Oil Market Shocks and Energy Transition: The Mediating Roles of Research Development and Environmental Regulation Stringency

Abstract

The literature shows that oil shocks have heterogeneous effects on economic activities, energy markets and transition to renewable energy (RE) which are also determined by aggregate factors. The transition from non-RE to RE is only one part of the comprehensive energy transition (ET) framework. Our study provides the first empirical examination of how differing oil market shock types affect this comprehensive ET and considers the mediation effects of key aggregate factors including economic growth, financial development, stringency of environmental regulation, and research and development (R&D) expenditure. Using the Panel Structure Vector Autoregression modeling approach for OECD countries over the period from 1991 to 2020, our results provide several important findings. First, the impacts of oil market shocks on the ET are heterogeneous across the shock types and time horizons. Short-run oil price and aggregate demand shocks encourage more ET (on-impact by 0.05% and 0.01% respectively), and these effects converge in the long run. Oil supply shocks while encouraging ET (by 0.06%) in the long run discourage ET in the short run (on impact by 0.02%). R&D expenditure is the most effective factor in reducing the impact of the oil price shock on ET (from 60 percent in the baseline model to 10 percent when R&D is introduced). The distinct role of the R&D expenditure becomes less dominant for oil supply and aggregate demand shocks.

JEL Classification: O3, Q2, Q3, Q4

Keywords

oil price shock oil demand shock oil supply shock energy transition financial development research and development environmental regulation stringency

1. Introduction

In response to the growing threat posed by climate change, global economies, especially those of OECD countries, have shifted their economies toward sustainability. The energy transition (ET) is core to this transition to sustainability, especially in achieving net-zero greenhouse gas (GHG) emissions by 2050 (OECD 2020). The conventional literature analysis of ET focuses on the transition from non-renewable energy (non-RE) sources to RE sources (Ahmad et al. 2023; Singh et al. 2019). The literature highlights that innovation, production and consumption in RE markets varies across countries due to many factors including research and development (R&D), environmental regulation and energy policy (Consoli et al. 2023; Lindberg et al. 2019; Schmidt and Sewerin 2019), system of knowledge and technological factors (Costantini et al. 2017; Moreno and Ocampo-Corrales 2022), and other aggregate factors including economic growth and population, international trade (Bourcet 2020; Wang et al. 2023). Equally importantly, there exists a growing number of empirical studies that examine the impact of uncertainty induced by oil price shocks on ET (Balcilar et al. 2019; Murshed and Tanha 2021), the role of environmental, fiscal and monetary policies on ET (Campiglio 2016; Chen et al. 2019; Nyambuu and Semmler 2023; Ullah et al. 2023; Wang et al. 2022), geopolitical risk (Wang et al. 2023), international trade (Lui and Lin 2024), and the role of financial and technological development on ET (Alariqi et al. 2023; Irfan et al. 2023; Lin and Bai 2023; Liu et al. 2023).

However, recent literature postulates that ET is much broader than just the transition from non-RE to RE sources. For example, the World Economic Forum’s ET framework is built to monitor the global transition as it shifts toward an energy system that supports sustainability, security and access (Singh et al. 2019). Where sustainability is concerned, taking the broader approach as suggested by Lu and Nemet (2020), ET refers to a paradigm shift of primary energy from carbon-intensive sources to low-carbon ones. From this carbon-emission perspective, ET also includes the transition from more polluting sources such as coal and fossil fuels to less polluting sources such as natural gas and nuclear power. Similarly, ET indicates that economies should move from biofuels and solar sources which have the lowest emission reduction capacity to hydro, geothermal, and wind as these energy sources offer higher emission reduction capacity. Using this broad ET approach, Hu et al. (2022) propose a new metric to calculate a country-level ET index based on consumption data of various types of energy sources. Recently, Sinha et al. (2023) uses this index in their empirical work that examines the determinants of ET across thirty-seven OECD countries using the data covering the 2000 to 2019 period.

In brief, under the broader sustainability framework, ET refers to the shift of energy sources toward cleaner and environmentally sustainable sources (Hu et al. 2022). Historically, as shown in Figure 1, the shift in the energy supply in OECD shows a remarkable decrease in oil and coal sources, dropping from 75.1% in 1973 down to 47.2% by 2020. At the same time, other non-renewable sources such as natural gas and nuclear have increased from 20.2% to 40.2% while the RE sources increased just from 4.7% to 12.6% during the same period. Undoubtedly, the transition toward cleaner non-RE energy used to be a critical part of the overall ET in OECD countries in the last few decades, not at all less important than the non-RE to RE transition as conventionally examined in the existing empirical literature. Failure to capture the ET within non-RE sources for OECD countries implies that the analysis would have mis-specified the research problems at hand. More specifically, using new metrics for ET proposed by Hu et al. (2022), Figure 2 clearly shows that two types of ET (i.e., transitions within non-RE and non-RE sources as the two components of the overall ET index) are very distinct in both the levels as well as the fluctuations during the period from 1991 to 2020.

Figure 1.

OECD total energy supply by source, 1971 to 2020 (EJ).

Figure 2.

Energy transition index (ETI) in OECD, 1991 to 2020.

This distinction is crucial for policy design, as different interventions are required for each type. Policies targeting transitions within non-RE sources, such as shifting from coal to natural gas, often focus on short-term emission reductions and improving energy efficiency. In contrast, policies promoting transitions from non-RE to RE emphasize long-term sustainability, including incentives for renewable energy investment, carbon pricing mechanisms, and infrastructure development for clean energy integration. Understanding these differences enables policymakers to design more targeted policies.

