The impact of environmental regulation on capacity utilization of China’s manufacturing industry: An empirical research based on the sector level

Xie, Bingyao; Liu, Yang; Song, Wan; Song, Linshu; Wang, Hao

doi:10.1016/j.ecolind.2023.110085

Cited by 25 publications

(7 citation statements)

References 39 publications

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“…Zhang et al (2023) [10] found that environmental regulation accelerates the transfer of equipment manufacturing industry, but its impact on its international competitiveness presents a "U-shaped" pattern. Xie et al (2023) [11] found technology innovation can be stimulated by combining environmental regulation and other policies to improve capacity utilization in moderate and severe pollution sectors. Wen et al (2022) [12] found that environmental regulation can force manufacturing enterprises to transform and improve total factor productivity.…”

Section: The Concept Of Urban Advanced Manufacturing Developmentmentioning

confidence: 99%

The Evaluation Prediction System for Urban Advanced Manufacturing Development

Dou,

Sun,

Zhu

et al. 2023

Systems

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With the rapid development of the economy, it is important to reasonably evaluate the development status of the regional manufacturing industry. Given this, this article expands the evaluation indicators of urban advanced manufacturing (UAM) from the perspective of the push–pull-mooring (PPM). Then, it uses a machine learning (ML) method to predict the evaluation results of other cities through a small amount of sample data. The results show that: (1) From the current development status of UAM in Guangdong Province (GD), cities in the Pearl River Delta region occupy a dominant position. However, cities in eastern, western, and mountainous regions have strong development potential and lead cities. Therefore, each region has cities with high levels of development and has a demonstrative role. (2) By comparison, it was found that the overall development level of UAM in GD is not significantly different from that of the Yangtze River Economic Belt. However, due to significant differences in their extreme values, the proportion of cities above the average in the overall population is relatively small. This indirectly proves that GD’s UAM not only has a phased nature, but also has a demonstrative role. (3) The prediction effect of the perceptron model is better than other methods. Although neural network models have better prediction performance than other machine learning models, they should not overly rely on complex network structure prediction data. By comparing the results, the reliability is verified. Finally, according to the life cycle theory, we propose a targeted development path for different UAM.

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Section: The Concept Of Urban Advanced Manufacturing Developmentmentioning

confidence: 99%

The Evaluation Prediction System for Urban Advanced Manufacturing Development

Dou,

Sun,

Zhu

et al. 2023

Systems

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“…Although responsible and sustainable management is the commercial basis of the global product market, various countries and regions have formulated different environmental policies according to their economic and social development and residents' disposable income (Xie et al, 2023). Differences in the severity of environmental regulations may lead to differences in the role of fintech in generating green innovation.…”

Section: Theoretical Analysis and Hypothesismentioning

confidence: 99%

Impact of fintech and environmental regulation on green innovation: inspiration from prefecture-level cities in China

Ni,

Yu,

Wen

2023

Front. Ecol. Evol.

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Environmental regulations may promote regional ecological evolution, but they also increase the need for financing green innovation activities. This study uses panel data from prefecture-level cities in China to examine the impact of fintech and environmental regulation on regional green innovation in the digital economy era. Empirical evidence shows that fintech significantly promotes regional green innovation, and fintech has a positive interaction effect with environmental regulation. While the evidence generally supports the role of environmental regulations in promoting green innovation, the evidence is insignificant in some models. The synergistic effect of fintech and environmental regulation on utility model green innovation is significant, but not on invention type green innovation. Climate policy, as a carbon regulatory policy, does not directly lead to green innovation, but it significantly collaborates with fintech to promote green innovation. The effects of fintech and environmental regulation on green innovation also have heterogeneity effects between resource-based and non-resource-based cities, and non-resource-based cities have a greater effect on achieving green innovation through fintech and environmental regulation. Our findings contribute to optimizing the coordination system between financial and environmental policies, thereby driving regional green innovation development with fintech in the digital age.

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“…First, in terms of the theoretical framework, there have been many studies on ER, but most have focused on the one-way impact of regulatory means on research subjects (Altman, 2001;Chen et al, 2018;Huang & Tian, 2023). Second, in terms of the selection of research subjects, there are many studies on the impact of ER on the carbon emission intensity of cities and industries; however, less research has been conducted on the impact of ER on ACEE (Zhang & Song, 2021;Lena et al, 2022;Xie et al, 2023). Third, in terms of research indicators, existing studies have explored the dynamic relationship between ER and food security, but the relevant indicators have mainly focused on food quantity security, and insu cient attention has been paid to FQS (Andrade & Satorre, 2015;Tenorio et al, 2020).…”

Section: Literature Reviewmentioning

confidence: 99%

Examination of the Relationship between Agricultural Carbon Emission Efficiency and Food Quality and Safety: From the Perspective of Environmental Regulation

Deng

Fang

Bai

et al. 2023

Preprint

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An important breakthrough in the coordinated development of China's low-carbon goals and food security strategies is agricultural development oriented toward quality, safety, green, and low carbon. This study integrated command-control and market-incentive environmental regulation (ER), agricultural eco-efficiency (ACEE), and food quality and safety (FQS) into a unified theoretical framework. The unexpected output-oriented Super-SBM model was used to calculate the ACEE of China's provinces and cities from 2011 to 2020 and test the bidirectional causality between ACEE and FQS through the system generalized moment estimation model. A dynamic panel smooth transition (PSTR) model was used to explore the nonlinear impact mechanisms of different types of ERs on ACEE and FQS. The results showed that there was a long-term, two-way causal relationship between ACEE and FQS. The impact of environmental regulations on ACEE and FQS has a nonlinear relationship. Among them, the role of market-incentivized ER is more significant. Therefore, building an inter-regional coordinated development mechanism, improving the utilization rate of agricultural resources such as fertilizers and pesticides, and coordinating the positive effects of different types of ERs are the keys to improving the ACEE and ensuring the coordinated development of FQS.

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The impact of environmental regulation on capacity utilization of China’s manufacturing industry: An empirical research based on the sector level

Cited by 25 publications

References 39 publications

The Evaluation Prediction System for Urban Advanced Manufacturing Development

The Evaluation Prediction System for Urban Advanced Manufacturing Development

Impact of fintech and environmental regulation on green innovation: inspiration from prefecture-level cities in China

Examination of the Relationship between Agricultural Carbon Emission Efficiency and Food Quality and Safety: From the Perspective of Environmental Regulation

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