Empirical analysis of energy consumption transfer in China's national economy from the perspective of production and demand.

Given the context of the global energy shortage and the deterioration of the ecological environment, this paper uses industrial linkage as a starting point to deeply explore the energy consumption of different sectors and its transfer characteristics. First, a dual evaluation of energy consumption in various sectors is conducted from the perspectives of production and demand to realize an integrated analysis from the energy consumption perspective. Second, based on industrial linkage, the economic input-output life cycle assessment (EIO-LCA) model is used to quantify the net transfer of energy consumption and identify the transfer of energy consumption pressure embodied in economic activities by various sectors. Finally, the energy consumption of various sectors is decomposed, and the transfer flow of energy consumption is measured to accurately quantify the degree of linkage in the energy consumption of sectors. The results show that the current direct energy consumption intensity of various sectors in China is continuously decreasing, but this decrease is insufficient to reverse the upward trend in energy consumption demand. From the perspective of production and demand, non-energy industrial sector is a key sector for reducing energy consumption, and the intersectoral linkage between the subsectors caused by product trade flows has a greater impact on the level of direct and embodied energy consumption. Direct and embodied energy consumption in the same sector can be ranked quite differently, and their levels may not be equal. Energy sector is the main net outflow sector of energy consumption, and the level of its direct energy consumption is much higher than that of its embodied energy consumption. It is worth noting that the scale of embodied energy consumption in construction industry is much higher than the scale of direct energy consumption. Construction is the sector with the greatest net inflow of energy consumed and is the main driver of the energy consumption transfer of energy sector and non-energy industrial sector.

Analysis of China's carbon emission driving factors based on the perspective of eight major economic regions.

Low-carbon transition has gradually become the focus of research on environmental issues. This paper takes China's eight major economic regions as the entry point. First, carbon emissions are measured according to United Nations' baseline methodologies. Second, the stochastic nonparametric data envelope analysis (StoNED) model is used to measure energy efficiency to improve the accuracy of the measurement. Finally, considering the temporal and spatial nonstationarity of carbon emission data, this paper constructs geographically and temporally weighted regression-stochastic impacts by regression on population, affluence, and technology (GTWR-STIRPAT) model, which can accurately analyze the impact of each driving factor of carbon emissions. This paper also explores efficient emission reduction paths in conjunction with the forcing mechanism. According to the study, China's carbon emissions show a decreasing trend from coastal areas to inland areas. In addition, there are significant problems with carbon emissions in China: some regions focus on improving energy efficiency but neglect increasing energy consumption; some regions focus on industrial development but neglect long-term emission reductions. Among the driving factors, energy efficiency, foreign trade, environmental regulations, and industrial structure have the effects of spatiotemporal heterogeneity, spatial heterogeneity, and time lag on carbon emissions, respectively. Graphical abstract.

Research on the relationship between China's greenhouse gas emissions and industrial structure and economic growth from the perspective of energy consumption.

A comprehensive understanding of the relationships between greenhouse gas (GHG) emissions and industrial structure and economic growth holds great significance for China to realize the development of a green economy. This paper calculates GHG emissions based on China's energy consumption, divides the industrial structure in detail, and uses the extended Stochastic Impacts by Regression on Population, Affluence, and Technology model that is realized by PLS method and Tapio decoupling model to study the relationship of GHG emissions to industrial structure and economic growth. The results show that (1) China's total GHG emissions showed a year-on-year growth trend from 2000 to 2017. For CO2, CH4, and N2O, only N2O emission showed a significant downward trend, while CO2 and CH4 emissions showed a slow growth trend. (2) The proportions of added value of industry and construction are positively correlated with GHG emissions, while those of farming, forestry, animal husbandry, and fishery; wholesale and retail trade; transport; and accommodation and catering are negatively correlated with GHG emissions. (3) China's GHG emissions and overall economic growth are in a decoupling state, but in the energy field, N2O emission reduction control has the best effect. Additionally, the overall economic growth of China's industrial sector and GHG emissions have experienced the process of decoupling-link-negative decoupling-link-decoupling. Graphical abstract.