From these backgrounds, our paper aims to address the following research questions: “How do different types of shocks in the oil markets affect ET in OECD countries in short- and long-run, taking into account the mediation effects of key aggregate factors including GDP growth, financial development, stringency of environmental regulation and research and development?.” Our paper uses data from thirty-seven OECD countries over the period 1991 to 2020 to examine how different types of shocks in the oil markets affect ET under the mediation of several important aggregate factors. Specifically, following Kilian (2009) our empirical approach considers all three types of shock in the oil market: oil price shock, oil supply shock, and aggregate demand shock. Also, to the best of our knowledge, our study is among the first few that use a newly developed ET index of Hu et al. (2022) as the dependent variable. We use four mediation factors including economic growth, financial development, stringency of environmental regulations, and research and development (R&D) expenditure. As informed by previous literature, these variables have causal relationships with each other, hence we use Panel Structure Vector Autoregression (PSVAR), a commonly used econometric technique for macroeconomic dynamic analysis. The PSVAR model is increasingly utilized in economic analysis due to its ability to handle complex interdependencies in macroeconomic data across multiple countries. This model effectively addresses individual heterogeneity and captures dynamic relationships among variables, making it a powerful tool for both forecasting and structural analysis. The models are extensively used for shock analysis and forecasting in commodity markets including the oil market (Xu 2018, 2020). For instance, Škare et al. (2022) utilized PSVAR to study the long-term effects of energy price shocks on global inflation, demonstrating their utility in analyzing persistent economic phenomena.

Our study contributes to literature in three distinct ways. First, the study uses comprehensive ET that captures not only the transition from non-RE to RE but also the shifts within the non-RE sector toward cleaner energy sources. Capturing both dynamics is pivotal to understanding the complexities of ET, as transitions often occur in phases rather than through an abrupt shift from fossil fuels to renewables. For instance, several OECD countries, such as Germany and France, have relied on natural gas as a relatively cleaner fuel while expanding their renewable energy capacity. Similarly, Japan’s post-Fukushima energy strategy has involved cleaner fossil fuel technologies alongside gradual renewable energy adoption. Ignoring these intermediate shifts would lead to an incomplete investigation of ET process.

Second, our study examines the impact of different types of oil market shocks on ET. While previous studies have primarily focused on the impact of aggregate oil price shocks alone, we extend this by considering the decomposition of oil price shocks into demand-driven oil shocks, supply-driven oil shocks, and precautionary shocks separately. This distinction is important because these shocks may have heterogeneous effects on ET such as precautionary shocks may encourage ET in the short run by making fossil fuels less competitive, as seen in Denmark and Sweden, where past oil crises accelerated investments in wind and bioenergy. Oil supply shocks may have mixed effects, depending on how countries respond where some countries may move toward energy diversification, while others may revert to domestic fossil fuel sources. Aggregate demand shocks can either promote or hinder ET, depending on how economic growth influences energy investment. For instance, South Korea’s Green Growth Strategy emerged as a response to economic slowdowns, leveraging government intervention to boost renewable energy adoption.

Third, our study extends the analysis by distinguishing between the short-term and long-term effects of these shocks on ET by incorporating macroeconomic, financial, environmental, and technological mediating factors. This approach allows us to examine how these factors affect the transmission of oil market shocks to ET processes. For instance, financial development may play a critical role in facilitating ET and may hedge ET against oil market shocks, such as initiatives in countries like Sweden and the Netherlands that are leveraging green finance and carbon markets to sustain long-term renewable energy investments. Similarly, Environmental regulations, such as the EU’s Fit for fifty-five policies, ensure that short-term fluctuations in oil prices do not derail decarbonization efforts. Meanwhile, technological advancements and R&D expenditures accelerate the transition by making renewables more competitive, as evidenced by the rapid decline in solar and wind energy costs across OECD countries.

Our empirical results uncover several new important insights. Different types of oil market shocks have differing impacts on ET. The effect also varies between short-run and long-run restrictions. Specifically, price and aggregate demand shocks in the oil market increase energy transition in the short run, and these effects converge in the long run. On the other hand, shocks in the supply side discourage the energy transition process in the short run while encouraging it in the long run. The R&D expenditure is found to be the most effective in reducing the impact of the oil price shock on ET. However, the distinct role of R&D expenditure becomes less dominant in the context of oil supply and aggregate demand shocks. This can be attributed to several economic and policy mechanisms. Unlike oil supply or demand shocks, which are often driven by geopolitical factors or macroeconomic conditions, oil price shocks create market-based incentives for innovation and technological advancements. Higher oil prices encourage investment in alternative energy sources, efficiency-enhancing technologies, and energy diversification strategies. In contrast, oil supply shocks (e.g., sudden disruptions due to geopolitical tensions) may require immediate responses such as strategic petroleum reserves rather than long-term innovation. Similarly, oil demand shocks, which often arise from global economic fluctuations, may have ambiguous effects on R&D investment, as firms and governments adjust spending based on broader economic conditions. Thus, R&D expenditure proves to be effective in responding to oil price shocks by enhancing long-term resilience through technological advancements and energy transition initiatives.

The remainder of this paper has five sections. Section 2 provides a brief literature background. Sections 3 and 4 describe the ETI and empirical strategy. Section 5 presents empirical results and relevant discussions. Section 6 concludes the paper.

2. Literature Background

According to Kilian (2009), the shocks in the oil market can be of three types: oil price, oil supply, and aggregate demand. Given that each type of oil shock has differing macroeconomic impacts across countries, undoubtedly the fluctuation in prices can substantially affect energy costs for both production and consumption in all sectors of any economy, leading to more aggregate impacts at national, international, and global scales (Kilian 2009; Lin et al. 2023; Nasir et al. 2018; Rentschler 2013; Sheng et al. 2020). In OECD countries, Caraiani (2019) shows that oil demand and supply shocks positively impact the GDP in oil-producing economies and negatively impact most other countries. Similarly, Sheng et al. (2020) using data from forty-five countries report that oil supply and demand shocks are important drivers of economic uncertainty.

The literature also argues that the transition from non-RE to RE in many oil-importing economies is an adaptation strategy that offers the potential to reduce the long-term uncertainties associated with fossil fuel dependence (Krane and Idel 2021). Empirical research shows that expanding RE can reduce an economy’s vulnerability to oil volatility (Rentschler 2013). Literature also investigates the impacts of oil shocks on RE across countries. Apergis and Payne (2014) using the panel data for twenty-five OECD countries for the period 1980 to 2011 show that in the short-run term, an increase in real oil prices increases RE consumption per capita. Deniz (2019) reports the same positive impacts of oil price and price volatility on RE for the top twelve oil-importing countries but negative impacts for the top oil-exporting countries during 1995 to 2014. Using a global panel consisting of sixty-four countries over the period 1990 to 2011, Omri and Nguyen (2014) show that oil price increases harm RE consumption. Theoretically, as oil and RE are possible substitutes, one would expect that an increase in the oil price should ceteris paribus lead to an increase in RE (Sadorsky 2009). However, as reviewed in Bourcet (2020), nearly half of the studies do not integrate this theoretical foundation. While empirical literature does not reach a clear consensus on the impact of oil price on RE, a positive influence of oil price on per capita RE consumption is well reported.

