Showing papers on "Data envelopment analysis published in 2021"
TL;DR: In this paper, the impact of energy reforms on energy efficiency was analyzed using data envelopment analysis (DEA) and the difference-in-difference (DID) method.
186 citations
TL;DR: In this article, the authors tried to connect sustainable development goals with energy efficiency for 20 Asian and Pacific (AP) countries using Data Envelopment Analysis (DEA) from 2000 to 2018.
131 citations
TL;DR: Zhang et al. as discussed by the authors applied a three-stage Data Envelopment Analysis (DEA) method combined with the Slack-Based Measurement (SBM) model to eliminate the influences of environmental factors and random errors and explore the real AGTFP of 30 provinces in China from 2000 to 2017.
97 citations
TL;DR: In this article, the authors present a measurement and analysis of G7 countries' energy, economic, social, and environmental performance associated with energy poverty indexes using data envelopment analysis (DEA).
Abstract: The aim of the study is to estimate the nexus between energy insecurity and energy poverty with the role of climate change and other environmental concerns. We used DEA like WP methods and properties of MCDA, a most common form of data envelopment analysis (DEA) to estimate the nexus between constructs. This paper presents a measurement and analysis of G7 countries’ energy, economic, social, and environmental performance associated with energy poverty indexes. The study used the multiple, comprehensive, and relevant set of indicators, including energy economics and environmental consideration of energy poverty. The net energy consumption of al G7 economies is equal to 34 percent of the entire world along with the net estimate GDP score of around 50 percent. Using DEA modelling and estimation technique, our research presented valuable insights for readers, theorists and policy makers on energy, environment, energy poverty and climate change mitigation. For this reasons, all these indicators combined in a mathematical composite indicator to measure energy, economic, social, and environmental performance index (EPI). Results show that Canada has the highest EPII score, which shows that Canada’s capacity to deal with energy self-sufficiency, economic development, and environmental performance is greater than the other G7 countries. France and Italy rank second and third. Japan comes next with 0.50 EPI scores, while the USA has the lowest average EPI score environment vulnerable even though have higher economic development among the G7 group countries. We suggest a policy framework to strengthen the subject matter of the study.
94 citations
TL;DR: A cross-regional multi-objective planning model combined the data envelopment analysis method and the results show that each province can realize the reasonable distribution of industries through the industrial transfer, and complete the phased goals in the 13th Five-Year Plan.
67 citations
TL;DR: In this paper, the effect of government subsidies and tax rebate policies on renewable energy firms' investment efficiency using China's renewable energy firm-level panel data was explored using Banker, Charnes and Cooper's data envelopment analysis (DEA) approach.
Abstract: This article measures renewable energy firm-level pure innovation efficiency, green productivity, technical efficiency, scale efficiency and total investment efficiency from micro input–output factors using Banker, Charnes and Cooper’s (BCC) data envelopment analysis (DEA) approach. Its main novelty is that it clearly explores the effective impacts of government subsidies and tax rebate policies on renewable energy firms’ investment efficiency using China’s renewable energy firm-level panel data. Our observational findings indicate that between 2001 and 2018, the aggregate degree of total investment performance from renewable energy firms rose steadily before declining. Renewable energy firms had larger ranges of total investment efficiency and size efficiency, and their levels of pure technological efficiency were both greater than 0.457%. At the 16% trust mark, current government subsidies and taxation rebates had dramatically positive effects on pure technological efficiency and total investment efficiency; additionally, government subsidies have a stronger positive impact on total investment efficiency and pure technical efficiency than taxation rebates. Furthermore, the ownership concentrations of renewable energy companies greatly encourage pure technological efficiency, size efficiency and total investment efficiency, and asset returns will significantly increase their average degree of total investment efficiency and pure technical efficiency.
64 citations
TL;DR: Wang et al. as mentioned in this paper used a super-efficiency SBM model to construct the relative effective frontier, and then machine learning algorithms were used to construct a regression model and establish the absolute effective frontier.
