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Article

Do the Reduction of Traditional Energy Consumption and the Acceleration of the Energy Transition Bring Economic Benefits to South America?

by
José Castro Oliveira
1,
Manuel Carlos Nogueira
1,2,* and
Mara Madaleno
2
1
ISPGAYA—Higher Polytechnic Institute of Gaya, Avenida dos Descobrimentos, 303, Santa Marinha, 4400-103 Vila Nova de Gaia, Portugal
2
GOVCOPP—Research Unit in Governance, Competitiveness and Public Policy, Department of Economics, Management, Industrial Engineering and Tourism (DEGEIT), University of Aveiro, 3810-193 Aveiro, Portugal
*
Author to whom correspondence should be addressed.
Energies 2023, 16(14), 5527; https://doi.org/10.3390/en16145527
Submission received: 17 June 2023 / Revised: 14 July 2023 / Accepted: 19 July 2023 / Published: 21 July 2023
(This article belongs to the Special Issue Energy Intensity, Economic Growth and Environmental Quality)
Versions Notes

Abstract

:
By considering a panel dataset between 1995 and 2019 including several countries in South America and methodologically using the fixed effect and GMM methods in first differences, the authors sought to empirically determine the relationship between traditional energy consumption, renewable energy consumption, and economic growth. The results show that the two main variables studied (fossil energy consumption and renewable energy consumption) are statistically significant and contribute to economic growth per capita in all nine South American countries studied. Furthermore, it should be noted that this significance persists in the four models discussed in this study, demonstrating a link between the positive economic impact of reducing traditional energy consumption and increasing renewable energy consumption in the South American countries studied. This article also contributes to the existing literature by highlighting the fundamental role of gross capital formation, labor force participation, and tertiary school enrollment in the economic growth of these countries. Two rather small effects on the aforementioned growth are the corruption perception index and domestic lending to the private sector by banks. This paper calls on policymakers to reconsider increasing energy production using renewable sources and to promote measures for its consumption.

1. Introduction

Renewable energy production and consumption emit few harmful gases into the environment; when countries replace fossil fuels with renewable energy, they do not want to sacrifice economic growth, which must be achieved in a sustainable way [1]. According to a comprehensive literature review of articles published between 2010 and 2021 [2] on the use of renewable energy and its impact on economic growth, the existent literature reports that this growth is not affected by the increased production and consumption of energy from renewable sources in both developed and developing countries.
In one of the first studies to attempt to establish the link between the use of energy from renewable sources and economic growth, the authors concluded that at the time, there was no consensus on the direction of the impact [3]. Other studies have indicated that the search for greener and inexhaustible energy alternatives has paved the way for research into the impact of this greener consumption on economic growth while ensuring a sustainable environment [4] as we move rapidly towards the depletion of fossil fuels. The world is forced to embark on a path that leads to the reduction of energy consumption from fossil resources and to direct this consumption towards production using renewable and, thus, inexhaustible sources [5,6,7]. On the other hand, there is a consensus that the use of fossil fuels causes the emission of greenhouse gases that lead to global warming [3,4].
This article follows the work of Madaleno and Nogueira [1] on the 27 countries of the European Union. Considering suggestions for future work, we extend the analysis to nine countries in South America and include two new variables (the corruption perception index and domestic credit to the private sector by banks). This article aims to contribute to the academic debate on the substitution of energy consumption and production using fossil fuels with the use of renewable energy sources and the impact of this substitution on economic growth.
As mentioned recently by several authors, we can divide the empirical literature on renewable energy production and consumption and its impact on economic growth into two categories: those that demonstrate a positive impact on growth and those that find a negative impact and assume that the economy will shrink as renewable energy increases. This article also aims to strengthen the empirical debate and generate more academic interest in more in-depth studies on the impact of the decrease in fossil energy dependence and the increase in renewable energy consumption on economic activity [2,6].
Using fixed effects panel data and the GMM (generalised method of moments) method in first differences over a long 25-year period, we find that both fossil fuel energy consumption and renewable energy consumption contribute to economic growth. Another contribution of our study to the scientific literature is that it has been shown, for South American countries, that replacing energy consumption from fossil sources with that from renewable sources benefits economic growth, overturning the arguments of those who defend the economic notion of this energy transition causing energy losses. This evidence could also mean that the current energy transition, which aims to expand renewable energy, can contribute to economic growth, to some extent, for South American countries.
Higher skill levels, lower perceptions of corruption, and higher domestic bank lending to the private sector also contribute to economic growth in the countries considered. We also found econometric evidence that investments in fixed assets and labor force participation strongly influence economic growth; meanwhile, perceptions of corruption and domestic bank lending to the private sector contribute only marginally. Surprisingly, the R&D variable does not show statistical significance.
Another valuable element that this study brings to the empirical debate on economic growth and renewable energy consumption concerns the fact that the vast majority of studies already conducted cover European Union or OECD countries or combine a large number of countries from different geographical settings, like G-7 and E-7 countries [8]. As far as we know, this is the first time that South American countries have been studied exclusively on this topic.
This paper is organized as follows: it starts with an introduction; Section 2 provides a brief review of the recent literature on the subject; Section 3 presents the data, variables, main statistics, and correlations and highlights the relevant results; Section 4 contains the empirical results; Section 5 discusses the results and highlights some policy implications; and finally, Section 6 concludes with the general conclusions, practical and theoretical contributions, limitations, and directions for future research.

