In this section, the articles identified by our search criteria are further classified and discussed. More specifically, we will classify our list of articles in terms of the type of energy studied and by primary research area of emphasis. Similar to before, we maintain two sets of articles: (i) the set of 31 most influential articles across all journals, and (ii) the 58 articles published on the topic in Energies. This will enable us to compare the highly cited articles in other journals to the articles published in Energies to pinpoint their differences in terms of research emphasis, methodology, and fuel types examined.
3.1. Renewable and Conventional Energy
The largest category across the two groups contains articles that study both renewables and conventional (i.e., oil, gas, and coal) energy. We found 33 total papers offering broad coverage of energy choices. Our review of these articles begins with the most influential papers, and then we discuss papers in Energies.
In the cluster of studies containing both renewable and conventional energy sources, eight of the ten influential articles cover topics in the areas of energy and finance. The most cited papers are by Sadorsky [
22] with 381 citations, Reboredo et al. [
23] with 199, and Ferrer et al. [
24] with 196. These articles are all published in Energy Economics. Using multivariate GARCH models, Sadorsky performed volatility analysis between crude oil prices and share prices of green energy and technology firms. He concludes that while green energy stock prices are sensitive to technology stocks, they are less related to the movements in crude oil prices. Reboredo et al. [
23] use nonlinear Granger causality and wavelet functions to examine the correlation and causality between crude oil prices and renewable energy stock prices for the period 2006–2015. They conclude that the relationship between these assets is not strong in the short term, but that a relationship in the long term does exist. Moreover, they revealed that stock prices for renewable energy have non-linear effects on the price of crude oil at different time horizons. Additionally, Ferrer et al. [
24] look at US data involving renewable energy stock prices, oil prices, and critical financial variables (such as profit and cost) to examine time and frequency dynamics. They use impulse response analysis. Their key finding is that there is strong short-term connectedness between their volatilities and returns. Furthermore, they show that the price of oil does not affect the performance of renewable energy firms both in the short and long term. In connection with the renewable energy and fossil fuel markets, Song et al. [
25] investigate information spillovers between these markets by examining their returns and volatilities. They demonstrate that stock prices for renewable energy firms and conventional energy firms are closely related. Very similar to Song et al. [
25], Corbet et al. [
26] use a spillover index and a volatility analysis (called DCC-FIGARCH) to investigate volatility spillovers among energy stocks during the COVID-19 era. They find positive and significant spillovers ranging from oil prices to clean energy and coal prices. Dawar et al. [
27] use weekly data to detect if the price of crude oil affects the prices of clean energy stock indices. They run a quantile regression to show that renewable energy stock returns depend less on crude oil returns. On a similar topic, Ji et al. [
28] compare renewable energy fund returns to conventional energy fund returns in Europe during 2010–2019. Their observation is that renewable energy funds do not perform as well as conventional energy funds and market benchmarks. More recently, Samour and Pata [
29] examined the U.S. interest rate and crude oil price spillovers on renewable energy utilization in Turkey using annual data from 1985–2016. They utilized an econometric method based on the bootstrap autoregressive distributed lag approach and showed that there exist significant spillover effects from the U.S. federal funds rate to renewable energy usage, and this spillover happens via effects on local interest rates and real income.
There are two remaining influential articles in this fuel category, and both have a focus on energy, growth, and productivity. First, Feng et al. [
16] used data envelopment analysis to examine factors that affected green total-factor productivity (TFP) for China’s metal industry from 2000–2015. They illustrate that the green TFP in this industry has risen at an annual rate of 11.52%. This has been explained by technological progress. Despite these improvements, green TFP levels remain low in this industry, and this stems from scale and technical inefficiencies. Similar to Feng et al. [
16], Zhu et al. [
17] also studied green TFP in the mining and quarrying industry in China during 1991–2014. They also used data envelopment analysis and showed that the green productivity in this sector increased by approximately 72%. However, productivities vary considerably across the sub-sectors.
