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The excessive growth of carbon emissions (CO2E) from industrial energy use not only exacerbates global warming and severely curbs the sustainable development of the economy and society. As a high energy-consuming sector second only to the fossil energy division, the power and heavy division, China's chemical industry should have received more attention for its CO2E. However, there are limited literatures on energy CO2E in China's chemical sector at present. Based on this fact, this current paper uses the energy utilization approach, the input–output analysis approach, and the extended structural decomposition method to evaluate the energy-related CO2E of China's chemical sector from 2007 to 2017. (1) China's chemical sector energy-related CO2E showed a trend of first growth and then a slow decline, demonstrating that the rapid growth of China's chemical sector energy-related CO2E has been effectively controlled; However, it should be noted that the chemical industry is still dominated by high-CO2E energy-related CO2E at the current stage. (2) Input structure and energy intensity effects have a reduced influence on the growth of energy-related CO2E in China's chemical sector. This is due to upgrading energy use technology and optimizing the generalized technology progress rate in the chemical sector. (3) Energy structure and final demand effects have encouraged the growth of the chemical sector's energy-related CO2E. It shows that the industrial system's demand for chemical products is constantly expanding, and the chemical products still have the characteristics of high carbonization. Also, the chemical sector's supply-side energy utilization structure has not been significantly enhanced. [ABSTRACT FROM AUTHOR]

China's economy has transtioned into the “new normal”, which demands higher standards for energy utilization efficiency. Meanwhile, the spatial distribution of China's energy consumption and economic development exhibits a significant imbalance, complicating efforts to achieve Pareto optimization of regional energy allocation efficiency. Addressing this issue, this study explores the heterogeneity of the factors influencing energy consumption in China from the dual perspectives of economic “new normal” and geographic space, using an exponential decomposition model. The results of the study show that: (1) the inhibitory effect of the energy intensity effect on the growth of regional energy consumption is differentiated, with stronger inhibitory effects in Guangdong, Jiangsu and other provinces, and weaker inhibitory effects in Hainan, Qinghai, and other provinces. Living standard effect on the regional energy consumption growth of the promotion of the effect also has differences, Jiangsu, Shandong and other provinces of the promotion of the effect is stronger, while Hainan, Qinghai, and other provinces of the promotion of the effect is weaker. (2) Population size effect on regional energy consumption growth is not consistent in the direction of the role of Guangdong, Zhejiang, and other provinces have a promotional effect and the role of the effect of the stronger, on the contrary, the provinces of Heilongjiang, Jilin, and Gansu has an inhibitory effect. (3) In the late stage of the economic “new normal”, the effects of energy intensity effect, living standard effect and population size effect on the growth of energy consumption in the four regions show a weakening trend, and this weakening trend is more obvious in the northeast region. This study expands theoretical research on factors affecting energy consumption and offers practical guidance for China's government to coordinate regional energy allocation under the economic “new normal” and geographical considerations. [ABSTRACT FROM AUTHOR]

Green finance is a market‐driven approach to achieve the “double carbon” goal. However, the existing research predominantly focuses on the connotations and empowerment aspects of green finance. Notable regional disparities exist in China's financial markets and resource endowments, resulting in uneven levels of green finance development among various areas, significantly impeding the overall green transformation of the economy and society. In light of this, our paper explores the spatial patterns and association network of green finance development in China. The research findings indicate that: (1) The development of green finance in China exhibits an “east high, west low” spatial distribution pattern, with significant spatial clustering observed among neighboring provinces. The eastern region displays a “H‐H” clustering, while the western region shows an “L‐L” clustering. (2) The spatial correlation distribution of provincial green finance in China demonstrates an eastward concentration and westward dispersion trend, with strong correlation areas gradually expanding. Notably, Jiangsu, Zhejiang, Shanghai, and the Beijing‐Tianjin‐Hebei urban agglomerations lead nationally in attractiveness. (3) The external radiation capability of provincial green finance is progressively improving, with provinces such as Beijing, Hebei, Hubei, and Sichuan likely to occupy strategic structural positions, possessing greater development advantages. This study not only expands the theoretical scope of existing green finance research but also provides a practical reference for coordinating and achieving the high‐quality development of regional green finance from a practical perspective. [ABSTRACT FROM AUTHOR]

