Prefecture-level Cities In China Research Articles

The Impact of Carbon Emission Intensity on High- Quality Economic Development: An Empirical Study from 284 Prefecture-Level Cities in China

The Impact of Carbon Emission Intensity on High- Quality Economic Development: An Empirical Study from 284 Prefecture-Level Cities in China

Digital Economy, Environmental Regulation and Urban Green Development- A Study Based on 282 Prefecture-Level Cities in China

Digital Economy, Environmental Regulation and Urban Green Development- A Study Based on 282 Prefecture-Level Cities in China

Impact of Low-Carbon City Pilot Policies on Regional Green Total Factor Productivity: A Study

Based on data from 246 prefecture-level cities in China spanning the years 2006 to 2019, this paper employs a multi-temporal double-difference model to empirically examine the effects of low-carbon pilot policies implemented since 2010 on green total factor productivity. Furthermore, the paper conducts a mechanism analysis and heterogeneity analysis to explore the policys impact mechanisms and effects on different regions. The study reveals that low-carbon pilot policies have enhanced Chinas green total factor productivity. Results from the mechanism analysis indicate that these policies increase green total factor productivity by promoting the agglomeration of productive service industries and the innovation of green technologies, with the mechanism test for manufacturing industry agglomeration showing non-significant effects. Heterogeneity analysis results suggest that the impact of low-carbon pilot policies on the improvement of green total factor productivity is significant in Chinas eastern and western regions as well as in non-resource-based cities.

China's environmental tax reform achieves the co-management of carbon and haze: The role of optimizing energy utilization

Environmental tax reform in China is an essential initiative to achieve carbon and haze co-management by shifting the Environmental Fee to Tax (EFT). Therefore, we employ a Coupling Coordination Degree (CCD) model to measure the level of carbon and haze co-management across three distinct scenarios in China. Treating the EFT policy as a quasi-natural experiment, the study explores its impact on carbon and haze co-management using data of 282 prefecture-level cities in China during 2015–2020. Furthermore, the study further analyzes the regional heterogeneity and the transmission mechanisms of optimizing energy utilization. The principal findings of this investigation are as follows. First, the co-management levels of carbon and haze in China show a discernible upward trend from 2015 to 2020. A heightened emphasis on carbon reduction is helpful in improving the co-management level of carbon and haze. Co-management levels of carbon and haze are lower within the treatment group compared to the control group, but their growth rates are higher. Second, the EFT policy in China has significantly enhanced the co-management level of carbon and haze. Third, the impact of China's EFT policy has a more pronounced effect in western China and a comparatively weaker effect in middle China, which may be attributed to variations in industrial infrastructure, supporting facilities, availability of clean energy resources, and prevailing policy inclinations across different regions of China. Finally, China's EFT policy is an instrumental factor in augmenting the co-management level of carbon and haze, primarily through pathways of upgrading energy structure and improving energy efficiency.

How does digital inclusive finance promote the journey of common prosperity in China?

In contrast to previous studies by using provincial data, this paper employs city-level data from 260 prefecture-level cities in China between 2011 and 2020 to explore the underlying mechanism of how digital inclusive finance promotes the realisation of common prosperity in China. In addition, this paper incorporates financial deepening theory, financial exclusion theory, long tail theory, digital divide theory and technology driven theory to examine the proposed research questions. Through system-GMM and IV estimation, the results show that common prosperity is composed of development, sharing and sustainability. The high level of the weight of the first-level indicators for development and sharing reflects the importance of “prosperity” and “sharing” in common prosperity. Digital inclusive finance plays a significant role in promoting the development of urban common prosperity, with a more pronounced effect in the Eastern cities when compared to the Central and Western cities. In addition, digital inclusive finance is found to promote urban common prosperity by overcoming the digital divide and by improving the activity of urban innovation and entrepreneurship. Furthermore, the degree of marketisation is suggested to exert a positive role in regulating the impact of digital inclusive finance on common prosperity.

