Renewable Energy Generation Efficiency of Asian Economies: An Application of Dynamic Data Envelopment Analysis

Abstract

Due to the continuous growth of global energy demand and the urgent pursuit of sustainable development goals, renewable energy development has become a vital strategy to deal with energy challenges and environmental issues. Renewable energy generation efficiency (REGE) around the world has begun to be examined, and ambitious goals with a sense of mission within a predetermined timeline have been set. The goal of this paper is to use the dynamic slacks-based measure (DSBM) data envelopment analysis (DEA) method to obtain the REGE for 44 Asian economies from 2010 to 2021. This paper also uses Tobit regression analysis to explore the factors that may affect the REGE. The empirical results indicate that the REGE in 17 economies reached the efficiency target during this period. When classified by income level, differences in average REGE are observed among high-income, upper-middle-income, lower-middle-income, and low-income economies. Additionally, differences in average REGE exist between tropical and temperate economies when classified by geographic latitude. Furthermore, through the Tobit regression model, we determine that information digitalization, financial openness, technological innovation ability, and renewable energy device capacity share all have significant positive effects on REGE, but life quality and democracy degree have significant negative impacts on REGE. Moreover, it has been found that the REGE scores of Asian economies exhibit a status similar to the middle-income trap. The outcome of the research provides Asian governments and those middle-income economies with ways to enhance REGE. Due to data limitations, this study cannot estimate the convergent solution based on the data of the research sample, and a new advanced Panel Tobit model is required.

1. Introduction

Energy is one of the most important driving forces for economic growth, national security, and sustainable development [1]. Due to the continuous growth of global energy demand and the urgent pursuit of sustainable development goals, the development of renewable energy has become a vital strategy to deal with energy challenges and environmental issues, and people pay attention to green energy priority [2]. Mitigating climate change has become one of the biggest challenges for people after 2000 [3]. People have noticed that energy supply is likely to exceed demand in the future, and greenhouse gas emissions or other pollutants caused by the excessive burning of fossil fuels may also increase the risk of global climate change. Therefore, people started to think about how to respond to these issues, and renewable energy has received unprecedented attention [4].

Based on the dual challenges of climate change and energy security, countries are committed to developing more powerful and stable green energy as part of their low-carbon development strategies. Since the mid-2000s, the growth rate of the renewable energy industry in East Asia has significantly exceeded that in other regions [5]. The Paris Agreement, which came into effect in 2016, further promoted the global focus on energy transformation, and increased the proportion of renewable energy use [6]. In 2021, the International Energy Agency (IEA) released the landmark report, “Net Zero by 2050: A Roadmap for the Global Energy Sector”, which is the world’s first formal document planning how to achieve a net zero energy system transition by 2050. In 2023, the IEA launched a new version, “Net Zero Roadmap: A Global Pathway to Keep the 1.5 °C Goal in Reach”, outlining the top priorities during this critical period before 2030 in more detail [7].

In addition, Renewable Energy 100 (RE100), the international renewable energy initiative, brings together the most influential companies in the world, and is committed to achieving the final goal of being powered by 100% renewable energy. More than 400 companies have joined RE100, and it has become a global trend for international private companies to declare their determination to use 100% renewable energy in the future by joining RE100 [8,9]. Therefore, the international community must find alternative solutions to achieve the goal of sustainable development to reduce the generation of greenhouse gases and solve the problem of global warming. Further, countries have begun to adopt renewable energy to reduce the use of fossil fuels. At the same time, the renewable energy power generation efficiency around the world has begun to be examined, with the aim being to achieve ambitious goals with a sense of mission within a predetermined timeline [10,11].

In the past two decades, the concept of low-carbon development has attracted widespread attention and become a research focus. Governments and enterprises around the world are scrambling to pay attention to the issue of energy carbon reduction, and low-carbon technology innovation (especially innovation for renewable energy generation technology) is regarded as a core consideration to achieve this goal [12,13,14]. Whether it is for developed or developing countries, economic development with limited resources is undoubtedly a complex challenge [15]. Cao [16] mentioned that the economic conditions and population of developing countries are growing rapidly, causing them to become major emitters of greenhouse gases, so they have a strong incentive to make more efforts to promote energy efficiency and use renewable energy.

By understanding the long-term sustainable energy policy development history of other countries, we can derive a reference guide for countries in designing appropriate and effective energy policies [17]. Although Asian countries have made a lot of progress in the field of renewable energy development, there are still a series of challenges and problems relating to renewable energy generation efficiency. In understanding the renewable energy generation efficiency of neighboring countries, a country can use this as a reference benchmark to formulate and implement appropriate renewable energy development policies in the future.

Furthermore, Tran [18] pointed out that a common problem faced by many members of the Association of Southeast Asian Nations is whether they could avoid falling into the middle-income trap, and elevate themselves into high-income countries successfully. Felipe et al. [19] discussed the so-called “Middle-Income Trap”, which means that some countries fall into the predicament of stagnant economic growth after reaching the middle income level; that study pointed out that the “Middle-income Trap” includes the characteristics of difficulty in improving labor productivity, bottlenecks in technological innovation, and obstacles to industrial transformation.

Since Asian economies have become the rising stars of the global economy due to their natural resources, abundant labor force, and demographic dividends, it is also vital to pay attention to their renewable energy generation efficiency in relation to sustainable development. There is research exploring the renewable energy generation efficiency in single or several countries, such as in Germany, the United States, Japan and China, and in countries with different income levels, but not in Asian economies. This paper aims to analyze the characteristics of renewable energy generation efficiency in Asian economies and explore the factors that have a significant impact on renewable energy generation efficiency using data from Asian economies. Besides this, this paper also explores whether there is a phenomenon similar to the middle-income trap in the scope of renewable energy generation efficiency in Asian economies.

The structure of this article is as follows: Section 2 reviews the theoretical framework and related literature on the dynamic slacks-based measure (DSBM) and factors affecting renewable energy generation efficiency. Section 3 discusses the research methods and data. Section 4 defines the data sources and presents the research results. The fifth section ends with conclusions, research limitations, and future study suggestions.

2. Theoretical Framework and Literature Review

2.1. Dynamic Slacks-Based Measure (DSBM)

The dynamic slacks-based measure provides a more accurate performance evaluation (discriminative power) for measuring renewable energy generation efficiency due to its structural features. Tone [20] proposed an efficiency measurement method in the field of Data Envelopment Analysis (DEA), called the slacks-based measure (SBM). By introducing the concept of “slacks”, SBM aims to respond directly to the input excesses and output shortfalls of decision-making units (DMUs) so as to facilitate a more comprehensive assessment of the relative performance of decision-making units. The structure of SBM has unit invariance and shows a monotonic decrease in the difference, and the goal is to maximize the use of the resources of the decision-making unit, so this method is more flexible and accurate in terms of measuring efficiency. The methods Charnes et al. [21] and Banker et al. [22] proposed involve assessing relative efficiency mainly based on the proportional change of input and output vectors. Both methods do not take into account the slacks, and struggle to reflect the actual situation of the decision-making unit. Therefore, compared with the previous two traditional methods, SBM appears to be a better method for capturing the relative efficiency of decision-making units, and provides a new perspective for efficiency analysis in the field of DEA.

Then, for long-term evaluations, Tone and Tsutsui [23] developed the DSBM model based on SBM, which can evaluate the overall efficiency of all decision-making units and the dynamic efficiency of the period, aiming to solve the limitations of traditional methods in evaluating the efficiency changes of carry-over activities during a connected period. There are four categories (Desirable Carry-Overs, Undesirable Carry-Overs, Free Carry-Overs, and Fixed Carry-Overs), divided based on the characteristics of carry-overs. Through the DSBM model, long-term optimization can be carried out throughout the entire activity period, and the efficiency of the decision-making unit in a specific period can be comprehensively evaluated to provide a more accurate performance evaluation.

