Total-Factor Eco-Efficiency and Its Influencing Factors in the Yangtze River Delta Urban Agglomeration, China

Geng

et al. 2020

IJERPH

The measurement of eco-efficiency is an important tool to evaluate the level of urban sustainable development. Therefore; improving urban eco-efficiency in the lower reaches of the Yellow River ensures the implementation of ecological protection and high-quality development strategies in the Yellow River Basin. In this study; the dynamic changes of urban eco-efficiency and spatiotemporal differences in the lower reaches of the Yellow River were investigated using the Super-SBM (Super-Slack measure model) model with undesirable outputs and standard deviation ellipse. The STIRPAT (Stochastic Impacts by Regression Population; Affluence and Technology) model was introduced to analyze the factors affecting the change in urban eco-efficiency. The results showed that the overall urban eco-efficiency in the lower reaches of the Yellow River has not reached the optimal level. The overall eco-efficiency in the lower reaches of the Yellow River in Shandong Province was higher than that in Henan Province but the gap is narrowing. The spatial differentiation of urban eco-efficiency in the lower reaches of the Yellow River showed the following trends: “blooming in the middle and reverse development at both ends” in the high-value area and gradual decrease in the low-value area. From 2007 to 2018; a direction was notable with respect to the development of urban eco-efficiency in the lower reaches of the Yellow River; with the centripetal force weakening. Although the mean center of urban eco-efficiency located in Shandong Province; it notably shifted to the west during the study period. In terms of driving factors; affluence and technological progress play positive roles in driving eco-efficiency; while investment intensity; industrial structure; and foreign investment intensity hindered the optimization and improvement of urban eco-efficiency in the lower reaches of the Yellow River. The results of this study show that urban eco-efficiency in the lower reaches of the Yellow River is improving; but the regional coordination is poor. The main methods promoting the sustainable development in the study area include changing the mode of extensive investments and the introduction of foreign capital; which improve the energy efficiency and promote faster and better economic development

Section: Discussionmentioning

confidence: 95%

Dynamic Changes, Spatiotemporal Differences and Factors Influencing the Urban Eco-Efficiency in the Lower Reaches of the Yellow River

Geng

et al. 2020

IJERPH

“…An increase in population density increases the labor force and the number of consumers, which can promote economic growth; however, it also results in greater consumption of resources and increases the pressure on the ecological environment. Following Guan and Xu, [62], Zhou et al [80], Díaz-Villavicencio et al [87], and Chen et al [88], the study selected population density as a control variable (Table 3).…”

Section: Indicators Of Ee Evaluationmentioning

confidence: 99%

China’s Eco-Efficiency: Regional Differences and Influencing Factors Based on a Spatial Panel Data Approach

Zeng

2021

Sustainability

From the Kyoto Protocol to the Copenhagen Conference and the Paris Agreement, eco-environmental problems have gradually become a matter of common concern worldwide. Eco-efficiency (EE) is an essential indicator for measuring levels of sustainable development. This study uses an epsilon-based measure (EBM) model with undesirable outputs to evaluate the EEs of 30 Chinese provinces during the research period 2008 to 2017, and a spatial Durbin model (SDM) to search for the impact factors of EE. The results indicate that most provinces in China have a low EE level. The EE value of the eastern area is higher than are those for the central, western, or northeastern areas. The EE in China as a whole demonstrates an inverted V-shaped trend with a high point in 2011. The SDM shows that economic development level, foreign trade dependence, and technological progress exert significant positive effects on EE, while population density exerts significant negative influences on EE. This paper provides scientific bases for the formulation of policies resulting in sustainable development.

“…Total-factor ecological performance, which simultaneously takes economic growth, resource consumption, and environmental cost into consideration, is an effective tool to evaluate the comprehensive development level of regions [27]. Data envelopment analysis (DEA) is widely used in effectively evaluating ecological performance, which is the basis for further exploring its relationship with urban clusters [28,29]. The general DEA method is based on the assumption of homogeneity; that is, each decision-making unit (DMU) has the same technological frontier.…”

Section: Related Research On Ecological Performancementioning

confidence: 99%

“…Li et al [35] developed a comprehensive method to measure eco-efficiency in the manufacturing process by considering energy efficiency and resource efficiency. Cheng et al [28] measured the ecological efficiency of China's Yangtze River Economic Belt by taking energy and water as resource indicators and wastewater, sulfur dioxide, soot, and dust as environmental indicators. In addition, scholars considered a broader resource conception in the input-output indicators, such as construction land [27].…”

Section: Related Research On Ecological Performancementioning

confidence: 99%

“Resource Conservation” or “Environmental Friendliness”: How do Urban Clusters Affect Total-Factor Ecological Performance in China?

Chen

Xie

2020

IJERPH

Urban clusters are important carriers for cities to participate in international competition and cooperation, and a booster for urban sustainable development. This study measured the degree of urban clusters by utilizing the panel data of 278 cities in China during 2004–2016. Then, an extended meta-frontier data envelopment analysis (EM-DEA) model was applied to estimate the total-factor ecological performance (UTEP) and decompose it into two sub-index from the perspective of “resource conservation” and “environmental friendliness”. On these bases, we employed a dynamic panel data approach to examine and demonstrate the relationship between urban cluster and UTEP in two dimensions, and further explored transmission channels of urban clusters on UTEP by adding the mediating effect. The results show that resource conservation increases first and then decreases with the increasing of urban clustering level, while environmental friendliness showed the opposite trend, making the latter become the main way for urban clusters to improve the UTEP. Industrial structure supererogation and rationalization are transmission channels for environmental friendliness rather than resource conservation in the way of improvement of UTEP. Technology innovation, as well as technology diffusion, also improves UTEP to some extent. In addition, urban clusters in eastern and central China have the greatest improvement in UTEP, while such effects are not the case in western China. Urban clusters in the second half sample period are more conducive to the improvement of the UTEP, with these potentially being the gains from the improvement of the level and quality of urban clusters.