Evolution Characters and Influencing Factors of Regional Eco-Efficiency in a Developing Country: Evidence from Mongolia

Xia, Bing; Dong, Suocheng; Yu, Li; Li, Zehong; Sun, Dongqi; Zhang, Wenbiao; Li, Wenlong

doi:10.3390/ijerph182010719

Cited by 10 publications

(8 citation statements)

References 76 publications

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“…In agriculture, eco-efficiency is a critical indicator for measuring agriculture's sustainable development level [9]. Scholars from different nations have differing angles, including investigating the agricultural eco-efficiency of EU countries, other developing nations [10,11], and China from the macro-perspective [12]. Researchers are also working at the industry level, as evidenced by Muller's investigation into the role of eco-efficiency in assessing the sustainability of kiwifruit production in New Zealand [13], Masuda's assertion that understanding the eco-efficiency of wheat production can contribute to sustainable agriculture [14], and Ounsaneha's assessment of eco-efficiency as a critical indicator in the evaluation and analysis of the expansion of rubber plantations in Thailand [15].…”

Section: Literature Reviewmentioning

confidence: 99%

Spatial Disequilibrium and Dynamic Evolution of Eco-Efficiency in China’s Tea Industry

Fan

2023

Sustainability

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Eco-efficiency is a significant target for evaluating the agricultural ecosystem and measuring sustainable agricultural development through quantitative analysis. It is also an essential part of constructing the ecological tea garden, which offers a directional function in realizing the green development of the tea industry. After measuring the eco-efficiency of China’s tea industry using the super-efficiency SBM model, this paper analyzes the spatial disequilibrium and dynamic evolution trend of the eco-efficiency in China’s tea industry through the method of Dagum Gini Coefficient and Kernel Density Estimation. The results show that the level of eco-efficiency in China’s tea industry was improved overall, and the spatial disequilibrium was significantly reduced. The differences within the tea region decreased as follows: tea regions in Southwest China, South China, south of the Yangtze River, and north of the Yangtze River; the overall difference in the eco-efficiency in the tea industry mainly comes from the contribution of the interregional difference in tea regions, and the second contribution comes from the intraregional difference in tea regions and the difference in super-variable density. The eco-efficiency of the tea industry has been improved both nationally and within the top four tea regions; the disequilibrium between areas and within the tea region has been largely alleviated, but there is still room to optimize the input–output structure and promote the eco-efficiency.

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Section: Literature Reviewmentioning

confidence: 99%

Spatial Disequilibrium and Dynamic Evolution of Eco-Efficiency in China’s Tea Industry

Fan

2023

Sustainability

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“…In politics, environmental regulation is the most common representative indicator which quantifies the policy efforts imposed by the government on environmental governance (Chen et al 2021, Hou andSong 2021). In economy, the indicators are usually GDP (Liu et al 2017, Xia et al 2021, industry structure (Xiang et al 2021, Han et al 2021, and external openness (Xiang et al 2021, Sun andWang 2022). In society, the specific indicators mainly include education level (Bonfiglio et al 2017, Diaz-Villavicencio et al 2017, and population density (Xia et al 2021, Diaz-Villavicencio et al 2017 Meanwhile, the representative indicator of technology are the investment in science and technology (Song et al 2022), and the research and development (Luo et al 2021).…”

Section: Influencing Factors Studymentioning

confidence: 99%

“…In economy, the indicators are usually GDP (Liu et al 2017, Xia et al 2021, industry structure (Xiang et al 2021, Han et al 2021, and external openness (Xiang et al 2021, Sun andWang 2022). In society, the specific indicators mainly include education level (Bonfiglio et al 2017, Diaz-Villavicencio et al 2017, and population density (Xia et al 2021, Diaz-Villavicencio et al 2017 Meanwhile, the representative indicator of technology are the investment in science and technology (Song et al 2022), and the research and development (Luo et al 2021). However, most of the current measurements of influencing factors were based on the calculation results of a single-stage DEA model that regards the ecological civilization construction process as a "black box," which inevitably failed to scientifically characterize the real situation within the sample, thus reducing the credibility of the conclusions.…”

