How to improve the energy-saving performance of China's transport sector? An input-output perspective.

The transport sector proves a major energy consumer in China, but improving energy-saving performance in China's provincial transport sector from the lifecycle perspective remains unresolved. Thus, this study employs the environmentally extended multi-region input-output (MRIO) method, structural path analysis, and the newest MRIO table of China from 2017, to investigate how to improve the energy-saving performance from final demand structure, supply chain, and pathway perspectives. The relevant results are threefold. (1) Regarding the final demand structure level, the embodied energy consumption of China's transport sector is predominantly driven by investment from the production side, while that of the consumption side is primarily caused by exports. (2) At the supply chain level, production-side embodied energy consumption primarily occurs along a three-echelon supply chain, while that from the consumption side mostly occurs via a two-echelon supply chain. (3) At the pathway level, the production-side energy-saving performance of China's provincial transport sector is dominated by two pathways along the construction sector, including transport sector → construction sector → final demands, and transport sector → intermediate inputs → construction sector → final demands, while that of the consumption side is chiefly determined by three pathways along internal transportation chains.

Do renewable energy consumption and service industry development contribute to CO2 emissions reduction in BRICS countries?

In recent years, BRICS countries have attached great importance to renewable energy development and actively promoted the shift of economic structure towards service industry, in order to achieve the decoupling of economic development from carbon emissions. However, relevant studies mostly neglect the cross-sectional dependence and heterogeneity issues, which may cause biased results. Therefore, this paper selects the panel data of BRICS countries during 1996-2017 and employs the common correlated effects mean group (CCEMG) method, which are based on the cross-sectional dependence and heterogeneity assumptions, to explore the influence of renewable energy consumption and service industry development on CO2 emissions in BRICS countries. Besides, we also use the random effects model and pooled estimated generalized least squares model, as well as fully modified OLS model for comparison. The results indicate that enhancing the proportion of renewable energy consumption in the total energy consumption is an effective measure to reduce CO2 emissions in BRICS countries. Moreover, the steadily rising contribution of service industry to economic growth in BRICS countries during the sample period does not necessarily contribute to reduce CO2 emissions.

Drivers Analysis of CO2 Emissions from the Perspective of Carbon Density: The Case of Shandong Province, China.

Against the backgrounds of emission reduction targets promised by China, it is crucial to explore drivers of CO2 emissions comprehensively for policy making. In this study, Shandong Province in China is taken as an example to investigate drivers in carbon density by using an extended Kaya identity and a logarithmic mean Divisia index model (LMDI) with two layers. It is concluded that there are eight positive driving factors of carbon density during 2000⁻2015, including traffic congestion, land urbanization, etc., and seven negative driving factors comprising energy intensity, economic structure, etc. Among these factors, economic growth and energy intensity are the main positive and negative driving factor, respectively. The contribution rate of traffic congestion and land urbanization is gradually increasing. Meanwhile, 15 driving factors are divided into five categories. Economic effect and urbanization effect are the primary positive drivers. Contrarily, energy intensity effect, structural effect, and scale effect contribute negative effects to the changes in carbon density. In the four stages, the contribution of urbanization to carbon density is inverted U. Overall, the results and suggestions can give support to decision maker to draw up relevant government policy.