mainly in its being applicable over a wide range of the independent variable. With respect to Nigeria specifically there are two estimates to which references can be made. One of these, Forecasting Potential Con- ggmption Reqpirements for Nigerian Forest Products, by E. E. Enabor (46) covers estimates of annual consumption of fuelwood, other roundwood, sawnwood, wood panels, paper and paperboard for up to 1985 but assumes that the current per capita consumption level of different wood products will always remain the same and that population change is the only vari- able that determines wood consumption. This allows the consumption of all the wood products to grow at the same rate of 2.5 percent per annum or the assumed rate of growth of total pOpulation. The other estimate of wood consumption for Nigeria is contained in FAQ. Agricultural Devel- meent in Nigeria 1965-1980 (15) but the report is not detailed enough to indicate the assumptions made in arriving at the estimates. The Objectives of the Study The survey of the estimates of annual consumption of wood for Nigeria presented above indicates that the available consumption infor- mation is not adequate to form a basis for decisions concerning expanding investments in Nigerian forestry industries. The duration of the estimates are not long enough for an industry where investments mature with long time lags. Some of the available estimates have also ignored important variables that determine consumption of wood, such as changes in the level and distribution of income, urbanization, public investments, etc. 14 The present study based on a national wood consumption survey considers not only changes in population levels as determinants of demand for wood but also changes in level and distribution of income, educational opportunities, urbanization, public policies, and distribu- tion of population. The specific objectives of the study are: l. to specify a general model for the forestry sector in prepara- tion for the formulation of a specific wood consumption model. 2. to formulate a specific simulation model of the wood consump- tion component which can always be updated and used to estimate annual consumption of wood products when necessary data are available and when desired. 3. to use the model of the wood consumption component to estimate annual consumption of wood products for Nigeria from 1965 to 1974 and project future consumption from 1975 to 1990. 4. to make limited prescriptions for actions that would lead to the attainment of the forestry sector objective of providing the needs of the country in timber. 5. to identify aspects of the forestry sector where further economic studies are necessary. It is particularly intended that information provided by the study will be useful for long range planning of public and private investments in forestry industries in Nigeria. The Approach of the Study The approach employed in this study is the General Systems Analysis and Simulation Approach (GSASA). The GSASA model which we shall construct for the wood consumption component of the Nigerian forestry sector is general with respect to techniques, kinds of data and information used in formulating and operating it, and.with respect to its philosophic orientation. .0 u‘ .. ., ~ .-0- ...u g. a §1>-. a \ - - -. '.'.l v n u. :- I. ll! 1‘ 15 Multidisciplinary Nature of the Approach The model of wood consumption is general with respect to sources of data and techniques. It accepts data and information from many sources including time series, survey data, normative and non-normative judgements of informed people, etc., and uses many specialized tech- niques including mathematics, econometrics, statistics, input-output, systems science, simulation, etc. The general systems analysis and simulation approach recognizes that various specialized techniques are often too limited in scope and by their assumptions to be used alone to solve problems involving change in technology, institutions, and people. For example, if we restrict our definition of economics to that which considers technology, tastes, income distribution, population, institutions, perfect knowledge, etc., as given, the problem under investigation here will virtually disappear because wood consumption is a function of changes in those variables. Economists who define economics this way often prescribe the right action as that which maximizes the difference between the sum of 922§§ and the sum of pads. Well trained economists and the general systems analysts recognize that such prescriptions are based On certain assump- tions which are often not met for problems involving change and uncertainty with respect to technology, institutions, and people. These assumptions are that a normative common denominator is available which permits different pa§§_to be added together, different gggg§_to be added together, and the subtraction of the total pagg from the total gggg_; that the normative common denominator is interpersonally valid; that actions can be ranked in the order of decreasing net advantage per unit 16 of sacrificed ggg§_or incurred 23g; and that the rule of defining the right action is simply one of subtracting the sum of the PEQE from the sum of the gggg§_and adopting that which maximizes the difference. Many problems which involve_change and uncertainty do not meet these assumptions. When generalized systems analysis and simulation investigators encounter problems in which the above preconditions for maximization are not met, they try to approximate them by designing a general model which utilizes normative and non-normative information from various sources including interaction with experienced persons, by iteratively making adjustments in the relevant variables and by tracing the conse- quences of the changes in the relevant variables through time. It is the ability to trace the consequences through time that makes such a model a simulation model. It can do this with minimum cost in person- nel, time. and other resources because it is computerizable. The wood consumption model is also a systems model because the wood consumption component is viewed as a system made up of subsystems, but itself is also a subsystem of a still larger system, the Nigerian forestry sector. When the general systems simulation model of the forestry sector is developed the wood consumption model will be a sub- model of it and it will then be easier to study the interactions among the components of the forestry sector including the wood consumption component and between the forestry sector and the other sectors of the national economy. In summary, systems analysis and simulation studies offer a multidisciplinary approach for interrelating the different aspects of complex problems involving change and uncertainty to see the 17 consequences through time of alternative choices of actions. Its pro- jections are objective estimations of the consequences through time of alternative choices in terms of attaining several relevant goods and incurring several relevant bads for which there may be no common denomi- nator for evaluation. Philosophic Position of the Approach The model is also general with respect to philosophic orienta- tion. It recognizes that practical problems involve change in technology, institutions, and people. Such problems require normative as well as non-normative knowledge to reach prescriptions for their solutions. Undue adherence to any one or unthoughtout combinations of philosophic positions such as normativism, positivism, pragmatism, etc. may not lead to adequate definitions and solutions of practical prob- lems. The normativists are sometimes able to define and arrive at solutions to practical problems when they hold that objective knowledge both abstract and prescriptive of goodness and badness is possible because prescriptive knowledge about what is right and wrong depends on both normative and non-normative knowledge. But when certain forms of normativism hold that non-normative knowledge is impossible they lose that part of rational prescriptive power which depends on non-normative information and lack adequate non-normative knowledge to work on the practical problems.‘ Outright non-normativism prevents the attainment of objective prescriptive knowledge since prescription requires both objective norma- tive as well as objective non-normative knowledge. Adherence to 18 outright non-normativism precludes problem definition as well as pre- scriptive solutions to problems and it is an inadequate philosophic position for practical problems. Pragmatism is based on the presumption that normative and non- normative knowledge are interdependent in the context of the problem they are being used to solve. Its strength lies on its focus on the problems of society. Pragmatists attach importance on tests of worka- bility in solving problems while, perhaps. underestimating the importance of the hard descriptive and abstract normative and non- normative knowledge of the traditional academic disciplines. This makes problem solving techniques more important than knowledge. For more complete discussion of philosophic position, see Johnson (29). Before we summarize the philosophic position of GSASA we shall stop to define some terms like practical problems, objectivity, and goodness and badness which we have used sloppily. The words gggd and pad_are primitive undefined terms like weight and distance on the non- normative side which we either do or do not know their meanings from experience and which are hence, basically undefinable. Objectivity is used to qualify both an investigator and his investigation. An investigator is objective if he does not identify himself and his prestige with some particular concept so that in the absence of pride or humiliation which follows self-identification with a concept the investigator will be willing to subject any relevant concept to certain tests and is willing to revise a concept that may fail any of those tests. In the same way we use objectivity to qualify a concept and define as objective those concepts that would pass those 19 tests established by practicing economists such as the tests of consis- tency. clarity, and workability. The test of consistengy is both an internal and an external one. The internal test requires that a set of concepts must bear logical relationships to each other whether they pertain to the past, the present, or to the future. The test of external consistency is a test of experience. An existing concept is compared with concepts based upon new experience. The text of clarity requires that a concept has a clear and specifiable meaning. A concept is clear if it can be under- stood and communicated from one person to another who has the relevant knowledge. The test of workabili§y_is a pragmatic test. Concepts are often used to solve problems; for instance, they are often used to pre- dict certain outcomes. If that outcome fails to materialize the concept used in predicting it has flunked the test of workability. Objectivity is not limited to the physical or biological sciences; it is also applicable to social sciences. It is not confined to non-normative as contrasted to normative concepts. Both may pass or flunk any of the tests of objectivity. A practical problem as contrasted with a theoretical one is that which decision makers have to live with. Even when they decide not to solve it a decision is, in effect, made to live with it and its conse- quences. In contrast a theoretical problem is a problem of belief about whether alternative normative and non-normative concepts describe or might describe reality. Such a problem may exist for years in an academic discipline without pressing for a solution. we can summarize the philosophic positions of the generalized systems analysis and simulation approach by saying that its investigators 20 show a willingness to estimate the future consequences of actions with- out being constrained by the economists' tendency to maximize unless the preconditions for maximization are met. If not met, before they maxi- mize the generalized systems analysis and simulation investigators first approximate those conditions by building a general model which can utilize both normative and non-normative information, can make adjust- ments in relevant variables by iterative methods and trace the consequences of those adjustments through time. Generalized systems analysis and simulation investigators also show a willingness to work objectively with the normative as well as the non-normative concepts. The process of systems analysis and simulation involves team work among interdisciplinary researchers and policy-makers thus making the policy- makers a part of an objective research team. Application of the Approach The approach is a flexible iterative process involving (1) prob- lem formulation, (2) mathematical and computer modeling, and (3) testing and validation of the model as presented in Figure 1. 1. Problem Formulation: An early step in problem formulation is the recognition of what normative and non-normative knowledge are relevant to the problem under study. The intent is to identify the major questions which will be put to the model after it is formulated such as what will be the consequence on annual consumption of various wood products if national income grows at a different rate or if the public sector changes its investment priorities, etc. During the problem definition and subsequent stages of model formulation, improve- ments are made in both the normative and non-normative information which 21 I—__—_———_—_ 7 l prop'le-m I Real World I defInItIon Observation i ' | | I I I | | I I | I I Data I I Collection I 8 Analysis I ' I | I ._ __l _| I Modeling I I I l I I I _l “I Validation I §§ I I is? l I ‘3 | L ____ ___llJ I FIGURE I: APPLICATION OF GENERALIZED SYSTEMS ANALYSIS AND SIMULATION APPROACH TO WOOD CONSUMPTION MODEL. 22 contribute to the model. The process of problem formulation includes, in addition to a wood consumption survey to collect both normative and non-normative data on wood consumption behavior of people, a series of interactions in formal and informal meetings with decision makers, and businessmen in the forestry sector, consumers of various wood products in Nigeria and with professors and graduate students at Michigan State University who have experience in Nigerian economy and in the systems analysis and simulation approach. The major policy questions and vari- ables which determine wood consumption behaviors and the interrelation- ships between them were isolated more easily than they might have been without such interactions. 2. Modeling: Mathematical modeling involves specifying in mathematical symbols the relationships between state variables involving the level of a variable at a given time, a set of parameters that defines the structure of the system like elasticity coefficients, a set of exogenous variables that influence the system behavior, and a set of variables which can be controlled to alter the systems performance in various directions. The mathematical model also specifies a set of intermediate output variables which measure how well the system model corresponds to reality and a set of output variables. In general the mathematical model illustrates how the variables which define the state of the simulated system in time period t + l is a function of the state of the system and the values of the parameters, exogenous variables, and the control variables at time t. For programming the computer version the entire model is pro- vided both in mathematical form and in detailed flow diagrams using variable names compatible with the programming language of the CDC 6500 23 computer at Michigan State University, but in such a way that it is transferable to some other computers like the CDC 3600. Both the mathe- matical and flow diagram forms of the model of the wood consumption component are presented in_Chapter IV. 3. Model Testing and Validation: One problem of general models is the need for testing and validating them and their projections. Unlike specialized models using data from controlled sources general models use data from different sources including judgement, time series, etc., brought together from many different sources and with different degrees of validity. This implies that several kinds of information with unknown levels of validity are being used to forecast the attain- ment of a wide variety of goods and avoidance of a similarly wide variety of bads. The consequences of making wrong decisions on the basis of the projections provided by a general model will be incurrence of a wide variety of bads or attainment of a wide variety of goods if the decisions are right. The rate of wood consumption depends a great deal on the rate of growth of national income. The national income estimates available do not go far enough into the future and are of uncertain accuracy. But we want to be sure that the future rate of wood consumption which we estimate is the rate that will actually prevail if we can avoid the consequences of wrong decisions. The theory of statistics is not adequate at its present level of advancement for establishing appropriate confidence intervals for the various kinds of data used in a general model. It is not adequate for making choices between more than two alternatives, particularly when the alternatives involve utilization of several kinds and sources of data or if the alternatives are possible prescriptions to solve more than one 24 problem each of which may result in the attainment of multiple goods and avoidance of multiple bads for which a common denominator may not be found. The need for validation is intensified by the fact that machine rather than man is used to put together the many different parts of the model and to make the complex computations involved in a large model. As long as the programming rules are met the coumputer will provide mathematical solutions even though the procedure may be illogical. If a model is large a programming error can elude a good programmer but can be detected by appropriate validation and verification tests. In general, the more rigorous statistical and econometric methods of verification and validation involve the application of tests of coherence with observed and recorded experience, logical internal consistency of the concepts, interpersonal transmissability of the con- cepts between persons equipped with relevant knowledge, and workability when applied to problems. The predictions which our model has reached can be accepted or rejected as false according to the same tests. These tests have been repeatedly applied at different degrees of rigor in bringing our model to its present stage. They were applied in assem- bling data, modifying and deve10ping model components, and in combining various components of the model. Later in Chapter VII they will be applied in evaluating model output. In that chapter we also introduce more information about the inaccuracies by changing some of the para- meters used by the model to obtain an indication of the consequences of possible variations and errors in the data. This process of testing the sensitivity of the projections yielded by the model to possible errors in the data is helpful in finding out how much poor information is 25 generated by a wide variety of unvalidated data which are used in making the projections. Model testing never stOps; it takes place each time a new use is made of the model. The normative content of the model deals with goodness and bad- ness of things, situations, and conditions or with the questions of value. Both the normative and the non-normative content are verifiable according to the procedures described above. The factual elements of the earlier versions of the wood consumption model which have not met these tests were replaced. Those elements still retained in the model are those which have not yet failed the tests. In testing the normative concepts. reliance was placed on experienced Nigerians to verify the validity of weights attached to such things, conditions and situations as wood versus non-wood in various parts of buildings, wood versus alternative sources of fuel, rural-urban distribution of population, etc., in the future. We want to stress the fact that the normative and the non-normative concepts, information, estimates and forecasts of this model are only tentatively true even if they survive the four tests described above. Time will reveal inaccuracies in both the normative and non-normative information used in reaching these estimates and pre- dictions. Time will also reveal relevant normative and non-normative information not conceived at present. Cgmplexity of the Approach Some object to generalized systems analysis and simulation studies because they are complex and not easily described. They involve a wide variety of techniques and model components. Attempts to make the models conceptualizable by means of flow diagrams scarcely improve 26 clarity. The diagrams are replete with boxes, lines, and arrows but still messy. The analysis of a problem involving technological, human, and institutional changes is difficult to describe in terms of any one dis- cipline be it mathematics, economic theory or any other. Also. the closer to reality a model approximates, the more techniques and sub- models it necessarily involves and the more complex it is likely to become. This truth is not unfortunate because the policy makers do not have to construct the general models by themselves. The situation is comparable to our transportation needs. We do not build the cars we drive: further, most of us do not understand how the engine works yet we drive our car and meet our daily transportation needs. We do this by simply specifying our transportation needs in terms, for instance, of size, reliability, safety, economy, comfort, speed and so on and rely on automobile engineers to provide them. Similarly if general systems models are complex and messy because they approximate real world situations and if approximation of real world is desirable, then systems analysts should be furnished with information about systems needs and be relied upon to construct and interpret general systems models. Organization of the Thesis The thesis is organized in such a way as to provide general information on Nigeria in Chapter I for the reader who is not familiar with that country. In the same chapter we also present the coverage and the specific objectives of the study as well as the general approach to studies employed by the thesis. In Chapter II, we present a framework 27 for a general systems analysis and simulation model for the Nigerian forestry sector.) We do not formulate this model in this study because it would be too large for a thesis and because doing so requires more resources than are available for the present study. Chapters III to VII concentrate specifically on the wood con- sumption component. In Chapter III we describe some of the terms we shall use in developing and Operating the model of the wood consumption component. We shall also discuss in the same chapter the important variables in the model. The discussions in this chapter will facili- tate the reader's understanding of the aggregations we have made in wood products and in wood using subsectors to simplify the analysis, and make it easier for the reader to follow the structure of the model. The model of the wood consumption component is presented in Chapter IV in three different versions; a general non-quantitative conceptualization, the mathematical model, and the computer models. Depending on his quantitative background and interest, the reader can conceptualize what the model is about by reading only the first and/or the second version or all three versions. In Chapter V, we present and discuss data used to operate the model so that anyone wanting to make use of the projections provided by it should be aware of the strengths and weaknesses of the data on which those projections are based. Projections generated with the model are summarized in Chapter VI. Some of the validation tests discussed in Chapter I are applied to the model output in Chapter VII. In Chapter VIII, the last chapter, we summarize the thesis, make suggestions for actions to achieve the goals of the forestry sector, and conclude by identifying important aspects of the sector that need further study. CHAPTER II SPECIFICATION OF A SIMULATION MODEL FOR THE FORESTRY SECTOR Introduction We mentioned in Chapter I in the section on the scope of the study that the emphasis of the study will be on the consumption compon- ent. Due to lack Of time, research resources, and the "normal" size of a doctoral dissertation, we shall merely specify the model of the whole sector and develop only the consumption component in detail leaving to later and, perhaps, others the task of creating the whole model. Thus. in this chapter we shall specify the whole sector model to provide the setting for our detailed work in later chapters on the consumption component. This will involve discussions of (1) background of the forestry sector including a definition of the sector, the extent Of the forest resources of Nigeria and the ownership of forestry industries in Nigeria, (2) interrelationships among the various components of the sector and between the sector and other sectors including description Of the various components of the sector, and (3) decision making in the forestry sector including public decisions with some examples and also private decisions. We shall summarize the chapter by pointing out the importance of developing the whole model before we can make a serious 28 29 analysis of the consequences of decisions made and actions taken in the sector. Background on the Forestry Sector What is Included as the Forestrnyector The forestry sector in Nigeria includes the industries related to the production, processing, and distribution of wood and minor forest products; the conservation of wildlife, soil, and watersheds; and the development of tourism and other recreational facilities. In this study we shall be concerned with only those industries related to production, processing, distribution, and the consumption of wood products. Forest Resources of Nigeria There are about 96,000 square kilometers of natural forest reserves (21). Virtually all of these are secondary-growth forests. About 22 percent of the natural forest reserves is located in the high rainforest zone and produces about 90 percent Of total sawlogs. Seventy- eight percent is located in the savannah and subsahahel zones and produces mainly fuelwood and building poles (40). In addition to this, in 1972 there was about 74,000 hectares of forest plantations growing mainly teak. Gmelina, and a range of indigenous species in the rain- forest areas and eucalyptus, neem, and acacias in the savannas. Wood is also supplied from various non-forest sources including homesteads and farm lots. It was estimated in 1964 that this source supplied up to 50 percent of all sawlogs processed in Nigeria (48). Nigerian forests contain a wide variety Of timber species, per- haps as many as 600. Only twenty-four (48) of those including mahogany 30 (Khaya ivorensis), sapele-wood (Entandrophragina cylindricum), iroko (Chlorophora exdelsa), African walnut (Lovea klaineana), guarea (Guarea thompsonii), opepe (Sarcocephalus diderrichii), agba (Gossweilerodendron balsamiferum), obeche (Triplochiton scleroxylon), etc. are being exten- sively utilized at present. It is estimated that up to 100 of them are utilizable but they are unknown in the world market and wood industries are generally conservative with respect to accepting new species. Felling of trees is regulated in the reserves and it is the policy of the government that steps be taken to assure that timber yields from them is sustained. But felling of trees in the non-forest reserves is not controlled and no efforts are made to replace the trees once felled. As sawlog trees from the non-forest sources become exhausted, production will have to be expanded in forest reserves. Domestic consumption Of wOOd is growing very fast as a result of increasing population, changing characteristics of the population, and high rate of spending on construction industries in recent years. The rate of felling has, as a result, been such that there is now a scarcity of sawlogs. Immature and lower grade logs are now being har- vested. Replacement efforts are no longer keeping pace with removals in the forest reserves. In the Western State, for example, which supplies most of the sawlogs from forest reserve sources only 57 out Of 85 square kilometers exploited annually receive limited regeneration treatments consisting Of enrichment planting and periodical Opening of the canopy and cleaning of undergrowth. Ironically, in spite of these shortages usable trees are felled and burnt or abandoned during farming operations in the rainforest zones. Because of limited transportation and processing facilities and because of lack of information about 31 availability of markets for wood in other parts of the country, it is not worth the efforts of the farmers to salvage wood for utilization. It is asserted that the present forest lands cannot sustain the growing domestic need for sawlongs (48). Because of the expanding population, urbanization, industrial estates, modern road systems, and agricultural tree crop plantations, there is little hope of increasing the area of forest land. The natural forests are gradually being con- verted into forest plantations in the effort to increase wood production by intensive methods. The annual rate of expansion of forest planta- tions has grown from 1,700 hectares in 1960 to over 6,000 hectares in 1970. It is prOposed by the various state governments owning forest reserves that the annual rate of conversion of natural forest reserves into forest plantations should be as much as 20,000 hectares by 1985. Research in various tropical countries including Malaysia has, however, shown that there is a limit to which wood production per hectare of forest land can be increased by intensive methods (3). This limit is imposed by maximum attainable limits in the number of species, basal area. and crown size per stand observed in the tropical rain- forests for most of the species. The extent of these limits has not been verified for Nigeria. If they exist it means that the potential for domestic production Of wood by extensive methods is limited by land area and the potential for domestic production by intensive methods is limited by ecological conditions. Under such conditions Nigeria must consider the possibility of looking beyond her borderS'for some of her future consumption needs. Wood processing going on in Nigeria includes conversion of sawlogs into lumber, plywood and veneer, conversion of wood into 32 charcoal, conversion of pulp into various products including paper and building boards, and wood seasoning. Conversion of wood into pulp will start soon when the four pulp mills planned by the Federal Government are set up. The lumber industry is diverse with respect to both the size and technology of operation. It varies from a few very large mills like those of the African Timber and Plywood Company to small one or two-man operated mills. The technology varies from the most modern techniques employed by large mills to the traditional techniques employed by pitsawyers. For example. in 1970, there were 171 wood processing mills which employed more than ten people per mill. Four Of these produced 90 percent Of all processed wood exported from Nigeria the same year (45). Ownership Of Forestry Industries All forest reserves and forest plantations are publicly owned by state and local governments. Exploitation is controlled by the state governments which also are responsible for various forest maintenance practices. The Federal Government assists the state governments in major investment projects with financial and personnel aids. The local governments receive a share of the revenue accruing from the forest products even though their contributions towards investment costs are minimal. This is partly because the land belongs to the local community and partly because the cooperation of the local governments facilitates forest protection from illegal removal of sawlogs, purposeful forest fires for hunting purposes, and encroachment by farmers on forest lands. Non-forest sources of wood are privately owned and controlled. Privately owned forest lands are limited although it is a written 33 objective of the public forestry sector to encourage the establishment of private forests (46). Private investments in forest plantations are beginning in the wood scarce states of the north. The reasons for limited private forest lands are not well known. One of them may be the traditional land tenure system. Land is owned by the community and allocation and use is controlled by emirs, chiefs and elders who act as trustees for the community. The plots worked by the individual farmers are small and scattered. This has been a deterrent to the investment of private capital on land and to the establishment of plantations. Another reason may be that individuals are reluctant to invest their land in forest trees because of low growth and high interest rates. Although fast growing species of trees are being introduced