Measuring the Inclusive growth of rural areas in China

Jiang, Aichun; Chen, Chuan; Ao, Yibin; Zhou, Wenmei

doi:10.1080/00036846.2021.1923640

Cited by 24 publications

(11 citation statements)

References 8 publications

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“…Thirdly, we employed kernel density distribution and the Dagum Gini coefficient to analyze and trace the regional differences in inclusive green growth in the Yangtze River basin. The most conventional models are the entropy method [16], the entropy TOPSIS analysis method [17] and slack-based measure [18,23], which ignore interactions between cities and fails to identify the contributions of differences within cities. To compensate for such shortcomings, we adopted the kernel density distribution to derive the dynamic distribution of the absolute difference, which was assessed and decomposed with the Dagum Gini coefficient.…”

Section: Discussionmentioning

confidence: 99%

“…Inclusive green growth is evaluated from different perspectives, such as economic, environment and social equality [15], to establish an index system, and the entropy method [16], entropy TOPSIS method [17] and factor analysis method have been used to get the weight. Some scholars discussed this issue with the input and output method [18].…”

Section: Related Literaturementioning

confidence: 99%

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Inclusive Green Growth and Regional Disparities: Evidence from China

Liu

Zhang

et al. 2021

Sustainability

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It is determined that inclusive green growth comprises processes of economic development and inclusiveness as a system of inclusions, taking into account the anthropogenic burden on the ecosystem, as well as the relational nature of socio-economic transformations. This article is an evaluation of this issue in the context of a contemporary Chinese society beset by regional inequalities that uses the Yangtze River basin as a case study. An index system has been constructed for inclusive green growth measurement, and kernel density and the Dagum Gini coefficient are used to analyze and describe characteristics regarding the distribution and spatial disparities within and between city clusters. The article then concludes that all city clusters are developing towards an inclusive green economy. There are still significant inequalities in inclusive growth among city clusters. Most city clusters are converging so slow that it will take a long time for weaker cites to catch up with stronger cites. City clusters also suffer major inner imbalances and gaps are widening. This paper argues that the profession needs to be more proactive in promoting strategic and targeted policies within such an unequal growth context.

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Section: Discussionmentioning

confidence: 99%

Section: Related Literaturementioning

confidence: 99%

Inclusive Green Growth and Regional Disparities: Evidence from China

Liu

Zhang

et al. 2021

Sustainability

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show abstract

“…According to the concept of IG in previous studies [ 1 , 3 , 5 ], we design a framework of China's urban IG evaluation system composed of 3 levels and 15 specific indicators. The target level reflects that the process of pursuing IG should achieve the balance of economic growth, opportunities, and sharing.…”

Section: Methodsmentioning

confidence: 99%

“…According to the report of the World Institute for development economics of the United Nations University, the average income of the richest 1% has increased by 10.1 times in absolute terms compared with that of others since 1980 [4]. To address this situation, the Asian Development Bank (ADB) established a new principle-inclusive growth (IG), which posited that apart from guaranteeing a wide participation and role to the growth procedure, issues of gender, ethnicity, and race and even environment should be part of IG [5][6][7]. e World Bank on the other hand stated that the issues of fairness of opportunity, social protection, and productive employment as contrasting to short-term income redistribution should be enshrined in IG [7].…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Performance of Inclusive Growth Based on the BP Neural Network and Machine Learning Approach

Fan

Liu

2022

Computational Intelligence and Neuroscience

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In this paper, we use the panel data of 281 cities in China from 2005 to 2020 for capturing the factors driving urban inclusive growth (IG). In doing this, we employ the BP neural network algorithm combined with the DEA model to measure the urban inclusive growth efficiency (IGE). Furthermore, a nest of machine learning (ML) algorithms are introduced to explore the drivers of urban IGE, which overcomes the defects of endogeneity and multicollinearity of traditional econometric methods. We find for the overall sample that entrepreneurship and innovation contribute the most to IGE, accounting for about 35%, respectively, and they are the most critical drivers, while the heterogeneity test results reveal that the contribution of influencing factors has changed for different regions such as the eastern region, the central region, and the western region. Based on the experimental results of the ML model, we provide some policy suggestions for China and similar developing countries and emerging economies to promote IG.

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“…Different measurement approaches have been used to study the influencing factors of poverty reduction efficiency. Models evaluating poverty reduction efficiency parameters include the stochastic frontier approach [15], regression analysis [16], the TOPSIS method [17], and intuitionistic fuzzy analysis [18]. The influencing factors of poverty reduction efficiency were analyzed based on natural resource endowment [19], socio-economic development [20], and the implementation of poverty reduction policies.…”

Section: Literature Reviewmentioning

confidence: 99%

How Do Ecosystem Services Affect Poverty Reduction Efficiency? A Panel Data Analysis of State Poverty Counties in China

Cao

Ouyang

Jun

2022

IJERPH

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Scientific evaluation of the interaction between poverty reduction efficiency (PRE) and ecosystem services (ES) in state poverty counties is essential in promoting the rural revitalization strategy and the construction of an ecological civilization. Using the DEA model, the InVEST model, and fixed-effect panel data, this study was analyzed using the panel data of 832 poverty counties in China for 2010–2019 to evaluate the relationship between poverty reduction efficiency and ecosystem services. The main results are as follows: (1) The overall poverty reduction efficiency showed an upward trend, while ES exhibited a declining trend with spatial heterogeneity. The poverty reduction efficiency of state poverty counties in the western region increased rapidly. (2) The impact of different types of ecosystem services on poverty reduction efficiency varied considerably. Habitat quality was significantly negatively impacted, while food production and carbon storage showed significant positive effects. There was a significant positive relationship between ecosystem services and poverty reduction efficiency in all regions, with the eastern region having the strongest correlation. (3) The panel regression analysis showed a significant positive impact. The environmental parameters were the primary factors affecting poverty reduction efficiency, while economic and social factors were the driving and external factors. The rural revitalization strategy should strive towards the win-win effect of ecological protection and economic development.

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Measuring the Inclusive growth of rural areas in China

Cited by 24 publications

References 8 publications

Inclusive Green Growth and Regional Disparities: Evidence from China

Inclusive Green Growth and Regional Disparities: Evidence from China

Evaluating the Performance of Inclusive Growth Based on the BP Neural Network and Machine Learning Approach

How Do Ecosystem Services Affect Poverty Reduction Efficiency? A Panel Data Analysis of State Poverty Counties in China

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