The Drivers of Renewable Energy: A Global Empirical Analysis of Developed and Developing Countries

Abstract

The need for renewable energy is regarded as a major component in the move towards achieving sustainable development. Using a large sample of 177 countries over the period 1990 to 2020, this research explores the impact of the most significant drivers of renewable energy. Findings from this work contribute to the literature by identifying the most significant drivers of renewable energy deployment and their different responses in developed and developing economies. Empirical results suggest that GDP, oil price, access to electricity, and CO₂ and methane emissions are significant determinants of renewables both as a share in energy consumption and as a share in electricity production. Additionally, trade is found to be a significant driver for the share of renewables in total energy consumption but not for the share of renewables in the electricity production model. Finally, our findings indicate that the factors influencing the development of renewables vary significantly between developed and developing countries, necessitating distinct approaches for each group of countries. These results can play a significant role from a policy perspective in designing and implementing specific policies to increase renewable energy deployment.

1. Introduction

The world is not decarbonising fast enough. The National Aeronautics and Space Administration (NASA) highlights that global surface temperatures have risen by over 1.1 °C (degrees Celsius) compared to pre-industrial levels, mainly influenced by human-induced climate change [1]. Projections by [2] indicate that if climate change accelerates, the average temperature could exceed the 1.5 °C threshold soon and potentially reach extremes of more than 4 °C within the next century without a global transition towards green energy. Transitioning from fossil fuels to renewable energy is essential for mitigating climate change. However, achieving this transition at the necessary speed may have significant economic and societal consequences.

Without resolute action on simultaneously promoting the development of renewables and improvements in energy efficiency, the required energy transition and the 1.5 °C trajectory agreed upon in the Paris Agreement 2015 are unlikely to be successful. According to [3], increasing the use of renewable energy sources, enhancing energy efficiency, reducing methane emissions, and expanding electrification through existing technologies can achieve over 80% of the necessary emissions reductions by 2030. However, stronger policies and international support may be required in developing economies where there is significant potential for carbon dioxide (CO₂) emissions savings by improvements in energy efficiency as suggested by [4], but satisfying the increasing appetite for energy is of crucial importance. It is, therefore, important for global policy makers to understand the drivers of renewable energy deployment and whether there are differences between developed and developing countries. Hence, the approach taken in this paper.

According to [5], non-fossil fuel renewable energy sources, such as windmills, watermills, and wood, have been available for centuries, primary forms of renewable energy that have been used in areas all around the world. In the more modern era, as Koch [6] notes, hydroelectricity was created in the 1890s on an industrial scale, with solar and wind renewable energy following decades later [7]. However, until relatively recently, these sources of energy have been a small proportion of the overall energy mix; over recent decades, renewable energy has developed rapidly as an alternative source of energy to fossil fuels encouraged by the Kyoto Protocol in 1997, the 21st Conference of the Parties (COP21) in 2015, with the Paris Agreement, and the 2030 Sustainable Development Goals (SDGs) in 2015 [8,9].

Consequently, it is generally agreed that the further development of renewable energy is essential to avert the climate crisis and promote sustainable development. Renewable energy can be applied on a large scale at the industrial level but also the household level. Moreover, according to [10], renewable energy at the household level is more efficient, compared to fossil fuels, and has the potential to help households alleviate rising living costs—especially given the global energy crisis following the Russian energy supplies disruption. Furthermore, the International Energy Agency [10] suggests that renewable energy helps to improve energy security, since countries that develop significant levels of indigenous renewable energy can better manage their energy system, avoiding many external impacts such as rising global market prices, transportation challenges, and political factors.

Given the perceived need for increasing renewable energy sources, it is crucial that researchers analyze the key drivers of renewable energy investment to aid understanding for relevant stakeholders, policy makers, and international organisations when developing appropriate policies for the promotion of the development of renewable energy. Hence, the research is undertaken here, despite the lack of consensus concerning the drivers of renewable energy development [11]. As explained below, we attempt to investigate as many drivers of renewable energy development as feasible using global data for 177 countries over the period 1990–2020. Moreover, we test whether there is a significant difference between the impact of the explanatory variables in driving the development of renewable energy in developed and developing countries.

This paper is organised as follows. Section 2 presents the literature review, building upon the extensive review by [11]. Section 3 outlines the model specification and estimation strategy adopted in the research and details the data used, followed by Section 4 that presents the empirical results, with a summary and conclusion given in Section 5.

2. Literature Review

Bourcet [11] published a comprehensive, extensive, and systematic review of the empirical determinants of renewable energy deployment. Bourcet reviewed 48 papers that were published between 2009 and 2017, clearly summarising them in a table (Table 8 in [11] pp. 11–14). We therefore build on this contribution by taking a similar approach. We review a further 41 papers published between 2016 and 2024 (not included in the [11] review), which are summarised in a similar way in

Table 1. Summary of reviewed papers on renewable energy deployment * in addition to those included in Table 8 in [11].

