Research on the Effect of Urbanization on China’s Carbon Emission Efficiency

Li, Lianshui; Cai, Yang; Liu, Liang

doi:10.3390/su12010163

Cited by 27 publications

(12 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The data envelopment analysis (DEA) model and its derivative models are often used in the study of carbon emission efficiency. Li et al (2019) used the DEA method to evaluate the carbon emission efficiency of each province and empirically investigated the impact of urbanization on carbon emission efficiency based on the stochastic impacts by regression on population, affluence, and technology extension. Atta et al (2022) evaluated the operational efficiency of Chinese listed real estate companies through SBM-DEA model and panel regression technology and studied its driving factors.…”

Section: Multifactor Carbon Emission Total Factor Efficiency Evaluationmentioning

confidence: 99%

The spatial-temporal evolution analysis of carbon emission of China's thermal power industry based on the three-stage SBM—DEA model

Feng²,

Guo³

et al. 2022

IJCCSM

View full text Add to dashboard Cite

Purpose China has proposed two-stage goals of carbon peaking by 2030 and carbon neutralization by 2060. The carbon emission reduction effect of the power industry, especially the thermal power industry, will directly affect the progress of the goal. This paper aims to reveal the spatial-temporal characteristics and influencing factors of carbon emission efficiency of the thermal power industry and proposes policy suggestions for realizing China’s carbon peak and carbon neutralization goals. Design/methodology/approach This paper evaluates and compares the carbon emission efficiency of the thermal power industry in 29 provinces and regions in China from 2014 to 2019 based on the three-stage slacks-based measure (SBM) of efficiency in data envelopment analysis (DEA) model of undesired output, excluding the influence of environmental factors and random errors. Findings Empirical results show that during the sample period, the carbon emission efficiency of China’s thermal power industry shows a fluctuating upward trend, and the carbon emission efficiency varies greatly among the provincial regions. The carbon emission efficiency of the interregional thermal power industry presents a pattern of “eastern > central > western,” which is consistent with the level of regional economic development. Environmental factors such as economic level and environmental regulation level are conducive to the improvement of carbon emission efficiency of the thermal power industry, but the proportion of thermal power generation and industrial structure is the opposite. Originality/value This paper adopts the three-stage SBM–DEA model of undesired output and takes CO2 as the undesired output to reveal the spatial-temporal characteristics and influencing factors of carbon emission efficiency in China’s thermal power industry. The results provide a more comprehensive perspective for regional comparative evaluation and influencing factors of carbon emission efficiency in China’s thermal power industry.

show abstract

Section: Multifactor Carbon Emission Total Factor Efficiency Evaluationmentioning

confidence: 99%

The spatial-temporal evolution analysis of carbon emission of China's thermal power industry based on the three-stage SBM—DEA model

Feng²,

Guo³

et al. 2022

IJCCSM

View full text Add to dashboard Cite

show abstract

“…Additionally, considering regional disparity, urbanization strategies should be adopted carefully by considering the local conditions. The eastern region, with the highest level of urbanization, should strengthen regional coordination and achieve a mutually beneficial regional model based on carbon mitigation cooperation [51]. Moreover, considering the urban-rural difference, it is of the same significance to promote integrated urban and rural development and implement rural revitalization strategy.…”

Section: Controlling Population Growth and Promoting Urbanization Ben...mentioning

confidence: 99%

Carbon Footprint of Residents’ Housing Consumption and Its Driving Forces in China

Geng

et al. 2021

Energies

View full text Add to dashboard Cite

A large population size and rapid economic growth have resulted in a huge amount of housing consumption in China. Therefore, it is critical to identify the determinants of housing carbon footprint (CF) and prepare appropriate carbon mitigation measures. By employing the IPCC accounting method, input-output analysis and the stochastic impacts by regression on population, affluence, and technology (STIRPAT) model, this study aims to study the spatio-temporal patterns and identify the driving factors of housing CF. The results show that regional disparities and urban-rural differences existed during the period 2012–2017. The results of the extended STIRPAT model show that population scale and energy consumption per unit building area are the two dominant contributors to the housing CF increments in all areas. While, family size only shows significant negative impact in eastern and western regions, the per capita disposable income only induces higher housing CF in rural areas, and energy structure had a remarkable positive impact in urban area of western region and all rural areas. Policy recommendations are proposed to mitigate the overall housing CF, including; controlling population growth and promoting urbanization benefits; encouraging green consumption; optimizing household energy consumption structure, and; enhancing residential building energy management.

