Spatial and Temporal Evolution and Driving Factors of Urban Ecological Well-Being Performance in China

Bian, Jing; Lan, Feng; Zhou, Yanyan; Peng, Zhenzhen; Dong, Mingfang

doi:10.3390/ijerph19169996

Cited by 9 publications

(17 citation statements)

References 57 publications

(52 reference statements)

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“…It is concluded that the spatial distribution of the YREB’s EWP has obvious spillover effects, and its distribution shows “H-H” and “L-L” agglomeration features. This finding confirms the spatial distribution characteristics of EWP observed at the national scale by Bian et al [ 33 ], indicating a cross-scale similarity in spatial clustering of the EWP.…”

Section: Discussionsupporting

confidence: 91%

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The Spatial Differentiation and Driving Forces of Ecological Welfare Performance in the Yangtze River Economic Belt

Bai

Guo

et al. 2022

IJERPH

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Ecological welfare performance contributes directly to human well-being and regional sustainable development. Improving the regional ecological welfare performance in the process of pursuing green and sustainable development demands theoretical innovation and empirical exploration. Based on the super-efficiency SBM model, this study evaluated the ecological welfare performance of 108 cities during the period of 2009 to 2019. The Dagum Gini coefficient decomposition and spatial convergence model were employed to analyze the differences in ecological welfare performance across and within the study area and explore the underlining causes of such spatial differentiation in the Yangtze River Economic Belt and the upper, middle and lower reaches. It can be seen from the results that: (1) the overall difference in the ecological welfare performance of the Yangtze River Economic Belt is associated with a fluctuating downward trend during the study period. Regional and inter-regional differences were revealed and hypervariable density was identified as the main source of the differences. (2) The ecological welfare performance of the Yangtze River Economic Belt has absolute and conditional β convergence, and the ecological welfare performance of each city-region and surrounding urban areas has a positive impact on each other. (3) The difference in the spatial-temporal differentiation trend is manifested by the difference in the convergence rate. The cities in the middle reaches of the Yangtze River have the fastest convergence rate, followed by the cities in the upper reaches, and the cities in the lower reaches are the slowest. This geographic difference is mainly driven by the combined effects of industrial structure, urban characteristics, environmental regulation, foreign direct investment, and transportation accessibility. Finally, it is proposed that future policies should focus on the imbalanced regional development in the study area, and each region needs to explore ways to improve local ecological welfare performance according to local conditions, and ultimately promote the overall green, coordinated and high-quality development in the Yangtze River Economic Belt.

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Section: Discussionsupporting

confidence: 91%

“…Regarding the research on the influencing factors of EWP, scholars have mainly investigated the effects of economic growth, industrial structure, urbanization, technological progress, and foreign direct investment [ 33 ]. However, the knowledge about the role of causal factors in influencing EWP patterns remains to be limited.…”

Section: Literature Reviewmentioning

confidence: 99%

The Spatial Differentiation and Driving Forces of Ecological Welfare Performance in the Yangtze River Economic Belt

Bai

Guo

et al. 2022

IJERPH

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“…Therefore, this paper uses environmental-pollution indicators as undesired outputs, and industrial wastewater, industrial waste gas, carbon emissions per capita, and industrial solid waste are chosen to characterize them. The accuracy of these indicators is confirmed by the studies of Xiao et al and Bian et al [27,43].…”

Section: Evaluation Index Systemsupporting

confidence: 52%

“…Therefore, modeling methods based on the multi-input-multi-output perspective were born, including the stochastic frontier approach (SFA) and data envelopment analysis (DEA) methods. For example, Xiao et [42,43]. However, the modeling approach has some drawbacks, such as multiple input and output indicators that may lead to problems in data acquisition and processing and limit its use to compare multiple countries and regions [36].…”

Section: Ecological Well-being Performancementioning

confidence: 99%

“…Hu et al found that EIWB is mainly driven by economic and technological effects, with economic effects being more dominant [46]. In addition, other studies have explored the extent of the role of industrial structure, urbanization, technological progress, and foreign investment on EWP through LMDI, regression models, and spatial econometric models [41,43,[47][48][49], but with different conclusions.…”

