Is Oil Really a Stumbling Block to Environmental Sustainability? From the Price Perspective

Abstract

The United States is exposed to the threats of massive carbon dioxide (CO₂) emissions generated by non-renewable energy in reaching environmental sustainability. With the wavelet-based quantile-on-quantile approach, this paper delves into the impact of the most typical fossil fuel, oil, on CO₂ emissions from a price perspective. The results highlight that oil is a mixed blessing for fostering environmental sustainability in the short–medium run. Mainly, CO₂ emissions are more susceptible to positively responding to the bullish oil market in the medium term. Nevertheless, it also reveals the underlying negative relationship rooted in the long run. The results are endorsed by the theoretical mechanisms between energy prices and emissions, which indicate the role of high oil prices in driving long-term CO₂ emissions mitigation and environmental sustainability. Therefore, industries should resist the temptation to indulge heavily in short- to medium-term price hikes instead of prudently reacting to the bullish signal. Moreover, under the environmental sustainability ambitions, the strategy of expanding investment in green technologies innovation to reduce the dependence on oil cannot be shelved.

1. Introduction

We are currently in an era when greenhouse gas (GHG) emissions threaten the sustainable development of the economy, society, and environment [1]. As the dilemma of global warming intensifies, concerns about the environmental deterioration brought on mainly by carbon dioxide (CO₂) emissions have grown [2]. According to the International Energy Agency (IEA), global CO₂ emissions, especially energy-related, have rebounded to the historically highest level in 2021 (IEA analysis. Available online: https://www.iea.org/news/global-co2-emissions-rebounded-to-their-highest-level-in-history-in-2021 (accessed on 31 January 2025)). This is largely attributable to the strong recovery of the global economy from the Coronavirus disease 2019 (COVID-19) challenge, as the pursuit of economic development often entails environmental degradation. Since the industrial expansion centres on fossil fuel consumption and the structural systems rely heavily on traditional energy in most countries, the quest for economic growth fuels the demand for fossil fuels. Crude oil, which comprises the most significant proportion of primary energy utilisation, reaching 33.1%, is considered a strategic commodity and the foundation of a country’s economy [3,4]. The fluctuations in oil prices (OP) are inextricably linked with economic growth, which may induce a change in CO₂ emission levels [5]. Considering that the goal of achieving environmental sustainability through CO₂ emissions mitigation has been established and prioritised within the framework of promoting green growth [6]. Examining the price perspective of oil’s impact on environmental sustainability is valuable.

As the largest oil consumer, the United States relies heavily on oil for its industrial expansion. Therefore, OP fluctuations may significantly affect production activities [7]. In addition, the shale oil industry is ramping up production, considerably changing the energy landscape and prices. However, some studies argue that it may take the place of conventional oil and lessen CO₂ emissions [8]. This view is not necessarily embraced by those who have dug deeper [9]. Based on the carbon curse hypothesis, shale oil and natural gas extraction enriches the oil resources, which is significantly associated with higher carbon intensity and thus exacerbates environmental issues [10]. Consequently, the U.S. ranks as the second-leading CO₂ emitter, posing serious threats to environmental sustainability [11]. In turn, the government’s pursuits of the energy transition for a green economy may also contribute to the long-term OP changes. Therefore, the U.S. is an attractive research candidate for investigating the relationship between OP and CO₂ emissions in our research.

Numerous works focus on the influence of OP on inflation, economic policy uncertainty (EPU), stock market, unemployment, and economic growth [12,13,14,15,16,17]. Recent studies have emerged to explore areas related to the environment. In this regard, Wang et al. [18] point out that the fuel vehicles market is negatively affected by the OP, regardless of the frequency they selected. The higher prices may also lead to an upsurge in renewable energy consumption, which can be regarded as a significant catalyst for innovation [19,20,21]. The development of technology prompts the initiatives to replace traditional energy sources with alternative energy to improve the efficiency of usage in pursuit of low-carbon goals [22]. Likewise, the technology effect of the Environmental Kuznets Curve (EKC) hypothesis suggests that more investment will be directed to research and development (R&D) as one country’s income increases [23]. Such initiatives not only decrease conventional energy consumption but also help to improve environmental quality [24]. Nevertheless, the high OP also witnesses enormous enthusiasm for oil corporations to invest more in exploration and production, which may run against urgent efforts to decarbonise the economy and foster environmental sustainability [5]. As a result, whether oil is a stumbling block to environmental sustainability remains to be further investigated. Therefore, this paper adopts a price-oriented perspective to offer valuable insights to industries and institutions dealing with environmental challenges.

