Environmental efficiency measurement with translog distance functions: A parametric approach

Cuesta, Rafael Álvarez; Lovell, C. A. Knox; Zofío, José L.

doi:10.1016/j.ecolecon.2009.02.001

Cited by 136 publications

(161 citation statements)

References 26 publications

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“…However, the modelling and estimation of both technical and allocative efficiency of agricultural production is often motivated by the need for a more complete representation of the economic or cost efficiency of farmers implied by the economic theory of production. All the studies mentioned, with the exception of those of Cuesta et al (2009) andHerrero (2005), compared results from deterministic distance functions with other approaches. Given the uncertainties surrounding agricultural production, the modelling of efficiency in a stochastic distance function framework is necessary.…”

Section: Introductionmentioning

confidence: 99%

Technological innovation and efficiency in the Nigerian maize sector: Parametric stochastic and non-parametric distance function approaches

Aye

Mungatana

2011

Agrekon

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Section: Introductionmentioning

confidence: 99%

Technological innovation and efficiency in the Nigerian maize sector: Parametric stochastic and non-parametric distance function approaches

Aye

Mungatana

2011

Agrekon

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“…Scholars showed that over the 20-year window some indicators have shifted in their importance in influencing the overall environmental index, while others have remained relatively irrelevant. Cuesta et al [48] implemented a parametric stochastic method to calculate the distance from the environmental efficiency frontier. Even though these methods have the potential to provide robust results, limitations due to sample size and data availability make it impossible to estimate weights from data.…”

Section: The Academy-oriented Contributionmentioning

confidence: 99%

“…In general, scholars implement different methods and approaches to generate measurements to evaluate sustainability and ecological efficiency of cities, i.e., ecological footprint analysis (EFA) [43,44], emergy accounting [45], urban metabolism analysis [46], ratio-approach [47], parametric and non-parametric methods (e.g., stochastic frontier analysis and DEA) [48][49][50], and Analytical Hierarchy Process (AHP) [51].…”

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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“…Numerous types of distance function include radical distance function [31], hyperbolic distance function [32][33][34], the environmental distance function [35,36] and the directional distance function (DDF) [20,37,38] while the treatments on undesirable outputs of these method are different. However, the flaw of the distance function is the slacks of the indexes could not be found.…”

Section: Undesirable Outputs Measurement In Deamentioning

confidence: 99%

Environmental Efficiency Analysis of Listed Cement Enterprises in China

Fang

et al. 2016

Sustainability

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China's cement production has been the highest worldwide for decades and contributes significant environmental pollution. Using a non-radical DEA model with slacks-based measure (SBM), this paper analyzes the environmental efficiency of China's listed cement companies. The results suggest that the average mean of the environmental efficiency for the listed cement enterprises shows a decreasing trend in 2012 and 2013. There is a significant imbalance in environmental efficiency in these firms ranging from very low to very high. Further investigation finds that enterprise size and property structure are key factors. Increasing production concentration and decreasing the share of government investment could improve the environmental efficiency. The findings also suggest that effectively monitoring pollution products can improve environmental efficiency quickly, whereas pursuit for excessive profitability without keeping the same pace in energy saving would cause a sharp drop in environmental efficiency. Based on these findings, we proposed that companies in the Chinese cement sector might consider restructuring to improve environmental efficiency. They also need to make a trade-off between profitability and environmental protection. Finally, the Chinese government should reduce ownership control and management interventions in cement companies.

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Environmental efficiency measurement with translog distance functions: A parametric approach

Cited by 136 publications

References 26 publications

Technological innovation and efficiency in the Nigerian maize sector: Parametric stochastic and non-parametric distance function approaches

Technological innovation and efficiency in the Nigerian maize sector: Parametric stochastic and non-parametric distance function approaches

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

Environmental Efficiency Analysis of Listed Cement Enterprises in China

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