both from outside and by research in Nigeria, forest trees are generally long-range, low return investments. Some will not mature in the life time of the investor. In addition, forestry generates many externali- ties which have no monetary values. The private entrepreneur will base his decision to put his land into forest on his valuation of risks that go with long range investments, the importance he attaches to who may inherit the investment, and also the many external benefits of the investment which have no monetary values. Such valuations Often result in the land being more valuable to the individual in other than forest uses. If the private sector cannot be relied upon to provide the forest products including wood products, the conservation of wildlife, soil, and watershed, etc., which the society needs, it may be right for the public sector to provide them. We should not, however, ignore some of the problems which public ownership Of productive resources may 34 create. Frequent occurrences of overexploitation, illegal removal of trees, purposeful forest fires, and overgrazing Of forest lands are traceable to public ownership of forest lands. Local authorities to whom forest revenue accrue and who have overhead cost in terms of land but zero marginal cost, will likely press for overexploitation if left uncontrolled since they do not bear the cost of regenerating the forests. Similarly, a hunter once sure that he will not be caught will burn public forest land to catch an animal as the burning has a zero cost to him. If held responsible for regrowing the forest, he will not burn it down in order to catch an animal. Similar reasoning can be used to explain the overgrazing of forest lands by nomadic herdsmen whose activities are uncontrolled. Since public ownership of forest reserves is desirable, more serious efforts are needed to control abuses of public forest lands by the private sector. There are at present forest ordinances enacted against such abuses as illegal removal of trees from forest reserves, etc., but most of the cases brought against violators are lost in the courts. The forestry departments have to secure lawyers to plead their cases from the justice departments. These lawyers lack forestry back- grounds and cannot realistically argue the case for forestry. Many bureaucratic procedures are involved in transferring services from one government department to another and this often results in loss of time. In addition, these government lawyers are civil servants who receive their pay whether the case is lost or not. Two alternatives can be recommended. the forestry departments should train their own lawyers and provide themwwith background in forestry or they should be free to hire 35 private lawyers so that they can get the most competent to argue their cases. Anybody who is benefiting directly from the forest products should where possible be made to pay for at least part Of what he gets. If nomadic herdsmen are to graze their cattle in the forest reserves, they should be taxed before they can do so. Before this can be done, however, the forest reserves boundaries should be well defined. The potential of the land for pasture and the number of cattle it can sup- port should be ascertained. This would involve extensive investigations and capital losses to some people. The situation is serious and calls for hard decisions and actions from policy makers. The processing industries, unlike other forest industries, are privately owned, partly be foreign investors and partly by Nigerians. In the very large concerns, a Nigerian government may be a shareholder. However, in general the large concerns are owned by foreign investors. In 1970 the four mills which produced 90 percent Of wood exported in processed form and which owned 80 percent of logging licenses were for- eign owned (45). In that year there were 124 mills owned by Nigerians, 44 owned by foreigners, and 3 owned together by both Nigerians and foreigners (45). There are no conditions for entry into the industry by Nigerian investors. The only obstacle is access to sawlogs which is not a problem to small scale millers who do not have high overhead costs since they can operate when they can get sawlogs and close down when sawlogs are not available without incurring large capital losses. The rela- tively free entry into the wood processing industries coupled with the excess profit which was observed in the industry by Okigbo (48) has 36 attracted many entrants over a wide range of scale and technique of operation into the industry. They include small owner-managed mills who employ traditional techniques such as pitsawing as well as very large integrated mills which employ the most modern techniques available to produce not only lumber but also plywood, veneer and other wood panels. The owner-managers according to Okigbo (48) more often than not possess neither the technical knowledge nor previous experience necessary for the success of the mill. They are able to make profit because their overhead costs are minimal. The consequence of this condition of the industry is much was- tage of wood at the processing stage. The average saw mill conversion factor for the country is 50 percent. The social cost of waste of this magnitude in a country which is facing a shortage Of wood products is high enough to inspire action from the public sector. Such actions can be minimum levels Of training and capital investment requirements for entry into the industry. It can be the use of tax policy to remove some of the excess profit so that only efficient producers can stay in the industry. At the present time Nigeria is insisting on exporting processed rather than unprocessed wood so that the value added in processing will be internalized. It is possible that the foreign companies which bought Nigerian wood as sawlogs would now want to process them in Nigeria to maintain the quality for their home consumers. They should be encouraged to stay because they are likely to be more efficient than their domestic counterparts as they have the necessary capital to take advantage of scale and are more likely to have the necessary training and experience. _In so doing, care should be exercised to assure that 37 non-monetary goals (e.g., increased employment, redistribution of popu- lation, utilization of by-products. utilization of lesser known species, etc.), which a profit motivated private entrepreneur is likely to ignore, are not lost to society. 'Foreign investors assume more risks than their domestic counter- parts. An example of this is the indigenization policy which politicians in some countries sometimes use to please their constituen- cies even when there are no other basis for such actions. Examples also exist in recent years where foreigners were expelled from some countries. Such policies usually involve capital losses by the foreign investors which should be borne in mind when specifying the conditions for their entry. The problems of ownership of resources in the forestry sector both in the forest industries and in the wood processing industries are serious but not enough is known about them. Detailed investigations are needed in this area to determine the extent of cost to society involved and what the solutions can be. How the Forestry Sector Interacts with Other Sectors The Compgnents of the Forestry_§ector Before we discuss the interrelationships among the various components of the forestry sector, we shall first describe the compon- ents. They include unprocessed wood supply, wood processing, wood (iistribution and consumption, forest education, forest research, and foreign trade in forest products. Each of these can be modeled indepen- Idently as a submodel of the forestry sector model. ll) 38 l. Unprocessed Wood Supply: The sources of supply of raw wood can be grouped into forest reserve sources, non-forest reserve sources, and possible foreign market sources. The forest reserve sources can further be grouped into unmanaged natural forests, managed natural forests and plantation forests. Non-reserve sources can also be further grouped into plantation forests and non-forest sources of wood (see Figure 2). Non-forest sources of wood include home- and farm-stead trees. As pointed out in the previous section, the possibility of expanding forest lands beyond the present 96,000 square kilometers is limited because of other demands on land. 2. WOod Processing Component: Wood processing includes conver- sion Of sawlog into lumber, plywood and veneer; conversion of wood into charcoal, and wood seasoning. Conversion of wood into pulp will start soon in Nigeria when the proposed four pulp mills are set up. The lumber industry is diverse with respect to both the size and technology of operation. It varies from a few very large mills like those of the African Timber and Plywood Company to small one- or two-man operated mills. Technology varies from the most modern technique employed in large mills to traditional techniques employed by pitsawyers. The greatest problem of small mills is the seasonal shortages of sawlogs. Small mills have been able to get by because they can shut down at Off seasons because of their low capital investments. The very large mills have integrated both logging at one end and more advanced processing like veneer and plywood manufacture at the other end. There are poten- tials for further integrations. Forward integration reduces wastage which is currently as high as 50 percent in small mills. Mills inte- grated with final product factories such as furniture factories, 39 All forest lands Forest Reserve _ Lands Non-reserve Forest 3 Forest . forest lands Reserve Reserve and other Wood _ Nan-wood sources _ * MGD’ * N0" forest Plantation Natural Sources Forest Supply of Supply of o—Logs, Poles and Fuelwood and Fuelwood l .— Logs, Poles —- Mkt for Logs, Poles and Fuelwood Supply of. Logs 8: Poles Foreign Saurces FIGURE 2: SOURCES OF UNPROCESSED WOOD. 2. v. b in uh. 40 building construction, charcoal manufacture or with a pulp factory can utilize additional volumes Of slabs, edgings, and sawdust. 3. Wood Distribution and Consumption: WOOd is consumed as unprocessed wood, sawnwood, plywood and veneer, paperboards, charcoal and paper pulp in various residential housing, non-residential building, road and vehicle construction, casket manufacture, paper, etc., sub- sectors and as fuelwood. Wood products are distributed to the various wood-using subsectors for final consumption both directly from the forest and indirectly through the market. A high proportion of the wood is used in the raw form mainly in the traditional sectors. Wood used in this form goes directly from the forest to the market or from the forest to the user if the user owns the forest. WOOd used in processed form goes from the forest to the proces- sing mill if the exploiter owns the mill. Sometimes logs reach the factory through the market when factory owners do not own exploitation concessions but buy their logs from those who own concessions. All processed wood is distributed through the market. Wood from the very large mills is distributed through company licensed distributors who sell both to the final product producers as well as to a large number of small retailers in market sheds. Smaller mills sell both directly to final product producers and through many small retailers. More highly processed wood like plywood, veneer, paper boards, and wood panels is distributed through larger scale retailers who can provide more adequate storage. Most savannah and sub-savannah states are self-sufficient in fuelwood but depend on the rainforest zones for sawnwood. Consequently. there is extensive internal trade in lumber, plywood and veneer. Wood 41 product prices vary widely between wood surplus and wood deficient zones. For example, by the end of 1974, the price of fuelwood was about four times in Enugu and eight times in Sokoto what it was in Ibadan. 4. Forest Education and Training: Professional training in forest education is carried out at a university department of forestry. Courses are available up to bachelors degree in forest production, wild- life, and wood technology. For specialization beyond the bachelors degree, students are sent to more advanced countries. Two year intermediate training program and some vocational training programs for forest attendants in forest production, sawmilling and wood technology are available in the three schools of forestry located in Benin, Ibadan. and Jos. These schools also offer short refresher courses and special courses. 5. Forest Research: Forest research is conducted in the areas of silviculture, mensuration, tree improvement, forest product utiliza- tion among other areas. The major problem of forest research in Nigeria is the non-availability of trained research manpower. By November 1974, out of some 116 research officers only 20 held research degrees. Eighteen held masters degrees and 2 held doctorate degrees. Public service in Nigeria is not attractive to people with the compe- tence and type of training needed for research in the forestry sector. Generally seniority is more important than productivity in pay and pro- motion. Opportunity for advancement is limited in the hierarchy where only a few will ever hope to attain the principal Officer position. 6. Trade with Other Countries: Nigeria exports sawlogs, lumber, and.p1ywood mainly to Western European countries. The volume of logs exported has, however, declined from 470,183 cubic meters in 1962 to 42 161,847 cubic meters in 1972 (21). The volume of lumber exported has also declined from 67,776 cubic meters in 1962 to 54,593 cubic meters in 1972 (21), while the value of plywood and veneer exported increased from £1.56 million in 1967 to N1.89 million in 1971 (10). Increased demand at home and government policy Of more home processing are responsible for these changes in export trade in wood. Interactions Among the Components and Between the Sector and Other Sectors A conceptual block diagram Of the wood forestry sector showing interdependences among various components Of the sector and between the sector and other sectors of the economy is presented in Figure 3. The forestry sector receives as input, revenue allocation in form of education, training, research and forest regeneration budgets, land, labor, and foreign exchange for purchase of capital equipment. It yields as outputs wood, minor forest products, environmental quality (soil conservation, watershed conservation, forest fire, recreation), employment. population distribution, income and income distribution, tax revenue, foreign exchange, etc. The wood supply component provides unprocessed wood directly to the agriculture and other sectors when farmers and other private owners of sources of supply obtain their wood directly from the forest and to the processing component through large sawmills who own logging conces- sions, and through losses by theft. Apart from these the center of interaction among the different components Of the sector and between the forestry sector and other sectors of the economy is the box labelled "Market and Intersectoral Trade." The unprocessed wood supply component provides unprocessed wood to the market and it is bought by smaller mills C O O ( C -‘ aomcuuunu SECTOR 8 C 0 IL ‘ E g z 3 i " In 9 O <- :/ .: g 3 f r. 5 b :4 a g u [m4 n O a. a In 0 g o (a: .3 g 8 o a 0 8K E ’ g 8 8 INPUT 5 g a o a g Prnronuaucc VARIAILES . g 3 g 8 o vacuums O O U m M wanna: 05“” a: O o u («murmur .I z :1 ¢ :3 O roam» I unnecesssco & a g .1 g p I wooo excumat 3 3 3 g k .5 «avenue l a, O O I . souncm 3 a 3 K ; noccssme I ——«-I \u 1 Income pom.” NATURAL oc ran rooo 1 £1,“ ron moo Iwooopuu may; I must 1' 0' '000 muumou cmnp " won '°°° """" a unnocesseo L- __ J mm. eaumma mousse w AND Pr uupnoccsseo "I ——" I1. unemcesseo w o moo s w - ____ In"... - Irma i—J-zu—LJ- mwooo uuo pun- n PROCESSED w m... nnou an autumn. "07”“ a an “rum. I Luca I Dd w u'r'L "“03 w an annum. I____1 .000 I I Ituvnouuum. '"I'ORMTIOIH non- IIsumLL I ouaurv roses: 0 o o .l I 'ORLD SOURCES 8 8 § 8 . I 00 '0' I'M-I on unnoctssm a a y a wooo 4eg—I ‘ - Ironic ‘3 o OI o I I a. or as I If. 8 g g g 5 3’. m «n In I g 8 8 3 I I ’ ° 2 2 2 .1 E n t a z 9 I 5'. S 3 o i I )—- 1L— — :F- 0—-I> - —- — _ — o i s ‘ a O ‘ ‘ O a 2 .n ‘4 c 3 §. 2 a. _ a 1 § 2 I: t: ' 5 I: 3 I. a. a a o o 0 0 NON'AORIOULTURAL SECTOR KEY 04 3 DEMAND DIST! 8 DISTRIBUTION MATL 8 MATERIAL P! I PRICE 9: I SUPPLY Id 3 WOOD FIGURE 3'. INTERACTIONS AMONG THE COMPONENTS AND BETWEEN THE FORESTRY SECTOR AND OTHER SECTORS. 44 which do not own log concessions and cannot obtain their sawlogs directly from the forest. The processing component provides processed wood tO the market which is bought by residential housing; non-residential building; farm, road, and vehicle construction; and other wood using subsectors. The agricultural sector supplies food to the market and the food is bought by the forestry and the other sectors. The other sectors supply raw materials like fertilizers, vehicles, machines, parts, etc. through the market to the forestry sector and to the agricultural sector. Prices of wood products, food stuff, and input raw materials are determined in the market. Information about supplies of products, inputs, and price levels are generated and disseminated. The forestry, agricultural, and other sectors compete for land and labor among other resources. The rainforest zone, which contains only 22 percent of the forest reserves but supplies 90 percent Of the sawlogs also supplies all the agricultural tree crops (such as cocoa, Oil palm, and rubber) and much Of the root food crops--cassava and yams. The agricultural tree crops also yield soil conservation, watershed conservation, employment, and income, as do the forestry trees and more tax revenue and foreign exchange than the forestry trees. For example, in 1967, forestry yielded N8.64 million in foreign exchange and contributed 4.1 percent of gross domestic product (10). In the same year cocoa, oil palm, and rubber together yielded N140.18 million in foreign exchange (10) and agriculture (excluding forestry, livestock, and fishing) contributed 43.0 percent of gorss domestic product. »In 1970 forestry yielded N8.1 million in foreign exchange earning and contributed 2.3 percent of gross domestic product. In the x. a 45 same year cocoa, oil palm, and rubber together yielded N173.7 million in foreign exchange (10) and agriculture (excluding forestry, livestock, and fishing) contributed 38.7 percent of gross domestic product (26). If foreign exchange, tax revenue, income, employment or any other commonly supplied product is presented as the major Objective of the forestry sector, it is possible that the forestry sector will lose resources to non-forestry tree crops. There are some forest products, like wood, which all sectors need but which no other sector can supply. The production of these exclusively forestry products have been more advantageously presented by the policy makers in the forestry sector as the major Objectives of the sector (46). Decision Making in the Forestry Sector The purpose of decision is to choose the right course or a right set of courses of action to achieve the desired goals. The Nigerian government has stated the provision Of the country's needs in wood as the number one goal (46) of the forestry sector. The need is the dif- ference between estimated annual supply and consumption. Some courses of action Open for choice include annual rates of exploitation of standing forests; annual rate of replanting of exploited forests; how much land, if any, shall be put into or taken out of forestry; how much wood should be imported or exported; how much processing should be done and how much foreign capital investment should be involved; and to what extent, if any, should consumption be controlled. The choice of course of action depends on the magnitude of the need and available resources. Some decisiOns are made by the public sector and others are made by the private sector. we shall discuss the decisions 46 made in both the public and private forestry subsectors, but first we shall describe briefly how the public forestry sector is organized. Organization and Functions of the Public Departments of Forestry We pointed out in Chapter I that Nigeria is at present divided into twelve states each with a government. Forestry is a division of an appropriate ministry in each state. The state divisions Of forestry control the forestry resource of the states. The Federal Government has two departments of forestry, namely, the Federal Department of Forestry and the Federal Department of Forest Research both in the Federal Ministry of Agriculture and Natural Resources. These do not own or con- trol forests. State and local but not the federal government own forests. The functions of the Federal Department of Forestry are "to advise the Federal Government on Forest DevelOpment throughout the country, to act as an advisory and liason body to the Nigerian States and to provide development services at Federal and interstate levels" (21). The Federal Department of Forest Research is responsible for forest and forest product research and forest education and training. Public Decisions Since most forest lands are publicly owned, most decisions Imelating to annual rates of cutting and replanting are made by the pnflolic sector in the box labelled "Public Decision" in Figure 4. Stxate governments which own forest lands decide the annual rate of cut armi'how much land will be put into forest uses. The Federal Government I—d _——dt E u- Re ener- Foreign Research cat on I 0 Ion sector '2) ”I g I '3 T e. I ‘3' '8 '5 I 3 I03 5‘3, £19. -- -- ._ 1‘5 2:. I, 3 gr 2?. '55 Is 2' I go I 3 '3. | I ‘3 I ~ I. _______ l I ______ __l I" _______ Decision % 2'0 C E -o 6' — E o o -. ‘o O 3 5. [3. 3 _ I3 §_' 9 l 3 2’ s I a 1; Forest ‘< 3 Con- Sources sump- of Wood .0 3 tion 4 9. a I I a a 1, . a O 3‘ a a :3. | 2%? 2 o 2,-.6 o - O -o-o — I 3 ‘11," a) g I 8 S «- o C I s I 3. L . . . 5' 1 "’ —————— PrIvate Daemon 3 1 1 ‘1 c: I I 3 . I o l 0 I 2 a l 8 3 Investment I Investment ° ° '" - I d- . I; .- rocessm Is ri u Ion Wood £———9—1 L—-——-IWO0d ‘—"‘ Procss- fl' Olstribu-‘—"’ sing Processed tion , Wood KEY Investment/Belle; NEIL Information flow Wood flow — FIGURE 4: DECISION MAKING IN THE FORESTRY SECTOR. 48 decides how much resources will be invested in forest research, educa- tion, and training. Although most wood processing industries are privately owned, the public sector has a control function over the private sector. When it becomes necessary the public sector can use its control power to eliminate inefficient operators in the processing industries and save some of the wood that is lost by poor processing techniques. Such controls can be the use of tax policy to remove excessive profit, establishment of minimum training requirements and minimum capital requirements for entry into the industries. These will be decided in the box labelled "Public Decision" in Figure 4. The public sector will also make the decisions on whether or not wood will be imported or exported and whether foreign capital will be used in the various forest based industries. The right course of action will depend not only on non-normative information about the magnitude of the need and on the available resources but also on the priorities between conflicting needs. Any chosen course of action which has desirable normative consequences for the forestry sector may have undesirable normative consequences for other sectors of the economy. we pointed out that land is needed in the agriculture sector for tree and food crOps and in the construction sectors for urbanization, industrialization. and road construction as well as in the forestry sector for producing more wood for increased consumption. Any decision to transfer land from one sector to another implies a judgement that one sector is more important than the other. Such decisions present problems because they cannot be easily made on 49 the basis of the calculus commonly used by economists to prescribe the right actions as that which maximizes the difference between the sum of the goods and the sum of the bads. This is because it is difficult to establish a common denominator which will permit the summation of the goods, the summation of the bads and the subtraction of the sum of the bads from the sum of the goods. Even if there were such common denomi- nators they may not be intersectorally valid and they should not be used to subtract bads imposed on one sector from the goods imposed on another. Some Examples of Public Decisions The Western State contributes over 50 percent of all sawlogs from public forest lands. In 1974, around 85 square kilometers of forest land were exploited and natural regeneration was supplemented with limited silvicultural operations consisting of enrichment planting, undergrowth cleaning, and climber cutting through the fourth Year. Research, has however shown that for maximum yield undergrowth cleaning and climber cutting should continue through the tenth year (3). Exploitation was by licensed logging contractors. Fees charged to the log contractors in 1974 varied from N1.04 per cubic meter for low grade species such as Berlinia, Canariium, and Daniellia, to N15.52 per cubic meter of such choice species as Tectona grandis. Seventeen percent of the revenue generated was paid directly as royalty to local government authorities, 58 percent was used to pay for forest administration, regeneration and replanting, and other recurrent costs. The balance over this type of costs is returned to local government authorities as further royalty. Twenty-five percent was put in trust funds not used for forestry services or payments. 50 We have pointed out that excess profit generated in the proces- sing industries has attracted many inefficient operators because entry into the industry is relatively free. This implies that the public decision with respect to inefficiency in the industry is to let it continue. Inaction when a practical problem exists implies a decision to live with the problem. Nigeria exports both sawlogs and processed wood but the volume of logs exported from Nigeria declined from 470,183 cubic meters in 1962 to 161,847 cubic meters in 1972 (21) while the value of plywood and veneer exported increased from N1.56 million in 1967 to 31,893 million in 1971 (10). Goals of more employment opportunities and more even distribution of population between rural and urban areas which are attained by processing wood in Nigeria for export are more important than the goal of additional foreign exchange which may be earned by exporting sawlogs. This is so probably because sufficient foreign exchange is currently being earned from petroleum. Priorities will likely be redefined when petroleum ceases to earn as much foreign exchange as it does at present. Private Decisions In so far as non-forest sources of wood are privately owned and controlled, how much to use this source is determined in the box labelled "Private Decision" in Figure 4. These decisions will probably be based on information on domestic and foreign market prices of wood, public decisions on importation and exportation of wood, and the rate of exploitation of public sources. Similarly, the amount of private land to be converted to forest plantations will be determined in the "Private Decision" box in Figure 4. This would probably be based on 51 present government forestry extension efforts and public investments in -forest research, education, and training. It will also depend on expected public import/export policy on wood products, and expected domestic and foreign market_prices of wood products, i.e., on private conception of the marginal productivity of land in forest trees relative to marginal productivity of land in other uses. Most of the wood processing industries, with the exception of the four pulp mills which are about to be established, are privately owned but publicly controlled to the extent that the public sector con- trols a high proportion of the raw wood supply, import/export of processed wood products, and importation of machinery and foreign capital. Foreign capital is important in the establishment of large plants. In 1970, four processing plants accounting for 90 percent of all processed wood exports and owning 80 percent (45) of logging con- cessions were foreign owned. There are numerous plants owned by Nigerians, but they are small scale. Private decisions, however, deter- mine the amount of wood processed within limits of constraints set by the public sector and private decision makers are free to decide the degree of processing to be done on the basis of information on consumer taste and market prices. Reaching private decisions also involves non-normative as well as normative concepts of good and bad. Private decisions differ from public decisions in that if there is a common denominator on the basis of which the goods and the bads can be evaluated, the problem of inter- personal validity of the common denominator may not be as serious in private decision as in public decision. Public decisions are made for more people and affect larger sectors. 52 Summary of Chapter We have in this chapter specified on a very broad basis some aspects of what will have to be considered when constructing a general model of the Nigerian forestry sector. The sections on interrelations among the components of the sector and between the sector and other sectors and the section dealing with decision making in the forestry sectors are not independent parts but aspects of each other. The rate of interaction among components and with other sectors will be influ- enced by the decisions and actions taken in the sector. A decision can be with respect to any of the components specified above, and the decision can be made by either the public or the private sector. Efforts in the forestry sector are to satisfy the goals of the sector, the foremost of which is the provision of wood to satisfy the increasing demand. The consumption estimates which the consumption model will yield will serve as feedback into the other components and will influence decisions and actions that will be taken with respect to them. We have discussed some of the decisions and actions that are being taken in the sector to realize the stated goals and other needs without tracing the time paths of their consequences. We cannot do this without constructing the model of the sector. And until we trace their time paths we cannot really say too much about them. We shall devote the rest of this study to organizing the con- cepts of the consumption component, constructing the model, making the projections of future consumption, and to discussing the consequences of the consumption estimates as much as we can without the entire sector model. CHAPTER III A MORE DETAILED DESCRIPTION OF THE WOOD CONSUMPTION COMPONENT Introduction In Chapter II we stated that although we shall specify a simula- tion model for the entire forestry sector, the major emphasis of this study will be on the model of the consumption component. The specification of the entire forestry sector was presented in the previous chapter. The rest of the study henceforth will be concerned with the consumption component. We shall develop the model of the consumption component in Chapter IV and discuss some of the inputs in Chapter V. In Chapter VI we shall present the projection estimates made with the model and apply some of the validation tests discussed in Chapter I to those model projections in Chapter VII. Before we do those things we shall describe in more detail in this chapter some of the wood products with which we shall be concerned. We shall describe the various subsectors where wood is used in Nigeria, the factors which shape the wood consumption characteristics of indi- viduals and how the model deals with them. We shall also discuss in this chapter some of the public actions affecting the rate of wood consumption and some other public actions which could be used if desired to further control the annual consumption of wood. 53 54 Nigerian Wood Products A wood product here means wood that is ready to be put in its end use. In this sense wood products in Nigeria include wood not processed at all, wood sawn into lumber, veneer and plywood, wood panels, paperboard, fiberboard, soft board, paper pulp, charcoal, fuel- wood, and so on all of which are used in Nigeria. For the purpose of the present study these wood products will be aggregated into five categories: namely unprocessed wood, processed wood, building board wood pulp, fuelwood, and paper wood pulp. Unprocessed Wood Unprocessed wood will here include wood cut and used directly in the farm for farm construction; round wood used in permanent posi- tions in various housing construction mainly in the rural areas but also in limited amounts in urban areas. We shall also include as unprocessed wood, wood used in carving household utensils like pounding mortars and pestles, and wood used for carving various farm hand tools like handles for hoes, axes, matchets, etc., used in the traditional sectors. Most of these wood products are not obtained from the "forest" but from the "bush" or the "farm." Also most of them are not distri- buted through the market but are cut from the so called "bush" and used directly on the farm. We have considered it necessary to include a crude estimate of consumption of them because if they were not available from those sources, the forestry or another sector would be required to supply them or alternatives to them. Not all unprocessed wood by our classification is from non-forest and non-market sources. “- ll I‘In! v. ‘ 55 Round wood used as scaffolds during construction of urban buildings or as props in mines, and round wood used in permanent positions in some rural and some urban buildings are supplied from the forestry sector and are distributed through the market. We shall include these in the aggregate we want to consider as unprocessed wood even though some of them may have undergone limited processing like trimming and seasoning. Processed WOod we pointed out in Chapter II that the wood processing going on in Nigeria includes the conversion of logs into lumber both by pit- sawing technique and by modern mill technique, manufacture of veneer, plywood, and wood panels. Imported wood pulp is also converted into paper boards for building constructions. We shall classify wood used for paperboards and other pulp products used for building construction separately as building board wood pulp later. We group here as processed wood only lumber, veneer, plywood, and other wood panels. Most of the processed wood products are distributed through the market. Most of the trees from which wood is cut for processing are grown specifically for wood even though as much as 50 percent (48) of sawlogs converted into lumber may not have come from forest reserves and forest plantations. Building Board Woodpulp The woodpulp used in making all woodpulp products like fiber 'boards, particle boards, hard.boards, and soft boards whidh are used for various building construction purposes are aggregated and referred to in this study as building board woodpulp. At present all pulp and pulp 56 products consumed in Nigeria are imported and distributed through the market. Paper wogdpulp WOodpulp used in making all newsprint, writing, and printing paper products are grouped as paper woodpulp. Some of these, like building board woodpulp, are imported as finished products and some are imported as pulp and converted into paper in Nigeria. All paper wood- pulp products are distributed through the market. Four woodpulp mills are being established in Nigeria at present to produce woodpulp both for building boards and for paper. Fuelwood I Although most of the fuelwood is consumed as unprocessed wood, some wood is converted into charcoal before it is burnt for fuel. Charcoal is more commonly used in urban areas than in the rural areas both for domestic cooking and for some industrial heating like black- smithing, goldsmithing, and.welding. Some of the fuelwood is obtained from trees grown specifically for fuelwood particularly in northern forest reserves but by and large most of the fuelwood comes from various salvage sources including bush fallow farming, logging, wood processing, building construction, wood works and other activities. All of the fuelwood supplied from forest reserves in some northern states and some of the fuelwood from some salvage sources are distributed through the market. But fuelwood not distributed through the market forms the greater proportion of total consumption. 