Comprehensive Evaluation of Green Development in Dongliao River Basin from the Integration System of “Multi-Dimensions”

Zhang, Jiquan; Tong, Zhijun; Du, Walian; Zhang, Jiquan; Liu, Xingpeng; Yang, Zhiyi

doi:10.3390/su13094785

Cited by 8 publications

(4 citation statements)

References 50 publications

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“…This paper answered the above questions through basic facts, theoretical analysis, and empirical tests. The marginal contributions are from the following three aspects: (1) We used a comprehensive index system to measure the development level of green finance rather than a single index, since China's green financial system is developing rapidly, involving securities, insurance, credit, and other fields [15]. According to the framework of green finance development in China, green credit, green securities, green investment, and green insurance are taken as the four indicators in this paper to measure the development level of green finance in Chinese 30 provinces.…”

Section: Introductionmentioning

confidence: 99%

Can Green Finance Development Reduce Carbon Emissions? Empirical Evidence from 30 Chinese Provinces

Chen

2021

Sustainability

164

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Dealing with the relationship between environment and economic development is the core issue of China’s sustainable development. At present, China’s economic transformation is urgent, and green finance is being widely concerned. This paper measured the development level of China’s green finance from the perspective of green credit, green securities, green investment, and green insurance. Then, it used a spatial dynamic panel model to empirically test the mechanism of the impact of green finance on carbon emissions with panel data of 30 Chinese provinces from 2005 to 2018. The following can be seen from the results: (1) The development of green finance contributes to carbon emission reduction. (2) The spatial spillover effect of green finance is significant. Specifically, the development of green finance can not only reduce the carbon emissions of the local region but also inhibit that of adjacent areas. (3) The development of green finance indirectly leads to a decrease in carbon emissions by reducing financing constraints and boosting green technology innovation. In order to stimulate the carbon emission reduction effect of green finance to a greater extent, we should further support the development of green finance, reduce the financing constraints of energy-saving and environmental-protection enterprises, and encourage the research and development of green innovative technologies.

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Section: Introductionmentioning

confidence: 99%

Can Green Finance Development Reduce Carbon Emissions? Empirical Evidence from 30 Chinese Provinces

Chen

2021

Sustainability

164

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“…Exploratory spatial analysis techniques can analyze the spatial correlation of GDE. The spatial clustering characteristics of GDE have been demonstrated in many studies [26,27], which indicates that green development levels between neighboring regions tend to be similar, implying a spatial spillover effect of its efficiency. Other studies have indicated that economic, financial, urbanization and resource utilization can drive the green development level of surrounding areas through spillover effects [28][29][30], thus establishing clustered green development.…”

Section: Introductionmentioning

confidence: 84%

Spatial Structure of China’s Green Development Efficiency: A Perspective Based on Social Network Analysis

et al. 2022

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Clarifying the spatial correlation network structure of green development efficiency (GDE) is of great significance for realizing coordinated and sustainable development in China. By constructing the evaluation index system of GDE, this study used the super epsilon-based measure (EBM) model that considers undesirable output to measure the GDE of China from 2000 to 2018, based on which the characteristics of the spatial correlation network characteristics and influencing factors were analyzed using social network analysis (SNA) and a geographical detector. The results indicated that: (1) The GDE of China as a whole remained relatively stable, and there was a significant spatial spillover effect of GDE between provinces; the spatial correlation network demonstrated complex and dense characteristics, and the closeness and stability of the network gradually increased. However, the strict hierarchical structure of the network still existed. (2) The eastern coastal provinces exhibited significant spillover effects and connectivity functions, while the northeastern and central-western provinces are located at the edges of the spatial correlation network. (3) The GDE spatial correlation network is divided into a leader subgroup, bridge subgroup and net benefit subgroup, with no isolated subgroup. (4) The economic development level, urbanization and financial development have a decisive impact on the formation of the GDE spatial correlation network.

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“…Jiang used indicators such as energy consumption per unit of GDP and pollution status in the study of green development level in the Shandong Peninsula urban agglomeration [31]. Wang used indicators such as environmental pollution and ecological space in the comprehensive evaluation of green development in the Dongliao River Basin [32]. Yue used indicators such as personnel efficiency, economy, and environmental impact in the evaluation of green development efficiency in resource-based cities in the Yellow River Basin [33].…”

Section: Construction Of An Evaluation Model For Rural Green Developm...mentioning

confidence: 99%

Evolving Trends and Influencing Factors of the Rural Green Development Level in Chongqing

Zhu

Song

Zhang

et al. 2023

Land

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Rural green development is a concrete practice of rural revitalization. Currently, research on quantitative evaluation methods for rural green development levels are not well developed. In this study, an evaluation model of the rural green development level in Chongqing City, China was developed based on the parameters of ecology, living, and production. An entropy weight method, Theil index, optimal scale regression model, and GIS were used to analyze the spatio-temporal characteristics, trends, and influencing factors of the rural green development level from 2018 to 2020 in Chongqing City. The results showed that: (1) the overall “ecology, living, and production” dimensions and the comprehensive index of the development level in the city were generally increasing, and the proportion of counties at a high-level increased from 23.68% in 2018 to 81.58% in 2020; (2) the Theil index of the city in was 0.0185, 0.0121, and 0.0114 in 2018, 2019, and 2020 respectively, indicating that the differences in development level among regions decreased as the development level increased; (3) the level of rural green development showed a clear upwards trend, and the proportion of counties with low-speed growth, medium-speed growth, and high-speed growth from 2018 to 2020 was 5.26%, 81.58%, and 13.16%, respectively; and (4) the optimal scale regression analysis showed that the factors with greater impacts on the rural green development level are social security and employment expenditure level of government finance, health expenditure level of government finance, with their contributions is 40.3% and 26%, respectively. The results from this study demonstrate the significance of exploring research methods for rural green development and ways to improve the level of rural green development.

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Comprehensive Evaluation of Green Development in Dongliao River Basin from the Integration System of “Multi-Dimensions”

Cited by 8 publications

References 50 publications

Can Green Finance Development Reduce Carbon Emissions? Empirical Evidence from 30 Chinese Provinces

Can Green Finance Development Reduce Carbon Emissions? Empirical Evidence from 30 Chinese Provinces

Spatial Structure of China’s Green Development Efficiency: A Perspective Based on Social Network Analysis

Evolving Trends and Influencing Factors of the Rural Green Development Level in Chongqing

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