Fossil fuel curse and green transition: The role of renewable energy and green R&D

Abstract

Many researchers have documented the effects of deploying natural resources on gross domestic product (GDP) growth and confirmed the existence of the resource curse phenomenon. Furthermore, several studies have proved that factors such as institutional quality, trade openness, and commodity price volatility can alter the effects of natural resource deployment on economic growth. However, few studies have considered the effect of green transition on the resource curse. While transitioning to a green economy poses the threat of reducing the potential revenues from exporting natural resources in resource-abundant economies, it can contribute to economic growth by reducing fossil fuel dependance, increasing energy efficiency, and increasing green competitiveness. In this study, we analyze how fossil fuel rents affect GDP growth and study the interactive effects of renewable energy and investment in green research and development. By using the data of 109 countries for the period 1990–2020, we conduct a panel analysis. The results confirm the existence of the resource curse effect, whereby fossil fuel rents hinder economic growth. In addition, increasing renewable energy deployment can alleviate the adverse effects of fossil fuel rents. This mitigation can occur through a reduction in the domestic consumption of fossil fuels, increased access to international trade, and infrastructure investments. Lastly, increasing investments in green research and development mitigates the adverse effects of fossil fuel deployment. This result is achieved because of increases in energy efficiency, investment in human capital, and green competitiveness. We conclude that active green transitioning, especially in fossil-fuel-rich economies, can lead countries to sustainable growth.

Keywords

Resource curse fossil fuel rent green transition renewable energy green R&D

Introduction

One of the great paradoxes in resource and energy economics is the long-observed phenomenon that countries with resource abundance have poor economic development, usually measured in terms of gross domestic product (GDP) growth.^1–3 A few examples of such countries are Venezuela,⁴ Congo,⁵ and Libya.⁶ By contrast, some of the most advanced economies, such as Japan, Singapore, and South Korea, have very few natural resources; yet, these countries have managed to reach unprecedented levels of economic growth.^7,8 Then, there are countries such as the United States, Canada, and China, which initially built their economies on the back of the capital generated by exporting natural resources, and they continue to use their resources to maintain their economic growth. Given the complex relationship between natural resource deployment and economic growth, as well as the greatly varied response of countries’ economies to this phenomenon, two main theories have been developed in the resource and energy economics literature: resource curse and resource blessing.

On the one hand, the resource blessing theory points out the irreplaceable role of natural resources in developing economies.^9,10 A few studies have contradicted other works, thereby highlighting the problem of endogeneity and reverse causality.¹¹ Some countries have benefitted economically by deploying natural resources, especially throughout the twentieth century, when many countries became industrial powerhouses.^12,13 Therefore, natural resource deployment seems to have accelerated the pace of development. However, resource limitation is a fact that must be addressed,^14,15 and reckless natural resource usage has led to negative externalities.¹⁶

On the other hand, the resource curse phenomenon suggests that dependance on natural resource deployment hinders economic growth because, instead of innovating, manufacturing, and adding value to produced goods, countries depend on the easy income generated by selling natural resources.^1,2,8,17 Countries such as Korea, Singapore, and Japan have built unprecedentedly robust economies in the short span of a few decades, despite having virtually no natural resources.

The resource curse manifests in many ways, with the Dutch disease being one of them. The Dutch disease is a phenomenon characterized by an appreciation of a country's currency through the export of natural resources, but it leads to an increase in the prices of manufactured goods. Corden (1984) pioneered theoretical explanations of the Dutch disease and discussed the cases of many countries, mainly oil exporters.¹⁸ Sachs and Warner (1995) argued that the Dutch disease was the primary reason for the slow growth of resource-abundant African countries.⁷

The scatter plot presented in Figure 1 depicts the relationship between the fossil fuel rent and GDP per capita of more than 100 countries in the year 2019. In most of the countries where fossil fuel rent accounts for a large percentage of the total annual GDP, the annual GDP per capita is less than $20,000. By contrast, in many countries where the annual GDP per capita exceeds $40,000, fossil fuel rent accounts for a small share of the annual GDP, aside from a few outliers.

Figure 1.

Distribution of fossil fuel rent and GDP per capita for 2019.

Figure 2 illustrates the 25th, 50th, and 75th percentiles of the fossil fuel rents of more than 100 countries from 1990 to 2020. The 25th and 50th percentile are not high, but the numbers in the 75th percentile are relatively higher; this result indicates that most of the fossil fuel reserves are concentrated in a few countries.

Figure 2.

The 25th, 50th, and 75th percentiles of fuel rent amount of more than 100 countries from 1990 to 2020.

We contribute to the resource curse literature by analyzing the data of more than 100 countries for a three-decade period and demonstrating that natural resource deployment decelerates economic growth. The contributions of this study are threefold. First, we use fossil fuel rent, calculated as the sum of the rents of oil, natural gas, and coal. Thus far, many studies have used oil rent or total natural resource rent. While oil rent does not include natural gas rent, which plays an important role, total natural resources rent includes categories such as forest rent and minerals rent, which have a different relationship with economic growth than fossil fuel rent.¹⁰ Since fossil fuel is the main source of energy for majority of the countries, using fossil fuel rent in that regard can provide more direct approach to energy transitioning concept, while rent from other natural sources such as agriculture or mining are rather different in their usage. Economic rent is the extra amount earned from a resource, such as land, capital, or natural resources. Therefore, it is calculated as the difference between the price of a particular commodity and the average cost of producing that commodity. Total natural resources include all the natural resources within a country. The revenue generated from natural resources is divided by the total GDP of a country, and therefore, resource rent is expressed as a share of annual economic performance. A high economic rent indicates fast liquidation of a country's capital stock. Therefore, natural resource deployment is akin to borrowing against a country's future. In a similar fashion, fossil fuel rent is defined as difference the price of three main fossil fuel sources, oil, coal and natural gas and the cost of producing each of these commodities. Then the total monetary value is divided by GDP to observe the share of fossil fuel rent in total domestic production.

