MULTI - SCALE APPROACHES TO GLOBAL CHALLENGES IN A TELECOUPLED WORLD By Zhenci Xu A DISSERTATION Submitted to Michigan State University i n partial fulfillment of the requirements for the degree of Fisheries and Wildlife Doctor of Philosophy 2019 MULTI - SCALE APPROACHES TO GLOBAL CHALLENGES IN A TELECOUPLED WORLD By Zhenci Xu Main findings from this dissertation include: At the global scale, These ACKNOWLEDGEMENTS I t hank many people for shaping my PhD study and my life. I am grateful for the help and instructions from committee members. I thank my advisor Dr. Jianguo (Jack) Liu for his insightful instructions about proposing ideas and writing papers , valuable comments and careful edits . I thank Dr. Ken Frank for his helpful social network analysis class . I thank Dr. Rafael Auras for his patience and passion in discu ssing with me about life - cycle assessment . I also thank Dr. Jinhua Zhao for his insightful suggestions in revising my dissertation proposal . I also thank Yunkai Li and Xiuzhi Chen for collecting part of the data. I greatly thank Jing Sun for his daily talk s with me at the beginning of my PhD study when I kn e w nothing about research. F rom these talk s I learn ed many tips about how to construct good ideas and writ e papers. I also appreciate the insightful comments and careful edits from many members in Center for Systems Integration and Sustainability, especially Shuxin Li, Yingjie Li , Sophia Chau , An na Herzberger, Ying Tang, Thomas Connor , Jinyan Wang, Di Zhang, Mimi Gong, Dapeng Li, Kelly Kap s ar, Ciara Hovis, Susie (Ruqun) Wu, Paul McCord, Julia W hyte, Sue Nichols and . I would like to thank my family and friends. I thank all my family members for their unconditional support and care . I would also like to thank my best friend Dequ Hong who was always willing to hear my emotion s , trust me and comprehend me . Finally, I acknowledge financial sup ports from the National Science Foundation, College of Agriculture and Natural Resources, and Graduate School at Michigan State University. TABLE OF CONTENTS ................................ ................................ ................................ ........................ ................................ ................................ ................................ ..................... ................................ ................................ ................................ ... ................................ ................................ ................................ ........................ ................................ ................................ ................................ ....... ......... ................................ ................................ ................................ .............................. ................................ ................................ ................................ .................... ................................ ................................ .............................. ................................ ................................ ................................ ................................ .. ................................ ................................ ................................ ........................... ................................ ................................ ................................ ....................... ................................ ................................ ................................ ................................ ................................ ................................ ................................ .............................. ................................ ................................ ................................ .................... ................................ ................................ ................................ . ................................ ................................ ................................ ................................ .. ................................ ................................ ................................ ........................... ................................ ................................ ................................ ....................... .............................. ................................ ................................ ................................ .............................. ................................ ................................ ................................ .................... ................................ ................................ ................................ ................................ ................................ ................................ ................................ . ................................ ................................ ................................ ....................... ................................ ................................ ................... ................................ ................................ ................................ .............................. ................................ ................................ ................................ .................... ................................ ................................ .............................. ................................ ................................ ................................ ................................ .. ................................ ................................ ................................ ........................... ................................ ................................ ................................ ..................... ................................ ................................ ................................ . ................................ ................................ ................................ ............................ ................................ ................................ ................................ ......................... ................................ ............................. ................................ ................................ ................................ .......................... ................................ .............................. ................................ ................................ ................................ ............................ LIST OF TABLES LIST OF FIGURES .21 CHAPTER 1 . INTRODUCTION national energy consumption and env ironmental pollutant emissions are growing rapidly. In 2013, nearly 22% of global energy consumption occurred in China . Consequently, China has surpassed the United States to be the world's largest energy consumer and the largest source of CO 2 emissions As China is still in the industrialization and urbanization process, energy consumption and pollutant emissions will increase in the near future (National Development and Reform Commission of the People's Republic of China 2013) . Developing strategies to maintain energy supplies while achieving economic and social sustainable development will be important issues to policy makers. CHAPTER 2 . EVOLUTION OF MULTIPLE GLOBAL VIRTUAL MATERIAL FLOWS Two kinds of directed weighted networks exist, each representing one of two potential flow di rections. Import - directed networks are those in which links represent imports, and export - directed networks are those in which links represent exports. Either kind of network represents the total global material flows for a given network because each flow is associated with both an import country and an export country. Thus, a certain flow is represented in both kinds of networks. For each kind of material flow, we constructed a global network by applying multi - regional input - output analysis, a method commo nly used to determine interdependencies between countries by tracking monetary flows. Assuming there are m countries and each country has n sectors, we can calculate the monetary output of sector i in country R by: Ribbon colors suggest the country of export. in w hich flow links represent exports) Global trade hubs such as the US and China dominate global networks. Environmental and socioeconomic shifts in these hubs result in consequences worldwide. For example, the 2008 financial crisis that originated in the US resonated across the globe; exceptionally rapid economic development in China led to increased importation of forest products, which led to deforestation in the . To manage global flows of virtual materials, policy mak ers could Safeguarding the environment through sustainable resource use will require a better understanding of the existing international mechanisms and structures of trade networks to target top trading countries. More governance aiming at virtual material trade should be developed to enforce trade regulations (Lenzen et al. 2012, Zhao et al. 2015) . Though some institutions and agreements like the Word Trade Organization and the Kyoto Protocol are able to promote the multilateral trade governance, there has been little focus on virtual material trade. Additi onal institutions should be established to promote multilateral and bilateral trade