Assessing Green Development Efficiency of Municipalities and Provinces in China Integrating Models of Super-Efficiency DEA and Malmquist Index

This paper proposes a green development growth index (GDGI) for measuring the changes in sustainable development over time. This index considers a wide range of pollutants, and allows for the incorporation of group heterogeneity and non-radial slack in the conventional green development index. The GDGI is calculated based on a non-radial directional distance function derived by several data envelopment analysis (DEA) models, and was decomposed into an efficiency change (EC) index, a best-practice gap change (BPC) index and a technology gap change (TGC) index. The proposed indices are employed to measure green development performance in 30 provinces in China from 2000 to 2012. The empirical results show that China has a low level of green development, with a 2.58% increase per year driven by an innovation effect. China's green development is mainly led by the eastern region, and the technology gaps between the eastern region and the other two regions (the central and western regions) have become wider over the years. The group innovative provinces have set a target for resource utilization of non-innovative provinces in order to catch-up with the corresponding groups, while the metafrontier innovative provinces provide targets for the technology levels of other provinces to improve their green development performance.

Section: Literaturementioning

confidence: 99%

Green Development Performance in China: A Metafrontier Non-Radial Approach

Song

2016

“…Average MAPE (%) DMUs Average MAPE (%) DMU 1 0.9462 DMU 11 1.4125 DMU 2 0.7709 DMU 12 1.227 DMU 3 0.27 DMU 13 0.6516 DMU 4 1.2972 DMU 14 2.742 DMU 5 1.983 DMU 15 0.384 DMU 6 0.505 DMU 16 0.469 DMU 7 1.263 DMU 17 1.083 DMU 8 1.4853 DMU 18 1.65 DMU 9 2.662 DMU 19 0.588 DMU 10 3.0697…”

Section: Dmusmentioning

confidence: 99%

“…To obtain a reliable prediction, certain laws governing the phenomena of system development must be understood through either natural principles or real observations [4,5]. Especially for logistics companies, which operate in the field of transportation, forecasting the demand for transportation plays a very important role.…”

Section: Introductionmentioning

confidence: 99%

A Grey Forecasting Approach for the Sustainability Performance of Logistics Companies

Wang

2016

Abstract:Logistics plays an important role in globalized companies and contributes to the development of foreign trade. A large number of external conditions, such as recession and inflation, affect logistics. Therefore, managers should find ways to improve operational performance, enabling them to increase efficiency while considering environmental sustainability due to the industry's large scale of energy consumption. Based on data collected from the financial reports of top global logistics companies, this study uses a DEA model to calculate corporate efficiency by implementing a Grey forecasting approach to forecast future sustainability values. Consequently, the study addresses the problem of how to enhance operational performance while accounting for the impact of external conditions. This research can help logistics companies develop operation strategies in the future that will enhance their competitiveness vis-à-vis rivals in a time of global economic volatility.

“…Chen et al [12] introduced the Likert scale in the processing of data to study the production and energy efficiency of each province. In addition, based on energy efficiencies, Yang et al [13] used the Super-efficiency DEA model to provide green development pathway plans for 31 provinces in China.…”

Section: Literature and Theoretical Backgroundmentioning

confidence: 99%

A Comprehensive Evaluation of Sustainable Development Ability and Pathway for Major Cities in China

Gao

Shiue

2017

Sustainability consists of economic, environmental, and societal aspects. Concomitant with China's rapid growth, the evaluation of China's ability for sustainable development (SD) has become a topic of interest. Unlike previous studies that are based on provincial regions and focus on economic and/or environmental evaluations, we have evaluated the comprehensive SD ability of 34 major cities in China using the context-dependent data envelopment analysis (CD-DEA) and proposed benchmark-learning pathways. The results indicate that the SD ability of China's major cities may be classified and ordered from low tiers to high tiers: high energy consumption and polluting industries-intensive industries-fixed asset investments and service industries-innovation, quality of life and societal services industries. As a whole, cities along the coast have a higher ability for SD than inland cities, and southern cities also rate higher than northern cities. Cities that are prioritized by government policies and funding also have higher SD abilities than other cities. We recommend that cities with lower abilities (i.e., cities in the second, third, and fourth tiers) should adopt the following points in their developmental pathways (in this order): the control of energy consumption and environmental pollution, industry upgrading and redevelopment of the city, and the development of an environment that encourages innovation and provides ample employment. This study finds that GDP is no longer an issue that restricts the SD ability of China's major cities, as compared to other factors. As China progresses towards the sustainable cities, focus should be placed on the development of a knowledge-intensive economy, balanced development, and pollution prevention.