For exploring the energy generation efficiency, Fallahi et al. [24] employed DEA to explore energy technology efficiency and productivity changes in 32 power management companies in Iran between 2005 and 2009, showing that productivity changes were modest and were driven primarily by inefficiencies rather than differences in technology. Sueyoshi and Goto [25] focused on comparing the efficiency changes of solar power plants in Germany and the United States, covering 80 power plants in each country. The empirical results indicate that the efficiency of German solar power plants is significantly higher even though the United States has great innate advantages in relation to solar energy and land resources, so the United States should think about how to coordinate energy policy with financial support and technological innovation to achieve a balance between benefits and costs in the process of implementing renewable energy policies. Wu et al. [26] used a two-stage analysis method to explore the production efficiency of 42 wind farms in China. In the first stage, DEA is used to obtain the efficiency value of the wind farm; in the second stage, Tobit regression analysis is used to discuss the relationship between the efficiency value and environmental variables that cannot be controlled by the wind farm. The empirical results show that the installed capacity and wind power density are key inputs affecting wind farm efficiency.

As for the energy generation efficiency in the United States and Japan, Sağlam [27] selected wind farms across 39 states in the United States as the research object and used a two-stage DEA analysis method to explore their relative generation efficiency, which showed that more than half of wind farms are highly efficient, but wind farms built earlier are more expensive and less efficient than those more recently installed. Hu et al. [28] used the output-oriented DSBM model to explore the differences in renewable energy generation efficiency in 47 administrative regions in Japan from 2016 to 2019, and then used panel data to conduct random effects on Tobit regression analysis to get the factors affecting the renewable energy power generation efficiency in various administrative regions in Japan. The empirical results show that Japan’s average renewable energy generation efficiency is generally lower than 30%, while renewable energy generation efficiency is negatively correlated with population density and positively correlated with regional income.

Considering the income level and generation efficiency, Rakshit and Mandal [29] used the DEA framework to analyze Environmental Energy Efficiency (EEE) and discussed the EEE results under different models for the high-income, middle-income, and low-income economies from 1993 to 2013. The empirical results show that global EEE has gradually improved, except for in the periods 1998 to 1999 and 2009, and the improvement was mainly led by high-income economies, while middle-income and low-income economies were lagging behind. It is worth noting that high-income economies account for a larger proportion of the use of renewable energy, which may be one of the reasons for their improvement in EEE.

The literature above applied the DEA analysis method to explore energy generation efficiency, and is summarized in

Table 1. Studies in the literature applied the DEA analysis method to explore energy generation efficiency.

Authors	Methods	Measurement Target	Inputs	Outputs
Fallahi et al. (2011) [24]	DEA	Generation efficiency	(1) Installed capacity (2) Fuel (3) Labor (4) Electricity used (5) Average operational time	Net electricity produced
Sueyoshi and Goto (2014) [25]	DEA	Generation efficiency	(1) Estimated annual insolation (2) Photovoltaic modules (3) Land area (4) Estimated annual average sunshine	(1) Installed capacity (2) Annual generation
Wu et al. (2016) [26]	Two-stage DEA	Generation efficiency	(1) Installed capacity (2) Auxiliary electricity consumption (3) Wind power density	(1) Electricity generated (2) Availability
Sağlam (2017) [27]	Two-stage DEA	Generation efficiency	(1) Installed wind capacity (2) Number of wind turbines (3) Total project(s) investment (4) Annual land lease payment	(1) Net generation (2) Percentage of in-state energy production (3) Number of U.S. homes powered (4) Wind industry employment (5) Annual water savings (6) CO2 emissions avoided
Hu et al. (2023) [28]	Output-oriented DSBM model, random effect Tobit regression analysis of longitudinal and cross-sectional data	Renewable energy generation efficiency	(1) Installed capacity (2) Forest size (3) Natural park size (4) Precipitation (5) Sunshine (6) Wind speed *	Renewable electricity generation

* Installed capacity is the adjustable input, forest size and natural park size are fixed inputs, and precipitation, sunshine, and wind speed are free carry-overs.

. All tables and figures in this paper were developed by Microsoft Excel 2019.

2.2. Technological Dimensions

The technology can have an impact on renewable energy generation efficiency through the degree of information digitization, financial openness, technological innovation capabilities, and renewable energy installation capacity. Chien and Hu [30] showed that increasing the use of renewable energy has a positive impact on the technical efficiency of the economy, while increasing the use of traditional energy has a negative impact on the technical efficiency of the economy. In addition, they found that the technical efficiency of OECD economies is higher, and geothermal, solar, tidal, and wind energy account for a higher proportion of renewable energy in these economies.

Concerning information digitization or financial openness, Rehman et al. [31] pointed out that the degree of information digitization has a positive impact on renewable energy generation. Lin and Huang [32] showed that digitization has positive moderating effects on the relationship between renewable energy device capacity and power generation. Zheng and Wong [33] stated that there is indeed a positive correlation between the digital economy and the development of renewable energy. Koengkan et al. [34] showed that the degree of financial openness has a positive impact on the development of renewable energy, and they mentioned that the KAOPEN financial index proposed by Chinn and Ito [35] is widely used as a proxy variable to measure financial openness. Wang et al. [36] stated that the degree of integration of financial openness plays a key role in driving the consumption of renewable energy, and therefore recommended that the financial systems of various countries should cooperate with each other, which will contribute to the comprehensive development of renewable energy systems in Asia.

As for technological innovation capabilities and renewable energy installation capacity, Abdmouleh et al. [4] stated that the key to determining whether renewable energy technologies can develop successfully is the integration advantages of mutually supporting software and hardware measures. Koengkan et al. [34] pointed out that the technological improvement of renewable energy is regarded as an ideal solution to achieve sustainable development, and Rehman et al. [31] showed that technological innovation capabilities have a positive impact on renewable energy generation. Dent [5] pointed out that developing East Asian countries tend to adopt an installation capacity-oriented strategy and pay attention to the generation efficiency and increase goals of renewable energy. Koengkan et al. [34] showed that increasing investment in renewable energy installation capacity has a positive impact on the development of renewable energy.

2.3. Social Dimensions

Society can have an impact on renewable energy generation efficiency through the degree of life quality or democracy. Shah et al. [12] showed that environmental policies have positive moderating effects on the relationship between life quality and renewable energy power generation. Dumbrell et al. [37] pointed out that safety is an important consideration for people in supporting the green energy industry. Huijts and van Wee [38] believe that psychological variables are the most critical factor affecting people’s acceptance of setting up hydrogen fuel stations, and the concept of the “NIMBY effect” is also echoed in the research results of Guo et al. [39]. Wolsink [40] and O’Neil [41] verified that the development of renewable energy will indeed be affected by the unfriendly sentiments of local residents. Based on concerns that life quality in the future will be affected, this has intensified the difficulty of building and operating renewable energy facilities.

As for the degree of democracy, Lio and Hu [42] pointed out that a higher degree of democracy is related to lower agricultural efficiency, and Chen et al. [43] showed that the degree of democracy will affect the relationship between economic growth and renewable energy consumption.

In addition to considering the factors of technical dimensions or social dimensions, there are three other factors that also have an impact on renewable energy generation efficiency based on the literature previously mentioned, as follows:

(1)

Population density—Hu et al. [28] showed that population density has a negative impact on the efficiency of renewable energy generation;

(2)

Population growth rate—Shah et al. [37] pointed out that population growth rate has a positive impact on renewable energy generation;

(3)

Per capita income—Koengkan et al. [34] showed that per capita income has a positive impact on the development of renewable energy. According to the conclusion of Nguyen et al. [44], increasing per capita income will be more conducive to the adoption of capital-intensive renewable energy generation technologies, especially for developing countries.