governance aimed at virtual material trade across national borders (Frankel 2009) . Consumption - based virtual material consumption/emission should be measured and then responsibility of consumption could be partly allocated to consumers (Frankel 2009, Peters 2010) . And more consu mption - based policies should be built to manage the virtual material trade. Research and policy - making should not be limited to a single sector like water or energy or CO 2 , but consider synergies and trade - offs between two or more sectors (Liu et al. 2015, Wicaksono, Jeong and Kang 2017, Miglietta, Morrone and De Leo 2018) . While nexus approaches (e.g., food, energy, and water nexus) have gained increasing attention to understand interactions among sectors, they mainly focus on sectors in a specific place such as within a country (Liu et al. 2018) . . Such integration would enhance our understanding of complex system dynamics and chart a path towards achieving human well - being and global sustainability (Liu et al. 2015, Lamastra et al. 2017) . CHAPTER 3. INTERACTIVE NATIONAL VIRTUAL WATER - ENERGY NEXUS NETWORKS most recently available multiregional input - output table for develop ing interprovincial energy and water trade networks simultaneously in China (Zhang et al. 2013, Zhao et al. 2015) We used the most recently available multiregional input - output table for developing energy and water networks simultaneously in China (Zhang et al. 2013, Zhao et al. 2015) . The Chinese 2007 interprovincial input - output table was constructed by the Chinese National Bureau of Statistics. Being consistent with previous research (Zhao et al. 2015) , water withdrawal data in sector at the provincial level were derived from Water Resource Bulletin at the provincial level and Chinese Economic Census Yearbook 2008 (Provincial Water Resources Bureau 2007, Census 2008, Zhao et al. 2 015) . The sources of water withdrawal wer e surface water, groundwater, and transferred water (Ministry of water resources of China 2007, Zh ao et al. 2015) . The energy consumption data for sectors in provinces were obtained from the 2008 energy balance table (National Bureau of Statistics 2008) . We ran the multivariate analysis to explore drivers associated with virtual water/energy transfer. Firstly, we performed the homogeneity test of variance for the virtual water and energy transfer data to evaluate the degree of heteroskedasticity of the var iables included in the analysis. The P - values for the homogeneity test of variance for virtual water and energy transfers are 0.080 and 0.162, respectively, indicating that the degree of heteroskedasticity in the data is not significant. Ordinary Least Squ are (OLS) regression was then applied in order to explore the drivers of virtual water or energy transfers. We used the ratio between internal water consumption (W C ) and energy consumption (E C ) (equation (14)) as the measurement for water - energy nexus rela tionship. interdependency between multiple issues and are addressed together (Liu et al. 2015) . Water and energy are consumed in most of the human activities involved in the sectors of the multiregional input - output table. Additionally, th e consumption of water and energy coincides with each other. For example, in agriculture, the water consumed in the food production process requires energy to pump the water. In industry, energy production requires the use of water to cool down machines an d plants, or produce the materials used to generate the energy (e.g., biofuel). In order to represent the interdependency between water and energy consumption, we calculated the ratio between their consumption. (Zhao et al. 2015, Wood et al. 2018) (Zh ao et al. 2015, Wood et al. 2018) - energy nexus ratio * under a without - trade scenario were therefore calculated by adding the trade balance (net import in our case) back to provinces in previous research (Zhao et al. 2015, Wood et al. 2018) - followed Zhao et al (201 production would supplement the original imported materials (Zhao et al. 2015, Wood et al. 2018) . For example, the original (Zhao et al. 2015, Wood et al. 2018) , 0% 10% 20% 30% 40% 50% 60% 70% scarce-to-abundant abundant-to-scarce scarce-to-abundant abundant-to-scarce water transfer energy transfer 0 50 100 150 200 250 300 350 Beijing Tianjin Hebei Shanxi Inner Mongolia Liaoning Jilin Heilongjiang Shanghai Jiangsi Zhejiang Anhui Fujian Jiangxi Shandong Henan Hubei Hunan Guangdong Guangxi Hainan Chongqing Sichuan Guizhou Yunnan Shaanxi Gansu Qinghai Ningxia Xinjiang Ratio between water and energy consumption (GJ/Thousand