Carbon emission efficiency measurement and influencing factor analysis of nine provinces in the Yellow River basin: based on SBM-DDF model and Tobit-CCD model

Zhang, Yuan; Xu, Xiangyang

doi:10.1007/s11356-022-18566-8

Cited by 52 publications

(30 citation statements)

References 46 publications

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“…The SBM-DDF can effectively solve the efficiency evaluation of non-desired outputs and the slackness of inputs or outputs (Fukuyama and Weber 2009 ). Therefore, in recent years, many scholars have combined SBM-DDF and ML index measures to analyze carbon productivity changes (Wang et al 2019 ; Li et al 2020a , b ; Yang et al 2021 ; Zhang and Xu, 2022 ). Chambers et al ( 1996 ) extended the Luenberger productivity index.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In terms of research methods, most of the existing studies have used the slacks-based measure of directional distance functions (SBM-DDF) method or combined with the Malmquist-Luenberger index to measure carbon productivity, environmental efficiency, etc. (Li et al 2018 ; Wang et al 2019 ; Zhang and Xu, 2022 ). This paper combines the SBM-DDF and the Luenberger index to measure the spatial and temporal developmental changes of TCP in both static and dynamic dimensions, and chooses the SYS-GMM regression model to explore the influencing factors of TCP.…”

Section: Introductionmentioning

confidence: 99%

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The evolutionary characteristics and influencing factors of total carbon productivity: evidence from China

Cui

Wang

et al. 2022

Environ Sci Pollut Res

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In order to systematically understand the evolution of total factor carbon productivity and explore its influence mechanism, based on panel data of 30 Chinese provinces from 2005 to 2019, the slacks-based measure of directional distance functions model and the Luenberger index are used to estimate the evolution of total factor carbon productivity, and the SYS-GMM model is constructed to explore the drivers of total factor carbon productivity and its influence effect. The results show that from 2005 to 2019, the overall level of total factor carbon productivity was low, but its growth index and decomposition term both showed an increasing trend; the development of total factor carbon productivity has regional differences. Only the eastern, northern, and middle Yellow River economic regions experience positive growth in total factor carbon production. The downward trend of total factor carbon productivity is most significant in the northwest and southwest economic regions, with − 2.577% and − 1.463%, respectively; improvements in scale technology are the main reasons for improving total factor carbon productivity across time and regions; economic growth and environmental regulations contribute to total factor carbon productivity at 1% significance level, and industrial structure has a negative impact. Foreign direct investment inhibits total factor carbon productivity, but the effect is not significant. Based on these findings, this paper provides an effective reference for achieving the goal of low-carbon sustainable development and improving total factor carbon productivity.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The evolutionary characteristics and influencing factors of total carbon productivity: evidence from China

Cui

Wang

et al. 2022

Environ Sci Pollut Res

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“…However, less research has been done on CEE at the special scale spatial unit of the river basin. The importance of the study of the YRB is further emphasized by its dominance by traditional resource-intensive industries [ 36 ]. Second, the Theil index has been widely used to study CEE differences [ 35 ], however, the differences between and within groups are less frequently compared.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Evolution and Influencing Factors of Carbon Emission Efficiency in the Yellow River Basin of China: Comparative Analysis of Resource and Non-Resource-Based Cities

Cheng

Zheng

et al. 2022

IJERPH

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Comparing the carbon emission efficiency (CEE) of resource and non-resource-based cities in the Yellow River Basin (YRB) can guide their synergistic development and low-carbon transition. This study used the super-efficiency slacks-based measure (super-SBM) model to measure the CEE of cities in the YRB. Kernel density estimation and Theil index decomposition methods were used to explore the spatiotemporal evolutionary patterns, and a panel regression model was established to analyze the influencing factors of CEE. The research results showed that the CEE of the two types of cities have an overall upward trend in time, with a widening regional gap. Resource-based cities mainly displayed the characteristics of decentralized regional agglomeration, while non-resource-based cities mainly showed the characteristics of convergent regional agglomeration. Panel regression results showed that the levels of economic development, indus-trial structure, and population density are significantly positively correlated with CEE in the YRB, while foreign direct investment and resource endowment are significantly negatively correlated with CEE. Except for economic development and industrial structure, there is some variability in the contribution of the remaining influencing factors to the CEE of the resource and non-resource-based cities. The research results suggest developing classification measures for low-carbon transition in the YRB.

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“…This type of research incorporates carbon dioxide emissions as undesirable output into the evaluation index system. The research areas include carbon emission efficiency for the cities [25], provinces [26], and the whole basin [27] in the YRB, green total factor productivity in the YRB [28], urban energy use efficiency in the YRB [29], water use efficiency in the YRB [30], agricultural ecological efficiency in the YRB [31], and carbon emission performance in logistics in the YRB [32], etc. The data envelopment analysis (DEA) method is mainly utilized in the above research for efficiency calculation.…”

Section: Introductionmentioning

confidence: 99%

Regional Differences and Convergence of Carbon Emissions Intensity in Cities along the Yellow River Basin in China

Chen

Meng

Shi

et al. 2022

Land

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Since the ecological protection and high-quality development of the Yellow River Basin (YRB) in China have become a primary national strategy, the low-carbon economy is crucial. To formulate effective emission mitigation policies for the YRB, we need to comprehensively understand the characteristics of the spatial agglomeration of the carbon emissions intensity in the YRB and its regional heterogeneity. Therefore, based on the relevant data from 2005 to 2017, we first scientifically measure the carbon emissions intensity of 57 cities along the YRB. Then, we analyze the spatial agglomeration characteristics and long-term transfer trends of carbon emission intensity using exploratory spatial data analysis methods and Markov chains. Finally, the Dagum Gini coefficient and the variation coefficient method are used to study the regional differences and differential evolution convergence of the carbon emissions intensity in the YRB. The results show that the carbon emissions intensity of the YRB has dropped significantly with the spatial distribution characteristics “high in the west and low in the east”, and there is a significant spatial autocorrelation phenomenon. In addition, the probability of a shift in urban carbon intensity is low, leading to a “club convergence” and a “Matthew effect” in general and across regions. Inter-regional differences have always been the primary source of spatial differences in carbon emissions intensity in the YRB, and the intra-regional differences in carbon emissions intensity in the lower YRB show a significant convergence phenomenon. The research results may provide a reference for the regional coordinated development of a low-carbon economy in the YRB, and serve to guide the win-win development model of ecological environment protection and economic growth in the YRB.

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Carbon emission efficiency measurement and influencing factor analysis of nine provinces in the Yellow River basin: based on SBM-DDF model and Tobit-CCD model

Cited by 52 publications

References 46 publications

The evolutionary characteristics and influencing factors of total carbon productivity: evidence from China

The evolutionary characteristics and influencing factors of total carbon productivity: evidence from China

Spatiotemporal Evolution and Influencing Factors of Carbon Emission Efficiency in the Yellow River Basin of China: Comparative Analysis of Resource and Non-Resource-Based Cities

Regional Differences and Convergence of Carbon Emissions Intensity in Cities along the Yellow River Basin in China

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