Why do many prospective analyses of CO2 emissions fail? An illustrative example from China

The main contribution of this paper is a demonstration of the importance of taking into account the evolution of the generation of value added and productivity to avoid biased prospective analysis of Chinese energy-related CO2 emissions. To that end, the paper delivers a Logarithmic Mean Divisia Index decomposition analysis of carbon emissions intensity from 1995 to 2014 for a balanced panel of disaggregated Chinese economic sectors. The paper shows that similar assumptions deliver different out-comes when prospective analyses are based on energy intensity, or alternatively its factorial decomposition, in order to take into account changes in the generation of value added (e.g., productivity). Following this hypothesis, the paper suggests that China will be unable to accomplish its pledged commitments under the Paris Agreement on climate change, despite strong reductions in energy intensity. Analyses based on the usual (standard) approach, by using intensity measures instead of its factorial decomposition, reach the opposite conclusion. The main lesson from this paper is that alter -native designs of prospective analysis (using alternative drivers or variables) leads to different lines of reasoning and conclusions. Therefore, researchers, consultants and policy makers will underestimate CO2 emissions if they continue to base their prospective analysis on intensity indicators. (C) 2022 The Author. Published by Elsevier Ltd.

Automated summary

The paper highlights the importance of considering the evolution of value added and productivity in accurately assessing Chinese energy-related CO2 emissions. The results show that different analytical methods lead to different conclusions, emphasizing the need for appropriate approaches in prospective analyses.