Second, we use renewable energy as an interaction term. By using the fixed effects model, we prove that fossil fuel rent has a negative relationship with economic growth (measured by the log of GDP per capita), and the interaction term with renewable energy deployment (solar and wind) has a positive impact. In other words, although fossil fuel rent adversely affects economic growth, renewable energy deployment mitigates this adverse effect by providing a sustainable energy alternative. The results are provided for three groups of countries: more than 100 world countries, OECD countries, and top 20 percentile fossil fuel-producing countries.

Third, we employ green research and development (R&D) investment as an interaction term with fossil fuel rent. By using the data of 71 world countries, we find that empirically, the investment in green R&D mitigates the adverse effect of fossil fuel rent on economic growth. To the best of our knowledge, this is the first study in which green R&D is considered as an interaction term with fossil fuel rent. We hope that this work will stimulate further interest and benefit future studies. Figure 3 shows the mechanism by which factors such as renewable energy transition and green R&D investment alleviate the adverse effect of fossil fuel rent on GDP. These factors are discussed comprehensively in the results section.

Figure 3.

Impact of fossil fuel rents on GDP, and mitigating role of factors such as renewable energy and green R&D.

The outline of this article is as follows: in the introduction section, we have discussed the background of our research and provided the description of our model and brief results. The literature review section provides a literature review. In the data and empirical framework section, we describe the data and econometric framework used herein. The results section provides a discussion of our empirical results. Finally, in the conclusion section, we provide a few concluding remarks and policy implications.

Literature review

The effects of natural resources on the growth of an economy have been covered extensively in energy economics. However, the results of various studies are far from unanimous. Although many studies have highlighted a negative relationship between resource abundance and economic growth, often called the resource curse,^7,19,20 other studies have supported the exact opposite phenomenon, that is, resource blessing, emphasizing the irreplaceable role of natural resources in generating economic output.^9,10,21,22

According to a third category of studies, the effects of natural resources depend on a few important factors that can mitigate the negative effects of natural resource deployment on economic growth, such as institutional quality, trade openness, and financial openness.^23–26 This strand of literature explains that we should carefully choose the control variables to explain the complex relationship between resource abundance and economic growth.

Resource blessing

Based on a study of the relationship between natural resource rents and economic growth in the Next-11 countries, Ben-Salha et al. (2021) reported that natural resources have a rather heterogeneous relationship with economic growth: Natural gas rents enhance economic growth, while oil and forest rents have adverse effects on growth.²² Additionally, financial development has a positive and significant effect on economic growth. They suggested that countries should try to use natural resources efficiently, improve their financial sectors, and increase investments in renewable energy.

Jaimes and Gerlagh (2020) studied the effects of natural resources, including agriculture, on economic development in the United States from 1997 to 2014 by using state-level data.¹⁰ They divided this period into two subperiods, namely pre-2007 and post-2008, because commodity prices were on the rise before the 2008 global financial crisis. They found that agricultural resources shared a positive relationship with the growth of states’ economies in all periods. Oil and gas rents positively affected economic growth only in the post-2008 period. Mining, by contrast, had a positive effect on economic growth in the pre-2007 period, when commodity prices were booming. In brief, the authors highlighted the complex relationship between natural resources and economic growth, and the importance of certain periods.

Resource curse

Sachs and Warner (1995) explored the negative relationship between resource abundance and economic growth and proved the existence of the Dutch disease phenomenon.⁷ They investigated the cases of 97 developing countries in the period 1970–1989 and demonstrated that most of the resource-abundant countries could not even reach 2% annual economic growth in this period. By contrast, resource-poor countries, such as Korea, Taiwan, Hong Kong, and Singapore achieved unprecedented levels of economic growth. The authors pointed out that manufacturing leads to a more sophisticated division of labor as opposed to the export of natural resources, thereby contributing to higher living standards. This study, although not the first in this field, laid the foundations for most of the similar research works that followed.

One of the explanations for the conflicting research results in this field is the lack of a theoretical framework for properly estimating the relationship between natural resources and economic performance. To address this gap, Gaitan and Roe (2012) developed a continuous-time infinite-horizon two-country model for comparing a country with abundant (but exhaustible) natural resources to a country with no resources.²⁰ The results obtained using this theoretical model proved the existence of the resource curse phenomenon and further suggested that the degree of inelasticity of the demand for exhaustible resources determines the severity of the phenomenon. Additionally, the results indicated that households in resource-rich economies tend to have smooth consumption: the increasing income from resource exports may cause households to invest less or save less than those in resource-poor countries. Consequently, future income growth declines.

Dutch disease

Dutch disease, a term coined by The Economist in 1977, refers to the scenario wherein a country discovers new natural resources; by exporting these resources, the country's currency appreciates relative to other foreign currencies, which makes the export of other goods expensive and imports cheap. Dutch disease has three main unintended consequences: currency appreciation, direct and indirect deindustrialization, and real exchange rate effects.¹⁸

Currency appreciation occurs when the currency of the resource-producing country appreciates relative to other foreign currencies owing to increased revenues from exports. Such currency appreciation increases the prices of other export goods (manufactured goods) from the country in the international market and reduces their sales. Next, direct industrialization occurs when one sector of the economy booms, which is the extraction of natural resources in our case, and labor and investment are shifted from other sectors to that specific sector. Indirect industrialization occurs when the substantial revenues generated from natural resources increase the demand for nontradeable goods, such as services, which contributes to a further shift away from manufacturing sectors.^2,19 As for real exchange rate effects, high demand for nontradeable goods, such as services, within the economy increases prices inside the country while the prices of tradeable goods remain the same, they are set internationally. These events lead to an increase in real exchange rates, which affects the overall competitiveness of the economy.