Author(s)	Period	Countries	Methodology	Dependent Var.	Independent Variables
Ackah et al. [12]	1971–2012	10 African countries	Dynamic panel model estimation techniques (GMM, FE, RE)	Renewable energy consumption per capita	Demographic and Geographical (Population), Economic and Financial (Energy prices, GDP per capita), Environmental (CO2 emissions, Energy resource depletion per capita), Social and Innovation (Human capital development)
Ito [13]	2002–2011	42 developing countries	Dynamic panel model estimation techniques (GMM)	Renewable energy consumption as % of total final energy use	Economic and Financial (GDP per capita), Environmental (CO2 emissions), Energy (Non-renewable energy consumption, Renewable energy consumption)
Narayan and Doytch [14]	1971–2011	89 countries globally	Dynamic panel model estimation techniques (GMM, FE)	Energy consumption per capita	Economic and Financial (GDP per capita)
Bellakhal et al. [15]	1996–2013	15 MENA countries	Static panel model estimation techniques (RE), IV, 2SLS	Share of RE in total primary energy produced, growth in the RE share	Economic and Financial (GDP per capita, GDP per capita2, Trade openness), Environmental (CO2 emissions), Energy (Energy Imports), Political and Regulatory (World Governance Indicators(wgi dataset), Existing RE policy, OPEC member, alternative measures of governance (ICRG database)
Ergun et al. [16]	1990–2013	21 African countries	Static panel model estimation techniques (FE, RE, GLS), Unit root tests, Causality tests	Renewable energy consumption (% of total final energy consumption)	Economic and Financial (FDI, GDP per capita, Trade), Political and Regulatory (Democracy), Social and Innovation (Human development index)
Hoang et al. [17]	1981–2018	USA	Wavelet techniques, time-varying Granger causality test.	Renewable energy consumption from different sources	Economic and Financial (Industrial production index, Oil price), Energy (Non-renewable energy consumption)
Dogan et al. [18]	1980–2016	72 developed and developing countries	Static panel model estimation techniques (OLS), Unit root test, AMG	Renewable energy (general), indicators: RE production, RE consumption, RE production per capita, RE consumption per capita, share of RE production in total energy production, share of RE consumption in total energy consumption, share of per capita RE production in per capita total energy production, share of per capita RE consumption in per capita total energy consumption	Economic and Financial (Crude oil price, GDP, GDP per capita), Environmental (CO2 emissions, CO2 emissions per capita)
Hao and Shao [19]	1995–2015	118 countries globally	Static panel model estimation techniques, Time series cross-sectional PW with panel-corrected standard errors, Unit root tests	Share of renewable energy in the final energy consumption	Demographic and Geographical (Population), Economic and Financial (GDP per capita), Environmental (Carbon intensity (CO2/GDP), Global Adaptation Index (Country’s vulnerability to climate change)), Political and Regulatory (Carbon tax policy adaptation)
Hussain et al. [20]	1996–2017	51 Belt and Road Initiative countries	Static panel model estimation techniques (FE, RE), IV, 2SLS	Share of renewable energy in total primary energy production	Economic and Financial (GDP per capita, Trade openness), Environmental (CO2 emissions), Political and Regulatory (Governance)
Khezri et al. [21]	2000–2018	31 mainly Asia-Pacific countries	Static panel model estimation techniques (POLS, RE, spatial FE, time period FE)	Renewable energy per capita (hydropower, solar, wind, bioenergy, geothermal)	Economic and Financial (Development of financial institution, Development of financial market, GDP per capita, Trade), Social and Innovation (Number of scientific and technical journal articles per capita)
Su et al. [22]	2000–2020	Global
Zhang et al. [23]	1999–2018	35 OECD countries	Panel smooth transition regression model, unit root test	Renewable energy consumption	Economic and Financial (FDI, GDP per capita, Inflation (domestic), International remittances, Trade (exports, imports, total trade)), Environmental (CO2 emissions), Energy (Access to electricity)
Zheng et al. [24]	1991–2017	87 countries globally	Static panel model estimation techniques (FE), Dynamic panel model estimation techniques	Patents related to renewable energy	Demographic and Geographical (Urbanisation), Economic and Financial (CPI, FDI, GDP per capita, Trade), Environmental (CO2 emissions), Energy (Share of renewables output in total electricity output), Political and Regulatory (Corruption, Stability, Terrorism), Social and Innovation (Government expenditure on education)
Amoah et al. [25]	1996–2019	32 African countries	Dynamic panel model estimation techniques (GMM, GMM-FE), IV, IV-FE	Share of renewable energy consumption	Economic and Financial (Economic structure, FDI, GDP per capita, GDP per capita2, Trade openness) Environmental (Environmental Performance Index (EPI)), Political and Regulatory (Corruption index (CPI))
Awijen et al. [26]	1984-2014	9 MENA countries	Panel smooth transition model, mediating model (nonlinear)	Renewable energy production	Economic and Financial (Domestic credit to private sector, FDI, GDP, Natural resources rents, Total factor productivity), Environmental (CO2 emissions), Political and Regulatory (Governance quality, Political stability and absence of violence), Social and Innovation (ICT, Percentage of internet users)
Fang et al. [27]	1990–2015	Brazil, India, China, South Africa	Static panel model estimation techniques (FGLS, FE, PCSE, RE), Dynamic panel model estimation techniques (GMM), Granger causality	Renewables consumption	Demographic and Geographical (Urbanisation), Economic and Financial (Economic globalisation, GDP per capita, Industrialisation), Social and Innovation (School enrolment primary, School enrolment secondary)
Huang et al. [28]	1980–2018	5 ASEAN countries	Static panel model estimation techniques (LLC, IPS, POLS, FMOLS), stationarity tests, Pedroni’s co-integration test, Dynamic panel model estimation techniques (DOLS)	Renewable energy consumption	Demographic and Geographical (Urbanisation), Economic and Financial (FDI, Trade), Environmental (CO2 emissions), Political and Regulatory (Governance)
Li et al. [29]	2015–2020	China (38 firms)	Static panel model estimation techniques (OLS), dynamic panel model estimation techniques (IV-GMM), Heckman two-stage model	Investments in renewable energy resources	Economic and Financial (Firm size, Green bonds, Oil price volatility), Political and Regulatory (Corporate governance, Geopolitical risk, green regulations)
Lu et al. [30]	1966–2016	36 OECD countries	Dynamic panel model estimation techniques (DSGMM, RE)	Renewable energy consumption	Economic and Financial (Economy-wide energy price, GDP per capita, International trade potential), Environmental (CO2 emissions)
Saba and Biyase [31]	2000–2018	35 European countries	Dynamic panel model estimation techniques (GMM, FMOLS, DOLS), Unit root test, Cointegration test, Causality test	Renewable electricity output	Demographic and Geographical (Land area, Population), Economic and Financial (Domestic credit to private sector, FDI, GDP per capita, Gross fixed capital formation, Industrial value added, Trade), Environmental (CO2 emissions), Political and Regulatory (Governance indicators), Social and Innovation (ICT, School enrolment secondary)
Shahbaz et al. [32]	1980–2018	China	Unit root test ADF, ARDL approach	Renewable energy consumption	Demographic (Urbanisation), Economic and Financial (Economic globalisation, Fiscal decentralisation, GDP, Income inequality)
Shinwari et al. [33]	1990–2020	China	Bayer-Hanck cointegration, Quantile Regression method, Frequency Domain Causality test, unit root tests ADF and DF GLS	Investment in renewable energy	Economic and Financial (GDP, Natural resources rents (% of GDP)), Energy (Energy efficiency), Social and Innovation (Innovation (patents by residents and non-residents))