2. Literature Review

Nowadays, countries all over the world have problems with climate change, pollution, and the burning of fossil fuels. For this reason, they are transforming energy production, using more and more renewable energy sources, and replacing traditional energy consumption, which leads to lower carbon emission impacts [9]. Many studies have claimed that renewable energy is a good strategy for preserving the environment [6] and have also confirmed a positive correlation between renewable energy consumption and economic growth [10].
Intended to decarbonize economies and promote development and economic growth, energy from renewable sources plays a crucial role in both more economically developed countries [11] and developing countries [12]. An analysis of 46 articles published between 2010 and 2021 that attempted to establish causal relationships between renewable energy consumption from different sources and economic growth also concluded that the consumption of this type of energy does not affect economic growth, both in more developed and less developed countries [2].
Recently, Mukhtarov et al., (2023) [13] used data for Azerbaijan, from 1993 to 2019, and employed the dynamic ordinary least squares method to examine the impact of energy consumption from renewable sources, real GDP per capita, and international trade on carbon emissions associated with this consumption. The authors found evidence that energy consumption from renewable sources decreases carbon dioxide emissions while real GDP per capita has a positive impact on the same emissions [13]. In particular, in Canada, Finland, Russia, Slovenia, South Korea, and the United Kingdom, energy from nuclear energy sources reduces carbon dioxide emissions more significantly than energy from renewable sources [4]. The authors confirm that nuclear energy and renewable energy are good options for the 22 nuclear-energy-producing countries to increase their economic growth without negatively contributing to the increase in carbon dioxide emissions [4]. In the literature, we also find results pointing out that the causality between renewable energy consumption and climate policy uncertainty is both negative and positive, which mainly stems from the attitudes of authorities towards climate change [14]. Thus, traditional energy is cheap and convenient to use during economic downturns, conditioning authorities’ decisions according to the period when decisions are to be made [14,15]. In fact, [15] also conclude that enhancing the green energy transition mitigates CO2 emissions and that reducing disaggregated and aggregated country-risks mitigates CO2.
The impact of traditional energy, the consumption of energy from renewable sources, foreign direct investment (FDI), and exports on economic growth in the BRICS countries (Brazil, Russia, India, China, and South Africa) from 2000 to 2018 are evident [16]. Using interest-rate level, labor force, degree of openness of the economy to foreign countries, and gross domestic savings as variables to determine the long-running relationship between the variables and using several econometric techniques, they concluded from the empirical results that traditional energy, consumption of energy from renewable sources, exports, FDI, and savings have positive and significant effects with long-running impacts on economic growth; meanwhile, interest rates and openness to foreign countries negatively affect this economic growth. The results show that an increase in the levels of traditional energy consumption and FDI contributes to an increase in economic growth [16]. Sadiq et al. [17] investigated the causality between energy use and growth-driven carbon emissions. Their empirical findings reveal the feedback effect between economic growth (GDP) and energy use in South Asia. Thus, the literature is almost unanimous in stating that there is a positive relationship between economic growth and traditional energy consumption.
Regarding a set of 20 developed and developing countries and the period between 1995 and 2016, Singh, N. et al. [18] it was concluded that renewable energy use can be an important pillar for sustained economic growth in the future as there is a positive and statistically significant relationship between renewable energy production and consumption and economic growth. Other authors have argued that variable energy consumption from renewable sources, with respect to global energy consumption, is better than renewable energy production, in relation to global energy production; this is because the cost of producing this type of energy is highly variable and, in the case of consumption, the customer pays the price when buying energy [19].
Other concerns with carbon emission reductions have recently emerged in the literature, in which countries like China and other similar countries, in terms of development, have been recommended to prioritize positive environmental externalities, in terms of reductions in carbon emissions caused by the high-speed railway [20]. For the transport sector, in the different veins of oil and gas transport and storage, [21] critical analyses suggest increased flexibility, energy saving, emission reduction, and changing the energy structure. The authors further emphasized the need to focus on improving energy efficiency further, reducing energy/water/material consumption and emissions [21].
Regarding the reality of the European Union and the OECD, there are important roles to be played in the consumption of renewable energy in relation to the total energy consumption in terms of economic growth, as well as roles in job creation, the level of R&D, investment, and the consideration that this relationship will be long-lasting [22]. For developing countries (such as some countries in South America), the negative effect on economic growth caused by renewable energy consumption is compensated in the long run if the consumption is intensified as some of these countries have very low values in terms of the consumption of these energies [23]. In a recent study conducted regarding several Asian countries, policymakers were advised to focus on the growth and production of renewable energy as a viable alternative to overcoming environmental degradation and they would promote the sustainable growth of the economy and the energy sector. A causality test has also shown that there is a bidirectional relationship between renewable energy consumption and economic growth [24]. Also, in the context of Asian economies, while studying the dynamic relationship between the various sources of renewable energy and economic growth in ten Asian countries, it was concluded that high economic growth requires higher energy consumption and that this energy consumption, when it comes from renewable sources, has a positive and significant impact on the level of economic growth. The authors also believed that countries should make efforts to reduce energy consumption from traditional sources to improve economic growth [25].
