Speaker

Tim Searchinger is a Senior Research Scholar at the School of Public and International Affairs of Princeton University. He is also a Senior Fellow and the Technical Director for Agriculture, Forestry & Ecosystems at the World Resources Institute. Searchinger's work today combines ecology, agronomy and economics to analyze the challenge of how to feed a growing world population while reducing deforestation and greenhouse gas emissions from agriculture. His publications include multiple papers in Science and Nature addressing the greenhouse and other environmental implications of agriculture, bioenergy, forestry, and nitrogen pollution. His reports include Creating a Sustainable Food Future, a comprehensive report addressing these issues published in 2019 by WRI, the World Bank and the United Nations, and a report setting forth a pathway for how Danish agriculture could achieve carbon neutrality. His research ranges from global analyses to projects in countries around the world, and focuses both on issues of science and of public policy. For the first part of his career, Searchinger worked as an environmental attorney, primarily at the Environmental Defense Fund, where he directed its work on agricultural policy, wetlands and restoration of several major aquatic ecosystems. He holds a J.D. from Yale Law School.

Talk: Agricultural and food systems models for influencing public policy: The good, the bad, and the ugly

Abstract: Officials developing policies to address agriculture and climate change are relying heavily on models developed by researchers, which often claim very broad results. These models are used to estimate global and regional mitigation potential from different measures and to project future land use changes, food prices, and indices of food security under baseline conditions and with different policy scenarios. Given the complexity of the topics addressed, models are necessary to address them, yet heavy uncertainties often lead to models built heavily on assumptions. Officials often prefer large-scale and detailed results that they can easily fit into policy justifications, and these require complex models, and yet, the more complex the model, the more assumptions on which it is based. Some models even endogenously build in estimated policy responses, which occur in the background of the models, effectively assuming and hiding policy responses even though the goal of the model is to recommend policies to achieve desired results. Some modelers offer biophysical models, others economic models, creating confusion about which are more appropriate or reliable for which purposes. This talk will use examples to outline best practices for using agriculture and food system models to influence public policy.

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