Interaction determinants and projections of China’s energy consumption: 1997–2030

Abstract

This study aims to provide a framework quantifying the interactions between drivers in determining energy consumption (EC) under different decomposition approaches and reduce the uncertainty of EC projection. Based on a new generalized complete decomposition framework, we propose a bootstrap two-step decomposition approach by combining logarithmic mean Divisia index with production-theoretical decomposition analysis and bootstrap technique rectifying the efficiency bias, further decomposing the drivers of EC, energy intensity (EI) and economic scale (SC), into twelve interaction determinants. We compare the projection results of EC to 2030 using the time -series models and scenario analysis with Monte Carlo simulation and fully considering the impacts of the COVID-19 pandemic on the economy. We examine the determinants of changes in EC for 30 Chinese provinces over 1997–2017. We show that the impact of potential economic development (PEDE) on SC and the impact of potential energy intensity (PEIE) on EI are main drivers contributing to the increase in energy consumption, while the impact of PEDE on EI, the impact of PEIE on SC and the impact of economic development technological change on SC are the major negative factors for most provinces over the period. We also confirm that scenario model may be more suitable for EC projection than that of time-series models especially in the case of social emergency such as the pandemic. We suggest the stockholders could establish an improved energy efficiency accounting system for monitoring and tracking energy performance based on the linked drivers.