3. Research Methods, Variables, and Data Sources

3.1. Research Methods

This paper first uses the output-oriented DSBM model to obtain the renewable energy generation efficiency of Asian economies. And then, it uses Tobit regression analysis to explore the factors that may affect the renewable energy generation efficiency.

3.1.1. DSBM

In traditional DEA models, only data from the same time point are usually processed. When considering the time factor and the performance of each decision-making unit at different points in time, DEA must be extended to dynamic situations, and correlations between data spanning multiple points in time must be considered. This paper uses the DSBM model that Tone and Tsutsui [23] developed. The model is as follows.

(1)

Restricted formula.

$$a_{it}\ge \sum _{j=1}^n{a}_{ijt}{\lambda }_j^t (i=1,\dots , h ; t=1,\dots ,T)$$

$$b_{it}\le \sum _{j=1}^n{b}_{ijt}{\lambda }_j^t (i=1,\dots , V ; t=1,\dots ,T)$$

$$c_{it}^{free}\le \sum _{j=1}^n{c}_{ijt}^{free}{\lambda }_j^t (i=1,\dots , n_{free} ; t=1,\dots ,T)$$

$${\lambda }_j^t\ge 0 (j=1,\dots , n ; t=1,\dots ,T)$$

$$\sum _{j=1}^n{\lambda }_j^t=1 (t=1,\dots ,T)$$

n is number of DMUs, T is number of periods, h is number of inputs, and V is number of outputs.

$${\lambda }_j^t\in R^n (t=1,\dots ,T):\mathrm{w}\mathrm{e}\mathrm{i}\mathrm{g}\mathrm{h}\mathrm{t} \mathrm{v}\mathrm{e}\mathrm{c}\mathrm{t}\mathrm{o}\mathrm{r} \mathrm{i}\mathrm{n} \mathrm{p}\mathrm{e}\mathrm{r}\mathrm{i}\mathrm{o}\mathrm{d} t$$

$$a_{ijt} \left(i=1,\dots , h\right):\mathrm{t}\mathrm{a}\mathrm{r}\mathrm{g}\mathrm{e}\mathrm{t} \mathrm{o}\mathrm{f} \mathrm{i}\mathrm{n}\mathrm{p}\mathrm{u}\mathrm{t}\mathrm{s}$$

$$b_{ijt} \left(i=1,\dots , V\right):\mathrm{t}\mathrm{a}\mathrm{r}\mathrm{g}\mathrm{e}\mathrm{t} \mathrm{o}\mathrm{f} \mathrm{o}\mathrm{u}\mathrm{t}\mathrm{p}\mathrm{u}\mathrm{t}\mathrm{s}$$

$$c_{it}^{free}:\mathrm{F}\mathrm{r}\mathrm{e}\mathrm{e} \mathrm{C}\mathrm{a}\mathrm{r}\mathrm{r}\mathrm{y}-\mathrm{O}\mathrm{v}\mathrm{e}\mathrm{r}\mathrm{s}$$

The following formula represents the restriction of the free carry-overs connecting period t and period t + 1:

$$\sum _{j=1}^n{c}_{ijt}^{free}{\lambda }_j^t=\sum _{j=1}^n{c}_{ijt}^{free}{\lambda }_j^{t+1} (\forall i ; i=1,\dots , T-1)$$

(1)

(2)

DMU_o (o = 1, …, n) can be expressed as follows:

$$a_{iot}=\sum _{j=1}^n{a}_{ijt}{\lambda }_j^t+k_{it}^{-} (i=1,\dots , h ; t=1,\dots ,T)$$

$$b_{iot}=\sum _{j=1}^n{b}_{ijt}{\lambda }_j^t-k_{it}^{+} (i=1,\dots , V ; t=1,\dots ,T)$$

$$c_{iot}^{free}\le \sum _{j=1}^n{c}_{ijt}^{free}{\lambda }_j^t (i=1,\dots , n_{free} ; t=1,\dots ,T)$$

$$\sum _{j=1}^n{\lambda }_j^t=1 (t=1,\dots ,T)$$

$${\lambda }_j^t\ge 0, k_{it}^{-}\ge 0,k_{it}^{+}\ge 0 (j=1,\dots , n ; t=1,\dots ,T)$$

(2)

(3)

Target formula.

The total efficiency of the output-oriented DSBM model is

$${\displaystyle \frac{1}{{\tau }_o^{*}}}=max{\displaystyle \frac{1}{T}}\sum _{t=1}^T\left[1+{\displaystyle \frac{1}{V}}\left(\sum _{i=1}^V{\displaystyle \frac{k_{it}^{+}}{b_{iot}}}\right)\right]$$

(3)

(4)

Combining the restricted formula and the target formula to reach the solution.

The efficient value of DMU_o in t period is

$${\tau }_{ot}^{\ast }={\displaystyle \frac{1}{1+\frac{1}{V}\left(\sum _{i=1}^V\frac{k_{iot}^{+\ast }}{b_{iot}}\right)}},t=1,\dots ,T$$

(4)

The total efficiency of the output-oriented DSBM model is

$${\displaystyle \frac{1}{{\tau }_o^{\ast }}}={\displaystyle \frac{1}{T}}\sum _{t=1}^T\left[{\displaystyle \frac{1}{{\tau }_{ot}^{\ast }}}\right]$$

(5)

where $k_{iot}^{+\ast }$ is the optimal slacks for the output; if ${\tau }_o^{\ast }=1$, this represents ${DMU}_o$ obtaining the total output efficiency.

3.1.2. Tobit Regression Model

Tobit regression analysis is a statistical method used to deal with restricted explained variables, especially when the explained variables are truncated or cut. Since the relative efficiency value obtained by the DSBM model must fall between 0 and 1, Tobit regression analysis is particularly suitable for this situation. Considering that the data used in this paper have both cross-sectional and time series properties, this paper originally planned to use random effects Tobit regression analysis in the second stage.

However, on the one hand, when using the fixed effects model to process longitudinal and cross-sectional data, it is assumed that different individuals have different individual effects, and their heterogeneity is captured through a specific intercept for each individual. When combined with the Tobit regression model, especially when the time dimension of the longitudinal and cross-sectional data is low, it increases the complexity of the model and makes it so that the fixed effect Tobit regression model is unable to find the closed-form solution directly, since Tobit regression analysis involves estimating values that are truncated or trimmed [45].

On the other hand, when using the random effects model to process longitudinal and cross-sectional data, individual differences are regarded as randomly generated, random intercept terms are further introduced to capture individual heterogeneity, and it is assumed that these random intercept terms come from certain population distributions. When combined with the Tobit regression model, especially for truncated observations, due to the high data requirements of the random effects model, when heterogeneous data in a short period of time are limited, this may lead to a slowing down of the convergence of the model, or it may affect the accuracy of parameter estimates [46].

Besides this, the estimation results of this paper’s random effects Tobit regression model show no convergence. Therefore, this paper does not use longitudinal and cross-sectional data analysis, but only uses Tobit regression to explore the positive and negative effects of explanatory variables on the explained variables.

3.2. Variables and Data Sources

Considering that this paper aims to explore the renewable energy generation efficiency in Asian economies based on an analysis of the possibility of maximizing the output of renewable energy generation, this paper uses the output-oriented DSBM model for research, which selects seven inputs and one output to measure the renewable energy generation efficiency in Asian economies, as shown in

Table 2. Output-oriented DSBM model.

