1. Introduction

Understood as the energy harnessed from naturally occurring sources that regenerate more quickly than their consumption rate, renewable energy (RE) holds a pivotal position in shaping a country’s long-term growth potential [1, 2, 3, 4, 5, 6].

Countries, with higher RE, are better positioned to drive down greenhouse emissions and other polluting substances, reduce the adverse effects of climate change, and address energy security, resulting in better growth performance and higher environmental quality. While the importance of RE is broadly acknowledged, significant differences exist between countries regarding their RE deployment [7]. In particular, many developing economies, compared to developed ones, where RE is a principal component of their national agenda, are not actively pursuing a transition to renewable energies [8]. From this perspective, there is a need for further investigation of the drivers of RE to gain a deeper understanding of the subject. An interesting line of research points to democracy as a potential crucial determinant of RE [9, 10, 11, 12, 13]. This research suggests that democracies might be more predisposed to embrace energy transitions for several reasons. Some of these reasons include the perceived higher value that democracies place on human life [14], the increased opportunities they offer for environmental interests [15, 16] and local interests [17] to influence policy decisions, the electoral incentives that drive politicians to pursue sustainable energy policies [18], and the democratic commitment mechanisms that encourage decarbonization efforts [19]. However, it is somewhat surprising that there has been relatively limited empirical exploration to ascertain whether or not democracy does indeed result in more RE. Additionally, the empirical evidence on the implications of democracy on RE is quite varied. While a number of studies do find the expected positive effect [20, 21], some suggest that democracy sometimes inhibits decarbonization [22]. Thus, we seek to make a meaningful contribution to this body of literature by tackling a crucial question: How does democracy shape developing countries’ renewable energy?

We focus on the developing countries for a number of reasons. Firstly, these countries often face a higher risk of political instability, as evidenced by the number of default episodes experienced by developing nations between 2000 and 2020. Secondly, many developing countries have a severe dependency on fossil energy; their economies are heavily relying on it. Cutting free from fossil fuels is considerably challenging, especially during current crises, as nations rush to implement short-term solutions to address fuel shortages. Based on the World Bank’s projections for the year 2023, in 2015, renewable energy accounted for just 22.02% of the total energy consumption in developing countries, which include both low- and middle-income nations. This figure was approximately 8 percentage points lower compared to the level observed in 2000. Consequently, it becomes apparent that instead of decreasing their dependence on fossil fuels, developing countries have, in fact, intensified their reliance on fossil fuels. Thirdly, many developing countries face challenges in accessing the necessary financing to build new renewable energy infrastructure and implement large-scale renewable power projects. We conducted an analysis covering the period from 1996 to 2020. To assess the relationship between democracy levels and RE, we employed a matching approach. This technique allowed us to compute the average difference in the outcome variable (in this case, RE) between countries characterized by high levels of democracy (the treated group) and countries with lower democracy levels (the control group). The strength of the matching approach lies in its ability to address issues such as self-selection bias, and endogeneity related to democracy. Our findings reveal a significantly positive treatment effect of democracy on RE, indicating that higher levels of democracy are associated with greater RE. Our chapter thus makes two important contributions. First, it provides explicit confirmation of the evidence supporting the idea that democracy promotes RE in developing countries. Second, it applies an innovative and less commonly employed approach, namely Propensity Score Matching (PSM), to explore the link between democracy and RE.

The rest of the document is structured in the following manner: Section 2 provides an overview of the relevant literature. Section 3 motives hypothesis. Section 4 presents the data. Section 5 introduces the methodology. Section 6 shows the empirical findings. Lastly, in Section 7, we draw our conclusions.

2. Literature review

Our chapter is related to two strands of literature.

First, there is a growing body of literature focused on examining the relationship between RE and economic growth. This literature recognizes that RE plays a pivotal role in shaping a country’s long-term growth prospects. This view can be supported theoretically and empirically.