Abstract: The traditional data envelopment analysis (DEA) method used for performance evaluation has inherent problems such as being easily affected by statistical noise in data. Furthermore, when new evaluation units are added, the performance of all the original units must be re-measured, which restricts the evaluation efficiency. In this study, machine learning algorithms were applied to make up for the shortcomings of the data envelopment analysis method. First, a super-efficiency SBM model was used to construct the relative effective frontier, and then machine learning algorithms were used to construct a regression model and establish the absolute effective frontier. After 15 machine learning algorithms were compared, BPNN demonstrated the best performance, and a SuperSBM-DEA-BPNN model was eventually established. The new model has the following advantages: First, compared with the traditional data envelopment analysis method, the absolute effective frontier displays better evaluation; second, compared with the data envelopment analysis and neural network fusion outlined in the previous literature, the new model can better overcome the problems associated with data envelopment analysis, thereby improving the fusion efficiency. Taking the innovation efficiency evaluation of China's regional rural commercial banks for instance, the new model is proven to be more applicable and offers more effective management tools to improve efficiency. On the whole, the new model not only provides a stable performance evaluation tool but also facilitates comparison, which has good application significance for organizations.
60 citations
TL;DR: Wang et al. as mentioned in this paper analyzed the regional differences and dynamic evolution of agricultural sustainable development and efficiency in 31 regions of Mainland China, based on a data envelopment analysis-Malmquist productivity index model.
59 citations
TL;DR: In this article, the authors investigated the efficiency and total factor productivity (TFP) growth of the Pakistani banking industry and determined the impact of risk and competition on the efficiency, and the results of the study show that the credit and liquidity risks have positive while insolvency risk has negative effect on the technical efficiency growth.
Abstract: This paper investigates the efficiency and total factor productivity (TFP) growth of the Pakistani banking industry and determines the impact of risk and competition on the efficiency and TFP growth. The data envelopment analysis (DEA)-based Malmquist productivity index is used to measure efficiency and TFP growth of the Pakistani banking industry. The generalized method of moments (GMM) model is applied to observe the impact of risk and competition on efficiency and TFP growth. The motivation behind the use of GMM model is its ability to overcome unobserved heterogeneity, autocorrelation, and endogeneity issues. The results of the study show that the credit and liquidity risks have positive while insolvency risk has negative effect on the efficiency and TFP growth. The competition leads to improve technological efficiency but declines the technical efficiency growth. Among other explanatory variables, operational cost management, banking sector development, GDP growth rate, and infrastructure development show significant relationships with various efficiencies and TFP growth. The banks also facilitate for the purchase of carbon-intensive products in order to reduce carbon emissions. Strong banking development successfully allocate their financial resources for the development of energy-efficient technology while banking sector development is found to be negatively related with environmental sustainability. The strong banking sector possesses a significant negative influence on carbon reduction and environmental degradation.
57 citations
TL;DR: Li et al. as mentioned in this paper presented a two-stage network DEA framework incorporating government and industrial sectors and measures the eco-efficiency of 84 resource-based cities during the post-financial crisis period (2007-2015).
55 citations
TL;DR: In this article, a metafrontier data envelopment analysis (DEA) decomposition framework was proposed to investigate the sources of inefficiency in coal-fired thermal power generation.
Abstract: China is the world's largest CO2 emitting country, and coal-fired thermal power generation accounted for over 50% of the total electricity generation in China in 2015. This study reports the changes in the power generation efficiency of coal-fired thermal power plants in China from 2009 to 2011 and elucidates how the differences in the production scale of the power plants and regional heterogeneity affect the power generation efficiency. We propose a metafrontier data envelopment analysis (DEA) decomposition framework to investigate the sources of inefficiency in power generation. The results suggest that, on average, the power generation efficiency of large-scale power plants is 13% higher than that of small-scale power plants. Although operational inefficiency is the main source of inefficiency in eastern and central China, the technology gap - the differences in the quality of coal consumed for electricity production and in the equipment of the power plants among regions - is the main source of inefficiency in western China. This study uses the results of the framework to discuss the scrapping policies for coal-fired thermal power plants in China. For large-scale power plants in western China, the components of inefficiency vary and, thus, policymakers should consider scrapping thermal power plants based not only on the level of inefficiency but also on their components.
