The Spatial Spillover Effect of Environmental Regulation and Technological Innovation on Industrial Carbon Productivity in China: A Two-Dimensional Structural Heterogeneity Analysis

Ren, Xiangshi; Wu, Xuting; Liu, Yujia; Sun, Sha

doi:10.1155/2021/5613525

Cited by 5 publications

(3 citation statements)

References 47 publications

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“…According to the above-mentioned numerical table of coupling degrees, combined with the classification criteria of coupling degrees in the literature on the study of coupling relationships [35,36], as shown in Table 8, this study will use Mobile Information Systems the resources based on business clusters and regional innovation linkages as the basis for its conclusions. e four levels of horizontal linkage, antagonistic linkage, run-on linkage, and high-level linkage are used to measure the linkage change process of assistance based on production groups and regional innovation networks [23].…”

Section: Time Evolution Of the Coupling Between Resource-basedmentioning

confidence: 99%

The Modal Analysis of Multifactor Coupling of Regional Industrial Innovation

Liu

Guo

2022

Mobile Information Systems

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In order to maintain the sustainable development of resource-based industrial clusters, research on the coupling relationship between resource-based industrial clusters and regional innovation networks is proposed. Combined with the historical data of 11 typical resource-based cities from 2013 to 2021, the grey relational model is used to measure the correlation between resource-based clusters and regional innovation networks. And the concept of capacity coupling and the capacity coupling coefficient model in physics is used as a reference to obtain the coupling degree model of resource-based industrial clusters and regional innovation networks. The coupling degree model is used to measure the coupling degree of the two, and a reasonable analysis is carried out on the result of the measurement. The results show that there is a correlation between resource-based industrial clusters and regional innovation networks. And the correlation between the two is above 0.65, indicating a strong correlation. The coupling between resource-based industrial clusters and regional innovation networks has entered the subsequent high-level coupling stage from the run-in coupling stage that began in 2013 and continues to maintain a high-level coupling. It is pointed out that cluster governance plans should be formulated according to regional differences and local conditions, so as to guide the transformation and upgrading of resource-based industrial clusters and to avoid the negative impact on the regional economy due to resource exhaustion.

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Section: Time Evolution Of the Coupling Between Resource-basedmentioning

confidence: 99%

The Modal Analysis of Multifactor Coupling of Regional Industrial Innovation

Liu

Guo

2022

Mobile Information Systems

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“…Sun et al (2020) used the DEA method to categorize the main influencing factors of total factor carbon productivity and CO 2 emissions as technical progress, scale efficiency and management efficiency, and found that technical progress is the largest driving factor, followed by scale efficiency and management efficiency. Ren et al (2021) used the STIRPAT model and the spatial panel Durbin model to investigate the spatial spillover effects of environmental regulation and technical innovation on industrial carbon productivity in China, and found that technical innovation was beneficial to industrial carbon productivity, but there was no significant regional spillover of technical innovation. Zhang et al (2014) decomposed the influencing factors of carbon productivity into technical progress and the substitution effect between capital and labor factors and energy factor, and through further empirical research proved that technical progress has a positive promoting effect on carbon productivity, while the substitution effect between labor factor and energy factor will not be conducive to the improvement of carbon productivity.…”

Section: Factors Of Influencing Carbon Productivitymentioning

confidence: 99%

Research on the impact of technical progress on the carbon productivity in China’s service industry

Zhen-qiu

Huang

et al. 2023

Front. Environ. Sci.

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The energy consumption and carbon emission of the service industry should not be ignored. In order to achieve green and low-carbon development, improving the carbon productivity of the service industry is an extremely important method, and technical progress is a key path to improving the carbon productivity of the service industry. This paper decomposes the technical progress of China’s service industry into technical progress of non-energy factors and technical progress of energy factor, and analyzes the impact and action mechanism of these two technical progress on the carbon productivity of China’s service industry respectively from the theoretical and empirical perspectives. The main conclusions of this paper are as follows: From 2003 to 2019, technical progress had a significant positive impact on the carbon productivity of China’s service industry. The influence coefficients of technical progress of non-energy factors and technical progress of energy factor are 0.285 and 0.306. In terms of the type of technical progress, the technical progress of energy factor has a greater impact. The technical progress of non-energy factors and energy factor have a significant promoting effect on the improvement of carbon productivity of service industry in all regions of China. In Eastern, central and Western China, the influence coefficients of the former are 0.318, 0.289 and 0.266, and the influence coefficients of the latter are 0.352, 0.296 and 0.273. The mechanism test finds that the technical progress of non-energy factors and energy factor directly affect the carbon productivity of China’s service industry on the one hand, and indirectly affect the carbon productivity of China’s service industry through the production efficiency and energy use efficiency of the service industry on the other hand.

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“…e implementation of cleaner production standards makes textile enterprises have technological innovation to avoid the cost of pollution control, technological progress to improve the production efficiency of enterprises, and then increase export technical complexity of enterprise. On the one hand, the implementation of cleaner production standards will promote a more rational allocation of resources and technological innovation through the cost effect [20], which will improve the productivity of enterprises [21,22]. On the other hand, in the face of environmental regulations on cleaner production, smaller or low-productivity textile enterprises cannot afford the high environmental costs and choose to exit the industry, while environmental regulations also impose sunk costs on enterprises that want to enter the textile industry, thus discouraging some enterprises from entering.…”

Section: Theoretical Mechanismsmentioning

confidence: 99%

Cleaner Production Standards and Increased Technical Complexity for Textile Exporters

Yao

2022

Mathematical Problems in Engineering

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An important prerequisite for promoting sustainable development and value chain climbing of textile enterprises is to achieve a win-win situation between environmental protection and export technical complexity. Thus, based on Chinese enterprises’ microdata, this paper investigates the impact of cleaner production standards implementation on the export technical complexity of Chinese textile enterprises using a double difference method. The results show that the implementation of cleaner production standards significantly increases the export technical complexity of Chinese textile enterprises. The results of the mechanism test indicate that cleaner production standards increase export technical complexity by enhancing capital and labor inputs of textile enterprises, as well as by promoting enterprise innovation and increasing productivity. Heterogeneity analysis shows that cleaner production standards have a significant role in increasing the export technical complexity of textile enterprises in the eastern region and larger scale; it is not significant in increasing the export technical complexity of textile enterprises in the central and western regions and smaller scale; in terms of enterprise ownership attributes, it has the greatest role in increasing the export technical complexity of state-owned textile enterprises, followed by foreign and private textile enterprises. The results of this paper show that cleaner production standards, as an environmental regulation policy, can achieve a win-win situation for environmental protection and export technical complexity increase.

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The Spatial Spillover Effect of Environmental Regulation and Technological Innovation on Industrial Carbon Productivity in China: A Two-Dimensional Structural Heterogeneity Analysis

Cited by 5 publications

References 47 publications

The Modal Analysis of Multifactor Coupling of Regional Industrial Innovation

The Modal Analysis of Multifactor Coupling of Regional Industrial Innovation

Research on the impact of technical progress on the carbon productivity in China’s service industry

Cleaner Production Standards and Increased Technical Complexity for Textile Exporters

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