Abstract:The green transformation of the manufacturing industry is crucial for high-quality development of the Yangtze River Economic Belt, and environmental regulation and technological innovation may play key roles. Considering the undesirable output of the manufacturing industry, this paper adopted the undesirable-SE-SBM Model to measure the green transformation efficiency, which can reflect the core transformation performance. On this basis, this paper respectively adopted system generalized method of moments (SYS-… Show more
“…For every 1% decrease in energy consumption per unit of GDP, green development efficiency increases by 0.1401%; therefore, only by improving energy utilization and vigorously promoting the use of clean energy can green development efficiency be improved. The finding of a significant inhibitory effect of external openings on green development efficiency demonstrates the existence of the pollution paradise hypothesis [ 59 ], which indicates that most of the foreign-funded enterprises introduced by the municipalities in the Yangtze River Economic Belt are polluting and have caused irreversible damage to the ecological environment.…”
In the context of continuing to promote the construction of an ecological civilization, it is of great significance to explore green development performance. However, most of the literature is based on a single perspective of level or efficiency, lacking a comprehensive examination of both. It is not scientific to explore how to promote green development only from a single perspective, which may be a new advancement by breaking the conventional thinking focusing only on level or efficiency. On this basis, we first established evaluation index systems of green development performance based on a theoretical framework. Furthermore, green development performance was measured with the entropy weight technique for order preference by similarity to ideal solution (TOPSIS) and super-EBM models, and finally, we analyzed the spatial and temporal evolution patterns of green development performance using the ESDA method and examined its influencing factors with a geographic detector (GD) and econometric models. The main results were as follows: (1) The trend of the green development level in the Yangtze River Economic Belt from 2004 to 2017 had an inverted “N” shape, while the overall average green development efficiency continuously increased. (2) In terms of spatial and temporal patterns, both the green development level and green development efficiency showed “high in the east and low in the west” spatial divergence characteristics. In terms of the spatial and temporal evolution pattern of the green development level, the L-L clusters were mainly distributed in the western region. However, for green development efficiency, the L-L clusters were mostly distributed around the H-H clusters. (3) The results of the influencing factor analysis indicated that industrial structure and people’s welfare are still important factors of the green development level. The improvement of green development efficiency was mainly driven by economic development, and the inhibiting effect of energy consumption is significant. In addition, the effect of opening up has not yet changed from a “pollution paradise” to a “pollution halo”.
“…For every 1% decrease in energy consumption per unit of GDP, green development efficiency increases by 0.1401%; therefore, only by improving energy utilization and vigorously promoting the use of clean energy can green development efficiency be improved. The finding of a significant inhibitory effect of external openings on green development efficiency demonstrates the existence of the pollution paradise hypothesis [ 59 ], which indicates that most of the foreign-funded enterprises introduced by the municipalities in the Yangtze River Economic Belt are polluting and have caused irreversible damage to the ecological environment.…”
In the context of continuing to promote the construction of an ecological civilization, it is of great significance to explore green development performance. However, most of the literature is based on a single perspective of level or efficiency, lacking a comprehensive examination of both. It is not scientific to explore how to promote green development only from a single perspective, which may be a new advancement by breaking the conventional thinking focusing only on level or efficiency. On this basis, we first established evaluation index systems of green development performance based on a theoretical framework. Furthermore, green development performance was measured with the entropy weight technique for order preference by similarity to ideal solution (TOPSIS) and super-EBM models, and finally, we analyzed the spatial and temporal evolution patterns of green development performance using the ESDA method and examined its influencing factors with a geographic detector (GD) and econometric models. The main results were as follows: (1) The trend of the green development level in the Yangtze River Economic Belt from 2004 to 2017 had an inverted “N” shape, while the overall average green development efficiency continuously increased. (2) In terms of spatial and temporal patterns, both the green development level and green development efficiency showed “high in the east and low in the west” spatial divergence characteristics. In terms of the spatial and temporal evolution pattern of the green development level, the L-L clusters were mainly distributed in the western region. However, for green development efficiency, the L-L clusters were mostly distributed around the H-H clusters. (3) The results of the influencing factor analysis indicated that industrial structure and people’s welfare are still important factors of the green development level. The improvement of green development efficiency was mainly driven by economic development, and the inhibiting effect of energy consumption is significant. In addition, the effect of opening up has not yet changed from a “pollution paradise” to a “pollution halo”.
“…It may be that the Chinese government promotes the transfer of industries to the west and central, including both high-tech industries and traditional manufacturing industries such as building materials and chemicals. The transfer of industries is accompanied by the transfer of pollution, which leads to a gradual increase in MCI from east to west (Zhao et al 2021 ). Fig.…”
Renewable energy technology innovation (RETI) is a crucial driver for promoting the manufacturing green transformation. However, few studies have explored the impact of RETI on manufacturing carbon intensity (MCI) from the perspective of spatial spillover and regional boundary. Based on the manufacturing panel data of 30 provinces in China from 2006 to 2020, this study examines the mechanism, spatial spillover effects, regional boundaries, and industry heterogeneity of RETI on MCI using the spatial Durbin model. The results show that (1) RETI significantly inhibits local and neighboring MCI. (2) The spatial spillover effect of RETI on MCI has a significant regional boundary, which is inhibitory in the range of 800 km and shows a significant “half-decay” characteristic at 400 km. However, in the range of 800 to 1400 km, RETI significantly promotes neighboring MCI. (3) The inhibitory effect of RETI on MCI has temporal and regional heterogeneity, which gradually increases over time, and the effect from high to low is central, west, and east. (4) RETI has a significant inhibitory effect on MCI of pollution-intensive, high-income, capital-intensive, and labor-intensive manufacturing in local and neighboring areas, but it has a more negligible effect on non-pollution-intensive, low-income, and technology-intensive MCI. The findings provide empirical evidence for formulating targeted and differentiated policy in manufacturing low-carbon development.
“…The driving factors of green transition are technological innovation, industrial structure, energy structure, foreign trade level, etc. Zhao et al (2021) found that technological innovation can improve production methods, increase the output level and promote energy conservation and emission reduction, while scientific research investment has not brought the corresponding improvement of technological innovation. Therefore, larger-scale promotion of low-carbon technologies, elimination of outdated production capacity, and reduction of excess capacity are critical to green development (Li and Lin, 2016).…”
Section: The Evaluation and Drives Of Manufacturing Industry Green Tr...mentioning
Accelerating the green transformation of manufacturing industry is the way to the sustainable development of China’s economy and an inevitable requirement for realizing the great vision of carbon peaking and carbon neutrality. This paper employs the global Malmquist-Luenberger productivity index based on the SBM-DEA method to calculate the green total factor productivity covering manufacturing industry of 30 Chinese provinces between 2006 and 2019. A difference-in-difference model is constructed to empirically study the direct impact and mechanism assessment of carbon emission trading policy on the green total factor productivity. The results show that the green total factor productivity of China’s manufacturing industry generally showed a dynamic trend of rising during this period. Critically, the implementation of the carbon emissions trading policy has a significant effect on promoting the green total factor productivity of China’s manufacturing industry. Interestingly, this promoting effect will be enhanced by higher technical levels, a cleaner energy structure, and a greener industrial structure. Additionally, our evidence also shows that the positive impact of carbon emission trading policies on green total factor productivity will be improved by higher carbon emission trading prices and a more active carbon emission trading market, thereby accelerating the green transformation process of manufacturing industry. Therefore, taking carbon emissions trading policy as a long-term strategy and improving its effectiveness is crucial to ensuring the green and sustainable development of China’s manufacturing industry.
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