Theoretically, the first explanation stems from the concept known as the Environmental Kuznets Curve (EKC). The EKC posits that the relationship between economic development and adverse environmental effects forms an inverted “U” shape. This suggests that in the early stages of economic growth, there is an increase in fossil-based energy generation, which is then followed by a shift toward reduced fossil fuel use and an increase in low-carbon energy sources [23]. Another possible explanation for the positive relationship between RE and economic growth can be found in the IS-LM-EE model. This ecological macroeconomic model proposes that the ultimate economy’s scale factor is the carrying capacity of the natural environment [24]. This latter is symbolized as a simplified EE schedule, which tracks the physical interactions between the economic system and its surroundings. According to this model, as an economy grows, it must also enhance its resource efficiency and reduce waste production to maintain a steady level of resource utilization [25, 26, 27]. A third theoretical explanation draws inspiration from the literature on pro-environmental behavior—the willingness to make sacrifices for the sake of the environment [28]. Some individuals are engaged with their environment on an ongoing basis and are willing to sacrifice some personal benefits to provide this public good at a private cost, meaning they are willing to embrace environmentally protective behaviors. Consequently, if consumer attitudes shift toward more sustainable choices, we anticipate that economic growth will also rise. Empirically, the link between RE and economic growth remains a subject of heavy discussion. Despite the growing body of research on this subject, there has been a persistent lack of agreement on a shared conclusion. The differing opinions on this issue can be summarized through four distinct hypotheses [29]: (i) the growth hypothesis suggests that energy conservation policies and increased usage of renewable energy might negatively affect economic growth. It posits that traditional energy sources play a significant role in driving economic growth, (ii) the conservation hypothesis that states that energy conservation policies or increased consumption of renewable energy may have little to no impact on economic growth, (iii) the feedback hypothesis, which contends that energy conservation and economic growth are interrelated and complementary. In this view, they positively influence each other; and (iv) the neutrality hypothesis, which posits that energy conservation policies and increased use of renewable energy will have an insignificant or no discernible impact on economic growth. The ongoing challenge of reaching a unanimous consensus regarding the link between RE and economic growth has spurred the investigation into the determinants of RE at the national level.

A second strand of literature examines the drivers of RE at the country level. Existing research in this area has identified a multitude of determinants, which can generally be grouped into two categories. Firstly, there are macroeconomic and environmental determinants, including economic growth, carbon dioxide emissions, energy efficiency, technological advancements, trade openness, employment rates, financial development, and crude oil prices (as referenced in [1, 30]). Secondly, there are institutional determinants, such as corruption, rule of law, democracy, bureaucratic efficiency, and political stability.

While the impact of macroeconomic and environmental factors on RE has been extensively studied, a little attention has been paid to the role of political and institutional variables in shaping RE [30]. This is striking, considering the fact that investing in RE is inherently a political decision [9, 31]. Why is that so? As RE deployment, is primarily initiated by governments. They have the power to develop energy infrastructure and formulate energy policy since large-scale investments in the energy sector need robust coordination, expertise, infrastructure, and experience. Consequently, the political authority and institutional framework play a crucial role in the formulation and implementation of RE policies.

The significance of democracy in shaping RE can be traced back to the body of literature that delves into the political economy analyses of energy and environmental policies. This literature comprises two main threads: the first concerns the quality of government and the second focuses on the ideology of the ruling government. The quality of government encompasses the institutional framework within which decisions related to energy and environmental policies are executed. The literature contains numerous papers that explore how institutional and governance factors affect RE, for example, corruption [32], bureaucratic quality [33, 34], protecting property rights, enforcing contracts [35], lobbying activities [36, 37], and political stability [7, 38]. The government ideology refers to the set of beliefs, values, principles, and political doctrines that guide the policies, actions, and decision-making of a government or a political party. It reflects overarching philosophy and goals that shape a government’s approach to various issues, including economics, social welfare, governance, foreign relations, and environmental policies. Government ideology can have a substantial impact on environmental quality and the strictness of energy policies. For example, left-leaning or progressive governments may be more inclined to implement stringent environmental regulations and promote renewable energy initiatives, while right-leaning or conservative governments might lean toward deregulation and favoring traditional energy sources. The ideological stance of a government can thus play a pivotal role in shaping the environmental and energy policy landscape [7, 39, 40]. The rationale behind choosing democracy is also linked to the understanding that the nexus between democracy and RE can be forged through non-monetary incentives. In democratic systems, these non-monetary incentives may include environmental preservation, public health improvements, and enhanced energy security. Such factors contribute to the alignment of democratic values and principles with the promotion of RE, making democracy an appealing choice for advancing RE initiatives. Ref. [31] argues that in democratic societies, there is often greater receptiveness to global discussions surrounding topics like human rights, justice, inequality, political participation, the environment, and RE when compared to autocratic regimes. Finally, the choice of democracy is interesting since it involves the clash of two contrasting perspectives: On one hand, there are arguments asserting that democracy can actively contribute to the reduction of carbon emissions in the energy sector, thereby having a positive impact on RE. On the other hand, there is counter-evidence suggesting conflicting relationships between democracy and RE, with some asserting that democracy hinders the development of RE. Below, we elaborate on these two lines of reasoning.