m3) without trade with trade However, virtual water and energy flows increased with increases in the proportion of industrial GDP in the importing province Provinces with greater local industry would have greater internal resource consumption and import more resources but provide less resources to other provinces. This may be because the surplus of cropland area can be used to produce food for export, which is one type of economic benefit that accelerates virtual water/energy export. Also, surplus cropland area may be indicative of less demand for trade since the province c an feed itself to an extent. For precipitation, it has positive effects on trade in export provinces but negative effects on trade in receiving provinces. The reason may be that precipitation increases water of local areas, therefore local area would deman d less resource from trade but can export more resource outside for profits. Notes: The number in table represents the coefficients of variables. *, ** denotes significance at 0.05, and 0.01 level. collaborative relationships collaborations it would be advantageous to Additionally, the results of the CHAPTER 4. SHIFT IN A NATIONAL VIRTUAL ENERGY NETWORK Due to persistent energy shortages, the virtual consumption and transfer of energy has attracted widespread attention in recent years (Liu et al. 2018, Xu et al. 2018) . . Our internal energy consumption refers to the internal energy fo otprint, which encompasses all local energy use (direct and indirect) associated with the final consumption in the studied area h, the national energy consumption and environmental pollutant emissions are growing rapidly. In 2013, nearly 22% of global energy consumption occurred in China . Consequently, China has surpassed the United States to be the world's largest energy consumer and the largest source of CO 2 emissions As China is still in the industrialization and urbanization process, energy consumption and pollutant emissions will increase in the near future (National Development and Reform Commission of the People's Republic of China 2013) . Developing strategies to maintain energy supplies while achieving economic and social sustainable development will be important issues for policy makers. We proposed the following hypotheses. First, more virtual energy was transferred from energy - scarce to energy - abundant provinces sine the 2008 global financi al crisis. Second, provinces tended to have more cross - border (including nearby and distant) virtual energy trade since the global financial crisis. Third, provinces depended more on distant rather than nearby virtual energy trade and this dependence incre ased over time. Fourth, net virtual energy transferred from the west of China to the east of China was greater than the physical energy transferred through ergy network s in 2007 and 2012. We applied the recent multiregional input - output table for China: the Chinese 2007 and 2012 interprovincial input - output table. the paper by 2 network between 2007 and 2012 (Mi et al. 2017) . The The energy consumption data for sectors within provinces were obtained from the energy balance table of the China Energy Statistical Yearbook 2008 and 2013 (National Bureau of Statistics 2 008) . based on the multir egional input - output analysis which catches both the direct and indirect energy use embodied in commodity trade (Kan et al. 2019) . Our internal energy consumption refers to the internal energy footprint, which refers to all local energy use (direct and indirect) associated with final consumption in the studied area. 0 2E+09 4E+09 6E+09 8E+09 1E+10 1.2E+10 1.4E+10 1.6E+10 1.8E+10 2E+10 energy-scarce to energy abundant provinces energy-abundant to energy-scarce provinces Total virtual energy transfer (GJ) 2007 2012 0 2E+09 4E+09 6E+09 8E+09 1E+10 1.2E+10 energy-scarce to energy- abundant provinces energy-abundant to energy-scarce provinces "Coal" virtual energy transfer (GJ) 2007 2012 0.0E+00 1.0E+09 2.0E+09 3.0E+09 4.0E+09 5.0E+09 energy-scarce to energy-abundant provinces energy-abundant to energy-scarce provinces "oil" virtual energy transfer (GJ) 2007 2012 0.0E+00 2.0E+08 4.0E+08 6.0E+08 8.0E+08 1.0E+09 energy-scarce to energy-abundant provinces energy-abundant to energy-scarce provinces " natural gas " virtual energy transfer (GJ) 2007 2012 0 1E+09 2E+09 3E+09 4E+09 5E+09 energy-scarce to energy-abundant provinces energy-abundant to energy-scarce provinces "others" virtual energy transfer (GJ) 2007 2012 0.35 0.4 0.45 0.5 0.55 total coal oil gas other ratio 2007 2012 0 0.5 1 1.5 2 2.5 3 Beijing Tianjin Hebei Shanxi Liaoning Jilin