TL;DR: In this article, the authors investigate energy efficiency via environmental innovation and the resulting degree of resilience and adaptation of both developed and developing countries, and show that knowledge spillovers from environmental innovations reduce inefficiency and therefore strengthen the resilience of economies that decide and manage to invest adequately in the transition to more sustainable technologies.
TL;DR: Wang et al. as discussed by the authors assessed the effects of these measures on China's CO2 emissions by using a newly proposed decomposition approach, which identified eight new factors related to the above realistic measures.
TL;DR: In this paper, the authors investigate the production efficiency in the USA and Europe through the channel of knowledge spillovers from technological innovation in the agricultural sector, and the empirical results evidenced a negative effect of land use spillovers on efficiency indicator for American firms and a positive effect for European ones.
TL;DR: In this article, the authors proposed a new data envelopment analysis (DEA) model to analyze the green growth degree of China's industrial sectors by considering the constraint of total energy consumption in order to conform to the policy of energy and emission reduction.
TL;DR: Examination of innovation performance of high-tech companies in China, using a dynamic network data envelopment analysis (DEA) approach, indicates disparities in innovation performance among different Chinese high- tech companies.
TL;DR: The authors investigated the relationship between environmental management practices (EMPs) and financial performance (FP), and consequently ascertain whether environmental performance can mediate the EMPs-FP nexus, finding that EMPs have a positive effect on FP.
Abstract: This study investigates the relationship between environmental management practices (EMPs) and financial performance (FP), and consequently ascertain whether environmental performance (EP) can mediate the EMPs–FP nexus. Distinctly using data envelopment analysis and generalised method of moments techniques to analyse a comprehensive dataset of Nikkei 225 listed firms from 2007 to 2018 (1,920 firm-year observations), our findings first suggest that EMPs have a positive effect on FP. Second, the desired EP can be achieved through the adoption of comprehensive EMPs. Third, improved EP has a substantial impact on shaping the EMPs’ effect on FP. These findings are consistent with the predictions of resource-based view and institutional theories. The results are robust to controlling for different types of alternative measures and endogeneities. The findings have important implications for academics, investors, managers, policy-makers, and regulators.
TL;DR: This work exploits several estimators from both static panel and dynamic panel data models to show that the GMM estimator is more adequate than the traditional estimators based on fixed or random effects.
TL;DR: In this article, the authors combine stochastic multicriteria acceptability analysis (SMAA-2) with data envelopment analysis (DEA) to evaluate the energy and environmental efficiency of Chinese transportation sectors in the presence of uncertain CO2 emission data.
Abstract: China’s transportation sector suffers from energy over-consumption and CO2 over-emission, resulting in increasing pressure to improve energy and environmental efficiency. Current measurement techniques cannot produce precise CO2 emission data, and this uncertainty makes previous approaches problematic for analyzing energy and environmental efficiency. This study combines stochastic multicriteria acceptability analysis (SMAA-2) with data envelopment analysis (DEA) to evaluate the energy and environmental efficiency of Chinese transportation sectors in the presence of uncertain CO2 emission data. The improved SMAA-DEA approach effectively handles CO2 data uncertainty and also considers all possible input and output weights, thus providing meaningful information (such as maximum efficiency, average efficiency, and rank acceptability index) to guide the development of effective policies to improve efficiency. This study’s empirical findings show that the energy and environmental efficiency of transportation sectors in 30 provincial regions is poor, great efficiency disparities exist between regions, and uneven development has occurred in China.
TL;DR: In this article, the authors used the modified and extended concept of the eco-efficiency, which on the output side includes proxy variables of environmental public goods, to assess the ecoefficiency of small-scale farms in Poland and to identify the relationship between ecoefficiency and institutional variables according to the framework of New Institutional Economics.
TL;DR: In this article, the authors adopted Data Envelopment Analysis (DEA) window analysis with an ideal window width to dynamically investigate the technological innovation efficiency of China's high-tech industry during 2009-2016, simultaneously from provincial, regional and industrial perspective.
