Industrial structure, urban governance and haze pollution: Spatiotemporal evidence from China

Shi, Tao; Zhang, Wei; Zhou, Qian; Wang, Kai

doi:10.1016/j.scitotenv.2020.139228

Cited by 101 publications

(38 citation statements)

References 36 publications

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“…Before using the GTWR model, it is needed to analyze the spatiotemporal weighted relationship between variables. The sample spatiotemporal distance d ST is the linear function of the distance d S and d T between the provincial capital cities of region i and region j [32,33], and thus: d ST = d S + d T , where γ, λ are the correlation coefficients. The spatiotemporal distance between region i and region j can be calculated as following:…”

Section: The Spatiotemporal Heterogeneity Analysis Modelmentioning

confidence: 99%

The Carbon Emission Reduction Effect of Technological Innovation on the Transportation Industry and Its Spatial Heterogeneity: Evidence from China

Shi

Chan

et al. 2021

Atmosphere

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The carbon reduction effect of technological innovation in the transportation industry is conducive to China’s anticipated realization of carbon neutrality. Therefore, we evaluated carbon emission reduction effect of technological innovation in the transportation industry in China. Based on the panel data of 30 sample provinces in China (excluding Hong Kong, Macao, Taiwan and Tibet) from 2012 to 2018, using the Moran’I index and Getis-Ord Gi index, this paper analyzes the evolutionary trend and spatial autocorrelation of carbon emission in the transportation industry, and analyzes the impact of technological innovation on carbon emission levels of the transportation industry and its spatiotemporal differences by using the geographical and temporal weighted regression (GTWR) model by using ArcGIS 10.4 software. The conclusions are as follows: The carbon emission level of China’s transportation industry generally has been rising steadily, showing a spatial distribution pattern of high emissions in the east and low emissions in the west. The cold spots are concentrated in the western region, and the hot spots are situated in the central and eastern regions. Technological innovation has a carbon reduction effect on the transportation industry in the eastern and north-eastern regions, while the effect in other regions is not obvious. However, there is an obvious “inverted U-shaped” relationship between technological innovation and the transportation industry’s carbon emissions. The technological innovation in the transportation industry will have a significant carbon reduction effect after breaking through the technical pain points. This carbon reduction effect has a higher effect on the western region than on the eastern region. In addition, the economic development level, the fiscal expenditure proportion of the transportation industry, the higher education level, and the proportion of fixed asset investment in the transportation industry have played a positive role in reducing carbon in the transportation industry, but the spatial heterogeneity of this carbon reduction effect is relatively strong. Therefore, during the “14th Five-Year Plan” development period in China, it is necessary to continuously promote the low-carbon development of the transportation industry with technological innovation, while highlighting the differentiated carbon reduction governance, and consolidating the role of talents and fiscal support.

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Section: The Spatiotemporal Heterogeneity Analysis Modelmentioning

confidence: 99%

The Carbon Emission Reduction Effect of Technological Innovation on the Transportation Industry and Its Spatial Heterogeneity: Evidence from China

Shi

Chan

et al. 2021

Atmosphere

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“…Structural upgrading is the goal of HQMD in terms of industrial structure. Since the industrial structure is a hierarchy of product structure, enterprise structure and industry structure [19], the primary index of structural upgrading was split into the structural upgrading of product, enterprise, and industry.…”

Section: Evaluation Index Systemmentioning

confidence: 99%

Competitiveness Evaluation of High-Quality Manufacturing Development in the Yangtze River Economic Belt