As one of the industries with high energy-extensive consumption in China, the carbon emissions (CO2-E) generated by heavy industry cannot be ignored. However, the related literatures that take heavy industry energy-related CO2-E as the research object in the past mainly focus on the index decomposition model (IDM), econometric analysis, and other aspects. At present, few literatures use the structural decomposition model (SDM) to analyze the CO2-E and influencing factors of the industry from the perspective of input–output (I-O) analysis. This perspective and model can unify the supply side and demand side energy-related CO2-E of the heavy industry, which is conducive to a more comprehensive analysis of the heavy industry energy-related CO2-E and further various direct and indirect influencing factors. Based on this, this paper employs the energy consumption method (ECM), the I-O analysis method, and SDM to examine the energy-related CO2-E and influencing factors of China's heavy industry. The findings show that while the growth tendency of China's heavy industry energy-related CO2-E has been effectively controlled within the sample interval. Simultaneously, the supply side's optimization of the energy consumption structure (ECS), the upgrading of energy utilization technologies, and the generalized technological progress rate (GTPR) reflecting the input structure effect (ISE) have an inhibitory effect on the heavy industry's energy-related CO2-E, while the final demand effect (FDE) has an increasing effect on the energy-related CO2-E in the heavy industry, indicating that the products produced by the heavy industry are still characterized by high carbonation. This paper not only further enriches the existing research literature on energy-related CO2-E from the heavy industry in theory, but also provides guidance for efficient control of excessive growth of energy-related CO2-E from the perspective of input and output for the heavy industry in practice. [ABSTRACT FROM AUTHOR]

With China's rapidly aging population, this paper constructs a policy model using overlapping generation (OLG) model and the computable general equilibrium (CGE) modeling to analyze the second-child policy and delaying retirement policy. Our research findings suggest that considering the short-term effects, delaying the retirement age imposes a greater impact on the economy than the second-child policy. Its economic impact increases initially, but then decreases to a stable level showing a diminishing influence. In the long term, the second-child policy has greater ability to boost the economy than the delaying retirement age policy and its economic impact gets stronger. From an industrial output perspective, the two policies exert greater influence on agriculture, light industry, finance and service sector than on construction and heavy industries. From an industrial import and export perspective, the two policies have great influence on finance, electric power, and fossil energy more than they do on the agricultural sector. From a monetary perspective, the impacts are greater on household income followed by the government income and corporate income, respectively. The policies also make a bigger difference to fixed capital than to changes in deposits and loans. [ABSTRACT FROM AUTHOR]

In recent years, the global greenhouse effect caused by excessive energy-related carbon emissions has attracted more and more attention. In this paper, we studied the dynamic evolution of factors driving China's energy-related CO2 emissions growth from 2007 to 2015 by using energy consumption method and input-output analysis and used the IO-SDA model to decompose the energy carbon emissions. Within the research interval, the results showed that (1) on the energy supply-side, the high carbon energy represented by raw coal was still the main factor to promote the growth of energy-related CO2 emissions. However, the optimization of energy consumption structure is conducive to reducing emissions. Specifically, the high carbon energy represented by raw coal exhibited a downward trend in promoting the increment of energy-related CO2 emissions, while the clean energy represented by natural gas showed an upward trend in promoting the increment of CO2 emissions. It is worth noting that there is still a lot of room for optimization of China's energy consumption structure to reduce emissions. (2) On the energy demand-side, the final demand effect is the main driving force of the growth of carbon emissions from fossil energy. Among them, the secondary industry plays a major role in the final demand effect. The "high carbonization" of the final product reflects the characteristics of China's high energy input in the process of industrialization. At the same time, since the carbon emission efficiency of the tertiary industry and the primary industry is better than that of the secondary industry, actively optimizing the industrial structure is conducive to slowing down the growth of carbon emission brought by the demand effect. (3) The input structure effect is the main restraining factor for the growth of energy carbon emissions, while the energy intensity effect has a slight driving effect on the growth of energy carbon emissions. The results show that China's "extensive" economic growth model has been effectively reversed, but the optimization of fossil energy utilization efficiency is still not obvious, and there is still a large space to curb carbon emissions by improving fossil energy utilization efficiency in the future. [ABSTRACT FROM AUTHOR]