Corruption, market segmentation and haze pollution: empirical evidence from China

Zhao, Junfeng; Jia-heng, Shen; Yan, Jinling; Yang, Xiaodong; Hao, Yu; Ran, Qiying

doi:10.1080/09640568.2021.2001316

Cited by 24 publications

(11 citation statements)

References 54 publications

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“…These production factors include technological innovation (Liu and Dong, 2021;Zhai and An, 2021) and human capital (Grimaud and Rouge, 2008). In addition to environmental regulations (Shuai and Fan, 2020), energy savings, and emissions reductions (Li and Xu, 2020;Hao et al, 2021) are all part of government regulations. In short, scholars have made abundant discussions on factors driving green economy development.…”

Section: Literature Reviewmentioning

confidence: 99%

“…extensive industrialization development model accelerated the urban economic development, nevertheless, environmental pollution problems have increasingly arisen (Guan et al, 2018;Hao et al, 2021). This is contrary to the green concept of "green mountains are gold mountains'', and restrains China's economic and social advancement.…”

Section: Introductionmentioning

confidence: 98%

“…The Chinese economy has seen a dazzling boom following decades of reform and opening up (Fan and Fang, 2020;Zhao et al, 2021). By 2021, the GDP has grown dramatically to 114.367 trillion yuan, up from 367.87 billion in 1978 ① .…”

Section: Introductionmentioning

confidence: 99%

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Can digital economy be a “new engine” for urban green economy development? An empirical analysis of Chinese cities

Zhao

Yan

2023

Preprint

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Digitization and greening are the vital driving force of China’s economic transformation. Whether they can drive collaboratively China’s economy is the key to realizing sustainable development. In this study, a super efficiency EBM model is applied to calculate the urban green economy development (UGED) level of 266 Chinese cities between 2011 and 2020, and then the dynamic SDM and intermediary effect models are employed to explore the impact and transmission mechanism of digital economy on UGED. Furthermore, it is also investigated whether digital economy and UGED have a nonlinear relationship. In the findings, digital economy is capable of significantly boosting UGED in the local city and its neighborhood, but significant differences exist in different regions and economic development stages. As a result of the transmission mechanism test, it may be found that digital economy could indirectly boost UGED through technology, structure, and allocation effects. The impact of digital economy on UGED has a double threshold due to the significant difference in the urban economic development level. The digital economy is increasingly contributing to UGED as the local economy develops. Therefore, local governments should develop digital economy in line with the local conditions so that its engine role in driving UGED can be given full play.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

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Can digital economy be a “new engine” for urban green economy development? An empirical analysis of Chinese cities

Zhao

Yan

2023

Preprint

Self Cite

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“…As the “invisible hand” and “visible hand” of macroeconomic development, the market and government both yield crucial moderating effects in the achievement of carbon neutrality and carbon peak goals. From one perspective, the government can appropriately adjust the imbalance of the indusial structure and spatial pattern, which can further improve resource allocation efficiency in the market ( 40 , 41 ). This is beneficial to carbon emission reduction in an indirect way.…”

Section: Mechanism Analysismentioning

confidence: 99%

Identifying impacts of industrial co-agglomeration on carbon emissions: Evidence from China

Shen

Pan

Feng

2023

Front. Public Health

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Based on panel data of 285 cities in China at the prefecture level and above from 2005 to 2020, this paper aims to study the nexus between industrial co-agglomeration and carbon emissions from dual perspectives including space and time. It adopts multiple approaches including a dynamic general method of moment, panel quantile regression model, panel threshold model, and dynamic spatial Durbin model. The non-spatial empirical results support the establishment of the threshold effect and the imbalance effect. The spatial empirical results indicate that industrial co-agglomeration poses a dramatic stimulating effect on urban carbon emissions, and its spatial spillover effect and spatial heterogeneity are conditionally established. Furthermore, heterogeneous effects are supported, such as the positive spillover effects of industrial co-agglomeration are more significant in western cities, resource-oriented cities, and non-low-carbon pilot cities. The heterogeneous influence of cost factors on industrial agglomeration and carbon emissions has also been partially confirmed. In terms of the channels and mechanism of action, the negative externalities of industrial co-agglomeration occupy a dominant position in the current status of economic development. The dynamic equilibrium between government intervention and marketization is a solid foundation for the optimization of carbon emission reduction paths.

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“…In recent decades, as a result of the drastic advancement of China's urbanization and industrialization, the surge in energy consumption is awfully daunting (Hussain et al, 2020;Zhao et al, 2021). The report on China's energy supply and demand reveals that 40% of the global energy consumption growth in 2020 is contributed by China.…”

Section: Introductionmentioning

confidence: 99%

Government competition, corruption and green total factor energy efficiency: An empirical analysis from china

Zhao

Yan

Yang

et al. 2022

Front. Environ. Sci.

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Currently, global warming and resource and environmental constraints are becoming formidable. Improving green total factor energy efficiency (GTFEE) is an effective tactic to alleviate carbon emissions and resource scarcity, and also a practical demand for the sustainable and healthy development of China’s economy. This study applies the super efficiency SBM model to calculate GTFEE of China’s 30 provinces over the period from 2006 to 2020, and then the dynamic spatial Durbin model (SDM) is employed to investigate the impact of government competition on GTFEE. Furthermore, the influence path between government competition and GTFEE is identified by the intermediary effect model. The findings reveal that as a whole, local government competition has undermined GTFEE both in local and adjacent areas, but significant differences exist in different regions. After the robustness tests, the conclusions remain valid. The intermediary effect results show that government competition indirectly inhibits GTFEE by triggering corruption. Based on this, policymakers should establish an effective performance appraising for local governments to spark a cycle of virtuous competition and cooperation among regions. Meanwhile, the anti-corruption system should be improved so as to shape a favorable institutional atmosphere for the improvement of GTFEE, and ultimately promote the sustainable development of China’s economy.

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Corruption, market segmentation and haze pollution: empirical evidence from China

Cited by 24 publications

References 54 publications

Can digital economy be a “new engine” for urban green economy development? An empirical analysis of Chinese cities

Can digital economy be a “new engine” for urban green economy development? An empirical analysis of Chinese cities

Identifying impacts of industrial co-agglomeration on carbon emissions: Evidence from China

Government competition, corruption and green total factor energy efficiency: An empirical analysis from china

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