Abstract
A coupled structural decomposition analysis (SDA) and sensitivity analysis approach is developed to explore the drivers of China’s CO2 emission intensity at both general and sectoral levels and from both ex-post and ex-ante perspectives. Two steps are involved—structural decomposition and sensitivity analysis. First, the popular factor decomposition method, SDA, is implemented to identify which drivers “have” made the largest contribution to emission intensity changes. Second, an emerging ex-ante approach, sensitivity analysis, is introduced to answer how and to what extent such drivers “will” influence future emission intensity at a sectoral level. Based on China’s input-output tables for 1997–2012, the empirical study provides a hotspot map of China’s energy system. (1) Direct-emission coefficient and technology coefficient are observed as the top two overall drivers. (2) For the former, reducing direct-emission coefficient in an emission-intensity sector (e.g., electricity and heat sectors) by 1% will mitigate China’s total emission intensity by at least 0.05%. (3) For the latter, future emission intensity is super-sensitive to direct transactions in emission-intensity sectors (particularly the chemical industry with elasticities up to 0.82%).
Highlights
China’s rapid economic growth has brought forth prosperity; such an economic boom has resulted in large environmental costs, such as uncontrolled CO2 emissions [1,2]
Based on the latest data (e.g., China’s IO table 2012 in the case of China), sensitivity analysis introduces the concept of elasticity to forecast “if” a target coefficient changes in the near future, and how the associate system “will” change [32,33]
China’s total emission intensity changes that “have” made the most contribution, and introduces sensitivity analysis to probe into their sectoral factors to capture the hotspots that “will” lead to massive future emissions in China
Summary
China’s rapid economic growth has brought forth prosperity; such an economic boom has resulted in large environmental costs, such as uncontrolled CO2 emissions [1,2]. Based on the latest data (e.g., China’s IO table 2012 in the case of China), sensitivity analysis introduces the concept of elasticity to forecast “if” a target coefficient changes (e.g., by 1% in practice) in the near future, and how the associate system (e.g., emission intensity for this study) “will” change [32,33]. We finely couple SDA and sensitivity analysis, to offer a systematical ex-post, ex-ante, and overall and sectoral analysis for exploring drivers of China’s emission intensity changes. China’s total emission intensity changes that “have” made the most contribution, and introduces sensitivity analysis to probe into their sectoral factors to capture the hotspots that “will” lead to massive future emissions in China. (1) By finely combining SDA and sensitivity analysis, this hybrid approach provides a systematical ex-post and ex-ante and overall and sectoral analysis on the drivers of China’s total emission intensity.
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