Method	Purpose	Input	Output
Output-oriented DSBM model	Obtain renewable energy generation efficiency for Asian economies	(1) Adjustable input: installed capacity (2) Fixed inputs: forest size, agricultural land size, and surface size (3) Free carry-overs: precipitation, sunshine, and wind speed	Renewable energy generation

. The selection of these inputs is based on the consideration of their potential impact on the renewable energy generation efficiency, following the approach of the studies above. For example, renewable energy installed capacity reflects the size of renewable energy equipment, while forest size, agricultural land size, and surface size may affect biomass energy output. In addition, precipitation has an important impact on hydropower generation, sunshine affects solar power generation, and wind speed is closely related to wind power generation.

Therefore, these inputs provide an analytical framework to help assess the renewable energy generation efficiency in Asian economies. And since precipitation, sunshine, and wind speed are all variables that cannot be changed under a given environment, there is no preset limit on the direction of their influence on the output (renewable energy generation), so the three are set as free carry-overs. The definitions and data sources of inputs, output, and carry-over variables in this paper are detailed in Table 3.

As for the Tobit regression model, this paper uses the renewable energy power generation efficiency of Asian economies obtained from the output-oriented DSBM model as the explained variable, and uses six main research variables as explanatory variables for analysis; the explanatory variables include information digitization, financial openness, technological innovation capabilities, renewable energy device capacity share, life quality and democracy degree. That is, Tobit regression is used to explore the positive and negative effects of explanatory variables on the explained variables, and to understand the characteristics of factors that have an impact on the renewable energy generation efficiency. The definitions and data sources of the explained variables, explanatory variables, control variables, and dummy variables in this paper are shown in Table 4.

According to geographical classifications (bounded by the equator (0 degrees latitude), the Tropic of Cancer (23.5 degrees latitude), and the polar circle (66.5 degrees latitude)), the southern and northern hemispheres are divided into tropical, temperate, and cold zones. This paper classifies each economy by its geographical latitude, indicated by NASA—Giovanni. As for the income classification, the World Bank classifies economies for analytical purposes into four income groups (low-, lower-middle-, upper-middle-, and high-income) by using gross national income (GNI) per capita data in U.S. dollars, converted from the local currency using the World Bank Atlas method, which is applied to smooth exchange rate fluctuations. The data were collected in January 2024, and each economy has been classified as low-income (≤1135), lower-middle-income (1136~4465), upper-middle-income (4466~13,845), or high-income (>13,846) according to World Bank analytical classifications [47,48].

Table 3. Definitions and data sources for DSBM model.

Indicator	Measurement	Definitions	Data Sources
Adjustable input
Installed capacity	MW	Renewable energy installed capacity	International Renewable Energy Agency [49]
Fixed input
Forest size	km2	Forest size	World Development Indicators [48]
Agricultural land size	km2	Agricultural land size	World Development Indicators [48]
Surface size	km2	Surface size	World Development Indicators [48]
Free carry-overs
Precipitation	mm/day	Average monthly total surface rainfall	NASA—Giovanni [47]
Sunshine	W/m2	Average monthly incident shortwave radiation on the surface	NASA—Giovanni [47]
Wind speed	m/s	Monthly average surface wind speed	NASA—Giovanni [47]
Output
Renewable energy generation	GWh	Renewable energy generation	International Renewable Energy Agency [49]

Table 3. Definitions and data sources for DSBM model.

Indicator	Measurement	Definitions	Data Sources
Adjustable input
Installed capacity	MW	Renewable energy installed capacity	International Renewable Energy Agency [49]
Fixed input
Forest size	km2	Forest size	World Development Indicators [48]
Agricultural land size	km2	Agricultural land size	World Development Indicators [48]
Surface size	km2	Surface size	World Development Indicators [48]
Free carry-overs
Precipitation	mm/day	Average monthly total surface rainfall	NASA—Giovanni [47]
Sunshine	W/m2	Average monthly incident shortwave radiation on the surface	NASA—Giovanni [47]
Wind speed	m/s	Monthly average surface wind speed	NASA—Giovanni [47]
Output
Renewable energy generation	GWh	Renewable energy generation	International Renewable Energy Agency [49]

Table 4. Definitions and data sources for Tobit regression model.

Variable	Code	Measurement	Definitions	Data Sources
Explained variable
Renewable energy generation efficiency	REGE	0 to 1	The value obtained from output-oriented DSBM model	DSBM model
Explanatory variable of technological dimension
Information digitization	DIGI	% of population	Proportion of population using the Internet	World Development Indicators [48]
Financial openness	KFI	Chinn-Ito Index	KAOPEN, an index measuring a country’s degree of capital account openness	The Chinn–Ito Index [50]
Technological innovation capabilities	INV	Quantity	Patent applications of domestic residents and non-residents	World Development Indicators [48]
Renewable energy device capacity share	RESHARE CAP	%	Proportion of renewable energy installation capacity to overall power installation capacity	International Renewable Energy Agency [49]
Explanatory variable of social dimension
Life quality	QOL	% of population	Proportion of population with basic drinking water service	Health Nutrition and Population Statistics [51]
Democracy degree	EDI	Electoral Democracy Index	V-Dem Democracy Index	V-Dem (Varieties of Democracy) [52]
Control variable
Population density	POP DENS	People per sq. km	Population per unit land area	World Development Indicators [48]
Population growth	POP GR	Annual %	Proportion of increasing in the population from year t−1 to t	Health Nutrition and Population Statistics [51]
GDP based on PPP	PPP GDP	GDP per capita, PPP (2017 constant international $)	GDP based on purchasing power parity	World Development Indicators [48]
Geographical latitude	TROP	Categorical variable	TL: Tropical (23.5° S–23.5° N), TE: Temperate (23.5° N–66.5° N), CZ: Cold zone (66.5° N–90° N)	NASA—Giovanni [47]
Dummy variable
Middle-income economy	MI (including middle–high income and middle–low income)	Categorical variable	HI (high-income), UMI (upper-middle-income), LMI (lower-middle-income), LI (low-income)	World Development Indicators [48]

Table 4. Definitions and data sources for Tobit regression model.

Variable	Code	Measurement	Definitions	Data Sources
Explained variable
Renewable energy generation efficiency	REGE	0 to 1	The value obtained from output-oriented DSBM model	DSBM model
Explanatory variable of technological dimension
Information digitization	DIGI	% of population	Proportion of population using the Internet	World Development Indicators [48]
Financial openness	KFI	Chinn-Ito Index	KAOPEN, an index measuring a country’s degree of capital account openness	The Chinn–Ito Index [50]
Technological innovation capabilities	INV	Quantity	Patent applications of domestic residents and non-residents	World Development Indicators [48]
Renewable energy device capacity share	RESHARE CAP	%	Proportion of renewable energy installation capacity to overall power installation capacity	International Renewable Energy Agency [49]
Explanatory variable of social dimension
Life quality	QOL	% of population	Proportion of population with basic drinking water service	Health Nutrition and Population Statistics [51]
Democracy degree	EDI	Electoral Democracy Index	V-Dem Democracy Index	V-Dem (Varieties of Democracy) [52]
Control variable
Population density	POP DENS	People per sq. km	Population per unit land area	World Development Indicators [48]
Population growth	POP GR	Annual %	Proportion of increasing in the population from year t−1 to t	Health Nutrition and Population Statistics [51]
GDP based on PPP	PPP GDP	GDP per capita, PPP (2017 constant international $)	GDP based on purchasing power parity	World Development Indicators [48]
Geographical latitude	TROP	Categorical variable	TL: Tropical (23.5° S–23.5° N), TE: Temperate (23.5° N–66.5° N), CZ: Cold zone (66.5° N–90° N)	NASA—Giovanni [47]
Dummy variable
Middle-income economy	MI (including middle–high income and middle–low income)	Categorical variable	HI (high-income), UMI (upper-middle-income), LMI (lower-middle-income), LI (low-income)	World Development Indicators [48]

4. Data Collection and Empirical Findings

4.1. Data Collection

The economies included in this paper mainly cover Asia (including East Asia and Southeast Asia), Eurasia, and the Middle East, classified by the International Renewable Energy Agency (IRENA), the National Aeronautics and Space Administration (NASA), and the World Bank.

