Meta-frontier Data Envelopment Analysis Research Articles

How do seasonal and technical factors affect generation efficiency of photovoltaic power plants?

Regions with limited space for constructing renewable power generation systems need to maximize electricity generation by optimizing the operational efficiency of existing plants and selecting an optimal location for the new construction of PV power plants with favorable weather conditions and surrounding environment. Utilizing monthly input and output data, including four inputs (solar irradiation, temperature, number of modules, and photovoltaic (PV) array rated capacity) and one output (electricity generation) from utility-scale PV power plants, meta-frontier data envelopment analysis was employed in this study to identify factors contributing to power generation inefficiency. Additionally, a Monte Carlo experiment analyzed the impact of solar irradiation uncertainty on power generation efficiency. The findings revealed that the average power generation inefficiency during the study period was 0.445, primarily attributable to seasonal and technical factors. Since these factors are difficult to control once a power plant is in operation, it is important to select an optimal site for power plants by considering meteorological and geographical data. Moreover, the results indicated a significant gap between observed and simulated values of power generation efficiency, arising from variations in weather conditions, power plant site area, and the data used in the research. Employing PV modules with higher electricity output levels can boost the DC/AC ratio, thereby increasing power generation, enhancing efficiency, and contributing to a stable power supply, thus reducing daily and seasonal fluctuations in power generation.

How can green finance promote the transformation of China's economic growth momentum? A perspective from internal structures of green total-factor productivity

Green financing is a crucial tool for promoting China's economic growth and helping to achieve carbon neutrality, but its implications on the internal dynamics of green economy remain uncertain. This study examines the impacts of green finance on China’s Green Total Factor Productivity (GTFP) and its internal factors, including technical innovation, industrial structure, balanced regional growth, scale, and resource allocation efficiency, by using a three-hierarchy meta-frontier Data Envelopment Analysis (DEA) and provincial panel data from 2008 to 2020. The findings show that: (1) regional green finance development may greatly boost the growth of GTFP, and the findings remain reliable after applying a series of robustness tests; (2) further research finds that green finance enhances GTFP by promoting technological innovation, facilitating the optimization of industrial structure, and improving resource allocation efficiency; and (3) there are significant spatial heterogeneities in this effect. The effect of green finance on GTFP is more pronounced in developed and market-oriented regions. This study helps to understand the pathways of green finance affecting the transformation of green economic growth and formulate differentiated regional policies in light of local conditions.

Convex or non-convex? A new meta-frontier data envelopment analysis framework considering technology compatibility

Convex or non-convex? A new meta-frontier data envelopment analysis framework considering technology compatibility

Does green finance agglomeration improve carbon emission performance in China? A perspective of spatial spillover

Promoting the synergies between economic growth and carbon reduction is crucial for achieving carbon neutrality goals. Understanding the determinants of carbon emission performance (COP) is important for improvement. However, empirical examinations of the linkages between green finance agglomeration (GFA) and COP remain limited. Utilizing a panel data of 30 Chinese provinces from 2007 to 2019, we measure COP via meta-frontier data envelopment analysis. We employ a spatial Durbin model (SDM) to investigate the spatial spillover effects of GFA on COP. Our findings reveal COP has a substantial positive spatial correlation between provinces. We further observe that GFA has a direct and spatially dependent impact on regional COP. The effects vary based on different green finance instruments and regions. GFA enhances COP through capital formation, energy transition, and technological advancement, although asymmetry effects exist between local and neighboring regions. Promoting regional GFA formation through interprovincial cooperation and coordination for optimizing green finance allocation is vital. Moreover, increased financial support towards green, low-carbon projects and enterprises can raise overall regional COP.