Zhang¹,

Mu²,

Zhang³

et al. 2020

IJSDP

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This paper aims to construct the evaluation index system and model for the competitiveness of high-quality manufacturing development in China, and put forward pertinent suggestions. To this end, the evaluation index system was designed in reference to the input-process-output model of the systems theory; the entropy method was improved through analytic hierarchy process (AHP), and then combined with the technique for order preference by similarity to ideal solution (TOPSIS) into the improved entropy TOPSIS model. On this basis, the competitiveness of the Yangtze River Economic Belt (YREB) in high-quality manufacturing development (HQMD) was evaluated with the panel data of 2013-2018, in terms of overall competitiveness, spatial and temporal trends, industrial landscape, and national ranking. The results show that: The overall HQMD competitiveness improved steadily, under the strong boost from two factors and slight constraint from two factors. In terms of space, the HQMD competitiveness was stronger in the east, with widening regional gaps; in terms of time, the HQMD competitiveness was improving across the board, especially in the central region. Five provinces maintain steady growth of HQMD competitiveness, and six provinces witnessed fluctuating growth. In the final manufacturing landscape, there were one leader, two subleaders, three supporters, and five followers. The YREB is the demonstration zone of China's HQMD, with 80% of its provinces falling in medium and high levels. Finally, several suggestions were presented to improve the HQMD competitiveness in the YREB. The research findings provide a good reference for policymakers to promote manufacturing and pursue sustainable development.

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“…As there were a large number of non-key industries, the HHI of these industries was determined separately. It was observed that Beijing, Tianjin, Langfang, Baoding, Hengshui, Xingtai, and Zhengzhou had high levels of pollution agglomeration; there was a correlation between industrial clusters and pollution clusters [2,3,7]. Therefore, follow-up studies analyzed and verified the correlation between industrial agglomeration and pollution agglomeration.…”

Section: Based On the Classification Standard Of The Ministry Of Ecology And Environment For Airmentioning

confidence: 99%

“…Industrial agglomeration promotes urban expansion, population concentration, and regional economic development; however, it also introduces a series of environmental problems [1][2][3]. As such, industrial agglomeration may have positive and negative environmental impacts.…”

Section: Introductionmentioning

confidence: 99%

Industrial agglomeration and air pollution: A new perspective from enterprises in Atmospheric Pollution Transmission Channel Cities (APTCC) of Beijing-Tianjin-Hebei and its surrounding areas, China

et al. 2021

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Air quality in China has gradually been improving in recent years; however, the Beijing-Tianjin-Hebei (BTH) region continues to be the most polluted area in China, with the worst air quality index. BTH and its surrounding areas experience high agglomeration of heavy-polluting manufacturers that generate electric power, process petroleum and coal, and carry out smelting and pressing of ferrous metals, raw chemical materials, chemical products, and non-metallic mineral products. This study presents evidence of the air pollution impacts of industrial agglomeration using the Ellison–Glaeser index, Herfindahl–Hirschman index, and spatial autocorrelation analysis. This was based on data from 73,353 enterprises in “2+26” atmospheric pollution transmission channel cities in BTH and its surrounding areas (herein referred to as BTH “2+26” cities). The results showed that Beijing, Yangquan, Puyang, Kaifeng, Taiyuan, and Jinan had the highest Ellison–Glaeser index among the BTH “2+26” cities; this represents the highest enterprise agglomeration. Beijing, Langfang, Tianjin, Baoding, and Tangshan also showed a low Herfindahl–Hirschman index of pollutant emissions, which have a relatively high degree of industrial agglomeration in BTH “2+26” cities. There was an inverted U-shaped relationship between enterprise agglomeration and air quality in the BTH “2+26” cities. This means that air quality improved with increased industrial agglomeration up to a certain level; beyond this point, the air quality begins to deteriorate with a decrease in industrial agglomeration.

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Industrial structure, urban governance and haze pollution: Spatiotemporal evidence from China

Cited by 101 publications

References 36 publications

The Carbon Emission Reduction Effect of Technological Innovation on the Transportation Industry and Its Spatial Heterogeneity: Evidence from China

The Carbon Emission Reduction Effect of Technological Innovation on the Transportation Industry and Its Spatial Heterogeneity: Evidence from China

Competitiveness Evaluation of High-Quality Manufacturing Development in the Yangtze River Economic Belt

Industrial agglomeration and air pollution: A new perspective from enterprises in Atmospheric Pollution Transmission Channel Cities (APTCC) of Beijing-Tianjin-Hebei and its surrounding areas, China

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