Geopolitical instability

Recent events related to the Middle East geopolitical conflicts and The Ukraine War have shown that the geopolitical instability can also exacerbate the resource curse by inducing countries to revert to conventional methods of energy, that is fossil fuels, such as coal and natural gas in the event of shortage of energy. For example, due to the war in Ukraine, many European countries began to increase the usage of natural gas and coal. Thus, the geopolitical conflicts can delay the sustainable development goals.²⁷

Institutional quality

One certainty pointed out in many previous works is the role of institutional and governmental quality in natural resource deployment. Superior management of institutions and political organizations can mitigate the negative effects of natural resource deployment. By contrast, a high level of corruption or control over natural resources by elites can intensify the negative effects.² Brunnschweiler (2008) pointed out that when institutional quality is considered, natural resources, especially mining goods, share a positive relationship with economic growth.⁹ Similarly, according to Boschini et al. (2007), the effect of natural resource deployment on economic growth is rather nonmonotonic in relation to institutional quality²¹; for certain types of resources, this effect is stronger. Especially, mineral-rich countries are cursed only if they have low-quality institutions, while the curse is reversed if their institutions are adequately good.

Antonakakis et al. (2017) confirmed the resource curse phenomenon mainly for developing countries and medium-high-income countries.²⁸ When the quality of political institutions is poor, natural resource deployment does not promote economic growth. More recently, Kerner et al. (2023) pointed out that the effect of an increase in GDP growth on natural resource deployment is stronger in countries with adequate institutional quality for natural resources.²⁴

Trade openness

The openness of a country's economy plays an important role in its economic development. Specifically, trade openness is crucial for accelerating the economic growth that trades goods and services. Additionally, trade openness mitigates the negative effects of natural resource deployment on economic growth.²³ Majumder et al. (2020) studied the mitigating effect of trade openness between oil rent and economic growth by using the data of 95 countries for the period 1980–2017. They concluded that trade openness reduced the negative effect of oil rent by 25%. Additionally, they argued that oil-rich countries can alleviate the resource curse by further opening their economies to international trade and international organizations; in this context, organizations such as the World Trade Organization are important facilitators. Arezki and Van der Ploeg (2011) studied the effects of trade openness, institutional quality, and initial income. They concluded that the resource curse phenomenon is more severe in countries with low trade openness, and the adoption of open trade policies can mitigate the negative effects of natural resource deployment.²⁹

Emerging trends: The role of artificial intelligence

From a rather recent developments, Wang et al. (2024)³⁰ showed the usage of artificial intelligence (AI) has an accelerating effect on the energy transition as well as carbon reduction. Besides, they have shown that trade openness has a mediating effect. Some other works have found similar trends, that AI can reduce the ecological footprint and carbon emission while promoting green transition.^31,32

Data and empirical framework

The data for this study were obtained from three sources, namely the World Bank website, OECD website, and International Energy Agency (IEA). GDP per capita, education, inflation, industry value, fertility rate, and unemployment data were obtained from the World Bank. The data pertaining to renewable energy, specifically solar and wind energy, were obtained from IEA. Green patent data were gathered from the OECD website. The size of data and the countries included in this study was based solely on the availability of data. We could collect data for 109 countries because variables such as renewable energy, green patent, and fossil fuel rent are not available for many countries. There is no particular criterion for the choosing the countries. Table 1 summarizes the sources, definitions, and measures of these data.

Table 1.

Descriptions and sources of variables.

Variable name	Measure	Unit	Data source
GDP per capita	Real gross domestic product per capita, base year = 2015	Percentage	World Bank
Fossil rent	Addition of oil, natural gas, and coal rents	Percentage	World Bank
Renewable energy, solar, and wind	Solar and wind production	A kiloton of oil equivalent	International Energy Agency
Green patents	Number of patents related to environmental innovations	Number of patents granted	OECD
Industry value addition	Value added to the industry, including manufacturing, mining, and construction	Percentage of GDP	World Bank
Inflation	Consumer price index	Percentage change	World Bank
Fertility rate	Births per woman	Number of births	World Bank
Education	Secondary school enrollment	Percentage of population	World Bank
Unemployment	Unemployment rate of total labor force	Percentage	World Bank

Based on the literature, we select three main categories for our control variables. All the variables are on an annual basis and are converted into natural log value by first adding the number one to the original value, to avoid null values, and then taking the natural log. The first category includes industry structure. We use a given industry's value creation, which is given as a percentage of GDP. This variable serves as a measure of the annual value created by various industries such as manufacturing, construction, mining, water, and electricity. Without this variable, our model can be affected by endogeneity because we would not be able to control for industry and manufacturing activities. Second, we control macroeconomic factors by including two main macroeconomic indicators: inflation and unemployment. Inflation, $l n I N F L$ , is given as the percentage change in the consumer price index, and unemployment, $l n U N E M$ , is the percentage of unemployed labor force in the total labor force. Third, for human capital, we control for demographic factors and education factors. We control for demographic changes by using fertility rate, $l n F E R T$ , which represents the quantitative aspect of human capital and is calculated as the number of births per woman. We use secondary school enrollment rate, $l n E D U$ , as our proxy for education as the qualitative aspect of human capital. The use of these variables is consistent with the literature.^33,34 Table 2 presents the summary statistics of the variables used herein.

Table 2.

Summary statistics.