Wang et al. [34]	1997–2019	32 OECD countries	Static panel model estimation techniques, Cross-sectional dependence test, Unit root test, PMG-ARDL	Renewable energy consumption	Economic and Financial (Economic globalisation, GDP per capita), Political and Regulatory (Institutional effectiveness, Political risk)
Xu et al. [35]	2001–2020	30 Chinese provinces	Gini coefficient, Moran’s I index, Spatial Durbin Model (SDM)	Renewable energy generation to reflect the renewable energy production capacity, Renewable energy installed capacity	Demographic (urban population, urbanisation), Economic and Financial (FDI, GDP per capita), Environmental (CO2 emissions, SO2 emissions by industry), Energy (Energy intensity, Transmission infrastructure), Political and Regulatory (Environmental regulation), Social and Innovation (R&D investment)
Zhu et al. [36]	2000–2019	Global	Dynamic panel model estimation techniques (SYS-GMM), Weighted global trade network	Renewable energy development, Renewable energy technological progress (mediating variable)	Economic and Financial (Critical mineral trade, FDI, GDP per capita), Energy (Energy intensity, Renewable energy consumption), Social and Innovation (Renewable energy technological progress)
Alharbi et al. [37]	2007–2020	44 countries globally	Static panel model estimation techniques, Cross-sectional dependence, Unit root tests, PMG	Net generation of renewable energy from biomass and non-biomass sources and these 2 categories separately in further tests, Share of renewable energy to total energy production	Demographic and Geographical (Population), Economic and Financial (Credit market, Equity market, GDP growth, GDP per capita, Green finance, Oil rent, Trade), Environmental (CO2 emissions), Energy (Fossil fuel energy), Social and Innovation (Innovation)
Appiah et al. [38]	2000–2021	21 Sub-Saharan African countries	Dynamic panel model estimation techniques, PQARDL technique, Cross-sectional dependence test, Unit root test, Panel cointegration, CSARDL technique	Renewable energy development	Economic and Financial (FDI, Financial development, Industrialisation), Political and Regulatory (Fiscal policy, Institutional quality)
Bei and Wang [39]	1990–2020	China	Time series techniques	Investments in renewable energy	Economic and Financial (GDP, Green finance, Investment in energy with private participation), Energy (Renewable electric output)
Chu et al. [40]	1990–2017	23 top energy consumers countries	Static panel model estimation techniques (FE), CD test, Quantiles technique, AMG technique	Renewable energy production per capita, Renewable energy consumption per capita	Economic and Financial (Economic complexity, GDP per capita, Trade), Energy (Energy security)
Dingru et al. [41]	1990–2015	Sub-Saharan Africa countries	ARDL technique, ADF approach, Phillip and Perron approach, unit root test	Renewable energy consumption	Demographic (Urbanisation), Economic and Financial (FDI, GDP per capita, Natural resources rents, Trade openness)
Foye [42]	1990–2020	Nigeria	ARDL technique, ADF approach	Installed renewable energy capacity	Economic and Financial (Exchange rate, GDP per capita, Inflation, Interest rate(domestic), Oil rent, Trade openness), Environmental (Climate change), PolItical and Regulatory (Governance), Social and Innovation (Government spending on human capital)
Hille [43]	1991–2021	37 countries in Europe	Dynamic panel model estimation techniques (FE-2SLS)	Renewable electricity generation per capita	Economic and Financial (Electricity price, FDI, Feed-in tariffs, GDP per capita, Investment tax credits, Public investment & capital subsidies, Quotas, Sales tax reductions, Trade), Environmental (CO2 emissions), Energy (Electricity consumption, Energy imports (coal imports, gas imports, oil imports)), Political and Regulatory (Geopolitical risks, Green parties’ seats, Regulatory quality), Social and Innovation (RE R&D budgets, Secondary education)
Iqbal et al. [44]	1980–2019	Pakistan	Time series, ARDL, NARDL, long-run, short-run	Renewable energy production	Economics and Financial (FDI, Financial development, GDP per capita), Environmental (CO2 emissions)
Lee et al. [45]	2001–2019	30 provinces in China	Static panel model estimation techniques (OLS, FE)	Renewable energy power	Demographic and Geographical (Population), Economic and Financial (Economic and financial openness, GDP per capita, Inflation, Trade (high technology exports (% of manufactured exports)), Environmental (CO2 emissions), Political and Regulatory (Political risk rating), Social and Innovation (Fixed broadband subscriptions (per 100 people), Individuals using the Internet (% of the population), Mobile cellular subscriptions (per 100 people), Secure Internet servers (per 1 million people), Unemployment)
Lee et al. [46]	2000–2019	126 countries globally	Static panel model estimation techniques (FE), MMQR technique	Renewable electricity output (GWh), Renewable electricity share of total electricity output (%), Renewable energy consumption (TJ), Renewable energy share of total final energy consumption (%)	Demographic and Geographical (Population), Economic and Financial (Economic risk rating, Financial risk rating, GDP per capita, Inflation, Trade), Environmental (CO2 emissions), Political and Regulatory (Political risk rating), Social and Innovation (Fixed broadband subscriptions (per 100 people), High technology exports (% of manufactured exports), Individuals using the Internet (% of the population), Mobile cellular subscriptions (per 100 people), Secure Internet servers (per 1 million people), Unemployment)
Liu and Feng [47]	2001–2020	129 countries globally	Static panel model estimation techniques (FE, RE), Dynamic panel model estimation techniques (SYS-GMM, Driscoll-Kraay, IV-GMM), Heterogeneity tests	All RE electricity generation, non-hydro RE electricity generation, Share of RE	Demographic and Geographical (Population), Economic and Financial (FDI, GDP per capita), Environmental (CO2 emissions per capita), Energy (Share of fossil energy), Political and Regulatory (Institutional quality, Legislative strength, Stock of older energy laws, Stock of recent energy laws)
Pata et al. [48]	2004–2018	G7 countries	Static panel model estimation techniques, CSD test, AMG approach	Renewable energy investments	Demographic and Geographical (Urbanisation), Economic and Financial (GDP), Political and Regulatory (Economic policy uncertainty, Geopolitical risk, Government efficiency, Regulatory quality)
Tinta [49]	2005–2022	33 Sub-Saharan Africa countries	Dynamic panel model estimation techniques (GMM), unit root test	Total renewable energy electricity consumption	Economic and Financial (Financial inclusion, GDP growth, Industrialisation), Energy (Total non-renewable energy electricity consumption), Political and Regulatory (Quality of institution), Social and Innovation (Schooling and returns to education index)
Wang et al. [50]	2003–2019	Asian countries	Dynamic panel model estimation techniques (IV-GMM)	Renewable energy generation	Demographic (Urbanisation), Economic and Financial (FDI, GDP growth), Social and Innovation (Digital economy, Industrial structural upgrading)
Zhao et al. [51]	1970–2019	20 OECD countries	Dynamic panel model estimation techniques (GMM), Cross-sectional dependence analysis, Unit root test, Cointegration tests	Renewable energy consumption	Economic and Financial (Economic globalisation index, GDP per capita, Natural resources rents), Environmental (CO2 emissions), Political and Regulatory (Geopolitical risk index)
Hassan et al. [52]	1990–2019	32 OECD countries	Dynamic panel model estimation techniques (GMM), Cross-sectional dependence analysis, Unit root test, Causality test	Renewable energy consumption	Economic and Financial (Consumer price index (CPI), GDP per capita, Trade), Energy (Renewable energy consumption), Political and Regulatory (Environmental policy stringency index (market based policies, non-market based policies, technology support policies)), Social and Innovation (Environmental innovation)