Regarding a group of 124 countries classified according to different income levels, using the PVAR (panel vector autoregressive) approach, it was found that gross capital formation only has a positive impact on economic growth in high-income countries. By using Granger causality, unidirectional relationships have already been found between gross capital formation and economic growth for countries of all income levels [26]. Additionally, considering a set of 24 emerging economies, evidence was found that fixed capital formation can increase economic growth, in addition to helping to protect these countries’ environments, if this investment is directed towards the production of renewable energies [27]. Other authors have also found evidence that gross capital formation contributes to economic growth [28].
Several authors have found links between increased spending on research and development (R&D) and economic growth around the world, as well as promoting social progress. For BRICS countries between 1961 and 2016, especially in terms of the long term, these correlations are evident [29]. If R&D is directed towards research on reducing the emission of carbon dioxide and other harmful gases to the environment, it has positive impacts on environmental protection without compromising economic growth [29]. Also, we find in the literature recent studies arguing in favor of the positive roles exerted by financial development, R&D expenditure, human development, and ICT on renewable energy transition, being considered its most prominent drivers [30], especially for China’s current economy.
Education is a determining factor of economic well-being, in addition to increasing human capital. Workers with higher education can improve in performance, as it constitutes a mechanism for absorbing knowledge, in addition to generating externalities for the economy and society. The higher the educational level of a country, the greater its economic growth; this growth reacts quickly to the increase in the level of education [31].
In terms of labor and resources, South American countries have abundant human capital and natural resources. A study of the BRICS countries highlighted that these countries have a high-quality and developed education system that can guarantee a highly skilled workforce for industry [16]. Countries that have abundant highly skilled labor forces achieve faster economic growth [16]. In addition, there is a large body of older literature that addresses the positive impact of the labor force on the economy [32,33,34]. Moreover, energy consumption contributes significantly to economic growth as a complement to labor and capital as factors of production. Therefore, policies that lead to energy savings or energy supply shocks, such as those that restrict traditional energy consumption, can negatively affect economic growth [35].
Regarding the relationship between corruption levels and economic growth, virtually all studies agree that the increase in corruption can affect economic growth, with this fact being more noticeable in countries with low investment rates and low qualities of political power. Countries with lower levels of corruption have better governance, which attracts productive and high-quality foreign direct investment [36]. In EU countries and Ukraine, from 2000 to 2016, a 1% increase in renewable energy (RE) consumption led to a decline in GHGs (greenhouse gases) and an increase in the Corruption Control Index, which provoked a GHG drop of 0.88% [37]. In the MENA region (13 countries in the Middle East and North Africa), economic growth reacts negatively to environmental degradation and political instability, which are characteristics of several countries in the region. Through static and dynamic empirical applications of panel data models, it became clear that an increase in the level of corruption has a strong impact on economic growth, environmental quality, and energy consumption. Thus, corruption has pernicious effects, in an indirect way, on economic growth through energy consumption and environmental quality; it also has indirect effects on environmental levels through economic growth, as well as indirect effects on energy consumption via carbon dioxide emissions and GDP [38].
Finally, we include in the estimates, as an explanatory variable, domestic credit to the private sector. It is common knowledge that financial development encompasses increases in the flow of foreign direct investment, dynamism in the stock market and in the banking sectors, a favorable legal environment, and internal credit to the private sector, concluding that the good functioning of financial markets can contribute positively to the economic growth [39]. Good financial development provides economic growth; it is empirically proven that this development, generally measured in terms of broad money supply and national credit to the private sector of the economy transacted through banks, contributes to an increase in carbon emissions and that, on the other hand, liabilities measured in net form have no impact on carbon emissions [39]. Regarding a study of 46 countries in sub-Saharan Africa, during the 2000–2015 period, the results were significant; considering the development of the financial sector, measured by broad money, credit granted internally to the private sector by banks causes increases in the carbon emissions used; only three moderate economic growth and promote the increase in carbon emissions. Moreover, the authors also confirmed that the number of residents, energy consumption, the country’s openness to trade, urbanization, and economic growth contribute to the increase in carbon emissions, with the results varying by geographic location and income groups [40]. Using a panel of five South Asian countries, from 1990 to 2014, financial development per capita (FIN) and domestic credit to the private sector of the economy divided by the number of residents were used as control variables; energy consumption from renewable sources and non-renewables, as well as investment in fixed assets, showed statistical significance for economic growth [41]. Thus, even though a lot of research has emerged recently discussing these variables’ impacts, less is known in the South American context, where resources are sometimes abundant but less-efficiently managed, where political will is reduced, and where social problems are more pronounced, with lower amounts of financial resources devoted to an effective and pronounced renewable energy transition.

