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With the continuous uptick in world energy consumption, green energy plays an increasingly significant role in alleviating energy depletion and promoting economic development. Due to regional differences, transportation restrictions, national policies and other reasons, there is a mismatch between green energy needs and resources across regions in China, which leads to misallocation of capital and labor between heavily polluting and nonpolluting enterprises. In this context, this paper first proposes a new measurement method: namely, the capital and labor mismatch index. Panel data of 30 Chinese provinces from 2008 to 2018 are utilized to calculate the index. The results show significant resource mismatch across provinces in China. In addition, we extend the green Solow model by combining capital mismatch, technological progress and environmental regulation into a dynamic analysis framework of economic growth. The empirical results show that all three factors have significant spatial spillover effects. Specifically, capital misallocation exerts a strong negative impact on economic growth, while the other factors increase it. [ABSTRACT FROM AUTHOR]

China has launched energy quota right trading system, as one of the sustainable operations, since 2016 to cap the total energy consumption, however the economic and energy saving effect of this new policy is unclear. Based on the input and output panel data of China's 29 manufacturing sub-industries, this paper constructs the non-parametric optimization model under the command-and-control and the energy quota right trading scenario, to compare their potential economic gains and energy savings of these two polices for Chinese manufacturing industries. Results show that, whether at overall level or at the sub-industries level, the economic potential and energy-saving potential from energy quota right trading are higher than those from command-and-control. However, a part of energy-saving potential will be squeezed out due to the negative externality of the energy market trading. We suggest the well integration of both command-and-control and the market mechanism to achieve a Win–Win development. [ABSTRACT FROM AUTHOR]

China's transport industry has made rapid progress, which has led to a great amount of carbon emissions. However, it is still unclear how carbon emissions from the transport sector are punctuated by shifts in underlying factors. This paper aims to examine the process of China's carbon emissions from the transport sector as well as its major driving forces at the provincial level during the period of 2000 to 2015. We first estimate the carbon emissions from the transport sector at the provincial level based on the fuel and electricity consumption using a top-down method. We find that the carbon emission per capita is steadily increasing across the country, especially in the provinces of Chongqing and Inner Mongolia. However, the carbon emission intensity is decreasing in most provinces, except in Yunnan, Qinghai, Chongqing, Zhejiang, Heilongjiang, Jilin, Inner Mongolia, Henan and Anhui. We then quantify the effect of socioeconomic factors and their regional variations on carbon emissions using a panel model. The results show that the development of secondary industry is the most significant variable for carbon intensity at both the national and regional levels, while the effects of the other variables vary across regions. Among these factors, population density is the main factor of the increasing carbon emissions per capita from the transport sector for both the whole country and the western region, whereas the consumption level per capita of residents and the development of tertiary industry are the primary drivers of per capita carbon emissions for the eastern and central regions. [ABSTRACT FROM AUTHOR]

To gain a deeper understanding of the carbon emission mechanism from transportation facilities, all system elements affecting carbon emissions from regional transportation facilities are identified and analyzed according to panel data from 30 regions in China. A spatial econometric model for carbon emissions from transportation facilities is constructed using the Spatial Dolbin model from 2004 to 2022 as the research period. From the results, the carbon dioxide emissions from transportation facilities added from 318 million tons in 2004 to 752 million tons in 2022, with an average annual growth rate of 4.9%. The global spatial auto-correlation coefficient was significant at the 5%, with an obvious spatial correlation between carbon dioxide emissions within a geographical range. In addition, through stability testing, the model showed high stability in both spatial lag testing and spatial error testing, demonstrating strong ability to interpret data. The research shows that the carbon emission is affected by independent variables, including population, economy, technology, and transportation, and exhibit significant spatial distribution characteristics in different regions and years, providing a basis for policy formulation and carbon emission management. [ABSTRACT FROM AUTHOR]