Regional differential decomposition and convergence of rural green development efficiency: evidence from China

Cui, Yu; Liu, Wenxin; Khan, Sufyan Ullah; Cai, Yu; Zhu, Jun; Deng, Yue; Zhao, Minjuan

doi:10.1007/s11356-020-08805-1

Cited by 58 publications

(18 citation statements)

References 38 publications

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“…Some studies emphasized that direct economic activities (such as cement production) are responsible for the emissions [26,27], while others [9,[11][12][13] revealed that urban economic polarization accompanied by rural population migration to urban areas, high energy consumption, urban agglomerations, fossil fuels, and household emissions are the main drivers of the carbon emissions. However, some studies considered the relation between urbanization and CO 2 emissions as being not very significant or inconclusive [28][29][30][31]. Another study [18] suggest that the relationship between urbanization and the environment is not monotonic, and it could have a non-linear effect on the environmental degradation.…”

Section: Introductionmentioning

confidence: 99%

National Carbon Accounting—Analyzing the Impact of Urbanization and Energy-Related Factors upon CO2 Emissions in Central–Eastern European Countries by Using Machine Learning Algorithms and Panel Data Analysis

Nuţă

Zamfir

et al. 2021

Energies

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The work at hand assesses several driving factors of carbon emissions in terms of urbanization and energy-related parameters on a panel of emerging European economies, between 1990 and 2015. The use of machine learning algorithms and panel data analysis offered the possibility to determine the importance of the input variables by applying three algorithms (Random forest, XGBoost, and AdaBoost) and then by modeling the urbanization and the impact of energy intensity on the carbon emissions. The empirical results confirm the relationship between urbanization and energy intensity on CO2 emissions. The findings emphasize that separate components of energy consumption affect carbon emissions and, therefore, a transition toward renewable sources for energy needs is desirable. The models from the current study confirm previous studies’ observations made for other countries and regions. Urbanization, as a process, has an influence on the carbon emissions more than the actual urban regions do, confirming that all the activities carried out as urbanization efforts are more harmful than the resulted urban area. It is proper to say that the urban areas tend to embrace modern, more green technologies but the road to achieve environmentally friendly urban areas is accompanied by less environmentally friendly industries (such as the cement industry) and a high consumption of nonrenewable energy.

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Section: Introductionmentioning

confidence: 99%

National Carbon Accounting—Analyzing the Impact of Urbanization and Energy-Related Factors upon CO2 Emissions in Central–Eastern European Countries by Using Machine Learning Algorithms and Panel Data Analysis

Nuţă

Zamfir

et al. 2021

Energies

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“…Absolute convergence test. Absolute convergence tested whether variations between different economies automatically vanished, including

convergence and absolute

convergence [ 19 , 20 ].…”

Section: Methodsmentioning

confidence: 99%

“…First, there were more studies on the evolution of the time dimension of AGTFP, but the studies from the spatial dimension still needed to be reinforced [ 7 , 18 ]. Second, the interpretation of the convergence mechanism of AGTFP was generally examined from a static perspective, and there was a lack of systematic research on the evaluation of its dynamic angle [ 19 , 20 ]. Based on this, the second section of this paper addresses research design and data sources, the next section examines the spatial-temporal and regional differences of AGTFP and its decomposition.…”

Section: Introductionmentioning

confidence: 99%

Spatial Distribution and Convergence of Agricultural Green Total Factor Productivity in China

Zhu

Shi

et al. 2022

IJERPH

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The power source, spatial-temporal differentiation and convergence of the growth rate of green total factor productivity in China’s agriculture were analyzed. The Malmquist index was used to measure the growth rate, and the spatial-temporal convergence was tested by σ convergence, absolute β convergence, conditional β convergence and dynamic spatial convergence. The study drew conclusions that the impetus for the intensive growth of green agriculture was insufficient, and the driving force for the growth of agricultural green total factor productivity (AGTFP) in the eastern, western and central region was green technology progress. In addition, AGTFP did not have an absolute σ convergence trend. Dynamic spatial absolute β and conditional β convergence indicated that regional differences were not completely related to regional endowment conditions, and regional green agricultural production was unbalanced. This study provides an important support for regional green development in China’s agriculture.

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“…Other researchers have focused on establishing the green total factor productivity to depict the green development efficiency among different regions. For instance, researchers have applied data envelopment analysis, including the nonradial direction distance function method [5], the slacks-based measure (SBM) model with undesirable outputs [7], and the SBM Malmquist-Luenberger model to evaluate the green total factor productivity [7,22,23]. By evaluating those green development indexes, researchers have found an imbalance in green development among different regions.…”

Section: Literature Reviewmentioning

confidence: 99%

Spatial spillover and impacting factors of green development: A study based on China’s provincial data

Huang

Chen²

2023

PLoS ONE

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Green development, an essential part of sustainable development transformation, is spatially correlated intra- and inter-regionally. However, previous research has not fully addressed the spatial characteristics of green development. This study investigates the spatial correlation structures, core–peripheral positions, and factors impacting the spatial network formation of China’s green development. Based on the green development evaluation index system modified by the entropy method, this study applies social network analysis, block model analysis, and quadratic assignment procedure analysis to data from 30 provinces in China. The results confirm the spatial spillover effect is overwhelmingly present in China’s green development. The findings further distinguish the core roles of provinces including Hunan, Tianjin, Zhejiang, Henan, and Xinjiang, and underline factors of green economic growth, governmental policy support, spatial adjacency, and geographic distance as significantly affecting the spatial network formation of China’s green development. Policy recommendations for green development are then put forward.

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Regional differential decomposition and convergence of rural green development efficiency: evidence from China

Cited by 58 publications

References 38 publications

National Carbon Accounting—Analyzing the Impact of Urbanization and Energy-Related Factors upon CO2 Emissions in Central–Eastern European Countries by Using Machine Learning Algorithms and Panel Data Analysis

National Carbon Accounting—Analyzing the Impact of Urbanization and Energy-Related Factors upon CO2 Emissions in Central–Eastern European Countries by Using Machine Learning Algorithms and Panel Data Analysis

Spatial Distribution and Convergence of Agricultural Green Total Factor Productivity in China

Spatial spillover and impacting factors of green development: A study based on China’s provincial data

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