3. Hypothesis

The reasons why democratic countries are more inclined to promote their RE deployment have been the object of a long-standing debate in economics. The academic literature attributes the anticipated positive correlation to three fundamental characteristics of democratic political systems: namely: (i) participatory decision-making, (ii) electoral accountability, and (iii) ideational values. The first argument revolves around the notion that the participatory decision-making process inherent in democracies enables open political systems to achieve energy transition. This is achieved by ensuring that diverse stakeholders, including the public, environmental groups, and local communities, collaborate to make socially acceptable decisions that maximize benefits for society as a whole [16, 41, 42]. The second argument suggests that democratic processes typically involve transparency, accountability, and access to information. This can facilitate open discussions about the benefits of RE, encourage citizen engagement, and help in the development of supportive policies and incentives for RE projects [43]. Ref. [44] observes that in democratic countries, freedom of information and transparency mechanisms empower individuals to readily access information about environmental matters. In this context, the capacity of people to openly voice their environmental concerns can exert pressure on governments and ultimately enhance environmental quality [45]. The third argument is related to the set of values and beliefs held by democratic societies that may influence their willingness to embrace lifestyle changes and ambitious climate policies, including the transition to RE. Some examples of such values are human rights, social justice, and political stability, among others [46]. The arguments outlined above indicate that democracy could have positive effects on renewable energy (RE): a higher level of democracy is associated with greater RE. All these developments are consistent with the findings of several research papers. As an example, a study conducted by Ref. [47] investigated the correlation between democracy and renewable energy across over 100 countries. The study found that all the democracy indicators utilized in the research had a positive influence on the adoption of renewable energy. Additionally, Ref. [48] reached the conclusion that democracy contributed to a reduction in air and water pollution. Furthermore, Ref. [42] asserted that governments in democratic systems responded favorably to the environmental concerns raised by their citizens. Lastly, Ref. [49] emphasized the significance of democracy as an effective tool for mitigating environmental harm.

On the other side, democratic institutions may discourage RE for several reasons.

The first one is that election campaigns need money to run, and in more competitive elections, political parties and politicians may be more susceptible to pressure from donors. Elected government may therefore defend the private interests of funders, particularly those operating in non-renewable energy sectors, rather than the interests of the wider public. In the same logic, it is harder for democratic governments to achieve agreements with a variety of stakeholders, especially in the presence of competing interests, namely, lobby groups calling for greater use of renewable energy, such as environmentalists and the green energy industry; or lobby groups opposing, such as the nuclear industry and the oil industry. As such, the democratic process will result in inaction or stopped transition [17, 50, 51, 52]. The second reason for the negative relationship between democracy and RE can be linked to the democratic system’s emphasis on short-term objectives, which can impede the transition to RE. This short-term focus is often attributed to the constant need for politicians in democratic systems to secure re-election [53]. However, voters themselves tend to prioritize immediate benefits, such as job opportunities and economic growth, over longer-term, diffuse advantages such as a stable climate. Consequently, democracy may inadvertently play a role in contributing to environmental degradation. According to Refs. [19, 54] democracy is intricately linked with economic advancement, fostering increased economic activities, which, in turn, can result in higher resource consumption and ecological degradation. This is in line with the assumption of modernization theory. Another reason that might deter democratic governments from investing in RE is the concern that such investments could yield a poor return in terms of expected votes. This fear arises because investments in RE often require extensive regulatory interventions and comprehensive policy changes on a large scale. These significant policy shifts may be perceived as potentially unpopular among certain segments of the electorate, impacting a government’s chances of securing votes in the next election. This consideration can influence the willingness of democratic governments to commit to RE initiatives. This is in line with the findings of several research papers. Building on all these reasons, numerous research papers have found that democracy can have a limiting effect on the adoption and promotion of renewable energy (RE). For instance, Refs. [55, 49] determined that democracy did not affect water pollution and particulate matter emission. Ref. [56] analyzed that the democratic transition increased CO2 emissions in Indonesia. Ref. [57] observe that democracies may focus on financial objectives leading to more ecological degradation. Ref. [29] also notice that democracy increases air pollution. Using the PMG method for nine nations, Ref. [58] illustrated that democracy alleviates CO2 emissions, while energy usage boosts emissions. Ref. [59] disclosed that democracy lessens environmental quality by expanding the ecological footprint.