Aggregation of meta-technology ratio in DEA framework using the evidential reasoning approach

The metafrontier data envelopment analysis (DEA) model is a popular evaluation technique when different decision-making units (DMUs) may exhibit production technology heterogeneity. In this framework, the group metatechnology ratio (MTR) is an important indicator to help measure the technology gap at the group level. The common approach to the group MTR aggregation is the arithmetic average approach, which requires the MTR of the DMU to satisfy the ‘additive independence’ condition. Because the MTRs are generated from the same data set and linked to each other, the MTRs do not meet the ‘additive independence’ condition. Therefore, a new aggregation approach is needed. This study applies the evidential reasoning (ER) approach to aggregate the group MTR by the transformation of the MTR of DMU to pieces of evidence. Moreover, this study proposes examples that verify the applicability and practicality of the MTR aggregation using the ER approach, including an empirical example of the evaluation practice of the transportation system of 30 provincial regions in mainland China for 2013–2019.

Sustainable development of rural regions: metafrontier data envelopment analysis of hometown tax in municipality K

Sustainable development of rural regions: metafrontier data envelopment analysis of hometown tax in municipality K

Achieving Net Zero—An Illustration of Carbon Emissions Reduction with A New Meta-Inverse DEA Approach

To achieve the goal of limiting global warming to 1.5 °C above preindustrial levels, net-zero emissions targets were proposed to assist countries in planning their long-term reduction. Inverse Data Envelopment Analysis (DEA) can be used to determine optimal input and output levels without sacrificing the set environmental efficiency target. However, treating countries as having the same capability to mitigate carbon emissions without considering their different developmental stages is not only unrealistic but also inappropriate. Therefore, this study incorporates a meta-concept into inverse DEA. This study adopts a three-stage approach. In the first stage, a meta-frontier DEA method is adopted to assess and compare the eco-efficiency of developed and developing countries. In the second stage, the specific super-efficiency method is adopted to rank the efficient countries specifically focused on carbon performance. In the third stage, carbon dioxide emissions reduction targets are proposed for the developed and developing countries separately. Then, a new meta-inverse DEA method is used to allocate the emissions reduction target to the inefficient countries in each of the specific groups. In this way, we can find the optimal CO2 reduction amount for the inefficient countries with unchanged eco-efficiency levels. The implications of the new meta-inverse DEA method proposed in this study are twofold. The method can identify how a DMU can reduce undesirable outputs without sacrificing the set eco-efficiency target, which is especially useful in achieving net-zero emissions since this method provides a roadmap for decision-makers to understand how to allocate the emissions reduction targets to different units. In addition, this method can be applied to heterogeneous groups where they are assigned to different emissions reduction targets.

Environmental Performance of China’s Industrial System Considering Technological Heterogeneity and Interaction

The industrial sector, the backbone of China’s economic development, is a key field that requires environmental management. The purpose of this study is to propose an improved data envelopment analysis (DEA) model to analyze the performance of provincial industrial systems (ISs) from 2011 to 2020 in China. To comprehensively characterize the operational framework of ISs, this study proposes an improved meta-frontier network DEA model. Unlike the existing models, the one proposed in this study not only considers the technical heterogeneity of ISs, but also reflects the interaction between IS subsystems. The empirical analysis yields valuable research findings. First, the overall environmental performance of Chinese ISs is generally low, with an average performance of 0.50, showing a U-shaped trend during the study period. Furthermore, significant regional differences are observed in the environmental performance of Chinese ISs. Second, the average performance of the production subsystem is 0.75, while the average performance of the pollution control subsystem (PTS) is 0.44. The low performance of the PTS pulls down the overall performance of Chinese ISs. Third, the technological level of Chinese ISs is low, with about 50% improvement potential. Finally, targeted suggestions to promote the green development of ISs are proposed on the basis of the empirical results.

Technology gap ratio decomposition in sugarcane farming in Indonesia

Indonesia's population and income growth have led to a higher demand for raw sugar and its derivative products; for this reason, sugarcane production must be improved, especially in central production areas. However, efforts are constrained by the disparity in resource endowment and technology. This study aimed to analyse technical, technological, and managerial gaps in sugarcane farming in different areas of East Java. The study used primary data collected through a survey during the 2019/2020 sugarcane planting season in Malang, Kediri, and Mojokerto Regencies. A structured questionnaire was used to collect data on inputs and outputs. A meta-frontier data envelopment analysis (meta-DEA) approach was used to measure the technical efficiency of sugarcane farming in each research area. The decomposition using meta-DEA showed that, on average, the meta-technical inefficiency of sugarcane farmers was caused mainly by managerial gaps, as the average managerial gap inefficiency (MGI) was more significant than the technological gap inefficiency (TGI). Malang regency showed the largest TGI and MGI, indicating that sugarcane farmers in Malang regency have lower managerial skills for decision-making and a lower level of production technology compared to the other two regions. Based on the findings of this study, the government should improve technological innovation to mitigate the technology gap that was found in our research location.