Section: Ecological Well-being Performancementioning

confidence: 99%

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Spatio-Temporal Evolution and Influencing Factors of Ecological Well-Being Performance from the Perspective of Strong Sustainability: A Case Study of the Three Gorges Reservoir Area, China

Lai

Zhuomin

et al. 2023

IJERPH

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The concept of strong sustainability suggests that natural capital is irreplaceable and emphasizes that human natural consumption cannot exceed the carrying capacity of the ecological environment. In the realistic context of tightening resource constraints and ecological degradation, how to explore the optimal economic and well-being output under certain ecological constraints has become an important topic in sustainability research. Ecological well-being performance (EWP) can comprehensively reflect the efficiency of natural resources and ecological inputs into the well-being level and has become an important tool for sustainable development research. Based on strong sustainability, this paper constructs an EWP evaluation index system. It decomposes EWP into two stages: ecological economy and economic well-being, which opens the “black box” of the ecological well-being conversion process. PCA-DEA, the network super-efficiency Epsilon-based measure (Network SEBM) model, and Geodetector are used to dynamically measure the EWP in the Three Gorges Reservoir Area (TGRA) from 2010 to 2020 and analyze its spatial and temporal characteristics and influencing factors. The results show that (1) temporally, the EWP in the TGRA shows an increasing trend from 2010 to 2020, but the overall level is low. Spatially, it shows a high pattern in the east and low in the west, and spatial differences are gradually decreasing; (2) ecological economic efficiency is significantly lower than economic well-being efficiency, and ecological economic efficiency is the main reason limiting the improvement in EWP in the TGRA. The ecological well-being situation of the TGRA is not optimistic; (3) there is an overall problem of excessive ecological input and insufficient per-capita GDP and well-being output in the TGRA, and decisions should be made according to local conditions; (4) the level of economic development has an EWP level that plays a dominant role and also has a greater relationship with the policy system, socioeconomic conditions, and natural environment.

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Using eco‐efficiency and eco‐wellbeing performance as indicators for urban sustainable development: A two‐stage network analysis

Zhang,

Cai,

Jiao

et al. 2023

Sustainable Development

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Sustainable development is one of the most significant issues of concern around the world today. Sustainable development goals cover natural resources, the environment, and human well‐being. It follows that eco‐efficiency (EE) and eco‐well‐being performance (EWP) are valid tools for measuring the level of different stages of sustainable development. This study uses a two‐stage super‐SBM model to gauge the EE and EWP of 281 cities in China from 2011 to 2019. The results show the following: (1) The national EE average is low, with M‐shaped “rising‐declining‐rising‐declining” fluctuation. The national EWP average is low, but there is a clear upward trend. (2) From a regional perspective, the best EE is in the eastern region, followed by the central and western regions, and the worst is in the northeastern region. The best EWP is in the western region, followed by the eastern and northeastern regions, and the worst is in the central region. (3) Among the 281 sample cities, approximately 12.8% are in the high EE‐high EWP development mode, 25.6% are in the high EE‐low EWP development mode, 18.2% are in the low EE‐high EWP development mode, and 43.4% are in the low EE‐low EWP development mode. Collectively, the three cities with the strongest sustainability are Shenzhen, Beijing, and Haikou, and the three weakest are Baise, Jinchang, and Baiyin. In addition, cities with larger populations tend to exhibit greater sustainability. These results provide the necessary reference for Chinese cities and cities worldwide in similar situations to formulate policies to improve sustainability.

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Spatial and Temporal Evolution and Driving Factors of Urban Ecological Well-Being Performance in China

Cited by 9 publications

References 57 publications

The Spatial Differentiation and Driving Forces of Ecological Welfare Performance in the Yangtze River Economic Belt

The Spatial Differentiation and Driving Forces of Ecological Welfare Performance in the Yangtze River Economic Belt

Spatio-Temporal Evolution and Influencing Factors of Ecological Well-Being Performance from the Perspective of Strong Sustainability: A Case Study of the Three Gorges Reservoir Area, China

Using eco‐efficiency and eco‐wellbeing performance as indicators for urban sustainable development: A two‐stage network analysis

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