Variables	Obs.	Mean	Std. dev.	Min	Max
LnGDPpc	3435	8.631	1.382	5.118	11.375
LnIND	3303	0.264	0.087	0.093	0.624
LnINFL	3241	0.114	0.334	−0.176	5.475
LnUNEM	2410	0.079	0.053	0	0.328
LnFERT	3534	0.911	0.487	−0.178	2.055
LnEDU	2657	0.582	0.174	0.053	0.971
LnFRR	3439	0.06	0.102	0	0.604
LnOil	3748	.048	0.927	0	0.604
LnRE	2959	7.40	2.68	0	12.596
LnGrPT	2382	1.540	2.00	0	8.198

As for our variables of interest, $l n F F R$ represents fossil fuel rent, which is calculated as the sum of three main fossil fuel rents, namely oil rent, natural gas rent, and coal rent. As mentioned in the Introduction, a few studies have used oil rent alone as a proxy for resource abundance. However, for many countries, coal rent and, especially, natural gas rent account for high proportions of the total resources rent. For this reason, we include all three main fossil fuel rents as a proxy for resource abundance. Nonetheless, as a robustness check, we run some regressions with oil rent alone. Resource rents are calculated as a percentage of GDP, and therefore, they represent the dependance of an economy on the revenues generated by fossil fuels.

In this study, we consider two types of renewables, namely wind and solar, because these two energy forms represent a country's advancement toward technology-based renewables. The variable $l n R E S$ represents the annual solar and wind electricity production. The unit of measure is kiloton of oil equivalent, and its natural log is taken.

The variable for green patents $l n G r P T$ represents the number of patents granted under the category of environment-related technological innovation. To the best of our knowledge, these data have not been used in the literature. This variable is important because it is indicative of the dedication of a country's resources toward technological innovation. Green patent data, collected from the OECD website, are limited to just over 70 countries.

As for which model to use, we use the fixed-effects model instead of the random-effects model for the following reasons. First, the number of countries is more than 100, which is three times greater than the number of periods. In panel data analysis, if the number of id-given characters, the number of countries in our case, is greater than the number of periods, the fixed-effects model is preferred because it offers better control over the individual heterogeneity of countries than the random-effects model. Consequently, we can obtain more reliable and unbiased results. When we run the fixed-effects model, the country-specific effects of most countries are significant at the 1% level, signifying the existence of heterogeneity. Therefore, the fixed-effects model provides better efficiency. In the results section, we provide Hausman test results to demonstrate that fixed effects model is indeed appropriate for our model.

Fixed effects model is a model used in econometric analysis when the sample has multiple entities over an observed timeline. It is characterized by a strict control over group-based differences, also known as group heterogeneity. As each entity may have inherent factor affecting the certain variable under study, fixed effects model treats these factors as constant and introduces a dummy variable for each group in the model.

However, as a robustness check, we run the regression using the random-effects model together with the fixed-effects model only for Table 3, and the results indicate the relationship between fossil fuel rents and economic growth. In addition, as part of further robustness analysis, we are including the Feasible Generalized Least Squares (FGLS) model results in Appendix, Table A1. FGLS model provides a unique aspect that fixed or random effects models do not include: it suppresses any form of heteroskedasticity that might exist in large group-based panel data. Therefore, by adding the FGLS model, we can ensure the absence of any form of heteroskedasticy. The results have been confirmed consistent with our main model, the fixed effects model, this means that our findings are indeed robust.

Table 3.

Results pertaining to the effects of fossil fuel rents (oil, natural gas, and coal) on the log of GDP per capita, as obtained using the fixed- and random-effects models.

	Fixed effects model			Random effects model
Variables	Entire sample	OECD	Top 20 percentile	Entire sample	OECD	Top 20 percentile
lnFFR	−0.769***	−2.075***	−2.251***	−0.675***	−1.219	−0.399
	(0.187)	(0.617)	(0.506)	(0.196)	(0.744)	(1.042)
lnIND	1.258***	1.148***	1.776***	1.198***	1.039***	3.661***
	(0.156)	(0.166)	(0.481)	(0.166)	(0.176)	(0.741)
lnINFL	−0.156***	−0.150***	−0.0946	−0.160***	−0.154***	−0.658**
	(0.0185)	(0.0184)	(0.0631)	(0.0198)	(0.0197)	(0.263)
lnFERT	0.232***	0.236***	0.476***	0.183***	0.186***	−0.235
	(0.0291)	(0.0320)	(0.166)	(0.0308)	(0.0337)	(0.198)
lnEDU	0.906***	0.855***	0.611*	1.020***	0.956***	2.971***
	(0.0707)	(0.0743)	(0.322)	(0.0750)	(0.0788)	(0.709)
lnUNEM	−0.790***	−0.758***	−0.900*	−0.821***	−0.793***	−9.139***
	(0.124)	(0.133)	(0.472)	(0.132)	(0.141)	(1.255)
Constant	7.772***	7.878***	7.501***	7.407***	7.556***	7.738***
	(0.0694)	(0.0751)	(0.353)	(0.104)	(0.112)	(0.734)
Year dummy	Included	Included	Included	Included	Included	Included
Country dummy	Included	Included	Included	Not included	Not included	Not included
No. of obs.	1760	841	135	1760	841	135
No. of countries	105	33	18	105	33	18
R-squared	0.754	0.776	0.893	0.223	0.450	0.810

Abbreviation: GDP: gross domestic product.