Note: * This table should be viewed in conjunction with Table 8 in [11] since it augments it by summarising, in a similar way, the papers published since Bourcet undertook her review.

, and draw conclusions from the combined reviews of the 89 papers in total.

In their review, Bourcet [11] highlights the distinct lack of consensus in the papers reviewed across many aspects of the research including the variables included in the research, the methods employed, the scope, and the results. And the additional papers included in Table 1 show that there is no difference and confirm the lack of consensus. Thus, as Bourcet [11] highlights and we also observe in the 41 papers included in Table 1, the choice of explanatory variables (or drivers) to include in an analysis of renewable energy deployment is extremely wide. Therefore, in this paper, we attempt to take as wide a perspective as possible by attempting to include as many countries as possible and as many explanatory variables (or drivers) as possible but using what [11] identifies as the most common explanatory variables: the share of renewables in total energy consumption and the share of renewables in total electricity production (see [11] Table 2 p. 4).

3. Methodology and Data

Given the discussion above, for our analysis, we attempt to test as many feasible drivers for as many global countries as possible, for both renewables as a share of total energy consumption $\left(R{S}_{it}^{TEC}\right)$ and renewables as a share of total electricity supply $\left(R{S}_{it}^{TEL}\right)$. This is illustrated by the general specification summarised in Equation (1). The full definitions and the sources of both dependent variables and the independent variables (i.e., the drivers) are given in

Table 2. Descriptive statistics.

Variable (Total Panel)	Label	Mean	Std. Dev.	Obs.	Source
Renewable energy consumption (% of total final energy consumption)	$R{S}^{TEC}$	34.267	30.709	5456	[53]
Renewable energy for electricity generation * (% of total electricity’s generation)	$R{S}^{TEL}$	34.872	34.465	5396	[54]
Gross Domestic Product (constant 2015 USD per capita)	$GDP$	11,231.78	16,515.62	5298	[53]
Crude oil price (WTI) (USD/bbl)	P	49.53	24.40	5487	[55]
Trade (% of GDP)	$TRADE$	81.35	48.53	4767	[53]
Access to electricity (% of Population)	$ACCEL$	78.92	30.52	4800	[53]
Surface (sq. Km)	$SUR$	750,787	1,993,359	5477	[53]
CO2 emissions (metric tonnes per capita)	$CO2$	4.25	5.41	5486	[53]
Methane emissions (Kt of CO2 equivalent)	$METH$	41,419.47	114,019.80	5487	[53]
Foreign direct investments (net, current USD)	$FDI$	−6.12 × 108	2.01 × 1010	4634	[53]
Government Expenditure in education (% GDP)	$GEXED$	4.45	1.91	3404	[53]
Research and Development expenditure (% GDP)	$RND$	0.96	0.96	2083	[53]
Patent applications	$PAT$	5515.77	23,706.28	3146	[53]
Total natural resources rents (% GDP)	$RENTS$	7.33	11.06	5353	[53]
Urban population (% of population)	$URPOP$	54.85	22.84	5487	[53]
Economic Development ** ($D=1$ for developing country, 0 otherwise)	$ED$	0.80	0.40	5487	[56]

Note: * For the share of renewables in electricity generation, we use data for electricity generation (renewables) and electricity generation (overall). ** Economic Development is captured by the Dummy Variable D that was constructed according to the International Monetary Fund (IMF) classification of countries. For more information about the panel of the countries in this study as well as the classification method, please see Table A1 in the Appendix A. Additionally, Table A2 in the Appendix A provides further descriptive statistics by the two groups of countries.

, which also provides the descriptive statistics for all variables. Note that in Equation (1), all variables are entered contemporaneously other than CO₂. In the previous literature reviewed by [11] and Table 1 above, when statistically significant, the coefficient on the contemporaneous CO₂ emissions variable is found to be negative—suggesting that an increase in current CO₂ causes the share of current renewable energy to fall. However, a priori, we would expect the opposite so that, as CO₂ emissions increase, this would drive an increase in the share of renewable energy, i.e., giving a positive sign. Thus, we suspect that this might be an endogeneity problem picking up that, as the share of renewable energy increases (for a given level of energy consumption or electricity production), this causes CO₂ emissions to fall. For this reason, unlike previous studies, we include CO₂ emissions lagged one year to analyze whether the same outcome is observed.

$$R{S}_{it}^j=E(GD{P}_{it},P_t,TRAD{E}_{it},ACCE{L}_{it},SU{R}_i,CO{2}_{it-1},MET{H}_{it},FD{I}_{it},GEXE{D}_{it},RN{D}_{it},PA{T}_{it},RENT{S}_{it},URPO{P}_{it},E{D}_i)$$

(1)

To operationalise Equation (1), we econometrically estimate the specification shown in Equation (2) where the variables in lower case are in natural logarithms, and the $\alpha$s represent the coefficients to be estimated. Equation (2) shows that, to analyze if the impact of the drivers differs from developed to developing countries, we include the drivers shown in Equation (1) plus a set of interactive variables, which are the same core drivers included in Equation (1) multiplied by the Developing Country Dummy $\left(D_i\right)$. Using this specification, the estimated coefficients show the effect of a change in the explanatory variables on the renewable share for the core variable group ($D_i=0$). The coefficients of these additional terms are, therefore, the interaction effects—i.e., the difference between the effects of the core variable group as compared to the other interactive group (for which $D_i=1$). The overall effect of the interactive explanatory variables is based on all the estimated coefficients with those from the base group, which we test by conducting an F-test of restricting all the interaction coefficients to be jointly zero to ascertain whether, generally, there is a significant difference in the way the independent variables drive the share of renewables for developed and developing countries, whereas to analyze the difference in the impact of the individual interactive drivers for the two sets of countries, we conduct a standard t-test on the individual interactive coefficients.