IRENA was formally established in 2009, and renewable energy became the focus of international attention afterward. After reviewing the renewable energy installation capacity and renewable energy generation data for each economy, which can be downloaded from the official IRENA website, we determined that many economies do not have renewable energy installation capacity data from before 2009, although the data can be traced back to 2000. Based on the characteristics of DEA, the output can be zero, but the input must not be zero. Therefore, in order to obtain more complete data, this paper selected data from 2010 to 2021 for analysis. In addition, there are still many missing data, such as those regarding the renewable energy installation capacity in Hong Kong, Kuwait, Macau, Oman, Palestine, and Qatar after 2010, and NASA does not have data on precipitation, sunshine, and wind speed for Myanmar, so these seven economies are excluded.

To sum up, 51 economies were originally covered, and this paper finally conducted an empirical study using data from 44 Asian economies from 2010 to 2021 after excluding missing data. The characteristics and geographical locations of 44 Asian economies are shown in

Table 5. The characteristics of the 44 economies.

No.	Economy	Code	Income	Latitude	No.	Economy	Code	Income	Latitude
1	Afghanistan	AFG	LI	Temperate	23	Lebanon	LBN	LMI	Temperate
2	Armenia	ARM	UMI	Temperate	24	Malaysia	MYS	UMI	Tropical
3	Azerbaijan	AZE	UMI	Temperate	25	Maldives	MDV	UMI	Tropical
4	Bahrain	BHR	HI	Temperate	26	Mongolia	MNG	LMI	Temperate
5	Bangladesh	BGD	LMI	Tropical	27	Nepal	NPL	LMI	Temperate
6	Bhutan	BTN	LMI	Temperate	28	Pakistan	PAK	LMI	Temperate
7	Brunei Darussalam	BRN	HI	Tropical	29	Philippines	PHL	LMI	Tropical
8	Cambodia	KHM	LMI	Tropical	30	Russian Federation	RUS	UMI	Cold zone
9	China	CHN	UMI	Temperate	31	Saudi Arabia	SAU	HI	Temperate
10	Georgia	GEO	UMI	Temperate	32	Singapore	SGP	HI	Tropical
11	India	IND	LMI	Tropical	33	Sri Lanka	LKA	LMI	Tropical
12	Indonesia	IDN	UMI	Tropical	34	Syrian Arab Republic	SYR	LI	Temperate
13	Iran, Islamic Rep.	IRN	LMI	Temperate	35	Taiwan	TWN	HI	Tropical
14	Iraq	IRQ	UMI	Temperate	36	Tajikistan	TJK	LMI	Temperate
15	Israel	ISR	HI	Temperate	37	Thailand	THA	UMI	Tropical
16	Japan	JPN	HI	Temperate	38	Timor-Leste	TLS	LMI	Tropical
17	Jordan	JOR	LMI	Temperate	39	Turkiye	TUR	UMI	Temperate
18	Kazakhstan	KAZ	UMI	Temperate	40	Turkmenistan	TKM	UMI	Temperate
19	Korea, Dem. People’s Rep.	PRK	LI	Temperate	41	United Arab Emirates	ARE	HI	Tropical
20	Korea, Rep.	KOR	HI	Temperate	42	Uzbekistan	UZB	LMI	Temperate
21	Kyrgyz Republic	KGZ	LMI	Temperate	43	Viet Nam	VNM	LMI	Tropical
22	Lao PDR	LAO	LMI	Tropical	44	Yemen, Rep.	YEM	LI	Tropical

.

There are 44 economies cited in this article, including Afghanistan, Armenia, Azerbaijan, Bahrain, Bangladesh, Bhutan, Brunei, Cambodia, China, Georgia, India, Indonesia, Iran, Iraq, Israel, Japan, Jordan, Kazakhstan, Korea, Dem. People’s Rep. (North Korea), Korea, Rep. (Republic of Korea), Kyrgyz, Lao, Lebanon, Malaysia, Maldives, Mongolia, Nepal, Pakistan, Philippines, Russia, Saudi Arabia, Singapore, Sri Lanka, Syria, Taiwan, Tajikistan, Thailand, Timor-Leste, Turkiye, Turkmenistan, United Arab Emirates, Uzbekistan, Viet Nam, and Yemen.

4.2. Empirical Findings

4.2.1. Empirical Findings of DSBM

The narrative statistics of the inputs, output, and carry-over variables of the output-oriented DSBM model are shown in

Table 6. Descriptive statistics of indicators of the DSBM model.

Indicator	Mean	Std. Dev.	Minimum	Maximum	Measurement
Adjustable input
Installed capacity	21,068.81	90,853.87	0.4680	1,020,234	MW
Fixed input
Forest size	319,004.5	1,245,718	5.2000	8,153,116	km2
Agricultural land size	423,557.4	926,832.7	6.6000	5,274,623	km2
Surface size	1,092,298	2,890,399	300.0000	17,098,250	km2
Free carry-overs
Precipitation	4.2396	4.2645	0.0539	21.9883	mm/day
Sunshine	214.3452	29.8243	116.4437	281.3223	W/m2
Wind speed	4.8560	1.0113	2.3678	6.7301	m/s
Output
Renewable energy generation	57,030.40	236,147.5	0.8100	2,405,538	GWh

, with 528 observations.

This paper uses DEA-Solver Pro. V13.0 software legally bought by National Yang Ming Chiao Tung University with the sponsorship of the National Science and Technology Council to conduct output-oriented DSBM model analysis.

Table 7. Renewable energy generation efficiency in 44 economies from 2010 to 2021.