57 Units of Measurement Unprocessed wood, and processed wood are measured in cubic meters and converted into round wood equivalence. Woods used for all types of building paper boards and for all writing, and printing papers and for newsprints are converted into wood pulp equivalence and measured in kilograms. Charcoal is converted into round wood and measured along with other fuelwood in cubic meters. Factors Which Shape the Wood Consumption Habits of Individuals The wood consumption habits of an individual are determined by the type of wood product the individual consumes most often, how sparingly or unsparingly he uses the wood, and by the use to which he puts it. Observation in Nigeria shows that these characteristics differ with people depending on whether they live in the rural or in the urban areas or on how close they are to the forest source of wood; on the ability of individuals to afford different types of wood pro- ducts; on the price of wood products relative to prices of substitutes for wood and also prices of other materials which complement wood in various uses; and on the previous exposures of individuals to alter- native living conditions. In summary, an individual's wood consumption behavior is shaped by some factors which approximately correspond to his location with respect to rural and urban areas, his income, availability and relative prices of substitutes and complements for wood in various uses, and the level of education attained by the individual. 58 Rural-Urban Location of Individuals Most of the forest and non-forest sources of wood are located in the rural areas. Rural people are more frequently associated with occupations like farming, logging, and forest tending which give them access to wood. Because of availability of wood, rural based people tend to consume proportionally more wood than their urban counterparts. They obtain most of their wood directly from the forest and use it in unprocessed form for both building and fuel. Individual's Income We discussed the economic dualism of Nigeria in Chapter I and pointed out that a great majority of Nigerians are very poor while a few who though may not be considered very rich by developed world standards are fairly well to do. The wood consumption characteristics of Nigerians have followed this dualism. Among the poor, particularly rural poor, dirt and farm salvage materials especially cork stock, stems of palm leaves, and livestock skins and droppings have substituted for wood extensively in various uses including farm construction, housing construction, furniture, and fuel. To the extent that wood is used, it is used in unprocessed form and is generally obtained directly from the forest or farm. In contrast, among the higher income classes, except temporary wood used during construction and sometimes in permanent positons in buildings for decorative purposes, wood is almost always used in processed form and is obtained through the market. When it is desirable to substitute other materials for wood which may be because wood with desired qualities including well formed grains is not available or 59 because weather conditions would not permit the use of wood in some out- side parts of buildings, the substitutes are metal, concrete, glass, and asbestos. Adeyoju believes that "the fortunes of the cash crOps with which most consumers' income is tied underlie the absolute demand for wood. . . . in periods of high producer prices for primary produce, there is a corresponding high demand for sawnwood as was the case for 1954-55, and in periods of market depression for primary produce as in 1958-59 a fall in demand is also recorded" (2). Availability of Substitutes and Complements for Wood All the building engineers and architects we interviewed during the wood consumption survey (carried out as part of this study and discussed in Chapter V) explained that the place of wood in a building is very elastic depending on availability of the right type of wood, availability of alternative materials and their relative prices. This statement implies that substitutability and complementarity between wood and other materials in buildings are imperfect. If substitution were perfect then either wood is used if it is cheaper or its alter- native is used if wood is more expensive. Conversely, if complementarity were perfect between wood and other materials, then the building is built with the required amount of wood or it would not be built. Under perfect complementarity, building technology would determine exactly how much of each material must be used. In the case of imperfect complementarity and substitutability which we face in the use of wood in a building a wide range of propor— tions is possible, the optimum prOportion depending partly on building 60 technology, cost of labor and general construction convenience, relative prices of the building materials, and the tastes of the individual owners of the buildings. Further, we note that if either wood or com- plements for wood in buildings become cheap more buildings will be set up and more wood will be required simply because of increased number of buildings. Conversely, if wood or its complements become expensive enough fewer buildings will be set up and less wood will be required. Also, if substitutes become more expensive than wood it is likely that more wood and fewer wood substitutes will be used, but if substitutes become less expensive than wood, less wood will be used. In recent years plastic products have substituted for wood in household utensils and in parts of household and non-household furni- ture because plastic industries established in Nigeria in recent years mass produced these products and made them cheaper than their wood alternatives. More wood will likely be displaced in buildings, utensils, and furniture when the integrated iron and steel mills which are planned in Nigeria start operating. The Federal Government plans to harness the natural gas associated with petroleum drilling which is presently wasted (7). Natural gas is more than a perfect substitute for wood as a source of fuel. If it becomes cheap enough when the initial cost involved in its use is considered, it will displace a large proportion of wood in the fuel consumption subsector. Educational Attainments of Individuals The process of formal education is not the only way individuals get exposed to alternative living conditions. Nigerians who have travelled to the countries of Western Europe and North America have 61 been known to try to live like people in those countries. We use educa- tional attainment only as index of exposure to different living conditions. Although college graduates would spend some time with their folks who may live in mud huts with thatched roofs, it is doubtful if any college trained individual would like to live permanently in any of those huts even if such people do not have jobs and hence incomes befitting their training. We did not come across any one among the college graduates we interviewed during the wood consumption survey mentioned above, who did not live in buildings with concrete walls, including those living and working in rural areas. Nor did they burn wood or charcoal for fuel. How the Determinants of Wood Consumption Habits of Indi- viduals are Incorporated in the Model Based on the wood consumption behavior of individuals, Nigerian people can be classified into three groups with fairly distinct wood consumption characteristics. Such grouping will approximately corres- pond to (1) rural poor; (2) medium income peOple whether rural or urban, urban poor, and college graduates who for any reason including unemployment may earn lower than average income irrespective of whether they live in the rural or in the urban area; (3) high income people irrespective of their educational attainment, and irrespective of their rural-urban location. We shall refer to these groups as traditional, semi-traditional, and non-traditional wood consumption population groups. Following this grouping of people we shall, where appropriate, subdivide various wood using subsectors which we shall describe in the next section into traditional, semi-traditional, and non-traditional because 62 of differences in size, and structure of buildings and in the building materials used in the buildings. Our model will use different parameters like average proportion of building per person, average amount of wood per building, average fuelwood per person per year, and average paper per person per year for each of these groups. This differentiation will account for differences due to rural-urban location, personal income, and educational attainment of individuals. Wood consumption estimates based on these distinctions should be closer to reality than estimates based on averages for all Nigerians. The model will account for the effect of relative prices of wood and of wood substitutes by estimating average wood per building in the different wood consumption groups as functions of the ratio of the index of price of wood to the index of prices of substitutes for wood. This will allow for substitution of metals, glass, and so on to displace wood as they become more available and relatively cheaper than wood in various uses where those products are substitutes for wood. Wood Using Subsectors The wood using subsectors we are considering in this study are grouped into residential housing construction subsector, non-residential building construction subsectors, farm construction subsector, casket manufacturing, vehicle body and bridge construction subsectors, fuel- wood.consumption subsector, and paper consumption subsector. Within each subsector, there is a wide range of variation in size, structure, and building materials among products produced. In the following sub- sections we shall describe each of these groups briefly and point out 63 what subsectors are in each group and explain how we want to deal with the variations in the size, structure, and building materials in the products of each subsector. Residential Housing Construction Subsector Only dwelling houses are included in residential housing con- struction except in the business districts of urban centers where some buildings are used partly for dwelling and partly for commercial pur- poses. Such buildings are included in the residential building subsector. Student dormitories, hospitals, hotels, etc. where people may live but are not homes are not included in the residential housing construction subsector. Dormitories are under school buildings, hospital buildings are under hospital buildings, and hotels are under commercial buildings, all of which are treated as non-residential buildings. The residential housing construction subsector is subdivided into traditional, semi—traditional, and non-traditional subsectors because of variation in the size, structure, and building materials in residential houses. Non-Residential Building Construction Subsectors Schools, religious, hospital, and public administration buildings, and commercial buildings like shopping centers, market places, workshops, and so on are grouped under the non~residential sub- sectors. Elementary schools, high schools, and colleges are treated as three separate subsectors because of the differences in size of buildings and types of building materials used. Elementary schools, commercial, and religious building construction subsectors are further divided into 64 traditional, semi-traditional, and non-traditional. Residential buildings within institutions such as homes for professors on a uni- versity campus are considered residential rather than non-residential. Farm Construction Subsector Farm crop storage buildings, livestock sheds, farm fences, farm crop staking, and manufacture of farm implements such as hoes, matchets, and axes with wooden handles are grouped in the farm construction sub- sector. These activities are different for traditional and non- traditional farms. Since the level of transformation from traditional to non-traditional agriculture which has occurred in Nigeria is insig- nificant, it was difficult to find an adequate definition of a non- traditional farm on which we could base our simulation of non-traditional farming activities, which will likely be expanding in the future. Eventually we classified as a traditional farm any tree crOp farm less than one hectare or a food crop farm less than two hectares. During the wood consumption survey which will be described later in Chapter V, we observed that traditional farms livestock mix with people in living rooms and farm crOps are stored until ready for market in the rooms in which people live. Such multi-purpose houses are more conveniently regarded as residential in the study. Estimates of annual wood consumption are made separately for traditional and for non- traditional farms. Casket Manufacture Vehicle Body and Bridge Construction Subsectors The containers in which the dead are buried in Nigeria vary with the people, religions and economic conditions. AAmong some peOple and 65 religious groups the dead are not buried in any type of container. Even among some people and religious groups where ordinarily the dead will be buried in a standard casket made of processed wood some cannot afford such a casket and are buried in caskets made of other materials. Our estimate here included only caskets made of processed wood and we assume that only about 40 percent (2), of the dead in Nigeria are buried in such caskets. Some commercial vehicles are imported as chassis and the body is added by local carpenters. Among these are "lorries," "pick-up," "mini-buses," and some "non-luxury" buses. There are also, particularly in urban areas, a number of manually powered carts which are made almost entirely of lumber. "Pick-ups" and "mini-buses" are small vehicles and most of the bodywork is metal. The wood content consists merely of one or two boards of plywood or paperboards used in the roof as insulation material against heat. "Lorries, non-luxury" buses and manual trucks are included in what is here referred to as "lumber- trucks." Bridges on modern tarred roads are frequently made of concrete and steel. Formwork materials like props and concrete containers are more often than not metal. On untarred roads, however, bridges or parts of bridges are still made of wood; only the latter type is included here. Furniture and Utensils Household furniture includes seats, tables, trunks, beds, shelves (cupboards), and clothing hangers. Household utensils are pounding mortars and pestles, bowls, and spoons. Non-household 66 furniture includes seats, tables, desks, shelves, writing boards and dividing screens. Furniture and utensils are included as parts of the building where used. Wood in a building is therefore wood used in formwork--props, concrete containers--during construction, in permanent positions--shutters, frames, roof--in the building, and in the furni- ture and utensils used in the building. Fuelwood Consumption Subsector We mentioned under the section above dealing with wood products that some of the wood burnt for fuel is in the form of charcoal and some is round wood. We also mentioned that some of the fuelwood is distributed through the market but that most consumers of fuelwood obtain their supplies directly from various salvage sources. There are significant differences in per capita consumption of fuelwood between those who obtain their supply of fuelwood through the market and those who obtain theirs from non-market sources. Separate estimates of annual consumption of fuelwood are made for fuelwood obtained from market and non-market sources. Paper Consumption Subsector Paper here includes newsprint, printing paper and writing paper. Estimates will be made separately for low income, medium income, high income and student population groups. Other Uses of WOod In Nigeria wood is also important in the construction of boats and canoes, and railroad sleepers. Wood is used for power and trans- mission poles and pit props in coal mines. Some packaging materials are 67 also made of wooden boxes or paper bags. Wood used in these ways is not estimated in this study because of time constraint. Government Actions Which Affect Wood Consumption Discussions of wood products, wood using subsectors, and factors influencing wood consumption indicate that certain actions of the governments do affect wood consumption. Government investments in agriculture, education, health, and transportation are some of these actions. Other actions not being taken at present could further con- trol the rate of consumption of wood in Nigeria. These will include pricing of wood products, substitutes, and of complements in various uses and the establishment of building construction codes which specify where and when wood may or may not be used in buildings. Government Investments A passage from Adeyoju (2) quoted earlier in this chapter indicates that higher producer prices for agricultural products in 1954-55 stimulated demand for sawnwood. Any public investment in agriculture which will improve the real income of the rural people whether the investment is in the form of higher producer prices or in the form of subsidized input prices will affect the rates at which various wood products are consumed in two ways. First, agricultural activities will be stimulated by the public investment. Then there will be increased consumption of the wood products used in traditional farm construction. This will occur with minimum time lag. After a longer time lag, there willbe a shift toward increased consumption of more processed wood, the consumption of which is associated with semi- and non-traditional wood consumption groups. This will happen 68 because increased investment in agriculture will eventually, result in higher income for rural people, enabling some of the rural poor to enjoy the living conditions of the semi-traditional population groups. Government investments in education have similar consequences. Investments in education will include expansion of school buildings and furniture which will result in almost immediate increases in wood con- sumption. A more important consequence of such investment in education will be apparent after the appropriate time lag. When people benefiting from such investments begin to graduate from high schools or colleges and take appropriate jobs, they will acquire the living habits associ- ated with the non-traditional population groups and consume more processed wood. If investments in the various sectors are sustained, the lagged consequences will continue. The increased rates of consumption of various wood products which are being experienced at present in Nigeria are merely some of the unlagged consequences of the government invest- ments in various sectors including agriculture, education, health, and transportation which began with the "oil boom." If such heavy public investments are sustained, the lagged consequences for wood consumption will be large. Decision makers for the forestry industries should only anticipate them and plan for their consequences. The model of the wood consumption component is structured to take government investments into consideration. The annual changes of the various wood consumption population groups are estimated.by the model partly as functions of projected government investments in different sectors. The numbers of students who go to high schools and colleges annually and who will eventually be classified in the 69 semi- or non-traditional wood consumption population groups are esti- mated as functions of government investments in education as well as functions of GDP. Similarly, the number of people who will be reclas- sified from traditional to semi-traditional population groups because of increased personal income are estimated in the model as a function of public investments in agriculture as well as of agriculture's contribution to GDP. Other Public Actions Other public actions which can be used to control wood consump- tion include artificial pricing of wood products, substitutes, and complements, and the establishment of building codes which will specify the use for wood in buildings. We pointed out earlier that since the substitutes for wood are imperfect, an increase in the price of wood relative to the price of the substitutes will result in the substitutes displacing some but not all wood in some uses. Thus, the government can use this relationship to encourage or discourage wood consumption if desired. Wood prices can be controlled by various tax measures including producer or consumer tax. Producer tax is convenient in Nigeria at the logging stage. Since the various state governments are the owners of forest sources of wood, they can control the prices of wood products by raising or lowering the fee they charge for logging rights. The Federal Government can control the price of wood by manipulating the importation and exportation of ‘wood into and out of the country. If it is desirable to encourage wood consumption, the Federal Government can relax the conditions for importation and at the same time tighten the conditions necessary for 70 exportation of wood products from Nigeria. This will expand domestic supplies and reduce domestic prices relative to the prices of substi- tutes for wood. Administered prices may have undesirable consequences in the long run. Suppose that due to certain reasons, one of which can be shortage of wood products, it is desirable to discourage the consump- tion of certain wood products and this is done by artificially increasing the price of wood relative to prices of substitutes. Con- sumers will begin to substitute other materials for wood and producers will begin to invest in forest industries in response to the artifi- cially high prices. Eventually when these investments begin to mature in form of increased supply of wood products, there may be a reduced market for the wood products because peOple have learned to use other materials in place of wood. Establishment of building codes which will specify the uses of wood may create complementarity where little exists. If a code speci- fies that wood must be used in certain positions in a building, either the required amount of wood is used or the building is not built, as under perfect complementarity. Such a measure can also lose markets for wood products which are hard to reestablish if it is later desirable to encourage wood consumption. Because of their undesirable consequences, artificial pricing and building codes should be very carefully con- sidered before used to control the consumption of wood. CHAPTER IV THE MODEL OF THE WOOD CONSUMPTION COMPONENT Introduction In Chapter I the complex nature of general models was discussed and we pointed out that a generalized systems analysis and simulation model is typically complex and disorderly. We also discussed other characteristics of such studies and pointed out that they proceed from conceptualization to mathematical and then computer modelling. In this chapter we shall discuss the three stages beginning with general con- ceptualization. This will make it possible for a reader who finds mathematical and computer models complex to conceptualize what the model is about without reading the mathematical and computer versions. Conceptualization of the Model We shall discuss the conceptualization in two stages: first, how the model is structured and, second, how it Operates through time. How the Model Is Structured In Chapter III we discussed various uses of wood products, including construction, fuel, and paper. Some of the construction sub- sectors considered are residential, school and hospital, housing, bridge, and farm constructions. Estimates of wood consumption in these subsectors are based on the independent adult in the case of residential 71 72 housing, students for school housing, hospital beds for hospitals, kilo- meters of road for bridges, and farms for farm construction. For lack of common terminologies, we use "building" to commonly refer to a residential house, school building, hospital building, a bridge, or a farm building and "unit" to refer to an independent adult person, a student, a hospital bed, a kilometer of road, or farm. The general procedure of the building construction submodels is to estimate the rate of setting up new buildings in each subsector as the sum of the rate of replacements of existing buildings and the product of average proportion of building per EEEE and the rate of change in the number of lggi£§_in the subsector. Once this is done, average wood per building in the subsector is applied to the number of new buildings to arrive at the rate of wood consumption in that building construction subsector. This is described in Figure 5. Wood can be unprocessed wood, processed wood, or building board woodpulp. If wood is building board woodpulp, the unit is kilograms/year rather than M3/year. The subsector can be residential housing construction, non-residential housing construction, farm construction, or bridge construction. If we eliminate the compo- nent which deals with the replacement of old buildings, the general procedure will also apply to other subsectors namely lumber truck con- struction, where the structure is not replaced, and in fuelwood consumption and paper consumption subsectors where the wood is used once and for all. For modeling convenience, the wood consumption model is divided into six submodels correSponding to (1) population; (2) residential housing construction; (3) non-residential housing construction, including school, hospital, commercial, religious, and public 73 .mJMOOEmDm zozoamhmzoo 02.04.35 “.0 mmDHUDmhm 4an 203395239 233*. .oacc< .20» .2335 new: 5.2 .2 3523 as: 35:63 2: 5 e8; .0 5363.80 _o:cc< ins: 3.239803 Eczeoqotav £5 E 283%: 2:225 9.2335 .550 E 683 .6 cozaEamcoo 335.4 74 administration building construction; (4) farm construction and fuel- wood; (5) other construction and manufactures, including caskets, vehicles, and bridges; and (6) paper subsectors. These submodels are independently modeled, but the outputs of the population submodel are inputs to other submodels. There is a lag between the time a building is built and replace- ment. During this lag, losses occur in the total number of buildings due to such random factors as wind or fire which add to the number of buildings to be replaced. Though these losses are stochastic, this model treats them as constants. The rates at which buildings are set up in the elementary school, commercial, religious, and public administration building con- struction subsectors are estimated as constant proportions of the rates of construction of residential buildings. Buildings in some subsectors are classified as traditional, semi-traditional, and non-traditional following the disaggregation of the population into those wood consumption groups as discussed in Chapter III. Residential and elementary school buildings are disaggre- gated into traditional, semi-traditional, and non—traditional residential and elementary school buildings, depending on whether they are used by traditional, semi-traditional, or non-traditional population groups. Commercial, farm, and religious buildings are classified as either traditional or non-traditional. Buildings in other subsectors (such as hospitals, high schools, and colleges), vehicles, bridges, and caskets, cannot be conveniently classified into any of the three classes. These latter subsectors are considered as nonspecific. 75 Annual consumption of fuelwood is estimated for fuelwood from market sources and for fuelwood from non-market sources for traditional and semi-traditional wood consumption population groups. We assume that non-traditional wood consumption habits do not include the consump- tion of fuelwood. The rate of consumption of paper is estimated on the basis of per capita consumption per year for individuals in the low-, mediumr, and high-income brackets and for elemehtary, high-school, and college student population groups. The average annual per capita con- sumption increases with per capita income in different income groups except in the low-income bracket. The output of the model includes estimates of annual consumption of five wood products--namely, unprocessed wood, processed wood, building board woodpulp, fuelwood, and paper woodpulp for the three wood consumption population groups. The estimated annual consumption of these wood products will also be classified on the basis of the sub- sectors where wood is consumed--namely, residential housing construction; non-residential building construction; caskets, lumber trucks, and bridges; farm construction; fuelwood; and paper. It is possible to get estimates of the annual wood consumption in the non-residential building construction separately for each of the subsectors that make up that aggregate from the model. Fuelwood will be classified on market, non- market basis, as well as by traditional and semi-traditional population groups. The model will generate some demographic information as inter- mediate outputs. These include the classification of adult population into traditional, semi-traditional, and non-traditional groups approximately corresponding to low, medium, and high income groups ‘ql 76 respectively; estimates of annual high school and college enrollments; high school and college graduation rate; and total number of deaths. Other intermediate outputs of the model include estimates of annual number of management level job openings, number of buildings set up each year in different building construction subsectors for different popula- tion groups; and the number of trucks added on annually. How the Model Operates Through Time In the previous subsection we pointed out that the rate at which buildings are set up in the various subsectors is estimated as the sum of the rate of replacements of old buildings and the product of the rate of change of the number of ggitg in each subsector and the average proportion of buildings per unit, Average proportions of buildings per gniE_in the subsector are nonvariable parameters in the model. But the rates of replacements of the old buildings and the rates of change of the number of gni£§_in each subsector vary with time. These two variables determine the time paths of the rates of consumption of the various wood products. Both variables are endogenously generated in the model. A population submodel generates rates of change in independent adult population and in student population. The inputs of the population submodel are the population of twelve-year-old children, income differential between rural and urban areas, and the rate of growth of the gross domestic product and government investments in education and agriculture. These are some of the variables discussed in Chapter III as the determinants of the annual wood consumption. A proportion of the twelve-year-old population, depending on the rate of growth of GDP and government expenditure on education, will go v.11. $08 A son“ . vu'v’ll C . . .QV‘ .