Within this energy category, articles in Energies are similar to the set of influential articles from other journals in that a high share (11 of 23) cover topics in the area of energy and finance. The two most cited articles in this area, each with fifteen citations, are those of Hsiao et al. [
30] and Czech and Wielechowski [
31]. While the former uses data for oil prices and stock prices of renewable energy companies in China to examine the relationship between these prices, the latter uses renewable and conventional energy data to examine the impact of COVID-19 on stock market indices. Other articles by Nie et al. [
32], Attarzadeh and Balcilar [
33], Jaworski and Czerwonka [
34,
35], De Blasis and Petroni [
36], Velasquez-Gaviria et al. [
37], Fuentes and Herrera [
38], Dominioni et al. [
39], and Chang et al. [
40] studied energy and finance. The first two articles are the most recent. Nie et al. [
32] studied return and volatility spillovers between renewable energy and technology stocks, oil futures, and carbon allowances over different investment horizons. They found a strong spillover effect between these assets and showed that technology stocks influence clean energy stocks more than oil futures. In a related recent paper, Attarzadeh and Balcilar [
33] studied the connectedness and spillovers between the renewable energy, common stock, oil, and technology markets. They showed that the oil and clean energy markets have bidirectional volatility spillover. They suggest that if the price of oil remains low, then industries producing alternative energy will not need tailored policy interventions to lessen their susceptibility to shocks to crude oil prices.
In this energy category, there are four papers in Energies in the area of energy and the environment by Mehmood et al. [
45], Hsieh et al. [
46], Zhou et al. [
47], and Li et al. [
48]. Mehmood et al. [
45] assessed intertemporal changes in energy efficiency and
emissions for select developed and developing countries from 2001 to 2011. They also employed a network data envelopment analysis and found that the countries examined do not exhibit perfect efficiency in the production and distribution of economic outputs simultaneously. They suggested that efficiencies could be improved if countries prioritized economic restructuring and the expansion of the middle-income class. Hsieh et al. [
46] measured environmental efficiency in EU countries from 2006 to 2013. They used dynamic data envelopment analysis and showed the predictive power of their model. Specifically, they measured environmental efficiency in Europe and reported that most countries needed to improve their environmental efficiencies. Zhou et al. [
47] measured the environmental efficiency in Chinese provinces for the period 2006–2015. They also used data envelopment analysis to quantify the environmental efficiency in each province. Their results revealed that Beijing, Guangdong, and Shanghai showed high performance in unified environmental efficiency and scale efficiency. Finally, Li et al. [
48] analyzed energy and air pollution efficiency scores in China during 2013–2016. Using a meta-frontier approach, they observed large differences between western and eastern cities. The western part of China had the lowest scores for efficiency.
There are also four articles in Energies covering energy efficiency and sustainability, including Hsiao et al. [
41], Moutinho and Madaleno [
42], Fidanoski et al. [
43], and Wang et al. [
44]. Hsiao et al. [
41] measured the total factor energy efficiency (TFEE) in ten countries across the Baltic Sea region during 2004–2014. The input variables in their analysis include labor, capital, energy, and carbon dioxide (
). For the output variable, they used real gross domestic product, and their environmental variables included consumption of renewable energy and population in urban areas. Using a stochastic frontier analysis, they demonstrated that Finland, Latvia, Norway, and Sweden performed well in energy efficiency performance compared to other Baltic countries. Moutinho and Madaleno [
42] measured environmental and economic efficiencies in Asian and African countries between 2005 and 2018. Using stochastic frontier analysis, they found an inverted U-shape function for eco-efficiency concerning the shared consumption of fossil fuels. They showed that technical eco-efficiency increased labor and renewable energy shares, and decreased fixed capital. Similar to this paper, Fidanoski et al. [
43] assessed energy efficiency in OECD countries from 2001 to 2018. They considered renewable energy capacity and environmental constraints for energy efficiency estimations. Using data envelopment analysis, they found that OECD countries exhibited inefficiency margins of 16.1% from primary energy sources (e.g., oil and gas) and from 10.8 to 13.5% for electricity. However, environmental constraints and renewable energy did affect efficiency figures. Finally, Wang et al. [
44] quantify energy efficiency in the Americas, Asia, and Europe. They used the data envelopment analysis method and found that the energy efficiency in European countries was higher than that in the Americas (except the U.S.) and Asian countries. They pinpointed the source of inefficiency, which stems from excessive consumption and waste.