3. Data, Variables, Main Statistics, and Correlations

The sample used in this study covered the period between 1995 and 2019 and accounts for 9 countries in South America (Argentina, Bolivia, Brazil, Chile, Colombia, Ecuador, Paraguay, Peru, and Uruguay). As some observations were missing, we were dealing with an unbalanced panel of 156 observations. The database was created by accessing the World Development Indicators (WDI), the World Bank’s main collection of development indicators based on officially recognized international sources. The only country in South America not included in this study was Venezuela as most of the data on this country were not available.
Table 1 presents the variables used in the analysis carried out, the definition of each of the variables, the intended objectives with each variable, and the unit of measurement. In turn, Table 2 shows the average of the same variables by country.
As we can see in Table 2, for the analyzed period, the country with the highest average GDP per capita (constant, 2015) was Uruguay (USD 12,408) and the one with the lowest average value was Bolivia (USD 2415).
In terms of average CO2 emissions per tonne and per capita, Chile was the most polluting country (3.8786) and Paraguay was the least polluting (0.873). In terms of average renewable energy consumption relative to total energy consumption, the highest value was found in Paraguay (66.49%) while the lowest value was found in Argentina (9.81%).
Regarding investment, as a percentage of GDP, the highest average was seen in Chile (25.154%) and, on the other hand, the lowest value occurred in Argentina (17.509%). In relation to the average expenditure on research and development as a percentage of GDP, the highest value was found in Brazil (1.122%) and the lowest (0.0765%) occurred in Paraguay.
The average labor force participation rate was the highest in Peru (73.75%) and the lowest in Chile (57.622%); meanwhile, gross enrolment, i.e., the ratio between total enrolment, regardless of age, and the population of the age group officially corresponding to the tertiary education level, was the highest in Argentina (67.872%) and the lowest in Paraguay (23.431%).
The country where the average corruption perception index was the lowest was Chile (68.116) and the highest was in Paraguay (21.96). The higher the corruption perception index, the more transparent the country was perceived to be. Finally, regarding the average amount of credit provided by the banking sector, as a percentage of GDP, the value was the highest in Chile (67.680%) and was the lowest in Argentina (15.550%).
Table 3 shows the main descriptive statistics, namely, the maximum and minimum values, means, and standard deviations of the variables considered.
As we can see in Table 3, the maximum value of GDP per capita was USD 16,192. This value was verified in Uruguay in 2019; the minimum value of 1866 occurred in Bolivia in 1995.
Regarding CO2 emissions, the highest value occurred in Chile in 2016 and the lowest occurred in Bolivia in 2001. In terms of renewable energy consumption in relation to total energy consumption, the highest value was verified to be in Paraguay in 1995 and the lowest was verified to be in Argentina in 2007.
As for the variable quantifying investment, as a percentage of GDP, the highest value was in 2019 in Chile and the lowest value was in 2019 in Argentina. As for labor force participation, the highest value was recorded in Peru in 2010 and the lowest was recorded in Chile in 1995.
Regarding the schooling variable, the highest value was recorded in Argentina in 2019 and the lowest was recorded in Paraguay in 1995. The highest value for the corruption perception index was found in Chile in 2001 and the lowest was found in Paraguay in 1998. Finally, the highest value for banks’ domestic credit to the private sector was found in Chile in 2019 and the lowest was found in Argentina in 2004.
Table 4 shows the Pearson correlation coefficients. In order to obtain more reliable results from the empirical analysis, it is necessary to take into account the possible problem of multicollinearity. The Pearson correlation test applied to the variables in our study suggested that there was no high multicollinearity between the variables; here, we considered the value of -0.80 or 0.80 as the reference value, as was already considered in other studies [37,38]. The highest correlations were found between the variables GDP per capita and schooling, between CO2 emissions and schooling, and between GDP per capita and CO2 emissions. As already mentioned, in none of the cases was this value greater than 0.80.