$$\begin{array}{cc}\hfill R{S}_{it}^j=& \alpha +{\alpha }^{gdp}gd{p}_{it}+{\alpha }^p{p}_t+{\alpha }^{trade}trad{e}_{it}+{\alpha }^{accel}acce{l}_{it}+{\alpha }^{sur}su{r}_i+{\alpha }^{co2}co{2}_{it-1}+{\alpha }^{meth}met{h}_{it}+{\alpha }^{fdi}fd{i}_{it}\hfill \\ \hfill +& {\alpha }^{gexed}gexe{d}_{it}+{\alpha }^{rnd}rn{d}_{it}+{\alpha }^{pat}pa{t}_{it}+{\alpha }^{rents}rent{s}_{it}+{\alpha }^{urpop}urpo{p}_{it}\hfill \\ \hfill +& {\alpha }^{dgdp}gd{p}_{it}\ast D_i+{\alpha }^{dp}{p}_t\ast D_i+{\alpha }^{dtrade}trad{e}_{it}\ast D_i+{\alpha }^{daccel}acce{l}_{it}\ast D_i+{\alpha }^{dsur}su{r}_i\ast D_i+{\alpha }^{dco2}co{2}_{it-1}\ast D_i\hfill \\ \hfill +& {\alpha }^{dmeth}met{h}_{it}\ast D_i+{\alpha }^{dfdi}fd{i}_{it}\ast D_i+{\alpha }^{dgexed}gexe{d}_{it}\ast D_i+{\alpha }^{drnd}rn{d}_{it}\ast D_i+{\alpha }^{dpat}pa{t}_{it}\ast D_i+{\alpha }^{drents}rent{s}_{it}\ast D_i\hfill \\ \hfill +& {\alpha }^{durpop}urpo{p}_{it}\ast D_i\hfill \end{array}$$

(2)

Our modelling strategy is to initially estimate the two versions of Equation (2) by Ordinary Least Squares (OLSs) using an unbalanced panel called the Base Model (BM) and to check whether there is evidence of heteroscedasticity (using a Modified Wald test for groupwise heteroscedasticity) and, if so, re-estimate using heteroscedastic-consistent standard errors (BMR)—which proved to be the case as shown in the results section below. We then eliminated the insignificant core variables and re-estimated using various specifications (all with heteroscedastic-consistent standard errors): a Pooled Model (PMR); a fixed effects model with country fixed effects (FEMiR); a fixed effects model with country and time fixed effects (FEMitR); and a random effects model (REMR). From this, various tests are undertaken to determine the preferred model to give the final preferred specification for both the share of renewables in total consumption and total electricity which, as shown in the next section, is the fixed effects model with country and time fixed effects (FEMitR). The tests are as follows: a Wald Test to test between PM and FEM with a null hypothesis that all fixed effects are equal to zero; another Wald Test between FEMi and FEMit where the null hypothesis is that all time dummies as a group are statistically insignificant; the Breusch–Pagan Lagrangian Multiplier test for random effects where the null hypothesis is that the PM is preferred over the REM; and the Hausman test to choose between the FEM and the REM for which the null hypothesis is that REM is the preferred model to use.

4. Empirical Results and Discussion

4.1. Share of Renewables in Total Energy Consumption

The BM results for estimating Equation (2) for the share of renewables in total energy consumption $\left(R{S}^{TEC}\right)$ are given in

Table 3. Estimation results for $R{S}^{TEC}$.

	BM	BMR	PMR	FEMiR	FEMitR	REMR
${\alpha }^{gdp}$	0.105	0.105	0.320 ***	0.474 ***	0.392 **	0.363 **
	(0.086)	(0.275)	(0.032)	(0.181)	(0.192)	(0.185)
${\alpha }^p$	0.725 ***	0.725 **	0.139 ***	0.018	0.405 *	0.434 **
	(0.124)	(0.320)	(0.036)	(0.052)	(0.218)	(0.213)
${\alpha }^{trade}$	0.687 ***	0.687 ***	0.279 ***	0.662 ***	0.652 ***	0.653 ***
	(0.081)	(0.247)	(0.046)	(0.147)	(0.157)	(0.159)
${\alpha }^{accel}$	−0.190	−0.190	−0.232 *	34.753 ***	28.161 **	−1.022 *
	(12.649)	(11.152)	(0.131)	(11.605)	(10.961)	(0.579)
${\alpha }^{sur}$	−0.531 ***	−0.531 **	0.622 ***	−0.616 **	−0.695 ***	0.323 *
	(0.130)	(0.244)	(0.015)	(0.248)	(0.253)	(0.167)
${\alpha }^{co2(t-1)}$	−0.948 ***	−0.948 ***	−1.041 ***	−1.279 ***	−1.181 ***	−1.145 ***
	(0.072)	(0.284)	(0.046)	(0.208)	(0.218)	(0.222)
${\alpha }^{meth}$	−0.542 ***	−0.542 **	−0.553 ***	−0.371 *	−0.352 *	−0.303
	(0.077)	(0.218)	(0.016)	(0.212)	(0.212)	(0.210)
${\alpha }^{fdi}$	−0.006	−0.006
	(0.011)	(0.006)
${\alpha }^{gexed}$	0.204 *	0.204
	(0.108)	(0.227)
${\alpha }^{rnd}$	0.092 *	0.092
	(0.047)	(0.140)
${\alpha }^{pat}$	0.032 ***	0.032
	(0.009)	(0.036)
${\alpha }^{rents}$	0.001	0.001
	(0.052)	(0.109)
${\alpha }^{urpop}$	−0.915 **	−0.915
	(0.383)	(1.336)
$developing$  ${\alpha }^{gdp}$	0.187 **	0.187	−0.136 ***	−0.305	−0.290	−0.283
	(0.089)	(0.281)	(0.044)	(0.204)	(0.211)	(0.203)
$developing$  ${\alpha }^p$	0.035	0.035	−0.158 ***	−0.022	−0.024	−0.015
	(0.036)	(0.060)	(0.048)	(0.058)	(0.058)	(0.057)
$developing$  ${\alpha }^{trade}$	−0.888 ***	−0.888 ***	−0.124 **	−0.646 ***	−0.620 ***	−0.624 ***
	(0.094)	(0.271)	(0.058)	(0.157)	(0.165)	(0.167)
$developing$  ${\alpha }^{accel}$	0.077	0.077	0.646 ***	−34.755 ***	−28.195 **	0.991 *
	(12.651)	(11.165)	(0.129)	(11.605)	(10.959)	(0.583)
$developing$  ${\alpha }^{sur}$	−5.686	−5.686 *	−0.493 ***	0.492 *	0.561 **	−0.224
	(4.245)	(3.086)	(0.019)	(0.255)	(0.261)	(0.182)
$developing$  ${\alpha }^{co2(t-1)}$	0.222 **	0.222	0.033	0.831 ***	0.729 ***	0.666 ***
	(0.091)	(0.302)	(0.053)	(0.218)	(0.231)	(0.233)
$developing$  ${\alpha }^{meth}$	−0.236 **	−0.236	0.493 ***	0.304	0.281	0.238
	(0.107)	(0.294)	(0.022)	(0.224)	(0.225)	(0.222)
$developing$  ${\alpha }^{fdi}$	0.062	0.062
	(0.137)	(0.090)
$developing$  ${\alpha }^{gexed}$	−0.534 ***	−0.534 **
	(0.125)	(0.261)
$developing$  ${\alpha }^{rnd}$	−0.175 ***	−0.175
	(0.052)	(0.141)
$developing$  ${\alpha }^{pat}$	−0.073 ***	−0.073
	(0.015)	(0.046)
$developing$  ${\alpha }^{rents}$	0.079	0.079
	(0.059)	(0.113)
$developing$  ${\alpha }^{urpop}$	0.648	0.648
	(0.417)	(1.284)
Number of Obs	1383	1383	4158	4158	4158	4158
R Square	0.67	0.67	0.68	0.46	0.47	0.46
Heteroscedasticity Test	1.8 × 106
Prob > Chi2	0.00
Wald Test: PMR vs. FEMiR				2001.92
Prob > F				0.00
Wald Test: FEMiR vs. FEMitR					1.77
Prob > F					0.01
Breusch-Pagan LM: PMR-REMR						39,242.19
Prob > Chibar2						0.00
Hausman Test: REM vs. FEMit						133.64
Prob > Chi2						0.00
F Test for Dummy Variables as a group	11.79	10.63	151.02	74.44	58.51	39.80
p Value	0.00	0.00	0.00	0.00	0.00	0.00

