Measuring energy and environmental performance for regions in China by using DEA-based Malmquist indices

Abstract

China's rapid economic development has intensified the country's many problems in the areas of energy shortage and environmental pollution. However, little research has been done which pays close attention to the evaluation of energy and environmental performance even though such evaluation is considered a crucial method in the fight to save energy, protect the environment, and mitigate global climate change. In this study, we utilize improved data envelopment analysis (DEA) models to evaluate the regional total-factor energy and environmental efficiency of China during the 11th 5-year plan period (2006)(2007)(2008)(2009)(2010). The total-factor energy and environmental efficiency is considered using a joint production framework of both non-energy inputs and energy inputs, as well as desirable outputs and undesirable outputs. In addition, the DEA-based Malmquist index is applied to evaluate the dynamic productivity change considering the undesirable outputs and energy inputs. An empirical study is done on 30 of mainland China's provincial-level regions, showing that most of them have low energy and environmental efficiency. On average, eastern China had the highest energy and environmental efficiency, followed by central China, with the efficiency of western China being the worst.

Evaluation of Energy-Environment Efficiency of European Transport Sectors: Non-Radial DEA and TOPSIS Approach

“…The evaluation of transport EEE has been done under the joint production framework, using non-energy inputs (labor and transport assets) and energy input (energy consumption) to produce desirable outputs (volume of passenger and freight transport) and undesirable output (GHG emissions). Aside from other widely used non-radial DEA models such as Slack-based models, Russell measure models, and Directional distance function, in this paper, the non-radial DEA model has been chosen due to its ability to use different non-proportional adjustments, with decision maker specified weights assigned to each efficiency score, and because of its ability to proportionally decrease the amounts of energy inputs and undesirable outputs simultaneously as much as possible [5,6].…”

“…The evaluation of transport EEE has been done under the joint production framework, using non-energy inputs (labor and transport assets) and energy input (energy consumption) to produce desirable outputs (volume of passenger and freight transport) and undesirable output (GHG emissions). Aside from other widely used non-radial DEA models such as Slack-based models, Russell measure models, and Directional distance function, in this paper, the non-radial DEA model has been chosen due to its ability to use different non-proportional adjustments, with decision maker specified weights assigned to each efficiency score, and because of its ability to proportionally decrease the amounts of energy inputs and undesirable outputs simultaneously as much as possible [5,6].The main contributions of this study are: (i) a newly systematic literature review in the field of transport EEE evaluation, (ii) a new definition of transport EEE, (iii) the evaluation of EEE with an extended set of used inputs, (iv) the evaluation of EEE of road, air, and rail transport sectors of European countries and their changing tendencies in terms of the EEE, (v) use of non-radial DEA and introduction of the TOPSIS method through DMUs ranking in the evaluation of transport EEE, as well as the comparison of their results and the identification of the most suitable one for the evaluation of the transport EEE. Based on the evaluation with the non-radial DEA model all stakeholders can create a sense of tendencies in terms of EEE of EU transport sectors.…”

Application of Multicriteria Decision-Making Methods in Railway Engineering: A Case Study of Train Control Information Systems (TCIS)

“…Furthermore, radial and non-radial DEA models for evaluating DMUs total-factor energy and environmental efficiency have been presented in [74]. In order to overcome the discriminating power of the radial model, following [86,87] in [74], the radial DEA model for energy and environmental efficiency evaluation has been extended to a non-radial model.…”

“…In order to overcome the discriminating power of the radial model, following [86,87] in [74], the radial DEA model for energy and environmental efficiency evaluation has been extended to a non-radial model. Assuming that there are K DMUs, and each DMU has n desirable inputs (non-energy inputs) and l undesirable inputs (energy inputs) in order to produce m desirable outputs and j undesirable outputs …”

“…Additionally, unified efficiency can be calculated through decision-maker specified weights assigned to each of these two efficiency scores and depends on the preferences between undesirable inputs utilization and undesirable outputs. In the model proposed in [74], the weights were set to 1/2. Hence, besides additional widely used non-radial DEA models such as Slack-based models, Russell measure models and Directional distance function, in this chapter non-radial DEA model was chosen due to its ability to use different non-proportional adjustments, decision-maker specified weights assigned to each efficiency score, and because of its ability to proportionally decrease the amounts of inputs and undesirable outputs simultaneously as much as possible [73,74].…”