A causal analysis framework for land-use change and the potential role of bioenergy policy. (31st December 2016)

• Record Type: Journal Article
• Authors: Efroymson, Rebecca A.; Kline, Keith L.; Angelsen, Arild; Verburg, Peter H.; Dale, Virginia H.; Langeveld, Johannes W.A.; McBride, Allen
• Is Part Of: Land use policy. Volume 59(2016:31 Dec.)
• Page Start: 516
• Page End: 527
• Publication Date: 2016-12-31
• Subjects: Indirect land-use change -- Biofuel -- Attribution -- Causation -- Greenhouse gas emissions -- Deforestation; Land use -- Periodicals; Land use -- Government policy -- Periodicals; Sol, Utilisation du -- Périodiques; Sol, Utilisation du -- Politique gouvernementale -- Périodiques; Electronic journals; 333.7305
• DOI: 10.1016/j.landusepol.2016.09.009
• Languages: English
• ISSNs: 0264-8377

Abstract

Abstract: 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.