4. Model Specification, Estimation Methods, and Empirical Results

As mentioned earlier, we used an unbalanced data panel to estimate a model that seeks to explain the effects that carbon emissions from burning fossil fuels and the consumption of renewable energy have on economic growth in nine countries from South America in the period between 1995 and 2019. This resulted in the use of 156 observations. Additionally, as control variables, we added six variables into the analysis: renewable energy consumption (REC), gross capital formation (GCF), research and development expenditure (R&D), labor force participation (LFP), school tertiary (ST), corruption perceptions index (CPI), and domestic credit to the private sector by banks (DC). Since we opted for a logarithmic specification of the model, the coefficients obtained represent elasticities and, thus, show the percentage change in GDP per capita as a result of a percentage change in one of the explanatory variables. The model has the following form:
Ln GDPpc𝑖𝑡 = 𝛼𝑖 + 𝛽1 ln CO2𝑖𝑡 + 𝛽2 ln REC𝑖𝑡 + 𝛽3 ln GCF𝑖𝑡 +
+ 𝛽4 ln R&D𝑖𝑡 + 𝛽5 ln LFP𝑖𝑡 + 𝛽6 ln ST𝑖𝑡 + 𝛽7 ln CPI𝑖𝑡 + 𝛽8 ln DC𝑖𝑡 + 𝑢𝑖𝑡
We could have used three alternative panel data methods to estimate Equation (1). The ordinary least squares approach would mean we would not assume country- and time-specific effects. This estimation method would be more suitable if the sample consisted of, for example, a cluster of homogeneous countries, which was not the case with our sample as we were dealing with very disparate data. An example of this disparity was the average GDP per capita in Uruguay and Argentina, which was over US$12,000; meanwhile, in Bolivia, this value was US$2415. There were also large amplitudes in the other variables between the averages of some countries and the averages of other countries. Another possible estimation approach that had the advantage of capturing country-specific heterogeneity was the fixed effects model, which captures the heterogenity in the constant term. This model could be estimated using the least squares method with dummy variables (LSDV), assuming country-specific dummy variables or, alternatively, using the time-constrained estimation approach [42]. Finally, the model could have been estimated with random effects. In this last estimation method, country heterogeneity is unobservable and is captured by the error term. The estimation method used is generalized least squares, which is applied to the reduced partial model [43]. In the case of random effects, the assumption that the unobserved error term is not correlated with any of the independent variables is important to obtain consistent and unbiased estimates.
The first step was to decide which estimation method to use (OLS, LSDV, or GLS). Three statistical tests were devised for this procedure. The F-test, which tested the clustered model against the FE model, the Breush-Pagan test LM, which tested the clustered model against the RE model, and the Hausman test, which tested the RE model against the FE model. When performing the three statistical tests, the FE model had the best fitting specification (F-test p-value = 0; Breush-Pagan LM test p-value = 0; Hausman test p-value = 1.67267 × 10−77).
A very common problem with panel data that is often overlooked is endogeneity, which, if not tested for, will not be corrected for. The results in Table 5, indicating the statistics of the Hausman test, suggest that three variables could be identified as possessing endogeneity (p-value less than 0.05). Given the results of the Hausman test, we could reject the null hypothesis that there is no correlation with the error term. Given these results, the best solution was determined to be to use instrumental variables to obtain more consistent estimators, e.g., by simplifying the model since the estimation gives biased results in the presence of endogeneity [44].
According to several authors, the inclusion of the lagged dependent variable on the independent side of the equation simplifies the model but could potentially lead to endogeneity that cannot be remedied by resorting to traditional methods (e.g., 2SLS (two-stage least squares), 3SLS (three stage least squares) or SUR (apparently unrelated regression)). Thus, according to these authors, in cases like the present one, the best estimation method is the generalized method of moments (GMM) method in first differences [42,43,44].
The estimation of the first differences for the variables related to the growth rate of GDP per capita, carried out with the GMM method, made it possible to take into account the persistence of the dependent variable over time. It also took into account possible problems due to endogeneity, which was demonstrated for three variables (renewable energy consumption, R&D expenditure, and corruption perception index) [45,46].
The GMM model in first differences has the following structural form:
Ln GDPpc𝑖𝑡 = 𝛼𝑖 + 𝛽1Ln GDP(−1)pc𝑖𝑡 + 𝛽2 ln CO2𝑖𝑡 + 𝛽3 ln REC𝑖𝑡 + 𝛽4 ln GCF𝑖𝑡 +
+ 𝛽5 ln R&D𝑖𝑡 + 𝛽6 ln LFP𝑖𝑡 + 𝛽7 ln ST𝑖𝑡 + 𝛽8 ln CPI𝑖𝑡 + 𝛽9 ln DC𝑖𝑡 + 𝑢𝑖𝑡
As we can see, Table 6 reports the results obtained with the panel data fixed effects methodology and the GMM model in first differences methodology.
An important conclusion we could draw, which was only possible with this estimation method, was that the coefficient of the dependent variable lagged by one period was 0.814 (in Model 3). This result indicated that the effect of the previous year’s economic growth was very persistent, i.e., only about 19% of the economic growth was adjusted in the following year. In Model 4, the coefficient of the lagged dependent variable was 0.802, which was also synonymous with the presence of a high persistence of the previous year’s economic growth and, in this case, only about 20% of economic growth was adjusted in the following year.
In the fixed effects model, there was one more variable that carried statistical significance than variables in the GMM model; but, the GMM model had no endogeneity and, thus, the reliability of the estimated coefficients, as well as their statistical inference, was not affected.
Table 6 shows that the variable that contributed most to the economic growth of these countries, in all models, was labor force participation, soon followed by renewable energy consumption relative to total energy consumption. The consumption of energy from traditional sources also contributed significantly to economic growth in all models; but, this occurred with a lower intensity than the consumption of renewable energy. Furthermore, the variable gross capital formation was also of great importance, as it relates to investments in fixed assets. The tertiary school variable also had a high and significant impact on economic growth, in all models. Finally, the variables measuring corruption and domestic credit to the private sector contributed only marginally to economic growth.