Note: *** Significant at 1% level, ** Significant at 5% level, * Significant at 10% level. Standard errors are in parentheses. Stata 18 [57] econometric software is used for the estimation of an unbalanced panel of 177 countries over the period 1990 to 2020. Base Model is a fixed effect model.

. This shows that, given the limited data on some variables, the number of observations is restricted to 1383. As detailed above, we then experimented with a restricted set of core drivers by eliminating some statistically insignificant variables. (We also experimented with other energy price variables for this and the $R{S}^{TEL}$ equation but found that the results with the crude oil price (P) gave the most consistent and robust overall estimation results). This resulted in the restricted set of drivers utilised (with FDI, GEXED, RND, PAT, RENTS, and URPOP—and the associated interactive terms—being eliminated) in the other models given in Table 3 but with a notable increase in the number of observations to 4158. To choose between these models (PMR, FEMiR, FEMitR, and REMR), we undertook a series of tests detailed in the methodology section and presented in Table 3. These suggested that the preferred specification is the FEMitR.

Focussing on the preferred model, FEMitR, this shows that all remaining (non-interactive) core explanatory variables are significantly different from zero at the 10% level at least. Moreover, in general, there is a significant difference between the impact of the drivers on the renewable share of energy consumption between the developed and developing countries shown by the F test of linear restrictions—in fact, this applies to all the estimated equations, shown in Table 3. This suggests that as a group, the developing country interaction terms are statistically significant. Therefore, we interpret the individual driver results separately for the developed and developing countries where the developing country interaction term is significantly different from zero. Note also that, given the variables are in natural logarithms, we interpret the estimated coefficients as estimated elasticities.

For the key macro drivers, GDP and P, Table 3 shows that for the preferred specification FEMitR, the developing country interaction terms are not significantly different from zero; hence, the estimated elasticities are the same for both the developed and developing countries. The estimated income elasticity is 0.39, and the estimated crude oil price elasticity is 0.41, highlighting the substitute nature of oil and renewables. For TRADE, however, there is a difference in the elasticities for the two sets of countries given that the developing country interactive term for this driver is negative and significant, suggesting that for developed countries, the trade elasticity is 0.65, whereas for the developing countries, it is much smaller at 0.03 like that obtained by [15].

For the ACCESS and SURFACE variables, there is again a statistically significant difference between the impact for the different sets of countries. The estimated access elasticity for developed countries is positive, like the estimate obtained by [23], but a lot higher at 28.20, whereas for the developing countries, it is negative at −0.03. This probably reflects that for developing countries, access to electricity is already very high at almost 100%, whereas for the developing countries, it is somewhat lower. The estimated surface elasticities for developed and developing countries are −0.70 and −0.13, respectively—these being different to the estimates by [58,59], who found positive surface elasticities for developed countries.

For the final included explanatory variables, there is a significant difference for CO2(t−1), but not METH, between the different set of countries. The lagged carbon dioxide emissions elasticity for developed and developing countries are −1.18 and −0.45, respectively; hence, despite entering CO2 with a lag, the estimated CO2 elasticities are still found to be negative for both sets of countries. The estimated methane emissions elasticity, however, is −0.35 for both sets of countries.

4.2. Share of Renewables in Total Energy Consumption

The BM and the BMR results for estimating Equation (2) for the share of renewables in electricity generation $\left(R{S}^{TEL}\right)$ are given in

Table 4. Estimation results for $R{S}^{TEL}$.