No.	Code	Income	Latitude	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021	Total Efficiency	Rank
1	AFG	LI	Temperate	0.710	0.596	0.582	0.661	0.752	0.867	0.733	0.712	0.826	1	0.943	0.951	0.755	23
2	ARM	UMI	Temperate	0.400	0.379	0.341	0.281	0.242	0.254	0.256	0.265	0.302	0.380	0.336	0.502	0.313	39
3	AZE	UMI	Temperate	0.353	0.272	0.181	0.148	0.140	0.196	0.255	0.261	0.304	0.342	0.279	0.326	0.232	42
4	BHR	HI	Temperate	1	1	1	1	1	1	1	1	1	1	1	1	1	1
5	BGD	LMI	Tropical	0.297	0.303	0.300	0.271	0.238	0.326	0.466	0.485	0.508	0.542	0.518	0.477	0.364	35
6	BTN	LMI	Temperate	1	1	1	1	1	1	1	1	1	1	1	1	1	1
7	BRN	HI	Tropical	1	1	1	1	1	1	1	1	1	1	1	1	1	1
8	KHM	LMI	Tropical	1	1	1	0.999	0.999	0.566	0.570	0.556	0.720	0.625	0.520	0.706	0.721	24
9	CHN	UMI	Temperate	0.294	0.252	0.301	0.289	0.322	0.347	0.371	0.391	0.458	0.562	0.587	0.718	0.370	34
10	GEO	UMI	Temperate	0.392	0.329	0.308	0.334	0.382	0.455	0.543	0.531	0.707	0.821	0.775	0.931	0.473	30
11	IND	LMI	Tropical	0.233	0.255	0.280	0.269	0.299	0.312	0.285	0.299	0.359	0.464	0.548	0.600	0.321	38
12	IDN	UMI	Tropical	1	1	1	1	1	1	1	1	1	1	1	1	1	1
13	IRN	LMI	Temperate	0.102	0.129	0.126	0.146	0.143	0.196	0.245	0.282	0.449	0.798	0.685	0.471	0.203	43
14	IRQ	UMI	Temperate	1	0.999	0.999	0.999	0.999	0.999	0.999	0.999	0.999	1	1	1	0.999	18
15	ISR	HI	Temperate	0.999	0.999	0.999	0.999	0.999	0.999	0.999	1	1	1	1	1	0.999	18
16	JPN	HI	Temperate	1	1	1	1	1	1	1	1	1	1	1	1	1	1
17	JOR	LMI	Temperate	1	1	1	1	1	1	1	1	1	1	1	1	1	1
18	KAZ	UMI	Temperate	0.353	0.346	0.313	0.302	0.326	0.424	0.589	0.645	0.689	0.724	0.741	0.802	0.456	31
19	PRK	LI	Temperate	0.334	0.351	0.349	0.340	0.346	0.283	0.381	0.398	0.518	0.540	0.662	0.888	0.405	32
20	KOR	HI	Temperate	0.265	0.245	0.235	0.233	0.202	0.233	0.308	0.351	0.437	0.485	0.597	0.642	0.305	40
21	KGZ	LMI	Temperate	1	1	1	1	1	0.854	0.899	1	1	1	1	1	0.977	22
22	LAO	LMI	Tropical	1	1	1	1	1	1	1	1	1	1	1	1	1	1
23	LBN	LMI	Temperate	0.353	0.368	0.488	0.385	0.078	0.214	0.186	0.245	0.248	0.680	0.805	0.344	0.257	41
24	MYS	UMI	Tropical	0.279	0.284	0.357	0.435	0.521	0.919	0.774	0.928	0.901	0.941	0.821	0.836	0.541	27
25	MDV	UMI	Tropical	1	1	1	1	1	1	1	1	1	1	1	1	1	1
26	MNG	LMI	Temperate	1	1	1	0.999	1	1	1	1	1	1	1	1	1	1
27	NPL	LMI	Temperate	0.999	0.999	0.999	0.999	0.999	0.999	1	1	1	1	1	0.999	0.999	18
28	PAK	LMI	Temperate	0.571	0.560	0.537	0.538	0.551	0.591	0.610	0.620	0.600	0.755	0.977	1	0.630	25
29	PHL	LMI	Tropical	0.329	0.408	0.424	0.406	0.400	0.462	0.477	0.561	0.666	0.745	0.782	0.846	0.497	29
30	RUS	UMI	Cold zone	1	1	1	1	1	1	1	1	1	1	1	1	1	1
31	SAU	HI	Temperate	1	1	1	1	1	1	1	1	1	1	1	0.999	1	1
32	SGP	HI	Tropical	1	1	1	1	1	1	1	1	1	1	1	1	1	1
33	LKA	LMI	Tropical	0.347	0.301	0.207	0.418	0.394	0.438	0.338	0.354	0.613	0.555	0.609	0.901	0.397	33
34	SYR	LI	Temperate	0.328	0.408	0.396	0.342	0.215	0.040	0.090	0.082	0.095	0.117	0.143	0.176	0.125	44
35	TWN	HI	Tropical	0.349	0.354	0.420	0.363	0.300	0.300	0.340	0.341	0.351	0.414	0.425	0.474	0.362	36
36	TJK	LMI	Temperate	1	1	1	1	1	1	1	1	1	1	1	1	1	1
37	THA	UMI	Tropical	0.949	1	0.999	0.998	0.998	0.999	0.999	0.999	0.999	1	0.999	1	0.995	21
38	TLS	LMI	Tropical	1	1	1	1	1	1	1	1	1	1	1	1	1	1
39	TUR	UMI	Temperate	0.279	0.270	0.283	0.266	0.199	0.323	0.350	0.335	0.414	0.652	0.643	0.650	0.337	37
40	TKM	UMI	Temperate	1	1	1	1	1	1	1	1	1	1	1	1	1	1
41	ARE	HI	Tropical	1	1	1	1	1	1	1	1	1	1	1	1	1	1
42	UZB	LMI	Temperate	0.460	0.466	0.454	0.449	0.467	0.550	0.613	0.716	0.626	0.833	0.649	0.644	0.555	26
43	VNM	LMI	Tropical	0.365	0.370	0.430	0.404	0.431	0.438	0.526	0.785	0.852	0.606	0.542	0.737	0.499	28
44	YEM	LI	Tropical	1	1	1	1	1	1	0.999	0.999	1	1	1	1	1	1

Note: The 17 economies marked in bold are those that have been at the efficiency frontier during this period.

shows the renewable energy generation efficiency of the 44 economies from 2010 to 2021, as well as the overall efficiency and ranking order of each economy.

Among them, the 17 economies marked in bold in Table 7 are those that have been at the efficiency frontier during this period, including Bahrain, Bhutan, Brunei, Indonesia, Japan, Jordan, Lao, Maldives, Mongolia, Russia, Saudi Arabia, Singapore, Tajikistan, Timor-Leste, Turkmenistan, United Arab Emirates, and Yemen. In addition, the three with the worst generation efficiency are Azerbaijan (ranked 42nd), Iran (ranked 43rd), and Syria (ranked 44th). It is also worth noting that among the members of the Organization of the Petroleum Exporting Countries (OPEC), the renewable energy generation efficiencies of Saudi Arabia, the United Arab Emirates, and Iraq were high between 2010 and 2021, indicating that these major oil-producing economies have begun to plan for energy transformation, making advanced arrangements to spread risks in the post-oil era.

In order to analyze the current status and characteristics of renewable energy generation efficiency in the economies more clearly,

Table 8. Renewable energy generation efficiency in 44 economies from 2010 to 2021 based on classification.

Classification	Quantity	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021	Mean
Income
HI	9	0.846	0.844	0.850	0.844	0.833	0.837	0.850	0.855	0.865	0.878	0.891	0.902	0.858
UMI	13	0.638	0.625	0.622	0.619	0.625	0.686	0.703	0.720	0.752	0.802	0.783	0.828	0.700
LMI	18	0.670	0.675	0.680	0.682	0.667	0.664	0.679	0.717	0.758	0.811	0.813	0.818	0.719
LI	4	0.593	0.589	0.582	0.586	0.578	0.548	0.551	0.548	0.610	0.664	0.687	0.754	0.607
Latitude
Tropical	17	0.715	0.722	0.730	0.739	0.740	0.751	0.751	0.783	0.822	0.817	0.810	0.857	0.770
Temperate	26	0.661	0.653	0.650	0.643	0.629	0.647	0.670	0.686	0.718	0.796	0.801	0.809	0.697
Cold zone	1	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000
Asia	44	0.690	0.687	0.689	0.688	0.680	0.695	0.709	0.730	0.765	0.809	0.809	0.832	0.732

Note 1: High-income economies account for 20.5%, upper-middle-income economies account for 29.5%, lower-middle-income economies account for 40.9%, and low-income economies account for 9.1%. Note 2: Tropical economies account for 38.6%, temperate economies account for 59.1%, and coldzone economies account for 2.3%.

classifies the economies by income level and geographical latitude. When classified by income level, we find differences in the average renewable energy generation efficiency among high-income economies (0.858), middle–high-income economies (0.700), middle-low-income economies (0.719), and low-income economies (0.607). On the other hand, according to geographical latitude, there are also differences in the average of renewable energy generation efficiency between tropical economies (0.770) and temperate economies (0.697). In addition, in the sample used in this paper, only Russia is a cold zone economy, and its renewable energy generation efficiency is at the efficiency frontier (1). Overall, the average renewable energy generation efficiency of the 44 economies is approximately 0.732, and has shown a continuous improvement trend from 2010 to 2021, increasing from 0.690 to 0.832.

