As the world’s largest developing country, and as the home to many of the world’s factories, China plays a crucial role in the sustainable development of the world economy regarding environmental protection, energy conservation, and emission reduction issues. Based on the data from 2003–2015, this paper examined the green total factor productivity and the technological progress in the Chinese manufacturing industry. A slack-based measure (SBM) Malmquist productivity index was used to measure the bias of technological change (BTC), input-biased technological change (IBTC), and output-biased technological change (OBTC) by decomposing the technological progress. It also investigated the mechanism of environmental regulation, property right structure, enterprise-scale, energy consumption structure, and other factors on China’s technological progress bias. The empirical results showed the following: (1) there was a bias of technological progress in the Chinese manufacturing industry during the research period; (2) although China’s manufacturing industry’s output tended to become greener, it was still characterized by a preference for overall CO2 output; and (3) the impact of environmental regulations on the Chinese manufacturing industry’s technological progress had a significant threshold effect. The flexible control of environmental regulatory strength will benefit the Chinese manufacturing industry’s technological development. (4) R&D investment, export delivery value, and structure of energy consumption significantly contributed to promoting technological progress. This study provides further insight into the sustainable development of China’s manufacturing sector to promote green-biased technological progress and to achieve the dual goal of environmental protection and healthy economic growth.
To understand the status of air pollution in northeastern China, we explore the structure of air pollution transmission networks and propose targeted policy recommendations. Using air pollution data from 35 cities in northeastern China for a total of 879 periods from 6 January 2015 to 3 June 2017, this paper used social network analysis (SNA) to construct a spatial association network of air pollution in the region, and analyzed the spatial association of air pollution among cities and its causes in an attempt to reveal the transmission path of air pollution in the region. The results show that inter-city air pollution in northeast China forms a complex and stable correlation network with obvious seasonal differences of “high in winter and low in summer”. Different cities in the region play the roles of “spillover”, “intermediary” and “receiver” of air pollution in the network. Small respirable particulate (PM2.5) pollution constitutes a significant component of air pollution in northeast China, which spreads from Liaoning province to Heilongjiang province via Jilin province. Therefore, regional joint pollution prevention and control measures should be adopted to combat the air pollution problem, and different treatment measures should be developed for different city “roles” in the pollution network, in order to fundamentally solve the air pollution problem in the region.
This paper measures the transformation and upgrading of industrial structure from two aspects of rationalization and upgrading of industrial structure, and empirically analyzes the impact of environmental regulation on industrial structure transformation and upgrading by using data of 29 provinces in China from 2004 to 2015. It was found that there is a significant nonlinear effect between environmental regulation and the transformation and upgrading of industrial structure. Specifically, environmental regulation is not conducive to the rational development of industrial structure, but with the continuous improvement of economic development level and human capital level, the inhibitory effect of environmental regulation on the rationalization of industrial structure is gradually weakened. The influence coefficient of environmental regulation on the rationalization of industrial structure is 0.0619~0.2648. Moreover, environmental regulation effectively drives the upgrading of industrial structure, and when the level of economic development and human capital are higher than the threshold, the role of environmental regulation in promoting the high development of industrial structure is gradually enhanced. The influence coefficient of environmental regulation on the upgrading of industrial structure is 0.0540~0.5626. Therefore, it is of great significance to formulate appropriate environmental regulation policies according to local conditions in the transformation and upgrading of industrial structure.
Current research on technological progress does not focus on whether there is a biased selection of technological progress based on the resulting pollutant emissions and the emission reduction effect. This paper measures green total factor productivity for 30 provinces in China from 2004–2018 and tests whether technological progress is selectively biased towards the pollutants emitted. The results find a selective bias of technological progress on pollutant emissions, and there is also heterogeneity in the selective bias across regions. The current level of technological progress is on the right side of the inverted U-shaped inflection point for SO2 and PM2.5 and the left side of the inverted U-shaped inflection point for CO2. The improvement of technological progress can reduce the emissions of SO2 and PM2.5. Still, the results indicate that the reduction effect of these two pollutants originates from the treatment process rather than reducing the source of the production side. The inability of technological advancement to reduce CO2 emissions suggests some carbon lock-in in China’s technological advancement. The Chinese government should increase the proportion of new energy applications and reduce the production methods of polluting industries to reduce pollutants effectively.
Based on cross-sectional data from 30 Chinese provinces from 2004 to 2017, this paper systematically examines the nonlinear effects of economic policy uncertainty (EPU) on carbon emissions and its causes using the PSTR model. It is found that the impact of EPU on carbon emissions at the provincial level in China has significant nonlinear characteristics and shows a positive and then negative pattern as the level of EPU increases. Furthermore, increased levels of EPU also cause a nonlinear migration of the effects of provincial economic and financial development, industrial structure, government spending, and environmental regulation on carbon emissions, illustrating a large amount of heterogeneity among Chinese provinces. Specifically, provinces with higher levels of economic and financial development experience a greater positive carbon emission effect from EPU, whereas provinces with lower levels of such development experience a greater negative carbon emission effect. In contrast, in provinces with irrational industrial structures, lower fiscal expenditures, and weaker environmental controls, the nonlinear carbon emission consequences of EPU are greater. Therefore, local governments should prudently adjust economic policies, improve and perfect the market information disclosure system, and afford full play to regional comparative advantages to help achieve the “double carbon goal”.
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