However, even though the two scenarios are possible when examining the relationship between democracy and RE, we assume that the benefits of democracy prevail and we therefore hypothesize:

Hypothesis 1 : Democracy has a positive effect on country’s RE.

4. Data and variable

Our dataset includes observations from 84 developing countries for the period of 1996–2020 and is derived from three sources. First, the V-DEM database, or Varieties of Democracy database, is a comprehensive and widely used dataset in political science and social research. It provides information and measurements related to various aspects of democracy and governance in countries around the world. The database covers a broad range of indicators, including political institutions, civil liberties, electoral systems, rule of law, and more. Researchers and policymakers often use the V-DEM database to analyze and compare the state of democracy and governance across different countries and regions. From V-DEM, we extract information about democracy. Second, the EPI database, the EPI database is used to measure and compare how well countries are addressing environmental challenges and achieving environmental goals. It provides valuable information for policymakers, researchers, and organizations interested in environmental issues and sustainability. From this database, we download information regarding Ozone exposure. Thirdly, we utilize data from the World Bank, specifically the World Development Indicators (WDI), to gather information on our dependent variable, which is renewable energy consumption, as well as data on the control variables. Additionally, we extract data from the World Bank’s Worldwide Governance Indicators (WGI) to obtain information related to corruption control and rule of law.

The primary focus of our study is to examine the influence of democracy on renewable energy (RE). It’s important to note that there is not a universally accepted and well-justified definition of RE. This lack of consensus may account for the various approaches and measures employed to assess RE. However, from a practical standpoint, there is a general agreement that RE can be assessed through three dimensions: supply, consumption, or installed capacity. In our research, we concentrate on the consumption aspect and we recognize, in line with previous studies that RE consumption reflects a country’s actual level of RE.

Numerous sources offer ratings for the degree of democracy in different countries. However, as demonstrated, none of these democratic measures is flawless. For example, Ref. [60] contends that the measurement of democracy is contentious due to challenges related to conceptualization, measurement, and aggregation. Consequently, no single index provides a fully satisfactory solution to these issues, and even the most robust indices have notable limitations. For this study, the V-DEM is used as a measure of democracy. The V-Dem evaluates democracy on multiple levels and aspects, including political rights, civil liberties, electoral processes, the rule of law, and the functioning of government institutions. It uses a variety of indicators and expert assessments to provide a detailed understanding of the state of democracy in different countries. It does not simply classify countries as either “democratic” or “non-democratic” but rather offers a more granular assessment, allowing for the exploration of different aspects and strengths and weaknesses within a democracy. This index ranges from 0 to 10, where a higher rating implies higher levels of democracy.

Following the literature, a number of control variables have been included in the model. These variables could be divided into three categories: macroeconomic variables, namely the GDP per capita, FDI inflows, financial development, and Human Development Index; environmental variables, like CO2 emissions per capita, oil rents, and Ozone exposure, and institutional variables, as control corruption and rule of law.

Table 1 defines our variables, data sources, and their descriptions. Descriptive statistics along the correlation matrix for all variables are reported in Table 2.