The first model (1) depicts the analysis of the effect of fossil fuel rents on GDP per capita. The coefficient $β_{1}$ represents this effect. The sum of the control variables is given as $Γ_{i t}$ . $α_{i}$ denotes country-specific effects, while $θ_{t}$ represents year-specific effects. Finally, $u_{i t}$ represents the error term. The second model (2), that is, the random-effects model, is used as a robustness check for model 1. This model does not include the term $α_{i}$ , which incorporates the fixed effects of countries that do not vary over time. The results obtained using models 1 and 2 are listed in Table 3.

l n G D P C_{i t} = β_{0} + β_{1} l n F F R_{i t} + γ Γ_{i t} + α_{i} + θ_{t} + u_{i t}

(1)

l n G D P C_{i t} = β_{0} + β_{1} l n F F R_{i t} + γ Γ_{i t} + θ_{t} + u_{i t}

(2)

The third model (3) incorporates the interaction term with renewable energy. The control variables are identical to those of model one. Table 5 presents the relevant results.

l n G D P C_{i t} = β_{0} + β_{1} l n F F R_{i t} + β_{2} l n F F R_{i t} \times l n R E S_{i t} + γ Γ_{i t} + α_{i} + θ_{t} + u_{i t}

(3)

Table 4.

Hausman test results for three main models.

Regressions	Test results	Chi-square Statistic	p-value
Entire sample	Fixed effects	239.57	.0000
OECD	Fixed effects	225.10	.0000
Top 20	Fixed effects	99.75	.0000

Table 5.

Results for the effects of fossil fuel rents and an interaction term with renewable energy on the log of GDP per capita when using the fixed-effects model.

Variables	Entire sample	OECD	Top 20 percentile
lnFFR	−0.478**	0.966	−2.584***
	(0.217)	(1.020)	(0.497)
lnFFR*lnRE	0.182***	−1.194***	0.348***
	(0.0592)	(0.202)	(0.0766)
Control variables	Included	Included	Included
Year dummy	Included	Included	Included
Country dummy	Included	Included	Included
No. of obs.	1545	776	117
No. of countries	94	33	16
R-squared	0.760	0.797	0.949

Abbreviation: GDP: gross domestic product.

The fourth model (4) contains the interaction term with green patents. The results of this model are listed in Table 6.

l n G D P C_{i t} = β_{0} + β_{1} l n F F R_{i t} + β_{2} l n F F R_{i t} \times l n G r P T_{i t} + γ Γ_{i t} + α_{i} + θ_{t} + u_{i t}

(4)

Table 6.

Effects of fossil fuel rents and green patents on GDP per capita.

Variables	Fixed-effects model	Random-effects model
lnFFR	−1.130***	−1.199***
	(0.253)	(0.262)
lnFFR*lnGrPT	0.659***	0.720***
	(0.135)	(0.142)
Control variables	Included	Included
Year dummy	Included	Included
Country dummy	Included	Not included
No. of obs.	1481	1481
No. of countries	71	71
R-squared	0.762	0.273

Abbreviation: GDP: gross domestic product.

Based on the literature, the fifth (5) and sixth (6) formulas represent our robustness analysis, which was performed using oil rent instead of fossil rent.²³ The relevant results are given in Table 7.

l n G D P C_{i t} = β_{0} + β_{1} l n O i l R e n t_{i t} + β_{2} l n O i l R e n t_{i t} \times l n R E S_{i t} + γ Γ_{i t} + α_{i} + θ_{t} + u_{i t}

(5)

l n G D P C_{i t} = β_{0} + β_{1} l n O i l R e n t_{i t} + β_{2} l n O i l R e n t_{i t} \times l n G r P T_{i t} + γ Γ_{i t} + α_{i} + θ_{t} + u_{i t}

(6)

Table 7.

Effects of oil rent and renewable energy on GDP (fixed-effects model).

Variables	Entire sample	Entire sample	Entire sample
lnOilRent	−0.500**	−0.766***	−1.309***
	(0.221)	(0.235)	(0.281)
lnOilRent*lnRE		0.00168***
		(0.000517)
lnOilRent*lnGrPT			0.302*
			(0.180)
Control variables	Included	Included	Included
Year dummy	Included	Included	Included
Country dummy	Included	Included	Included
No. of countries	106	94	71
R-squared	0.753	0.761	0.760

Abbreviation: GDP: gross domestic product.

Results

Table 3 presents the results obtained considering the effect of fossil fuel rents on GDP per capita by using the fixed- and random-effects models. The results obtained using each of these models are listed in three columns. Column 1 lists the results of 105 countries, that is, our entire sample. Column 2 lists the results of OECD countries. Although the total number of OECD countries is 38, owing to limited data availability, we present the results of 33 countries. Finally, Column 3 lists the results of 18 fossil-fuel-rich countries. The same order applies to the random-effects model. As can be seen, all results indicate that fossil fuel rent, given as the log of fossil fuel rent percentage, shares a negative relationship with the log of GDP per capita, thereby proving the resource curse phenomenon. All three results are significant at the 1% level, and the negative effect is more severe for fossil-fuel-rich countries compared to that for the entire sample in the first column or that for OECD countries in the second column. (In this, standard errors in parentheses, significance levels are as follows: *** p < .01, ** p < .05, * p < .1.) Our results are consistent with the results of Majumder et al. (2020), who used oil rent as a proxy to measure the abundance of natural resources.²³

As for the control variables, $l n I N D$ consistently has a positive relationship across all the models. This result is intuitive because $l n I N D$ represents the value added to industry, including manufacturing and construction. $l n I N F L$ consistently has a negative relationship with economic growth, which is consistent with the findings of Kim and Jeon (2024).³³ $l n F E R T$ consistently has a positive relationship with economic growth, indicating that human capital plays an important role in economic development. $l n E D U$ has a positive relationship with economic growth. Following Majumder et al. (2020)²³ and Kim and Jeon (2024),³³ we used $l n U N E M$ to control for the effects of unemployment, and $l n U N E M$ consistently has a negative relationship with economic growth across all the results.