show abstract

“…At present, the long-term equilibrium relationship between urbanization and carbon emission efficiency has been confirmed by many studies. Li et al believe that urbanization can promote carbon emission efficiency, but the intensity of the impact is different in different regions, and the promotion effect is the strongest in western China [ 16 ]. According to Sun et al, there is an inverted U-shaped relationship between urbanization and carbon emission efficiency.…”

Section: Introductionmentioning

confidence: 99%

Research on the coordinated development of provincial urbanization and carbon emission efficiency of construction industry in China

Niu,

Xin,

Xin

et al. 2024

Carbon Balance Manage

View full text Add to dashboard Cite

Background Energy conservation and emission reduction policies restrict the economic and social development of all countries in the world, and the impact on China, which has low urbanization, is more serious. In the process of promoting urbanization, the pressure of carbon emission reduction in the construction industry has increased, and the high emissions of the construction industry have made the low-carbon development of cities face severe challenges. China is at a critical stage of urbanization development, and there is become a general consensus on how to improve the carbon emission efficiency of the construction industry. The interaction between urbanization and the carbon emission efficiency of the construction industry is a long-term and complex process. As one of the industries contributing to China’s urbanization process and carbon emissions, it is of great practical significance to explore the coordination relationship between urbanization and the carbon emission efficiency of the construction industry (CEECI) to realize the goal of “double carbon”, promoting urbanization construction and solving the problem of “green development”. Taking 30 provinces in China as the research target area, the double weighted summation method and the undesirable output superefficiency window-EBM-DEA model are used to measure the provincial urbanization level and CEECI, respectively. Then, the coupling coordination degree model of the relative development index is introduced, and the spatial autocorrelation model and the spatial and temporal differentiation characteristics of the coordination level of urbanization and the CEECI are analysed. Results From 2010 to 2021, China’s urbanization level increased steadily, but the growth rate gradually decreased. There were significant differences in urbanization levels among provinces. The eastern provinces have a higher level of urbanization but lack an impetus in the later period, while the western provinces have a lower level of urbanization but a faster growth rate. The low-carbon development trend of China’s construction industry is good, and the overall development of the CEECI shows an “N” type, and the inflection points appear in 2013 and 2018. The interprovincial urbanization level is significantly different from that of the CEECI, and the development form of the central and western provinces is better than that of the eastern provinces. The coordination level of urbanization and the CEECI in China is transitioning from the running-in stage to the coordination stage, and the coupling coordination degree between systems is on the rise, while the relative development degree is on the decline. The spatial distribution pattern is in a dynamic state of change, and the overall distribution pattern is “high in the east and low in the central and western regions”. The differences among provinces were significantly decreased, with 63.33% of provinces at the high running-in level. The provinces that entered the coordination stage were mainly located in the eastern region, and only Beijing was in the coordination stage by the end of the study. In addition, 90% of the provinces exhibited lagging efficiency, and only Jiangxi, Guangxi and Chongqing, which had low coordination levels maintained synchronous development of the two systems. The coordination level between provincial urbanization and the CEECI showed a positive spatial distribution, the global Moran index showed a “V” shape trend, and the spatial dependence of the coordination level between the two systems gradually weakened. In the local spatial distribution, there are two types of convergence: high and low. Conclusions The coordination degree of urbanization and the CEECI analysed in this study is an extension of the research on the relationship between the two. By integrating the two into a unified framework, the method of combining quantitative and qualitative analysis is used to further explore the coordination relationship between the two, which not only enhances the scientificity and accuracy of the research but also extends the breadth and depth of relevant theoretical research. At the same time, according to the coordination type between urbanization and the CEECI, China should propose corresponding targeted coordination and optimization paths from the perspective of urbanization and low-carbon development of the construction industry to achieve high-quality development of China’s economy and society.

show abstract

Research on the Effect of Urbanization on China’s Carbon Emission Efficiency

Cited by 27 publications

References 26 publications

The spatial-temporal evolution analysis of carbon emission of China's thermal power industry based on the three-stage SBM—DEA model

The spatial-temporal evolution analysis of carbon emission of China's thermal power industry based on the three-stage SBM—DEA model

Carbon Footprint of Residents’ Housing Consumption and Its Driving Forces in China

Research on the coordinated development of provincial urbanization and carbon emission efficiency of construction industry in China

Contact Info

Product

Resources

About