Variable name	Code	Description	Source
Renewable energy consumption	REC	Total energy generation from various renewable sources, including hydroelectric, includes geothermal, solar, tides, wind, biomass, and biofuels.	WDI
Democracy	DEM	Level of democracy in a country-year	VDEM
GDP per capita	GDPG	Gross Domestic Production per capita	WDI
Foreign investment	FDI	Foreign Direct Investment net inflows (% of GDP).	WDI
Financial Development	DCPS	Private credit by deposit banks and other financial institutions (% of GDP)	WDI
Human Development Index	HDI	Human development index	WDI
CO2 emissions	CO2	CO2 emissions (metric tons per capita)	WDI
Oil rents	OIL	Oil rents (% of GDP)	WDI
Ozone exposure	OZE	Ozone exposure	EPI
Control Corruption	CORR	Perception about public power to control all types of corruption	WGI
Rule of Law	ROLW	Rule of Law	WGI

Table 1.

Definition of variables and data source.

Table 2.

Summary statistics and correlation matrix.

5. Empirical methodology

In our study, we intend to evaluate the influence of the level of democracy, referred to as the “treatment” variable, on RE, which is the outcome variable. This “treatment” variable is binary, meaning it creates two distinct groups within our sample: one group consists of countries with a lower level of democracy, considered as treated countries, while the other group includes countries with a lower level of democracy that have not been exposed to this treatment. A straightforward method to estimate the impact of the treatment, democracy level, on the outcome, RE, is to compute the difference in the means for the two groups of countries. This involves calculating the average democracy level for each group and conducting a statistical test of equality. If RE is significantly higher in the group of countries having a high level of democracy compared to the group having a lower level of democracy, then it can be concluded that the level of democracy has a positive impact on RE. The resulting estimator is often considered naive because it does not account for the effects of selection. What does this mean? For instance, if there is a difference in renewable energy (RE) consumption between countries with a higher level of democracy and those without, we cannot be certain that this difference is solely due to the level of democracy. It’s possible that the difference arises because the countries in both groups are not identical. The distortion in the estimates caused by such situations is known as selection bias. To address this selection bias, we employ the PSM method with a Kernel Matching Function. The general concept is to create a group of untreated countries (control group) that are comparable to the treated group, ensuring an unbiased estimate of the treatment’s effect on the treated countries while correcting for selection bias.

Let us define Di as a binary variable that takes the value of “1” for countries with higher levels of democracy and “0” for countries with lower levels of inclusion. Di serves as the treatment variable. Di is designed as follows: First, for each country, we create a dummy variable, DUM1, that takes a value of “1” when the democracy level exceeds the mean of the distribution of this indicator, and it takes a value of “0” otherwise. Then we calculate the mean of DUM1 for each country and label this new variable MDUM. MDUM will have values ranging from 0 to 1, representing the proportion of countries where the democracy level exceeds the mean. Finally, we construct the binary treatment variable DEM based on MDUM. DEM takes a value of “1” if MDUM exceeds 0.5, indicating that the country has a higher level of democracy, and it takes a value of “0” otherwise.

Additionally, let Yi1 represent the RE for country i when it has a high level of democracy (i.e., it belongs to the treated group), and let Yi0 represent the RE for the same country when it does not have high democracy, assuming all other characteristics of the country remain equal. The treatment effect for country i can then be expressed as:

∆i=Yi1−Yi0E1

Since it’s not feasible to observe the same countries in both treated and untreated situations, we focus on measuring the Average Treatment Effect (ATE) on the treated population. In your specific case, the treated population consists of countries having a high level of democracy which can be defined as:

∆ATT=EYi1Di=1−EYi0Di=1E2

The second part of the equality (2) cannot be observed. By adding and subtracting in the second part of the above equation, we get:

∆ATT=EYi1Di=1−EYi0Di=1+EYi0Di=1)−EYioDi=1=EYi1−Yi0Di=1+EYi0Di=1−EYioDi=0E3

The first expression E(Yi1–Yi0|Di = 1) is the effect of the level of democracy that we are trying to isolate, the effect of having a high level of democracy on the country’s RE. The difference E(Yi0|Di = 1)–E(Yi0|Di = 0) corresponds to bias selection.