Abstract: This study firstly adopts Data Envelopment Analysis (DEA) window analysis with an ideal window width to dynamically investigate the technological innovation efficiency of China's high-tech industry during 2009–2016, simultaneously from provincial, regional and industrial perspective. The ideal window widths in the high-tech industry and its five sub-industries are all 4. The findings indicate that the efficiency of high-tech industry is low and presents a wave-shaped trend, as well as presents large inter-provincial and inter-regional differences. The efficiency in eastern region is always the highest, while the efficiency in northeastern region is the lowest. Moreover, the efficiencies in eastern region and western region both presented wave-shaped decrease trends, while the efficiencies in central region and northeastern region both presented wave-shaped increase trends. There are significant inter-regional and inter-provincial differences in efficiency of each sub-industry. The distributions of efficiencies of various provinces in five sub-industries are different. No province has always been on the innovation frontier for the entire evaluation period. The province with larger number of years on the frontier generally has the higher efficiency score, although there are some exceptions. Among the provinces on the frontier in various industries, the eastern provinces account for a large proportion.
TL;DR: The drag factors which affects the above four standpoints have been explored and removed, to mend the supply chain for better profits.
TL;DR: The presented fuzzy network DEA approach is implemented for performance appraisal and ranking of investment firms (IFs) with two-stage processes including operational and portfolio management process.
Abstract: This paper presents a novel approach for performance appraisal and ranking of decision-making units (DMUs) with two-stage network structure in the presence of imprecise and vague data. In order to achieve this goal, two-stage data envelopment analysis (DEA) model, adjustable possibilistic programming (APP), and chance-constrained programming (CCP) are applied to propose the new fuzzy network data envelopment analysis (FNDEA) approach. The main advantages of the proposed FNDEA approach can be summarized as follows: linearity of the proposed FNDEA models, unique efficiency decomposing under ambiguity, capability to extending for other network structures. Moreover, FNDEA approach can be applied for ranking of two-stage DMUs under fuzzy environment in three stages: 1) solving the proposed FNDEA model for all optimistic-pessimistic viewpoints and confidence levels, 2) then plotting the results and drawing the surface of all efficiency scores, 3) and finally calculate the volume of the three-dimensional shape in below the efficiency surface. This volume can be as ranking criterion. Remarkably, the presented fuzzy network DEA approach is implemented for performance appraisal and ranking of investment firms (IFs) with two-stage processes including operational and portfolio management process. Illustrative results of the real-life case study show that the proposed approach is effective and practically very useful.
TL;DR: In this article, a hybrid methodology that combines the data envelopment analysis (DEA) Window model, and fuzzy technique for order of preference by similarity to ideal solution (FTOPSIS) was presented to evaluate the capabilities of 42 countries in terms of renewable energy production potential.
Abstract: Climate change and air pollution are among the key drivers of energy transition worldwide. The adoption of renewable resources can act as a peacemaker and give stability regarding the damaging effects of fossil fuels challenging public health as well as the tension made between countries in global prices of oil and gas. Understanding the potential and capabilities to produce renewable energy resources is a crucial pre-requisite for countries to utilize them and to scale up clean and stable sources of electricity generation. This paper presents a hybrid methodology that combines the data envelopment analysis (DEA) Window model, and fuzzy technique for order of preference by similarity to ideal solution (FTOPSIS) in order to evaluate the capabilities of 42 countries in terms of renewable energy production potential. Based on three inputs (population, total energy consumption, and total renewable energy capacity) and two outputs (gross domestic product and total energy production), DEA window analysis chose the list of potential countries, including Norway, United Kingdom, Kuwait, Australia, Netherlands, United Arab Emirates, United States, Japan, Colombia, and Italy. Following that, the FTOPSIS model pointed out the top three countries (United States, Japan, and Australia) that have the greatest capabilities in producing renewable energies based on five main criteria, which are available resources, energy security, technological infrastructure, economic stability, and social acceptance. This paper aims to offer an evaluation method for countries to understand their potential of renewable energy production in designing stimulus packages for a cleaner energy future, thereby accelerating sustainable development.
TL;DR: In this article, a static and dynamic energy structure analysis method using the total factor productivity method based on slacks-based measure integrating data envelopment analysis is put forward to better assess energy structures of 24 countries for 2008-2018.
TL;DR: In this article, data envelopment analysis is used to evaluate the energy efficiency of municipal wastewater treatment plants (WWTPs) in China, and the six outputs were COD, BOD, SS, NH3-N, TN and TP pollutant removals per amount of electricity consumption.