Within this fuel category, there are four articles in Energies covering energy, growth, and productivity by Ding et al. [
18], Lin et al. [
19], Fujii et al. [
20], and Ziolo et al. [
21]. Ding et al. [
18] studied the impact of human capital on green production. They also considered physical capital to measure green and traditional GDPs for 143 countries using a regression model. They found that human capital was more important than physical capital and that green GDP had a higher sensitivity to variations in human capital compared to standard GDP. Note that before Ding et al., Lin et al. [
19] proposed a Green GDP index, considering both energy and pollution intensities, to measure the environmental and energy performance in China using a data envelopment analysis. Fujii et al. [
20] looked at technology and examined the relationship between productivity and technology within a distributed renewable energy system. They specifically differentiated technology between information technology and software and found that the technology contributed to increased productivity in output. Lastly, Ziolo et al. [
21] used data envelopment analysis and a regression model, to consider sustainable economic development in OECD countries. In the realm of growing renewable energy investments and pollution control programs in both developing and developed OECD countries, they found upward trends in energy efficiency that positively contributed to economic growth.
3.2. Renewable Energy Only
Twenty-nine total articles across the two sets are focused on renewable energy sources only. In this fuel subcategory, there are seven influential articles and nine papers in Energies that do not specify the specific type of renewable energy. Six of these seven influential papers are directly related to China and its renewable energy policies. Within Energies, there are also articles dealing with renewables that are focused on a particular type. For example, in the category of solar energy, seven papers specifically consider solar energy and its benefits. In the category of electricity, there are six papers in which electricity is generated using conventional and renewable energy sources. These papers address various issues in the electricity sector involving pricing, trade, outages, and emissions.
In the fuel category of renewable energy only, the most cited article is by Bai et al. [
49] in the Journal of Cleaner Production, with 125 citations. This article is in the research area of energy, growth, and productivity. The authors study companies that are energy-intensive during the period 2010–2015 to determine if government research and development subsidies impacted the amount of green innovation activity. Using a propensity score matching method, they show that subsidies facilitate energy firms’ innovation of green products. In this category, the second highly cited paper is by Wu et al. [
58], which falls in the research area of energy efficiency and sustainability. This paper is one of the hottest, having been cited 97 times since 2021. This paper has received so many recent citations that it is also on the ESI hot paper list, which is a designation given to very high-performing papers published in the most recent two years. Hot papers satisfy two conditions: (i) the publication date must be within the last two years; and (ii) over the most recent bimonthly period, these articles perform in the top 0.1% based on citations when compared to other articles in the same field. Their empirical study examines the relation between internet development and usage and green total factor energy efficiency (GTFEE) in China for the period 2006–2017. They find that internet development has significant positive and nonlinear effects on GTFEE and that these effects can spillover to nearby regions.
IN this fuel category, there are two articles in the influential group with an energy and finance focus. First, Xu and Li [
51] examine the impacts of green energy credit on the debt financing cost in China for the period 2001–2017. Using a fixed effect model, they find that both green credit policy and green credit development reduce the debt financing cost of green enterprises but have a small impact on their debt maturity. Second, Wen et al. [
52] investigate Chinese A-share listed renewable energy companies during 2007–2019 to study how corporate innovation investment is influenced by fiscal policy uncertainty. Based on a dynamic data envelopment analysis, they find that the research and development investment of renewable energy firms is negatively influenced by fiscal policy uncertainty. However, these effects are reduced by the amount of product market competition.