We can only obtain the real treatment impact when the selection bias equals zero, which means that

EYi0Di=1=EYi0Di=0E4

This equality holds true only if Yi and Di are independent. In econometric terms, this signifies that the treatment variable is not correlated with the outcome variable. In this context, the treatment is regarded as being distributed randomly and conditionally based on observable characteristics X.

Yi1,Yi0∪D∣X,∀XE5

We can express

EYi1X,D=1−EYi0X,D=1=EX∣D=1EYi0X,D=1−EYi0X,D=0E6

When dealing with a high number of features, matching all of them can be challenging. One potential solution is to perform matching based on one of the features, the propensity score π(x), rather than on all features X. The propensity score represents the probability that an individual with feature X will be assigned to the treatment, denoted as π(x) = Pr(D = 1|X). This can be expressed as follows:

Yi1,Yi0⊔D∣πx,∀XE7

The matching process can be formulated as follows:

∆ATT=EPXD=1EYiD=1PX−EYiD=0,PXE8

However, it is crucial to ensure that there exists a common interval in both propensity score distributions for the two groups. In other words, the condition of common support must be satisfied, which can be expressed as follows:

0<PrD=1X<1E9

In summary, the matching method relies on two critical hypotheses. Conditional Independence Assumption: This assumption posits that, given a set of observable variables represented by the vector X, the outcome variable becomes independent of the level of democracy. Common Support Condition: This condition ensures that there is sufficient overlap in the characteristics of both the treated (high democracy) and untreated (low democracy) groups. This overlap is necessary to find suitable matches between countries in these groups, facilitating a valid comparison. These two hypotheses are fundamental to the effectiveness of the matching method.

The PSM involves two steps. First, it entails estimating the propensity score through a logistic regression model. Second, it involves matching treated and control observations based on their propensity scores, ensuring that the two groups are comparable. This method helps to mitigate the issue of selection bias in estimating the treatment effect. There are several methods. We employed three distinct matching approaches: (1) One-to-one matching: this technique ensures that each treated unit is paired with a single control unit that is most similar in terms of the observed variables. This helps to reduce bias and allows for a more accurate estimation of treatment effects. (2) The Nearest-Neighbor Using Mahalanobis: this technique relies on Mahalanobis distance, a statistical measure that matches treated and control observations based on their inverse variance-covariance matrix. It helps to create pairs of similar entities [61]. (3) Caliper Width of 0.2 Standard Deviation: In this method, we utilized a caliper width of 0.2 standard deviations of the estimated propensity scores. This particular caliper width is often considered optimal for Propensity Score Matching (PSM) in observational studies [62].

6. Results

As we have already mentioned, we create a binary democracy variable to be used as the treatment variable: “the level of democracy.” Table 3 reports the distribution of this treatment variable. Table A1 in the Appendix provides an overview of countries with a high level of democracy (treated countries) and those without. Clearly, out of the 86 developing countries under the study, 44 are considered treated countries.

TREAT	Freq.	Percent	Cum.
0	1534	56.19	56.19
1	1196	43.81	100.00
Total	2730	100.00

Table 3.

Distribution of the treatment variable: Democracy.

Table 4 presents the sample means for both treated and untreated countries, along with the results of difference tests. These statistics are displayed for various classifications of countries, distinguishing between those that have received treatment and those that have not.

	Democracy level
	TREAT = 0	TREAT = 1	TEST
REC	2.109	3.474	−22.7^***
GDPG	7.708	8.347	−14.55^***
DCPS	2.887	3.337	−9.6^***
FDI	19.890	20.666	−8.5^***
HDI	0.592	0.677	−15.95^***
CO2	9.864	9.425	−4.4^***
OIL	7.897	0.918	17.7^***
OZE	49.799	39.287	−11.9^***
CORR	0.153	0.662	−18.85^***
ROLW	0.190	0.646	−21.75^***
N	1092	1140
Country	42	44

Table 4.

Sample means and differences test results for treated and untreated countries.

