Impact of urbanization on ecological efficiency in China: An empirical analysis based on provincial panel data

Yao, Jiadai; Xu, Pengpeng; Zhi-jin, Huang

doi:10.1016/j.ecolind.2021.107827

Cited by 108 publications

(71 citation statements)

References 50 publications

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“…Based on the literature review and combined with the research themes of this paper, we introduce other control variables that may affect GTFP, including industrial structure [50,51], energy structure [52], human capital [53,54], financial development level [55][56][57] and urbanization level [58,59]. (1) Industrial structure (is): expressed by the ratio of the output value of the tertiary industry to the GDP.…”

Section: Control Variablementioning

confidence: 99%

Research on the Impact Factors of Green Economy of China—From the Perspective of System and Foreign Direct Investment

et al. 2021

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The outbreak of COVID-19 has had an immeasurable impact on the global economy. It has damaged parts of the real economy, but also provided new opportunities for China’s green development. Both the system and foreign direct investment (FDI) have an important impact on China’s green recovery path. Based on the provincial panel data of China from 2007 to 2016, this paper uses a slacks-based measure (SBM) model and Malmquist–Luenberger (ML) index to measure the green total factor productivity (GTFP), and empirically analyzes the regulatory role of system in the influencing mechanism of FDI on GTFP. The results show that the overall level of FDI significantly inhibits the improvement of GTFP, and the interaction between system and FDI makes it shift from inhibition to promotion, but the promotion would be weakened with the improvement of the system. FDI in the eastern region shows a positive effect on GTFP, which will be weakened with the improvement of the system. FDI in central and western regions shows a negative effect on GTFP, and the negative effect in western regions will be increased with the improvement of the system. Then this article puts forward targeted policy suggestions for further improving the level of regional systems and introducing FDI of high quality.

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Section: Control Variablementioning

confidence: 99%

Research on the Impact Factors of Green Economy of China—From the Perspective of System and Foreign Direct Investment

et al. 2021

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“…When we consider the regional heterogeneity and spatial interconnection, traditional econometric models are inefficient [ 60 ]. Spatial econometric models consider the spatial dependence between observations, and different approaches are used when spatial effects are considered [ 61 ].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Zhang et al [ 63 ] use the SDM to examine the impact of high-speed rail on the consumption of urban residents, and found that HSR had different effects on consumption density in cities in eastern, central, and western China due to different levels of urban economic development and inter-city relationships. Yao et al [ 60 ] use the SAR, SEM, and SDM to analyze the impact mechanisms of urbanization dimensions and the internal structure effect of each dimension on eco-efficiency, and find that population and ecological urbanization have a significant positive impact on local eco-efficiency, while social and spatial urbanization have a negative impact. Chica-Olmo et al [ 64 ] use the SAR, SEM, and SDM to investigate the spatial dependence between GDP and renewable energy consumption for 26 European countries.…”

Section: Literature Reviewmentioning

confidence: 99%

The Impact of Green Technology Innovation on Carbon Emissions in the Context of Carbon Neutrality in China: Evidence from Spatial Spillover and Nonlinear Effect Analysis

Zeng

et al. 2022

IJERPH

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The Paris agreement is a unified arrangement for the global response to climate change and entered into force on 4 November 2016. Its long-term goal is to hold the global average temperature rise well below 2 °C. China is committed to achieving carbon neutrality by 2060 through various measures, one of which is green technology innovation (GTI). This paper aims to analyze the levels of GTI in 30 provinces in mainland China between 2001 and 2019. It uses the spatial econometric models and panel threshold models along with the slack based measure (SBM) and Global Malmquist-Luenberger (GML) index to analyze the spatial spillover and nonlinear effects of GTI on regional carbon emissions. The results show that GTI achieves growth every year, but the innovation efficiency was low. China’s total carbon dioxide emissions were increasing at a marginal rate, but the carbon emission intensity was declining year by year. Carbon emissions were spatially correlated and show significant positive agglomeration characteristics. The spatial spillover of GTI plays an important role in reducing carbon dioxide emissions. In the underdeveloped regions in China, this emission reduction effect was even more significant.

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“…Third, urbanization, as an internal manifestation of human social progress, will bring about the industry and population agglomeration, technological upgrading, and changes in human lifestyles, which will have different effects on ecological efficiency. Therefore, urbanization is the key factor in regional ecological efficiency [47]. Finally, the pollution paradise hypothesis states that foreign investment by developed countries will transfer polluting industries or products to developing countries with relatively loose environmental regulations.…”

Section: Literature Reviewmentioning

confidence: 99%

Research on the Influencing Factors of Urban Ecological Carrying Capacity Based on a Multiscale Geographic Weighted Regression Model: Evidence from China

Liu

Xie

Zhou

2021

Land

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Based on the comprehensive evaluation method, a comprehensive urban ecological carrying capacity (UECC) evaluation system is established. It includes ecological support, ecological resilience, and ecological pressure. Multiscale geographically weighted regression (MGWR) was used to conduct a thorough examination of the spatial and temporal patterns, and the factors that influenced the UECC of 286 prefecture-level cities in China from 2010 to 2019. The results show that (1) China’s UECC index ranges from 0.0233 to 0.2811 in 2019, which is still at a low level. (2) The spatial distribution is relatively stable: high-value agglomerations of UECC are distributed primarily in the Yangtze River Delta and Pearl River Delta, while low-value agglomerations are primarily distributed in the regions in the Central Plains. (3) All influencing factors have a positive effect on the improvement of UECC and are heterogeneous in spatial distribution. Lastly, this paper gives corresponding suggestions, so that governments can formulate differentiated policies and effectively improve UECC.

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Impact of urbanization on ecological efficiency in China: An empirical analysis based on provincial panel data

Cited by 108 publications

References 50 publications

Research on the Impact Factors of Green Economy of China—From the Perspective of System and Foreign Direct Investment

Research on the Impact Factors of Green Economy of China—From the Perspective of System and Foreign Direct Investment

The Impact of Green Technology Innovation on Carbon Emissions in the Context of Carbon Neutrality in China: Evidence from Spatial Spillover and Nonlinear Effect Analysis

Research on the Influencing Factors of Urban Ecological Carrying Capacity Based on a Multiscale Geographic Weighted Regression Model: Evidence from China

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