Distribution dynamics and quantile dynamic convergence of the digital economy: Prefecture-level evidence in China

The digital-substantive fusion is the realistic need to crack the problem of “out of the real to the virtual” in the real economy. Based on the panel data of 288 prefecture-level cities in China from 2011 to 2019, we use kernel density estimation and quantile convergence model to explore the distribution dynamics and convergence trend of the digital economy across the country and in the eastern, central, and western regions. In doing so, we unearth several key findings. First, the development of the digital economy in Chinese cities is on the rise, but absolute differences among cities still exist. Second, the overall development of the digital economy exhibits a dynamic q-σ convergence trend, but the development of the digital economy in the central region from the 0.75 to 0.95 quantiles shows a divergence trend. Third, our analysis reveals quantile heterogeneity and regional heterogeneity in the dynamic absolute q-β convergence of digital economy development. The tendency to converge is stronger in the extreme quantile regions than in other regions, while the convergence speed of central and western regions is higher than that of eastern regions. Finally, the dynamic conditional q-β convergence trend of digital economy development across the country and in the eastern, central, and western regions is stronger than the dynamic absolute q-β convergence, with the trend of convergence weakening only in the eastern high quantile regions. These findings provide empirical evidence for promoting regional coordinated high-quality development of digital economy, as well as theoretical references for vigorously promoting the construction of a modernized industrial system and accelerating the development of new-quality productive forces.

Higher education or scientific research: Which one contributes more to China’s green innovation?

Green innovation (GI) is a widely accepted driver of carbon neutrality and sustainable development. Human capital is the executor of green innovation, and both higher education (HE) and scientific research (SR) are vital to green innovation. This study explores the impact of HE and SR on GI in 285 prefecture-level cities in China from 2003 to 2019. The results show that both HE and SR contribute significantly to GI. Researcher density, regional innovation, and entrepreneurship capacity were introduced as mediating variables, and the results appear to be all significant mediating effects. Finally, corresponding recommendations were made: the government should strengthen the coordination of education, science, and technology resources, promote interactive enterprises and universities’ development, and strengthen GI in western China.

The path toward urban carbon neutrality: How does the low-carbon city pilot policy stimulate low-carbon technology?

To reduce carbon emissions, it is important to promote the development of low-carbon technology in urban areas. This study is the first to examine the impact of policies on the advancement of low-carbon technologies using the Low-Carbon City Pilot Policy as a quasi-natural experiment. Specifically, the study examines the impacts of the Low-Carbon City Pilot Policy on low-carbon and carbon-reducing, -neutral, and -negative technologies. It does so by analyzing data from 2006 to 2020 across 278 prefecture-level cities in China. The analysis is based on various models and includes use of multi-period difference-in-differences (DID), multi-period propensity score matching (PSM) DID, event study, staggered DID, and synthetic DID (SDID). The results indicate that the Low-Carbon City Pilot Policy has substantially impacted the development of urban low-carbon inventions, particularly evident in the ongoing growth of carbon-neutral and -negative technologies. The Low-Carbon City Pilot Policy is mainly achieved through industrial upgrades, green finance, and public behavior, but the effects on low-carbon technologies are heterogeneous due to environmental resources law and regional digital economic development. These findings offer more theoretical backing and empirical substantiation for the Chinese government's pursuit of “point-to-surface” implementation of low-carbon cities, and for the expedited achievement of its “carbon peaking, carbon neutrality” target.

The effects of sub-central development on the economic growth of provincial capitals: A study based on the DID and PSO-RF models

As a core component in deepening reforms and expanding the strategy of dual circulation development, the importance of developing sub-centers for the economic growth of provincial capitals is self-evident. This paper, based on panel data from 300 prefecture-level cities in China from 1998 to 2020, constructs a progressive double-difference (DID) model to assess the impact of sub-central development on the economic growth of provincial capitals. The research reveals that the development of sub-centers at the provincial level significantly promotes the economic growth of provincial capitals. Moreover, the policies of sub-center cities have a more pronounced stimulating effect on the economic growth of provincial capitals in the central and western regions. Additionally, the more sub-center cities there are, the more apparent the effect on promoting the economic growth of provincial capitals. Subsequently, this paper employs the Particle Swarm Optimization - Random Forest (PSO-RF) model through machine learning to validate the above conclusions, confirming the validity of the findings.