In a recent literature survey by Shah et al. (2018), most studies relating RE to oil prices and macroeconomic factors focus on how government policy can encourage investment in RE. Literature postulates that supply-side climate policies could decrease investment in fossil fuel (Bogmans et al. 2024) and increase oil prices (Boer et al. 2023; Harstad 2012). Not only climate regulations restrict directly oil production but also indirectly increase the cost of capital for oil production due to the shift of investment to RE (Ehlers et al. 2022; Seltzer et al. 2022). As an increasing number of nations pledge to decrease their GHG emission, the regulatory framework in the last few decades becomes more uncertain, affecting investment choices within the oil industry and exerting an influence on the worldwide shift toward non-oil energy alternatives. Hence, regulatory variables including the stringency of environmental regulations are important instruments analyzed in broad RE literature as reviewed in Sequeira and Santos (2018) and also in empirical studies on RE deployment as reviewed in Bourcet (2020). Similarly, more recent ET literature highlights the crucial roles of financial development from both micro and macro perspectives as well as the importance of technology (Belald et al. 2021; Dogan et al. 2022; Sachan et al. 2023).

Panel SVAR models have been applied to various economic issues, including the transmission of financial shocks, the impact of oil price fluctuations, and the effects of trade policies. They are also used to study regional economic dynamics and the interdependencies between different sectors (Liu et al. 2021). Their application in analyzing the impact of energy market shocks is still evolving. Meshari et al. (2023) utilized PVAR to analyze the effects of economic shocks on polluting emissions and found that developing countries’ growth relies heavily on CO2-emitting energies. However, the study also identified the potential for the substitution of polluting energies with renewable energies. Jin and Xu (2024) projected the prices of various energy commodities in nonlinear autoregressive neural network models.

The brief review of the literature shows that studies have examined the macroeconomic implications of oil price, supply, and demand shocks across countries, emphasizing their effects on economic uncertainty, energy costs, and overall economic performance. Several studies provide empirical evidence supporting the positive influence of oil price increases on RE consumption, particularly in OECD and oil-importing economies. However, findings on this relationship remain inconclusive, with some studies reporting negative effects of oil price volatility on RE adoption. Moreover, the literature does not provide evidence of the link between oil market shocks and the comprehensive ET process by distinguishing between supply-driven, demand-driven, and speculative oil market shocks. Additionally, studies investigating the mediating role of economic growth, financial development, environmental regulation, and technological development in shaping ET trajectories are scant.

To address these gaps, our study investigates how different types of oil market shocks affect ET in OECD countries in both the short- and long-run, incorporating the mediation effects of GDP growth, financial development, environmental regulation stringency, and R&D. By employing advanced econometric techniques and using novel data on energy transition our research aims to provide insights into the mechanisms through which oil market disruptions influence ET and how different macroeconomic, financial, and environmental characteristics affect this relationship.

3. Energy Transition Index

The growing literature on ET also broadens our understanding about various dimensions, moving beyond the transition from non-RE sources to RE sources (Singh et al. 2019). The World Economic Forum’s ET framework focuses on the global shift toward an energy system that supports sustainability (Singh et al. 2019). Similarly, OECD (2020) also position ET as a core component of the transition to environmental sustainability. In relation to environmental sustainability Lu and Nemet (2020) refers ET a paradigm shift of primary energy from carbon intensive sources to low-carbon ones. From this carbon-emission perspective, ET includes transition to cleaner energy forms within non-RE sources. Examples are the transition from coal and oil to natural gas and nuclear sources. Similarly, within the RE sources, economies should move from biofuels and solar sources to hydro, geothermal, and wind sources because the sources of the latter type have the higher GHG emission reduction capacity. As shown earlier during the period 1991 to 2022, OECD countries have experienced both forms of transitions; therefore, empirical analysis of ET for OECD should take into account these transitions. Empirical cross-country analysis of ET requires the use of quantitative metrics that capture ET. Following Hu et al. (2022), we use the ET index that captures broader concepts of ET but focuses on the energy deployment aspect. In doing so, we aim to model the relationships between this ET metrics with many other factors belonging to system performance imperatives and transition readiness enabling factors such as economic growth, human capital, financial capital and investment, regulatory and institutional environments as discussed in the literature of ET (Lu and Nemet 2020; Singh et al. 2019).

The energy transition index (ETI) newly constructed by Hu et al. (2022) is specified as follows:

E T_{i, t} = \sqrt{(aNR T_{i, t} + bR T_{i, t}) / (a + b)}

(1)

where a and b are respective weightages assigned to non-renewable transition (NRT) and renewable transition (RT). As the transitions from non-RE sources toward the RE sources are more preferable, and hence, a < b. The NRT and RT are respectively constructed as follows:

NR T_{i, t} = \frac{{\sum_{n = 1}^{4} n (N R_{n, t} / N R_{t}) {(Δ logN R_{n, t} - Δ logN R_{t})}^{2}}}{\sum n}

(2)

R T_{i, t} = \frac{{\sum_{r = 1}^{5} n (R_{r, t} / R_{t}) {(Δ \log R_{r, t} - Δ \log R_{t})}^{2}}}{\sum r}

(3)

where n indicates four types of non-RE sources (i.e., coal, crude oil, natural gas, and nuclear) and five types of RE sources (i.e., biofuels, solar, hydro, geothermal, and wind) while NR_n,t indicates the consumption of nth non-RE at time t and R_r,t indicates the consumption of rth renewable energy at time t. A higher value of NRT indicates transition toward a cleaner nonrenewable energy source while a higher value of RT indicates transition toward an more efficient renewable energy source. Note that in these formulas, because of its high carbon content, coal was given the least weight during the energy transition while the most weight is given to nuclear energy in the NRT. Similarly, biofuels reduce emissions least, followed by solar, hydro, geothermal, and wind; hence wind has the highest weightage and biofuels the lowest.