	BM	BMR	PM	FEMi	FEMit	REM
${\alpha }^{gdp}$	0.941 ***	0.941	0.917 ***	1.262 ***	0.875 **	0.912 **
	(0.146)	(0.591)	(0.056)	(0.352)	(0.403)	(0.428)
${\alpha }^p$	0.099	0.099	0.093 *	−0.037	1.095 ***	1.072 ***
	(0.212)	(0.402)	(0.053)	(0.086)	(0.343)	(0.329)
${\alpha }^{trade}$	−0.167	−0.167
	(0.138)	(0.424)
${\alpha }^{accel}$	186.430 ***	186.430 ***	−0.271	225.815 ***	187.547 ***	−0.831
	(21.532)	(43.534)	(0.172)	(38.301)	(46.877)	(0.907)
${\alpha }^{sur}$	−0.849 ***	−0.849 **	0.513 ***	−1.044 **	−1.435 ***	0.285
	(0.221)	(0.418)	(0.017)	(0.473)	(0.470)	(0.231)
${\alpha }^{co2(t-1)}$	−1.485 ***	−1.485 ***	−1.462 ***	−1.839 ***	−1.333 ***	−1.244 ***
	(0.122)	(0.421)	(0.079)	(0.354)	(0.371)	(0.387)
${\alpha }^{meth}$	−0.890 ***	−0.890 **	−0.413 ***	−0.649 **	−0.561 *	−0.466
	(0.130)	(0.377)	(0.027)	(0.327)	(0.323)	(0.313)
${\alpha }^{fdi}$	0.010	0.010
	(0.018)	(0.009)
${\alpha }^{gexed}$	−0.895 ***	−0.895 *
	(0.183)	(0.526)
${\alpha }^{rnd}$	0.837 ***	0.837 ***
	(0.080)	(0.301)
${\alpha }^{pat}$	0.023	0.023
	(0.016)	(0.076)
${\alpha }^{rents}$	0.110	0.110
	(0.088)	(0.172)
${\alpha }^{urpop}$	−0.230	−0.230
	(0.652)	(2.196)
$developing$  ${\alpha }^{gdp}$	−0.592 ***	−0.592	−0.698 ***	−0.981 ***	−0.906 **	−0.951 **
	(0.151)	(0.540)	(0.071)	(0.374)	(0.402)	(0.432)
$developing$  ${\alpha }^p$	−0.113 *	−0.113	−0.114	0.008	0.018	0.044
	(0.062)	(0.114)	(0.070)	(0.093)	(0.093)	(0.102)
$developing$  ${\alpha }^{trade}$	0.316 **	0.316
	(0.160)	(0.437)
$developing$  ${\alpha }^{accel}$	−186.514 ***	−186.514 ***	1.190 ***	−225.637 ***	−187.509 ***	0.884
	(21.536)	(43.534)	(0.157)	(38.301)	(46.875)	(0.909)
$developing$  ${\alpha }^{sur}$	5.098	5.098	−0.242 ***	0.878	1.267 **	−0.097
	(7.227)	(8.892)	(0.026)	(0.551)	(0.492)	(0.246)
$developing$  ${\alpha }^{co2(t-1)}$	0.985 ***	0.985 **	0.509 ***	1.527 ***	1.004 **	0.899 **
	(0.156)	(0.433)	(0.089)	(0.366)	(0.386)	(0.398)
$developing$  ${\alpha }^{meth}$	1.042 ***	1.042 **	0.318 ***	0.647 *	0.518	0.426
	(0.182)	(0.519)	(0.034)	(0.344)	(0.338)	(0.325)
$developing$  ${\alpha }^{fdi}$	0.172	0.172 *
	(0.233)	(0.092)
$developing$  ${\alpha }^{gexed}$	0.648 ***	0.648
	(0.213)	(0.523)
$developing$  ${\alpha }^{rnd}$	−0.760 ***	−0.760 **
	(0.089)	(0.308)
$developing$  ${\alpha }^{pat}$	−0.211 ***	−0.211 **
	(0.025)	(0.088)
$developing$  ${\alpha }^{rents}$	0.035	0.035
	(0.100)	(0.178)
$developing$  ${\alpha }^{urpop}$	1.502 **	1.502
	(0.710)	(2.165)
Number of Obs	1383	1383	4574	4574	4574	4574
R Square	0.6	0.6	0.38	0.22	0.28	0.26
Heteroscedasticity Test	8.3 × 106
Prob > Chi2	0.00
Wald Test PMR vs. FEMiR				1219.22
Prob > F				0.00
Wald Test FEMiR vs. FEMitR					7.62
Prob > F					0.00
Beusch-Pagan LM: PMR-REMR						41,342.92
Prob > Chibar2						0.00
Hausman Test: REM vs. FEMit						120.67
Prob > Chi2						0.00
F Test for Dummy Variables as a group	22.81	18.51	57.13	70.90	42.39	31.96
p Value	0.00	0.00	0.00	0.00	0.00	0.00

Note: *** Significant at 1% level, ** Significant at 5% level, * Significant at 10% level. Standard errors are in parentheses. Stata [57] econometric software is used for the estimation of an unbalanced panel of 177 countries over the period 1990 to 2020. Base Model is a fixed effect model.

. Again, these have only a limited 1383 observations, but following some experimentation and the elimination of some statistically insignificant variables, this increased to 4574 observations. This resulted in a further restricted set of core drivers since the TRADE variable was also eliminated. Also like before, we undertook a similar series of tests to choose between PMR, FEMiR, FEMitR, and REMR, presented in Table 4, and like with the $R{S}^{TEC}$ equation, the overall preferred specification is the FEMitR—Fixed Country and Time effects corrected for heteroscedasticity.

Focussing on FEMitR for $R{S}^{TEL}$, this shows that, again, all remaining (non-interactive) explanatory variables are significantly different from zero at the 10% level at least, and in general, there is a significant difference between the impact of the drivers on the renewable share of consumption between the developed and developing countries with the F test of linear restrictions (shown in Table 4) suggesting that as a group, the developing country interaction terms are statistically significant (again this is consistent across all estimated models). Thus, our interpretation of the individual impact of the drivers is similar to that above.

Table 4 shows that, unlike the $R{S}^{TEC}$, the estimated coefficient for the developing country interactive term is significantly different from zero suggesting the changes in GDP.

These estimates, therefore, indicate that the income effect is very small for developing countries and, possibly, is an inferior good, such that although the share of renewables in electricity production should continue to grow relatively strongly as developed countries continue to grow, this is unlikely to be the case across the developing countries. For the impact of P, however, there is not a significant difference between the two sets of countries (like $R{S}^{TEC}$) with an estimated crude oil price elasticity of 1.095, somewhat higher than for the $R{S}^{TEC}$ estimates but again highlighting the substitute nature of oil and renewables.

For the ACCESS and SURFACE variables, like the estimated $R{S}^{TEC}$ equation, there is a statistically significant difference between the impact for the different sets of countries. The estimated access elasticity for the developed countries is positive and very high at 187.55, whereas for the developing countries, it is just positive at 0.04. The estimated surface elasticities for developed and developing countries are −1.44 and −0.17, respectively—like those found for the $R{S}^{TEC}$ estimates.

Finally, like the $R{S}^{TEC}$ estimates, there is a significant difference for CO2(t−1), but not METH, between the different set of countries. The lagged carbon dioxide emissions elasticities are again still negative at −1.33 for developed countries (similar to [37]) and −0.33 for developing and −0.56 for the estimated methane emissions elasticity for both sets of countries—similar to [35].

5. Summary and Conclusions

In this paper, we conduct an empirical analysis of renewable energy drivers at the global level. We examine several potential drivers of the share of renewables in total energy consumption and the share of renewables in total electricity production allowing for a distinction of the impact of the drivers between developed and developing countries. The Base Models that we initially estimate incorporate many potential drivers; however, after econometrically estimating the models and statistical analysis and tests, several variables are excluded. Hence, for the share of renewables in energy consumption and the share of renewables in electricity production, we find no role for the following potential drivers: foreign direct investment; government expenditure in education; research and development expenditure; patent applications; total natural resources rents; and urban population. And, although for the share of renewables in total energy consumption, trade is significant and retained, it is excluded in the share of renewables in electricity production model.

Furthermore, the preferred model for both renewable share equations is the fixed effect model with country and year effects corrected for heteroscedasticity. These give similar results that retain the following drivers: GDP, the crude oil price, access to electricity, surface area, lagged CO₂ emissions, and methane emissions. Thus, for both preferred share equations, the results are similar in terms of the variables maintained, but as mentioned, the trade variable is also retained in the share of renewables in the energy consumption equation. Furthermore, for both share equations, there is a significant difference in the overall responses to the drivers in the developed and developing countries suggesting that researchers should not automatically assume that the responses by the two sets of countries are the same.

