Monitoring environmental efficiency: An application to Italian provinces

Coli, Mauro; Nissi, Eugenia; Rapposelli, Agnese

doi:10.1016/j.envsoft.2010.07.004

Cited by 35 publications

(15 citation statements)

References 23 publications

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“…Coli et al (2011) selected two environmental variables, namely nitrogen dioxide (NO 2 ) concentration and PM10 concentration when monitoring environmental efficiency in Italy, while Mandal and Madheswaran (2010) chose CO 2 emissions for calculating environmental efficiency for cement industry in India. In this section, taking low-carbon economy into account, we select energy consumption associated with carbon emissions resulting from the manufacturing A Three-stage DEA analysis sectors as the environmental variable.…”

Section: Data and Variablesmentioning

confidence: 99%

Choosing competitive industries in manufacturing of China under low-carbon economy

Fang

Hong

et al. 2013

International Journal of Climate Change Strategies and Management

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Purpose -This paper aims to explore true technical efficiency in order to select the most competitive manufacturing industries in China. And the paper intends to discuss how environmental variables measured by energy consumption affect performance in different industrial sectors under the restriction of low-carbon economy. Design/methodology/approach -In order to measure the calculated efficiency of industrial sectors more accurately, Three-stage DEA model is presented in the empirical analysis using data from 2007 to 2010 covering 29 manufacturing industries in China. The advantage of using this method is enabling us to separate the managerial factor from external environmental factors and random errors factors on the technical efficiency. Findings -The results using this Three-stage DEA model show that textile manufacturing sector has the highest technical efficiency, and when environment variables are not considered, efficiencies in machinery and electronics manufacturing industries have a significant increase. Moreover, this empirical model enables us to evaluate the technical performance in various manufacturing sectors more accurately. Practical implications -This study provides a useful efficiency measurement tool (Three-stage DEA model) to calculate technical efficiency among different industrial sectors. Technical efficiency plays a key role in building the competitiveness of manufacturing industry. Based on the objective efficiency evaluation, the paper can make a better selection of the most competitive industries. Originality/value -The paper contributes to the existing literature by developing a Three-stage DEA to examine the technical efficiency and competitive power of manufacturing sectors in China. This study has great policy implications for the research of China's manufacturing in both ideas and methodology.

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Section: Data and Variablesmentioning

confidence: 99%

Choosing competitive industries in manufacturing of China under low-carbon economy

Fang

Hong

et al. 2013

International Journal of Climate Change Strategies and Management

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“…Indeed, efficiency frontier analysis methods and, more specifically, the non-parametric method called data envelopment analysis (DEA, Charnes et al, 1978) have led to a different way of taking performance into account in the assessment of agricultural production systems. Including undesirable outputs in classical DEA models (Scheel, 2001;Coli et al, 2011) is a powerful way to assess eco-efficiency using observed databases. The non-parametric efficiency frontier method does not assume an a priori functional form of the production of technology (i.e.…”

Section: Introductionmentioning

confidence: 99%

A methodology to explore the determinants of eco-efficiency by combining an agronomic whole-farm simulation model and efficient frontier

Berre

Vayssières²,

Boussemart

et al. 2015

Environmental Modelling & Software

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“…The approach has gained significant popularity in energy and environmental modeling (Zhou et al ). Compared with the traditional DEA models, most environmental DEA models still feature inputs and outputs as the basic components and account for undesirable outputs by adjusting technical efficiency measures (e.g., Zhang et al ; Coli et al ). Unlike in previous studies, our approach is more similar to a DEA‐based weighting method for the aggregation of various indicators, not for the calculation of traditional or variant technical efficiency based on inputs and outputs.…”

Section: Methodology and Datamentioning

confidence: 99%

Environmental Performance Analysis of Eco‐Industrial Parks in China: A Data Envelopment Analysis Approach

Liu¹,

Tian²,

Chen³

et al. 2015

J of Industrial Ecology

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Summary In pursuit of more sustainable development of industry, China has been actively developing eco‐industrial parks (EIPs) for more than a decade. However, the environmental value of these EIPs remains largely unverified. This study aimed to evaluate the environmental performance of national EIPs in China using data envelopment analysis. Eco‐efficiency and environmental performance indices were used to represent the static and dynamic environmental performance of EIPs, respectively. An environmental performance index was formed by combining measures of eco‐efficiency in a dynamic setting with the sequential Malmquist index approach. We obtained three main empirical findings. First, 34 national EIPs exhibited a cumulative environmental performance improvement of 89.4% from 2007 to 2010, which is primarily the result of eco‐efficiency change rather than environmental technical change. Second, compared with the trial EIPs, the demonstration EIPs had a higher average eco‐efficiency (0.611 vs. 0.446 in 2010) and experienced greater average environmental performance improvement (129% vs. 60%). Third, the EIPs retrofitted from high‐tech industrial development zones exhibited much higher average eco‐efficiency (0.798 vs. 0.440 in 2010) than those retrofitted from economic and technical development zones. The key measures supporting the performance improvement and policy implications for the development of EIPs are also discussed.

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Monitoring environmental efficiency: An application to Italian provinces

Cited by 35 publications

References 23 publications

Choosing competitive industries in manufacturing of China under low-carbon economy

Choosing competitive industries in manufacturing of China under low-carbon economy

A methodology to explore the determinants of eco-efficiency by combining an agronomic whole-farm simulation model and efficient frontier

Environmental Performance Analysis of Eco‐Industrial Parks in China: A Data Envelopment Analysis Approach

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