The remaining three influential articles in this fuel category all cover topics related to energy and the environment. Zhang et al. [
61] study the environmental effects of green credit policy (GCP) in China and question the effectiveness of these policies to promote green development. Using an empirical difference-in-difference method, they show that, for firms with both high energy consumption and levels of pollution, the GCP provides incentives for short-term financing, but it restricts investment behavior in the long term. Most importantly, the GCP reduces sulfur dioxide and wastewater emissions. Additionally, the GCP has an asymmetric impact on the investment behavior of state-owned large firms and independent small firms. Deng et al. [
62] investigate the relationship between green technology innovation and tax competition. Using a dynamic spatial Durbin model along with a threshold panel model, they find that green technology innovation over different regions shows positive spatial agglomeration effects and that an inverted U-shape describes the relationship between tax competition and green technology innovation. Finally, Zeng et al. [
63] investigate green technology innovation (GTI) in 30 Chinese provinces for the period 2001–2019. Their empirical results demonstrate that innovation efficiency is low but show progress in that the GTI has increased over the years. Furthermore, while total emissions of carbon dioxide in China have been increasing at a marginal rate, the intensity of carbon emissions has been falling over the years.
In the renewable energy only category, the source of renewable energy was not explicitly specified by the authors in 9 articles of the 58 Energies publications covered by our search criteria. Five of these papers are in the area of energy and finance. Sotnyk et al. [
53] study renewable energy development in Ukraine’s regions to identify optimal investment strategies. Day et al. [
54] explore the impact of diverse technical trading investment strategies on investing in clean and traditional energy ETFs. Liu and Hamori [
55,
56] examine spillovers from conventional energies to renewable energy stocks in the US and Europe and the performance of the environmental, social, and governance indexes along with renewable energy securities. Reboredo et al. [
57] examine the dependence structure between renewable energy assets and non-energy assets in the US and EU markets. In the area of energy, growth, and productivity, Sart et al. [
50] study the relationship between educational attainment and renewable energy use. In the area of energy efficiency and sustainability, Liu et al. [
59] measure the technical efficiency of the green energy industry in China, whereas Chodakowska and Nazarko [
60] assess the functioning of sustainable development goals for EU countries. Finally, in the area of energy and environment, Wang et al. [
64] calculate the efficiency scores of highly industrialized and newly industrialized countries using renewable energy capacity and
emissions.
In terms of a specific renewable energy source, we observe that most of the papers published in Energies examine solar energy. Seven papers in this category are all empirical and mostly concern Asian countries. For example, Mariano et al. [
66] and Lee et al. [
67] analyze the performance of solar photovoltaic (PV) power plants in Taiwan. Yang et al. [
68] focus on China and evaluate the PV power generation efficiencies in 30 regions. Wang et al. [
70] examine a region of Vietnam and investigate the optimal location choice for solar farms. We observe only one paper on the USA, which is written by Assereto and Bryne [
65]. They investigate the impact of subsidy and electricity price uncertainty on solar investments in Pennsylvania, New Jersey, and Maryland electricity markets. In addition, two studies do not specify the location, and these are by Hajdukiewicz and Pera [
71] and Lee and Tong [
69]. While the former studies trade disputes in the solar sector, the latter examines the transfer efficiencies of PV systems. The research areas emphasized are energy efficiency and sustainability by Mariano et al. [
66], Lee et al. [
67], Yang et al. [
68], and Lee and Tong [
69]; energy and environment by Wang et al. [
70]; energy and finance in Assereto and Bryne [
65]; and energy and trade by Hajdukiewicz and Pera [
71].
Six papers in Energies focus on electricity as an intermediate energy input without explicitly specifying the fuel burned to generate electricity. Four of these papers are in the area of energy and finance. In the only theoretical paper in this group, Maekawa and Shimada [
72] examine power producers’ speculative behavior in the day-ahead electricity market of Japan. Haugom et al. [
73] examine the characteristics of forward premiums in the Nord Pool power market. Kaufmann et al. [
74] emphasize the reliability issue of wind power and its impact on electricity prices. They offer a risk assessment tool for power traders to navigate the risks involved in intermittent wind generation. To complement this paper, Lin et al. [
75] look at the impact of renewable energy from the system operator’s perspective and offer a risk assessment of renewable energy connected to a power grid. The remaining two papers are in the area of energy efficiency and sustainability. Siksnelyte and Zavadskas [
76] conduct an empirical study using a multi-criteria decision-making model to assess the EU’s energy policies in its electricity markets. Their paper reviews the EU’s renewable energy subsidies, which have aimed to bolster investments in renewable energy generation to diversify its power generation portfolio and achieve environmental targets for reducing greenhouse gas emissions in its power sector. Finally, Kufeoglu et al. [
77] look at power interruptions in the Finnish electricity market and compute the costs associated with power outages.