The Impact of the Digital Economy on Urban Ecological Resilience: Empirical Evidence from China’s Comprehensive Big Data Pilot Zone Policy

The present study examines the effects of China’s comprehensive big data pilot zone policy on urban ecological resilience. This is achieved through the utilization of a quasi-natural experiment, employing panel data from 217 prefecture-level cities in China spanning the years 2010 to 2021. The research revealed that China’s extensive policy on big data pilot zones has a notable and favorable influence on the ecological resilience of urban areas. This impact is both constant and subject to variation across different regions. The aforementioned impact is attained by means of progressions in industrial structure and the introduction of innovative green technologies. Furthermore, the strategy exerts a beneficial impact on the ecological resilience of urban areas in adjacent regions by means of spatial spillover effects.

Government digital transformation, resource curse and green total factor energy efficiency in Chinese cities

In the age of digitalization, Government Digital Transformation (GDD) stands out as a critical gauge of a nation's modernization drive. It reflects the extent of modernization within the governance framework and acts as a central catalyst for improving governance efficiency and the overall modernity of the system. Against this backdrop, this study empirically investigates the impact of GDD on green total factor energy efficiency (GTFEE) from 2011 to 2020, focusing on prefecture-level cities in China. Using a two-way fixed-effects model, we analyze how GDD influences GTFEE. Additionally, we explore the mediating effect and threshold models to elucidate the mechanisms underlying the resource curse (RC). The findings of the study are as follows: first, GDD has a promoting effect on GTFEE; second, the promoting effect of GDD on GTFEE is more obvious in southern cities and fourth-tier cities; third, the RC plays a mediating effect in the relationship between GDD and GTFEE, specifically, GDD promotes GTFEE by suppressing the RC effect and then promotes GTFEE; fourth, the present study also identifies a RC threshold effect; when RC < 0.5127, the energy efficiency promotion effect of GDD is relatively limited, while when RC ≥ 0.5127, the promotion effect of GDD on GTFEE is significantly enhanced. These findings are of great value for understanding and optimizing the strategic significance of GDD in enhancing GTFEE. They provide a scientific basis and directional guidance for formulating and implementing related policies.

Transportation infrastructure upgrading and green development efficiency: Empirical analysis with double machine learning method

In order to deal with the environmental problems such as pollution emissions and climate change, sustainable development in the field of transportation has gradually become a hot topic to all sectors of society. In addition, promoting the green and low-carbon transformation of China's transportation is also an important issue in the new era. Thus, it is particularly important to correctly identify the green effect of high-speed rail. However, the traditional causal reasoning model faces several challenges such as ‘dimensional curse’ and multicollinearity. Based on the panel data of 283 prefecture-level cities in China from 2003 to 2019, this study uses the double machine learning model to explore the impact of transportation infrastructure upgrading on the efficiency of urban green development in China. The research shows that the upgrading of transportation infrastructure can effectively improve the efficiency of urban green development by 4%. Service industry agglomeration and green innovation are verified as two mediating channels. Moreover, the synthetic difference in difference model is employed to evaluate the regional impact of high-speed rail, and finds that the regional impact of transportation policies often exceeds the impact of individual cities. We further apply the conclusions of this paper to the research at the micro enterprise level. Goodman-Bacon decomposition and a variety of robustness tests confirm the validity of our conclusions. The study's comprehensive empirical analysis not only validates the positive effects of transportation upgrades on green development, but also offers novel insights into the underlying mechanisms and policy implications of transportation upgrading.

Energy and environment: How could energy-consuming transition promote the synergy of pollution reduction and carbon emission reduction in China?

In order to attain the mutually reinforcing advantages of pollution reduction and carbon emissions, it is imperative to give precedence to the prevention and control of carbon pollution from its source, hence promoting a forward-thinking approach to governance. Hence, there is an urgent need for theoretical explanation and empirical verification to determine whether the energy-consuming right trading system, which serves as a conventional method for preventing and controlling pollution, has effectively achieved the desired outcome of reducing carbon emissions and promoting a mutually beneficial outcome. This study employs a panel data analysis of 266 prefecture-level cities in China spanning the years 2011 to 2020. The objective is to investigate the influence and underlying mechanism of the energy-consuming right trading system on the synergistic effects of pollution reduction and carbon emissions. At the same time, the heterogeneity analysis is carried out at three levels, including geographical location characteristics. The findings indicate that the energy-consuming right trading system has a substantial impact on enhancing the synergistic effects of pollution reduction and carbon emissions. In comparison to cities that did not implement the system, the pilot cities experience a reduction of 84.8% in CO2 and 34.5% in SO2. Eco-environment attention, clean energy substitution, energy consumption structure, and green technology innovation are the key transmission mechanisms of the energy-consuming right trading system affecting the synergistic effects of pollution reduction and carbon emissions. In addition, the effect is significantly different in different cities.