‘"‘¢. - \Au ‘ d n; ’1 (D 77 to high school each year. Similarly, a proportion of high school graduates, also depending on GDP and government investment in education, will proceed to college. These people, after the appropriate time lags, will be classified with the semi—traditional population group if they stop at high school or with the non-traditional population group if they graduate from college and take befitting jobs. A proportion of the twelve—year-old children who do not go to high school are in urban areas. After some time lag, ranging from nine to twelve Years, these peOple mature into independent adults to be classified with the semi-traditional population group. Similarly, after a period of time between twelve and eighteen years, the proportion of the twelve-year-old children who are in the rural areas mature into independent adults and are classified with the traditional population. Migration from the rural to urban areas takes place during this delay period in response to expected better economic opportunities available in the cities. The model can reclassify an individual already classified in a group if his personal income improves enough. For example, if the per— sonal income of an individual classified as traditional improves enough, he will be reclassified as semi-traditional, even though he may be living in the rural area. In the same way, if the income of an indi- vidual in the semi-traditional population group rises enough, he is reclassified as non-traditional by the model. The traditional population group consists of the low-income rural people. The semi-traditional population is made up of the urban population who are not in the high income bracket, rural population who are in the medium income bracket, and high school graduates and college {I I 78 graduates who do not have college-level jobs irrespective of whether they live in the rural or urban areas or whether their income is below average. The non-traditional population consists of all people in the high income brackets irrespective of whether they live in the rural or in the urban area and whether they went to school or not. In Chapter V we shall explain what are meant by low-, medium-, and high-income brackets. At high rates of growth of GDP and government investments to stimulate economic activities, the number of peOple who will go to high schools and colleges and eventually be classified as non-traditional will be high. Similarly, the number of individuals in the traditional population groups whose income will rise enough to enable them to be reclassified as non-traditional will be higher at higher rates of economic activities. In response to improvements in economic condi- tions, the proportion of the traditional population is expected to decline exponentially over time without reaching zero. The proportion of semi-traditional population will first grow, then level off, and eventually decline but never reach zero. The proportion of the non- traditional group is expected to grow exponentially without attaining unity. The time paths of these population groups are represented in Figure 6 as functions of changes in income. In Figure 6 the total population is represented by one while each population group as a proportion of the total is represented by the distance from the hori- zontal axis to the curve with the corresponding label. If economic conditions deteriorate, the time paths will be reversed in that non- traditional population will be expected to decline as a proportion of total and traditional population to grow proportionally over time. 79 Amxm20rrm0dom$ NICO! do .mzoiozam m4 manomo 20....443m0m 9554) “.0 th4m m2; ”0 MSGE we: .33 2:8,: do 3:05 Co 2.5 o ‘4 0'-“ "'I' \ III! I ’I I II I .2228: -23.... I \\ I I 38:39.7]: . I l l .cotoSood .20... tom» can t cottoooi ‘[ pawn UEVaube {3285 O J‘ heme t‘ ‘ and 3E l]. 4 a 80 The rates of setting up new buildings in different subsectors will follow comparable time paths. If economic conditions improve over time, the rate of new building construction will be expected to decline because of declining rates of growth of traditional populations. The rates of non-traditional building will be expected to grow with non- traditional population. Lumber trucks will tend to be replaced with modern vehicles, and traditional bridges will be fewer because dirt roads will be converted to modern-tarred roads. Consumption of fuelwood and unprocessed wood both used relatively more in the traditional sectors, will be expected to decline as economic conditions improve. Consumption of processed wood, building board woodpulp, and paper, which are relatively more important in the non-traditional sectors is expected to grow over time. There are some time lags which elapse before changes in economic conditions are translated into changes in the rates of wood consumption. FOr instance, by 1976 primary education will be free in Nigeria. It will take at least fifteen years including six years in the primary school, five in high school, and four in college, before the first group of beneficiaries from free primary education can graduate from college, find befitting jobs, and adopt non-traditional wood consumption habits. This is why consumption of fuelwood and unprocessed wood estimated in Chapter VI have not declined substantially in spite of the fact that GDP has grown at relatively accelerated rates in recent years because of availability of petroleum in Nigeria. The duration of our projections, which is only fifteen years, is not long enough for most of the conse- quences of the present changes in economic activities to be fully felt. 81 The other variable on which new building depends is the rate of replacement of old buildings. When a building is set up, it deterio— rates after a delay period at a rate determined by the rate of extraction of services from that building as well as by environmental conditions. A group of buildings of the same subsector, type, and vintage will deteriorate over time at different rates depending on the rates of use. The order of deterioration in a group of buildings can be dif- ferent for different groups of buildings. In one group most buildings may deteriorate early and only a small proportion experience long delays while most buildings in another group may experinece long delays; conversely a small proportion of buildings in a group may experience short delays and deteriorate quickly while another small prOportion deteriorates much later. and most of the buildings deteriorate near the mean life for the group. This later order will be likely for buildings with uniform rates of use, building materials and environmental condi- tions. A graphical representation of the number of buildings which deteriorate in each group as a function of time is shown in Figure 7. If in a group of buildings, the rate of deterioration follows the first order described, the rate of deterioration will follow the time path labelled k = l (36) in Figure 7. The model assumes that the rate of deterioration in different buildings will follow the time path labelled k = 10 in which most of the buildings will decay near their mean life. This assumption is realistic because we have disaggregated buildings into groups of uniform characteristics such as purpose (rate of use), building materials, etc. and we use different mean lengths of life for W3~Lw\v\\avfl \C L..~»N:\‘u2 82 moz_o.=3m mo mdaomu .rzwmmuma no zo_bN.0 h 00H.0 000.0 0ma.0 Sham aocowuwpmua 00.0 NH 00H.0 mmH.0 hm0.0 wooflum mm.0 NH 000.0 000.0 000.0 Hmuwmmom 00.0 NH 000.0 000.0 000.0 mmmaaoo NN.0 NH 000.0 000.0 000.0 Hoocom :00: mm.0 ma 0m0.0 0H0.0 H00.0 Hagucmowmmm Hmsowuwcmuancoz mm.0 0H 0m0.0 0N0.0 000.0 Hmwucovwmmm Hmcowuflvmualflamm «0.0 0H 000.0 mm0.0 hm0.0 Haaucmvwmmm HmGOwuwomua nodumaum> macaum>ummno A.w>\.doumv A.u>\.moumv A.uw\.moumv oaucwaom moo o.um : mumm mmoa mmoa mmoq mcfloawnm mo mm>9 m . .mu w QE 2 Hmcofluuomoud manmmumHmmmlcoz manmmomHmmm .maoafism mo mm>a wa mommmooum amHmo may , ca mmcfloawsm mo mumbEsz Hmuoe mo mcofiuuomoum mm mommmooum awaoo unfluso umoq mmcwoawsm mo mnmbfi:2:u.v manna 101 reasons are discernible at present to expect nonreplaceable losses in school and hospital buildings. 5. Other COnstant Inputs: The ratios of number of buildings in the elementary school, commercial, religious, and public administration building construction subsectors to number of buildings in the residen- tial housing construction subsector in the base year (1974) are needed in the non-residential building construction submodel. The number of buildings set up each year in the commercial, religious, and public administration subsectors are estimated as proportions of the number of buildings set up each year in the residential housing construction sub- sector. Our estimates of those ratios are based on the enumeration of all buildings in Ibadan province by Ibadan City Council in 1974. The relative proportions of numbers of different buildings in Ibadan are not representative for all of Nigeria. A more realistic ratio would be based on the average of those ratios from the other parts of Nigeria. The number of buildings at the base year are also needed in residential housing constructions, non-residential building construction, farm con- struction, and bridge construction submodels, where time delays are simulated. They are used to estimate the number of buildings that are at their various stages of deterioration in the different building con- struction subsectors. The number of buildings in each subsector at the base year is calculated as the product of number of units and the average proportion of buildings per unit in the subsector. The average amount of fuelwood from nonmarket sources per adult per year is the fuelwood from nonmarket sources as a prOportion of the total amount of fuelwood consumed by the household which obtained all or part of its total consumption from nonmarket sources divided by the 102 number of adults in the household. The mean of this for a number of households is used to estimate the total amount of fuelwood obtained from market sources. Income per adult for low-, medium-, and high-income groups were adapted from Aboyade (l). Aboyade's observed average income per family was divided by our observed average number of adults per family. Individuals in the low-income group earn less than H400.00, individuals in the medium-income group earn between N400.00 and l2,000.00, and individuals in the high-income group earn more than N2,000.00 per adult per year. It is assumed that the rate of growth of per capita income for the high-income group will lead gross domestic product by 5 percent, while that for medium-income group will lag the GDP by 2 percent. The per capita income for the low-income group will not change significantly as GDP grows. Estimation of Regression Parameters Ordinary least squares techniques were employed to estimate the regression parameters for some of the equations. Some of our data, particularly the time series data, may not meet most of the assumptions of ordinary least squares technique. Time series data in Nigeria are often incomplete and of short duration. There is a reasonable chance that a high degree of measurement and other statistical errors were involved in collecting, analyzing, and presenting them. Also, a civil war disturbed economic activities in many parts of the country between 1967 and 1970. The range of the time series data are not wide enough to deter- mine the functional form by plotting the data. We have substituted a 103 linear approximation to a functional form which is consistent with our assumptions (see page 77). We deemphasized these problems because the regression equations serve predictive purposes. We retained variables at moderate levels of significance and coefficients of determination (Rz's) where other experiences and judgements have given us strong reasons to believe that such variables are important determinants of the dependent variables. 1. Parameters of Processed Wood Per Building as Function of Prices: The average amounts of processed wood in residential buildings were estimated as variable functions of the ratio of the price index of processed wood to the index of prices of substitutes for processed wood in various uses. The cross-sectional data on processed wood used for the regression analysis were derived by grouping residential buildings enumerated during the wood consumption survey according to the years they were built. Arithmetic means of amounts of processed wood per building (defined as processed wood in furniture and utensils used in the building, in formwork during construction and in permanent positions in the building) were found for the buildings in each group. These means are presented for traditional, semi-traditional, and non- traditional residential housing construction subsectors in Tables 5 through 5B. Price information is derived from the import and export values of processed wood products, and non-wood products which are used in the various building construction. We present the results of the regression analyses below in Table 6. For each equation the standard errors of the estimates and the levels of significance of the variables are in parentheses. R2 and Dw stand for coefficient of determination and Dubin-Watson statistics. 104 Table S.--Average Amount of Processed Wood Per Traditional Residential Building by Year(s) of Construction. Average Amounts Year(s) of of Processed Number of Coefficient1 Construction Wood/Building Observations of Variation (Cubic Meters) 1952 and earlier 1.80 20 0.52 1953 - 1956 1.01 27 0.52 1957 - 1960 1.58 17 0.87 1961 - 1962 1.55 33 0.64 1963 - 1964 1.65 38 0.65 1965 - 1966 1.61 15 0.49 1967 - 1968 1.77 21 0.53 1969 - 1970 1.68 24 1.27 1970 - 1974 1.42 31 0.29 1See Note 2, Table 1. Table 5A.--Average Amount of Processed Wood Per Semi-Traditional Residential Building by Year(s) of Construction. Average Amounts 1 Year(s) of of Processed Number of Coefficient Construction Wood/Building Observations of variation (Cubic Meters) 1949 and earlier 6.20 15 .43 1950 - 1956 4.58 9 .50 1957 - 1959 4.61 16 .43 1960 3.81 11 .71 1961 3.62 18 .59 1962 4.15 24 .48 1963 6. 1o 11 ' .06 1964 - 1966 6.42 14 .20 1967 - 1972 5.03 18 .53 1See Note 2, Table 1. 105 Table SB.--Average Amount of Processed Wood Per Non-Traditional Residential Building by Year(s) of Construction Average Amounts Year(s) of of Processed Number of Coefficient1 Construction Wood/Building Observations of Variation (Cubic Meters) 1949 and earlier 4.57 8 .30 1950 - 1956 3.26 6 .29 1957 - 1959 3.69 8 .28 1960 3.30 11 .38 1961 3.85 17 .44 1962 - 1963 4.89 10 .59 1964 - 1965 5.32 5 .31 1966 - 1969 5.61 16 .74 1970 - 1974‘ 5.67 8 .16 See Note 2, Table 1. Table 6.--Parameters of Processed Wood Consumption in the Construction of Residential Buildings as Function of Prices. EQ. 1 PRlz = 1.83 - 0.94 ° P R2 .50 (0.11) (0.34) (0.36) DW = 1.75 (.01>L>0) (.03) EQ. 2 PR22 = 11.40 - 7.45 - PSI R2 .63 (6.69) (2.07) (2.17) DW = 1.73 (.01>L>0) (.01) EQ. 3 PR32 = 8.48 ' 4.28 ° PSI R2 = .24 (0.90) (2.69) (2.84) DW = 1.69 (.02) (.18) L = level of significance 106 PRiZ' i = 1, . . ., 3 are the average amounts of processed wood in cubic meters per traditional, semi-traditional, and non-traditional residential building respectively. P is the price index of processed 2 wood at 1956 level and PSI is the ratio of the price index of processed wood to the price index of substitutes for processed wood in various uses. The price index of processed wood rather than the ratio of the price index of processed wood to the price index of substitutes for wood is used to estimate the parameters in EQ. 1 in the above table because substitutes for wood in the traditional residential building construc- tion as pointed out in Chapter III are mainly dirt, and farm salvage materials which are not usually obtained from the market. The low R2 in these equations may be explained by the fact that not only prices but also other variables including personal income and tastes of individuals for wood and non-wood in various parts of buildings and furniture deter- mine how much wood is used in a piece of construction. Prices are significant at 18 percent level in the non-traditional, at 3 percent level in traditional and only at 1 percent level in the semi-traditional residential buildings. The level of substitution for wood for other materials is least in the semi-traditional residential construction and highest in the non-traditional sectors where concrete, steel, asbestos, glass, etc. are used in place of wood in parts of buildings, furniture, and utensils. 2. Parameters of Market Supplied Fuelwood as Function of Fuel- wood Price: Some households obtain part or the whole of their fuelwood supply from the market. For each of these households the quantity bought was divided by the number of adults in the household and converted 107 to annual quantities in cubic meters. The households were grouped according to their local market sources of fuelwood and the arithmetic means of the market supplied fuelwood per adult per year were calcu- lated. These means are presented in Table 7. They were regressed on the local market prices to obtain the average amount of fuelwood from market sources per adult per year separately for the traditional and the semi-traditional population groups. These equations are presented in Table 8. Table 7.--Average Amount of Fuelwood Per Adult Per Year by Local Market Area. Local Market Average Amount of Price (N/m3) Fuelwood/Adult/ Number of Coefficient1 (Aug. - Dec. 1974) Year Observations of Variation (Cubic Meters) 8.62 7.30 35 0.57 7.75 10.59 25 0.21 6.89 10.95 28 0.35 6.03 8.03 27 0.58 5.69 9.86 15 0.47 5.17 15.33 18 1.27 4.31 14.24 27 0.64 3.96 13.14 30 0.81 3.45 14.97 14 0.82 2.86 14.60 17 0.76 1.03 15.33 8 1.76 1See Note 2, Table l. PFi, i = 1 or 2 is the average amount of fuelwood in cubic meters from market sources per adult per year in the traditional or semi-traditional wood consumption population group who obtained part or the whole of his 108 Table 8.--Parameters of Market Supplied Fuelwood Consumption as Function of Fuelwood Price. EQ- 1 PFl = 2.9 - 0.18 - 84 R2 — .66 (1.84) (1.42) (0.05) 0w = 1.88 (.01>L>0) (.01>L>O) EQ. 2 PF2 = 2.1 = 0.12 . P4 R2 = .71 (0.83) (1.23) (0.04) 0w = 1.65 (.01>L>0) (.01>L>0) L = level of significance ' supply from market sources. P4 is market price of fuelwood (N/cubic meters). 3. Parameters of Paper Consumption as Function of Per Capita Income: The per capita consumption of paper for the low-, medium-, and high-income population groups are calculated as functions of changes in per capita income in the various income groups. The parameters here are estimated by judgement rather than by regression methods because of insufficient data on per capita consumption of paper. Average annual consumption of paper per adult in the various income brackets in the base year (1974) were estimated by asking the respondents the number of periodicals including newspapers, magazines, and journals they read and how often each was published, and how many printed books and writing books they buy in a year. Respondents in the medium and high income groups were also asked in addition how much toilet paper they buy per month. These were converted to annual quantities. Average amounts of paper in kilograms were found per person in each income group. The averages for the medium and high income groups were doubled assuming that peOple use as much paper at home as at work. These averages were used as constant parameters. 109 The same respondents as above were asked how many more magazines and reading and writing books they would buy if their incomes doubled. These were calculated as percentages of periodicals and books now bought. Arithmetic means for these were taken for different income groups and multiplied by 1.5 assuming that the volumes of newspapers and periodicals will probably increase as income increases in the aggregate. These were used as the coefficients of per capita income in the consumption of paper. These estimates are presented below: . + . - P PAPl = 03 0 0 (15>c1l 011(0)) PAP2 = 2.9 + 0.0013 (PCI2 - PCI2(0)) PAP3 = 31.0 + 0.0023 (PCI3 - PCI3(0)) PAPi is per capita consumption of paper in kilograms in the income group i, PCIi is the per capita income (N/year) in the income group at time t, and PCIi(0) is the per capita income (N/year) at the base year. i = l, 2, 3 represents low-, mediumr, and high-income brackets respec- tively. The coefficient of change in income for the low-income group is zero. If the income of anyone in this group changes enough, he is clas- sified in the medium-income group. Because of lack of information we assume that changes in per capita consumption of paper for high school and college students will be proportional to changes in medium and high per capita incomes respectively. 4. Other Parameters Estimated: In Table 9 the results of the regression estimates of other parameters are shown: 110 Table 9.--Estimates of Other Parameters. EQ. 1 HST = ~31721 + 36.02-GDP 283.87-EB R2 = .57 (15810) (1312) (16) (309) DW = 1.69 (.39) (.05) (.38) EQ. 2 CST = -88 10.420-EB 55.912-THG R2 = .85 (69) (35) (19) (17.319) DW = 1.54 (.32) (.17) (.08) EQ. 3 JOB = 2863 9.09'GDP 5.31-SB R2 = .82 (1737) (7812) (4.85) (15.34) DW = 2.43 (.12) (.ll) (.74) EQ. 4 RSI = 327 0.07-AGP 5.19-AB R2 = .96 (173) (54) (0.02) (7.25) DW = 2.02 EQ. 5 SNI = -41776 0.23‘GDP 1.96'SB R2 = .87 (2771) (21546) (0.07) (0.20) 0w = 2.12 (.02) (.04) (.34) EQ. 6 BED = 106 0.04-GDP 188.92-HB R2 = .72 (101) (48) (0.01) (6.17) 0w = 1.83 (.26) (.76) (.02) EQ. 7 PRD = 333 0.15-GDP 2.32'TB R2 = .84 (214) (139) (0.07) (0.59) 0w = 1.69 EQ. 8 ETAl = 0.97 0.02-AB 226.74-PD R2 = .92 (.01) (.01) (.01>E>0) (.01>E>0) DW = 2.57 (0>.01) (.01>L>0) (.15) EQ. 9 ETA2 = 0.02 0.02-AB 16.63'PD R2 = .93 (.01) (.01) (.01>E>0) (.01>E>0) DW = 2.54 (.06) (.01>L>0) (.09) EQ. 10 TMT = 37 4.03'BLN R2 = .91 (12) (73) (.69) DW = 1.37 (.62) (.01>L>0) EQ. 11 RIG = .19 0.00099'PDF R2 = .72 (.02) (.04) (.01>E>0) 0w = 2.41 (.01) (.03) E = Standard error of coefficient L = Level of significance HST = Number of incoming high school students (people/year) 111 Table 9.--Continued. SNI BED PRD ETA ETA TM? GDP EB THG SB AGP HB TB PD BLN PDF Number of incoming college students (people/year) Number of management level job openings (jobs/year) Number of people in the traditional population group reclassi- fied as semi-traditional (people/year) Number of people in the semi-traditional population group reclassified as non-traditional (people/year) Number of additional hospital beds (beds/year) Additional kilometers of dirt road (kilometers/year) Proportional rate at which people enter traditional agriculture (proportion/year) Proportional rate at which people enter non-traditional agri- culture (proportion/year) Number of new lumber trucks (trucks/year) Proportional rate of rural to urban migration (proportion/year) Gross domestic product (Rmillion/year) Government education expenditure (Nmillion/year) Number of fresh high school graduates (thousand people/year) Total government fiscal expenditure (Nmillion /year) Agriculture's contribution to GDP (Nmillion/year) Government expenditure on agriculture (fimillion/year) Government health expenditure (Nmillion/year) Government transportation expenditure (Nmillion/year) Population density (people/square kilometer) Commercial bank loans for transportation and communication industries (Nmillion/year) Rural-urban income differential (N/year) 112 All the parameters except those of EQ. 11 were estimated on the basis of time series data. The parameters in EQ. 11 were based on cross-sectional data provided by Mabawonku (33). Sources of the Data Data presented above came from both primary and secondary sources. The primary sources are the national wood consumption surveys carried out between July and December 1974 cooperatively with the Federal Department of Forestry and Food and Agriculture Organization working on the High Forest Development Project in Nigeria. The second- ary sources are published and unpublished sources of government departments, commercial banks, the Central Bank, and so on. The National Wood Consumption Survey The objective of the survey was to observe how much of different wood products are used in the residential housing construction, non- residential building construction, casket manufacturing, vehicle body and bridge construction, fuelwood consumption, and paper subsectors. The survey was conducted at the point of consumption or at the house- hold, institution, etc., levels, i.e., we observed the wood that is already in use. We could have as well observed the wood at the produc- tion point or at the distribution level at a point in time and derive estimates of consumption. This would be probably easier but it would not provide sufficient insight into the use to which the wood products are put. Ideally, observation should be made at the three points and comparison made but we could not do this because we were limited by resources . 113 1. Some Problems Encountered in the Survey: The enumeration included both counting of pieces of wooden items, taking measurements and photographs of them as well as of buildings to be later presented to building architects and engineers for the estimates of amounts of wood involved. We could not trust that hired enumerators would do these as carefully as required, so the investigator with an assistant carried out all the interviews and completed all the questionnaires personally. This practice limited the sample size but it makes us aware of all the measurement errors and other biases that are involved in the field work. Four provinces approximately representing four natural forest zones, namely, Ibadan (rain forest), Enugu (derived savannah), Zaria (savannah), and Sokoto (subsahelian) were surveyed. The original arrangement was to include Port-Harcourt, Calaber, and Jos provinces. The bureaucratic processes in the Federal Government resulted in loss of time. Vehicles were available but government drivers were in short supply and they alone can drive government vehicles. Travelling advances were approved for the assistant who went out with me on the field trips but never made available in time. One or the other person who would sign the voucher would not be "on seat." The FAO project director tried to help out by providing the travel advance on loan for the assistant but when the loans were not refunded, he ceased. When we were able to go out, we were not able to work long hours as we would have wanted since our time was limited and since we would meet more peo- ple at home in the evenings. Civil servants were reluctant to work out- side office hours. A guide and interpreter was provided in each province by the arrangement of the Federal Department of Forestry. The guides who were all forest guards were very useful. 114 It was our intention to take measurements of buildings and fur- niture and observe their wooden parts personally. We could not always do this in the provinces surveyed in the north because of religious restrictions. In those areas we relied on the respondents for the measurements. In the same areas too, we were required to get permit from the chief and the sub-chief before we carried out any interviews in each village. The permit was never refused once we could locate the apprOpriate chief but further time was lost in locating him. Except in the Ibadan province where some interviewees were reluctant, most of our respondents were quite polite although some of them believed that through our study they could get scarce wood products cheaper. We tried to explain that through our study wood products may become cheaper but not right away. For this reason we suspect that some of the price information we got, particularly in Enugu province, was exaggerated. We avoided questions on the sensitive area of personal income and tried to classify people in income Groups on the basis of type of house in which they lived. Except in a few cases where the date the residential buildings were set up was carved on the wall in front of the buildings, the ages of buildings were often guessed. There were no other written records and memories were short. Measurements of houses and furniture presented problems, especially in the rural areas where there was absence of regularity in size and design of buildings. In the non-residential buildings, too, the sizes of buildings varied widely from single-floor to multi-floor buildings. In such cases, it becomes unrealistic to count number of houses. 115 2. Sampling Procedures: Rural household surveys were limited to villages located between ten and thirty kilometers from the outskirts of the major city in each province included. We selected five routes leading out of the city, including branch routes, and making sure that they were as far as possible in different directions around the city. Starting at the tenth kilometer from where by judgement we considered the city limit on the route we chose and enumerated the first five, ten, or fifteen households (see Table 10) on the right-hand side of the road. It will be relatively easy to recognize and repeat or avoid the same households if another survey is desired. In the urban residential wood consumption survey the major city in each province, namely, Ibadan, Enugu, Zaria, and Sokoto, were included. Each city was stratified into high and low income neighbor- hoods which were distinct and easy to recognize. The high income neighborhoods were not many, in most cases one or two. From a list of streets obtained from the city council office, one street was selected by a random method in one high income and in one low income neighbor- hood. Any twenty households in the selected street in the high income neighborhood and any thirty households in the street selected in the low income neighborhood were enumerated. Very frequently people were not available at home by the time we visitied so we continued until we got enough households where we could find respondents. The survey in the non-residential building construction sub- sectors included hospitals, elementary schools, high schools, religious, commercial, and public administration centers. These were also surveyed in the same provinces as were the residential constructions. Lists of elementary schools, high schools, hospitals, public administration 116 Table 10.--Locations of Households Included in the Rural Residential Wood Consumption Sample Survey. Distance From Number of Province Route Outskirts of City Households (Kilometers) Included Zaria Samaru - Hunkui 15 10 Slika - Tandama 18 15 Zaria - Kaduna 21 10 Zaria - Soba 21 10 Kuolan - Roga 15 5 Total 50 Sokoto Sokoto - Kware 18 15 Kware - Gande 18 10 Sokoto - Bodinga l3 5 Sokoto - Gusau 21 10 Sokoto - Gorougo 20 10 Total 50 Ibadan Ibadan - Imaw ll 5 Ibadan - Ijebu - Igbo 14 10 Ibadan - Apomu 13 15 Ibadan - Moniya ll 15 Ibadan - Oshielo 15 5 Total 50 Enugu Enugu - 010 17 10 Ninth Mile - Nsukka 5 15 Enugu - Okbokubono 21 10 Emene - Abakaliki 10 10 Oji River - Awgu 7 5 Total 50 Grand Total 200 117 centers, and churches in both rural and urban areas and mosques in the urban areas were available in the various offices of the ministries, city councils, church headquarters, etc., if one searched hard enough. Rural mosques were often open grounds. From the various lists, ten elementary schools, three from the urban low income neighborhood, two from the urban high income neighborhood and five from the rural area were selected by a random method in each province. Five high schools in each province were selected from the list. Effort was made to include one of secondary grammar, secondary modern, secondary commercial, vocational, and teacher training schools if available. Five hospitals were selected from the list in each province. Where available at least one public or quasi-public, and one private hospital, one "health center" and one maternity home were included. Ten religious centers, five in the rural and five in the urban areas, were selected in each province. They included at least one church, one mosque, and one shrine where these were available. The religious centers included were only those with erected buildings and where more than one household worshipped. Actually no religious center was enumer- ated in the rural areas of the two provinces in the north because none were found. One public administration center in each province was included. Besides differences in size of buildings they were fairly uniform in terms of furniture in the buildings and the building materials. Commer- cial centers in rural areas are market places. We included one of these in each province. In the urban areas, we included at least one retail shop, one workshop, and one factory if they were available. 