Measuring capacity utilization under the constraints of energy consumption and CO2 emissions using meta-frontier DEA: A case of China's non-ferrous metal industries

Currently, China's non-ferrous metal industry (NMI) still has the problem of overcapacity, measuring its capacity utilization (CU) and revealing its determinants is an important way to resolve the overcapacity problem of China's NMI. Using the panel data of China's 29 NMIs, this paper constructs a meta-frontier data envelopment analysis (DEA) to measure CU by taking into account the energy consumption and CO2 emissions constraints, and decompose the CU into four parts: technical efficiency, technology gap, scale efficiency, and equipment utilization. The main findings indicate that during the period of 2004–2017, the CU of China's NMI is generally low. Non-ferrous metal alloy manufacturing and rolling processing has the highest CU, while non-ferrous metals mining and dressing shows the lowest CU. From the dynamic perspective, mining and dressing experienced CU decrease, while other two sub-industries achieved CU increase. Technical inefficiency and technology gap enlargement are two main barriers of China's NMIs' CU. Besides, among 29 NMIs, there are only ten industries witnessed a CU improvement, while the remaining nineteen industries all experienced decrease of CU. CU and its determinants across China's 29 NMIs are quite different. Thus, the policies to improve CU should be made tailored to each industry's features.

Using nonradial metafrontier data envelopment analysis to evaluate the metatechnology and metafactor ratios for the Taiwanese hotel industry

AbstractThe main purpose of this study was to develop nonradial metafrontier data envelopment analysis (DEA). Previous studies commonly used assumptions involving the redial measure and convexity of metafrontier to evaluate efficiency and the technology gap. The two assumptions have limitations, and a model that modifies the two assumptions simultaneously has not been proposed. In this study, a nonradial metafrontier DEA is proposed that incorporates the assumptions of nonconvexity as well as the nonradial measure. The study also developed the MFR index for measuring the technology gap based on individual factors.

Does financial inclusion improve energy efficiency?

This paper extends the empirical debate on the effects of financial inclusion on energy efficiency. We employ a non-convex metafrontier data envelopment analysis approach to estimate energy efficiency, and examine the impacts of financial inclusion on energy efficiency using a dataset of China's 251 prefecture-level cities during 2011–2015. Our results document that financial inclusion significantly improved energy efficiency. Specifically, a unit increase in financial inclusion would lead to an increase in energy efficiency by approximately 6.5 %. To address the endogeneity, we introduce two instrumental variables into our baseline model. Estimates of two stage least squares also support our main findings. Moreover, we find that marketization is one of the primary channels for financial inclusion to improve energy efficiency. These findings are robust across estimation method, model specifications and alternative measures of both financial inclusion and energy efficiency. Policy implications are presented based on the empirical results.

Decomposition of carbon emission reduction efficiency and potential for clean energy power: Evidence from 58 countries