As stated above, we ran the regression using the random-effects model to compare the results. Here, we determine which model is more suitable for use in this study. The results of a Hausman test indicate that the fixed-effects model is preferred over the random-effects model for all our regressions. Hausman test checks whether there are unobserved group heterogeneity effects. It runs on the hypothesis that random effects by default are preferred. But when we have p-value that is close to 0, we reject the null hypothesis and choose the fixed model instead. Since, our results have shown that p-value is indeed virtually zero for all samples, we conclude that fixed effects model is preferred, as it controls for unobserved group heterogeneity effect, in our case country-specific unobserved effects. The results of the Hausman test are listed in Table 4.

Table 5 lists the effects of the interaction term between fossil fuel rent and renewable energy on the log of GDP per capita. Renewable energy is presented as the log of the amount of solar and wind power produced annually in kilotons. As explained earlier, positive results indicate that renewable energy mitigates the negative effects of fossil fuel deployment on economic growth. According to our results, the interaction term is positive at the 1% significance level for the entire sample, thus proving our theory.

The results of OECD countries indicate that the interaction term is negative. One explanation for this is that many OECD countries have already made substantial investments in renewable energy and have reached the point of stability or nexus in terms of maximum solar and wind energy production. Therefore, additional solar and wind energy production in these advanced economies would not be as beneficial as it would be in emerging economies. The size of the entire sample and that of the top fossil-fuel-producing countries are reduced slightly owing to limited availability of renewable energy data from the IEA. As for the top oil-producing countries, the interaction term is positive at the 1% level, and its magnitude is larger than that of the entire sample. This result demonstrates that the countries that base their economy on fossil fuel production can benefit greatly by deploying renewable energy. It is worth noting that certain group of countries, top 20 percentile fossil-fuel-producing countries, can benefit from the deployment of renewable energy more than other groups such as OECD countries. This is partly because the energy infrastructure of these economies is largely based on fossil fuels rather than renewable energy. This means that, given that many of the fossil-fuel-producing countries are located in Middle East regions, the geographic location of these countries is well-suitable for solar and wind.^35,36 This implies that the capacity for growth in terms of transforming the economy from fossil-based fuels to renewable-based ones is greater for these countries than others where, for example, the amount of annual sunny or windy days may not be as adequate. The explanation for the positive interaction term of renewable energy is threefold.

Replacement of domestic fossil fuel consumption by renewables

When fossil-fuel-rich countries increase renewable energy deployment, they can replace domestic fossil energy consumption with renewable energy and, in turn, export more fossil fuels, which will increase the GDP per capita. Usually, domestic energy prices are lower than international prices, and if countries can manage to service a larger proportion of domestic consumption of energy consumption through renewables, they can reap substantial benefits by exporting fossil fuels at higher prices.

This process may come at the cost of the Dutch disease. As revenues increase owing fossil fuel exports, the country's currency may appreciate, which would make other exported products more expensive. However, if countries reinvest the revenues generated by fossil fuels into the development of human capital and enhancement of the manufacturing sector, they may be able to offset the potential effects of the Dutch disease.¹⁶ Nonetheless, sooner or later, such countries will need to replace their entire energy production with renewables.

Enhanced export competitiveness by meeting CBAM and RE100

Another important factor to consider is the increasing awareness and policy regulations pertaining to CO₂ emissions. Many developed countries have started to implement policies related to the import of goods; these policies require the exporting countries to use environmentally friendly technologies to produce the goods. For example, in 2023, the European Union proposed a new regulation called the carbon border adjustment mechanism (CBAM) to control carbon reduction, which will be in effect from 2026. According to this regulation, imported goods will be taxed depending on the amount of CO₂ produced while manufacturing those goods. Accordingly, countries with low levels of technological efficiency may end up losing in price competition. Therefore, it is important to increase the share of renewable energy, especially in domestic consumption and production.³⁷ According to a few theoretical works, the sectors that are not covered by CBAM and are considered high-emitting industries are likely to face losses.³⁸

Similarly, another initiative called renewable energy 100% (RE100) has been established not by countries but by multinational companies. This initiative aims to implement tariffs on the quality of production of raw materials. When these companies manufacture products, they import raw materials from all over the world, but the RE100 initiative aims to ensure that the imported goods are produced in an environmentally friendly way.³⁹ The reduction of fossil fuel footprint in manufacturing and the use of renewable energy instead can play a major role in ensuring the supply of environmentally friendly products.⁴⁰

On this note, it is important to mention environmental, social, and governance (ESG) investment. ESG investment includes investing in environmentally responsible projects that provide sustainability and deviate from socially irresponsible investments. Over the past two decades, investments of this nature have attracted global attention. Today, more than ever, ESG-based investments benefit investors, firms, and governments worldwide. For example, Xue et al. (2024) pointed out that high ESG ratings can significantly increase a company's total factor productivity by easing financial constraints and helping it qualify for government subsidies.⁴¹ For corporations, ESG ratings provide clearer visibility, enhanced reputation, and high audit quality in the global market. Similarly, Aydoğmuş et al. (2022) reported that ESG scores have a positive and statistically significant impact on a firm's profitability.⁴²

An alternative instrument for directing investments into socially responsible and environmentally friendly channels is green bonds. Huang (2024) mentioned that green bonds and ESG investments can improve sustainable economic growth in resource-rich economies, such as China, Brazil, and Saudi Arabia.⁴³

Investment in sustainable energy infrastructure that supports economic growth

Without investing in sustainable renewable energy, countries only reduce the rent of fossil fuels, which are limited in stock, and they may eventually end up with no fossil fuel reserves and no renewable energy. Therefore, the income from fossil fuel rents should be redirected as investments in renewable energy to ensure sustainable growth. This income should be transferred into productive, sustainable capital stock to provide sustainable energy infrastructure. Increasing the amount invested in renewables not only helps improve sustainable energy infrastructure but also reduces CO₂ emissions.⁴⁴

Moreover, dependance on fossil fuels increases a country's energy volatility, also called energy security risk. However, replacing fossil fuels with renewable energy reduces this dependance.⁴⁵ Therefore, even a country with a scarcity of fossil fuels need not depend solely on energy imports. The first step toward reducing a country's energy volatility is investing in renewable energy, especially solar and wind, and supporting environmental innovation. Only through properly channeled investments can a country transform its conventional energy infrastructure into renewables-based infrastructure.

