Estimation of eco-efficiency and its influencing factors in Guangdong province based on Super-SBM and panel regression models

Zhou

et al. 2018

Water

Abstract:Water resources are important and irreplaceable natural and economic resources. Achieving a balance between economic prosperity and protection of water resource environments is a major issue in China. This article develops a data envelopment analysis (DEA) approach with undesirable outputs by using Seiford's linear converting method to estimate water use efficiencies for 30 provinces in China, from 2008-2016,and then analyzes the influencing factors while using a DEA-Tobit model. The findings show that the overall water use efficiency of the measured Chinese provinces, when considering sewage emissions as the undesirable output, is 0.582. Thus, most regions still need improvement. Provinces with the highest water efficiency are located in economically developed Eastern China. The spatial pattern of water use efficiency in China is consistent with the general pattern of regional economic development. This study implies that factors like export dependence, technical progress, and educational value have a positive influence on water use efficiency. Further, while industrial structure has had a negative impact, government intervention has had little impact on water use efficiency. These research results will provide a scientific basis for the government to make plans for water resource development, and it may be helpful in improving regional sustainable development.

Section: Methodsmentioning

confidence: 99%

Water Use Efficiency and Its Influencing Factors in China: Based on the Data Envelopment Analysis (DEA)—Tobit Model

Zhou

et al. 2018

Water

“…For the second issue, many studies have focused on industrial structure, economic development, technical progress, and expansion policies to determine the key factors that influence industrial water consumption [6,9,[15][16][17][18][19][20][21]. However, most of these factors are affected by the rapid urbanization; thus, urbanization is the main factor that has influenced the efficiency of industrial water consumption in China.…”

Section: Of 17mentioning

confidence: 99%

Does Urbanization Improve Industrial Water Consumption Efficiency?

Liu

Hou

et al. 2019

Sustainability

Although some studies have focused on the logical connection between industrial water consumption in the industrial economic development (IED) and industrial wastewater treatment (IWT) stages, the master–slave game relationship between these stages has not been considered. This study selected panel data from 30 provinces in China from 2011 to 2015, divided these provinces into IED- and IWT-dominated regions, and developed a two-stage data envelopment analysis (DEA) model based on the master–slave game relationship between the IED and IWT stages. In addition, a regression model based on the Simar–Wilson approach was constructed to reveal the effects of urbanization on industrial water consumption efficiency. The results show that the industrial water consumption efficiency in China slightly fluctuated from 2011 to 2015, and there was no significant efficiency improvement. The efficiency of the IED stage was generally higher than that of the IWT stage, and the efficiency gap between stages was smaller in IED-dominated regions than in IWT-dominated regions. Urbanization has different effects on industrial water consumption efficiency, and the same factor can have significantly different effects in different regions. Some policy implications are proposed for the different types of regions.

“…The coordinated growth between economic growth and the eco‐environment is the key to realize sustainable economic development. Eco‐efficiency is an important indicator which serves as a helpful instrument for the studies on sustainable development (Zhou, Shi, Wang, & Zhang, 2018). Thus, fulfilling the efficient resource utilization and eco‐environmental protection without compromising the steady economic growth highlights the importance of the studies on eco‐efficiency.…”

Section: Introductionmentioning

confidence: 99%

“…Gross domestic product (GDP), per capita GDP, added value of industry, production capacity, and sales volume (Moutinho, Madaleno, Robaina, & Villar, 2018; Ounsaneha & Rattanapan, 2016; Zhang, Liu, Chang, & Zhang, 2017) were selected as economic indicators. Energy consumption and energy intensity, water consumption and water intensity, electricity (Camarero, Castillo, Picazo‐Tadeo, & Tamarit, 2013; Guan & Xu, 2016; Mehmeti, Todorovic, & Scardigno, 2016; Zhou et al, 2018) were selected as the indicators of resource utilization. Sulfur dioxide, soot and dust emission, waste water, and solid waste (Gómez‐Calvet, Conesa, Gómez‐Calvet, & Tortosa‐Ausina, 2016; Yang, Zhou, & Xu, 2015) were selected as the environmental indicators.…”

Section: Introductionmentioning

confidence: 99%

“…DEA (Zhang, Bi, Fan, Yuan, & Ge, 2008), life‐cycle assessment (LCA) (Ullah, Perret, Gheewala, & Soni, 2016; Xing, Wang, & Zhang, 2018), and stochastic frontier analysis (SFA) (Reinhard, Knox Lovell, & Thijssen, 2000) were the main methods to estimate the eco‐efficiency. In comparison with other methods, DEA is a non‐parametric method where inputs and outputs are combined with self‐defined weighting coefficients in order to come up with an aggregate score (Zhou et al, 2018). Thus, DEA was the most widely used method.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Estimation of eco‐efficiency and identification of its influencing factors in China's Yangtze River Delta urban agglomerations

Zhang

2020

Growth and Change

Eco-efficiency is an important indicator which serves as a helpful instrument for the studies of sustainable development. This paper estimated the eco-efficiency of 26 cities in China's Yangtze River Delta urban agglomerations. Based on different matrixes, Moran's I was applied to analyze the spatial correlation. The spatial Durbin model was applied to analyze the direct and indirect effect of influencing factors. Some conclusions were drawn. (a) The average eco-efficiency showed an increasing trend. The regional difference of ecoefficiency decreased and then increased. Shanghai's ecoefficiency had been the highest. (b) The eco-efficiency showed positive spatial autocorrelation and it also exhibited significant characteristic of local spatial autocorrelation. (c) Economic development, fixed assets investment, and technological level of one city had a significant positive impact on the eco-efficiency of the city itself. Industrial structure, fiscal decentralization, and foreign direct investment of a city had a significant negative impact on the eco-efficiency of the city itself. Changes in the economic development and technological levels in a city had positive impacts on the eco-efficiency of its neighboring cities. Foreign direct investment had a negative spatial spillover effect. (d) This paper suggested that it is necessary to pay attention to the interaction of influencing factors among cities.