Concerning the average generation efficiency trends of different categories of economies each year, this paper further plots the information shown in Table 7 in Figure 1 and Figure 2 to present the differences more clearly. In Figure 1, the average generation efficiency of high-income economies is significantly better than that in countries with other income levels, while the average generation efficiency of middle–high-income and middle–low-income economies is similar to the average generation efficiency of all the economies. It is worth noting that before 2014, the average generation efficiency of middle–low-income economies was better than that of middle–high-income economies, and the performance of middle–high-income economies finally surpassed that of middle–low-income economies after 2015. However, the average generation efficiency of middle–low-income economies became better than that in middle–high-income economies between 2018 and 2020. The average generation efficiency of middle–high-income economies once again slightly exceeded that of middle–low-income economies in 2021. As for the average generation efficiency of low-income economies, there was a slight decline between 2014 and 2017, but the annual improvement was most obvious after 2017, and the gap between them and middle-income economies was significantly reduced by 2021.

Figure 2 shows that, excluding 2020, the average generation efficiency of tropical economies is the highest, followed by the Asian economies, while the average generation efficiency in temperate economies is the lowest. As mentioned above, the average generation efficiency of Russia as the only coldzone economy in the sample data of this paper is at the top throughout the entire data period.

4.2.2. Empirical Findings of Tobit Regression Model

The narrative statistics of the explained variables, explanatory variables and control variables of the Tobit regression model are shown in

Table 9. Descriptive statistics of variables in the Tobit regression model.

Variable	Mean	Std. Dev.	Minimum	Maximum	Measurement
Explained variable
Renewable energy generation efficiency	0.7199	0.3027	0.0781	1	0 to 1
Explanatory variable of technological dimension
Information digitization	53.9479	28.0135	3.7	100	% of population
Financial openness	0.2809	1.5179	−1.9311	2.2994	Chinn-Ito Index
Technological innovation capabilities	64,388.03	225,139.4	4	1,585,663	Quantity
Renewable energy device capacity share	28.5001	26.9735	0.01	100	%
Explanatory variable of social dimension
Life quality	94.2145	6.3019	69.2777	100	% of population
Democracy degree	0.3973	0.229	0.015	0.866	Electoral Democracy Index
Control variable
Population density	536.4035	1437.274	1.7354	7965.878	People per sq. km
Population growth	1.2103	1.3933	−4.1703	11.794	Annual %
GDP based on PPP	21,705.3	20,777.36	2359.996	107,741.1	GDP per capita, PPP (2017 constant international $)

, with 333 observations. According to the content described in Section 3.1.2, using EViews 13 software, this paper performed Equation (6) to conduct Tobit regression analysis in order to explore the positive and negative effects of explanatory variables on the explained variables, which helps us to understand the characteristics of factors having an impact on the renewable energy generation efficiency. Based on the fact that the explained variables in the Tobit regression model are truncated or aligned, the common method of assessing “whether the Variance Inflation Factor (VIF) is greater than 10” cannot be used to detect whether there is multicollinearity between each explanatory variable.

Therefore, this paper takes an alternative approach, and performs Ordinary Least Squares (OLS) regression analysis on each explanatory variable in the Tobit regression model one by one. If the obtained determination coefficient (R-squared) is greater than 0.9, it means that there is high correlation between the explanatory variable and other explanatory variables, and there may be a collinearity problem. In that case, it is necessary to consider deleting relevant explanatory variables in order to avoid collinearity problems affecting the empirical research results.

As mentioned above, in order to explore whether there is a phenomenon similar to the middle-income trap in the renewable energy generation efficiency of Asian economies. Mid-income economies are followed by low-income economies and are blocked by high-income economies from progressing, such that they fall into the predicament of not being able to achieve a high level but also not being able to achieve a low level, so the real income per person is replaced by the dummy variable MI (including middle-high income and middle-low income) representing a middle-income economy.

The Tobit regression empirical model is as follows:

  REGE_it = β₀ + β₁DIGI_it + β₂KFI_it + β₃INV_it + β₄RESHARE CAP_it + β₅QOL_it +
 β₆EDI_it + β₇POP DENS_it + β₈POP GR_it + β₉MI GDP_it + β₁₀TROP_it + ε_it

(6)

Each item represents the following: REGE_it—renewable energy generation efficiency, DIGI_it:—information digitization, KFI_it—financial openness, INV_it—technological innovation capabilities, RESHARE CAP_it—renewable energy device capacity share, QOL_it—life quality, EDI_it—democracy degree, POP DENS_it—population density, POP GR_it—population growth, MI GDP_it—middle-income economy, TROP_it—geographical latitude, ϵ_it—residuals.

It has been confirmed that there is no collinearity problem in the explanatory variables in Equation (6), following collinearity testing. According to the Tobit regression results in

Table 10. Results of Tobit regression model.

	Explained Variable
	Equation (6) Renewable Energy Generation Efficiency
Explanatory Variable	Coefficient		Standard Errors
Constant	3.0995	***	0.5377
Explanatory variable of technological dimension
Information digitization	0.0030	**	0.0013
Financial openness	0.0888	***	0.0220
Technological innovation capabilities	2.82 × 10−7	***	1.02 × 10−7
Renewable energy device capacity share	0.0039	***	0.0011
Explanatory variable of social dimension
Life quality	−0.0216	***	0.0055
Democracy degree	−0.4661	***	0.1260
Control variable
Population density	3.41 × 10−5		3.16 × 10−5
Population growth	0.0365	*	0.0198
Geographical latitude	0.0574		0.0552
Dummy variable
Middle-income economy	−0.4766	***	0.1037

Note: *: p-value < 0.10, **: p-value < 0.05, ***: p-value < 0.01.

, this paper has obtained the following empirical results: information digitization, financial openness, technological innovation capabilities, and renewable energy device capacity share have a significant positive impact on the renewable energy generation efficiency. However, life quality and democracy degree have a significant negative impact on the renewable energy generation efficiency. The results are consistent with those from the literature above.

In addition, we have also seen a phenomenon similar to the middle-income trap in the renewable energy generation efficiency of Asian economies. According to the sample data of this paper, if a country is at the middle-income level (including middle–high income and middle–low income), its renewable energy generation efficiency will be significantly negatively affected.

5. Implications

5.1. Technological Dimensions for REGE

Based on the research findings of this paper, suggestions are put forward to promote renewable energy generation efficiency in Asian economies. First, regarding information digitization, Hu et al. [53] stated that energy–ICT refers to the combination of energy and information and communication technology. By combining energy systems with ICT technology, the integration of energy production, storage, transmission, distribution and use can be achieved. In addition, applying energy–ICT can help improve energy efficiency, promote the use of renewable energy, reduce carbon emissions, achieve green energy development, and contribute to the energy system. Through ICT technology, energy producers and users can monitor and manage energy usage more effectively and achieve more environmental energy utilization. Since the cycle and frequency of renewable energy generation are not fixed [54], there is greater reliance on the cooperation of smart grid dispatching and energy storage devices. Therefore, it is recommended that the governments of Asian economies strengthen the implementation of information digitization in order to improve renewable energy generation efficiency.

Second, for financial openness, Song et al. [55] pointed out that foreign direct investment can promote technology transfer and technological innovation, thereby improving the technical level and industrial competitiveness of local enterprises. Therefore, foreign direct investment usually has a positive catalytic effect on the development and application of technology. Based on this concept, it is recommended that governments of Asian economies strengthen the promotion of green finance and integrate ESG concepts into deposits, lending, loans, and other businesses, so as to improve renewable energy generation efficiency.

