This research proposed to apply the DEA method for the assessment of renewable energy efficiency. Therefore, the review of the literature with identical concepts as summarized in Table 1, is organized into two subsections including the review of efficiency analysis in renewable sectors and the review of DEA application in renewable energy. Then, the research gap and the contribution of the current study are introduced in the last subsection.
2.1. Literature Review on Efficiency Analysis
The research on renewable energy seems to be matured in terms of technology, policy, and other executed factors. Hence, the impact of renewable alternatives on sustainable development has become a further concern to scientists. That can be mentioned in the study of Muradin et al.(2018) when they compared the eco-efficiency of two biogas plants in Poland with the implementation of the life-cycle assessment (LCA) to analyze the environmental impact and the levelized cost of electricity (LCOE) to calculate the cost of the produced electricity throughout its life cycle. Some studies, on the other hand, investigate the factors of productivity like Moutinho et al.(2021) who introduced the stochastic frontier analysis (SFA), a parametric method, to interpret the incorporation of determinants for enhancing the eco-efficiency in Asia and Africa.
2.2. Literature Review on Methodology
From 2001 to 2021, there was an increase in interest to apply DEA in the evaluation of renewable energy as concluded in the systematic review of Mohd Chachuli et al. (2020). In general, the review found that the popular topics mostly concentrated on observing the efficiency of renewable resources either in the geographical areas or the technology or the policy. Also, the literature work demonstrated the trend of application in which the two-stage DEA and the advanced DEA such as SBM, and three-stage DEA emerged to be the dominant ones due to the increasing demand for data complexity.
For example, the DEA-SBM approach was employed to measure the performance of the learning effect of the municipal solid waste recycling system in 23 Taiwanese organizations (Chang, Liu and Yeh, 2013). Meleddu et al. (2018) analyzed the public spending on renewable energy in 21 Italian regions by DEA-Simar Wilson bootstrapped model. Park et al. (2018) suggested a hybrid method of the Malmquist index and DEA to review the effect of policy transition on the efficiency of South Korea’s renewable energy operation. Zeng et al. (2020) did research on the evaluation and optimization of the renewable energy plans through the two-stage DEA method.
Even though the hybrid method has been more favorable, the traditional DEA still gained its certain interest in the renewable energy field. Mezősi et al. (2018) proposed the cost-efficiency to benchmark the renewable electricity support schemes of 25 European countries by approaching only DEA. Similarly, Chachuli et al. (2021) did a study of performance evaluation of R&D activities in five types of renewable resources with regards to the policy in Malaysia.
Lately, several theories of DEA have explored the performance of renewable sectors with consideration of the ecological and economic aspects. For instance, Moutinho et al. (2017) determined the eco-efficiency in 26 countries of the EU while proposing the hybrid method of DEA and quantile regression which can check the determinants of DEA efficiency scores. The inputs were collected including labor productivity, capital productivity, the weight of fossil energy, and the share of renewable energy in GDP meanwhile the ratio GDP per Greenhouse Gas (GHG) emissions was considered as the output. The environmental taxes revenues, resource productivity, and domestic material consumption were used to examine the dependency of efficiency ranking. They concluded that the taxes on pollution/resources have a substantial influence on eco-efficiency. Ezici et al. (2020) addressed the eco-efficiency of manufacturers in the US considering the renewable and non-renewable resources with an integrated method of time series multi-region input-output (MRIO) and DEA. MRIO was referred to as an advanced method of input-output LCA since it can project the sustainable impact of the product/process on a global scale. The economic share, as well as the use of renewable and non-renewable energy, were estimated by MRIO before the determination of the economic-output-induced renewability ratio obtained by DEA. The results showed no improvement in the trend of renewable resource uses in contrast to the growth of non-renewable resource uses between 1995 and 2014. Demiral et al. (2021) established their study on the assessment of eco-efficiency and eco-productivity among 50 states in the United States by employing a two-stage analysis that involved DEA and non-parametric test as well as Tobit regression. At the first stage, CCR (developed by Charnes, Cooper, and Rhodes) and BCC (developed by Banker, Charnes, and Cooper) models of DEA was applied to define the efficiency oriented to inputs like capital, employment, and energy consumption with the outputs as CO2 emission and GDP. Meanwhile, the key performance indicators including eco-efficiency and eco-productivity were observed with DEA-SBM. Although the performance was revealed, the relationship between the input and outcome was unclear. Hence, the non-parametric method at the second stage was conducted to test the hypotheses implied for the impact of each input on states’ performance. To make clear the effect of renewable energy on the states’ outcomes, the Tobit regression was proposed to break down the contribution of green energy and fossil fuel consumption. As the result, nearly 70% of the states were efficient. Also, low CO2 emission and renewable energy use have a positive effect on the efficiency scores. Other cases like Wang et al. (2021) evaluate the capability of renewable energy production with DEA and fuzzy TOPSIS (FTOPSIS) model. To calculate the capability of 42 countries, they used population, total energy consumption, and total renewable energy capacity as the inputs to compare with the GDP and total energy production. Then, 10 countries were top-down to be ranked by FTOPSIS, a qualitative method, regarding the availability of resources, energy security, technological infrastructure, economic stability, and social acceptance. It revealed that the United States, Japan, and Australia were the top three of most potential countries in terms of renewable energy production. Rodríguez-Lozano et al. (2021) assessed the efficiency of electricity generation from both renewable and non-renewable energy sources using DEA for 126 countries across continents. In this paper, generation capacity from fossil fuels, solar, biomass, and waste, nuclear, hydroelectricity, wind, geothermal, and tide was adopted as the input while power generation of corresponding sources and CO2 equivalent were considered as the outputs. It revealed that both Africa and Oceania reached their peak of performance in 2001 while Europe achieved the best in 2008. On the other hand, both America and Asia fluctuated from 2000 to 2016. Wang et al. (2020) compared the eco-efficiency among EU countries by DEA-SBM including the Malmquist Productivity Index (MPI) so that the total productivity trend throughout the time as the result of both efficiency and technological changes can be captured. The results showed that France, Hungary, Greece, Italy, the Netherlands, Poland, Portugal, Switzerland, and the United Kingdom were efficient from 2013 to 2017. Despite that, the regression of total productivity during the period was witnessed for all countries in the investigation. In this context, Sharma (2022) studied the possibility of reducing CO2 emissions and fertilizer usage to improve the environmental efficiency in South Asian agriculture. The methodology was proposed to measure the total factor productivity by Malmquist Index incorporating DEA-SBM to obtain the technical efficiency. The efficiency of renewable energy utilization in the Asian economies was investigated by Wang et al. (2022) considering the DEA with undesirable output. In this investigation, the labor force, total energy consumption, the share of renewable energy, and total renewable energy capacity of 14 countries were compared to GDP as a desirable goal and CO2 emission as an undesirable goal. They reported that UAE, Saudi Arabia, Japan, and South Korea can utilize green energy at the most. It was also noted that the more consumption of renewable energy the less CO2 emissions.