5. Discussion

As we can see in Table 6 and the results obtained in Model 1, only the research and development expenditure variable did not present statistical significance for levels normally considered acceptable. Still, in relation to Model 1, the two most important variables under study (traditional and renewable energy consumption) were statistically significant and contributed to the per capita economic growth of the nine South American countries considered in this study. It should be noted that this significance remained in the four models considered.
The effects caused by the consumption of fossil energy, despite being harmful to the environment, still represented an important source of energy for these countries. The use of fossil fuels is still the main source of energy; but, the positive effect, in economic terms, of the consumption of energy from renewable sources cannot be overlooked.
Thus, in this way, an important piece of evidence should be highlighted. The coefficient associated with the consumption of renewable energy, in relation to total energy consumption, was higher in all models than the coefficient associated with traditional energy consumption. For example, in the case of Model 1, under the condition ceteris paribus, while an increase of 1% in the consumption of renewable energies in relation to the total energy consumption impacted 0.35% in the economic growth per capita, under the same conditions, and in the case of traditional energy consumption, this growth was only 0.21%. This important indication could mean that the current energy transition, which aims to reduce the consumption of energy from fossil sources, and the accompanying increase in the production and consumption of energy from renewable and environmentally friendly sources, will have a net positive effect on economic growth. Similar effects have already been found in other studies and for other geographic realities [1,47,48,49].
In all of the models considered, the variable that, in an isolated way, contributed the most to economic growth in per capita terms in the countries considered was labor force participation, which could mean that it greatly contributes to the economy. In the particular case of Model 1, for each 1% increase in this variable and under the ceteris paribus condition, economic growth of 0.51% in the countries of South America is expected on average. Its role as a driver of economic growth has also been noted by other authors [34,50].
Regarding Model 2, also estimated using the fixed effects of panel data and without the variables CO2 and R&D, it appeared that all of the remaining variables maintained their statistical significance, with only a few variations in the intensities of the coefficients. This fact may mean that even after removing the variable that represents the traditional consumption of energy, the remaining variables manage to contribute to economic growth.
Considering the investment in fixed assets, and as already verified by other authors [51,52,53,54], we also found a positive and significant contribution to economic growth. Investments and innovations that promote productivity translate into economic returns for society in the future.
Regarding the school tertiary variable, its positive effect on economic growth demonstrated the importance of investments in education as a way of preparing current students and future workers for the job market and improving their productivity. Upon entering the labor market, these workers will be better prepared, the potential spillover effects will increase, and the knowledge of these workers will spill over to the rest of the economy [55].
In all four estimated models, we could see that countries that simultaneously increased renewable energy consumption, labor force participation, and school tertiary were expected to obtain important contributions for their economic growth. Despite minor contributions, the corruption perceptions index and domestic credit to the private sector by banks variables also presented their contributions to economic growth.
The relationship between levels of corruption and economic growth has been the subject of much debate in the literature in recent decades and, in general, researchers find evidence of negative correlations between these two variables that are detrimental to economic growth [36]. In our case, we also saw that the decline in the corruption perception index contributed to economic growth; although, it did not show statistical significance in Models 3 and 4, i.e., when endogeneity was taken into account, the sign was preserved but the significance was lost.
Finally concerning domestic credit, according to the existing literature, there is a positive correlation between this variable and economic growth. As for several authors [56,57,58], in these South American countries, the granting of credit by the financial system to the private sector contributes to economic growth. Financial institutions thus play an important role in the economy, as a result of credit granted for investments in the private sector investments.
The impact of fossil fuel consumption, while environmentally damaging, is still an important source of energy for these countries. The use of fossil fuels remains the most important source of energy; but, the positive economic impact of the consumption of energy from renewable sources cannot be overlooked.
This is therefore an important indication that should be highlighted. The coefficient associated with renewable energy consumption in relation to total energy consumption was higher than the coefficient associated with the traditional energy consumption variable, in all models. For example, in the case of Model 1, a 1% increase in renewable energy consumption relative to total energy consumption had a 0.35% impact on economic growth per capita under the ceteris paribus condition; meanwhile, this growth was only 0.21% in the case of carbon emissions under the same conditions. This is an important indication that the current energy transition, which aims at decarbonization, and the resulting increase in the production and consumption of energy from renewable and environmentally friendly sources, will have a net positive impact on economic growth. Similar effects have already been found in other studies and other geographical settings [1,48,49].
The biggest difference between these two types of estimations was that in the GMM model in first differences (without endogeneity), the variable CPI lost its statistical significance and the other coefficients associated with the other variables suffered slight decreases. This factor may be because the high persistent effect of the previous year was excluded and, therefore, a small adjustment was made in the following year (as we discussed in the previous point).