We explicate our contribution to the literature through the following aspects. First, although several endeavours have been made to identify the underlying influence of OP [5,6,7,25,26,27,28,29,30], the relationship with CO₂ emissions is relatively less explored. Consequently, there is limited consensus in the research regarding the function of OP in achieving ecological sustainability. Likewise, some studies focus exclusively on their complete linear correlation [31,32,33]. This is not always the case due to the fact that OP is frequently vulnerable to uncertain exogenous shocks, resulting in changes in oil consumption and CO₂ emissions. In addition, some studies consider the effect of OP on CO₂ emissions in the short or long run. However, they fail to consider that their relationship may vary depending on oil market conditions and emission levels across different temporal scales. So, we employ the wavelet-based quantile-on-quantile method to study the relationship between OP and CO₂ emissions. This novel non-parametric approach enables us to intuitively derive correlations between various oil markets and emission levels in different periods, thereby addressing whether oil indeed impedes environmental sustainability.

The remaining part of this article is structured as follows: Section 2 provides a comprehensive literature review. Section 3 elaborates on the theoretical mechanism between energy price and CO₂ emissions. Section 4 introduces the empirical methods used in our work, including the quantile-on-quantile technique and wavelet analysis. In Section 5, a particular description of these data is provided. Section 6 details the empirical analysis regarding the correlation between OP and CO₂ emissions. Section 7 outlines the discoveries and policy recommendations.

2. Literature Review

Prior research has explored the impact of OP on CO₂ emissions from multiple perspectives, yielding diverse and occasionally conflicting findings. On the one hand, research reveals that rising OP can be an appealing alternative to climate policies and further contain environmental degradation. Mohamued et al. [6] explore the sustainability potential of global OP and innovation. They believe that higher OP is favourable for the environment, and the endowment and availability of oil resources dictate the ability to which economies contribute to the environment [25]. Okwanya et al. [34] indicate an uneven link between OP and CO₂ emissions, where an increase in oil prices leads to decreased CO₂ emissions. Furthermore, over the long run, oil-importing countries exhibit a stronger response in CO₂ emission reduction than oil-exporting countries when OP rises. Islam and Sohag [35] reveal that oil price shocks’ transmission and spillover effects positively influence solar and wind energy production over extended periods, ultimately contributing to reduced carbon emissions. Malik et al. [24] validate this hypothesis by discussing the symmetric and asymmetric effects of OP in Pakistan. They also embrace the perspective of the conditional negative response from OP to CO₂ emissions over different time periods [36]. Katircioglu [36] arrives at the same conclusion by studying the case of Turkey. Mukhtarov [19] has evidence that an increase in oil prices can bolster the appeal of renewable energy consumption because of its potential cost advantage, which may expand as oil prices climb, thereby fostering greater adoption and reduced CO₂ emissions [37]. According to Liu et al. [26], the emission levels of the G7 (The Group of Seven (G7) is a political forum comprised of Canada, France, Germany, Italy, Japan, the United Kingdom, and the United States) area are inversely correlated with the volatility in oil rents. Using data from 16 European economies, Bekun et al. [27] indicate that the hazards associated with oil rent have a lasting impact on environmental sustainability. In this regard, rising OP can be a feasible option for limiting pollution in general and towards environmental sustainability systems [38].