With more countries committing to achieving carbon neutrality targets by midcentury to mitigate global climate change, it is necessary to replace the combustion of fossil fuels in the power industry with clean energy and improve the CO2 reduction efficiency (CRE) of clean energy. Therefore, this study introduces a multi-hierarchy meta-frontier data envelopment analysis (DEA) approach to estimate the CO2 reduction inefficiency (CRI) and potential in nuclear power and three renewable power industries of 58 countries, which are decomposed into three components of industrial technology gaps, regional technology gaps, and management levels derived from the heterogeneities of regions and power industries. The dynamic performance of CO2 emissions reduction is then analyzed. The main results are as follows. (1) Among the countries in each region, China, the USA, and Russia have greater average CRE values of 0.6605, 0.4817 and 0.7486 for clean-energy power industry from 2011 to 2018, respectively. The assessment of CRE by using the regional frontier as the evaluation criterion yields the greatest efficiency. The CRE is greater when using the industrial frontier as the benchmark compared to the use of the meta-frontier. (2) Currently, Asia, America, and Europe have the CRI values of 0.3262, 0.4706 and 0.3831 for nuclear power industry, and need improvements in technology structure, regional technology gaps, and internal management, respectively. Hydropower has a lower CRI than solar and wind power, with Asia having the highest CO2 reduction potential at nearly 700 Mt; The primary reason for the CRI in Asia and Europe is management inefficiency, while in America it's the inefficiency of regional development. The large CRI values are recorded for solar and wind power, especially in Europe where the values are 0.7957 and 0.7320, respectively. (3) The CRE of the global clean-energy power industry improves by an average of 2.27% from 2011 to 2018, primarily due to technological progress and improvements in technical efficiency. In response to these findings, targeted policy implications are developed to strengthen the transition to the advanced technologies and management options for maximizing CO2 mitigation.

Route-based performance evaluation for airlines – A metafrontier data envelopment analysis approach

The international aviation industry is essential for both passengers and commodities. Previous literature tends to evaluate aviation performance from the perspective of airlines or airports. However, each route faces a different operating environment and thus has a different profit portfolio. Therefore, a route-based performance evaluation can better reflect the range of operating characteristics by considering the differing characteristics of routes. This study conducted an empirical analysis of 112 international routes from two Taiwanese airlines. To first identify the key variables that have a significant impact on airline performance, a systematic literature review was conducted using a meta-analysis approach. This study then used those key variables to measure route-based performance with data envelopment analysis (DEA) modeling. Finally, k-medoids cluster analysis was used to explore successful factors for air route performance as complementary research to the DEA modeling. Model results show three groups of routes can be clearly identified by considering route efficiency. Each group has very distinct route characteristics (e.g., Asian routes vs US routes) and some route planning recommendations can be developed. The results of this study can help operators and policy makers improve operating performance and identify key factors for route development and planning.

Integration and convergence in efficiency and technology gap of European life insurance markets

This paper applies the meta-frontier Data Envelopment Analysis and the main concepts of convergence from the economic growth literature (β-convergence and σ-convergence) to analyze integration and convergence both in efficiency and in technology gap of European Union (EU) insurance markets. We evaluate 10 EU life insurance markets over the 17-year-period 1998–2014. Results show convergence in cost/revenue efficiency among major EU life insurance markets during the sample period. These findings indicate that the least efficient countries in 1998 have shown a higher improvement in cost/revenue efficiency than the most efficient countries in the same year as well as that the dispersion of the mean efficiency scores among EU life insurance markets decreased over the sample period. We also find convergence in cost/revenue technology gap among these markets, suggesting that they become more technologically homogeneous during the sample period. However, results show that the global financial crisis has led to a slowdown in the progress of integration and convergence in efficiency and technology gap of EU life insurance markets in terms of cost efficiency but not in terms of revenue efficiency.

The Green Innovative Power of Carbon Neutrality in China: A Perspective of Innovation Efficiency in China’s High-Tech Industry Based on Meta-Frontier DEA

The high-tech industry plays a crucial role in reducing carbon emission and achieving green economic development. This research uses Meta-Frontier data envelopment analysis to measure the innovation efficiency level of the high-tech industry in China’s provinces from 1999 to 2018, compares the difference in this industry’s innovation efficiency under the regional Frontier and common Frontier, and inspects the convergence condition of its innovation efficiency in the three major areas of eastern, central, and western China. The results show under the regional Frontier that the difference in innovation efficiency of the western region’s high-tech industry is the biggest, while the difference in the central region is the smallest, and under the national common Frontier the innovation efficiency level of the eastern region’s high-tech industry is the highest, while that of the western region is the lowest. The regional pattern of innovation efficiency in the high-tech industry is consistent with the development trend of the regional economy. Moreover, by using the ratio of the technology gap ratio, we find that the eastern region has the potential optimal technology in China, whereas the central and western regions have large room for improvement. Lastly, the stochastic convergence test shows that the innovation efficiency of the central region’s high-tech industry presents a convergence trend, but the same trend does not occur in the western and eastern regions as well as for the whole country.