Heilongjiang Shanghai Jiangsu Zhejiang Anhui Fujian Jiangxi Shandong Henan Hubei Hunan Guangdong Guangxi Hainan Chongqing Sichuan Guizhou Yunnan Shaanxi Gansu Qinghai Ningxia Xinjiang ratio 2007 2012 0 2 4 6 8 10 12 Beijing Tianjin Hebei Shanxi Liaoning Jilin Heilongjiang Shanghai Jiangsu Zhejiang Anhui Fujian Jiangxi Shandong Henan Hubei Hunan Guangdong Guangxi Hainan Chongqing Sichuan Guizhou Yunnan Shaanxi Gansu Qinghai Ningxia Xinjiang ratio 2007 2012 0 0.5 1 1.5 2 Beijing Tianjin Hebei Shanxi Liaoning Jilin Heilongjiang Shanghai Jiangsu Zhejiang Anhui Fujian Jiangxi Shandong Henan Hubei Hunan Guangdong Guangxi Hainan Chongqing Sichuan Guizhou Yunnan Shaanxi Gansu Qinghai Ningxia Xinjiang ratio 2007 2012 0 2 4 6 8 10 Beijing Tianjin Hebei Shanxi Liaoning Jilin Heilongjiang Shanghai Jiangsu Zhejiang Anhui Fujian Jiangxi Shandong Henan Hubei Hunan Guangdong Guangxi Hainan Chongqing Sichuan Guizhou Yunnan Shaanxi Gansu Qinghai Ningxia Xinjiang ratio 2007 2012 0 0.5 1 1.5 2 2.5 3 Beijing Tianjin Hebei Shanxi Liaoning Jilin Heilongjiang Shanghai Jiangsu Zhejiang Anhui Fujian Jiangxi Shandong Henan Hubei Hunan Guangdong Guangxi Hainan Chongqing Sichuan Guizhou Yunnan Shaanxi Gansu Qinghai Ningxia Xinjiang ratio 2007 2012 0 2 4 6 8 10 12 Beijing Tianjin Hebei Shanxi Liaoning Jilin Shanghai Jiangsu Zhejiang Anhui Fujian Jiangxi Shandong Henan Hubei Hunan Guangdong Guangxi Hainan Chongqing Sichuan Guizhou Yunnan Shaanxi Gansu Qinghai Ningxia Xinjiang ratio 29.1 2007 2012 0 2 4 6 8 10 12 Beijing Tianjin Hebei Shanxi Liaoning Jilin Heilongjiang Shanghai Jiangsu Zhejiang Anhui Fujian Jiangxi Shandong Henan Hubei Hunan Guangdong Guangxi Hainan Chongqing Sichuan Guizhou Yunnan Shaanxi Gansu Qinghai Ningxia Xinjiang ratio 30.4 2007 2012 0 5 10 15 Beijing Tianjin Hebei Shanxi Liaoning Jilin Heilongjiang Shanghai Jiangsu Zhejiang Anhui Fujian Jiangxi Shandong Henan Hubei Hunan Guangdong Guangxi Hainan Chongqing Sichuan Guizhou Yunnan Shaanxi Gansu Qinghai Ningxia Xinjiang ratio 45.8 2007 2012 0 10 20 30 40 Beijing Tianjin Hebei Shanxi Liaoning Jilin Heilongjiang Shanghai Jiangsu Zhejiang Anhui Fujian Jiangxi Shandong Henan Hubei Hunan Guangdong Guangxi Hainan Chongqing Sichuan Guizhou Yunnan Shaanxi Gansu Qinghai Ningxia Xinjiang ratio 2007 2012 0 2 4 6 8 10 Beijing Tianjin Hebei Shanxi Liaoning Jilin Heilongjiang Shanghai Jiangsu Zhejiang Anhui Fujian Jiangxi Shandong Henan Hubei Hunan Guangdong Guangxi Hainan Chongqing Sichuan Guizhou Yunnan Shaanxi Gansu Qinghai Ningxia Xinjiang ratio 23.3 2007 2012 The virtual energy flowing from west to east China is much greater than the physical energy transferred through the WTEETP. Currently, en ergy policy primarily focuses on physical supplies, not virtual energy. This may overlook the important influence that virtual energy has on the regional energy mechanism, thus leading to less efficient policy management. Because the nergy management should target at specific type of virtual energy, since the flow pattern of different types of energy differed from each other. This was e specially true for virtual oil as more than half of the virtual oil was flowed from energy - sca rce to energy abundant provinces . Therefore, government should consider policies (e.g., applying oil virtual energy export tax) that may regulate the virtual oil flows to change their spatial patterns (Cherniwchan et al. 2016) . CHAPTER 5 . SPATIAL - TEMPORAL ASSESSMENT OF WATER FOOTPRINT, WATER SCARCITY AND CROP WATER PRODUCTIVITY AT THE COUNTY LEVEL IN CHINA S MAJOR CROP PRODUCTION REGION ABSTRACT INTRODUCTION Irrigated more than 70% of the total water use, and more than 90% of total consumptive water use worldwide ( ) (Döll 2009, Food and Agriculture Organization of the United Nations 2018) consumption and crop yields for more sustainable development. Many public policies have been applied and billions of dollars spent to save water in irrigated a griculture (Ward and Pulido - Velazquez 2008) . The water footprint, water scarcity and crop water productivity are used as indicato volume of freshwater consumed to produce the product (Liu, Zehnder and Yang 2009, Mekonnen and Hoekstra 2011) . WF includes not only direct water consumption of products, but also indirect water consumption water indirectly consumed and water polluted throughout the produ ction chain. Water scarcity Crop water productivity refers to Evaluating water footprints presents a comprehensive picture of the relationship between water consumption and human appropriation, becau se a footprint includes both direct water consumption of products and water indirectly consumed and