Digital inclusive finance, green technological innovation, and carbon emissions from a spatial perspective

Based on the panel data of 276 prefecture-level cities in China from 2011 to 2020, this study explores the impact of digital inclusive finance (DIF) on carbon emissions and the intrinsic mechanism of green technological innovation from a spatial perspective by constructing a spatial econometric model, a mediating effect model, and a threshold model. The results show that DIF significantly inhibits carbon emissions, exhibiting a spatial spillover effect. The transmission mechanism from a spatial perspective shows that green technological innovation plays a partial mediating role between DIF and carbon emissions, with the mediating effect accounting for approximately 59.47%. The heterogeneity analysis suggests that the impact of DIF on the reduction of carbon emissions is more pronounced in large and medium-sized cities and eastern regions. Further discussion reveals that the carbon reduction effect of DIF is also influenced by green technological innovation and industrial structure upgrading, showing threshold effects with marginal decreases and gradual increases, respectively.

Towards Carbon Neutrality in Agglomeration: Impact of Eco-Industry Development on Urban Carbon Emission Efficiency

Ecological industrial parks (EIPs) play a pivotal role as primary drivers of China’s industrial green transformation, facilitating the enhancement of urban carbon emission efficiency (UCEE) and the realization of green sustainable development. This study empirically investigates the effects of EIP policies on UCEE through quasi-natural experiments, utilizing data from 282 prefecture-level cities in China spanning from 2006 to 2021. Employing a multi-period difference-in-difference (DID) method, the findings are as follows: (1) The implementation of EIP policies leads to a 2.5% average increase in UCEE. (2) Event analysis reveals certain lagging characteristics in the promoting effect of EIP policies on the carbon emission efficiency of pilot cities. (3) EIP construction primarily enhances UCEE by reinforcing agglomeration effects and elevating innovation ability. (4) The promoting effect of EIP construction is more pronounced in the eastern and central regions, as well as in non-resource-based cities within different regions. Drawing from the empirical results, this study provides pertinent recommendations for EIP construction, offering theoretical guidance to policymakers and managers in crafting sustainable development strategies.

Beggar-thy-neighbor: Carbon leakage within China's pilot emissions trading schemes

The differences in the market liquidity of eight pilot emissions trading schemes (ETS) and the economic development of cities within the provincial-level ETS raise carbon leakage risks among the pilot cities. Utilizing panel data and DID models for 266 prefecture-level cities in China from 2006 to 2019, the empirical analysis reveals several key findings. Firstly, at the provincial level, the Tianjin pilot is found to have significantly increased CO2 emissions, potentially linked to carbon spillover from Beijing. Additionally, at the prefecture-level city level, certain cities in Guangdong Province and Fujian Province exhibit significant increases in CO2 emissions, indicating carbon transfer within the pilot ETSs. Secondly, all three spatial spillover effect tests demonstrate a notable carbon spillover effect between pilot cities. Lastly, the analysis of the moderating effect suggests that administrative intervention within the ETS and market mechanisms have limited impact on carbon spillover between pilot cities, while administrative intervention outside the ETS significantly enhances carbon leakage between pilot cities, primarily driven by carbon transfer resulting from state-owned enterprises' support for government emission reduction actions.

The Influence of Digital Economy on The Vitality of Urban Double Innovation

Under the new development pattern, the rapid development of digital economy is becoming the core force to change the global economic and social structure. At the same time, China is speeding up the conversion of old and new kinetic energy, and how to activate China's dual-innovation vitality is the focus of attention from all walks of life. Based on this, this project, based on the panel data of 285 prefecture-level cities in China from 2011 to 2019, studies the influence of regional digital economic development index on urban innovation vitality through two-way fixed effect model and intermediary effect model. It is found that the digital economy can promote the promotion of urban innovation vitality, and this conclusion is still valid after replacing the robustness test of the explained variables. The further mechanism test shows that the upgrading of industrial structure is an important intermediary variable for the digital economy to enhance the dual-innovation vitality of cities, and the digital economy can promote the dual-innovation vitality of cities through the upgrading of industrial structure.