118 3. Estimating the Amount of Wood in an Item: At the end of the enumerations we went to the senior architect in the Western State Minis- try of WOrks and Housing in Ibadan, a foreign building contractor who has worked in Nigeria for twenty-six years, and to a building engineer in Enugu with our photographs and measurements of buildings and the records of parts of them made of wood. These people made independent estimates of the average amounts of different wood products in each group of houses, furniture, and utensils as well as in caskets, lumber trucks and bridges. We calculated the arithmetic means of the esti- mates by the three people and used them in our model. Sources of Time Series Data Government expenditures for agriculture (AB), education (EB). transportation (TB), economic services (SB), and commercial bank loans (BLN) for transport and communication were obtained from various issues of the Standard Bank Group, Annual Economic Review, Nigeria (53), Central Bank of Nigeria, Economic and Financial Review (4), and from John W. Hanson et al., Report on the Supply of Secondary Level Teachers in English-Speaking Africa (28). AB and EB are recurrent expenditures. TB which is transport and communication and SB which includes education, health, agriculture, construction, transport and communication and other economic services expenditures are both capital and recurrent expenditures. Gross domestic product (GDP) and agricultural gross product (AGP) were obtained from Gross Domestic Product of Nigeria, 1958-59 to 1969-70, Federal Office of Statistics (26), and from various issues of the Standard Bank Group, Annual Economic Review, Nigeria (53). 119 Number of new timber trucks, additional kilometers of dirt road per year and price indexes at 1960 level were obtained from various issues of Digest of Statistics, Federal Office of Statistics (10). New lumber trucks are new registrations of commercial vehicles excepting trailers, cars, and light trucks. Additional kilometers of dirt roads are the sum of tarred and gravel or earth road in year one minus the same in year zero assuming that every road starts as dirt road. First-year high school and college students respectively were obtained from various issues of Statistics of Education in Nigeria (55). High school includes secondary grammar, secondary commercial, secondary modern, vocational schools, and teacher training. College includes universities, polytechnics, schools of arts, science and technology, and advanced teacher training colleges. The preportion of farms that are traditional and the proportion that are non-traditional are derived from Rural Economic Survey of Nigeria, Consolidated Results of Crop Estimation Surveys, 1968-69, 1969-70, and 1970-71 (52). A commercial farm is a farm household farming two hectares or more of food crops or one hectare or more of tree crops . The numbers of new college-level jobs were estimated from various issues of Digest of Statistics (10), Statistics of Education in Nigeria (55), and Investment in Education, the Report of the Commis- sion on Post-School Certificate and High Education in Nigeria. College- 1evel jobs were defined to include managerial and professional positions and positions for teachers with Nigerian Certificate of Education and higher qualifications. The sectors included are government services, manufacturing, construction, electricity, transport, and teaching. 120 The rural-urban income differential (PDF) and the proportional rate of rural-urban migration were adapted from Mabawonku, Impact of Rural-Urban Migration on the Agricultural Economy in Western Nigeria (33). PDF is government minimum wage rate in 1971-72 minus average cash earnings per head of rural household in the same year in the selected communities. Proportional rate of rural-urban migration is the rate of rural-urban migration in the selected communities in 1970-71. Reliability of the Data: The accuracy of the projections yielded by the model will depend partly on the assumptions we make about the relationships between variables and the values we assign to the various variables and constant inputs. We have regretted that the average amounts of some wood products in some uses could not be varied as func- tions of prices in the present projections. We are, however, more concerned about the accuracy of the values we assigned to them and to other inputs which the model receives as constants. The major source of this concern is the size of the sample covered in the national wood consumption survey from which most of those constant inputs were derived. The differences in the living conditions in Nigeria with such variations in vegetation, economic conditions, educational backgrounds, etc., described in Chapter I cannot be observed in four provinces. We have three main reservations about the variable inputs, GDP, government investments, and others. One major reservation is about the assumptions we have made about their future values. The economic condi- tions in Nigeria are so unstable and the information base so scanty that it is very difficult to make dependable assumptions about income and 121 expenditure for any extended lengths of time. We shall discuss this reservation further in Chapter VII. The second source of concern about variable inputs is the time series data used to estimate most of the coefficients. We have pointed out that they are generally incomplete, of short duration and are likely to have high degrees of errors. We attempted to make up for some of the incompleteness by trying to assemble some data from unanalyzed informa- tion in the files of the Federal Office of Statistics. We discovered many empty cells in the questionnaires. Because of the short duration of most of the time series, our degrees of freedom in the regression analysis presented above were between seven and ten. The next reser- vation is the simple nature of the equations. In some of the equations we wanted to use exponential functional forms which will be more consis- tent with our basic assumptions but we needed more and better data to do so. Since we are using these equations for the purpose of predicting the time paths of modernization processes, we are not as much concerned with the functional form and the numbers of degrees of freedom as we are with the accuracy of the data. We recommend that when the model is rerun with a new set of data to make fresh projections some coefficients should be reestimated with cross-sectional data and that the sample size be extended in the next survey to cover various geographical, vegeta- tional, and economic backgrounds in Nigeria which we could not cover. CHAPTER VI SUMMARY OF BASE RUN ESTIMATES AND PROJECTIONS Introduction In Chapter I it was stated that an objective of the present study was to develop a computerized simulation model which could be used to estimate wood consumption levels for Nigeria whenever the necessary data are available. In Chapters IV and V this model was developed and computerized. On the basis of data presently available, the model was used to estimate the annual wood consumption in various wood-using subsectors in the past from 1965 to 1974 and to make three sets of pro- jections of those estimates into the future up to the year 1990. Projections were also made for each of the population groups, high school and college enrollments, management level employment opportuni- ties and other intermediate outputs. One set of projections, called the base run projections, is based on projected GDP for the period 1975 to 1980 made by the Federal Government of Nigeria. Estimates and projections are also made on the basis of two alternative assumed rates of growth of GDP. We shall pre- sent the base run estimates and projections in this chapter and present the projections under alternative assumptions in the next chapter. 122 123 Assumptions About Variables GDP and Government Investments In the Fourteenth Independence Anniversary broadcast General Gowon said that during the period 1975-80 the gross domestic product of Nigeria is expected to grow from N13,962 million in 1974-75 to 824,235 million in 1979-80, indicating an average compound rate of growth of 11.7 percent per annum (7). In the same broadcast General Gowon summarized the highlights including government expenditures of the Second National Development Plan ending in March 1975 and of the Third National Development Plan beginning in April 1975. These govern- ment estimates form the basis for the base run projections made with the model. We assumed that the gross domestic product will continue to grow at 11.7 percent per annum between 1975 and 1990. But the base figures of N13,962 million for 1970 and 824,235 million for 1975 were reduced to their values at 1960 price levels. It is also assumed that government expenditures will continue to grow at the governmentally projected rates of the period 1975-80. Expenditures at the final year of each five year period are assumed to be 25 percent higher than expen- diture for the first year of the same five year period. General Gowon stated in the broadcast cited above that "by 1973 about N315 million (out of the 1970-75 plan transportation budget) had been spent by the governments of the Federation on the various transport modes. By March 31, 1975, it is expected that expenditure (on the various trans- port modes) will be at least twice that figure" (7). Frequently a greater prOportion of budgets for various National DevelOpment Plans are 124 spent towards the end of the plan period. The assumptions about the gross domestic products, and government expenditures for agriculture, education, and transportation for the base run are summarized in Table 11. Table ll.--Annual Values (at Five Yearly Intervals) of GDP and Govern- ment Expenditures Assumed for the Base Run (N million/year), 1965-1990. Agriculture Education Transportation Year GDP Budget Budget Budget 1965 2,990 30.0 26.1 1.7 1970 3,700 43.1 56.1 10.5 1975 10,254 226.0 380.0 784.0 1980 16.840 372.8 610.0 1,098.0 1985 26.120 515.4 852.6 1,516.0 1990 37.920 786.4 10.446.0 2,306.0 Other Variables Some of the other variables about which assumptions were made are agriculture's contribution to GDP, the ratio of price index of wood to the price index of substitutes for wood, average per capita incomes for the medium and for the high income brackets, and the loans provided for the transportation industry by commercial banks. Agriculture's contribution to GDP is assumed to continue to grow at its 1958-74 average rate of 3 percent per annum. 125 Projections of Intermediate Variables Some of the intermediate variables projected by the model are population of adult persons by traditional, semi-traditional, and non- traditional population groups corresponding approximately to low income rural population, medium income and urban low income population, and high income population groups. Projections were also made for high school and college enrollments, deaths, management level job oppor- tunities, numbers of lumber trucks and hospital beds, and annual rates of construction of buildings in the various wood using subsectors. The projections of these intermediate variables are based on various defini- tions and assumptions which are adequate for estimation and projection of wood consumption, but may not always be adequate for the estimation and projection of the intermediate variables because the estimation and projection of the intermediate variables are not the primary objectives of the study. We shall summarize the projections of some of these intermediate variables. Population Population projections are needed and made for the total and for the annual growth of traditional, semi-traditional, and non—traditional wood consumption population groups. Projections are also made for totals and changes in high school and college enrollments and for total deaths. The projections of the population component of "A Generalized Simulation Approach to Agricultural Sector Analysis with Special Refer- ence to Nigeria (GSAASA)" (35) form the basis of our projections of traditional, semi-traditional, and non-traditional wood consumption population groups. The GSAASA estimates of the total population are 126 lower than the results of 1973 population census in Nigeria by 7.6 per- cent. But as pointed out in Chapter I above some of the unexpected increase in the Nigerian population from less than 56 million in 1963 to over 79 million in 1973 can be attributed to statistical errors. The 1973 population results are still controversial and not completely accepted in Nigeria. The resort of the present study to GSAASA for population projections is not due to a confidence in its accuracy. It is rather because it is the most up-to-date population estimate that we could find for Nigeria. Population projections of our model are pre- sented in Appendix C and summarized in Table 12 and Figure 8. Table 12.--Summary of Some of the Population Estimates and Projections, 1965-1990. High School College Traditional Semi-tra- Non-traditional Students Students Population ditional Population Year (Total) (Total) (Rate/Year) Population (Rate/Year) (Rate/Year) 1965 251,424 7,750 355,380 181,171 7,380 1970 352,059 11,501 278,920 181,912 9,909 1975 960,574 21,546 256,644 198,972 24,201 1980 2,001,755 55,408 232,955 356,753 41,365 1985 3,210,549 102,128 240,227 509,278 68,790 1990 4,827,840 153,868 249,760 702,165 104,475 Building Construction Rate Estimates and projections are made of rates of construction for residential, school, farm, commercial, religious, public administration, and hospital buildings; bridges; and lumber trucks. These are presented 127 ‘0‘) «0a00_3000 «upon—4&3: .m.aoa .u.aoa .wuaoc 00, c0 0 O C O O O O O O O O O O O 0.. 00 06.00.... occwonou. 00.0000». IL“; on ex 1). < o 1 o 00 cu ooowaoun. coowuoun. 00.00000. apt—on ut_» Na» co 1" on :0 o o o o o 0 n 0 0 0 Q C e 0 0 O c a 0 a 0 c a c c c o o 4 o 0 an co ooow~nnn. ocou~nun. «coumoan. not .030 50 «03000 (CC. ‘C‘CCC (C‘CC Lo 0N ca 9 o . u o b u n a c u u u 0 O C U u u v e u u u u C O C m I r on o o D o O a D Q a D 8 c 0 o a m o n 0 I o n c o. o 0 cm an en odoxndcu. 00.00000. oaounacu. 0000 0000 000000 0.0.0000 0.0.0000 0.0.0.04 0.0.000“ 000.000“ ene.oooa cae.saoa can.0ooa 000.0000 eoe.¢0ou eno.moou . «~0.noou one.voou ena.voou 0.0.0000 one.roou eae.~ooa eao.~oou ene.uoeu 000.000“ one.ecou . eno.eaoa 0.0.oao can.o~o en0.o~ou eaa.¢~ou ene.useu 0.0.ssou aao.0so« 0.0.059“ en0.rsou 000.000“ 0~0.0soq 0.0.0sou u 0~0.nso« u 0.0.rson 0~0.~nou eoo.~aoa one.usou ona.«~ou cae.esou ene.esou eno.ooo« 000.000“ ear.¢oou 0.0.coeu cac.uoon rar.soou 050.coon 0.0.009“ ene.rooa 0.9.000“ O O OUUUUUUUUUDUUUUUU .enps .0... .e..a has. Run Projections of Annual Growth of Traditional (A). Semi-Traditional Figure 0: (l). and Ion-Traditional (C) Population Groups (people/year). 1965-10“". 128 in Appendix C. These estimates are subject to the same errors and follow similar time paths as the estimates and projections of the rates of wood consumption. The rates of construction of residential, farm, school, and hospital buildings and bridges is not exactly proportional to the rates of growth of independent adult populations, farm populations, student populations, hospital bed, and road kilometers. Replacement of decayed buildings after losses resulting from deaths, leaving farm business, conversion of dirt roads to modern ones and losses of buildings by accident are considered. The rates of construction of commercial, religious, public administration, and elementary school buildings are proportional to the rates of construction of residential buildings for the traditional, semi-traditional, and non—traditional population groups because they are estimated as constant proportions of residential buildings. These projections are presented in Appendix C. Other Intermediate Variables The projections of other intermediate variables namely manage- ment level job openings, deaths, numbers of lumber trucks and hospital beds, and the rate of rural-urban migration, are presented in Appendix C. Projections of Annual Wood Consumption The wood using subsectors discussed in Chapter III are residen- tial housing construction, non-residential (school, religious, commercial, public administration, and hospital buildings) building construction, farm construction, casket manufacture, bridges and vehicle construction, fuelwood, and paper. Estimates of the past annual wood 129 consumption made for these subsectors are based on actual levels of GDP, government expenditures, and other variable inputs of the model in the past. Projections of annual wood consumption in the future in the subsectors are made on the basis of assumed levels of GDP, public investments, and other variables and on model projected levels of pOpu- lation and other intermediate outputs. The projections of annual consumption of unprocessed wood, processed wood, building board woodpulp, fuelwood, and paper woodpulp in the aggregate are presented in the Appendix D and summarized in Table 13 and Figures 9, 10, and 11. Table 13.--Summary, Base Run Projections of Overall Annual Consumption of Different Wood Product, 1965-1990. Building Unprocessed Processed Board Year Fuelwood Wood Wood Woodpulp Paperpulp (000.3) (000M3) (000M3) (000 kg) (million kg) 1965 39,876 3,738 700 239 147 1970 57,822 2.972 640 245 175 1975 65,693 2,744 801 382 1,761 1980 70,804 2,666 942 548 3,518 1985 75,565 2,897 1,168 771 7,949 1990 79,740 3,290 1,501 1,055 16,876 We shall next discuss these estimates of annual wood consumption made for the base run in more details under the following subtitles: (1) wood for construction and manufacturing purposes, (2) fuelwood, (3) wood consumption requirements by wood consumption population groups, and (4) wood for paper. 45‘ “BJ‘J’ “-b() ‘0‘ 130 nubqupat3c cow 00.00.3000 .0. ...ex» ~0.u...p. c. :0 cs :0 C O O. O O c c d C C a 1 0 C O O 0 ‘0 c 4 a a o o o o O O O O o a O O .C 0 d 4 a c a o o o o co c. as so soomuaun. apt—ad wt—» and «a co :0 cu O O O O O O O O < a u c 1 C o o o o d 0 G c c c 4 c c a o o o o c c c o 0 c O C O O O O O 0 co co :1 «w eeounvan. booms-e~. 8c; peas 5‘ awacuv O O pqr.eeou cqe.e09« ¢a0.009« .ae.eoou —~$0f83d 0.0.csau ear.sson eno.noo« enr.ccou 0.0.ccou .nr.reou pae.rcou ann.vra« e.0.000« pnr.reou pae.noon onr.ncou .ao.weeu eno.uno« enc.ucon car.eao« 090.009“ can.eaou eac.oseu eeo.cpou rea.eseu can.nso« 0.0.ssoa rar.oson cae.onou 000.0000 coo.rso« cno.osou pae.osou eep.nsoa eno.nso« can.«so« ceo.ws9« can.«son ooa.«nou cer.asou 000.csou cor.ooau rae.ooeu o.r.909d 0.0.000“ 0.0.koau 9.0.009“ e_o.eoe« .ne.coo« .ar.roeu ae.iar.0. loo. Run Projections of Annual Consumption of Unprocessed Hood. 1965-1990. figure 9: I.. 1'”. c .‘KC'OK (N C ....n.nm trikbnnn n 113]. mu»040 05 awaoun O 0 000.0000 000.0000 000.0000 000.0000 000.0000 0.0.0000 000.0000 000.0000 000.0000 000.0000 000.0000 000.0000 000.0000 000.9000 000.0000 000.0000 000.0000 000.0000 000.0000 000.0000 000.0000 000.0000 000.9000 000.0000 ene.rsou 000.0000 000.0000 000.0000 000.0090 000.0000 090.0000 000.0000 000.0000 000.0000 000.0000 000.0000 000.0000 000.0000 000.0000 000.0000 000.0000 000.0000 000.0000 000.0000 000.0000 000.0000 000.0000 000.0000 000.0000 000.0000 000.0000 000.0000 ocomaene. Base Run Projections of Annual Consumption of Processed Hood. 1965-1990. Figure 10: 14.0.: it.» flu“ 0:. t. 0990‘.- .0 .00 0041...; 0.. QN OBthfikfl. 00.00.00.- .. 132 mu..u_4;:c mup¢u_4a3c 441) co, 0 cap avouo.=u. co en es :0 o o o 0 g o o o o c c c c c c o o a o O O O O o o o o o o o o co no us ca ooomomos. aha—on at.» ~uu cu cv O 6 cm :9 ocomacsn. ac. bean ha mn4¢um 0 0 cm .~ oa.ucns~1 “ cu ooe.eoou . ene.eoou ena.oooa coe.ooo« e.o.coou aaa.caoa one.soou eoe.soo« eao.¢ooa coe.coou cae.nooa o one.noou cae.voou one.ooou ooe.nooa ooe.noou one.woou eoe.moou aeo.«oou eno.«oou coo.ooou o eoa.eoou eeo.o~oa eoe.o~ou eac.¢nou eoe.¢so« one.ssou cae.ssoa eoe.o~ou coo.cson aoo.nkou o ooe.n~ou eoa.osou aoe.vson eoe.nsou e.e.nsou o.a.-o« ego.~soa noe.usou eae.«~ou < cae.esou c . eoe.eaoq o.o.eoou age.ooon eae.ooou o—e.eoou « eee.soo« 4 cae.soou c one.cooa . cae.ooou 4 rae.eoou <. poa.roou c geomornn. C‘C‘ Base Run Projections of Annual Continual: of Paper Hood Pulp. 1965-1990. tuna! 11: . l a: P; 3‘ 0 CK AJ» m C» L A. .VIJ $ :3 I1. .74 i {JOE/OI 3* h IL‘C" ~ ,5. «Ir-eat 133 Wood for Construction and Manufacturing Residential housing construction, non-residential building construction, and casket manufacturing, lumber trucks and bridge con- struction are the construction and manufacturing subsectors for which annual wood consumption are projected. Projections of annual wood con- sumption in the residential housing construction subsector both in absolute amounts and as proportions of overall annual wood consumption are summarized in Table 14 and Figure 12 and presented in Appendix D. The absolute amount and the relative proportion of unprocessed wood required in the residential housing construction subsector increases with increasing income. The relative proportions of processed wood and building board woodpulp decline while their absolute amounts increase with improving economic conditions, implying that other sub- sectors using processed wood are expanding faster than residential housing construction subsectors. We have asserted in the previous chapters that most of the pro- jected increases in the annual consumption of various wood products up to 1990 are only the partially lagged consequences of the present high rates of public spending. The governments are currently spending money in building hospitals, roads, schools, etc. and wood is being used in increasing proportions for those purposes. Some lagged consequences of large expenditures on wood consumption will appear later as increased proportions of processed wood in residential housing and furniture con- struction when the consequences of the current high rate of spending on people materialize. This will happen when the people who are currently benefiting from the present high public spending on education 134 NMbo. Han vmmm. own gmmm. MHH.H Gama momm. mom amom. How osmm. mum mmma ammo. omm mmoo. Ham omam. omm omma momm. mmm ammo. mam Hmmm. «ms msma move. «ma mmfln. Hoe mmsm. Hmm osma acme. oma came. «we mmmm. mom mood ~u>\.moumv Ausxmx oooc Au>\.moumv Ausxmzoooo Au>\.moumo Aus\mzooo. newuuomoum undead cofluuomoum undead cofluuomoum ucaoam m>quHmd quHOmQ‘ w>flUMHQm mufiflomfi m>flUMme QubHOmnfiw H00» masmpooz canon mcwwawsm @003 @mmwmooum II. 0093 commoooumco .ommaumoma .coHuQasucoo oummmuomt mo aco«uuomoum no can up::0&4.mus~onn¢ umcwmsom Hmwucmcammm ca coHumESmcoO c003 danced no mcowuoonoum and moon .auaalsmn|.va canoe II 1' .- .KJCU' .HOfittlm. 135 “CL un».u.a.:c ... c. a. .5 .o on o. .. o~ c” c a a o o...oooa c o o o t o o o o u o o one.eocn a a u c......« a a c c......« c a u coo.cooa q a o coo.eoou a F u noe.ucou c a u eno.s.ou coo.o-o« ooe.coou c I 0 900.0904 0 o c o o o c o o o o co 0 0.0.9..” 1 c u . oca.v¢ou c eoe.ooou a c o eoa.noon a a u eoe.noou c o o eoo.waou a a u e...~oou a n u ego.uooa a a o ..-.uoon c a o ooe.eoou o eoc.oco« eee.o~ou one.o~on a . ooa.oson a o ooo.¢~o« c n eoo.snou a - one.sson a - coo.onoa a on..oso« a o 09¢.rsou o o o o o a...nson c a coo.vson - oa9.vsou . - .o..n.o« . o .oe.nsou a - eoe.nsou c - a.o.~sou a - 9.9.«sou a - ano.usou a a oao.euon o 4 o o o a o I O o O O ane.en0¢ . a eoo.ooou c . ace.ooou < a ooc.¢oou - c eoc.coou c ene.noo« . 99-.soou a coo.eoou a rue.coou . eao.roou o o o o o a. c O o o 0 999.9008 ma..o.u¢:¢ co. co co o~ .o an o. o u an .n.mwza acouowau. eoowooco. ooowc-c. 9o..wo-num acouooqu. .~.u.za .c...ua.. ooowoooo. oeom.-o. o..u.-.n. o..u...u. ...m.¢a ......«_. e..m..oo. .e.w.-o. o..u...nm o..m...«. n»z_o. us.» «an me. sad. sq .u4.un C . 90. In: Construction. Hood (A). MM loo-1hr:trajecthuusoflhunlu.Couuuqnflou oflluxvccsoad Hood (I) and Building Intro Ibod Pulp (C) in lauidontlal flout isusqsso. fluufirl2: 365:2 2 to .7017. longer 1 5cm pro 136 graduate from high schools and colleges and adopt non-traditional living standards for housing and other consumption goods. Projections of annual wood consumption in the non-residential housing construction (school, religious, hospital, commercial, and public administration buildings) are presented in Appendix D and sum— marized in Table 15 and in Figure 13. The absolute amounts of building board woodpulp increases while its relative proportion declines. The absolute amounts and relative proportions of processed wood and unprocessed wood in the non-residential housing construction increase with improving economic conditions for the same reasons that those pro- portions decline in the residential housing construction. The model can supply the information on absolute rates and proportional rates of wood consumption separately if desired for each of these subsectors aggregated together under non-residential housing constructions. Table 16 and Figure 14 summarize the estimates of annual con- sumption of processed wood in casket manufacturing, lumber truck, and bridge construction subsectors. These estimates are presented in detail in Appendix D. Assumption was made that these subsectors do not utilize unprocessed wood and building board woodpulp in significant amounts. The rate of wood consumption in casket construction increases at decreasing rate in absolute amounts and decreases in relative propor- tions as economic conditions get better. This results from declining death rates which will be expected as pOpulation shifts from traditional to non-traditional groups as economic conditions improve. Bridge and lumber truck construction subsectors individually do not consume signifi- cant pr0portions of processed wood. :UFD We .11, F. m .04 137 masmpooz oumom mcapawsm @003 pmmmoooum @003 pommmooumco Hemm. mam.m>a «mum. mmm.vam NmHo. moa.mm omma Nomm. mnm.ama momm. moo.®mm vmao. hmm.vv mmma amen. m¢~.hm mhwm. mmw.mmm NvHo. mum.hm omma mvwv. www.mh mmom. mom.mm~ Hmao. omm.mm mhmH ¢mmm. mmm.ov mqam. mmm.mma mmoo. HN¢.@N Chad when. mom.mm HmoN. www.mma mhoo. For.mm mood Au>\.moumv Au>\mxv AH>\.moumv Au>\mzooov Au>\.moum. Au>\mfiooov noduuomoum u::o&¢ newuuomoum undead cowuhomoum undoE¢ m>wumamm quHOmnm m>wumawm musHOmn¢ m>wu~amm mandamnd How» .ommanmoma .cofiuQESmcoo Hamum>o mo mCOwuuomoum nu 0cm mucaoE¢ mandamnd "mewpaasm Hmwucmpammmacoz ca coflumsnmcou @003 Hmsccc mo mcowuoonoum cam mmmm .aumeasmlu.ma manna 0..., ‘u1fikflfkn u...‘ DdObhdck. n.\l.§ :e. .c;& an ocean. 138 muhau_gaac cow 0. O O O 0 O O a a a a c a o o O O O O 0 O mu..u_laao co« co .n.vwgzz o¢.uoc~a. ...mm-za o..u.n... ...uuaza o‘.u-r~«. a. on o o on as onomso¢~. coousoa~. oceanooa. ups—on at.» «ua co c. o o u o u u u u c u o o u 0 o u o o o o a a c a a a o t o o o a o a o o c 9 n O o 0 en av oeouvnoa. ogowouou. ooououou. can bean to awacum UH. O 9 9* an n..msuos. n..wnu.s. n..usaosu CCCC ‘Q‘C UUUU 000090 UDUUULGU CQCC‘. cu CCCC C... CC.‘ CC. .CC“C O o.-.oooa a....-oa noo.oooa e...ooou .00....“ a......« a»..soou 9......“ 9.....oa 2......” o~¢.noou e...o-oa .o..ooo« e...oooa 0...».on o...»¢.u o...~oou an?“ o no o...uooa 9......“ u...eoon ecu.ouoa e-o.o~o« e...oson eco.osoa e...-oa ea-.ssou e...osoa ea..osoa coo.osoa cno.rson eac.v~oa 9......“ ..-.n.oa o...»uou an..~soa one.wsou c...«~oa a...nso« eqo.osou eoe.onou eoe.ooou ea-.ooou one..oou ene.coou an-.soou «no.50ou «aa.ooon one.ooou pne.ooon o 9.0.0.0u .o... ...o. .o..» COIIIIDQICI of "Ivonne-cod flood (A). Ploeooood Incl III. (C! In the Ica-Ioold-ntlal lulldln. bail (I). d mun. but (h-tnnnthlulllumuln.lllbdwil Ila-lhnhProfiunda-Icfl'lllunl figunrlla 139 cofluoauumcou mopwum How 0003 pommmooum cofluosuumcoo xosue umnfisq How @003 @wmmmooum mufluomuscmz noxmmo How @003 pwmmmooum Nmoo. Nmo.m NmHo. ohm.om Hmao. 5mm.vm omma mvoo. mmm.m hmao. moH.oH ammo. ohmxvm mmmn Nvoo. mmm.m ooao. hma.m mmmo. Hmo.¢m omma mmoo. wom.m mnoo. hmm.m NmNo. HmH.NN mhma haoo. omo.a NNoo. hwm.a ommo. mmm.om Ohma vmoo. mmm.a mmoo. omm.a Hamo. mam.om moma Au>\.moumv Au>\mzv AM>\.moumv AH>\mzv AH>\.moumv Au>\mzv cowuuomoum undoam coHuuomoum uc5054 cowuuomoum uczofic m>flumamm musaomna m>wumHmm musaomnm m>aumHmm GHSHOmnfl Mom» .ommalmoma .coHumEsmcou p003 pommmooum HHmuo>o mo chHuuomoum up now mucsoac manaomnm uxosua Honsaq .mumxmmu Ca @003 wommmooum mo cofiumsswcoo Hmzccfi mo unawuomnoum cam mmmm .mumafismll.oa manna 140 mupcu—Jnao cow :0 e. vs a o o o o o s o c c a q a . c o. 0 O O a . a a . ‘u o o . 0 n O O O c a c a a a . a . o o < o o a a a a a a a c c o O c O a a . a c c a a a. o o o u o mu—au.aaaa go. a. a. o. .~.oaaa n¢.uoo.~. ooowsoon. .~._x.a n¢.u..... coouuooa. .u.».ua .¢.a.o.~. r..w~vo.. nut-ac ux—p c. c. .o o o o o o o o o c a o o a o a o O o o O o 0 O O O O O O o. .n no noowooou. n-ano..a. neouoooa. ~¢~ so. scan sq umacun O O O rue.eaoa e.o.cooa eno.oeoa cae.ocoa ene.coou cae.cren one..oon o.o.sooa a...oooa one.¢ooa o.e.¢ao~ 9.9.rooa «no.vooa a...oeoa can..aoa ene.poou ooo.«aoa 9.9.woon o...¢ooa e.e.uaou roe.euoa ena.ooou coo.osoa o...o~oa on..-sou e.a.csou o.o.~uoa .e..u.oa 9.0.csoa o...¢sou eoo.nsou oco.ono« eco.osou .no.vsoa c...psou e...»sou euo.n~oa o...~sou ooe.usou a...usoa =3» c...oooa .90...." eao.oooa oae.ooou eaa.soou ene.soou one.cooa eao.coou ena.nooa ene.roon .oaan .eau— .oaa. and Bridge Construction. l965-1990. of Annual Cantu-prion of Processed Hood in Casket lunufucturu (A). Lulbor Truck (3). lamnlunlhofiunflonu ”sun ll: T. in the fa detail i: skmly d tion. T sdbsect< traditi Spendir 'J / I'," If) 141 Table 17 summarizes the projections of annual wood consumption in the farm construction subsector. The projections are presented in detail in Appendix D. The farm construction subsector consumes a slowly declining large proportion of overall unprocessed wood consump- tion. The proportion of processed wood used in the farm construction subsector is insignificant. This proportion tends to increase as non- traditional agriculture expands with income and increased public spending in agriculture. Table 17.—-Summary, Base Run Projections of Annual Wood Consumption in the Farm Construction: Absolute Amounts and as Proportions of Overall Consumption, 1965-1990. Unprocessed WOod Processed Wood Year Absolute Relative Absolute Relative Amount Proportion Amount PrOportion (000M3/yr) (Prop./yr) (000M3/yr) (Prop./yr) 1965 2,761 .7384 57 .0139 1970 2,114 .7114 23 .0060 1975 1,926 .7007 18 .0037 1980 1,792 .6722 36 .0063 1985 1,903 .6570 68 .0097 1990 2,123 .6454 147 .0171 Fuelwood Estimates are made for the annual consumption of fuelwood in the traditional and the semi-traditional wood consumption population groups. It was assumed that the non-traditional wood consumption popu- lation group does not burn wood for fuel in significant amounts. Estimates market an the annua populatic consumpt m Table large p; with ch. 1I‘ll]. be do not “305. g. from v ‘11 on "a m 3’ ’ —l (J 3 T» f-’ 142 Estimates are also made for the annual consumption of fuelwood from market and from non-market sources. We shall discuss the estimates of the annual fuelwood consumption by traditional and semi-traditional population groups under the next subtitle. The estimates of the annual consumption of fuelwood by market and non—market sources are summarized in Table 18 and Figure 15 and presented in detail in Appendix D. The large prOportion of fuelwood coming from non-market sources declines with changing economic conditions implying that in future more reliance will be made on the market for the supply of fuelwood. Market sources do not necessarily mean non-salvage sources of fuelwood. Some of the wood sold in the market in the form of wood or charcoal are salvages from various activities including farming, wood processing, and construc- tion industries. Table 18.-~Summary, Base Run Projections of Annual Fuelwood Consumption by Market and Non-Market Sources: Absolute Amounts and as Proportions of Overall Fuelwood Consumption, 1965-1990. Fuelwood from Market Fuelwood from Non—Market Sources Sources Year Absolute Relative Absolute Relative Amount Amount Amount Amount (000M3/yr) (Prop./yr) (000M3/yr) (Prop./yr) 1965 17,977 .1871 78,101 .8129 1970 .19,183 .1957 78,834 .8043 1975 19,096 .1971 77,714 ' .8029 1980 19,479 .2024 76,765 .7976 1985 20,420 .2122 75,812 .7878 1990 21,902 .2252 75,359 .7748 dc. V‘ m V9 " :5 C n ‘ H (9 M’J u [0‘ .1 ::::3 '3 khfi fl I‘D no 0 whh. O Luv. . . . O 0 4w 0 .J.) .0 mm 0 O O 2 O O O O O u - CCQC‘CCGCCCCCCCCQC . (dd C(Cdddd :0 .‘C- O O O C(‘Cddddddi 0 n .. '.. 11“...” .1 n O. WU'u 0-1 0 7mm ufi Oflnu~ Q o no 0 . . . . n K K In T on p N .4 .4 1.30 O O O 0 0 u ‘ a- Y 0 IL p.1' 1‘ Oct!) .16. Bonn“. 0 0 (10m . . . S utJ'J W .3 NM (3 o 0 on .0 d L‘ (I) a. O O O O O u ’ o a. O O O . . ‘ ‘7 In or .0 o 0 Mn Ito-w “ D a ”V mommmm .0-0 1)"? Y" ”'6‘")""r"‘ o o a. 11.16:) '3. "33313 roam-nrronmncnaca 4'0 O o . '1 '1 '\ worm a, ffif‘“ 3. O O O Q . . d N O O , . 6 O U'JJ. O O . O O t ‘ n J J o- F)’3"'3\J!MJ'$« oo-Jsa13n0'JOO-QOO'JLI'J- ouoo loo J'JO-D‘lao-Duu-J-aotvu n: N\ .J li'JJJJ-OJUIJ I ’JJJJ A‘JJ DJJ;JJ& 1.156.013.) 3.0.! aJ DJ OJJJ ‘JJJ doc ., v- : I 3J0) a a"; J p 0.331-3'93 l-J J 1') AJ'SD'IJJ ' 04") I.) 2 A") J to A" 1'. .0 o one.000.000.00.00...000.00.00.00...OOOIOOOOQOOOOO. -.