TL;DR: Wang et al. as mentioned in this paper empirically analyzed the total factor energy efficiency of 19 Chinese manufacturing sub-sectors in 9 provinces from 2001 to 2011 and further analyzed its influencing factors from the perspective of the dual heterogeneity.
Abstract: At present, there are serious problems of excessive energy consumption and carbon emissions in Chinese manufacturing industry. To solve these problems, it is of great importance to measure and improve total factor energy efficiency reasonably. At the same time, Chinese manufacturing industry differs significantly in various sub-sectors and regions, which means the prevalence of dual heterogeneity. Such strong heterogeneities can have a significant impact on the accurate measurement of total factor energy efficiency. However, it is unfortunately neglected in existing studies. In this paper, we empirically analyze the total factor energy efficiency of 19 Chinese manufacturing sub-sectors in 9 provinces from 2001 to 2011 and further analyzes its influencing factors from the perspective of the dual heterogeneity. To model industry and regional heterogeneities, we propose a three-level meta-frontier slacks-based measure (SBM) approach based on data envelopment analysis (DEA). The results show that: (1) The energy efficiency of Chinese manufacturing industry is generally low, and varies significantly across different industries and regions. Moreover, the energy inefficiency mainly comes from management inefficiency; (2) The dual heterogeneity affects the technology gap rate (TGR) deeply. The technology gap rate caused by industry heterogeneity (ITGR) is larger than that caused by regional heterogeneity (RTGR); (3) Enterprise scale, foreign direct investment, economy development level and industrial structure affect energy efficiency positively, while energy consumption structure and energy price affect energy efficiency negatively. In response to the above research findings, some policy recommendations are put forward in the end.
TL;DR: This paper argues that most empirical studies using frontier estimation methods such as data envelopment analysis (DEA) over-estimate MACs, and develops a novel MAC estimation approach based on convex quantile regression.
TL;DR: A comprehensive review of 161 articles published since 2000 on the application of data envelopment analysis (DEA) in supplier selection is presented in this paper, where the authors present various classifications of DEA methods based on input criteria, sectors of application, and industry-wide case studies.
Abstract: Purchasing occupies a strategic role in supply chain management for a firm and is the driver of competitive advantage Owing to the high purchase cost to revenue ratio, decisions such as evaluation, selection, and performance management of suppliers are of the matter of immense interest to firms Multi-criteria decision making tools allow the purchasing managers to evaluate the suppliers holistically One such tool, data envelopment analysis (DEA) has been used extensively for supplier evaluation and selection This paper presents a comprehensive review of 161 articles published since 2000, on the application of DEA in supplier selection These articles are located from the Scopus database With little existing literature on a full-fledged review, this work envisages to be first of its kind, by aiding DEA practitioners in purchasing function The analysis of the study indicates the emergence of the theme of green supply chain and sustainability in recent years as well as the adoption of hybrid approaches to solving the problem of supplier selection using DEA The paper presents various classifications of DEA methods based on input criteria, sectors of application, and industry-wide case studies, which can be used as a quick reckoner by an academician or a purchasing manager
TL;DR: Wang et al. as mentioned in this paper proposed a slacks-based measure network data envelopment analysis (SBM-NDEA) model with undesirable outputs to evaluate the performance of production processes that have complex structure containing both series and parallel processes.
Abstract: This paper proposes a new slacks-based measure network data envelopment analysis (SBM-NDEA) model with undesirable outputs to evaluate the performance of production processes that have complex structure containing both series and parallel processes. We demonstrate the proposed approach by evaluating Chinese commercial banks during 2012–2016. The operational process of these banks could be divided into deposit producing and deposit utilizing processes connected serially, while deposit utilizing process is further divided into profit generating and deposit reserve interest earning processes, which are parallel. The overall efficiency is decomposed into deposit producing and deposit utilizing efficiency. Deposit utilizing efficiency is further decomposed into profit generating and deposit reserve interest earning efficiency, respectively. Our empirical results suggest that the overall inefficiency is mainly from the profit generating process. The results also estimate the adjustment of variables for the network process of an inefficient bank.