Methods for Long - Term Environmental Policy Challenges

How do we study long-term policy challenges where the outcomes are difacult to anticipate far into the future? After a brief overview of the broad classes of methods used to inform long-term policy decisions, this article will review in more depth three analytic methods—statistical methods, robust decisionmaking, and adaptive agent-based models—that provide innovative examples. The article considers the application of each method to climate change, the quintessential example of a difacult, long-term policy challenge. Statistical methods, which and past patterns in data and project them into the future, are an example of the broader class of quantitative forecasting methods. Applied to climate change, statistical methods have been employed in the debate on the Environmental Kuznets Curve (EKC) which envisions a model of the various stages of pollution intensity as economies become substantially richer. Robust decision-making (RDM) draws from both the broad classes of formal decision analysis and scenario planning. RDM relates short-term policy interventions to different clusters of long-term environmental futures. Adaptive control, along with multi-agent modeling, provides a novel ability to analyze cooperation and conoict among multiple agents in their choice of strategies. The comparison of all three methods focuses on the challenge of global climate change and a potential transition to a low-carbon future. In the anal section, we will offer guidance on choosing among methods.