-L:~ A ossfvfiflfi- ’- n-ooo'wu-"o y’u‘ mo ossfivfi‘fl‘w-s-«u‘v'un'fi trnmc Msnr*1 s 1 . .‘ . ‘u‘ .4. rANNNKKNNNKNKNIersNKKNKf1'1‘v‘f‘rf'l (‘v‘rv‘a‘v‘c 1 f9? 1"!“ “AvQQQQQQQ’QQQhym,$ffi%a‘90$39G.@-flfim$fi0f‘fia-‘.’9,fi‘Q . o. a. a" 0‘0 o “0.00-‘ o.- ooaocduoo Qflfi-Cfioud«do.00.0.1.4«(c.0404NO‘WOQOONH'OVQ-CoCdoOH-Odotdod Figure 15: Base Run Projections of Annual Consumption of Fuelwood from lon-lurket (A) and Market Sources (B). 1965-1990. 144 Wood Consumption Projections by Wood Consumption Population Groups In Chapter III the adult pOpulation was disaggregated into traditional, semi-traditional, and non-traditional groups on the basis of observed wood consumption habits of individuals in the groups. Activities in some of the wood using subsectors were also disaggregated into traditional, semi-traditional, and non—traditional activities according to the population group engaged in them. Some of the sub- sectors like hospital, high school and college building construction, casket manufacturing, vehicle and bridge construction subsectors could not be easily classified as traditional, semi-traditional, or non- traditional. These subsectors are considered as non-specific sub- sectors. Other subsectors like commercial, and religious building construction, farm construction, and fuelwood consumption subsectors were classified into traditional and semi-traditional only. Except for fuelwood consumption, which is not observed in the non—traditional group, the disaggregation of these subsectors into only traditional and semi- traditional does not mean that they will disappear if all Nigerian population became non-traditional. These disaggregations are convenient and adequate at present time. Estimates of the annual consumption of the three wood products required for various construction purposes by different wood consumption groups and in the non-specific subsectors are summarized in Tables 19 to 21 and in Figures 16, 17, and 18, and presented in details in Appendix E. The traditional group accounts for a large proportion of unprocessed and processed wood but this declines rapidly both in relative proportions and in absolute amounts with increasing income. The proportions of 145 mmoo. omH.om owmo. hmm.mm omhm. 0mm homo. mmm.m omma mnoo. mmo.mm mmao. mam.mm mmma. 0mm Hmmn. mom.m mmma vooo. mn0.hH mNHo. mwm.mm puma. mom vmvm. mv~.m omma vmoo. man.va Hsoo. mmm.mH mmmo. omm Hmom. vmv.m mhma HHoo. Hm~.m hmoo. omo.m mnho. 0mm mmam. Hmw.~ onma mooo. mmm.m oaoo. ovw.m memo. mmm maam. mov.m mood .Hs\mzv Au>\mzs AnsmeOOov Aus\mzooov .moum DGDOE¢ .moum ucsoem .moum unsofid .moum unsoac m>wumHom mDDHOmnd m>flumaom muDHOmnc wbfiumHmm ousHOmnd m>wum~om wuzaomnm Mum» mmwuw>wuo¢ coflumHsmom coaumHnmom coHumHsmom onmuommm Hmconunomne Hmaonuucmue Hmcouunomue aflsmw >9 confisvmm @003 commououmcs an Umuwswmm @003 oommmooumcs Icoz cw wouwsvmm 0003 commoooumca scoz >n ponflsqmm poo: pommoooumca .ommalmoma .mmsouu cofiumEsmcou woos >3 cowumfiomcoo 0003 pommmooumca Hamno>o mo woodpuomoum no can muGSQE4 muSHomn¢ "@003 pommmooumcs mo scaumezmcoo Hmsccd mo mcofluoonoum com ommm .>um&sdma|.ma manna 146 Obha. «um mmmm. mam mmHH. mod mmaa. 0mm omma mamm. hum @mmo. moa ommo. mom FNNN. mvm mmma hmON. mma ovuo. mo ovno. ham mvmm. com ommH boom. mod hmmo. av hmmo. mmm Howm. mom mhma mnmo. om Homo. 0H ammo. Ham moom. cam osma mono. we mbao. Ha mhao. 0mm momm. mmm moma .moum .ucaosd .moum assofid .moum u:50&< .moum ucsofifi m>wumHmm musHOmnd o>wumHmm musHOmnm m>wumHmm wusaomnd m>fluwamm musaouna Mum» mmfluw>fluo¢ coflumHsmom :owumadmom coHumHsmom oflmflowmmucoz Hmcofluwpmuelcoz HMdOAuapmuenwEwm Hmcoflufiwmua cfl pmufiswmm >3 pmuwswmm >n pmuflswom an pmuflsvmm 6003 pomwmooum @003 commwooum @003 commoooum @003 pommmooum .ommalmoma .mmsouo coflumfismcoo @003 >n coflumesmcoo @003 pomwoooum HHmum>O mo mcofiuuomonm no new Amz.ooov mucsoad ousHOmnd "c003 commoooum mo coHDQESmcou Hmscc¢ mo mcofluoomoum com ommm .thsfismun.om magma momm. mmm avam. omm momm. Ham 0 o omma mmvm. hma vama. mva vomm. hmv o o mmma thm. flea Hama. mm hamm. mam o o ommH mmmm. mmH mmma. Hm mmvm. mom 0 o mhma 7, M mmoa. hm homo. Hm mvow. hma o o onma mnmo. mm hvoo. 0H Hmmm. com o o moma .moum pascad .moum ucsoem .Qoum DCDOE¢ .moum ucsosm m>wumamm musHOmnd o>flumHmm muDHOmnd m>wuwaom wusaomnm o>wum~mm mDsHOmnd mmauw>wuo¢ coflumHsmom cofiumHsmom coHumHsmom Hum» oamwommmlcoz HMCOADAUMMBICOZ Hmcowuflomualflaom Hmcofluflpmua cw @muHDWmm masmpooz unmom manuausm >n wouwsvom QHDQUOOB oumom manoansm >2 cmuwswmm manmoooz pumom mcfiwawzm xn pmuflswom maszOOS oumom mcwoawsm .ommH moma .wmsouo :oHumEsmcou @003 >n codumESmcou Hamno>o mo mcoHuuomoum no can Aux \oxooov mu2508¢ mDDHOmn¢ .manmpooz Unmom ocapaasm mo coHumESmcou mo macauomnonm com ommm .xumEEsmul. Hm magma DC .I. II: II ‘QU' a.“I' .euom 148 C .9“ nubcu—Jtac nubao.4;aa ...oaaa» .u.~.uaa» .u.~.naa> ...¢.-aap ca— :9 can scan-.vn. scowceon. ago-.con. user-con. c. as 0‘ o as soomunnuo snowman". scowunnuo s-owunru. arr—on wt—h own ca 0 on soomnosn. scownosu. soounosu. soouoasu. cc can roam so unacon on 9 an oe.u~.o.. ...m~on.. o..w~ono. .a..~on.. MM") CI. EGO QQQC CTIQ QQQQOC‘Q r...uoou ran..oou c...eeon can.ocou ego.oco« can.ooou coe.aoo« eaa.soou eao.soou can.ooou eoe.ooou eon.roou ene.roou eaa.oooa ea...ooa can.noou eao.noou c.n.~oon eac.~aou can.uoou one..oon oqn.eoou c.a.ooo« nnn.osou en-.osou «on.onou eac.¢sou e.m.ssou pac.nnon can.osou aae.osou ear.anou one.nhon ace.osou one.v~o« can.nsou cae.nso« ene.-ou e-.~son eon.usou ene.usou «ar.asou e.c.e~ou enr.ooou eae.ooou oe:.ecou eac.noo« can.soou cae.scon pao.ooou 9......“ eno.roou .onon .0... .onon .000: III. lullhudccthuulc! Aanuul .~...mnn» so. a. a. O o.— econ-mac. oaaunnno. oaaucnao. acouorno. c. c. as a o co c. as coowoaov. ooowoaoc. ooowooov. ocowooov. mun—oa 5:.» «a» oo 0. .0 a o a o a a o a o O a a a c a n a n 0 do a c w a a a. a a a o a a a o a a do a o a a a c a c a c a o c a c O O o O 0. on .o o..w.-.n. a..u.¢an. occwooun. ooouooqn. no; ~CJO an ”madam ‘C‘C‘. cm on c« d u c o u. o u u u U . u u a u a u a u a o o u o a n a n . u m u c a c o o o D o O . a a) c t e a a 0 on c a n a O O O O c a o o o a. on ad ooouoouu. enema-on. ...u..... o..u¢.ou. ran.eoo« o e.e.eaon ene.ooo« can.ono~ e~e.roo« cao.ecoa pae.koou eno.soe« nae.eoou e.e.eoo« one.rao« A one.reou eaa.oeou paa.vmou one.nnoa eao.neeu oae.~¢on oeo.~oou coo.uaou eaa.«oou one.eooa . mao.eaon ena.onoa raa.o~oa one.c59u e~e.rsoa o u u e~e.ssou u e~a.nsou u c.e.¢noq u coo.esoa u enn.rnou o o ono.e~ou u one....« u ono.osou u eee..gon u ooo.n~ou u ooo.~soa u ono.-o« u oao.usou u aao.asoa u cao.eso« u eno.asoa u cae.oooa u use.ooo« a eao.ooo« a eoe.¢ooa cae.sooa one.soou ego.aooa e.e.ooou a .no.roou a m e.e.rooa anon-mnu. meow-cum. noowoonn. naomoouw. Base Run Projections of Annual Consumption of Processed Hood in the Traditional figure 17: (A). Sell-Traditional (B). Ion-Traditional (C). and in the Non-Specific (D) Sectors. 1965-1990. Had rcn km: a 55 nuacun (I H"‘.1 IO.‘°'OK. 150 WU nu.au—Jo>c nu»¢u_4aac .m.uaoo» .n n.ncn> .n.m.mon» cor ogauouan. . . oaowoa occuganu. :9 Ci .- oucunnon. o ownnoa. coownnou. upzuoo uz_— uuu cm coon ..ou...u. sums" c. o no poJa sq nuaauo cm on a o u o o u o u u o u u o O o a a a a o c o o o. o o a a c a a c c c o o c a 4 a c c a c a c o o. c a a a c a a c c a o o c» cu nu.- was. a .u a... anew-nos. IN- IHI‘DEI (”i C! en UH, ena.e.o« o cae.e~o« ooa.ooo« e.n.oooa cae.ceo« eoe.crou coo.soou a.e.scoa eoe.eoou cae.eoo« paa.roou o eaa.roou oae.veou eae.ooo« eac.nooa one.rcou o.e.«eoa eao.~oou ee¢.a¢o« oo..uooa eee.evo« o e.a.ece« ooe.osou coo.oaou coo.o~ou o.o.¢noa eoe.s~ou eac.sso« ace.onou eao.osou .ao.onoa o one.rso~ rue.osou eno.o~ou aao.nsou one.n~ou ene.~so« cae.~soa c cae.nsou a one.usou c ene.anou o eae.asoa a one.eooa a e.e.eooa eae.ooou o.e.¢oau can.soou one.sooa eno.ooou eao.ooou one.ooou o oaanroou . neowoeou. accuse-u. noowanou. Soul-Traditional (A). Ion-Traditional (B) and in tho Ion-Specific (C) Sectors. 1965-1990. Base Run Projections of Annual Consulption of Building Board Hood Pulp in the Figure 10: 151 unprocessed and processed wood required by other wood consumption popula- tion groups as well as in the non-specific subsectors increase both in absolute amounts and in relative proportions as economic conditions improve. Building board woodpulp consumption was not observed in signi- ficant amounts in the traditional sectors. The proportions of this product required in the semi-traditional population group and in the non-specific subsectors are increasing in absolute amounts but slowly declining in relative proportions. These declining relative proportions are absorbed by the non-traditional group where building board woodpulp needs are increasing both in absolute amounts and in relative propor- tions. The estimates of the annual consumption of fuelwood by wood consumption population groups are summarized in Table 22 and Figure 19 and presented in Appendix E. Changes in the annual fuelwood consumption are rather sluggish as increased consumption of fuelwood, which would follow increased population, is eliminated by substitution of other sources of fuel for fuelwood as income increases. This trend will likely be speeded up resulting in more significant declines in the annual consumption of fuelwood in future when the consequences of current public investments in peOple through investments in education materialize. The traditional population accounts for most of the fuel- wood consumption. Wood for Paper Table 23 summarizes the projections of annual consumption of paper pulp by wood consumption population groups. Consumption of paper is highly sensitive to income changes. The rate of consumption of an! m u: .- A“ «ND ave-u 4.0.. (a) :3: (LEO on DD 6 0 mm. 6 non. NM 04" IB’ 0 C U. o O ““‘du d‘d‘ ‘C (1 I3. 0 O .0 DO 6 0 (Raw nun-Ha 2.0.. .10.: Conn noun. 0 K (a) f to .- N .4 u o. O .D 0. O In .C‘.‘ at“! J. 0 ammo. 0 «no: (500 mm NN In 0 o ‘3 U n O. 0 J '30 O n ‘00 no 6 6 mu: '3») our MN «4.. o 0- no 0 \I 3' an!!! 9‘! roman 8‘ 0‘ ‘5 '0 26 ~30 0 ca 019 -' I 0 O Mine). 0 J J c of: -a¢:avo'30¢na-Jm‘ao'm .‘l '0') ;4 34.01.. 1.!) a ’0 ago J dd gr) 1"”) r: ‘I J'J‘D',J”.I O. OOOOOOOOIOOOOOCOO 'FIh-flflhhfifi’m'D ’d-‘NN'Q' A: DAM 0‘ 0:“ AKKKKKNN . 07‘000‘04‘0" OOQF“‘ oodudH-‘Nddd-Qdddddd figure l9: Base Run Projectim Seni-‘haditional (B) 152 VI m p q O .4 .1 o 3 O O 0 . . O o on O 0 . . U 0 ‘z'l ((4.... C111. d...‘ CCCCQC . O O C«C.¢ 0 an <1 6‘. O 0 O o a h 0 O O O O O O O o o a to O O O 6 o a O O O 0 MD M o 0 9 ‘I ' 1 .o QMfiM K! mama ‘Hflf‘smcfi vaccines-Mn ,~;~ " «core . . . . I t 90 0 O 0 o - » vO‘JOOtJUQ ac'aouooaoouujeaa'a'uautz . ~ v-IoJUJJJJJJJ-OaJ-‘IJJQJ oJJaJ'Jo-aua - .. "30.) 3'0'8'1"'131.‘J¢1“30m3 n-ro no): o.o.ooooooooocoo-ooooooooooooooo.o- or:amonsseem’aoqa-:normwoossoe-r-- - sskmnrrrv‘ errccccrfrr ' ‘ ‘NNKKNAK "“00900003m9~$%9093$090$*090"' acoocaoa~0ca-u-o-4-aou-ooc-coooc.4.coc.a.oo4uo.¢-c.4-4 o.... I 9 of Amuol Min of Finland by ‘i’roditional (A) and Populatim Group. 1965-l9”. 153 Table 22.--Summary, Base Run Projections of Annual Consumption of Fuel- wood: Absolute Amounts (000 M ) and as Proportions of Overall Consumption by Wood Consumption Groups, 1965-1990. Fuelwood Required by Tra— ditional Population Fuelwood Required by Semi- Traditional Population Year Absolute Relative Absolute Relative Amount Amount Amount Amount 1965 82,537 .8591 13,541 .1409 1970 83,224 .8491 14,793 .1509 1975 81,551 .8435 15,259 .1563 1980 79,987 .8311 16,258 .1689 1985 78,130 .8119 18,102 .1881 1990 76,665 .7882 20,596 .2118 paper declines in the traditional population group in response to declining population in that group. The non-traditional and student populations account for most of the paper pulp consumption. The rate of growth of annual consumption of paper in the non-traditional population groups will be stepped up further later when the present high rate of annual college enrollment results in high rate of college graduation and also in high rate of growth of the non-traditional population group. The projections of annual consumption of paper pulp up to the year 1990 are presented in Figure 20. Summary of Chapter In discussing the time paths of the annual consumption of various wood products in Chapter IV we pointed out that as the economy grows and as increased public investments are used to stimulate economic activities, the pOpulation in the traditional sector and the activities 154 mama. Hmo.oHN momw. 0mm.vhv.a have. 5mm.om Hooo. mmm coma moma. Hmm.hoa whom. vwh.mmo mmmo. www.mm vooo. mom mmma mmaa. va.vv mmmh. oma.HHm osmo. oom.mm mooo. mom owma mmno. mom.va mmom. Hoo.vma vaH. mna.mm maoo. ohm mhma mmmo. mmv.m mvmv. mom.ma mmmv. vah.ma woao. 0mm ohma mmho. mv¢.~ ammo. mom.ma mbav. www.ma haao. ohm mama Aus\mx ooov Aus\mx ooov Ausxmx oooc Ausxmx ooov Au>\.moumv ac505¢ AH>\.moan unsos< Au>\.moumv uczosd AH>\.moumv uc50E< m>wumHmm oudHOmnd w>fiumHom oDDHOmn¢ o>flumaom muSHOmn< o>wumHmm mUDHOmnm How» coaumHsmom cofiumasmom COAUMHsmom cofiumHsmom unopzum . HmcoHquMHEncoz Hmcoflufipmueuflamm Hmcofluwpmua you Gaza ummmm wow masm nommm Mom masm woman Hem manm “warm .ommalmwma .msouo meoocH >n coaumezmcoo HHMHO>O mo wcofluuomoum up new unadofim ouDHOmnd "masmpooz uommm mo coflumEdmcoo Hmsccd mo mcofluoonoum cam mmmm .>HMEE5mII.mm manna 155 ‘I-‘Qus mu.¢u_3n:c «wean—Jase moan» .n.a4o .«.a¢a .—.o 05Hp> pounce nonpoum @003 Umumsflpa omumfifiumm Hmumfimumm omma sou seeumsdwcou Hmuoe mo oumeeumm .moDMEwumm HmuoEmumm 050m nufl3 momma >uw>wuflmcwm mo muHSmmm .mumsfidmll.vm manna 163 resulted in an average 24 percent change in consumption estimate. This means that the structure of the model is good enough to absorb some of the effects of errors in the estimation of the parameters. In Chapter V we pointed out that the regression equations were predictive in purpose, not structural. The errors in parameter estimates which are due in part to poor data and which have high probabilities of occurrence do not greatly affect the consumption predictions. Summary:L of Model Runs Under Alternative Assumptions About GDP and Govern- ment Investments The two alternative rates of growth of GDP to the base run rate considered are 6 percent and 15 percent. Public investments in agri- culture, education, health, and transportation and the loans made available by commercial banks for the transportation industry are assumed to grow at the same rates as GDP for both alternatives. In making these assumptions, we imply that government investment priori- ties will remain the same even if national income grows at different rates. This is not necessarily correct--if national income changes, the government may alter its investment priorities. The rate of growth of average per capita income for the medium income bracket is assumed to lag the rate of growth of GDP by 2 percent per annum and the rate of growth of per capita income for the high income bracket is assumed to lead the rate of growth of GDP by 5 percent per annum. In both alternatives, the rates of population growth, the ratio of price index of wood and price index of substitutes for wood, 1See Appendix F for complete Run results. 164 agriculture's contribution to GDP, and rural-urban migration are not altered but are assumed to grow at their base run rates. Assumed levels of GDP and public investments in agriculture, education, health, and transportation are summarized in Table 25. Table 25.-~Va1ues (N Million/Year) of GDP and Government Investments Under Alternative Assumptions About Rates of Growth of GDP, 1975-1990. Government Investments in Sectors Gross Domestic Year Product Agriculture Education Transportation 6% 15% 6% 15% 6% 15% 6% 15% Rate Rate Rate Rate Rate Rate Rate Rate Alt. Alt. Alt. Alt. 1975 10,254 10,254 266 266 380 380 784 784 1980 15,440 16,840 374 392 534 672 1,094 1,154 1985 20,060 29,720 441 576 632 1,178 1,290 1,696 1990 25,460 50,520 522 879 751 1,798 1,540 2,586 Projections of wood required for residential construction under the two alternative rates of growth of GDP are summarized in Table 26. These amounts are also summarized as proportions of aggregate wood requirements under the two alternatives in Table 27. Both the absolute amounts and relative proportions of unprocessed wood are higher under the 15 percent alternative. Absolute amounts of processed wood and building board woodpulp are higher under the 15 percent alternative but the relative proportions of both are lower under the 15 percent alterna- tive indicating that the consumption of both wood products increase faster in the more immediate future in other subsectors where the impact 165 Table 26.--Summary, Projections of Annual Wood Requirements for Resi- dential Construction Under Alternative Rates of Growth of GDP, 1975-1990. Unprocessed Wood Processed Wood Building Board (000 M3) (000 M3) W00dpulp (000 kg.) Year 6% 15% 6% 15% 6% 15% Rate Alt. Rate Alt. Rate Alt. Rate Alt. Rate Alt. Rate Alt. 1975 782 782 512 512 223 223 1980 836 836 569 570 348 350 1985 933 952 637 660 476 516 1990 1029 1165 690 851 586 797 Table 27.-~Summary, Projections of Annual Wood Requirements for Resi- dential Construction as Proportions of Overall Wood Requirements Under Alternative Rates of Growth of GDP, 1975-1990. Unprocessed Wood Processed Wood Building Board Wood- (Proportion/year) (Proportion/year) pulp (Proportion/year) Year 6% 15% 6% 15% 6% 15% Rate Alt. Rate Alt. Rate Alt. Rate Alt. Rate Alt. Rate Alt. 1975 .2851 .2851 .6521 .6521 .5803 .5803 1980 .3138 .3135 .6387 .6226 .6581 .6341 1985 .3256 .3269 .6396 .5660 .7187 .6164 1990 .3311 .3414 .6190 .5148 .7258 .6024 166 of public spending on wood consumption is felt without time lags. In the more distant future, this situation will likely be reversed, the proportion of processed wood consumed in residential housing construc- tion will be increasing faster if GDP and public spending are growing at 15 percent per year at present than if they are growing at 6 percent per year. The effects of public investment on wood consumption for residential purposes are lagged depending on the time it takes invest- ments in people to affect their housing and living standards. Projections of wood required for farm construction are sum- marized in Table 28. These amounts as proportions of aggregate wood requirements under the two alternative rates of growth of GDP are summarized in Table 29. Unprocessed wood required for farm construction will be higher in absolute amounts at higher rates of growth of income but its relative proportion will be lower. Processed wood requirements are higher at higher rates of GDP and public investments both in the absolute amounts and in relative proportions. Both of these observa- tions are probably due to the expansion of non-traditional agriculture which will be expected at higher levels of income and public investments in agriculture. The estimates of amounts of wood required for non-residential housing construction (including school, hospital, commercial, religious, and public administration buildings) under the two alternatives are summarized in Table 30. These values, as proportions of aggregate wood requirements under the two alternatives, are summarized in Table 31. The absolute amounts and the relative proportions of all wood products required for these purposes are higher for the 15 than for the 6 percent alternative. 167 Table 28.--Summary, Projections of Annual WOOd Requirements for Farm Construction Under Alternative Rates of Growth of GDP, 1975-1990. Unprocessed Wood Processed Wood (000 M3/year) (000 M3/year) Year 6% 15% 6% 15% Rate Alt. Rate Alt. Rate Alt. Rate Alt. 1975 1,926 1,926 2,935 2,935 1980 1,793 1,792 5,813 5,920 1985 1,899 1,908 9,526 11,596 1990 2,039 2,174 1,403 27,940 Table 29.--Summary, Projections of Annual Wood Requirements for Farm Construction as Proportions of Overall Wood Requirements Under Alternative Rates of Growth of GDP, 1975-1990. Unprocessed Wood Processed Wood (Proportion/year) (Proportion/year) Year 6% 15% 6% 15% Rate Alt. Rate Alt. Rate Alt. Rate Alt. 1975 .7018 .7018 .0037 .0037 1980 .6728 .6721 .0065 .0065 1985 .6621 .6554 .0099 .0108 1990 .6562 .6368 .0128 ‘ .0169 168 Table 30.--Summary, Projections of Annual Wood Requirements for Non- Residential Housing Construction Under Alternative Rates of Growth of GDP, 1975-1990. Unprocessed Wood Processed Wood Building Board Wood- (M3/year) (M3/year) pulp (kg.) Year 6% 15% 6% 15% 6% 15% Rate Alt. Rate Alt. Rate Alt. Rate Alt. Rate Alt. Rate Alt. 1975 35,836 35,836 239,805 239,805 73,685 73,685 1980 35,761 38,373 279,669 303,186 91,565 98,577 1985 35,432 51,345 308,424 457,136 104,536 149,695 1990 39,240 74,274 364,717 718,973 124,596 236,673 Table 31.--Summary, Projections of Annual Wood Requirements for Non- Residential Housing Construction as Proportions of Overall Wood Requirements Under Alternative Rates of Growth of GDP, 1975-1990. Unprocessed Wood Processed Wood Building Board Wood- (Proportion/year) (Proportion/year) pulp (Proportion/year) Year 6% 15% 6% 15% 6% 15% Rate Alt. Rate Alt. Rate Alt. Rate Alt. Rate Alt. Rate Alt. 1975 .0131 .0131 .3053 .3053 .4245 .4245 1980 .0134 .0144 .3138 .3309 .3655 .3789 1985 .0124 .0176 .3096 .3874 .3202 .3721 1990 .0126 .0178 .3273 .4351 .3081 .3674 169 Table 32 summarizes the projections for processed wood consumed in casket manufacturing, and lumber truck and bridge construction under alternative rates of growth of GDP. These amounts are also summarized as proportions of overall processed wood consumption under the two alternatives in Table 33. The amount of wood that will be needed f0r the manufacture of caskets declines indicating declining death rates under improved economic conditions. Wood required for the construction of lumber trucks is higher in absolute amount but lower in relative proportion under 15 percent alternative indicating declining relative importance of lumber trucks that would be expected at higher levels of income. Wood needed for construction of bridges is higher both in absolute amounts and in relative proportions under the 15 percent than under the 6 percent alternative. Table 32.--Summary, Projections of Annual Processed WOOd Requirements for Casket, Lumber Trucks, and Bridges Under Alternative Rates of Growth of GDP, 1975-1990. Processed Wood for Processed WOOd for Processed Wood for Casket (M3/year) Trucks (M3/year) Bridges (M3/year) Year 6% 15% 6% 15% 6% 15% Rate Alt. Rate Alt. Rate Alt. Rate Alt. Rate Alt. Rate Alt. 1975 22,151 22.151 5,887 5,886 2,504 2,504 1980 24,045 24,024 8,680 8,731 3,751 3,892 1985 24,776 24,507 11,295 12,689 4,638 5,870 1990 25,446 24,469 14,444 20,600 5,717 9,691 170 Table 33.--Summary, Projections of Annual Processed Wood Requirements for Casket, Lumber Trucks, and Bridge Construction as Proportions of Overall Requirements of Processed Wood Under Alternative Rates of Growth of GDP, 1975-1990. Processed Wood for Processed Wood for Processed Wood for Casket (Prop./year) Trucks (Prop./year) Bridges (Prop./year) Year 6% 15% 6% 15% 6% 15% Rate Alt. Rate Alt. Rate Alt. Rate Alt. Rate Alt. Rate Alt. 1975 .0282 .0282 .0075 .0075 .0032 .0032 1980 .0270 .0262 .0095 .0095 .0042 .0042 1985 .0249 .0208 .0113 .0108 .0047 .0050 1990 .0228 .0148 .0130 .0125 .0051 .0059 The projections for annual consumption of fuelwood from market and non-market sources under alternative rates of GDP are summarized in Table 34. The rates of fuelwood consumption are not very different between the two alternative rates of growth of GDP because the substitu- tion of other materials for fuelwood which will follow increased income is partially eliminated by population growth. Besides this, the effect of income and public spending on fuelwood consumption is lagged. This implies that the annual consumption of fuelwood will decline faster later. Projections of annual consumption of paper woodpulp under alternative rates of growth of GDP are summarized in Table 35. The annual consumption is higher under 15 percent of GDP as expected than under 6 percent rate. A summary of aggregate requirements of unprocessed wood, pro- cessed wood, building board woodpulp, paper woodpulp, and fuelwood under 171 .UH< Guam wmd .UH¢ mumm we mmouaom umxumz Scum ©003Hosm .uafl wumm mmH .udd wumm we moonsom umxumzlcoz Bonn ooozamdm Homm. moo.m~ bemm. hmm.am mmnn. omm.mh moor. mam.mn coma HNHN. mov.o~ mmam. hov.om ohms. mom.mh hump. mam.mh mmma vNom. www.ma vmom. mmv.ma ohms. moh.oh ohms. ooh.mn omma mood. omo.ma mead. omo.oH hmom. vah.bh hmom. ona.>n mood A.us\mz ooov ..us\mz ooov ..us\ms ooos x.us\mz ooos .u>\.moum undead .w>\.moum unsofid .H>\.m0um ucsoe< .u>\.moum uchEd o>eum~om muaaomoa 0>Huoaom mosaOmnm o>wum~om muaHOmn< o>wum~om ouzaomnd Hum» .ommalmhma .mQU MO nuzouo «0 owner m>eumcuouad noon: coflumEDmcoo Haouo>o mo mc0wuwomoum mm 0cm mucaosd oua~0mnm "moousom umxumZIGOZ can umxumz Scum ooozaozm m0 COAuQEDchO amazed mo mc0wuomflonm .mhmsfiamlu.vm manna 172 Table 35.~-Summary, Projections of Annual Consumption of Paper Woodpulp Under Alternative Rates of Growth of GDP, 1975-1990. 6 Percent Alternative Rate 15 Percent Alternative Rate Year of Growth of GDP of Growth of GDP (Million kg/year) (Million kg/year) 1975 1,761 1,761 1980 3,503 3,521 1985 7,570 8,072 1990 14,809 18,428 alternative rates of growth of GDP is presented in Table 36. The amounts of unprocessed wood and fuelwood required are not very different for the two alternatives. This is because the total population in traditional group which accounts for most of the consumption of those wood products does not change substantially under either alternative. On the other hand the amounts of processed wood, building board wood- pulp, and paper woodpulp required are substantially higher under 15 percent than under 6 percent rate of growth of GDP. These numerical analyses indicate that while the consumption of fuelwood and unprocessed wood do not appear to be very sensitive to changes in income, the consumption of processed wood, building board woodpulp, and paper woodpulp are quite sensitive to changes in income. The implication of this is that errors of up to 50 percent in the assumed annual rate of growth of GDP will not cause up to 50 percent error in the predictions of annual consumption of fuelwood and unprocessed wood. Such an error will, however, likely cause more than 50 percent error in the prediction of annual consumption of processed wood, building board woodpulp, and paper woodpulp. 173 mhv.ma mom.va eHH.mo www.mo mmm.a hoe mme.a mHH.H mmav.m hoa.m ommH who.m Ohm.h mov.ev hmv.ev 5mm Nee omH.H emm mam.m hem.m mema Hmm.m mom.m mmm.ev mmm.eo New ewe eHm Hoe eee.~ mee.N oema Heh.a Heh.a ~>m.mv mnm.mv mmm mmm emu emu vow.m mvh.m mhma mumm mumm wumm wumm mumm mumm wumm wumm wumm mama .uafi .uad .uam .uam .uad .uH¢ .uac .uad .uafl .uam wed we wma we «ma we wma we emu we now A.us\.ox coeaauzv rumms\mz oooc rumms\.mx ooos rumms\mz ooov rumms\mz ooov » masm eOOBHosh QHDQoOOS 0003 0003 loco: Momma oumom oceeaezm emmmooowm commooowmca mo nuaouo m0 woumm o>eumcumuad Hooco .ommalmhma .mDO mucosouwoqom 0003 Hmsccd mummoumoc mo maceuomnowm .mumaadmnl.em wanna 174 Projections generated under the assumption that GDP will grow at the rate of 6 percent imply further that even if GDP and government investments grow at 6 percent rather than at 11.7 percent annually, the future wood needs of Nigeria will still be high. Policy makers in the Nigerian forestry sector should therefore do something to increase the supply of wood products even if lower rates of growth of GDP and public investments are projected by a new government. The income trend in Nigeria has been that of a steady growth (26). The government projected a growth rate of 6.1 percent in GDP for the five year period ending in March 1975 but the realized growth rate was 10.2 percent (6)--much higher than projected because domestic supply and world market prices for petroleum went up during the period. The future levels of both domestic supply and world prices for petroleum are uncertain, but the revenue currently being derived from the export of large quantities of petroleum at high prices is being invested in indus- trialization, road construction, education, agriculture, etc. and very soon such investments will begin to generate multiplier effects on income. These considerations seem to indicate that a growth rate of GDP between 11.7 percent and 15 percent is more probable than one between 6 percent and 11.7 percent. This implies that the projections of wood consumption under 15 percent alternative are more likely than those under 6 percent. The base run projections could be underestimated rather than overestimated. Further Tests of Objectivity_ The model was subjected to further tests of objectivity by comparing its outputs with estimates by other people and with recorded 175 experience for correspondence and consistency. These comparisons are discussed below. Comparisons of Model Estimates with Estimates by Other People Estimates by FAQ (15) for processed wood for the years 1974 and 1980 are available. Estimates for fuelwood, unprocessed wood, and processed wood by Enabor (46) for 1970 and 1985 are also available. A comparison of these estimates and estimates by our model are presented in Tables 37 and 38. Our estimates of consumption of processed wood are not too far from FAO estimates, but they diverge from estimates by Enabor. While our model projects an increase of about 79 percent in the annual consumption of processed wood between 1970 and 1985, Enabor pro- jects an increase of only 31 percent within the same period or about 2 percent per year. Probably Enabor based his estimate of 2 percent annual rate of growth of consumption of processed wood on the growth of popula- tion. In addition to population growth, however, annual consumption of processed wood is also responsive to other variables such as growth in income. Adeyoju (2) has observed that the consumption of processed wood has historically responded to changes in income. Income has recently been increasing in Nigeria as does population (7). One should therefore expect that annual consumption of processed wood would grow at a faster rate than population. Experience in Japan, a country that underwent the modernization process we anticipate for Nigeria, indicates that annual consumption of processed wood will grow at a faster pace than population if income is also growing. Table 39 compares changes in total national population, annual consumption of timber, and national income in Japan between 1953 176 Table 37.--Comparison of Estimates of Consumption of Processed Wood by FAO with Model Estimates. Year Estimated by FAO Estimated by Model (M3/year) (M3/year) 1974 752,000 777,558 1980 1,050,000 912,207 Table 38.--Comparison of Estimates of Rates of Consumption of Wood by Enabor with Model Estimates. Year/Estimates Wood Product 1970 1985 Estimates by Estimates Estimates by Estimates Enabor by Model Enabor by Model Fuelwood (Million M /year) 46.8 49.3 65.1 48.4 Unprocessed Wood (000 M3/year) 1,275 2,972 1,680 2,897 Processed Wood (000 M3/year) 581 620 840 1,112 177 Table 39.-~Popu1ation, Lumber Consumption, and National Income Trends in Japan, 1953-1963. 1953 1963 % Change Population (Million) 87 96 10 Lumber Consumption (Million M3) 19 65 240 National Income (Y 000 Billion) 5.8 22.5 291 Sources: Economic Survey of Japan, 1963-64 Economic Survey of Japan, 1954-56 Japan Economic Year Book, 1965 Japan Economic Year Book, 1959 and 1963. While population increased by only 10 percent, wood consump- tion increased by 240 percent consistent with the increase of 291 percent in national income. Our projections of consumption of unprocessed wood are more than 72 percent higher than projections by Enabor. We are not sure what wood using subsectors are included in his estimates, but Table 17 indicates that farm construction (which includes crop staking, farm fencing, live- stock sheds, and storage spaces) accounts for about 70 percent of total unprocessed wood consumption estimates by our model. We pointed out in Chapter III that we considered it necessary to include those uses because if their needs cease to be met from non-forest sources, the forestry sector will need to satisfy them. - Enabor's estimates indicate an increase of 38 percent and our estimates a decline of 1.8 percent in the consumption of fuelwood between 1970 and 1985. There is no logical reason other than 178 population growth to expect that the consumption of fuelwood will grow. But while population is expected to grow the characteristics of the population with respect to wood consumption is also expected to change if economic conditions are changing. It is likely that the proportion of urban population will be higher in 1985 than in 1970. Urban popula- tion buy more of their fuelwood than rural population and therefore consume less per head. As economic conditions improve, it will be expected that people will substitute electricity, natural gas, etc. for fuelwood. Comparison of Model Estimates with Past Consumption in Nigeria The model was used to track the past consumption of the various wood products. The backward projections of annual consumption of pulp products were compared with the past consumption of the pulp products derived from foreign trade statistics of Nigeria. All paper, paperboard, and similar products are until now imported either as finished products or as pulp. There are no pulp mills in Nigeria at present. Table 40 indicates that our model has tracked rather closely the past consumption of pulp products. There are no records of annual consumption of other wood products in the past for Nigeria. Our historical data were generated by running the model back- wards to track the past. The adequacy of this test depends on the availability of a sufficient number of degrees of freedom and of historical record with which to compare the tracked data. In our case there are no recorded data on the consumption of the various wood products except woodpulp in Nigeria, hence our reference to experience 179 Table 40.