We propose a causal analysis framework to increase understanding of land-use change (LUC) and the reliability of LUC models. This health-sciences-inspired framework can be applied to determine probable causes of LUC in the context of bioenergy. Calculations of net greenhouse gas (GHG) emissions for LUC associated with biofuel production are critical in determining whether a fuel qualifies as a biofuel or advanced biofuel category under regional (EU), national (US, UK), and state (California) regulations. Biofuel policymakers and scientists continue to discuss to what extent presumed indirect land-use change (ILUC) estimates should be included in GHG accounting for biofuel pathways. Current estimates of ILUC for bioenergy rely largely on economic simulation models that focus on causal pathways involving global commodity trade and use coarse land-cover data with simple land classification systems. This paper challenges the application of such models to estimate global areas of LUC in the absence of causal analysis. The proposed causal analysis framework begins with a definition of the change that has occurred and proceeds to a strength-of-evidence approach that includes plausibility of relationship, completeness of causal pathway, spatial co-occurrence, time order, analogous agents, simulation model results, and quantitative agent–response relationships. We discuss how LUC may be allocated among probable causes for policy purposes and how the application of the framework has the potential to increase the validity of LUC models and resolve controversies about ILUC, such as deforestation, and biofuels.
Land-use change (LUC) estimated by economic models has sparked intense international debate. Models estimate how much LUC might be induced under prescribed scenarios and rely on assumptions to generate LUC values. It is critical to test and validate underlying
assumptions with empirical evidence. Furthermore, this modeling approach cannot answer if any specific indirect effects are actually caused by biofuel policy. The best way to resolve questions of causation is via scientific methods. Kim and Dale attempt to address the question of if, rather than how much, market-induced land-use change is currently detectable based on the analysis of historic evidence, and in doing so, explore some modeling assumptions behind the drivers of change. Given that there is no accepted approach to estimate the global effects of biofuel policy on land-use change, it is critical to assess the actual effects of policies through careful analysis and interpretation of empirical
data. Decision makers need a valid scientific basis for policy decisions on energy choices.