CO2 Reduction Potential from Efficiency Improvements in China’s Coal-Fired Thermal Power Generation: A Combined Approach of Metafrontier DEA and LMDI

Among the G20 countries, China is the only country to experience an increase in electricity generation from coal-fired thermal power plants from 2019 to 2020. This study aims to develop an analytical framework combining metafrontier data envelopment analysis with the logarithmic mean Divisia index for a detailed decomposition analysis of ‘mass-based’ energy-related CO2 reduction potential through efficiency improvements in coal-fired thermal power plants in China. The results show that inefficiency in power generation can be largely attributed to differences in the location of power plants and the production scale. Moreover, the impact of regional heterogeneity on the changes in power generation efficiency is more notable for the small–medium power plants in the northeast region than the large power plants in the western region in China. However, when focusing on the mass-based CO2 reduction potential associated with the regional heterogeneity, its positive effects in the western region for the large power plants are 6.2 times larger than that in the northeast region for the small–medium power plants. These results imply that an analysis that focuses only on the efficiency score would ignore the production scale of coal-fired thermal power plants and thus would fail to properly evaluate the environmental impacts associated with efficiency changes.

Efficiency assessment of electricity generation in China using meta-frontier data envelopment analysis: Cross-regional comparison based on different electricity generation energy sources

China is the world's largest electricity producer and consumer, and the healthy development of the electricity industry contributes to its economic growth and social stability. Yangtze River Economic Belt (YREB) and non-YREB are two major economic engines of great strategic significance for China with distinct differences in the external development environment of their power industry. A cross-regional comparison of electricity generation efficiency in China is meaningful to obtain a more accurate understanding of the power generation efficiency of YREB and non-YREB, but there is currently no literature in this area as well as none for comparing electricity generation efficiency between different energy sources. Therefore, this research uses the meta-frontier epsilon-based measure (EBM) data envelopment analysis (DEA) model to calculate the efficiency of different electricity generation energy in YREB and non-YREB for the period 2013–2017. The empirical results show that: 1) YREB's electricity industry has higher electricity generation efficiency than non-YREB's, and the efficiency gap is continuing to widen; 2) YREB's average thermal electricity generation efficiency is higher than non-YREB's; 3) YREB's clean energy power generation efficiency is always higher than non-YREB's; 4) regional differences in YREB are smaller than those in non-YREB; and 5) the efficiency value of clean energy power generation is lower than that of thermal power generation. From our analysis results, this paper presents some policy recommendations.

Meta-Frontier Data Envelopment Analysis for Performance Improvement of Forest Industry Sub-Sectors in Ontario

Meta-Frontier Data Envelopment Analysis for Performance Improvement of Forest Industry Sub-Sectors in Ontario

A non-convex metafrontier DEA model with natural and managerial disposability for pollutant tax levels and environmental efficiencies analysis

Efficiency analysis is an important step in the environmental performance evaluation research, especially in the context of the implementation of the Environmental Protection Tax Law in China. In this paper, we first consider regional heterogeneity and divide regions into high, medium, and low-cost groups based on pollution tax levels. Then, we construct a non-convex metafrontier Data Envelopment Analysis (DEA) model that can solve the heterogeneity problem well and measure the environmental efficiency of each region. In addition to considering natural disposability, we further discuss managerial disposability here. The empirical results show the difference between the efficiency and the benchmark test based on the convex or non-convex metafrontier assumption. Besides, middle and low-cost groups are encouraged to increasing environmental protection expenditure under managerial disposability. Finally, we summarize the improvement methods of provincial administrative regions under different tax rates: high-cost groups develop a green environmental economy and control pollution emissions; middle-level groups optimize industrial structure and increase investment in technological innovation; the low-tax groups increase the tax on pollutants and strengthen government control.