Innovation agglomeration and urban hierarchy: evidence from Chinese cities

Government environmental information disclosure is an important means to promote environmental supervision and law enforcement, and improve the level of environmental management. In order to explore the impact of government environmental information disclosure on the sustainability of urban economic growth, this paper uses the Pollution Information Transparency Index (PITI) to measure the degree of government environmental information disclosure, studies its effect on green total factor productivity through two-way fixed effect model and systematic GMM estimation method, and further adopts threshold model to study whether there is heterogeneity in this effect. The results show that: (1) Each unit of government environmental information disclosure will increase green total factor productivity by 0.2 units. (2) Considering the endogeneity, the promotion of government environmental information disclosure to green total factor productivity has increased. (3) The degree of government environmental information disclosure plays a non-linear role in the path of green total factor productivity. The greater the degree of economic development, the more obvious the effect of government environmental information disclosure on green total factor productivity. Therefore, this paper believes that the government should strengthen the disclosure of environmental information based on the urban economic development to ensure the sustainability of urban economic development.

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“…The p -value is 0.1376, so it is acceptable that there is no cross-section. Assumptions of relevance are found in [ 49 ].…”

Section: Regression Resultsmentioning

confidence: 99%

Research on the Impact of Government Environmental Information Disclosure on Green Total Factor Productivity: Empirical Experience from Chinese Province

Liang

Chen

2022

IJERPH

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“…Often, principal component analysis is more of an intermediate means to an end than an end in itself, and it is often used as an intermediate step in many studies that continue with other multivariate statistical methods, after condensing the data in order to address the actual problem [71][72][73][74]. In our study, the principal component analysis is followed by a multiple regression analysis to explain the main influencing factors of carbon emissions.…”

Section: Principal Component Analysismentioning

confidence: 99%

Spatial Heterogeneity of Carbon Emissions and Its Influencing Factors in China: Evidence from 286 Prefecture-Level Cities

Qin

2022

IJERPH

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In the face of the severe challenge of global warming, promoting low-carbon emission reductions is an important measure to cope with global climate change and achieve a green cycle of sustainable development. The purpose of this study was to reveal the spatial heterogeneity of carbon emissions and the influencing factors in 286 prefecture-level-and-above cities in China, and to provide an empirical basis for the formulation of low-carbon emission reduction policies in China. This study used a combination of comparative analysis, regional difference analysis, correlation analysis, principal component analysis, and stepwise regression analysis to analyze the spatial differences in carbon emissions and their influencing factors in 286 prefecture-level-and-above cities in China, and draws the following main conclusions: (1) From 2005 to 2015, regional differences in six sectors, including household carbon emissions, widened in the 286 prefecture-level-and-above cities in China, while regional differences in 14 sectors, including rural household carbon emissions, narrowed. (2) There were significant intra-group differences in urban household carbon emissions, and the contributions to intra-group differences in carbon emissions differed across the six sectors in the northeast, east, central, and west regions. (3) Although the total and average carbon emissions of each sector increased from 2005 to 2015, China’s carbon emission intensity was decreasing, and carbon productivity is increasing. (4) Carbon emissions per capita (CCE) were positively correlated with GRP per capita, industrial SO2 emissions per capita, and the proportion of employees in the secondary sector, and negatively correlated with population density and the proportion of employees in the tertiary sector. (5) Resident savings and consumption factors, pollution emission factors, and economic structure factors had a facilitating effect on CCE, while population density factors and economic growth factors have a weakening effect on CCE.

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“…to use spatial econometric models to explore the mechanisms by which green technological innovation affects hazardous air pollutants. (2) Explanatory variable: green technological innovation (E) Patent data are a valid measure of the level of scientific and technological innovation in a particular field [48][49][50][51][52]. Therefore, the core explanatory variable in this study was green patents, with their definition obtained from the International Patent Green Classification List issued by the World Intellectual Property Organization.…”

Section: Selection Of Indicators and Data Sources 221 Selection Of Indicatorsmentioning

confidence: 99%

“…Patent data are a valid measure of the level of scientific and technological innovation in a particular field [48][49][50][51][52]. Therefore, the core explanatory variable in this study was green patents, with their definition obtained from the International Patent Green Classification List issued by the World Intellectual Property Organization.…”

Section: Selection Of Indicators and Data Sources 221 Selection Of Indicatorsmentioning

confidence: 99%

Can Green Technological Innovation Reduce Hazardous Air Pollutants?—An Empirical Test Based on 283 Cities in China

Liu

Xiao

et al. 2022

IJERPH

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Based on the panel data of 283 cities in China from 2009 to 2018, this paper analyzes the effect of urban green scientific and technological innovation enhancement on hazardous air pollutants using the GS2SLS method, which simultaneously controls for model endogeneity and spatial spillover effects and reveals the transmission mechanism of urban green scientific and technological innovation level. It was found that (1) There is a significant spatial spillover effect of hazardous air pollutants between regions, both in China as a whole and in the eastern, central, and western parts of the country, and the spatial spillover effect of hazardous air pollutants is significantly greater in the eastern and central parts of China than in the western parts. (2) Green technological innovation has a significant inhibitory effect on hazardous air pollutants in cities in eastern and central China. An extended study found that the improvement in green technology levels in innovative cities has a better effect on controlling hazardous air pollutants than in non-innovative cities. (3) The energy- saving and green economy effects have a mediating influence on the effect of green technological innovation on hazardous air pollutants in cities, and the simultaneous occurrence of these two effects in green technological innovation serves to enhance the transmission of hazardous air pollutants in order to facilitate the long-term management of haze.

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Innovation agglomeration and urban hierarchy: evidence from Chinese cities

Cited by 89 publications

References 47 publications

Research on the Impact of Government Environmental Information Disclosure on Green Total Factor Productivity: Empirical Experience from Chinese Province

Research on the Impact of Government Environmental Information Disclosure on Green Total Factor Productivity: Empirical Experience from Chinese Province

Spatial Heterogeneity of Carbon Emissions and Its Influencing Factors in China: Evidence from 286 Prefecture-Level Cities

Can Green Technological Innovation Reduce Hazardous Air Pollutants?—An Empirical Test Based on 283 Cities in China

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