Focussing on the individual elasticity estimates, the positive but relatively small income elasticity found for both the developed and developing countries suggests that as GDP grows, so will the share of renewables in energy consumption, but with an elasticity of just under 0.4, this does not suggest that the pace of increase would be dramatic. The situation is slightly different for the share of renewables in electricity production given the estimated income elasticity is just under 0.9 for developed countries, but for developing countries, the estimated elasticity is close to zero (being just negative); so, again, overall, as GDP grows, the increase in the share of renewables in electricity production is unlikely to be that dramatic. This all suggests that increases in renewable energy shares are unlikely to be enough to support the necessary increase for a sustainable global future.

The crude oil price elasticities are positive across the board, being about 0.4 for the share of renewables in energy consumption and about unity for the share of renewables in electricity production—showing that renewables are a substitute for oil (and other fossil fuels) and that higher prices could incentivise a greater uptake of renewables. This would be seen as being advantageous in terms of sustainability aspirations in the longer term. However, this would not be the case for oil/fossil fuel importing developing countries in the shorter term, given the effect of the higher fuel costs on the living standards of the poorer countries.

Trade openness could play an important role in shaping the global renewable energy landscape by either facilitating technology transfer, especially to countries that may not have the capacity to develop these technologies domestically or by enabling access to resources, influencing supply chain dynamics. This is reflected in the positive trade elasticities found for the share of renewables in energy consumption. However, our results suggest that this only really applies to developed countries, given the estimated elasticity of about 0.7 for developed countries and just above zero for developing countries (and no trade effect being found for the share of renewables in electricity production). The policy implication is critical at this stage to accelerate renewable energy deployment, given trade may drive policy alignment and economic development between countries, especially from more to least developed countries.

The estimated impact of access to electricity shows a stark difference between developed and developing countries, both renewable share measures being extremely strong for developed countries and negligible for developing countries, probably reflecting a saturation of access in the developed countries relative to the developing countries. As for the estimated surface area elasticities, they are all negative. For the developed countries, they are around −0.7 and −1.4 for the share of renewables in energy consumption and electricity production, respectively; however, they are somewhat lower (in absolute terms) for developing countries at about −0.1 and −0.2, respectively. This probably reflects that the relative size of a country influences the infrastructure needed to support renewable energy deployment, such as transmission, network upgrades, and storage facilities, and that larger countries have bigger cities that are difficult to create big renewable areas around or inside and more complex political and regulatory environments that suffer from administrative and/or bureaupathology that could hinder renewable energy deployment.

Finally, the two preferred equations retain the two environmental drivers, methane and CO₂ emissions. For methane, no difference is found for the two sets of countries with the estimated elasticities being about −0.4 and −0.6 for the share of renewables in energy consumption and electricity production, respectively. The negative results are generally consistent with the literature (although often not statistically significant), but arguably, this needs some further consideration. The negative elasticity suggests that as methane emissions increase, the share of renewables falls, but it is not intuitively obvious why this would be the case. The estimated CO₂ elasticities are equally puzzling. Again, we find negative elasticities for developing countries of around −1.2 to −1.3 for the share of renewables in energy consumption and electricity production, respectively, but somewhat lower estimates (in absolute terms) for developing countries of about −0.5 and −0.3, respectively. And again, it is not intuitively obvious why these should be negative. We had expected a priori that a rise in laggedCO₂ emissions would drive an increase in the share of renewables, reflecting increased environmental concerns encouraging stricter regulation towards limiting climate change and provoking pressure to increase renewable sources, but maybe the lag is too short, and these influences take longer to work through. Therefore, the negative elasticity might reflect a negative signal of social alienation about environmental concerns, suggesting that policy makers should act urgently and be more persuasive.

In summary, our results supply stakeholders and policy makers with additional information. Different approaches to renewables development are needed in developed and developing countries, given our results suggest that the drivers impact development in different ways across both sets of countries. In addition, our results suggest that more needs to be done if policy makers want the share of renewables to increase, given the environmental constraint and sustainability ambitions. In the aftermath of the Paris Agreement in 2015, many of the targets towards renewable deployment are very common among countries as depicted in their Nationally Determined Contributions, either in absolute numbers or in their nature, given the need to meet their international commitments [60]. The results found here, and in previous studies, suggest that left alone, these will not be achieved without further intervention, a view which is also echoed by [61]. Increased energy dependence hampers the deployment of renewables as this energy appetite is satisfied by utilising available non-renewable sources, acting as a significant mobility barrier to renewable ambitions.

Renewables are often still perceived as an expensive energy option, but in reality, their cost is rapidly falling. Barriers to renewable deployment are not static, they can vary from country to country and over time so putting in place the right array of measures at the right time is the key to successfully deploying renewables. Renewable deployment has tremendous potential to reduce emissions, help sustainability objectives, and alleviate poverty by extending energy access, but according to [62], their deployment in the developing world still faces severe economic, as well as non-economic, barriers. Additionally, many advanced economies face challenges to implementation, especially related to permitting and grid infrastructure expansion, while according to [63], in emerging economies, policy and regulatory uncertainties remain major barriers to faster renewable energy expansion. Moreover, in developing economies, weak grid infrastructure and a lack of access to affordable financing hamper the timely commissioning of such projects.

The deployment of modern renewable energy conversion technologies, such as solar photovoltaics and wind parks, is relatively recent in many countries; initiatives for the development of renewable energy sources have mainly focused on the economic factors which are also covered by a vast part of the literature. However, non-economic barriers to renewable energy deployment may play a significant role soon. Such barriers include environmental, administrative, regulatory, infrastructure, and public acceptance. A lack of data availability for many of the countries, however, makes it impossible to analyse the impact of such barriers.

Thus, policy makers need to develop policies that promote economic growth, trade openness, and increased access to electricity but with other interventions that strongly encourage or incentivise renewables deployment, such as the USA 2022 Inflation Reduction Act [64] and the European Green Deal [65] from the European Commission. However, these are very much developed country policies, and more tailored holistic policies are required for developing countries, maybe like the China Belt and Road Initiative [66], which includes investments in infrastructure projects, promotion of trade and new technologies, and improving financial inclusion for the countries involved in the project.

As stated above, we provide further useful information for policy makers; however, further work is needed. The distinction between developed and developing countries could be explored further by dividing developing economies into low- and lower-middle income economies as defined by the World Bank. The impact of policy such as feed-in-tariffs, legislation, etc., could be included in such analysis, but this was precluded here since it significantly reduced the number of observations; however, further analysis might address this by focussing on developed or OECD countries only. On the conceptual side, a deeper understanding of the relationship between emissions and renewables deployment is required to better allow for further robust statistical testing and verification. Finally, given the general lack of consensus in this area, as [11] has already suggested, there is a need for a robust meta-analysis to help fully understand the reasons for the different results generated in the research.