Impact of industrial policy on urban green innovation: empirical evidence of China’s national high-tech zones based on double machine learning

Effective industrial policies need to be implemented, particularly aligning with environmental protection goals to drive the high-quality growth of China’s economy in the new era. Setting up national high-tech zones falls under the purview of both regional and industrial policies. Using panel data from 163 prefecture-level cities in China from 2007 to 2019, this paper empirically analyzes the impact of national high-tech zones on the level of urban green innovation and its underlying mechanisms. It utilizes the national high-tech zones as a quasi-natural experiment and employs a double machine learning model. The study findings reveal that the policy for national high-tech zones greatly enhances urban green innovation. This conclusion remains consistent even after adjusting the measurement method, empirical samples, and controlling for other policy interferences. The findings from the heterogeneity analysis reveal that the impact of the national high-tech zone policy on green innovation exhibits significant regional heterogeneity, with a particularly significant effect in the central and western regions. Among cities, there is a notable push for green innovation levels in second-tier, third-tier, and fourth-tier cities. The moderating effect results indicate that, at the current stage of development, transportation infrastructure primarily exerts a negative moderating effect on how the national high-tech zone policy impacts the level of urban green innovation. This research provides robust empirical evidence for informing the optimization of the industrial policy of China and the establishment of a future ecological civilization system.

Understanding China's CO2 emission drivers: Insights from random forest analysis and remote sensing data

China has become the world's largest emitter of carbon dioxide, putting significant pressure on the government to reduce emissions. This study analyzes the driving factors of carbon emissions in 281 prefecture-level cities in China from 2003 to 2019, based on carbon emission data matched with the locations of thermal power stations and nighttime light data. Firstly, we compare the accuracy of multivariate linear regression and random forest models, finding that the random forest regression yields superior results. Then, we rank the impact of various factors using the random forest method, revealing that population, economic development, and industrialization are the top three influencing factors. The interaction between population and economic development explains 68.5% of carbon emissions, with regional variations in the ranking of influencing factors. The main policy implications of this study are as follows: firstly, there is no need to overly concern about the impact of population growth on carbon emissions, and policies regarding fertility can be adjusted flexibly; secondly, controlling urbanization to a certain extent is conducive to achieving efficient low-carbon cities; thirdly, during the process of industrialization, carbon emissions inevitably increase, and it is advisable to accelerate industrialization to reach a turning point as soon as possible.

Migrants Subjective Well-being in Big Cities of China: Based on Self-rating Social Status and Social Integration

Promoting subject well-being of migrants could give full play to the advantages of talent's resource. Social position and social inclusion of migrants could be the key to pursue a people-centered, new type of urbanization and bring about rural migrants settling in cities. There is a lack of studies focusing on social comparison and social inclusion of migrants with the point of geographical features. Using the data collected from China Migrants Dynamic Survey in 2014, the relations among object socioeconomic status and self-rating social status as well as subject well-being of migrants in one municipal district and seven prefecture-level cities of China was examined by adopting multilevel linear regression model, which includes both mediating variables and moderating variables. Several conclusions are drawn as follows: 1) Migrants subjective well-being is affected by their objective socioeconomic status. Whilst migrants comparing themselves with local relatives and colleagues, as we call it self-rating social status, plays a part in mediating relations between object socioeconomic status and subject well-being, especially when the object socioeconomic status refers to migrants household monthly income, certain types of occupations (including civil servants, clerks and the self-rated) and homeowners in the host city. 2) When compared their socioeconomic status with locals in the host cities, including economic status and respect for themselves, the deeper the migrants are merging into cities and social networks, the weaker the relationship between self-rating social status and subjective well-being will be. In other words, social inclusion among migrants do negatively regulates the relations above. 3) However, there is no evidence that the higher the acceptance degree of local residents in host cities to migrants, the deeper the impact of the respect for migrants on subjective well-being will be. Obviously, the acceptance degree of local residents could not significantly moderate the relations between respect for migrants and their subject well-being. Based on the above research conclusions, this paper suggests that for building a harmonious society and improving the quality of urbanization, equal treatment of migrants and locals should be identified and dealt with. Furthermore, Therefore, we must speed up migrants to blend into the society in the cities.