3.3. Conventional Energy
In the categories of oil, gas, coal, biogas, and biomass, there is one influential article and 6 papers published in Energies. Taghizadeh-Hesary et al. [
80] are the only influential paper in the conventional energy segment. While this article focuses on the linkage between oil prices and food prices, it also promotes renewable energy usage in food production to entail food security. Therefore, we classify the focus of this article as being in the area of energy efficiency and sustainability. The remaining papers in this fuel category were all published in Energies. Yan et al. [
84] are categorized in the area of energy efficiency and sustainability; Iotti and Bonazzi [
83], Jianu and Jianu [
78], and Puka et al. [
79] cover topics in energy and finance; Mo and Wang [
81] have an energy and environment focus; and Gorecka et al. [
82] have an energy and trade focus. For biofuel, Yan et al. [
84] investigate economic and technical efficiency in the Chinese biomass sector, whereas Iotti and Bonazzi [
83] examine biogas firms and their performance in Italy. For conventional energy, Jianu and Jianu [
78] focus on oil and gas companies and examine how their investments in infrastructure, including exploration, drilling, and extraction, impact their share prices in the London Stock Exchange market. Puka et al. [
79] provide a methodological contribution to examining risk mitigation for crude oil prices. Mo and Wang [
81] emphasize gasoline markets to search for the sustainability of road transportation. Finally, Gorecka et al. [
82] consider coal, oil, and gas markets to study energy trade in the EU.
3.4. Energy Type Unspecified
In the final category, which we call “Unspecified,” the energy source is not explicitly defined. This means that energy may be produced from a renewable source or a fossil fuel. There are thirteen influential articles in this segment, with five falling into the energy efficiency and sustainability category and eight in the energy and environment category. Since the articles covering energy efficiency and sustainability tend to be the most heavily cited, those are reviewed first. The most cited paper in this category is by Chang et al. [
85]. This article was published in Energy Policy and has been cited 292 times. They analyze the environmental efficiency of the transportation sector in China. Using a data envelopment analysis with a slacks-based measure, they show that the levels of environmental efficiency in most provinces are below half of their optimal levels. Second, Li and Hu [
86] compute the ecological total factor energy efficiency (ETFEE) in 30 regions of China during 2005–2009. Their empirical results reveal that regional ETFEE in China is low, but they observe a monotonically increasing relation between regional ETFEE and national GDP per capita. Furthermore, they find a positive correlation between the ETFEE and the ratio of R&D expenditure to GDP and the degree of foreign investment. Third, Huang et al. [
87] examine the dynamics of regional eco-efficiency using Chinese data for the period 2000–2010. Similar to others in this research stream, they use a data envelopment analysis model to show that the average eco-efficiency of China over years displays a V-shape. Furthermore, the eco-efficiency levels show significant differences between the regions in China. In another study with a focus on energy efficiency and sustainability in China, He et al. [
88] examine China’s iron and steel industry from 2001 to 2008 to evaluate the industry’s energy efficiency and productivity growth. Using a data envelopment analysis, they find that most of China’s steel and iron-producing plants are inefficient. However, productivity has increased, and they identify technical change as the main catalyst for this. Finally, Shao et al. [
89] studied China’s industrial sectors between 2007 and 2015 to evaluate the eco-efficiency of these sectors. Using a network data envelopment analysis, they showed that the eco-efficiency and process efficiencies of China’s industries have improved. However, there are important differences across the sectors in efficiency rates: the lowest eco-efficiency comes from the mining sector, whereas the electricity and gas production sectors have shown the highest rates.