4. Empirical Strategy

4.1. Econometric Model

The empirical strategy of the study combines the identification of oil market shock by Kilian (2009) with Panel Structural Vector Autoregressive (PSVAR) model of Pedroni (2013). Kilian (2009) decomposes oil shocks into shocks to the current physical availability of crude oil (oil supply shocks), shocks to the current demand for crude oil driven by fluctuations in the global business cycle (aggregate demand shocks), and shocks driven by shifts in the precautionary demand for oil also known as oil-specific demand shock (precautionary demand shocks).

Kilian (2009) and Stock and Watson (2016) explain that due to technological constraints in adjusting production levels at existing wells, shutting down wells, and initiating new wells, the response of crude oil production to demand shocks or other macroeconomic or global shocks is delayed. Consequently, an unexpected change in oil production within a period is considered exogenous and classified as an exogenous supply shock (εt OS). This innovation to oil production is equivalent to the oil supply shock. Furthermore, global economic activity promptly reacts to oil supply shocks and global aggregate demand shocks, but its response to other shocks within the period is sluggish. Real oil prices adjust to oil supply shocks, aggregate demand shocks, and other oil price-specific shocks within the period, but remain unaffected by other macroeconomic or global shocks. Kilian interprets other oil price-specific shocks as shocks to oil demand distinct from aggregate demand shocks. Examples include oil inventory demand shocks, potentially triggered by anticipated oil supply shocks or speculative demand shocks. Dagher and Hasanov (2023) and Kilian (2009) consider the real price of oil as oil-specific demand shock. According to Maghyereh and Abdoh (2021), energy transition is contemporaneously affected by all oil shocks.

These theoretical conjectures impart upper triangular identification scheme and Cholesky ordering to innovation. Following Pedroni (2013), the PSVAR representation, assumptions, and restrictions are described as follows. The PSVAR (q) model reads as follows:

B_{i} Y_{it} = A_{i} (L) Y_{it - 1} + μ_{it}

(4)

where $Y_{it}$ is $k \times 1$ vector of endogenous variables in log levels, $B_{i}$ the matrix of contemporaneous relationships between the $k$ variables in $Y_{it}$ , $A_{i} (L)$ the $k \times k$ matrix of autoregressive slope coefficients, and $μ_{it}$ the vector of serially uncorrelated structural shocks (Kilian and Lütkepohl 2017). The $B_{i}$ matrix is estimated using the Maximum Likelihood method, necessitating the imposition and identification of restrictions on the contemporaneous relationships among the variables being analyzed.

multiplying $B_{i}^{- 1}$ will yield the following equation.

Y_{it} = π_{i} (L) Y_{it - 1} + ϵ_{it}

(5)

Where $π_{i} (L) = B_{i}^{- 1} A_{i} (L) Y_{it - 1}$ and $ε_{it} = B_{i}^{- 1} μ_{it} ϵ_{it} ~ (0 Ω_{i})$ , $Ω_{i}$ is variance-covariance full rank and nondiagonal matrix of reduced-form error $ϵ_{it}$ . The restrictions are imposed k elements of $ϵ_{it}$ to make $Ω_{i}$ orthogonal and derive impulse response function (IRF).

The PSVAR model extends the VAR framework by assuming both cross-sectional and intertemporal dependence of data. Intertemporal dependence is added to the model by the standard autoregressive specification of the VAR models. Cross-sectional dependence is included by decomposing the ε _it into two shocks, which are orthogonal to each other.

ϵ_{it} = Λ_{it} {\bar{ϵ}}_{t} + {\tilde{ϵ}}_{it}

(6)

ε_it in equation (5) is the vector of exogenous structural shocks which are assumed to follow white noise processes that is, having identical, independent and mean-zero distribution. The common shock ( ${\bar{ϵ}}_{t})$ is given to all panel countries, but the shock received by each country depends upon the diagonal loading matrix, Λ_i. The idiosyncratic shock ( ${\tilde{ϵ}}_{it}$ ) is country-specific and thus determines the independent disturbances across cross-sections. The method of Pedroni (2013) has extensively been applied and discussed in the literature (Ha et al. 2019; Roch 2019; Wu and Xu 2021, among others), thus, for brevity complete exposition is not mentioned here. Moreover, the objective of the research pertains to the examination of global oil shocks which are common across the panel. This perspective prompts us to refrain from delving into the detailed discussion of idiosyncratic shock.

Given this background, for the baseline model of the study $Y_{it} = [Δ O S_{it}, Δ A D_{it}, Δ O P_{it}, Δ ET I_{it}]$ , whereas to include the intermediation through economic growth, environmental stringency, financial development, and research and development, $Y_{it}$ becomes

\begin{matrix} Y_{it} = [Δ O S_{it}, Δ A D_{it}, Δ O P_{it}, Δ E G_{it}, Δ ET I_{it}]' \\ Y_{it} = [Δ O S_{it}, Δ A D_{it}, Δ O P_{it}, Δ F D_{it}, Δ ET I_{it}]' \\ Y_{it} = [Δ O S_{it}, Δ A D_{it}, Δ O P_{it}, Δ ES I_{it}, Δ ET I_{it}]' \\ Y_{it} = [Δ O S_{it}, Δ A D_{it}, Δ O P_{it}, Δ R D_{it}, Δ ET I_{it}]' \end{matrix}

Where OS = Oil Supply, AD = Aggregate Demand, OP = Real Price of Oil, RD = Research & Development, ESI = Environmental Stringency Index, FD = Financial Development, GDP = Log of GDP, ETI = Energy Transition Index. For intermediating variables the Cholesky decomposition is retained by introducing intermediating variables before ETI and after oil shocks.

The method of Pedroni (2013) presents the median relationship and upper and lower confidence intervals depict the responses at 25th and 75th quantile. Following Pedroni (2013), we use General to Specific (GTOS) information criteria to fit an appropriate member-specific lag truncation in the panel SVAR specification. The study applied both short run and long run restrictions as above-mentioned identification scheme and following Pedroni (2013) by imposing restrictions on short run impact matrix and long run impact matrix separately. The long run identification scheme requires the examination of cross section dependence and associated unit root process even after time effect is extracted. It also requires that panel series are not cointegrated. Standard panel unit root and panel cointegration tests that allow for dynamic heterogeneity can be used to confirm this when they are unable to reject the null of a unit root for all members and the null of no cointegration for all members. These tests are conducted for panel variables and reported in Table 1. The table shows that all prerequisites for long run restrictions are met. It is worth mentioning here ETI responses to the idiosyncratic and common shocks of mediating factors are also extracted and attached in Supplementary 1.