6. Conclusions

This study focused on a sample of nine countries in South America (Argentina, Bolivia, Brazil, Chile, Colombia, Ecuador, Paraguay, Peru, and Uruguay) and used panel data for the period between 1995 and 2019. The authors examined the relationship between traditional and renewable energy consumption and economic growth in these countries.
It was found that the two main variables studied (consumption of traditional energy and renewable energy) were statistically significant and contributed to the per capita economic growth of all nine South American countries studied. Moreover, it should be noted that this significance was maintained in the four models studied; but, they have the peculiarity that the coefficients associated with the consumption of renewable energy were higher than the coefficient of energy consumption from fossil sources so that economic growth was not affected by the switch in energy supply.
In all estimated models, the variable that contributed most to economic growth was labor force participation, with both tertiary education and investment in fixed capital having a high impact on this growth. The variables corruption perception index and banks’ domestic credit to the private sector contributed, to a small extent, to economic growth in these countries; meanwhile, investment in research and development surprisingly did not show statistical significance.
In terms of literature, this work has helped to enrich the existing literature as no study has simultaneously examined the impact of traditional energy consumption and renewable energy in nine South American countries and their relationship with economic growth. It has been demonstrated that South American countries can achieve an energy transition, switching energy consumption from fossil fuels to renewable energy, and that this transition still generates additional economic growth.
Since fossil fuels are a significant source of carbon dioxide emissions that lead to environmental degradation, the results obtained could lead policymakers to reconsider increasing energy production from renewable sources and its subsequent consumption since all of the evidence suggests that this increase does not affect economic growth [2].
As all the countries studied are classified by the International Monetary Fund as emerging and developing economies, the transition to developed economies may also involve promoting the consumption of renewable energy to the detriment of the dependence on the burning of fossil fuels. Accelerating the energy transition would not only reduce the carbon footprint but would also demonstrate appropriate management of natural resources and compliance with international agreements to combat climate change and environmental degradation.
One of the limitations of this study is related to the time constraints of the available data. Although our sample spanned 13 years, a potentially larger dataset could have altered the conclusions or the strength of the coefficients. As mentioned earlier, the fact that not all South American countries were included could also be another limitation. Finally, the third limitation relates to the fact that the panel was unbalanced as it did not include all observations.

Author Contributions

Conceptualization, M.M., M.C.N. and J.C.O.; methodology, M.C.N.; software, M.C.N.; validation, M.M., M.C.N. and J.C.O.; formal analysis, M.M.; investigation, M.C.N.; writing—original draft preparation, M.M., M.C.N. and J.C.O.; writing—review and editing, M.M., M.C.N. and J.C.O.; supervision, M.M. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

All data may be downloaded freely from the cited sources.

Acknowledgments

The authors are grateful for the support provided by ISPGAYA—Higher Polytechnic Institute of Gaya.

Conflicts of Interest

The authors declare no conflict of interest.