Table 6 lists the results for the effects of fossil fuel rent and the interaction term with the proportion of green patents in the total number of patents.

Green patents represent a good proxy for a country's investment in green technologies and energy technologies. Therefore, we expect the sign of the interaction coefficient to be positive because it represents a movement toward more sustainable economic growth. However, data availability is limited. Even though our total sample includes 100 countries, green patent data are available for only 71 countries. Nonetheless, this variable serves as a measure for many important aspects of environment-related technology. A few important subcategories included in this variable are climate change mitigation technologies related to energy generation, transmission, or distribution; capture, storage, sequestration, or disposal of greenhouse gases; and mitigation technologies related to the production or processing of goods. In terms of the results, the coefficient of fossil fuel rent is negative and significant at the 1% level, confirming the resource curse hypothesis again. However, the coefficient of green patent data is positive and significant at the 1% level. Accordingly, a country's investment in green technologies mitigates the negative effects of fossil fuel production on its economic development. The reasons for this relationship are twofold.

High energy efficiency

One of the outcomes of investments in green patents is higher energy efficiency in the manufacture and processing of goods, that is, companies and governments try to achieve higher efficiency levels in manufacturing and reducing carbon intensity.^44,46 Green patents are related to the invention of technologies that minimize the energy cost of manufacturing goods, reduce inefficiencies, save energy, and facilitate the development of renewable energy technologies. In recent years, researchers have started to use machine learning for increasing energy efficiency.⁴⁷ Wang and Yan (2023) demonstrated that in China,⁴⁸ provinces with lower levels of economic development can achieve higher marginal energy efficiency and accelerate sustainable development goals.

R&D and human capital building

Transitioning the conventional economy into a human-capital economy is an important and inevitable phase in developing economies. Developing an intellect-based economy and intellectual capital requires investment in intangible assets, the value of which can be attributed to workers’ knowledge and intellectual property. Subsequently, revenue will be generated from this intellectual capital as opposed to physical capital under conventional economic models. For example, in the late 1990s, the Latin American and Caribbean countries started transitioning into “knowledge economy” by using the income generated through the export of natural resources.

Although the countries in these regions exported natural resources for a decade, the share of natural resources in their exports remained substantial. In other words, these countries failed to transition their economies by not focusing on building a knowledge-based economy, not increasing the share of manufactured goods in exports, not raising human capital, not providing enough funding for R&D, and not improving the quality of institutions.⁴⁹ Therefore, we provide empirical findings that investments in renewables and green technologies are crucial and cannot be separated from policies related to natural resource deployment. Moreover, a higher level of investment in green technologies is crucial for increasing renewable energy generation and reducing carbon emissions over the long term.⁵⁰

Green competitiveness

Another useful outcome of investing in green R&D is a substantial reduction in greenhouse gas emissions.^51,52 To participate in global trade, it is important to reduce carbon emissions, and countries have been advised to follow the terms of the Kyoto Protocol and Paris Agreement. In this context, investment in green technologies represents one route to reduce carbon emissions.⁵³ As mentioned above, global initiatives such as RE100 and CBAM are recent examples of such efforts. Therefore, countries need to invest in green R&D to achieve higher levels technological development and, by extension, reduce carbon emissions.⁵⁴ Many researchers have confirmed the strong correlation between environmental innovations and reduction of carbon emissions.^55,56 Our study confirms that environmental innovation helps to not only reduce CO₂ emissions but also achieve sustainable economic growth.

In addition, we further see the role renewable energy and green R&D play to mitigate the curse effect for emerging economies. This group of countries includes all the countries in our whole sample except for OECD countries. The results for this regression are provided in Appendix, Table A2.

Finally, we run a regression using oil rents alone because oil accounts for a major share of fossil fuel production. A few studies have argued that because oil is a highly tradeable commodity, it can have a sizeable effect on macroeconomic factors such as employment, inflation, and industrial output.^23,57 Table 7 presents the results obtained using the fixed-effects model. Column 1 lists the effect of oil rents on economic growth, while Column 2 lists the interaction term with renewable energy. When we use only oil rents, the results in the first column confirm the existence of the resource curse phenomenon, thereby reaffirming the results of the previous studies that used oil rent as a proxy for natural resources.²³ The interaction term in Column 2 indicates that renewable energy, specifically solar and wind energy, can mitigate the resource curse.

As confirmed by the results, the resource curse is consistent across all our models, namely the entire sample, OECD countries, or fossil-fuel-rich economies. Interestingly, stronger dedication toward renewable energy can negate the harmful effects of natural resource deployment and benefit the economy. As discussed earlier, we specifically selected solar and wind energy in this study because they require technology-oriented investments, while traditional hydropower and biomass-based energy do not require substantial technological improvements. Hydropower has been implemented for more than a century, and the technology to extract electricity from water dams is mature. Meanwhile, there is massive room for improvement in solar and wind energy technologies, and each year, countries are investing millions of dollars to achieve higher efficiency levels. This study proves that investing in solar and wind energy can help countries attain sustainable economic growth by mitigating the effects of traditional, fossil-fuel-based energy systems.