Table 1.

Diagnostics for Long-run Restrictions.

Variables	Results of Pesaran CD test		Pesaran CIPS test
	Null hypothesis: No cross section dependence		Null hypothesis: Unit root at level
	Test statistic	Prob	Test statistic	Prob (%)
Energy transition	15.48	.000	1.30	>10
Log of GDP	91.47	.000	8.85	>10
Financial development	76.58	.000	−1.23	>10
Environmental stringency	91.53	.000	1.20	>10
Research and development	56.89	.000	3.25	>10
Kao test of panel cointegration – Null hypothesis: No cointegration
ADF	−0.79	0.23	Null hypothesis accepted

Source. Authors own tabulation.

Note. Cross Section dependence test and unit root test are applied as pre-requisite for long run Cholesky restriction following Pedroni (2013). The null hypothesis of No Cointegration is required to be accepted to proceed with PSVAR.

Oil market shocks can lead to varied effects on ET. An increase in oil prices can positively influence the transition by rendering non-oil energy forms more cost-effective and competitive compared to oil. However, this trend is more likely to be sustained in countries that rely on oil imports. Conversely, in nations that are net exporters of oil, higher oil prices can hinder the energy transition. This is because it may lead to a reduction in investments in alternative energy sources, while driving up investments in traditional energy. This is driven by the increased profitability associated with higher oil prices in such contexts (Murshed and Tanha 2021).

Likewise, oil supply shocks can influence the ET process, potentially yielding both positive and negative outcomes. An increase in oil supply has the potential to dampen the ET by driving down oil prices, which could, in turn, diminish the competitiveness of RE (Boer et al. 2023). Additionally, an abundance of oil leading to lower prices may decelerate the adoption of renewable energy. However, sudden oil supply shocks prompted by geopolitical factors can introduce economic uncertainty and spur advancements in the energy transition. The impact of these shocks is also contingent on factors like sustained policy initiatives and technological progress (Özkan 2023).

An increase in aggregate demand can serve as a catalyst for investment and innovation in renewable energy technologies. Furthermore, heightened aggregate demand drives up the demand for oil, resulting in higher prices and subsequently facilitating the transition toward sustainable energy. Aggregate demand can also impede energy transition through the lock-in effect, as pointed out by Pieroni (2023). In regions where fossil fuel infrastructure is well-established and cost-effective, increased aggregate demand may lead to higher consumption of fossil fuels rather than investments in renewables. This can be due to the lower immediate costs and existing supply chains. Similarly, the direction of investment often depends on government policies and incentives. In countries with strong support for renewable energy through subsidies, tax incentives, or regulatory frameworks, increased demand is more likely to spur investment in green technologies. However, in places where fossil fuel industries receive more support, the opposite may occur.

Nevertheless, economic growth, incentives, and regulations, along with advancements in renewable technologies, have the potential to redirect energy demand toward cleaner sources, as emphasized by Özkan (2023). In this regard, it is postulated that economies characterized by high GDP growth and a well-established financial sector may exhibit greater resilience to oil shocks, potentially impeding the transition from non-RE to RE in the face of such shocks. Nonetheless, a strong economy and well-developed financial system can also furnish the resources needed for the adoption of cleaner energy. This can result in heightened innovation and a swifter transition toward sustainable energy sources, even in the face of oil shocks (Deka et al. 2024; Eren et al. 2019).

Financial development is crucial in managing oil supply shocks and facilitating the energy transition process. A well-developed financial sector offers tools for assessing and mitigating these effects, enabling capital allocation toward renewable energy projects, supporting risk management strategies, and encouraging innovative financing models. But the relationship may not be as straight forward as it seems to be as financial development may adversely affect the transition to renewable energy because the financial markets for the crude oil-related products are well-established and there is a little chance that the investors shift the investment toward the cleaner and greener energy sources.

In the presence of an oil supply shock, R&D can diversify the energy mix by developing alternative sources and technologies like renewables, energy storage, and efficiency improvements. R&D also promotes energy transition by driving innovations in efficiency, storage, and generation, making renewable energy solutions more accessible and affordable, according to the consumer demands and making them an attractive alternative even amidst fluctuations in aggregate demand. Additionally, R&D can facilitate the development of smart grid technologies by enhancing its adaptability (Cheon and Urpelainen 2012; Wong et al. 2013).

Environmental regulation stringency positively affects the energy transition through boosting technological innovation, leading to increased costs for pollution control and developing renewable energy technologies (Hassan and Rousselière 2022). Oil price, demand and supply shocks can temporarily slow down the energy transition by relying on fossil fuel infrastructure. However, stringent environmental policies can incentivize clean energy investments in the short run. But in the long run, these shocks, combined with strict policies, drive innovation and technological advancements in renewable energy, accelerating the transition to sustainable sources. Aggregate demand shocks along with stringent policies ensure a resilient and environmentally friendly transition over the long term (Li and Shao 2023; Wang et al. 2022).

3.2. Data

The panel data covers thirty-seven OECD countries from 1991 to 2020. Descriptions of variables used, and data sources are in Table 2 while descriptive statistics are reported in Table 3. It is important to acknowledge here that external shocks, such as the global financial crisis of 2008, and geopolitical tensions may have influenced energy transition patterns. These shocks can cause temporary deviations from long-term trends, affecting investment in renewable energy, regulatory responses, and overall economic stability. Although PSVAR inherently deals with structural time variations associated with these events, we still encourage future research to explore the role of these shocks more explicitly, possibly by incorporating crisis dummies or interaction effects to assess their differential impact.

Table 2.

Variable Description and Data Sources.

Variables	Descriptions	Data source
Energy transition	Energy transition index	Hu et al. (2022)
Oil price	Log of crude oil price	US Energy Information Administration (2021)
Oil supply	Log of oil supply	US Energy Information Administration (2021)
Aggregate demand	Global economic activity index	FRED
Gross domestic product	Log of gross domestic product (annual at Constant US $)	World Development Indicators by World Bank (2021)
Financial development	Financial development index	IFS (2021)
Environmental regulation stringency	Environmental regulation stringency index	OECD (2023) Statistics
Research and Development (R&D)	Research and development expenditures	OECD (2023) Statistics

Source. Authors’ tabulation.