However, on the other hand, this viewpoint is not universally shared by others who have performed similar research. According to Ghazouani [28], increasing OP is a great barrier to environmental sustainability in Tunisia, particularly about the positive influence of price on CO₂ emissions. Concerning the oil-exporting economies, higher OP is always accompanied by greater revenues and increased energy consumption, intensifying CO₂ emissions and environmental deterioration [39]. Lu et al. [7] demonstrate that the elevation of OP acts as a stimulus for oil exploration firms, encouraging them to escalate their production and exploration efforts, resulting in a direct surge in carbon emissions. Furthermore, exploiting natural resources stimulates the economy by raising rent levels. Still, the increased supply reduces the resource price (e.g., OP), which provides a potential way for manufacturers to produce more products. As a result, CO₂ emission levels have risen and threaten environmental sustainability [30]. Mahmood and Furqan [31] track the relationship between FDI, financial market development and oil rents on CO₂ emissions. There are negative correlations between CO₂ emissions and the first two variables, but the conclusion cannot be accepted when considering the oil rent, highlighting its driving force on emissions. Likewise, Adedoyin et al. [32] find that the area they explore has greater CO₂ emissions, mainly ascribed to the higher rents.

In addition, certain scholars hold the view that this effect is mixed. Barrales-Ruiz and Neudörfer [5] find that when OP rises because of supply-side factors, a notable decrease in CO₂ emissions is often seen, indicating a shift from fossil fuels to renewable energy sources in the market. Conversely, during periods of positive global economic activity shocks, higher OP leads to an increase in CO₂ emissions. Esmaeili et al. [29] highlight that economic disturbances that impact aggregate demand, such as those triggered by events such as the Persian Gulf War and the Asian financial crisis, have an adverse effect on renewable energy by diminishing investment opportunities and rendering oil a more economical choice. Conversely, shocks specific to oil demand, exemplified by the Iranian Revolution of 1979 and the 2008 financial crisis, initially decrease the appeal of renewable energy but eventually surge its consumption as oil prices skyrocket.

Currently, a limited corpus of literature explores the relationship between OP and CO₂ emissions. Likewise, some have concentrated on their complete linear connection [31,32,33]. In reality, this association cannot always be held since OP is frequently vulnerable to uncertain exogenous shocks that affect oil consumption and lead to changes in CO₂ emissions. Therefore, the confusion that stems from these two variables is still roaming, which imposes barriers for the government and industries that consider sustainable development concerning the oil market. In addition, although some studies consider the impact of OP on CO₂ emissions over different periods [24,29], they still fail to consider the likelihood that their relationship may also vary from oil market circumstances and emission levels during diverse time scales, which our study can address. We also analyse the potential impact of changes in CO₂ emissions on OP to understand the role of oil in achieving emission mitigation targets from the price point of view.

3. Theoretical Analysis and Research Hypothesis

This section presents an intuitive theoretical mechanism to channel the impact of energy prices and CO₂ emissions effectively. The emission levels are intricately connected to energy consumption. The global fervour for industrialisation has led to an escalation in the use of fossil fuels, thereby exacerbating the threat of CO₂ emissions. So, the initial Equation can be constructed as follows:

$$C=\beta E={\displaystyle \sum {\beta }_i{E}_i}$$

(1)

where C is the CO₂ emissions, and E means the energy consumption. In addition, β denotes the emission coefficient, which mainly depends on the efficiency and technical level. i represents different types of energy or other categories for the purpose of differentiation and calculation.

Then, considering the Cobb–Douglas production function affected by technological progress:

$$F=A(t)f(K,L,M,E)$$

(2)

where t means the total output, and A is technological progress. f(.) is a function, where K, L, and E represent capital, labour and energy factors, respectively. In addition, M represents the error term. We use the energy intensity model under the Solow production function as follows:

$$F=A(t)f\left(P_K,P_L,P_M,P_E,Q\right)=A(t)P_K^{{\alpha }_K}{P}_L^{{\alpha }_L}{P}_M^{{\alpha }_M}{P}_E^{{\alpha }_E}Q$$

