Forecasting fossil energy consumption structure toward low-carbon and sustainable economy in China: Evidence and policy responses. (November 2018) - Zhao, Xu; Luo, Dongkun
Abstract: As an emerging developing economy and one of the largest CO2 emitter, China is facing great pressure to reduce carbon intensity, while still maintaining its impressive record of economic growth. In this paper, we forecast the future energy consumption structure through estimating the short and long-run relationship between economic growth, carbon intensity and consumption of coal, crude oil and natural gas, respectively. Using time series data and employing the Autoregressive Distributed Lag (ARDL) bounds test and Granger causality test based on Vector Error Correction Model (VECM), the results show that there are positive elasticities of coal, oil and natural gas consumption with respect to GDP and carbon intensity in long run. The Granger causality results indicate that there is a set of unidirectional and bidirectional causality among the selected time series. The future energy consumption structure is predicted using above models with assumed scenarios of GDP and carbon intensity. We find that both the GDP and carbon intensity will decrease when we reach the expected energy consumption structure by the governments. This suggests that the government should consider the adverse effects of reducing carbon intensity on economic growth and make appropriate policy on adjusting energy structure. Highlights: The relationships between each fossil energy consumption, GDP and carbon intensity are estimated. There are positive elasticities of each energy consumption with respect to GDP and carbon intensity. The future energy structure is predicted using above models. Both GDP and carbon intensity decrease when adjusting our forecasting structure as government's expectation.