Eco-efficiency of the Western Taiwan Straits Economic Zone: An evaluation based on a novel eco-efficiency model and empirical analysis of influencing factors

Zhu, Wei; Xu, Liang; Tang, Lina; Xiang, Xueqin

doi:10.1016/j.jclepro.2019.06.157

Cited by 49 publications

(21 citation statements)

References 33 publications

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“…Previous studies have shown the spatial differences in EE among different regions in China, which were mainly because of the heterogeneity of resource endowment, technological progress, and other factors among different regions [18]. In summary, the existing studies had the following shortcomings.…”

Section: Literature Reviewmentioning

confidence: 98%

“…Chen et al [17] used the DEA to evaluate the provincial EE in China and tested the hypothesis of EE and suggested that improving the EE was important for reducing environmental risks. Zhu et al [18] constructed an extended DEA model, and used window DEA combined with undesirable output and super-efficiency slacks-based measure (WIN-US-SBM) model, to explore the impact on eco-efficiency in Western Taiwan Straits Economic Zone.…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Regional Differences in the Spatial Characteristics and Dynamic Convergence of Environmental Efficiency in China

Tao

et al. 2020

Sustainability

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This study uses the undesirable output and super-efficiency slacks-based measure combined with window (WIN-US-SBM) data envelopment analysis (DEA) to evaluate the environmental efficiency (EE) in 30 Chinese provinces, from 2005 to 2016, explores regional differences in the EE, and uses the dynamic spatial Durbin model (DSDM) to analyze regional differences in effects of important factors on the convergence of EE. It reveals that EE in the eastern area is higher than EE in the central and western areas, and a positive spatial autocorrelation exists in the interregional EE. The difference in provincial EE gradually narrows over time and tends to converge to its own steady-state level. Economic growth reduces EE for the central and western areas and improves efficiency for the eastern area; economic growth from surrounding areas indirectly promotes local EE for the eastern area. Foreign direct investment (FDI) promotes EE in the eastern and central areas, and FDI in the adjacent areas has a positive effect on local EE for the eastern area. Export reduces EE for all areas, and export in surrounding areas indirectly promotes local EE for the central area. Industrialization reduces EE in the western area, and industrialization in the surrounding areas increases local EE for the eastern area. Energy efficiency promotes EE for the central area, urbanization increases EE for the central area, and urbanization of the surrounding areas reduces local EE for the eastern area.

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Section: Literature Reviewmentioning

confidence: 98%

Section: Literature Reviewmentioning

confidence: 99%

Regional Differences in the Spatial Characteristics and Dynamic Convergence of Environmental Efficiency in China

Tao

et al. 2020

Sustainability

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“…Production with pollutant byproducts and pollution assumed as a two-step system have provoked growing attention in the sustainability management field. Undesirable aspects have been considered when evaluating the performance of suppliers [73][74][75][76][77][78][79][80][81], eco-efficiency [82][83][84][85][86][87][88][89][90][91], and resource and environmental [92][93][94][95][96][97] efficiency. Some studies also introduced DEA efficiency evaluation by taking into account unwanted issues, and unwanted factors can be considered inputs or unwanted outputs in the DEA models [98][99][100][101][102][103][104].…”

Section: Literature Reviewmentioning

confidence: 99%

An integrated Multi-Criteria Decision Making Model for Sustainability Performance Assessment for Insurance Companies

et al. 2020

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To stay competitive in a business environment, continuous performance evaluation based on the triple bottom line standard of sustainability is necessary. There is a gap in addressing the computational expense caused by increased decision units due to increasing the performance evaluation indices to more accuracy in the evaluation. We successfully addressed these two gaps through (1) using principal component analysis (PCA) to cut the number of evaluation indices, and (2) since PCA itself has the problem of merely using the data distribution without considering the domain-related knowledge, we utilized Analytic Hierarchy Process (AHP) to rank the indices through the expert’s domain-related knowledge. We propose an integrated approach for sustainability performance assessment in qualitative and quantitative perspectives. Fourteen insurance companies were evaluated using eight economic, three environmental, and four social indices. The indices were ranked by expert judgment though an analytical hierarchy process as subjective weighting, and then principal component analysis as objective weighting was used to reduce the number of indices. The obtained principal components were then used as variables in the data envelopment analysis model. So, subjective and objective evaluations were integrated. Finally, for validating the results, Spearman and Kendall’s Tau correlation tests were used. The results show that Dana, Razi, and Dey had the best sustainability performance.

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“…In this study, the Tobit regression model is applied to analyze the influencing factors that affect the efficiency of China's industrial circular economy. The standard form is as follows [67,68]:…”

Section: The Tobit Regression Modelmentioning

confidence: 99%

An Integrated Measurement of the Efficiency of China’s Industrial Circular Economy and Associated Influencing Factors

Zhang

et al. 2020

Mathematics

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Since the 1990s, the notion of a circular economy has been developing globally; countries all over the world have been considering the development of a circular economy as an important means of achieving sustainable development. As the development of an industrial circular economy can help promote the efficient recycling of resources, it is an important starting point for industrial transformation and upgrading, and represents a key factor that will lead to the development of a circular economy in China. China’s varying provinces (municipalities and autonomous regions) have successively implemented circular economy practices in the industrial field. The research object of the present study is 30 provinces, autonomous regions, and municipalities directly under the control of central government (Hong Kong, Macao, Taiwan, and Tibet were not included owing to lack of data). Through the integration of geographic information systems (GIS) technology and the spatial analysis model, data envelopment analysis (DEA) model, and Tobit regression model, a measure model and index system are constructed, in order to carry out a multi-angle comprehensive study integrating the efficiency evaluation, spatial analysis, and influencing factors analysis of China’s industrial circular economy. It is an important innovation, and an important contribution to the existing research system. The conclusions are as follows: (1) In general, the overall level of China’s industrial circular economy’s efficiency was not high, and there was still a lot of room for improvement. The integrated efficiency of the industrial circular economy in the eastern region was relatively high, followed by that in the western region, and the lowest level in the middle region. (2) The efficiency of China’s industrial circular economy displayed obvious spatial aggregation characteristics at the provincial level, including clear spatial dependence and spatial heterogeneity. High-value aggregation areas were mainly distributed in the eastern coastal areas, and low-value aggregation areas were concentrated and contiguously distributed in the middle and western inland areas. (3) The four elements of economic level, openness to the outside, government regulation, and industrialization aggregation each impose a significant positive impact on the efficiency of China’s industrial circular economy, which can promote its efficiency. The level of industrialization exerts a significant negative impact on the efficiency of the industrial circular economy, which hampers its improvement. The impact of technological innovation on the efficiency of the industrial circular economy is not statistically significant.

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Eco-efficiency of the Western Taiwan Straits Economic Zone: An evaluation based on a novel eco-efficiency model and empirical analysis of influencing factors

Cited by 49 publications

References 33 publications

Regional Differences in the Spatial Characteristics and Dynamic Convergence of Environmental Efficiency in China

Regional Differences in the Spatial Characteristics and Dynamic Convergence of Environmental Efficiency in China

An integrated Multi-Criteria Decision Making Model for Sustainability Performance Assessment for Insurance Companies

An Integrated Measurement of the Efficiency of China’s Industrial Circular Economy and Associated Influencing Factors

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