Conclusion

This study confirms the resource curse phenomenon and observes the role of renewable energy and investment in green R&D to mitigate the resource curse. By applying the fixed-effects model to the data of more than 100 countries, we find that fossil fuel rent adversely affects economic growth. However, renewable energy deployment and green innovation can mitigate these adverse effects. Our subsample analysis demonstrates that this mitigating effect is stronger for oil-producing countries in the top 20th percentile. An important policy implication for the countries that are heavily reliant on the deployment of fossil fuels, especially oil, is that they should increase their share of renewable energy production and invest in green innovation.

We have highlighted three main channels through which renewable energy can mitigate the adverse effects of fossil fuels. First, countries can replace domestic fossil fuel consumption with renewable energy. Second, countries can increase the share of renewable energy to lower the CO₂ emissions due to manufacturing and meet environmental standards, which would increase their competitiveness in the international market. Organizations such as CBAM and RE100 monitor the environmental compliance of countries, and it is increasingly important to follow international policies for reducing greenhouse gas emissions. Moreover, ESG ratings and ESG-compliant investments have proved to be beneficial in terms of increasing profitability of companies worldwide and enhancing their reputation. Third, resource-rich countries should deploy their diminishing resource capital toward building sustainable infrastructure. Only through reinvesting the revenues from fossil fuels into sustainable energy sources can economies achieve sustainable growth. The policy implications of this study are that countries should focus on improving renewable energy deployment, especially solar and wind. Many resource-abundant countries are also rich in terms of the annual number of sunny days. Therefore, with adequate R&D, these countries can increase the share of their annual energy consumption serviced by renewable energy. For example, according to the U.S. Energy Information Administration, in the United States, renewable energy serviced 10% of the domestic energy consumption for the first time in 2017. Although this event may not seem significant, given the annual amount of energy consumed by the United States, it is an important milestone.

Another factor for mitigating the resource curse phenomenon is investment in environmental innovation. We have highlighted three important channels through which investment in green R&D can help mitigate the adverse effects of fossil fuel deployment. First, by investing in technology, countries can innovate and achieve higher levels of energy efficiency. Second, countries can reduce the adverse effects of fossil fuel deployment by investing in human capital, thereby transforming the conventional, fossil-fuel-based economy into a human-capital economy. Because human capital is an essential component of economic growth, countries should reinvest the revenues generated by fossil fuel deployment into human capital. Finally, investing in environmentally friendly technologies helps increase the competitiveness of countries in the global arena by reducing carbon emissions. As mentioned above, countries are encouraged to comply with international regulations pertaining to carbon emission reduction and follow the guidelines formulated by organizations such as CBAM and RE100. Investing in green technology is the first necessary step in this direction. The important policy implications that emerge from our findings include increasing the investment in green R&D and supporting environmental innovation.

One of the reasons we have chosen solar and wind as renewable energy source in this study is due to the technology-oriented nature of these two variables. While there are many other renewable energy sources such as hydro, we think that further development of solar and wind actually requires investments in technological advancement and human capital in the long term. This helps countries achieve higher levels of technological infrastructure as well as sustainable generation of energy in the long term. In addition, solar and wind are cost efficient and requires very marginal production cost, which is limited to regular checkups in solar and wind plants and maintenance cost only. Therefore, producing additional energy from solar and wind requires virtually no extra cost. Therefore, further investment in solar and wind is the fastest way countries can achieve sustainable energy production.

This study can be extended by focusing on a few other channels through which renewable energy and green innovation can help mitigate the resource curse. Especially in terms of green innovation, there are a few opportunities to further study and analyze the factors that can help a country achieve sustainable growth by investing in environmental innovation. As for the future research, there is research opportunity for studying the impact of renewable energy and green R&D on various forms of sustainable development. One of such sustainable development indicators is adjusted net savings, calculated by adding the educational expenditure to the national net savings and subtracting the natural resources deployment. The data is provided by World Bank and is available for majority of countries. Since it factors the resource deployment, it can provide more reliable environment-related interpretation comparing to other development indicators such as GDP growth. Another important indicator for sustainable growth is sustainable development goals tracker provided by the United Nations. It has many indicators and elements, some important ones are Climate Action, Affordable and Clean Energy, and Industry, Innovation and Infrastructure. Integration of such variables into the topic of further deployment of renewables and investments in green R&D can be a promising field for future research.

In addition, the role of institutions and governments is essential in achieving sustainable growth. Therefore, integrating the institutional quality of countries can further provide promising insights. There are many indicators developed by different organizations around the world. One of the widely recognized indicators is the Worldwide Governance Indicator provided by World Bank. However, the institutional quality is not limited to the governmental actions but can be further extended to areas such as economic institutions, social institutions, environmental governance and human development indicators.

Supplemental Material

sj-docx-1-eae-10.1177_0958305X251315811 - Supplemental material for Fossil fuel curse and green transition: The role of renewable energy and green R&D

Supplemental material, sj-docx-1-eae-10.1177_0958305X251315811 for Fossil fuel curse and green transition: The role of renewable energy and green R&D by Nuriddin Makhmudov and Wooyoung Jeon in Energy & Environment

Footnotes

Data availability statement

All data generated or analyzed during this study are included in this article.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Nuriddin Makhmudov

Wooyoung Jeon

Supplemental material

Supplemental material for this article is available online.

Nuriddin Makhmudov is currently a master's degree student in the Department of Economics at Chonnam National University under the supervision of Prof Wooyoung Jeon. His research interests include resource management economics, energy economics, renewable energy economics, and environmental economics.

Wooyoung Jeon is a professor of economics at Chonnam National University. After receiving his PhD from Cornell University, he held research and academic positions at Chonnam National University in Korea. His teaching and research are focused on energy and environmental economics.

Appendix A

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