Forests are increasingly seen as cost-effective mechanisms to mitigate and adapt to climate change. However, significant uncertainties remain for how climate change may affect future forest carbon sink or source strength. This challenge is compounded by the fact that most forest policy and planning decisions made today will not manifest for years, decades, or centuries. To improve the outcomes of regional greenhouse gas emission reduction efforts, salient and robust forest carbon science and data are required. Few studies have assessed gaps and barriers to integrating forest carbon data and models into policy and planning. Furthermore, there is an increasing need to quantify the impacts of enacting specific policies and management strategies to inform decision-making across scales, as well as advancements of associated tools to provide robust quantification and characterization of disturbance impacts on future forest carbon dynamics. Given these challenges, the first chapter of this dissertation provides a brief overview of forests and global climate change and the role of forest carbon data and models to inform forest decision-making. The second chapter focuses on assessing barriers and gaps to integrating forest carbon data and tools into regional policy and planning initiatives. Our results provide a roadmap for more effective science-based communication and education to improve forest carbon outcomes. The third chapter explores a suite of alternative forest management and wood utilization scenarios, compared to a business-as-usual scenario, to quantify the impacts of specific forest policies in the mid-Atlantic region in support of net-zero greenhouse gas emissions targets. These results suggest that key climate-smart forestry practices can increase both the short-term and long-term forest carbon sink strength without hindering timber supplies or reducing forest resilience. The fourth analysis uses a Monte Carlo simulation approach and a random forest model to quantify and characterize model variability and sensitivity to future disturbance regimes. These findings suggest that disturbance, including land-use change, harvesting, and disease outbreaks, play an important role in driving net ecosystem carbon balances in Maryland’s forests. Together, these results exhibit the value of forest carbon models to inform forest policy and planning in support of decision-making to address the climate crisis. Future work should continue to address future barriers to enhancing forest carbon decision-making by further integrating climate considerations and leveraging data and tools to inform forest policy and planning.
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