Exploring the black box: Applying macro decomposition tools for scenario comparisons. (September 2022) - Koomey, Jonathan; Schmidt, Zachary; Hausker, Karl; Lashof, Dan
Abstract: To illustrate the power and utility of macro-level decomposition tools, this article presents a structured comparison of two all-sector global modeling exercises that assess emissions reductions compatible with climate stabilization at roughly 1.5C above pre-industrial levels. It uses an expanded Kaya Identity combined with the LMDI (Logarithmic Mean Divisia Index) method to decompose the effects of key drivers of changes in emissions over time in these scenarios. The most important drivers of emissions reductions include final energy intensity of economic activity, the fraction of primary energy delivered by fossil fuels, and emissions from non-CO2 warming agents. Land-use change and the carbon intensity of fossil energy are also important. The article suggests additional data modelers should release to allow more rapid analysis of results and ways to facilitate cross-study comparisons (such as adopting "best of breed" sectoral models instead of relying solely on in-house expertise for model development). Topics: Global change; Climate change; Emissions reduction modeling; Model comparisons; Energy resources; Environmental policy; Environmental technology; Energy Policy. Highlights: Detailed decomposition analysis gives visibility into key drivers and model structure. Key drivers include energy intensity, fossil fuel fraction, and non-CO2 warming agents. Analyzing "edge cases" can help suggest superior technology/policy combinations. Analyzing "edge cases" can help demonstrate the limits of feasible climate mitigation. "Best of breed" sectoral models are an essential complement to IAMs.