Section: Influencing Factors Studymentioning

confidence: 99%

The construction efficiency study of China National Ecological Civilization Pilot Zone with network SBM model: a city-based analysis

Liú

Liu

Zheng

et al. 2023

Environ Sci Pollut Res

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The Chinese government proposed the establishment of China National Ecological Civilization Pilot Zone in 2016 to further explore the coordinated development of economy and environment, and Fujian, Jiangxi and Guizhou provinces were selected as the first batch of pilot zones. After years of exploration, it is necessary to discuss and summarize the construction progress of the three pilot zones from the perspective of the city. In this study, first, the ecological civilization pilot zone construction system was decomposed into an economic construction subsystem (ECS) and an environmental optimization subsystem (EOS). Then, a two-stage network SBM model was adopted to calculate the efficiencies of the subsystems, and the Kruskal-Wallis test was used to measure the efficiency difference. Finally, a panel data regression model was applied to explore the influencing factors of both subsystems. The results show that the ECS efficiency is higher than that of the EOS, and the ECS efficiency in Fujian is significantly better than that in Jiangxi and Guizhou, however, there is no significant difference in EOS efficiency in the three provinces. Furthermore, industrial structure and population agglomeration have a significant effect on ECS efficiency, environmental regulation has a significant impact on EOS, and the technology level has a significant impact on both subsystems. Based on the results, policy implications for improving the efficiency of the two subsystems were given respectively.

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“…Regional population quality had a significant impact on the ECILUs of all economic zones, and the impact coefficients were all negative. The allocation of labor among industries and regions is a direct manifestation of the efficiency of economic operation [61]. The negative correlation shown in the regression results may have been due to the existence of a large surplus of industrial labor, which had a negative impact on the ECILUs [16].…”

mentioning

confidence: 93%

Ecological Efficiency of Urban Industrial Land in Metropolitan Areas: Evidence from China

Pan

Ling

et al. 2022

Land

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Industrial land is an indispensable strategic resource in urban development that plays an indispensable role in ensuring the industrial space of urban construction and development. Measuring and analyzing the eco-efficiency of industrial land utilization (ECILU) can provide insights into how to maximize the input–output ratio of industrial land and ensure the sustainable development of land resources and economies. Based on the undesirable output slacks-based measure (SBM) model, choosing land, capital, and labor as input indicators, and the industrial added value and carbon emissions as desirable and undesirable output indicators, this study measured the ECILUs in 78 cities and 13 metropolitan areas in four Chinese major economic zones from 2007 to 2018, analyzed their spatial and temporal evolution characteristics and regional differences, and constructed a Tobit regression model to test the influence mechanism of each variable on the ECILUs in different regions. This has important theoretical and practical significance for the Chinese government in formulating relevant policies and realizing the green utilization of urban land in the future. Empirical results showed that the ECILUs in most cities were low and that the differences between regions were large. The ECILU in the Western Economic Zone was relatively high, followed by the Eastern, Central, and Northeastern Economic Zones. According to the ECILU value and urban synergy degree of each metropolitan area, this study divided the 13 metropolitan areas into four categories. The regression analysis results showed that the variables had different effects on the ECILUs of all cities and the four economic zones in China. It is suggested that all economic zones should reinforce the optimization of industrial structure, control industrial pollutant discharge, and solve the phenomenon of labor surplus. The Eastern Zone should maintain the growth of its economy while focusing on soil quality. The Central Zone should focus on the efficient use of infrastructure, and the Western, Northeastern, and Central Zones should balance the green coverage area and the industrial land area to ensure the efficient use of urban industrial land.

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Evolution Characters and Influencing Factors of Regional Eco-Efficiency in a Developing Country: Evidence from Mongolia

Cited by 10 publications

References 76 publications

Spatial Disequilibrium and Dynamic Evolution of Eco-Efficiency in China’s Tea Industry

Spatial Disequilibrium and Dynamic Evolution of Eco-Efficiency in China’s Tea Industry

The construction efficiency study of China National Ecological Civilization Pilot Zone with network SBM model: a city-based analysis

Ecological Efficiency of Urban Industrial Land in Metropolitan Areas: Evidence from China

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