A prediction on the impacts of China’s national emissions trading scheme on CO2 emissions from electricity generation - Solaymani, Saeed (Frontiers in Energy Research, 2022)
One of the government policies that can reduce CO2 emissions is the Emissions Trading Scheme (ETS), which was implemented in the Chinese economy on 16 July 2021. It is the largest ETS in the world, covering 12% of global CO2 emissions. Since this policy has not been experienced in China, it is necessary to predict its impact on CO2 emissions in this country. Furthermore, electricity and heat production is the major contributor to total CO2 emissions from fuel combustion. Therefore, this study attempts to predict the impact of the emissions trading scheme on CO2 emissions from the combustion of coal, oil and natural gas in electricity generation using annual data from 1985 to 2019. For this purpose, this study first predicts CO2 emissions from the combustion of coal, oil and natural gas for electricity generation in power plants using ARIMA and structural Vector Autoregression (SVAR) techniques over the 2020–2030 period. It then estimates the short- and long-run impact of the ETS policy on CO2 emissions from the combustion of coal, oil and natural gas in power plants over the projected period (2020–2030) by employing the ARDL methodology. The results suggest that the ETS policy is effective in reducing the CO2 emissions from the combustion of all fuels in electricity generation over the long-run. This is because of the increase in CO2 emissions from the combustion of these fuels in power plants in the long run, which exceed the threshold value. But in the short-run, it has a negative and statistically significant impact only on CO2 emissions from the natural gas power plants. These results suggest that improving the efficiency of all fuels can significantly reduce CO2 emissions in electricity generation from coal, oil and natural gas in the short- and long-run. They also enable China’s energy policymakers to update the ETS policy in its next phases.