The remaining eight influential articles in this fuel category are all in the area of energy and the environment. Sueyoshi et al. [
7], which is published in Energy Economics and has citations totaling 261, provide a literature review of papers that use data envelopment analysis and have a research focus in the area of energy and the environment and that were published starting in the 1980s. Chen and Jia [
93] examine the environmental efficiency in China’s regional industry during 2008–2012. Using a data envelopment analysis, they show that the environmental efficiencies of China’s industries tend to be low and are not showing improvement. However, some regions in China show better efficiency factors than others. Asongu et al. [
94] examine the relationship between information and communication technology (ICT) and carbon dioxide emissions in Africa during 2000–2012. Using a generalized method called the moments method, they find that ICT (measured by internet and cellphone usage) can be employed to reduce environmental pollution. Miao et al. [
95] explore China’s energy use and pollution levels. Using a data envelopment analysis, they show that the source of air pollution and the environmental inefficiency comes from SO
2 emitted from the industrial sector and NO
x emitted from the transportation sector. They suggest governments increase air pollution regulations. Chen et al. [
96] study the relationship between industrial agglomeration and environmental quality. Using a spatial econometric analysis involving the Durbin model, they show that significant spatial spillover effects exist between industrial agglomeration, pollution, and ecological efficiency. They find a U-shaped relationship between industrial agglomeration and wastewater, SO
2, and soot emission. In a related study, Zhu et al. [
97] measured energy efficiency and the impact of energy usage on the environment. They use data envelopment analysis to analyze industrial production and pollution control data and provide remedies to improve environmental efficiency in transportation sectors across Chinese provinces. In the nexus of trade, growth, and environmental pollution, Wang et al. [
98] use an empirical threshold model to show that when the degree of trade is below a threshold, technological progress may raise the level of pollution; otherwise, technological progress can diminish emissions. The final paper in this category is a very recent one by Khan et al. [
99]. Their study examines how the quality of institutions impacts foreign direct investment inflow and improves environmental quality. They use static and dynamic panel models to show that institutional quality affects foreign direct investment positively and significantly. Additionally, energy usage and carbon emissions are positively related to economic growth.
Within Energies, seven papers do not specify the fuel type explicitly. They mainly use energy as an input, which could come from any source, to explain the production of intermediate products and their environmental impacts in various sectors. Four of these articles focus on energy and the environment. The focus of these papers is diverse, with wide coverage of industries. In particular, Tu et al. [
100] investigate environmental efficiency in the Chinese cement industry. Their methodology relies on data envelopment analysis. They show that the environmental efficiency of this industry in China is very low. They suggest several remedies, including the reduction of air emissions through pollution control measures, and forcing cement producers to carry out technological innovations on scrubbers, filters, and carbon capture mechanisms. Wang et al. [
101] use data from China’s mining industry for 2007–2016 to study total factor environmental efficiency. Using an epsilon-based measure model, they find that China’s provincial mining industry has a low average total factor static environmental efficiency and that there exist significant spatial-temporal differences. Feng et al. [
102] use industrial production and pollution control data for China from 2013 to 2017 to study the influence of pollution control measures on industrial production efficiency. Their empirical study is based on the directional distance function and the technology gap ratio. They compute the technology gap ratios for 31 provinces, cities, and administrative regions. They offer remedies to improve resource allocation and pollution prevention and control. Debkowska et al. [
103] examine whether public funds for climate policies have been effectively and efficiently utilized in the EU during 2005–2019. They carry out an empirical study relying on a data envelopment analysis. They show that public funds have not been efficiently used to mitigate climate change. However, a case study based on the replacement of heating resources in Poland indicates some efficient use of public funds.
The remaining three publications in Energies that do not specify the fuel type explicitly all have a focus on energy efficiency and sustainability. Sueyoshi et al. [
90] study the sustainable development of Chinese provinces to detect patterns of economic and environmental performance at a regional level. Similar to the other papers in this category, their empirical study relies on a data envelopment analysis. Their analysis reveals that the pace of sustainable development is stable in some provinces, whereas other provinces reveal radical swings in performance. In addition, they find sustainable development performance to be low in some provinces with fast-growing economies. In another study for China, Yang and Li [
91] evaluate efficiency scores in relation to industrial wastewater for Chinese sectors. Finally, Hernandez and Prakoso [
92] emphasize the difficulties with Indonesia’s energy transition. Their study relies on a learning-activation approach. They essentially offer a teaching guideline that provides recommendations as to how Indonesia’s energy transition stages could be applied to other countries that have just started working on transitioning their energy industries to include renewable energy.