Table 3.

Descriptive statistics.

Variables	Obs	Mean	SD	Min	Max
Energy transition	1,110	0.18	0.18	0.002	2.4
Log of oil price	1,110	5.5	0.67	4.38	6.55
Log of oil supply	1,110	4.41	0.13	4.42	4.6
Aggregate demand	1,110	1.84	55.24	-90.08	144.02
Log of GDP	1,110	26.46	1.60	22.69	30.62
Financial development	1,110	0.57	0.21	0.008	1
Environmental stringency	780	2.19	1.05	0.00	4.89
Research and development	1,110	1.63	0.99	-0.01	5.44

Source. Authors own calculations.

Following Pedroni (2013), data is demeaned, and all variables are integrated of same order.

5. Empirical Results and Discussion

The impact of three types of oil market shocks (oil supply, aggregate demand, and real oil price shock depicting oil-specific demand shock) on energy transition are evaluated both in the short and long run across five model specifications: the baseline model as well as four separate models controlling the individual effects of four aggregate factors: GDP growth, financial development, R&D expenditure, and environmental regulation stringency. In all models, all variables are introduced with positive standard deviation shocks. The x-axis of all the graphs presents time (ten periods) and y-axis measures the response of ETI. Moreover, upper and lower red lines depict the responses at 75th and 25th quantiles. Table 4 summarizes the differing impacts of the three types of shocks in the oil market on energy transition in the baseline model as well as models with mediating role of GDP, FD, R&D and ES. The effects of shocks are analyzed over both the short and long run. Sections 5.1 to 5.3 provide more detailed discussions on the results for each of the types of shocks.

Table 4.

Summary of Results.

Shocks	Short-run effects on ET	Long-run effects on ET
Real oil price shock (OP) as the baseline	Positive and diverging; reduced with time	Negative and converging; vanishes intermediately
OP and GDP	Positive and diverging; increases with time	Negative and converging; vanishes intermediately
OP and FD	Positive and diverging most of the time	Negative and converging; vanishes intermediately
OP and R&D	Positive and diverging; more dominant from intermediate years	Negative and converging but diverging once intermediately
OP and ES	Positive and diverging, more in the initial years	Negative and converging; vanishes intermediately
Oil supply shock (OS) as the baseline	Negative and diverging; converges only once intermediately	Positive and converging; vanishes intermediately
OS and GDP	Negative and diverging, persistent	Positive and converging, vanishes quickly
OS and FD	Negative and diverging, most of the time	Positive and converging, vanishes quickly
OS and R&D	Negative and diverging becomes more divergent in the long run	Positive mostly and converging; vanishes in the longer-term
OS and ES	Negative and diverging, less diverging intermediately	Positive and converging; vanishes intermediately.
Aggregate demand shock (AD) as the baseline	Positive and diverging, less divergent intermediately	Negative but converging; vanishes intermediately
AD and GDP	Positive and diverging, persistent	Negative but converging; vanishes intermediately
AD and FD	Positive and more divergent in the initial time than later	Negative and converging; vanishes intermediately
AD and R&D	Positive and diverging; becomes convergent once in the later period	Negative but converging; vanishes intermediately
AD and ES	Positive and diverging, stronger initially then becomes negligible	Positive and converging; vanishes in the later period

Source. Authors’ Tabulation from the results of IRFs and Variance Decomposition presented in sections 5.1 to 5.3.

5.1. Energy Transition Responses to the Real Oil Price Shock

Top Panel of Figure 3 contains the IRFs of ET to a short run oil price shock in the baseline model and also controlling for mediating factors. In the bottom panel the contribution of short run oil price shock in the variance of ET is captured in baseline model and also controlling for mediating factors. In Figure 4 long run results are presented. In the short term, a one-standard-deviation positive real oil price shock triggers an immediate increase in ET, but this effect starts leveling off by the third period. However, a sustained small elevation in ET can be observed after a short-term oil price shock. The findings align with the theoretical predictions that oil price volatility facilitates the energy shift toward non-oil alternatives as countries attempt to mitigate the negative impacts of oil price fluctuations (Henriques and Sadorsky 2008; Shah et al. 2018). Empirically, this finding supports the view that oil price dynamics could drive the development of cleaner as well as renewable energy forms (Zhao 2020).

Figure 3.

Short run oil price shock and ETI-IRFs (upper panels) and variance decomposition (lower panels).

Figure 4.

Long run oil price shock and ETI-IRFs (upper panels) and variance decomposition (lower panels).

The four right boxes in Figure 3 show how the real oil price shock influences ETI with the mediation effects of economic growth, financial development, R&D, and environmental regulation stringency respectively. With economic growth, the short-run impact is raised to a more consistent higher level (with the response value of just above 0.004). This suggests that economic growth bolsters the ET process in the context of an oil price shock. When accounting for financial development or environmental regulation stringency, the ET displays slight fluctuations, yet the impact consistently remains positive. These imply that the oil price shock leads to higher ET in those countries with higher levels of financial development and more stringent environmental regulations. These findings reinforce previous literature showing the enabling roles of both financial markets as well as environmental regulatory environments with respect to the comprehensive ET framework (Singh et al. 2019). Nonetheless, the relationship between oil-specific demand shocks and environmental regulations may vary across countries with different environmental policies, economic structures, and policies related to renewable energy adoption. For instance, countries with strong environmental regulatory frameworks and effective enforcement mechanisms may see a stronger correlation between these factors (Ebaid et al. 2022). Similarly, countries that impose higher carbon taxes or have more stringent emissions standards may see a more pronounced impact of oil price shocks on their energy transition efforts (Achuo 2022).

Interestingly, the R&D factor plays a distinct role with respect to how ET responds to the oil price shock. The magnitude of the response is much higher in the model with the R&D variable in comparison with other model specifications. In fact, the response reaches its peak in period 10 at the magnitude more than 0.01 in comparison with the peak of 0.005 in other models. This highlights a new finding that R&D expenditure appears to be the most effective safeguard against the impact of the oil price shock within OECD countries on ET. The overall findings suggest that countries experiencing higher economic growth and have more R&D expenditure have experienced stronger transition to cleaner and renewable energy sources. These empirical results are reasonable in the sense that with stronger economic growth, countries would have increased the capacity to invest in R&D in general, hence R&D and innovation for the energy sector.