--Comparison of Model Estimates of Annual Consumption of Pulp Products with Consumption Data Derived from Statistics of Import of Pulp Products. Year Model Estimates Derived Consumption Data (000 kg./year) (000 kg./year) 1965 50,506 46,482 1966 59,972 66,853 1967 42,677 38,354 1968 38,710 36,169 1969 59,924 61,516 1970 80,686 89,509 1971 108,397 113,610 1972 115,135 114,401 in Japan. The number of degrees of freedom available in a generalized model is difficult to count because sometimes personal judgements are employed. However, we believe that there were degrees of freedom adequate for realistic prediction of the past. We conclude from these comparisons that it is both logically consistent as well as consistent with recorded experience to expect that as economic conditions improve the annual consumption of fuelwood will decline and the annual consumption of processed wood will grow at a faster pace than population. Our projections may not be accurate to the last figure, but they are likely to be right in pointing at the general direction of future trends in the consumption of various wood products. 180 Summary of Validation and Verification Tests The model concepts seem to be clear and easily transmitted between people with relevant knowledge as was done in various discus- sions of the concepts at the various stages of the study. But the test of clarity is a continuous test; the model is tested whenever anyone with relevant knowledge reads this thesis. Although the probabilities of errors in the parameter estimates were found to be fairly high, the sensitivity tests on a limited number of parameter estimates indicate that the model outputs are not very sensitive to such errors. The consumption of some wood products-- processed, building board woodpulp, and paper woodpulp--was found to be sensitive to changes in the rate of growth of GDP and some other vari- ables while the consumption of others--fuelwood and unprocessed wood-- was not. We do not know the probability that GDP will grow at any of the considered rates, but from various considerations discussed under "Summary of Model Runs Under Alternate Assumptions About GDP and Govern- ment Investments" an annual rate of growth of 15 percent seems to be the most likely of the three rates considered. If this is true the base run projections of consumption of fuelwood and unprocessed wood may still be expected but those of processed wood, building board woodpulp, and paper woodpulp are likely to be underestimates. These tests of the model concepts for workability in case of errors in parameter estimates or in input variable assumptions are merely tentative. The final test of the workability of the concepts will be in their application to real problems. 181 The model concepts were found to be consistent with experience in other parts of the world, notably Japan, where the modernization process similar to that we anticipate for Nigeria took place recently. The concepts are also consistent with projections by other people except in the case where some of the important determining variables were omitted in making the projections, therefore making such projec- tions logically inconsistent. ' The historical consumption of woodpulp products tracked with the model corresponds closely with recorded consumption of the wood product assuming that we had sufficient number of degrees of freedom as we believe. We cannot count our degrees of freedom because we employed many techniques and derived our data from a variety of sources including judgements. Passing these tests does not make the model indefinitely accep- table. Time will reveal further information and inconsistencies of which we are not presently aware. But in the mean time our tests indi- cate that the concepts of the model are quite consistent, fairly easily understood when discussed with knowledgeable people, and are likely to work when applied to real world problems. CHAPTER VIII CONCLUSION 81111111135! In this thesis we specified a general model for the Nigerian forestry sector without constructing it. However, we constructed a model of its wood consumption component and operated it to make pro— jections of annual consumption of various wood products for Nigeria from 1965-1990. We shall summarize these under (1) the structure of the model, (2) data used in operating the model, and (3) projections by the model. The Structure of the Model The various wood products consumed in Nigeria were aggregated into unprocessed wood, processed wood, building board woodpulp, paper woodpulp, and fuelwood. The wood using subsectors were also aggregated into residential housing construction, non-residential building con- struction (schools, hospitals, religious, commercial, public administra- tion, etc. buildings), casket manufacture and vehicle and bridge construction, farm construction, fuelwood consumption, and paper consumption subsectors. The variables which determine the consumption of those wood products in these uses were identified as rural-urban location, income and educational attainment of individuals, availability and relative prices of substitutes and complements for various wood 182 183 products in various uses, and public investment in education, agricul- ture, and other key sectors of the national economy. These variables were incorporated into the structure of the model by classifying Nigerian people into traditional, semi-traditional, and non-traditional wood consumption population groups on the basis of rural-urban location, income, and education attainment of the peeple. Some of the wood using subsectors were also classified into traditional, semi-traditional, and non-traditional depending on which population group is engaged in them. Each group had a different set of parameters such as average residential building per person, average amount of various wood products per building, average life span of buildings, etc. Variables which may be used to control annual consumption of various wood products, if it ever becomes necessary, were identified as domestic pricing of wood products, and establishment of housing codes. These may, however, lead to undesirable cycles in the production and consumption of wood products. The annual consumption of a wood product in a construction subsector (residential housing, non-residential, farm, vehicle, or bridge construction) was estimated as the product of the average amount of that wood product in cubic meters per building and the number of buildings set up in the sector each year. The average amount of the wood product per building in each subsector was estimated as a function of the ratio of the price index of the wood product to the price index of substitutes for the wood product. The number of buildings set up in a subsector each year is the sum of new buildings for incoming individuals and old buildings being replaced. An individual may be an independent adult person in residential housing construction, a student 184 in school construction, a hospital bed in hospital construction, or a kilometer of road in bridge construction subsector. The time path of the model is determined by changes in the number of individuals and the order of deterioration of buildings in each subsector. The order of deterioration of buildings in each building construction subsector is determined by the rate of use of the buildings, the environmental conditions, and the building materials. The annual rate of change of school (high schools and colleges) populations (students/year) are determined as functions of GDP and public investment in education. The annual rates of maturity by rural and urban non- school going youths into traditional and semi-traditional independent adult population groups are determined as lagged functions of time. School going youths will, on graduation and on taking appropriate jobs, be classified as semi-traditional if they stop at high school and as non-traditional if they obtain college degrees. If the personal incomes of some people already classified in the traditional groups improve enough they are reclassified in the non-traditional groups. This time path is a modernization time path such that as economic conditions improve the traditional population declines and the non- traditional population grows in an exponential fashion. The moderniza- tion process which depends on income and government investments in education and other key sectors occurs with considerable time lag to which wood consumption particularly in residential houSing construction subsector is very sensitive. Separate estimates are made for fuelwood from market and non- market sources for rural and urban population groups. Fuelwood from non-market sources is the fuelwood obtained and used directly by the 185 consumer from various own sources like the farm, etc. Annual consump- tion of fuelwood from each source is computed as a product of average amount of fuelwood from that source per adult per year and the number of adults. The average amount of fuelwood from the market sources per adult per year is estimated as a function of the prices of fuelwood. Annual consumption of paper is estimated as a function of changes in per capita income separately for traditional, semi-traditional, non- traditional, and student population groups. The number of caskets manufactured annually is estimated as a constant proportion of number of deaths each year. Some people are not buried in caskets, others are buried in caskets made of materials other than wood for various reasons including religious prescriptions, inability to afford a wooden casket, etc. Our estimates are for only those made of wood. Data Used in Operating the Model The data used in the model were assembled from a national wood consumption survey, published and unpublished secondary sources, and from the judgement of knowledgeable individuals. The model is designed to receive some of the data as constants and other as variables. The fact that some data are fed into the model as constants does not mean that the variables which they are functions of are not varied in making the projections if they are real world variables. For instance, the model receives average proportions of residential building per person as constants. In reality this varies with time because as income improves, people want to live in less crowded houses. There are three values of average proportion of residential building per person in the model for traditional, semi-traditional, and non-traditional residential buildings 186 respectively. Each one is a constant but as economic conditions improve, the value for non-traditional buildings is used more frequently and the value for traditional buildings is used less frequently in making the projections. Thus, the average proportion of residential building per person is varied in the model hopefully as in reality. The coefficients of the variables are estimated in a total of nineteen equations. Sixteen of these are simple linear regression equations. Exponential forms of equations would be consistent with our assumptions but the range and the durations of time-series data available (the maximum of which is twelve years) were not sufficient to estimate the coefficients of exponential equations. The simple form of the regression equations does not concern us greatly because they are used as predictive equations. The coefficients in the other three equations which are those of changes in per capita incomes in the per capita consumption of paper by the low, medium, and the high income population groups were estimated by judgement because we could not gather enough data to estimate them by regression techniques. Projections from the Model The model is operated to make historical projections of annual consumption of the various wood products for the period 1965-1974. This was based on the actual values of the various variables like GDP, govern- ment spending, etc. Three sets of projections based on three alternative rates of growth of GDP were made for annual consumption of the various wood products in the future for the period 1975-1990. The rate of growth of GDP assumed for the base run of the model is 11.7 percent per annum which is that estimated by the Federal Government for 187 the Third National Development plan. The other two rates which are alternatives to the base run are 6 and 15 percent respectively. Govern- ment spending in the various sectors was assumed to grow at the same rate as GDP in each alternative run although we recognize that public investment priorities will vary if national income changes. We pointed out that changes in income and public spending have two consequences for wood consumption, one is in the near future and the other more lagged. The duration of our future projections is not long enough to cover that lag and we cannot extend it without making undue assumptions about future levels of income and public investment priorities. One should therefore note that the future projections presented in the previous chapters do not tell the whole story. Wood consumption in the residential housing construction is particularly sensitive to the lagged consequences. We pointed out that Tables 14, 15, 27, and 31 show that the proportion of processed wood consumed in the residential housing construction subsector is declining while that of non-residential building construction subsectors is increasing as income and government spending increase. This is because while the consequences of increasing income and government spending take effect instantaneously in the non-residential building construction subsectors, they are delayed in the residential housing construction subsector. When we consider that 70 percent of overall processed wood consumption is in the residential housing construction subsector, we realize that the lagged consequences of the current high public investments on wood consumption which are unobservable in our projections will be substan- tial. This is based on the assumption that the high rates of spending are sustained over time so that the modernization process is continuous. 188 The projected annual consumption of various wood products indi- cates that future wood consumption in Nigeria will be phenomenal. If the production challenge is not met, it will result in high domestic prices for wood products to ration the available supply. One of the consequences of domestic prices higher than world market prices for wood would be pressure to import wood products into Nigeria and for exports of wood from Nigeria to decrease. This would result in loss of foreign exchange contribution from the forestry sector. If it happens when Nigeria will no longer be able to export petroleum in large quantities and at high prices, this consequence will be regrettable. High domestic prices for wood products would also encourage overexploitation of the existing forests. This would conflict with other forestry sector objectives such as environmental quality control. The undesirable effects of overexploitation of forest lands for various purposes including farming, logging, etc. are already apparent in many parts of Nigeria. Examples of these adverse effects are the southward movement of the desert and the water erosion areas in many parts of East Central state. Overexploitation of forests today will lead to more acute shortages in future and to higher prices and so on. Another consequence of high domestic prices for wood products would be the introduction of substitutes for wood in various uses. Some of the materials that are extensively substituted for wood in many countries are plastics made from petroleum products, steel, concrete, and glass. Nigeria is blessed with an abundant supply of natural resources from.which these materials are made. But these natural resources are exhaustible and cannot be relied upon forever. Wood on the other hand is a renewable natural resource. 189 High prices for wood products would encourage investments in the forestry industries at the same time as other materials are being substituted for wood in various uses. Investments in some forestry industries mature with long time lags. It is possible that when they mature, there may be reduced markets for wood products as peOple may have learned to use wood substitutes. This could lead to cycles in the production and consumption of forestry products. The likely situation of high annual consumption of wood products in future should be of concern to Nigerian policy makers in the forestry sector. The sensitivity analyses presented in the previous chapter indicate that even if our estimates are high by as much as 50 percent, which is unlikely, the expected change is still phenomenal. It is unlikely that the potentials of Nigerian forests will be able to meet such requirements or that more land will be put into forestry because of other demands on land consequent on increasing income such as increasing industrialization, urbanization, roading, and demand for food. Prescriptions The first goal of the forestry sector is to "provide for the needs of the country in timber and other forest produce adequate for the requirements of the community under a fully developed national economy and to provide the greatest possible surplus of those products for export markets" (46, p. 2). The projections of annual wood consumption from our model just summarized show that the need of the country for timber is high and will likely be higher. In this section we summarize some of the actions we believe would help in meeting that need as follows: 190 l. A continuation of the current efforts being made to increase the supply of forestry products by intensive cultural methods. These efforts include: (a) gradual conversion of natural forest reserves to forest plantations. This process is important not only because it results in higher yield of wood of desired species per acre, but also because of the uniform characteristics of plantations, logging opera- tions are more convenient and less expensive than in natural forests. The tendency of the private sector to regard forests as "god given" resulting in such abuses as stealing of forest products, forest fires, etc., will likely be less in forest plantations which are man-made than in the natural forests. The rate of conversion of forest lands to agricultural lands may be reduced when natural forests are converted into forest plantations. Additional problems associated with man-made forests should be borne in mind. Man-made forests need more silvicultural care and are more susceptible to diseases than natural forests. The high cost of conversion is reduced by the "taungya" method which permits the farmer to clear the forest land and grow his food crops for the first few years thus transferring the costs of clearance and initial maintenance to the farmer. In addition to the problem of maintenance, man-made forests will yield wood the quality of which will differ from that yielded by natural forests. (b) More intensive management of natural forests. Barnard (3) has shown that maintenance practices in the Western Nigerian natural forest reserves are sub-optimum and that such will result in low yield. In view of the rapidly expanding demand for wood, it is necessary that maintenance practices in the natural forests be stepped up. The additional cost of this should be financed by 191 increasing the proportion of forest revenues reinvested in the industry. (0) The practice of multiple use of forest lands for agriculture, recreation, etc., as well as for tree growing should be pursued more vigorously. This practice will reduce the cost of forest maintenance for the forestry sector as well as help bring some agricultural land into forestry. 2. Modernization of processing facilities including equipment and management and encouragement of horizontal and vertical integration of wood processing with final product industries. Inefficiency resulting in 50 percent conversion rate for the country has been observed in the wood processing industry (48). The slabs, edgings, and sawdust which make up the other 50 percent are not entirely wasted since some of them are utilized in various other ways including fuelwood and those not utilized at all return to the soil to enrich it. Yet in view of the phenomenal increase in demand for wood for construction and paper, it is necessary that some of those byproducts should be salvaged for direct utilization. The efficiency of the industry can be improved in various ways including requiring a minimum capital and training for entrance into the industry. Minimum capital requirement will encourage horizontal integration among the small mills to take advantage of economy of scale which is high in the industry. The school of wood technology should be expanded to accommodate prospective private wood processing industri— alists. Vertical integration of wood processing with final produCt industries will internalize some of the so called wastes and encourage their direct utilization. For example, integration of the manufacture 192 of matches or of charcoal with wood processing will utilize most of the slabs and edgings which result from sawmilling. Some of these actions will likely have some adverse effects on some peeple currently employed in the industry. The plans of action should include alternative arrangements for the compensation of such people. 3. Encouragement of substitution of other materials for wood in various uses where such is possible to reduce the amount of wood per unit of structure. Since steel, concrete, and plastics are acceptable substitutes for wood in certain uses, the establishment of iron and steel mills, cement factories, and oil refineries which are planned for the country will likely increase the degree of substitution of these products for wood in certain uses. The net social advantage of such substitution may be high. For example, the prOportion of the cost of fuelwood in the household budget is substantial in some parts of the country such as Sokoto province. One way to make fuelwood less expen- sive for the individual is to grow more trees for fuelwood in the forest reserves. The social opportunity cost of the land and other resources put into growing such trees is high because of the long time lags required to grow trees. Alternative sources of fuel like electricity, natural gas, solar energy, etc. are likely to be eventually cheaper both for the individual and for the society. Some of the sources of substitutes for wood such as iron ore and petroleum deposits are, however, exhaustible and cannot be relied upon indefinitely. Therefore, the use of substituties should not be regarded as a permanent solution for the wood shortage problem. In addition, any iron ore or petroleum product used in Nigeria cannot be exported; the social advantage of substituting them for wood should be weighed against 193 the social opportunity cost in terms of foregone foreign exchange earnings. These recommendations are arrived at from the analyses of the projections of annual wood consumption presented in the last two chap- ters. While in Nigeria gathering information for the model and projections of wood consumption, the author discussed the organization, problems, and other general aspects of the forestry sector with various people including public employees of the sectors as well as some people privately engaged in forestry industries. Based on these discussions as well as on readings of materials (published and unpublished) about the sector we have arrived at additional recommendations. These recom— mendations should be investigated further before they are considered for implementation because they are not based on an objective study. They are as follows: 1. The present set-up in which public bodies own forest lands should be maintained because forests yield socially important exter- nalities which are not of direct interest to the private entrepreneur. But the protective power of the public bodies responsible for public forests should be strengthened against abuses of such forest lands from the private sector. In Chapter II we pointed out that there are forest ordinances in Nigeria enacted against such abuses but most of the cases brought against the violators are lost in the courts. The forestry departments have to secure from the justice departments lawyers who lack ferestry backgrounds and who cannot realistically argue the case for forestry. Bureaucratic red tape involved in transferring services from one government department to another generally result in loss of time. 194 Forest production divisions in the various departments of forestry should be reorganized to include legal subdivisions whose staff should consist of legal men with forestry backgrounds. In the absence of this, forest protection divisions should be empowered to hire private lawyers when they need them so that the best lawyers will be secured at the right moments. 2. The social advantages of the current practice of indiscrimi- nately grazing cattle on public forest lands by nomadic herdsmen should be weighed against the social costs and stopped or the herdsmen made to pay for grazing on forest lands if such a practice proves to be socially disadvantageous. 3. The advisory services of the Federal Department of Forestry needs to be enhanced. The department is established to advise the Federal Government on forest development including location of federally funded forest projects throughout the country, to act as an advisory and liaison body to the Nigerian states, etc. This function is very impor- tant if the Federal resources are to be invested where they are most productive and if unhealthy competition among state departments of forestry is to be avoided. To provide such advisory service, however, the Federal Department of Forestry needs to have adequate information about the supply and productivity of resources in different states. The present capacity of the department to provide this information is heavily on the side of the technical forestry disciplines almost to the neglect of the social science disciplines. An economics division which will be staffed with economists and other social scientists is needed. Such a division would deal with such human problems as land tenure, political problems, etc., and in cooperation with the technical 195 divisions carry out project appraisals. The information provided by the economics division will be used in the planning division. 4. We pointed out in Chapter II that a problem of the Federal Department of Forest Research (FDFR) is shortage of qualified research officers. The employment policy of the Nigerian governments is at least partially responsible for this. For example, FDFR employs a fresh first degree graduate and after a year or two sends him for a graduate work to study for Master's degree for two years. While in training the individual's training expenses are paid, he is given maintenance allowance in addition to his salary. His promotion is withheld but he maintains his seniority in the civil service. His colleague who is not a government employee pays his own expenses, does not receive any allowance or salary, and gets a doctorate degree after four or five years from first degree. The FDFR offers him a junior officer grade position which is likely to be inferior to that of his colleague who in addition to his training expenses being paid by the government earned his salary and maintenance allowance while in training. This set-up is unattractive to individuals who invested their own time and money to _ earn higher degrees. To meet its manpower needs FDFR should step up its training program by increasing the number of employees sent to graduate school each year and by training them up to doctorate degree level because for some of the FEFR research projects Master's degree may not be enough preparation. The cost of this can be reduced by making the department attractive to individuals who have trained themselves up to the doctorate degree level in relevant areas. This can be done by employing such 196 people at least as senior officers to compensate them partially for their efforts to train themselves. Our recommendations are made on the basis of information generated by an incomplete model--the wood consumption component is only a part of the entire forestry sector model--because we believe that no recommendation will ever be made if we have to wait until we have a complete model. Even a national economy model is incomplete because a national economy interacts with economies of other countries. The only complete economic model will be a world economy model. However, in the next section entitled "unfinished work" we shall discuss the need f0r the complete forestry sector model and summarize some other aspects of the sector where investigation will be of importance. Unfinished Work Need for a Generalized Model of the Entire Forestry Sector We presented a general model for the Nigeria forestry sector without formulating the model specifically because we lacked the neces- sary resources (time, information, and money) to do so. The model of the wood consumption component needs to be used in conjunction with models of the other components with which it interacts in the forestry sector system. This has left it open ended without a feedback 100p. This loop is needed before we can make any meaningful analysis of policy alternatives. For example, we have merely mentioned that artificial pricing of wood products may lead to undesirable cycles in the produc- tion and consumption of wood products. If we had the model of the production component we would generate the equilibrium prices 197 endogenously by equating production with consumption. We could generate those cycles specifically for different pricing policy alternatives by changing the prices from their equilibrium levels. Similarly, with a complete model of the sector we can analyze the consequences of alternative public and private decisions on the forestry and national objectives. Specifically for example, we can analyze the consequences of alternative forestry management practices (such as natural regeneration, natural regeneration supplemented with limited silvicultural operations, and outright conversion to forest plantations) on the number one objective of the forestry sector, supply of timber (46) as well as on the gghgg national objectives (such as employment, income, revenue, and foreign exchange generation). We would be able to analyze and display on the basis of a complete forestry sector model the consequences over time of alternative uses of land in forestry and in agriculture, of alternative import and export policies for forest products and forest industry inputs, of various profit margins for producers of forest products, etc. on the various forestry sector and national objectives. A general systems simulation model should make these analyses better than specialized technique models because it can utilize any technique available to handle the full range of relevant normative and non-normative information from a wide range of sources to estimate the consequences through time and over space of alternative actions in terms of various goods and bads. If the necessary preconditions for maximi- zation are not met, a general model can trace the consequences of actions through time and through a series of interactions and generate the ,necessary normative and non-normative information to approximate the 198 preconditions for maximization. Preconditions for maximization are not generally present for public decisions and actions which involve a multiplicity of goals, people, change and uncertainty. A model which is generalized with respect to technique, kind, and sources of information, and with respect to philosophic orientation is more likely to provide a multidisciplinary investigation base which no specialized model can provide. This is particularly important in forestry, the components of which including production, processing, distribution, consumption, education and training, land tenure, etc. span a wide range of disciplines such as biology, ecology, engineering, education, and sociology, to mention a few. Other Areas for Further Research All through this thesis we have identified portions in the forestry sector where causes and consequences are either not well known or not known at all. These areas need research attention. They include: 1. Constraints on forest land despite the stated public forest sector objective of encouraging private forestry. 2. Wastage of utilizable trees in the forest zones in the presence of markets for them in other parts of the country. 3. Abuses of the public forest lands by the private sector such as overgrazing of forest lands, purposeful forest fires, illegal removal of trees, etc. 4. Adverse consequences on the production of forest products of the present system of distributing investment-costs and revenue fer the public forests. 