*, ** and *** mean for significance at 10, 5% and 1% level. TEST is the Student t-test.

From the data presented in Table 4, it becomes evident that countries with a higher level of democracy tend to have a greater share of renewable energy (RE). Additionally, it suggests that economic conditions play a significant role in the selection of countries for treatment. Specifically, countries with a higher level of democracy are characterized by the following factors: Higher GDP per capita; Increased DCPS (Democracy and Political Stability Index); Greater FDI (Foreign Direct Investment); Higher HDI (Human Development Index); Lower CO2 emissions; Reduced OIL rents; Lower OZE (Ozone Depleting Substances); and better governance. This highlights the importance of creating an appropriate control group through the matching approach before calculating treatment effects. Failing to do so could result in an incorrect estimation of the impact of democracy levels.

Figure 1 provides an insight into the common support. It clearly shows that there is sufficient overlap in the covariate distribution between treated group and the control group. This overlap instills confidence in our ability to compare outcomes between these two groups, ultimately mitigating selection bias and bolstering the validity of our causal inferences.

Figure 1.
Distribution of the common support.

The PSM approach involves two steps. We begin by estimating the propensity score using a benchmark logit equation. This equation aims to explain the likelihood of a country receiving the treatment, which, in your case, means having a high level of democracy. We take into account various potential economic environmental and institutional determinants that may influence this treatment. Table 5 below reports the results of the logit. First, it appears that economic variables wield considerable explanatory influence. For example, the variable “GDPG” exhibits notable significance at the 1% confidence level, demonstrating a positive association. This implies that countries with higher economic growth rates are more inclined to exhibit increased levels of democracy. Second, the environmental variables play also a crucial role in explaining the likelihood of a country having a high level of democracy. In particular, while CO2 emission and Ozone exposure positively affect the country’s probability of being treated, the oil rents have a negative influence. Finally, governance factors seem to have no impact on the treatment.

TR	Coefficient	Std. Err.
GDPG	0.8044***	0.1234
DCPS	−0.4160***	0.0577
FDI	−0.0380***	0.0433
HDI	0.9039	0.8559
CO2	0.1358***	0.0491
OIL	−0.2567***	0.0191
OZE	0.0163***	0.0027
CORR	0.4660	0.1752
RLOW	1.0674	0.1787
Constant	−5.9979	0.7796

Table 5.

PSM for democracy level: Logit estimation.

*, ** and *** mean for significance at 10, 5% and 1% level. TEST is the Student t-test.

Then, by employing the propensity score, we proceed with estimating the impact of level of democracy (the treatment variable) on renewable energy (the outcome variable). The average treatment effect on the treated estimations are reported in Table 6.

Variable	Naïve	PSM using radius 0.2	Nearest-Matching using Mahalanobis	One-to-one matching
ATT	0.3915***	0.9160***	0.3579***	0.97201***
	(0.0701)	(0.1556)	(0.0908)	(0.1079)

Table 6.

Average treatment effect on the treated estimation from four matching methods.

*, ** and *** mean for significance at 10, 5% and 1% level. TEST is the Student t-test.

Standard errors in parentheses.

As observed, the findings indicate that high democracy levels have a statistically significant impact on increasing renewable energy consumption, with a significance level of 1% across all specifications. The ATT estimates remain relatively consistent across the three different matching approaches utilized, providing us with confidence in the reliability of our results. Considering the matching techniques and the evaluated control variables, the ATT coefficients range from 0.36 to 0.97 percentage points. This suggests that high levels of democracy are associated with a predicted enhancement in renewable energy consumption. However, the extent of this improvement depends on the initial level of renewable energy in each specific case. The significant rise in renewable energy consumption associated with greater democracy levels exhibits varying degrees of magnitude before (Naïve) and after matching using the propensity score. This discrepancy indicates the presence of significant selection bias. These findings corroborate our hypothesis and provide strong evidence that the level of democracy has a substantial impact on renewable energy consumption in developing countries. Specifically, these findings indicate that countries with higher levels of democracy tend to experience significantly higher levels of renewable energy consumption. This aligns with prior research that has recognized the advantages associated with greater democracy in promoting RE.