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Table
Table 1. Variable definition and data source.
Table 1. Variable definition and data source.
VariableDefinitionObjectivesUnit
GDPpcitGross domestic product per capita, in the country i and year t (constant, 2015)Achieve the growth of an economyUSD
CO2itCarbon emissions, in the country i and year tQuantify traditional energy consumption from burning fossil fuelsTons per capita
RECitRenewable energy consumption, in the country i and year tMeasure renewable energy consumption in relation to final energy consumptionPercentage
GCFitGross capital formation, in the country i and year tInvestments made in fixed assets in relation to GDPPercentage
R&DitResearch and development expenditure, in the country i and year tMeasure investment in R&D in relation to GDPPercentage
LFPitLabor force participation, in the country i and year tAmount of the population providing labor for the production of goods and servicesPercentage
STitSchool tertiary, in country i and year tLevel of human capital qualificationsPercentage
CPIitCorruption perceptions index, in country i and year tAssess the perception of corruption in a countryIndex
DCitDomestic credit to the private sector by banks, in country i and year t Amount of credit granted to the private sector Percentage
Source: Authors’ elaborations.
Table
Table 2. Average of variables for each country (1994–2019).
Table 2. Average of variables for each country (1994–2019).
CountryGDPpcCO2RECGCFR&DLFPSTCPIDC
Argentina12,0263.8319.810017.5090.489759.80767.87233.06815.550
Bolivia24151.37520.17417.7140.281169.461-28.50048.300
Brazil77741.93144.95818.3161.122166.15236.52537.59245.885
Chile10,8823.87829.62025.1540.358057.62260.49568.11667.680
Colombia50121.52230.03621.0330.223365.21636.27034.80034.071
Ecuador51542.16815.91223.9910.180265.22034.48127.39224.695
Paraguay48850.87365.49121.0710.076569.53623.43121.96026.411
Peru46131.34733.05321.6880.107473.75137.82238.52029.822
Uruguay12,4081.87046.85717.8340.330162.63648.49162.36530.959
Source: Authors’ calculations.
Table
Table 3. Main descriptive statistics.
Table 3. Main descriptive statistics.
MaximumMinimumAverageStd Deviation
GDPpc16,192186677893877
CO24.78270.9202.0861.069
REC70.1717.72132.8517.09
GCF29.85914.2120.463.783
R&D0.62260.0420.3550.314
LFP79.24153.9265.475.875
ST95.4479.35443.89819.58
CPI751538.93716.34
DC87.2329.50136.09317.79
Source: Authors’ calculations.
Table
Table 4. Correlations.
Table 4. Correlations.
GDPpcCO2RECGCFR&DLFPSTCPIDC
GDPpc-0.7379−0.0307−0.01630.3763−0.52980.78710.62490.0973
CO2--−0.5400−0.06500.0074−0.62490.77470.44880.2604
REC---−0.06500.00740.2387−0.40350.06310.0088
GCF----−0.2457−0.0416−0.01490.21890.2338
R&D-----−0.28280.20720.15970.2796
LFP------−0.3327−0.4428−0.1441
ST-------0.41930.3356
CPI--------0.4177
DC---------
Source: Authors’ calculations.
Table
Table 5. Hausman test specification results.
Table 5. Hausman test specification results.
GDPpc − p value = 0.4532LFP − p value = 0.1478
CO2 − p value = 0.1028ST − p value = 0.2501
REC − p value = 0.0411CPI − p value = 0.0207
GCF − p value = 0.2138R&D − p value = 0.0257DC − p value = 0.3457
Source: Authors’ calculations.
Table
Table 6. Results from the estimations.
Table 6. Results from the estimations.
Dependent Variable: Ln GDPpc
Fixed EffectsGMM First Differences
Model 1 CoefficientsModel 2 CoefficientsModel 3 CoefficientsModel 4 Coefficients
Intercept2.41051 ***2.95214 ***4.05478 ***4.35780 ***
Ln GDPpc (-1) 0.81478 ***0.80214 ***
Ln CO20.21525 *** 0.19478 **
Ln REC0.35584 ***0.33514 **0.28254 ***0.25147 ***
Ln GCF0.17584 ***0.18357 **0.12478 **0.13547 **
Ln R&D0.01887 0.05647
Ln LFP0.51961 ***0.48324 ***0.41478 **0.40328 **
Ln ST0.24904 ***0.28478 *0.20147 **0.19325 **
Ln CPI0.02381 **0.03014 **0.03147
Ln DC0.04385 **0.05391 ***0.06478 ***0.07025 ***
R-Squared0.981410.97847
F-test (p-value)3.50147 × 10−1133.54780 × 10−115
Breus-Pagan test (p-value)00
Hausman test (p-value)2.32851 × 10−123.46214 × 10−11
Observations156156
Note: ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels of significance, respectively. Source: Authors’ estimations.
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Oliveira, J.C.; Nogueira, M.C.; Madaleno, M. Do the Reduction of Traditional Energy Consumption and the Acceleration of the Energy Transition Bring Economic Benefits to South America? Energies 2023, 16, 5527. https://doi.org/10.3390/en16145527

AMA Style

Oliveira JC, Nogueira MC, Madaleno M. Do the Reduction of Traditional Energy Consumption and the Acceleration of the Energy Transition Bring Economic Benefits to South America? Energies. 2023; 16(14):5527. https://doi.org/10.3390/en16145527

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Oliveira, José Castro, Manuel Carlos Nogueira, and Mara Madaleno. 2023. "Do the Reduction of Traditional Energy Consumption and the Acceleration of the Energy Transition Bring Economic Benefits to South America?" Energies 16, no. 14: 5527. https://doi.org/10.3390/en16145527

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