5. Inefficiency in the wood processing industries. 6. Alleged excess profit in the wood processing industries. 7. The relative advantages with respect to the national objectives of importing sawlogs for domestic processing for reexport. 199 8. Need for assembling, analyzing, and publishing for public access of information on prices, production, and domestic consumption of wood products. Causes and consequences in the areas have always been guessed at but what is needed is objective investigations to determine their extent and magnitude as a first step for reaching prescriptions to solve problems. APPENDICES APPENDIX A MATHEMATICAL MODEL OF THE WOOD CONSUMPTION COMPONENT OF NIGERIAN FORESTRY SECTOR APPENDIX A MATHEMATICAL MODEL OF THE WOOD CONSUMPTION COMPONENT OF NIGERIAN FORESTRY SECTOR Types and Models of Time Lags Encountered It was pointed out in the section above dealing with the general conceptualization of the model that the response of wood consumption habits to changes in economic conditions involve time lags of different durations. Two types of time lag are encountered in this model. In one of them, described as discrete delay, individuals undergoing the delay process change at a uniform rate. A discrete delay model is used to model the time delays involved in going through high school and college, where most students pass from freshman to sophomore to senior grades and so on at approximately the same rates. Another discrete delay model is used in modeling the time lag involved in the first stages of the aging process of non-school-going rural and urban youth. The young people mature from thirteen to twenty-one years of age in urban areas at the same rate. Similarly, the young people in the rural areas mature from thirteen to twenty-four years of age also at the same rate. In general, a discrete delay model can be represented by (36): l. 0(t) = I(t-DT) 200 201 where: 0(t) is the lagged output of the delay in period t, I(t-DT) is the input to the delay in period t—DT. UT is simulation time interval. A distributed delay model is used to simulate the time lag involved in (l) the final stages of the process of maturity in the non- school—going rural and urban youth, and in (2) deterioration of various buildings as described in the previous sections. In general a distributed delay is defined as a linear differen- tial equation as follows (36): k k-l 2. x(t) = akdy(:) + d k_{(t) + ... + aly(t) dt (it where: x(t) is the unlagged value. y(t) is the lagged value. A delay model is defined by its order, k. The order of a delay was described in Figure 7 above. The output y(t) of the delay model repre- sented above is distributed over a number of periods so that it adjusts slowly to changes in input x(t). Pppulation Submodel (See Figure 21) Young people who do not proceed to high school generally stay dependent until about twenty—one or twenty-four in the urban and rural areas, respectively, at which ages individuals begin to assume personal responsibilities, such as separate residence, separate business, marriage, etc. The first thirteen to twenty-one years in the urban areas and thirteen to twenty-four years in the rural areas of this delay are 202 kzuzouzoomnm 20:41:36.“. “.0 24194.0 30...... ...N maze—... U401» ”I: an an: 3.3. n o o o n ..oa N h ... u...» 3.3. ...: i .4 ... [Ir .1 .81 ...» u _ _ 2:... stand... stand stand _ to: .4 w . w . .23.»: 5.3-5 1.8.5 n I... 2.2. o 52 out. a 2.9.8 u .338 . 23:3 _ h n _ -2526 52.86— .3888 65:83 _ £23 3 J: ... . aa .2 cc _ I I I :26» 415.. .343 2:3) I .l I. .l. .I II I. :59 23.3 .233 2:: l I I. l N o n n , n .0 8.. _ o. .8. 958 3.3.6 .3308 _ a w N + .2342 .53.... .... 2-0: .2. 2.». h . . «.56.. _ out. n 32.8 ~ 29.60 _ 28:8 _ a h . _ 12.226 6.5.6»... .3333 65:33 h“: "Oav Q“ 1.1.“ 1 - , .4 r r, ...—4 .2 ac U. ~33 U .....zu l :8 I ”no. no , ...x h #80 UJO200 Faom 5:80:23 up 6mm #0 .04m a. use» Tll 3d 25... BuoEnsm , cozoSood .aoa 212 t RRij(t) = rate out of the j h stage for house type i (houses/year). DELRi = mean delay time for houses of type i (years). KR = the order of the delay for houses of type i. The rate of change of STRij is the net flow into the jth stage for houses of type i: Euler's integration formula is used to solve the differential equation: dSTR. . (t) ____££L___ = RR. . (t) - RR_,(t) - RAC..(t) ° STR..(t) ° dt i,j+l 1] 13 13 RR. (t) = RAD. ' POP.(t) + ROUT.(t) + RAC.(t) - STR. (t) i,KR+l i i i i i is the rate at which new houses of type i are built (houses/year) where: RADi = proportion of house of type i per adult (proportion). ROUTi(t) = RRil(t) is the output of the delay, number of decayed houses of type i to be replaced (houses/year) RACi(t) = number of standing buildings during the entire delay destroyed prematurely as a proportion of total number of standing buildings for population group i (proportion/year). RAC..(t) - number of standing buildings in jth stage of delay 1] destroyed prematurely as a proportion of total number of standing buildings in the same stage of delay for pOpula- tion group i (proportion/year). ij - STRi(0)/D‘ELRi assuming steady state initial conditions. The intermediate rates RR . are initialized to RRij(0) - Total number of houses of type i per unit time (stock) is given by: RESi(t) = DT - RRi,KR+1(t) (houses/unit time) The total amount of wood of type w used in residential house construction per unit time (stock) is given by: 213 WRES (t) = w PR. (t) - RES.(t) 1 1w 1 1 "Mu where: w = l, 2, or 3 indexes unprocessed wood, processed wood, and building board wood pulp. PRiw(t) = CRiw + BRiw ° P (t) is the amount of wood of type w per residential house of type i (PRil and PRiz in cubic meters, PR. = PR. - PE in kilograms). 13 i CRiw and BRiw = regression parameters (see Chapter V). Pw(t) = index of price of wood, w, to the index of prices of sub- stitutes for wood. PB = proportion of paperboard that is pulp. WRESw(t) as a prOportion of total wood w used for all construc- tion purposes is given by: PWRES (t) = WRES (t)/TWD (t) w w w where: TWDw(t) = total wood w used for all construction purposes. Non-residential Housing Construction Submodel (see Figures 23-26) It was pointed out in earlier sections that the school, hospital, commercial, religious, and public administration building construction subsectors are considered as non—residential building construction subsectors. The rates of setting up elementary school, commercial, religious, and public administration buildings are estimated as constant proportions of building rates for residential buildings. We shall present these first and later present high school and college (as school) and hospital submodels. 214 ZOEmeHmZOU suds @. HZMZOQEOQmDm 02.0.55 moozom >m23 .59.. 5:96:20 218 1. Elementary School (see Figure 23). The elementary school component computes the number of new houses and furniture set up for elementary schools for traditional, semi-traditional, and non-traditional populations per unit time and the amount of unprocessed wood, processed wood, and building board woodpulp used in this construction. The rate at which elementary school (of type i) buildings are built is given by: ELE.(O) = 1 REi(t) - RR (t) (houses/year) '. + RESi(O) 1 “I where: i = 1, 2, or 3 indexes traditional, semi-traditional, and non- traditional respectively. RRi KR+1(t) = the rate at which residential houses of type i are set ' up (houses/year). ELEi(O) = total number of elementary school buildings of type i at base time (houses). RESi(0) = total number of residential buildings of type i at base time (houses). Total number of elementary school buildings of type i per unit time (stock) is given by: ELEi(t) = DT ° REi(t) (houses/unit time) Wood of type w used in elementary school housing and furniture per unit of time is given by: WELE (t) = w PE, (t) - ELE.(t) i 1w 1 1 HMO) 219 where: w = l, 2, or 3 indexes unprocessed and processed wood, and building board woodpulp respectively. WELEl 2 are in cubic meters, WELE3 = WELE3 ' PB in kilograms per ' unit time. PE. (t) = CE. + BE, ° P (t) is amount of wood w per elementary 1w iw 1w w school building of type i. CEiw and BEiw = regression parameters (see Chapter V). PB = proportion of paperboard that is woodpulp (proportion). 2. Commercial Building Construction (see Figure 24). This submodel computes the number of new houses and furniture built per unit time for commercial uses by traditional and semi- traditional p0pu1ation groups and the amounts of unprocessed wood and processed wood used (in cubic meters) per unit time and building board woodpulp used (in kilograms) per unit time in this construction. The rate at which commercial buildings of type i are built is given by: COM.(0) = 1 - RR (t) (houses/year) RESi(O) CMi(t) i,KR+1 where: i = l, or 2 indexes traditional and semi-traditional commercial buildings respectively. RR. (t) = the rate at which residential houses of type i are 1,KR+1 set up (houses/year). COM,(O) = total number of commercial buildings of type i at base 1 time (houses). RESi(O) = total number of residential houses of type i at base time (houses). Number of commercial buildings of type i per unit time (stock) is given by: 220 COMi(t) = DT ‘ CMi(t) (houses/unit time) Wood of type w used in commercial housing construction is given by: "MN WCOM (t) = W PC. (t) ' COM. (t) 1 1w 1W 1 where: w = 1, 2, 3 indexes unprocessed wood, processed wood, and building board woodpulp. PC. (t) = CC. + BC. - P (t) is average amount of wood w per 1w 1 . 1w . g. . commerc1a bu1l ing of type 1. CCiw and BCiw = regression parameters (see Chapter V). P (t) = the ratio of the index of price of wood, w, to the index of w . . pr1ces of subst1tutes for wood. PC. and PC. are in cubic meters. 11 12 . = P . . . . . . PCl3 Cl3 PB is in kilograms PB = proportion of paper board that is woodpulp. 3. Religious Building Construction (see Figure 24). The religious housing construction submodel computes the number of new houses and furniture set up per unit time for religious uses by traditional and semi-traditional population groups and the amounts of unprocessed wood and processed wood in cubic meters per unit time and building board woodpulp in kilograms per unit time used in those con- structions. The rate at which religious buildings of type i are set up is given by: REL.(0) 1 RR (t) (houses/year) RESi(O) mi“) = i,KR+l 221 where: i = 1, or 2 indexes traditional, and semi-traditional respectively. RELi(O) = total number of religious buildings of type i at base time (houses). Number of religious houses of type i set up per unit time is given by: RELi(t) = DT - RLi(t) (houses/unit time) Wood of type w used in religious housing construction is given by: (t) II II MN ":1 t" E t" WREL (t) . . W 1w 1w where: w = 1,..., 3 indexes unprocessed wood, processed wood, and building board woodpulp. PL. (t) = CL. + BL. - P (t) is the average amount of wood, w, per 1w 1w 1w w religious building of type i. Chiw and BLiw = regression parameters (see Chapter V). P (t) = the ratio of the index of price of wood, w, to the index of w . . pr1ces of subst1tutes for wood. PL. and PL. are in cubic meters. 11 12 PL. = PL, - PB is in kilo rams. 13 13 9 PB = proportion of paper board that is woodpulp. 4.. Public Administration Building Construction (See Figure 24). This submodel computes the number of new houses and furniture built per unit time for public administration uses and the amounts of unprocessed wood and processed wood (in cubic meters per unit of time) and building board woodpulp (in kilograms per unit of time) used in those constructions. 222 The rate at which public administration buildings are set up is given by: AD(t) = ADM(O) - (RR + RR ) (houses/year) I + I + REs(0)+REs(O) ZKRI 3““ 2 3 where: RR3 KR+1(t) = the rate at which non-traditional residential houses ' are set up (houses/year). RR2 KR+1(t) = the rate at which semi-traditional residential houses are built (houses/year). ADM(O) = total number of public administration buildings at base time (buildings). RE82(O) = total number of semi-traditional residential houses at base time (houses). RES3(O) = total number of non-traditional residential houses at base time (houses) Number of public administration buildings set up per unit of time (stock) is given by: ADM(t) = DT - AD(t) (houses/unit time) Wood of type w used in public administration housing construction is given by: WADM (t) = PA (t) - ADM(t) w w where: w = 1,..., 3 indexes unprocessed wood, processed wood, and building board woodpulp. PAw(t) = CAw + BAw ' Pw(t) is average amount of wood w per public administration building. CAw and BAw = regression parameters (see Chapter V). Pw(t) = the ratio of the index of price of wood, w, to the index of prices of substitutes for wood. 223 Pnl and PA2 are in cubic meters. PA3 = PA3 - PB is in kilograms. PB = proportion of paper board that is woodpulp. 5. School (High School and College) Building Construction (see Figure 25). This submodel computes the number of new houses and furniture set up per unit of time for school uses by high school and college levels and the amounts of unprocessed wood and processed wood in cubic meters per unit time and building board woodpulp in kilograms per unit of time used in this construction. The rate at which high school or college buildings are set up is computed as the sum of the rate of replacements of existing ones and a proportional multiple of the increment in the number of students. The rate of replacements is computed following DELLVF (11) to simulate the time delay involved in the decay process and losses due to accidental demolition of school buildings and furniture as follows: . . .th STSij is the storage of school house of type 1 in 3 stage of delay: STSij(t) = BEEEi_° Rsij(t) (houses) KS where: i = l, 2 indexes high school and college. j = 1,..., KS indexes stage of delay. RS..(t) = rate out of jth stage for school building of type i 13 (houses/year). DELSi = mean delay time for school building of type 1 (years). KS = order of the delay for school building of type i. 224 The rate of change of STSij is the net flow into the jth stage; Euler's integration formula is used to solve the differential equation: ds'rsi.(t) ——-1-—— = RS.. (t) - RS..(t) - SAC..(t) ~STS..(t) ° dt 1.3+l 1) 13 13 RS. (t) = SAD. - CHST.(t) + sou'r.(t) + SAC.(t) ' STS.(t) 1,KS+1 1 1 1 1 1 the rate at which new school buildings of type i are built (houses/year) where: SACi(t) = number of school buildings of type i destroyed prematurely during the entire delay as a proportion of all school buildings of type i (proportion/year). SOUTi(t) = R311(t) is the output of the delay, the rate at which school buildings of type i decay (houses/year). CHSTi(t) = increment in student enrollment for school type 1 (students). SAD. = proportion of school building of type 1 per student 1 (proportion). SACi.(t) = number of school buildings of type i destroyed pre- 3 maturely during the jth stage of delay as a proportion of school buildings of type i in the jth stage (proportion/ year). Intermediate rates of Rsij are initialized to: RS..(O) = STS.(0)/DELS 13 1 assuming steady state initial conditions. Total number of school buildings of type 1 per unit time (stock) is given by: SCHi(t) = DT ' Rsi,KS+l(t) (houses/un1t time) Wood of type w used in school housing construction is given by: IIMN wnsca (t) = w PS. (t) - SCH.(t) .1 1w 1 1 225 where: w = 1,..., 3 indexes unprocessed wood, processed wood, and building board woodpulp. PS. (t) = C8. + 88, ° P (t) is average amount of wood, w, per 1w 1w 1w w school building of type i. CSiw and BSiw = regression parameters (see Chapter V). Pw(t) = ratio of the index of price of wood to the index of prices of substitutes for wood. PSi1 and P512 are in cubic meters. PSi3 = PSi3 ° PB is in kilograms. PB = proportion of paperboard that is woodpulp. 6. Hospital Building Construction (see Figure 26). This submodel computes the number of new houses and furniture set up per unit of time for medical uses and the amounts of unprocessed wood and processed wood in cubic meters per unit of time and building board woodpulp in kilograms per unit of time used in this construction. The rate at which hospital buildings are set up is computed as the sum of the rate of replacement and a proportional multiple of increment in the number of hospital beds. The rate of replacement is computed following DELLVF (ll) to simulate the time delay involved in the delay process and losses due to accidental demolition of hospital buildings. .th 5THj is the number of hospital buildings in the 3 stage of delay: STH.(t) = DELH . RH.(t) (houses) 3 75:“ 3 where: RHj(t) = rate out of jth stage (houses) 226 j = 1,..., KB indexes delay stage. DELH = mean delay time for hospital buildings (years). KH = order of delay for hospital buildings. The rate of change of STHj being the net flow into the jth stage. Euler's integration formula is used to solve the differential equation: dSTH.(t) ——3—-dt = RHj+1(t) - RHj(t) - HACj (t) - STHj(t) RHKH+1 = HAD - BED(t) + HOUT(t) + HAC(t) ° STHj(t) is the rate at which hospital buildings are set up (houses/year). where: HAD = hospital building per hospital bed (proportion). BED(t) = AH + DH ' GDP(t) + VH ' EB(t) is increment in number of hospital beds (beds/year). AH, DH, and VB = regression parameters (see Chapter V). GDP(t) = gross domestic product divided by price index (N/year). HB(t) = government health budget divided by price index (N/year). HOUT(t) = RH1(t) is the output of the delay, worn out hospital buildings (houses/year). HAC(t) = number of hospital buildings destroyed prematurely during the entire delay as a proportion of the total number of hospital buildings (proportion/year). HAC.(t) = number of hospital buildings in the jth stage of delay 3 destroyed prematurely as a proportion of the total number of hospital buildings in the jth stage (proportion/year). The intermediate rates RHj are initialized to RHj(O) = STH(O)/ DELH assuming steady state initial conditions. The number of hospital buildings per unitcaf time (stock) is given by: 227 HOSP(t) = or - Wheel“) Wood of type w used in hospital building construction per unit of time is given by: WHOS (t) = PH (1:) ' HOSPU’.) W W where: w = 1,..., 3 indexes unprocessed wood, processed wood and building board woodpulp. PH (t) - CH + BH ' P (t) is average amount of wood, w, per w w w w hospital building. CHw and BHw = regression parameters (see Chapter V). Pw(t) = ratio of the index of price of wood to the index of prices of substitutes for wood. PH1 and PH2 are in cubic meters. PH3 = PH3 ' PB is in kilograms. PB = proportion of paper board that is woodpulp. Wood w used in non-residential housing construction per unit of time is given by: WNRES (t) = WBLE'(t) + wcom (t) + WREL (t) + WADM (t) + wnscn (t) + W W W W W W WHOS (t) w WNRESw(t) as a proportion of all wood w used for all construction purposes is given by: PWNR(t) = WNRESw(t)/TWDw(t) Farm Construction and Fuelwood Submodel (see Figures 27-28) 1. Farm Construction (see Figure 27). 228 .0."— Fzmzoasoomnm wzozbamhmzoo 2¢030 8:90:28 5.24.1 ...Q .bzwzomzoomam 20....m23m200 0003.53...“ “.0 241045 304... Hmm manor. mmam 229 + Ema... + 6.6:... ram Lb 1m“... f _ .3055 Ken: #32238 230 This submodel computes the number of new farms by traditional and non-traditional, the rate at which farm buildings are set up, and the amounts of unprocessed wood and processed wood in cubic meters per unit time used in the farm construction. The number of new farms per unit time is given by: PAM, (t) = ETA, (t) ‘ POP. (t) l l 1 where: i = 1,..., 2 indexes traditional and non-traditional. POPi(t) the rate at which population group i grows (people/year). ETAi(t) AGi + DGi - AGB(t) + VGi - PD(t) is the proportion of population group i that are farmers (proportion/year). AGi, DGi' and VGi = regression parameters (see Chapter V). AGB(t) = state government's budgets for agriculture (N/year). PD(t) = population density by state. The rate of construction of new farm buildings is computed as the sum of replacements of existing farm buildings and a preportional (buildings per farm) multiple of new farms. The rate of replacements is computed following DELLVF (11) to simulate the time delay involved in the decay process and losses due to accidental demolition of farm buildings. .th STGij is the storage of farm buildings, 1, in the 3 stage of the delay: STG..(t) = 93151 . RG..(t) (farm buildings) where: i = l, 2 indexes traditional and non-traditional farm buildings respectively. 231 j = l, .... KG indexes stage of delay. RG., = rate out of the jth stage for farm buildings of type i (farm buildings/year). DELGi = mean delay time for farm buildings of type i. KG = the order of the delay for farm buildings of type i. The rate of change of STGij is the flow into the jth stage for farm buildings of type i; Euler's integration formula is used to solve the differential equation: dSTG..(t) ____$J__.= RG, , (t) — RS..(t) — GAC..(t) ° STG..(t) dt 1.3+1 13 l] 13 RG. (t) = GAD. - FAM.(t) + GOUT.(t) + GAC.(t) ° STG(t) is 1,KG+1 1 1 1 1 the rate at which new farm buildings of type i are set up (farm buildings/year) where: GAD. = average number of farm buildings of type i per farm (farm buildings/farm). GOUTi(t) = RGi (t) is the output of the delay, number of decayed farm buildings of type i to be replaced (farm buildings/ year). GACi(t) = number of farm buildings of type i destroyed prematurely during the entire delay as a proportion of total number of farm buildings (preportion/year). . ‘ .th GAC..(t) = number of farm buildings of type i in the 3 stage of 13 delay destroyed prematurely as a prOportion of total number of farm buildings in the jth stage (proportion/ year). The intermediate rates RGij are initialized to: RGij(0) = STGi/DELGi assuming steady state initial conditions. Total number of new farm buildings of type i per unit time (stock) is given by: 232 FAMBi(t) = DT - RGi,KG+l(t) (farm buildings/UT) Wood, w, used for farm constructions per unit time is given by: 2 WFAM (t) = 22 PG. (t) ° FAMB.(t) w . 1w 1 1=l where: PG, (t) = CG. + BG. - P (t) is average amount of wood, w. per farm 1w 1w 1w w of type i. CGiw and BGiw = regression parameters (see Chapter V). w = 1,..., 2 indexes unprocessed wood and processed wood. Pw(t) = ratio of the indexes of price of wood to the index of prices of substitutes for wood. PG, is in cubic meters. 1w WFAMw(t) as a proportion of wood, w, used for all construction purposes is given by: PWFAM (t) = WFAM (t)/TWD (t) w V w where: w = 1,..., 2 indexes unprocessed wood and processed wood in cubic meters. TWDh(t) = wood w used for all construction purposes. 2. Fuelwood Consumption (see Figure 28). This submodel computes the amounts of fuelwood from market and nonmarket sources in cubic meters per unit time for the traditional and non-traditional population groups. Fuelwood from market sources is given by: 2 FUEM(t) = Z PF.(t) ° PBF. - TPOP,(t) i=1 1 1 1 233 where: i = 1,..., 2 indexes traditional and semi-traditional. PFi(t) = CFi + BFi ° P4(t) is average wood in cubic meters per adult in population group i who obtained his fuelwood from the market. CFi and BFi = regression parameters (see Chapter V). P4 = price per cubic meter of fuelwood. TPOPi(t) = total adult population in group i. PBFi = the proportion of population group i that obtained fuelwood from market sources. Fuelwood from nonmarket sources is given by: FUES(t) = i IIMN PFS, - PSF. - TPOP.(t) 1 l l 1. where: i = 1,..., 2 indexes traditional and non-traditional. PFSi = fuelwood in cubic meters per adult in population group i who obtained their fuelwood from nonmarket sources. PSF. = preportion of population group i that obtained fuelwood from nonmarket sources. Total fuelwood per unit time is given by the following identity: TFUEL(t) = FUEM(t) + FUES(t) Submodel of Other Manufacture and Construction Subsectors (see Figures 29-30) Casket manufacture and lumber trucks, and bridge construction subsectors are grouped together as other manufacture and construction subsectors. It is assumed that none of these subsectors consumes unprocessed wood and building board woodpulp in significant amounts. 234 #3:; ...m .mhzmzoazoomnm zozuamhmzoo xoamh cumin.— oz< wm3h0030 FDOQ COtOLOmgfluflo w ... h ... 2.0 04m 9mm . a 236 l. Casket Manufacture (see Figure 29). The rate at which caskets are built is given by: CKT(t) = PBC - DTHS(t) (caskets/year) where: PBC = proportion of dead buried in caskets (proportion/year). DTHS(t) = total number of deaths per unit time. Processed wood used for casket construction per unit time is given by: WCKT(t) = CKT(t) - PK(t) (cubic meters/year) where: PK(t) = CK + BK - P (t) is average amount of processed wood used per casket (cubic meters). CK and BK = regression parameters (see Chapter V). P2(t) = ratio of index of price of processed wood to the index of pr1ces of subst1tutes for processed wood. 2. Lumber Trucks Construction (see Figure 29). This submodel computes the number of lumber trucks per unit of time and the amount of processed wood in cubic meters used in the con- struction of the trucks. The number of new lumber trucks per year is given by: RTM(t) = AU + DU ° BLN(t) where: AU and DU = regression parameters (see Chapter V). BLN(t) = commercial bank loan funding available for transportation industry (N/year). 237 The number of new lumber trucks per unit of time is given by: WTMT(t) = PT(t) ' TMT(t) where: PT(t) = CT + BT - P (t) is average amount of processed wood used for lumber trucg (cubic meters). CT and BT = regression paramters (see Chapter V). P2(t) = ratio of index of price of processed wood to the index of pr1ces of subst1tutes for processed wood. 3. Bridges Construction (see Figure 30). This submodel computes the number of bridges built on dirt roads per unit time and the amount of processed wood in cubic meters used for the construction of the bridges. The rate at which bridges are set up is computed as the sum of the rate of replacements and a proportional multiple of additional miles of dirt road each year. The rate of replacements is computed following DELLVF (11) to simulate the time delay involved in the decay process and losses due to accidental demolition as follows: STBj is the storage of bridges in the jth stage of decay: s'rsj(t) = Egg; . RBj(t) KB where: j = 1,..., KB indexes delay stage. RBj(t) = rate out of jth stage for bridges (bridges/year). DBLB = mean delay time for bridges (years). KB = order of the delay for bridges. The rate of change of STBj is the net flow into jth stage; vEuler's integration formula is used to solve the differential equation: 238 dSTB.(t) ____J__. = RB. (t) - RB.(t) - RB.(t) - BAC.(t) ° STB.(t) dt 3=1 3 J J J RBKB+1(t) = BAD ' PRD(t) + POUI‘(t) + BAC(t) ' STB(t) is the rate at which new bridges are built (bridges/year). where: BAD = bridges per mile of road (preportion). PRD(t) = AD + DD ' GDP(t) + VD - TB(t) is additional miles of road (miles of road/year). AD, DD, and VD = regression parameters (see Chapter V). GDP(t) = gross domestic product divided by price index (N/year). TB(t) = government transportation budget divided by price index (N/year). BOUT(t) = RB1(t) is the output of the delay or worn-out bridges on old roads (bridges/year). BAC(t) = number of bridges destroyed prematurely during the entire delay as a preportion of the total number of bridges (proportion/year). . .t BACj(t) = number of bridges destroyed prematurely during the 3 h stage of delay as a proportion of the total number of bridges in the jth stage (prOportion/year). The intermediate rates RBj are initialized to RBj(O) = STB(O)/ DELB assuming steady state initial conditions. Number of bridges per unit time (stock) is given by: BRIG(t) = DT ° RBKB+1 Processed wood used for bridge construction per unit time is given by: WBRG(t) = PB(t) - BRIG(t) (cubic meters/unit time) PB(t) = CB + BB ' P2(t) is average amount of processed wood per bridge (cubic meters). where: CB and BB = regression parameters (see Chapter V). P (t) = ratio of index of price of processed wood to the index of 2 . subst1tutes for processed wood. Paper Submodel (see Figure 31). The paper component computes the amount of paper used by elemen- tary school population, high school population, college population, traditional adult population, semi-traditional adult population and non-traditional population. Total wood (pulp) used for paper per unit time (stock) is given by: 3 3 WPAP = Z TPAPSS + Z TPAPi S=l i=1 ' TPAP.(t) = PAP.(O) - (1 + PB - CP,(PCI.(t) - PCI.(O))) : TPOP, 1 1 1 1 1 1 is per capita consumption of paper by pepulation group i (kg./year) where: i = l, 2, 3 indexes traditional. semi-traditional, and non- traditional population groups respectively. PAPi(O) = per capita consumption of paper by population group i at base time (kg./year). PCIi(t) = per capita income for population group i (N/year). PCI.(O) = per capita income for population group i at base time (N/year). TPOPi = total adult population in group 1 (people). PB = proportion of paper that is woodpulp. CP. = proportional increase in per capita consumption of paper per unit increase in per capita income for the income group i. 240 .hzmzoazoomzm 20....mEszoo mmm<¢ ...O 2CuhfiHXEb(1Hw .t().fl2bGr7:;9HRR}\ ( .(( 232? P1 H..I(l)t((|. (nu . C nfilTor¥Ihla. .9.“H09:FHHML2U19quPF. F TY. 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Newao ckew. cued. mmow. :woa. m6... . .«5\.z. 5.y..z 2... aces. newumESmcoo -nuw>o mo accuuuomoum mm ”a mugg OHDHOmn‘ CH @0030“ UQXHQZOCOZ can uoxua: an voozaosh uo couumisacoo danced 2:031 uhadcmot Du .sm~o.:«u ......~.. .no~m~c.. .n095mcou .~5acooo. .....M... ......»o. .55onoNou .nom04uo. .o.oo.oau .wenooow .muuwcoo .ommmucw .....N5. .cm..~5@ .....w5. .~.5wn5. .mosmc30 .n~..... .5.~.4.. ..55.n5. .....~n.. ...~.... .ooo~om¢ .o-mm5o ..mm:~.m omwu moau coma Luau mood momu Java no¢u womu «ecu coma «hmu chad shew oNOu msou than nsou «sad «sou ohmu mowu coma soou coon mood “cub APPENDIX E BASE RUN PROJECTIONS OF ANNUAL CONSUMPTION OF VARIOUS WOOD PRODUCTS BY WOOD CONSUMPTION POPULATION GROUPS, 1965-1990 1272 005:. .an.m~ awoa. .«3mc~ coco. .anhw noaa. .m:om~ coco. .«cn:~ csoa. .noomw nsoo. .Noouw asaa. .swaou coco. .-o¢« Goon. .oouca 309°. .ouosa omen. .~mmmu chc. .:~nm« mace. .oomhu oweo. .:ocm« omen. .muscu s:oo. .osswu «tau. .«smcu once. .nosa case. .05:: «ace. .«mmn auaa. .uumn auoa. .o3nn coca. .«oow .99.. .nccu cage. .c~c~ ¢>\.ao¢m .¢>\nx.hx< m>~h<4um ubaaomo¢ Duunoummozoz gown. ammo. mnwo. nuNo. no~o. nwuo. asua. omao. cede. onuo. mmao. ouuo. owoo. omoa. choc. «sea. noon. 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APPENDIX F PROJECTIONS OF ANNUAL CONSUMPTION OF DIFFERENT WOOD PRODUCTS BY USES UNDER ALTERNATIVE ASSUMPTIONS ABOUT THE RATES OF GROWTH OF GDP, 1975-1990 277 y ....5.. ....o.. ....5... ....... -.5...... ........ .... .5..... ....... ......5 .....5. ........ ......... a... ......n ....... ....5.5 ..5.5.. ........ ....5.o .... ..5.5.. ..5.... ......5 ....... ........ ...»... .... .5..... .5..... ......5 .....u. ..5.... 3...... .... ....... ....... ....5.. ....5.. ......o ..5.... ..c. .9:.9m~ .9999.m .umr9a0 .909059 .«.u9~9 ...¢.m9 4.0. ..5.5.. ....... ....... ....... .5..... ....5.. .... ....... .55.... ...5... ....... ....... ....... .... .....5. ....... ......m ......m ....... ....... .... ..5.... ....... ......m ......m .....w. ..5.... .... ....... .5..... ....5u. ...5... ....... ..cu... a... ....... ....... ......m ......m .5..... .5..... .... ...5... ...5... ......m ....5.. ......5 ......5 5... ....... ....... ..5.... ..5.... ......5 ......5 .5.. ...... ...... ......m ....... .5....5 .5....5 .5.. ...< ... ...< .. .... ... ...< u. ...< ... ...< N. «waomxmxv www.95 xv A. 095 :0 .00» ..s. ...: n m 9.009 90.9..09 9003 vommmooum 9003 vmmwmoounca O99.Im599 $.00 .90 $930.90 .90 09.9.95. mm... 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BIBLIOGRAPHY Aboyade, O. Incomes Profile. Ibadan, University of Ibadan, 1973. Adeyoju, S. K. A Synopsis of the Nigerian Timber Economy, Ibadan, 1971. Barnard, R. C. "Silviculture in the TrOpical Rainforest of Western Nigeria Compared with Malayan Methods," The Commonwealth Forestry Review, Vbl. 34, p. 355, London: Commonwealth Forestry Association. Central Bank of Nigeria, The Economic and Financial Review, Lagos. Coleman, J. S. Nigeria Background to Nationalism, Berkley and Los Angeles: University of California Press, 1958. . Daily Times, Lagos: (August 24, 1974). . Daily Times, Lagos: (October 2, 1974). Dawkins, H. C. "The Volume Increment of Natural Tropical High- Forest and Limitations on Its Improvements," The Commonwealth Forestry Review, v01. 38, p. 175, London: Commonwealth Forestry Association. . Demography with Discrete Age Cohorts (DEMOGD), CLASS Document, Michigan State University, East Lansing. . "Digest of Statistics," Federal Office of Statistics, Lagos, Nigeria. . 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Stanford Research Institute. Stanford, california, America's Demand for Wood, A Report to Weyerhaeuser Timber Compan , Tecoma, Washington, Sunnyvale, California, 1954. . Statistics of Education in Nigeria, Lagos: Federal Ministry of Information. . Table Function with Equal Intervals and Extrapolation (TABEX), CLASS Document, East Lansing: Michigan State University. volkomies, P. J. "Wood Raw Materials for Pulp and Paper in the Tropical Countries," Unasylva, V01. 23 (3), Rome: FAO.