For the long run as depicted in Figure 4, the IRFs suggest that ETI exhibits an initial positive response to oil price shocks, which stabilize over time. However, the magnitude and persistence of this response vary across different model specifications. Notably, controlling for FD, R&D, and ESI introduces some degree of fluctuation in the response pattern, potentially indicating non-linearities in the relationship. In particular, the responses when R&D and ESI are controlled show a more distinct upward trajectory in later periods, suggesting that innovation and sustainability policies may reinforce the ET process following oil price shocks. Additionally, while most responses converge toward stability, certain models exhibit temporary reversals, hinting at short-term market adjustments before a long-run transition is established. These variations highlight the importance of policy interventions in shaping the long-run effects of oil price fluctuations on ET.

The bottom panels of Figures 3 and 4 show the contributions of oil price shocks to explain the variations in ETI over a horizon of ten years in short- and long-run model specifications. In the baseline model, a short-lived oil price shock contributes substantially (40–60 percent) to ETI. The inclusion of economic growth into the model significantly reduces the role of oil price shock in shaping ET (10–15 percent). When controlling the R&D expenditure, the contribution reduces significantly to the level of 0.04 percent, which highlights the pivotal role of R&D expenditure in moderating the effect of oil price shock on ETI. The contributions of oil price shocks are lower in models with financial developed and environmental regulation stringency factors than in the benchmark models, confirming the role of these enabling factors for the ET.

5.2. Energy Transition Responses to the Oil Supply Shock

Top Panels of Figure 5 contain the IRFs of ET to a short run oil supply shock in the baseline model and also controlling for mediating factors. In the bottom panel the contribution of short run oil supply shock in the variance of ET is captured in baseline model and also controlling for mediating factors. In Figure 6 long run results are presented. Figures 5 and 6 depict how ETI responds to the shock in the oil supply and the contribution of the oil supply shock in both short- and long-run specifications of the base line model and the four variations controlling the mediation roles of the four aggregate factors. All the short-run models show a consistent and permanent negative impact of the oil supply shock, suggesting that an oil supply shock presents a great threat to ET in OECD countries. While the four aggregate factors help to mediate the effects of the oil price shock as discussed earlier, these factors do not provide any hedge against short run oil supply shocks. These results conform to the theoretical prediction that with an increased oil supply, oil prices tend to drop, making oil a competitive commodity. Consequently, oil supply shocks tend to increase the attractiveness of the oil sector, which leads to increased investment in oil exploration, transportation, and infrastructure development. These, however, make cleaner or renewable energy sources less competitive, affecting the overall ET (Zhao 2020). Moreover, the effect of the supply shock is more dominant in short-run similar to the findings of Maghyereh and Abdoh (2021).

Figure 5.

Short run oil supply shock and ETI-IRFs (upper panels) and variance decomposition (lower panels).

Figure 6.

Long run oil supply shock and ETI-IRFs (upper panels) and variance decomposition (lower panels).

As shown in Figure 6, the ETI responses converge in the long run specification in most of models except in the model that controls the effect of the R&D expenditure. The aggregate factors such as economic growth, financial development, and environmental regulation stringency extend the periods for ETI to converge in comparison to the baseline model. On the other hand, while the R&D expenditure reduces the divergence in the intermediate years, it increases the divergence from the eighth period. This could suggest some delayed effects of R&D expenditure as highlighted in the literature (Sovacool 2016; Tian et al. 2022).

In the short-run model, as shown in Figure 6, the oil supply shock contributes 60% in baseline model and remains the same even after controlling the effect of financial development. Whereas, along with GDP it is recorded around 32%, with environmental regulation stringency 40%. With R&D, the contribution was recorded at 5%, which increases to approximately 7% at the end. Fluctuations in the ETI responses is less in the long run model than in the short run model. Also, the fluctuations decline over time.

5.3. Response of Energy Transition to the Aggregate Demand (AD) Shock

Top Panels of Figure 7 contains the IRFs of ET to a short run aggregate demand shock in the baseline model and also controlling for mediating factors. In the bottom panels the contribution of short run aggregate demand shock in the variance of ET is captured in baseline model and also controlling for mediating factors. In Figure 8 long run results are presented. In the short run, the positive AD shock causes a positive and persistent impact on ET as depicted in Figure 7. The finding that AD shock has an overall positive impact on the energy transition is reasonable because as demand for oil rises, the oil price will increase, which makes oil less competitive than the alternative energy sources. Also, increased AD may encourage investment in RE technologies and other climate change adaptations that promote energy transition (Maradin et al. 2017). The short-run positive impact heightens in those countries with more stringent environmental regulations but levels off after three years. On the other hand, the role of R&D remains modest throughout in its role as a safeguard against AD shock. Economic growth consistently and steadily facilitates ET in the event of shock.

Figure 7.

Short run aggregate demand shock and ETI-IRFs (upper panels) and variance decomposition (lower panels).

Figure 8.

Long run aggregate demand shock and ETI-IRFs (upper panels) and variance decomposition (lower panels).

In a model with long run restrictions (Figure 8), a one standard deviation positive AD shock has a negligible negative impact on ETI. After the third period, the impact reduces to zero and ETI converges to the steady state equilibrium. An increase in aggregate demand could reduce ET because of the lock-in effect (Pieroni 2023), as the consumers rely significantly on the energy from the fossil fuels, so it becomes financially difficult to change the traditionally entrenched energy system and infrastructure. In addition, an AD shock can introduce macro-economic uncertainty, leading to a delay in investment decisions, especially for green investment in general and investment in cleaner and renewable energy forms. Incorporating GDP, financial development, and R&D reveals transitory and negative responses of ET to the AD shocks. Conversely, when taking the effect of environmental regulation stringency, there is an initial positive impact, albeit short-lived. In the presence of stringent environmental regulations, an aggregate demand shock can trigger an increase and eventual convergence of energy transition. This is because the heightened demand for energy combined with more stringent environmental regulations could present an opportunity for investors to channel resources into clean and sustainable technologies, thus facilitating the transition toward sustainable energy.

