Environmental governance effect of the transformation of export trade mode: empirical evidence from 194 cities in China

Li, Guangqin; Fang, Xubing; Liu, Maotao

doi:10.1007/s11356-021-17110-4

Cited by 2 publications

(5 citation statements)

References 38 publications

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“…On that account, in this paper, the method of first-phase lag of core explanatory variables is adopted to validate the results of baseline regression [59]. As shown in Table 8, the effects of the agglomeration of manufacturing, the agglomeration of producer services, and co-agglomeration on PM 2.5 , SO 2 , and NO x after first-phase lag are consistent with the baseline regression results.…”

Section: First-phase Lagsupporting

confidence: 59%

“…First, when we clean the underlying data, we mainly use methods such as logarithmic and uniform measures of the main variables to reduce the heteroskedasticity in the regressions, so the effect of bias from outliers can be greatly reduced. Second, Lag term, spatial correlation test, and instrumental variables are used to test the baseline regression [59][60][61][62][63].…”

Section: Robustness Testmentioning

confidence: 99%

“…As shown in the above analysis, the energy consumption structure and human capital of a region affect the air pollutant emissions of the region. Therefore, energy consumption structure and human capital are used as mediating variables to set up the econometrics model and verify whether they have mediating roles [59,[64][65][66][67].…”

Section: Mechanism Testmentioning

confidence: 99%

“…According to the above elaboration on mechanisms, the human capital of a region may mitigate the air pollution of that region. To test whether human capital plays a mediating role between industrial agglomeration and air pollution, this paper has designed the following mediating effect model [59,[64][65][66][67]:…”

Section: Human Capitalmentioning

confidence: 99%

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Impact of Collaborative Agglomeration of Manufacturing and Producer Services on Air Quality: Evidence from the Emission Reduction of PM2.5, NOx and SO2 in China

et al. 2022

Atmosphere

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Industrial agglomeration is a major source of regional economic development and the main pattern enterprises employ after having developed to a certain stage. Industrial agglomeration also affects the emissions of air pollutants in production. Based on provincial panel data for China from 2006 to 2019, this paper introduces the full generalized least squares (FGLS) panel econometrics model. By considering spatial correlation, the potential endogenous problem has been controlled using the instrumental variable and the effects of the co-agglomeration of manufacturing and producer services on three major air pollutants, i.e., SO2, PM2.5, and NOx, have been empirically estimated. The empirical results show that: (1) The agglomeration of manufacturing increases the emission of PM2.5 in the air, while the agglomeration of producer services and the co-agglomeration of manufacturing and producer services reduce it. Moran correlation index test showed that SO2 and NOx had no significant spatial correlation. (2) The agglomeration of manufacturing, the agglomeration of producer services, and co-agglomeration exert the most significant effects on PM2.5 in the air in central and western China. This is probably because of the availability of basic natural resources in these areas. (3) The energy consumption structure mediates the effect of the agglomeration of manufacturing on PM2.5, and human capital mediates the effect of the agglomeration of producer services on PM2.5 emissions. Based on the results, policy suggestions to improve the atmospheric environment during the process of industrial agglomeration are proposed.

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Section: First-phase Lagsupporting

confidence: 59%

Section: Robustness Testmentioning

confidence: 99%

Section: Mechanism Testmentioning

confidence: 99%

Section: Human Capitalmentioning

confidence: 99%

See 2 more Smart Citations

Impact of Collaborative Agglomeration of Manufacturing and Producer Services on Air Quality: Evidence from the Emission Reduction of PM2.5, NOx and SO2 in China

et al. 2022

Atmosphere

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“…In this context, this paper mainly examines the impact of the transformation of processing trade enterprises to general trade enterprises on carbon emissions. Therefore, this paper draws on the research of Li et al ( 2022 ), adds up the general trade export value and processing trade export value of enterprises to obtain city-level data, and uses the ratio of general trade export value to processing trade export value to measure the degree of export trade transformation. This paper takes the natural logarithm of the above ratios to narrow the absolute difference between the data.…”

Section: Model Settings and Variable Descriptionsmentioning

confidence: 99%

Can the transformation of trade patterns achieve carbon reduction? Empirical evidence from a panel of 201 Chinese cities during the period 2000–2016

Liu

2023

Environ Sci Pollut Res

Self Cite

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How the transformation of trade patterns affects carbon emissions is an important topic in the field of trade and the environment. Based on the panel data of Chinese cities from 2000 to 2016, this paper uses fixed effect model, instrumental variable model and threshold estimation model to investigate the impact of the transformation of export trade patterns on carbon emissions and its internal mechanism. We find that during the investigation period, the transformation of export trade pattern has a significant “U”-shaped nonlinear impact on carbon emissions, and the research results still hold after a series of robustness checks and instrumental variable estimation; through the mechanism analysis of the threshold effect, it is found that in the process of the impact of trade patterns transformation on carbon emissions, there are double thresholds for energy structure transition; the nonlinear impact of export trade pattern transformation on carbon emissions has significant heterogeneity in different regions, city categories, and different periods; the mechanism analysis results show that industrial structure upgrading, industrial co-agglomeration, and green technology innovation are the main paths that the transformation of export trade patterns affects carbon emissions. Under the dual goals of fulfilling China’s commitment to “carbon peak in 2030 and carbon neutrality in 2060” and implementing corporate social governance to promote green and sustainable development, this paper will provide suggestions for realizing the goal of low-carbon emission reduction from the perspective of the transformation of export trade patterns.

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Environmental governance effect of the transformation of export trade mode: empirical evidence from 194 cities in China

Cited by 2 publications

References 38 publications

Impact of Collaborative Agglomeration of Manufacturing and Producer Services on Air Quality: Evidence from the Emission Reduction of PM2.5, NOx and SO2 in China

Impact of Collaborative Agglomeration of Manufacturing and Producer Services on Air Quality: Evidence from the Emission Reduction of PM2.5, NOx and SO2 in China

Can the transformation of trade patterns achieve carbon reduction? Empirical evidence from a panel of 201 Chinese cities during the period 2000–2016

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