An interpretation of our results is that democratic governments may have a stronger capacity to implement policies and initiatives that facilitate increased renewable energy consumption, potentially due to their ability to garner broad support and effectively address environmental concerns.

7. Conclusion

This chapter examined whether the level of democracy has an impact on renewable energy in developing countries. To that end, we use a sample of 86 developing countries over the period of 1996–2020. It’s worth noting that our approach to study this link, particularly the use of the PSM approach, is innovative and not commonly employed in this context. The main result of our study indicates that democracy plays a significant role in increasing RE, suggesting that higher levels of democracy are associated with increased RE in these developing economies.

The practical implications of our study are twofold. First, democracy plays a direct and influential role in promoting renewable energy (RE) in developing countries. This finding is highly relevant for policymakers who are concerned with understanding the key drivers of RE in these regions. It suggests that fostering democratic institutions and values can be an effective strategy for advancing RE. Second, alterations in a country’s democracy levels can function as a significant indicator of its dedication to transitioning toward renewable energy. This signal can be especially enticing to investors, making democracy a favorable consideration for them when assessing countries for potential investments in the renewable energy sector.

In future research endeavors, it would be intriguing to delve into the short- and long-term effects between the democracy and RE, utilizing a variety of indicators. Unfortunately, there are significant challenges related to the scarcity of reliable data on RE and the absence of a well-defined methodology for assessing RE, which hinders the execution of such analyzes. One promising direction for future research lies in addressing the limitation of matching techniques, which predominantly rely on observable covariates to create comparable groups. While matching enhances the comparability of groups based on known characteristics, it cannot fully account for the influence of unobservable variables that may significantly impact the outcome. To overcome this limitation, a more in-depth exploration of disparities between countries within our database is warranted. This exploration should encompass a broader spectrum of factors, including geographical and cultural differences, which can play pivotal roles in shaping outcomes and may shed light on previously unaccounted-for variations. Finally, an interesting avenue for future research involves the application of spatial panel data estimation techniques to investigate the impact of democracy on RE while accounting for spillover effects and interactions between neighboring countries. Such research could provide valuable insights into the complex relationship between democracy and renewable energy.

Appendix

TR = 0, lower democracy level countries		TR = 1, higher democracy level countries
Algeria	Mozambique	Albania	Indonesia
Angola	Nepal	Argentina	Israel
Azerbaijan	Nicaragua	Benin	Jamaica
Bangladesh	Nigeria	Bolivia	Lesotho
Belarus	Pakistan	Bosnia and Herzegov	Mali
Bhutan	Papua New Guinea	Botswana	Mauritius
Burma/Myanmar	Qatar	Brazil	Mexico
Burundi	Republic of the Congo	Bulgaria	Moldova
Cameroon	Russia	Burkina Faso	Namibia
Chad	Rwanda	Chile	Panama
China	Saudi Arabia	Colombia	Paraguay
Comoros	Seychelles	Costa Rica	Peru
Democratic Republic	Singapore	Croatia	Philippines
Egypt	Sudan	Cyprus	Romania
Equatorial Guinea	Tajikistan	Dominican Republic	Senegal
Fiji	Tanzania	Ecuador	Sierra Leone
Gabon	Thailand	El Salvador	South Africa
Guinea	The Gambia	Georgia	South Korea
Guinea-Bissau	Togo	Ghana	Sri Lanka
Haiti	Tunisia	Guatemala	Trinidad and Tobago
Iran	Uganda	Guyana	Turkiye
Iraq	Ukraine	Honduras	Uruguay
Ivory Coast	United Arab Emirates	Hungary
Jordan	Vietnam	India
Kazakhstan	Yemen
Kenya	Zambia
Kyrgyzstan	Zimbabwe
Lebanon
Libya
Madagascar
Malaysia
Morocco

Table A1.

Distribution of countries by treatment variable.

Pedro Gamito

Chapters authored

Related collaborators

Fabio Solari

Samir Otmane

Floriana Renna

Giovanni Attolico

Gert Pfurtscheller

Christophe Domingues

Frederic Davesne

Malik Mallem

Fabio De Felice

Arcangelo Distante