Figure 7-bottom panels present the contribution of the AD shock in the responses. In the short term, the AD shock causes no immediate variations in ET, with and without controlling for GDP, financial development, R&D, and environmental stringency. The variations increase to 39 percent initially and continue to increase over time. A higher-income country experiences shock variations of 17 percent, which reduces with time while in a financially developed economy, the variations in energy transition due to the shock are recorded at 25 percent, which remain almost the same over time. Controlling for R&D, the shock variations are recorded at only 6 percent and then further reduced to 5 percent. With the inclusion of stringency in environmental regulation, our model predicts that AD shock causes around thirty fluctuations in the ET, which decreases a little at the end of the time period.

In the long run (Figure 8-bottom panels), the baseline model shows that AD shock initially causes ET to fluctuate by approximately 35 percent. The impact increases further to 50 percent in the next period and then declines continuously over time. Controlling for the effect of economic growth, the contribution of the AD shock on ETI reduces to only 12 percent which remains almost same over the time period. Respectively controlling for financial development, R&D expenditure, and environmental regulation stringency, the contribution of the AD shock stays around 15 to 20 percent in initial periods and start reducing further till the end of the period.

6. Conclusion

There are three types of shocks related to the oil markets: oil price shock, oil supply shock, and aggregate demand shock. The interplay between these shocks can be complex. For instance, an oil supply shock that reduces the availability of oil can lead to higher oil prices, which in turn can reduce aggregate demand as consumers and businesses face higher costs. Similarly, an increase in aggregate demand can lead to higher oil prices, which can then impact oil supply if producers respond by increasing production. Literature rightly argues that these shocks impact economic activities of different types including investment, production, and consumption at both micro and macro levels, which affect energy markets. The literature presents an intensive amount of work that focuses on the impact of the oil price shock on the deployment of renewable energy and other forms of non-renewable energy. However, our comprehensive literature review shows two important gaps about the impact of the shock in the oil market on the ET performance of OECD countries.

The first literature gap is related to the focus of empirical literature on the transition from non-RE to RE. In the spirit of carbon emission reduction, this is one of many forms of ET. Two main forms of ET include the transition from dirty to clean non-RE and the transition of less to more efficient forms of RE. Therefore, it is desirable to model ET using more comprehensive index such as the ET index proposed by Hu et al. (2022). This newly developed ETI is more comprehensive than a simple indicator such as per capita RE consumption. The second literature gap refers to the emphasis of the empirical literature on the impact of oil price shock, leaving the impact of oil supply and aggregate demand shock unknown. We fill in these two gaps by presenting our empirical analysis with respect to thirty-seven OECD countries for the period from 1991 to 2020. In addition, our analysis assesses how the response of ETI was mediated by key important aggregate factors including GDP growth, financial development, environmental regulation stringency, and R&D expenditure. Our empirical results make several important new contributions to the growing literature.

Firstly, energy transition is facilitated by oil price shocks more persistently in the short-run as compared to in the long-run. The oil price shock has an overall positive and persistent effect in the short-run but a negative and transitional effect in the long-run. The effects remain robust after controlling the mediation effects of the aggregate factors. Among the four aggregate factors, the R&D expenditure has the strongest effect in safeguarding the impact of the oil price shock on the ET. Secondly, the oil supply shock has a negative and persistent effect on ET in the short-run but a positive and transitional effect in the long-run. While the aggregate factors do not show any significant role in reducing the impact of the supply oil shock on ETI in the short run, the moderating roles of these factors vary in the long run. We highlight our finding that when controlling R&D expenditure, our long-run model shows that the contribution of the oil supply shock on ETI drops to less than 8 percent in the tenth period in comparison to around 20 percent in other models. Similar observations for the distinct role of R&D expenditure also apply for the short-run models. Third, the aggregate demand shock has a positive and persistent impact on energy transition in the short-run and negative and temporary in the long-run. The distinct features related to the mediating role of R&D experience become less significant, especially in the long-run specification.

Given the key findings following policy implications are presented. R&D expenditure happens to be the strongest safeguard against the negative long-term effects of oil market shocks, OECD countries may prioritize sustained R&D funding in renewable energy technologies. Similarly, short-run negative effects of oil supply shocks on ET suggest that energy diversification strategies may be strengthened. As financial development plays a strong role in mediating energy transition, green financing policies may be expanded. Carbon pricing, green bonds, and ESG-driven investment strategies should be embedded in macroeconomic and financial policies to accelerate long-term ET. Since aggregate demand shocks positively impact ET in the short-run as compared to long-run, governments may implement structural policies (such as green infrastructure spending) that sustain the ET process beyond temporary demand shocks. This includes linking ET policies to fiscal stimulus measures during economic downturns and ensuring that aggregate demand fluctuations do not disrupt long-term ET progress.

We acknowledge limitations in our empirical analysis that we examined the mediating impact of various variables individually, even though PSVAR enables the incorporation of a range of endogenous variables. However, due to the uncertain endogeneity order of mediating factors in our study, we chose to introduce them separately for better control. Moreover, the data can be extended to more recent time periods in future research. Future research may be extended to include digitalization, geopolitical, and other relevant variables pertinent to this relationship. Moreover, research can also be undertaken to examine the response of the ET process to oil market shocks in developing countries.

Supplemental Material

sj-rar-1-enj-10.1177_01956574251368290 – Supplemental material for Oil Market Shocks and Energy Transition: The Mediating Roles of Research Development and Environmental Regulation Stringency

Supplemental material, sj-rar-1-enj-10.1177_01956574251368290 for Oil Market Shocks and Energy Transition: The Mediating Roles of Research Development and Environmental Regulation Stringency by Shahzad Alvi, Saira Tufail and Viet-Ngu Hoang in The Energy Journal

Footnotes

Correction (October 2025):

The article has been updated to reflect the correct ORCID ID for Saira Tufail.

Ethical Considerations

No animal and human experiments and primary data are involved in this research.

Funding

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

Data Availability Statement

The data and software files are attached as a supplementary.

Supplemental Material

Supplemental material for this article is available online.

AI-Statement

The AI tool is not used in this study.

ORCID iDs

Shahzad Alvi

Saira Tufail

Viet-Ngu Hoang

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