polluted during production. Exploring crop water produ ctivity can facilitate understanding the trade - offs between food production and water consumption. Holistically, understanding all three variables can illuminate pathways to alleviate conflicts between water security and food security. Many studies have fo cused on water footprint, water scarcity and crop water productivity separately . Yet to our knowledge water footprint, water scarcity, and crop water productivity ( ) have not been assessed simultaneously at the county level in large plains over a temporal scale. This will help to construct targeted policies to achieve both food security and water security in irrigated agriculture Different from most water footprint studies at coarse spatial scales (e.g., global and national scales) or focused on geographic units , a study at the knowledge The aim of this study was to assess the water footprint, water scarcity and crop water productivity of irrigated ag riculture at the county level in the NCP from 1986 to 2010. We calculated the blue, green, and grey water footprint to illustrate the dynamics of total water footprint (WF total ) in the whole NCP; applied the water scarcity index to study the impacts of water consumption from irrigated agriculture on water scarcity in each county; and measured the grain yield per unit water use to represent crop water productivity (Mekonnen and Hoekstra 2011) . MATERIALS AND METHODS (Kang 2007) We assessed the WF for the entire grain production chain, which included both the consumptive water usage for crop growth (WF cons ) and the fresh water to dilute associated pollutants (WF grey ). According to the s ources of water, WF cons were further divided into WF blue ( ) and WF green ( ). More detailed procedures for the WF assessment methods (i ncluding all calculation equations) can be found in Supplementary Information. (Allen et al. 1998; Hoekstra et al. 201 1) . Where A(km 2 ) is the total planation area; Y (kg) is the total crop yield; R n (MJ m - 2 d - 1 ) is net radiation on surface of crop; G (MJ m - 2 d - 1 ) is soil heat flux; T ( ) is average air temperature; U 2 (m s - 1 ) is wind speed at 2 meters above ground; e s (kPa) is saturation vapor pressure; e a - 1 ) is the slope of the curve between saturation vapor - 1 ) is hygrometer constant. For our proposed water consumption method, the actual crop y = aET a 2 + bET a + c (4) (5) Where a,b,c are regression coefficients; and y(kg/ha) is unit area crop yield. Considering that the actual crop water consumption might differ from the estimated amount in the conventional water requirement method, we proposed a new water consumption method based on the crop water production function and compared it with the conventi onal water requirement method. (Zhang et al. 200 8) (Zhao, Chen and Zhang 2009) (Xu et al. 2013) The water scarcity index of grain production from a WF perspective can be reflected through the ratio (I total ). The higher the water scarcity index, the less sustainable water use for grai n production. The water scarcity index can be calculated as follows: I total = WF grain,total / WR agri, total (13) Where I total is water scarcity due to agricultural use, I total >1 I total >2.5 indicates severe water scarcity due to grain production. WF grain,total is the total WF for winter wheat and summer maize here; WR agri, total refers to the renewable agricultural water resources. Crop water productivity refers to (a) (a) y = 0.0412x - 80.919 R² = 0.8192 0.50 0.70 0.90 1.10 1.30 1.50 1.70 1.90 2.10 2.30 1986 1989 1992 1995 1998 2001 2004 2007 2010 kg/m 3 0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 1980 1985 1990 1995 2000 2005 2010 2015 Percent dercrease in water footprint and water scarcity in the NCP due to increases in crop water productivity year CHAPTER 6. CONCLUSIONS Below are main conclusions and research directions: g lobal trade hubs such as the United States and C hina dominate global networks . To manage global flows of virtual materials, policy - mak ers could Safeguarding the environment through sustainable resource use will require a better understanding of the existing international mechanisms and structures of trade networks to target top trading countries. nt should target specific type s of virtual energy, since the flow pattern s of different types of energy differed from each other. Especially for the virtual oil , more than half virtual oil was flowed from energy - scarce to energy abundant provinces, which c ould exacerbate energy shortage and lead to more severe uneven energy distribution. APPENDICES REFERENCES