Measuring the urban land use efficiency of three urban agglomerations in China under carbon emissions

Lu, Xinhai; Zhang, Yanwei; Li, Jiajia; Duan, Kaifeng

doi:10.1007/s11356-021-18124-8

Cited by 50 publications

(24 citation statements)

References 106 publications

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“…The DEA-Solver Pro 5.0 software is used to make an empirical analysis of the carbon emission efficiency of the Yangtze River Delta and the Guangdong-Hong Kong-Macao Greater Bay Area urban agglomeration during 2010-2021. The result shows that the carbon emission efficiency of the sample is in an unbalanced state, as demonstrated in the previous academic literature (Lu et al, 2022). Thus, considerable work has to be carried out to promote the green and low-carbon transformation of the energy and resource structure, and the Yangtze River Delta and the Guangdong-Hong Kong-Macao Greater Bay Area city clusters still have a lot of room for development to improve the carbon emission efficiency.…”

Section: Analysis Of Carbon Emission Efficiency Resultsmentioning

confidence: 53%

How does education affect urban carbon emission efficiency under the strategy of scientific and technological innovation?

Tang

Dong

Liu

2023

Front. Environ. Sci.

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Low-carbon economy is not only an important topic for the globe but also a serious challenge for China with its economy entering a new level. Based on the DEA-undesirable model and Malmquist index model, urban agglomeration of the Yangtze River Delta and the Guangdong–Hong Kong–Macao Greater Bay Area from 2010 to 2021 were selected as research samples. Based on that, a panel generalized method of moments model was constructed to analyze the effects of the education level, technological development, and their interaction on urban carbon emission efficiency. It found that 1) the carbon emission efficiency of the Yangtze River Delta and the Guangdong–Hong Kong–Macao Greater Bay Area urban agglomerations shows a steady growth trend, but the overall level is low and there are regional differences, among which pure technical efficiency mainly limits the improvement of comprehensive efficiency; 2) the education level and technological development have a high positive correlation on urban carbon emission, and their interaction is conducive to the improvement of carbon emission efficiency. The carbon emission efficiency has a significant advantage under the influence of control variables, such as the economic development level, industrial structure upgrading, opening-up degree, and Internet penetration rate. 3) According to the economic dimension and population dimension, the samples of the Yangtze River Delta and the Guangdong–Hong Kong–Macao Greater Bay Area were divided into large cities and small cities, and regression results showed no substantial changes. It shows that the research conclusion is scientific. According to the aforementioned conclusion, this paper puts forward corresponding countermeasures and suggestions.

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Section: Analysis Of Carbon Emission Efficiency Resultsmentioning

confidence: 53%

How does education affect urban carbon emission efficiency under the strategy of scientific and technological innovation?

Tang

Dong

Liu

2023

Front. Environ. Sci.

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“…The Beijing–Tianjin–Hebei urban agglomeration and the Shandong Peninsula urban agglomeration in the region reached the mature stage, and the levels of urban land use and emission reduction are high. The region is the political, cultural, international communication, and innovative center of China [ 48 ]. Beijing was on the production frontier surface during the research period, with Tianjin, Shandong and Hebei behind.…”

Section: Resultsmentioning

confidence: 99%

The Driving Mechanism of Urban Land Green Use Efficiency in China Based on the EBM Model with Undesirable Outputs and the Spatial Dubin Model

Zeng

2022

IJERPH

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Green development is necessary for building a high-quality modern economic system. The contribution of the article mainly includes the following three parts: First is the study on the urban land green use efficiency (ULGUE) in 30 provinces of China from 2008 to 2018 by adopting the epsilon-based measure (EBM) model with undesirable outputs to yield a more accurate and reasonable assessment result. In addition, the spatial agglomeration characteristics were analysed according to the spatial autocorrelation analysis. Thirdly, the spatial Durbin model was applied to analyse the driving factors of the WRGUE, which considers the spatial effects. The findings are as follows: (1) The regional differences in ULGUE were very significant, with the number decreasing from the coastal region to inland. (2) ULGUE showed a significantly positive spatial autocorrelation, and the spatial homogeneity was more significant than the spatial heterogeneity for ULGUE. (3) Economic development level, technical progress level, and urban population density have a significant impact on ULGUE, while the higher the proportion of the secondary industry in GDP, the lower the level of ULGUE. The research results may be a useful reference point for policymakers.

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“…Obviously, when β * j,it = 0 (j = L, Y, C), it indicates that urban i has reached the optimum in terms of j input (output) in year t. Based on the optimal solution, this study calculated the LCULUE, which is calculated as in Equation (5).…”

Section: Methodology and Datamentioning

confidence: 99%

“…At the same time, it has the advantages of being less dependent on the length of the study and of allowing intuitive observation of the dynamic evolution of random variables and is widely used in the field of describing the unbalanced distribution of economic variables. In view of this, this paper uses the kernel density estimation method to explore the dynamic evolution characteristics of LCULUE, which is constructed as shown in the following paper [5].…”

Section: Kernel Density Estimation Methodsmentioning

confidence: 99%

“…On the one hand, rapid urbanization is accompanied by a rapid expansion of urban land, which usually comes at the expense of large amounts of agricultural and environmental land, posing a threat to food security and the ecological environment [2][3][4]. On the other hand, urban land is the spatial carrier of urbanization, carrying the development of economic, social, political and cultural activities in the city, and its efficiency of use directly affects the social and economic development of the region and the construction of the human living environment [5][6][7]. Therefore, governments and scholars are actively exploring and practicing ways to improve the efficiency of urban land use, especially the Chinese government.…”

Section: Introductionmentioning

confidence: 99%

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Measurement and Influencing Factors of Low Carbon Urban Land Use Efficiency—Based on Non-Radial Directional Distance Function

Chen

Meng

Cao

2022

Land

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The development and use of urban land is a major source of carbon emissions. How to reduce carbon emissions in the process of urban land use without harming the economy has become an extremely important issue. This paper integrating carbon emissions into the urban land use efficiency evaluation index system, measures low-carbon urban land use efficiency using a non-radial directional distance function and analyses its spatial and temporal evolution and its influencing factors using a combination of a kernel density estimation method and a Tobit model. The study found that: (1) China’s low-carbon urban land use efficiency shows a fluctuating development and tends to converge; (2) there is much room for reducing land input and carbon emissions in China, and in 2016 alone, land input and carbon emissions in the sample could be reduced by 10.38% and 5.31%, respectively; (3) at the national level, land finance, economic level and population density have a positive impact on low-carbon urban land use efficiency, while the traffic level has negative effects, and these effects show regional heterogeneity. Accordingly, the paper proposes corresponding policy recommendations.

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Measuring the urban land use efficiency of three urban agglomerations in China under carbon emissions

Cited by 50 publications

References 106 publications

How does education affect urban carbon emission efficiency under the strategy of scientific and technological innovation?

How does education affect urban carbon emission efficiency under the strategy of scientific and technological innovation?

The Driving Mechanism of Urban Land Green Use Efficiency in China Based on the EBM Model with Undesirable Outputs and the Spatial Dubin Model

Measurement and Influencing Factors of Low Carbon Urban Land Use Efficiency—Based on Non-Radial Directional Distance Function

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