Third, for technological innovation capability, Mohsin et al. [56] mentioned that technological innovation and progress will help promote the growth of the green economy, and also emphasized the importance of technological reorganization capabilities in the energy sector. Therefore, it is recommended that cooperation channels be established between various research institutions and universities to create more opportunities for technological innovation. This move is expected to further promote renewable energy generation efficiency.

Finally, regarding renewable energy device capacity share, energy is indispensable in promoting human progress and social and economic development. In order to cope with the imminent energy crisis and promote sustainable development, a solution would be to develop the scope for and application of various renewable energy sources. However, the deployment of renewable energy often faces various challenges, which may hinder the rapid promotion of renewable energy technologies [57]. Since the issue of deploying renewable energy facilities will inevitably involve the choice of plant site and the acceptance of residents, it is recommended that governments of Asian economies should actively assist the private sector in removing obstacles; if appropriate plant site selection can be made, the capacity share of renewable energy devices could be greatly expanded. This could improve renewable energy generation efficiency.

5.2. Social Dimensions and Middle-Income Trap for REGE

As regards life quality, the public has little understanding of the serious impacts that their collective actions will have on climate change. However, this is an important cause of long-term environmental damage and a major obstacle to all parties seeking solutions. Therefore, it is recommended that both the governments of Asian economies and green energy-related industries should strive to strengthen communication with the community, eliminate objections based on life quality considerations, and create a social environment that allows correct green energy information to circulate, as well as inspiring the public to understand the seriousness of the environmental degradation currently faced by mankind. This is bound to be a worthwhile long-term investment that will help to realize the global vision of a green and clean energy transition.

Democracy is a modern way of life. Everyone has the sacred right to vote and the right to express different opinions. However, this also means that the burden of persuasion will be higher. Therefore, good communication skills are particularly important in a democratic society. It is recommended that governments of Asian economies with a higher degree of democratization should pay more attention to communication with the public, spend more resources in gaining the recognition of the majority of people, and make greater efforts to improve renewable energy generation efficiency.

In addition to the specific technical action plans that governments of Asian economies can implement to improve renewable energy generation efficiency through the implementation of information digitization, financial openness, technological innovation, and increasing renewable energy device capacity share, it is recommended that middle-income economies could also seek opportunities for technological cooperation with high-income economies. In addition, since middle-income economies face more difficult financial challenges, it may also be worth considering that encouraging private investment or negotiating financial cooperation with international organizations are feasible approaches.

6. Conclusions, Research Limitations, and Future Suggestions

6.1. Conclusions

As global energy challenges and climate change become increasingly severe, renewable energy has become an important way to achieve sustainable development goals, and it has attracted widespread attention. In order to improve the renewable energy generation efficiency, it is important to deeply explore the impacts of different aspects.

This paper first used the output-oriented, dynamic, and slacks-based DSBM to obtain the renewable energy generation efficiency of 44 economies from 2010 to 2021. Then, Tobit regression analysis was conducted to explore the positive and negative effects of the variables on the renewable energy generation efficiency, with the variables involving two aspects: the technical system and the social system.

According to the empirical results of the first stage, 17 economies have been at the frontier of renewable energy generation efficiency during this period, including Bahrain, Bhutan, Brunei, Indonesia, Japan, Jordan, Lao, Maldives, Mongolia, Russia, Saudi Arabia, Singapore, Tajikistan, Timor-Leste, Turkmenistan, United Arab Emirates, and Yemen. In addition, the three with the worst generation efficiency are Azerbaijan (ranked 42nd), Iran (ranked 43rd), and Syria (ranked 44th). It is worth noting that among the members of the Organization of the Petroleum Exporting Countries (OPEC), the renewable energy generation efficiencies of Saudi Arabia, the United Arab Emirates, and Iraq are the highest, indicating that these major oil-producing economies have begun to plan for energy transformation, making advanced arrangements to spread risks in the post-oil era.

If classified by income level, we see differences in the average values of renewable energy generation efficiency among high-income economies (0.858), middle–high-income economies (0.700), middle–low-income economies (0.719), and low-income economies (0.607). There are also differences in the average renewable energy generation efficiency between tropical economies (0.770) and temperate economies (0.697) when classified by geographical latitude. Overall, the average renewable energy generation efficiency of 44 Asian economies is approximately 0.732, and showed a continuous trend of improvement from 2010 to 2021, increasing from 0.690 to 0.832.

After further comparison, we see that the average generation efficiency of high-income economies is significantly better than that of those with other income levels, while the average generation efficiency of middle–high-income and middle–low-income economies is similar to the average generation efficiency of all Asia economies. It is worth noting that before 2014, the average generation efficiency of middle–low-income economies was better than that of middle–high-income economies, and the performance of middle–high-income economies finally surpassed that of middle–low-income economies after 2015. However, the average generation efficiency of middle–low-income economies became better than that of middle–high-income economies between 2018 and 2020. But the average generation efficiency of middle–high-income economies once again slightly exceeded that of middle–low-income economies in 2021.

As for the average generation efficiency of low-income economies, there was a slight decline between 2014 and 2017, but the annual improvement was most obvious after 2017, and the gap between these and middle-income economies was significantly reduced by 2021. On the other hand, excluding 2020, the average generation efficiency of tropical economies was the highest, followed by whole Asian economies, while the average generation efficiency of temperate economies was the lowest.

According to the empirical results of Tobit regression in the second stage, the information digitization, financial openness, technological innovation capabilities, and renewable energy device capacity share all have a significant positive impact on the renewable energy generation efficiency, and the life quality and democracy level have significant negative impacts on the renewable energy generation efficiency.

Finally, this paper finds that there is a phenomenon similar to the middle-income trap in the renewable energy generation efficiency of Asian economies. According to the sample data used in this paper, if a country is at the middle-income level (including middle–high income and middle–low income), its renewable energy generation efficiency will be significantly negatively affected.

6.2. Research Limitations and Future Suggestions

The data used in this paper have both cross-sectional and time series properties. A random effects Tobit regression model of the longitudinal and cross-sectional data was developed in the second stage of analysis, but the estimation results show no convergence. Therefore, this paper could not perform longitudinal and cross-sectional data analyses, and so only used Tobit regression to explore the influences of explanatory variables on the explained variables.

However, Tobit regression does not consider the structure of longitudinal and cross-sectional data; that is, all data are directly regarded as coming from the same cross-section in estimating the positive and negative impacts of the main research variables on the renewable energy generation efficiency. When only using Tobit regression, without considering the cross-sectional and time series structure of the data, it is likely that the heterogeneity between individuals and over time will be ignored, and this heterogeneity may contain important information. This may lead to biases in the estimation results of the model.

The random effect of the Tobit regression model based on the current longitudinal and cross-sectional data is not guaranteed to produce a convergent solution, so this paper cannot offer a convergent solution based on the data of the research sample. In the future, if scholars develop a new advanced Panel Tobit model, able to definitely produce a convergent solution, it can be used to deal with the type of data used in this paper, and it is expected that the estimated results will be able to more accurately explain the main research variables, and their impact on the renewable energy generation efficiency.

In addition, this paper found a phenomenon similar to the middle-income trap in the renewable energy generation efficiency of Asian economies; when a country is at the middle-income level (including middle–high income and middle–low income), its renewable energy generation efficiency will be significantly negatively affected. This “middle-income trap in renewable energy power generation efficiency” is a consequence for which the reasons still need to be explored; that is, more variable data still need to be collected in order to outline the cause, given the lack of antecedent variables.