Spatial-temporal Heterogeneity of Green Development Efficiency and Its Influencing Factors in Growing Metropolitan Area: A Case Study for the Xuzhou Metropolitan Area

Fang-dao, Qiu; Chen, Yang; Tan, Juntao; Liu, Jibin; Zheng, Ziyan; Zhang, Xinlin

doi:10.1007/s11769-020-1114-3

Cited by 35 publications

(15 citation statements)

References 20 publications

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“…Secondly, as for the research on the influencing factors of IGDE, some studies focused on the role of space [ 46 ], but few studies adopted the MGWR model to explore the degree and direction of influence of influencing factors on different units. Through research, this paper found that the scale of impact of different variables on the IGDE is quite different.…”

Section: Discussionmentioning

confidence: 99%

Analysis of China’s Industrial Green Development Efficiency and Driving Factors: Research Based on MGWR

Qiao

Zhou

2021

IJERPH

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With increasingly severe constraints on resources and the environment, it is the mainstream trend of economic development to reduce industrial pollution emissions and promote green industrial development. In this paper, a super-efficiency slacks-based measure (SBM) model is adopted to measure the industrial green development efficiency (IGDE) of 289 cities in China from 2008 to 2018. Moreover, we analyze their spatiotemporal differentiation pattern. On this basis, the multiscale geographical weighted regression (MGWR) model is used to analyze the scale differences and spatial differences of the driving factors. The results show that the IGDE is still at a low level in China. From 2008 to 2018, the overall polarization of IGDE was relatively serious. The number of high- and low-efficiency cities increased, while that of medium-efficiency cities greatly decreased. Secondly, the IGDE presented an obvious spatial positive correlation. MGWR regression results show that the technological innovation, government regulation, and consumption level belonged to the global scale, and there was almost no spatial heterogeneity. Other driving factors were urbanization, industrial structure, economic development, and population density according to their spatial scale. Lastly, the influence of economic development and technological innovation had a certain circular structure in space; the influence of population size mainly occurred in the cities of the southeast coast and northeast provinces; the influence of urbanization was more obvious in the most northern provinces of the Yangtze River, while that of industrial structure was mainly concentrated in the most southern cities of the Yangtze River Economic Belt (YREB). Spatially, the influence of consumption was manifested as a distribution trend of decreasing from north to south, and the government regulation was manifested as increasing from west to east and then to northeast.

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Section: Discussionmentioning

confidence: 99%

Analysis of China’s Industrial Green Development Efficiency and Driving Factors: Research Based on MGWR

Qiao

Zhou

2021

IJERPH

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“…In order to alleviate the contradiction between man and earth, we must understand the relationship between human activities and GUEUCL. According to the existing literature and research, this paper studies the driving factors of spatial differentiation of green use efficiency of urban construction land in Jilin Province from three aspects: social economic factors, natural science and technology factors, and urban development factors [24][25][26][27][28][29][30][31][32][33].…”

Section: Driving Factors Of Green Utilization Efficiency Of Urbanmentioning

confidence: 99%

“…Land is a complex system of socioeconomic-natural environment, and the efficiency of traditional urban construction land use is limited from the socioeconomic perspective, ignoring the green development concept of land use efficiency following the principle of coordination of economic, social, and ecological benefits. erefore, in the current background of urban sprawl, resource waste and environmental pollution and ecological civilization construction require "green development," and the GUEUCL has attracted wide attention of the state and society [24][25][26][27][28][29][30][31][32][33].…”

Section: Introductionmentioning

confidence: 99%

Spatial Pattern Characteristics and Influencing Factors of Green Use Efficiency of Urban Construction Land in Jilin Province

et al. 2020

Complexity

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How to explore the allocation and green utilization level of urban construction land resources has an important role in the sustainable development of the city. Taking 47 counties and cities in Jilin Province as an example, this paper evaluates the green utilization efficiency of urban construction land (GUEUCL) in 2011 and 2015 by using the unexpected output super-SBM model and explores the spatial-temporal differentiation characteristics and influencing factors of GUEUCL by using GIS and machine learning methods. The results show that (1) the GUEUCL in Jilin Province is low, mainly distributed in small- and medium-sized areas, with significant positive spatial correlation. The L-L concentration area is mainly distributed in the eastern region, but the degree of spatial concentration is small, the spatial structure characteristics of the two periods are different, and the spatial heterogeneity is large; (2) the internal factor decomposition shows the impact of pure technical efficiency on the comprehensive efficiency and the restriction ability is stronger than the scale efficiency, that is to say, the factors such as management and technology have a greater impact on the comprehensive efficiency; (3) the relative importance of external factors has always been ranked as socioeconomic factors, urban development factors, and natural science and technology factors. This paper focuses on the temporal and spatial characteristics of each county and city and the influencing factors, which provides a certain value reference for the pilot of ecological construction and the development of ecoenvironmental benefit economic system in Jilin Province.

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“…Because the DEA method does not need dimensionless processing of data, nor any weight assumption and strong objectivity, it has been more recognized and applied in the academic circle. For example, Qiu et al used the Slack Based Model (SBM) model, spatial autocorrelation, and geographically weighted regression model to analyze the spatio-temporal heterogeneity and influencing factors of green development efficiency in the Xuzhou metropolitan area of China ( 14 ). Zhang et al, Shao et al, and He et al researched green transformation from the perspective of total factor productivity by constructing the DEA model ( 15 – 17 ).…”

Section: Introductionmentioning

confidence: 99%

Evaluation and Influencing Factor Analysis of Sustainable Green Transformation Efficiency of Resource-Based Cities in Western China in the Post-COVID-19 Era

Zhu

Wang

2022

Front. Public Health

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Under the impact of the coronavirus disease 2019 (COVID-19), green, low-carbon, and sustainable development has become a global consensus, and the world will enter a low-carbon and intelligent production mode faster. As the largest contributor to world economic growth and an active participant in global environmental governance, achieving green recovery and the high-quality economic and social development of China is of great significance to promote the global sustainable development strategy. The green transformation of resource-based cities in Western China is the key factor for China to build a high-quality modern economic system and promote long-term sustainable development. This article used the Super Efficiency Slack Based Model (Super-SBM) model and Malmquist index model of the Data Envelope Analysis (DEA) method to measure the static and dynamic green transformation efficiency of resource-based cities in Western China. It investigated the impact of different factors on the static and dynamic efficiency by constructing panel Tobit and dynamic panel models. The research found that the static efficiency of the green transformation of resource-based cities in Western China is low, and the development is uneven. The dynamic efficiency of green transformation showed a fluctuating upward trend first and an accelerating upward trend later. Different factors have different effects on green transformation efficiency. This article holds that the combination of post-epidemic economic recovery and green transformation is expected to promote the green transformation of western resource-based cities while injecting new vitality into China's green sustainable development in the post-COVID-19 era.

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Spatial-temporal Heterogeneity of Green Development Efficiency and Its Influencing Factors in Growing Metropolitan Area: A Case Study for the Xuzhou Metropolitan Area

Cited by 35 publications

References 20 publications

Analysis of China’s Industrial Green Development Efficiency and Driving Factors: Research Based on MGWR

Analysis of China’s Industrial Green Development Efficiency and Driving Factors: Research Based on MGWR

Spatial Pattern Characteristics and Influencing Factors of Green Use Efficiency of Urban Construction Land in Jilin Province

Evaluation and Influencing Factor Analysis of Sustainable Green Transformation Efficiency of Resource-Based Cities in Western China in the Post-COVID-19 Era

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