Industrial agglomeration and haze pollution: Evidence from China

Chen, Yufeng; Zhu, Zhitao; Cheng, Shuiping

doi:10.1016/j.scitotenv.2022.157392

Cited by 58 publications

(26 citation statements)

References 32 publications

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“…Column (3) reveals a partial mediating effect between INSA and carbon efficiency and finds that the relationship between them is U-shaped. This is consistent with Hypothesis 3, the finding by Li and Liu (2022), and the research of Chen et al (2022). Initially, most of China's industries were processing, resembling, and resources extraction, were still on the path of transformation from labor-intensive to capital-intensive, and were characterized as low added value, high pollution, and high energy consumption.…”

Section: Industrial Agglomerationsupporting

confidence: 86%

“…Although the economic growth rate was quite high, it came at the cost of the environment. After the agglomeration reaches a certain level, the spillover effect of the knowledge alongside the transition to technology-intensive helps to increase the added value and reduce the emissions, thus improving carbon efficiency (Chen et al, 2022).…”

Section: Industrial Agglomerationmentioning

confidence: 99%

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The influence of digital development on China’s carbon emission efficiency: In the view of economic and environmental balance

Yuan

Yang

2023

Front. Environ. Sci.

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Introduction: With the worsening global climate problem, carbon peak and carbon neutrality have become crucial objects to realize sustainable development. Regarded as the engine of economic development in the new era, it is worth exploring whether digitalization could contribute to carbon goals. Meanwhile, carbon reduction should not be advocated at the cost of economic recession and contains the risk of reversal when the economy renews in growth.Material and Methods: This paper evaluates carbon efficiency using the total factor non-radial directional distance function, which reflects the economic performance and environmental performance of 246 China’s prefecture-level cities during 2011–2019. Fixed effect and mediation effect models are used to explore the non-linear relationship and transmission channels between digital development and carbon efficiency.Results: It is found that: 1) digital development would hinder carbon efficiency first and then promote it after reaching a certain level; 2) digital development could indirectly affect carbon efficiency through industrial agglomeration, industrial structure upgrading, and industrial electricity productivity in non-linear ways. 3) Heterogeneity exists in the relationship between digital development and carbon efficiency due to different regions and development types.Discussion: Due to digital development itself having high carbon-negative externalities at the initial stage, its impact on carbon efficiency is complex and non-liner even when decomposing through multiple channels. A well-structured development strategy is needed during the digitalization process in order to prompt carbon efficiency.

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Section: Industrial Agglomerationsupporting

confidence: 86%

Section: Industrial Agglomerationmentioning

confidence: 99%

The influence of digital development on China’s carbon emission efficiency: In the view of economic and environmental balance

Yuan

Yang

2023

Front. Environ. Sci.

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“…It was consistent with the fact that developed provinces in the eastern regions tend to have a higher eco-efficiency in Stage Ⅰ. It can be explained that developed provinces pay more attention to environmental protection and these provinces might be on the right side of the environmental Kuznets curve (EKC); the scale effect and agglomeration effect brought by economic development can effectively realize the balanced development of economy and environment [ 46 ].…”

Section: Resultssupporting

confidence: 71%

Exploring the Ecological Performance of China’s Tourism Industry: A Three-Stage Undesirable SBM-DEA Approach with Carbon Footprint

Chen

Zhu

Zhang

2022

IJERPH

Self Cite

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The environmental impact of carbon emissions and the carbon footprint from tourism activities are significant for promoting low-carbon development in the tourism industry. This paper employed a bottom-up approach to estimate the carbon footprint and energy consumption of China’s tourism industry. Then, the three-stage undesirable SBM-DEA model was employed to evaluate and decompose the eco-efficiency of China’s provincial tourism industry from 2008 to 2017. The results showed that the eco-efficiency of most provinces has experienced a slight increase during the past ten years, while management inefficiency within the tourism industry has been the main restriction of the utilization of tourism resources in most regions. The decomposition and quadrant analysis indicated that scale efficiency was the direct driver of the poor ecological performance in Northeast China, while technical efficiency dominated the tourism eco-efficiency in South-Central China. These two issues have together led to the poor utilization of the rich tourism resources and the natural environment in Southwest China. On the basis of these discussions, differentiated policy implications towards different kinds of regions were provided to promote low-carbon development and to realize the potential of tourism resources in China’s tourism industry.

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“…Or surrounding carbon emissions are impacted but not those in regional cities 19 – 21 . In terms of methodology, scholars have evaluated industrial agglomeration by calculating the location quotient index and Gini coefficient 22 , 23 . The agglomeration of producer services is evaluated by calculating the producer services agglomeration index 24 .…”

Section: Introductionmentioning

confidence: 99%

Spatial correlation between producer services agglomeration and carbon emissions in the Yangtze River Economic Belt based on point-of-interest

Zeng

Shang

Xing

2023

Sci Rep

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Agglomeration of the industry significantly impacts economic performance and environmental sustainability. In line with its strategic context of striving to achieve carbon reduction targets, China is making efforts to optimize the producer services landscape to reduce carbon emissions. Understanding the spatial correlation between industrial agglomeration and carbon emissions is particularly crucial against this background. Based on POI and remote sensing data of China’s Yangtze River Economic Belt (YREB), the paper adopts the mean nearest neighbor analysis, kernel density analysis, and standard deviation ellipse to portray the agglomeration of producer services. Then uses Moran's I to present the spatial distribution characteristics of carbon emissions. Accordingly, the spatial heterogeneity of producer services agglomeration and carbon emissions is showed using the Geographic detector so as to provide strong support for industrial structure optimization and sustainable development. Here are some of the conclusions drawn from the study: (1) Producer services are a significant state of agglomeration in the provincial capitals and some central cities, with similar agglomeration patterns. (2) Carbon emissions exhibits significant spatial aggregation characteristics, with the spatial distribution pattern of "High west–Low east". (3) Wholesale and retail services industry is the primary risk factor that causes spatial differentiation of carbon emission intensity, "leasing and business services industry-wholesale and retail services industry" is the key interaction factor of the spatial differentiation. (4) Carbon emissions shows a downward trend followed by an upward trend as producer services agglomeration increases.

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Industrial agglomeration and haze pollution: Evidence from China

Cited by 58 publications

References 32 publications

The influence of digital development on China’s carbon emission efficiency: In the view of economic and environmental balance

The influence of digital development on China’s carbon emission efficiency: In the view of economic and environmental balance

Exploring the Ecological Performance of China’s Tourism Industry: A Three-Stage Undesirable SBM-DEA Approach with Carbon Footprint

Spatial correlation between producer services agglomeration and carbon emissions in the Yangtze River Economic Belt based on point-of-interest

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