Measuring environmental performance under different environmental DEA technologies

Zhou, P.; Ang, B.W.; Poh, Kim-Leng

doi:10.1016/j.eneco.2006.05.001

Cited by 453 publications

(149 citation statements)

References 37 publications

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“…The conceptual definition of environmental production technology can be approximated by piecewise linear combinations of the observed data, which is similar theoretically to DEA form; as such, it can also be called "environmental DEA technology" [49,50]. Under CRS assumption, environmental production technology T can be approximately formulated as follows:…”

Section: Environmental Dea Technologymentioning

confidence: 99%

Optimal Path for Controlling Sectoral CO2 Emissions Among China’s Regions: A Centralized DEA Approach

2015

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This paper proposes a centralized data envelopment analysis (DEA) model for industrial optimization based on several different production technologies among several regions. We developed this model based on improved Kuosmanen environmental DEA technology, which avoids positive shadow price on undesirable outputs. We also designed a dual model for our centralized DEA model, and used it to analyze shadow prices on CO 2 emissions. We further employed the proposed model to determine the optimal path for controlling CO 2 emissions at the sector level for each province in China. At sectoral level, manufacturing showed the highest potential emissions reduction, and transportation was the largest accepter of emission quotas. At regional level, western and northeastern areas faced the largest adjustments in allowable emissions, while central and eastern areas required the least amount of adjustment. Because our model represents increase or decrease in emissions bidirectionally in terms of shadow price analysis, this setting makes the shadow price on CO 2 emissions lower than strong regulation (decreasing CO 2 emissions along with increasing value added) used by directional distance function (DDF).

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Section: Environmental Dea Technologymentioning

confidence: 99%

Optimal Path for Controlling Sectoral CO2 Emissions Among China’s Regions: A Centralized DEA Approach

2015

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“…In recent years, the environmental efficiency evaluation based on DEA is widely favored by environmental economists interested in China's environmental performance, due to its unique advantage on the efficiency evaluation for homogeneous decision making units (DMUs) with multi inputs and outputs. In these early studies, traditional DEA models are adopted to measure the environmental efficiency, emphatically discussed the treatment of pollutants, i.e., undesired outputs (Zhou et al, 2008) [3]. To access the provincial energy efficiency in China, Shi et al (2010) [4] proposed a traditional DEA method, which includes energy consumption and undesired output, simultaneously.…”

Section: Literature Review: China's Regional Environmental Efficiencymentioning

confidence: 99%

Does the Central Government’s Environmental Policy Work? Evidence from the Provincial-Level Environment Efficiency in China

Xia

et al. 2016

Sustainability

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This paper aims at checking the effectiveness of environmental policy pushed by the Central Government at provincial level since China's entry into the World Trade Organization (WTO). For this purpose, the industrial system of each province is divided into industrial production sub-system and pollution treatment sub-system, and a novel slack-based measure data envelopment analysis (SBM-DEA) model with non-cooperative game is proposed to evaluate the environment efficiency of both industrial production sub-system and pollutant treatment sub-system. The results show that the proposed model can describe the environmental efficiency more precisely than the traditional DEA models. During 2003-2012, the efficiencies of industrial production sub-system and pollution treatment sub-system at the provincial level are both relatively low. Specifically, the efficiency of industrial production is not only lower than pollution treatment efficiency, but is falling generally, especially in the Eastern area. However, in the Central and Western area, the efficiency of industrial production remains relatively stable, and pollution treatment efficiency is rising steadily. The technology gap between the Central/Western area and the Eastern area is narrowing, particularly concerning pollution treatment technology. We thus conclude that though the Central Government's environmental policies fail to solve the inner contradiction between economic and environmental systems, and they indirectly contribute to the expansion of pollutant treatment technology among the Eastern, Central, and Western areas, which is conducive to the coordinated development among different areas.

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“…Zhou et al [72] conducted an in-depth literature survey on the application of DEA to environment and energy studies. Zhou et al [73] discussed the environmental DEA technologies that exhibit either non-increasing returns to scale or variable returns to scale. Zhou et al [74] developed a non-radial DEA model and a non-radial Malmquist environmental efficiency to measure change of environmental performance of 26 OECD countries from 1995 to 1997.…”

Section: The Academy-oriented Contributionmentioning

confidence: 99%

Ecological Efficiency Based Ranking of Cities: A Combined DEA Cross-Efficiency and Shannon’s Entropy Method

Storto

2016

Sustainability

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Abstract:In this paper, a method is proposed to calculate a comprehensive index that calculates the ecological efficiency of a city by combining together the measurements provided by some Data Envelopment Analysis (DEA) cross-efficiency models using the Shannon's entropy index. The DEA models include non-discretionary uncontrollable inputs, desirable and undesirable outputs. The method is implemented to compute the ecological efficiency of a sample of 116 Italian provincial capital cities in 2011 as a case study. Results emerging from the case study show that the proposed index has a good discrimination power and performs better than the ranking provided by the Sole24Ore, which is generally used in Italy to conduct benchmarking studies. While the sustainability index proposed by the Sole24Ore utilizes a set of subjective weights to aggregate individual indicators, the adoption of the DEA based method limits the subjectivity to the selection of the models. The ecological efficiency measurements generated by the implementation of the method for the Italian cities indicate that they perform very differently, and generally largest cities in terms of population size achieve a higher efficiency score.Keywords: cross-efficiency; data envelopment analysis; Shannon's entropy; ecological efficiency; cities; ranking; Italy BackgroundIn the last decade, cities have gained a greater centrality in the economic and social growth of nations. The recent report delivered by the Brookings Institution indicates that in 2014 the economies of the 300 largest metropolitan areas accounted for 47% of global gross domestic product (GDP) and 38% of GDP growth [1]. According to recent estimates provided by Seto et al.[2], more than 80% of the national gross domestic product (GDP) is generated in urban areas. A recent worldwide research conducted by the McKinsey Global Institute on a sample of 600 cities estimates that between 2010 and 2050, the GDP of these cities is expected to double, while 23 megacities-cities having more than 10 million inhabitants-in 2007 generated 14% of global GDP [3].People move to and live in cities to have access to better jobs, education, health care, goods and services. More than half of the human population over the world is living in cities and towns, and, in the next decades, the number of people expected to live in cities will grow to 75%, while population growth over the next 25 years will be concentrated in cities and towns [4,5]. The most urbanized areas are located in the American and Europe continents, respectively having about 80% and 70% of all inhabitants residing in cities and towns. However, while cities are the primary source of economic development and social prosperity, and house more than half of the world population, they are large users of resources, responsible for about 2/3 of energy demand and greenhouse gas emissions [5]. The recent rapid and intense urbanization has often resulted in an over consumption of water, energy, raw

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Measuring environmental performance under different environmental DEA technologies

Cited by 453 publications

References 37 publications

Optimal Path for Controlling Sectoral CO2 Emissions Among China’s Regions: A Centralized DEA Approach

Optimal Path for Controlling Sectoral CO2 Emissions Among China’s Regions: A Centralized DEA Approach

Does the Central Government’s Environmental Policy Work? Evidence from the Provincial-Level Environment Efficiency in China

Ecological Efficiency Based Ranking of Cities: A Combined DEA Cross-Efficiency and Shannon’s Entropy Method

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