(3)

where Q is the gross domestic product, P is the corresponding price, and α represents the elasticity of energy demand. According to Shepard’s lemma (Shepard’s lemma shows that the demand for a product equals the partial derivative of its cost function with respect to its price at a fixed level of output), the energy prices are derived as follows:

$$E={\displaystyle \frac{{\alpha }_{E}A(t)P_K^{{\alpha }_K}{P}_L^{{\alpha }_L}{P}_M^{{\alpha }_M}{P}_E^{{\alpha }_E}Q}{P_E}}$$

(4)

We suppose that:

$$P_Q=P_K^{{\alpha }_K}{P}_L^{{\alpha }_L}{P}_M^{{\alpha }_M}{P}_E^{{\alpha }_E}$$

(5)

to obtain:

$$C=\epsilon E={\displaystyle \frac{\partial {\alpha }_{E}A(t)P_{Q}Q}{P_E}}$$

(6)

As can be seen from the above equation, there is a correlation between energy prices and CO₂ emissions. Likewise, the OP may affect CO₂ emissions in indirect ways [34]. The consideration of OP in an environmental setting arises from price volatility’s substitution and income effects [40,41]. The movements in OP may have an effect on CO₂ emissions associated with fossil fuel usage. The falling price has darkened the halo and attraction of renewable energy, which creates obstacles to the green transition and sustainability. However, the increased OP will help pave the way to more efficient and cleaner energy sources [42]. It also contributes to reducing the use of fossil fuels. Concerning the income effect, the soaring oil costs raise overall income pressure, leading to a decline in demand for fossil fuel energy [43] and thereby lowering carbon emissions.

The impact of oil price on CO₂ emissions is mixed, varying across short, medium, and long-term periods. Short-term fluctuations in oil prices can alter energy consumption patterns and production decisions, influencing CO₂ emissions [33]. However, it is crucial that these short-term behavioural adjustments may fall short of significantly altering the overall carbon emission trend, given that the transition of the energy consumption structure necessitates a considerably more extended timeframe. In the medium- and long-term, the impact is more complex and is influenced by technological progress, policy adjustments, and changes in economic structure. High prices may stimulate renewable energy and energy efficiency investments, reducing emissions, while low prices may discourage energy transformation, leading to increased emissions.

The relationship between oil prices and CO₂ emissions is non-linear, varying across different oil prices and emission levels. At low oil prices, the energy substitution effect is weak, with minimal impact on CO₂ emissions. As oil prices rise, the substitution effect becomes significant, increasing demand for renewable and clean energy and resulting in more pronounced emission reductions [5]. Similarly, emission sensitivity to oil price changes differs at various emission levels [7]. In high-emission areas, the potential for emission reduction through energy substitution and efficiency improvements is greater, amplifying the impact of oil prices on emissions.

Based on these discussions, we formulate the following hypotheses to guide our empirical investigation:

H1. 

The impact of oil price on CO₂ emissions is mixed, and vice versa, with potential variations across short, medium, and long-term periods.

H2. 

The relationship between oil prices and CO₂ emissions might be non-linear and varies across different oil prices and emissions levels.

4. Wavelet-Based Quantile on Quantile Regression Approach

The adoption of this approach is motivated by its ability to effectively capture nonlinear relationships between variables and analyse them across different time scales [44], thus enhancing the validation of our hypotheses. Specifically, H1 posits differential impacts of oil prices on CO₂ emissions in the short, medium, and long term, which can be explored separately across these time dimensions through wavelet analysis’s multiscale decomposition capability [45]. Furthermore, H2 suggests a potential nonlinear and heterogeneous relationship between oil prices and CO₂ emissions, varying across different levels. Integrating wavelet analysis with quantile-on-quantile regression (QQR) allows us to assess these variable relationships across time scales and quantiles [46,47], offering a comprehensive understanding of the intricate dynamics between oil prices and CO₂ emissions.

4.1. Quantile on Quantile Regression

Although the traditional quantile regression (QR) assesses explained variables across different quantiles, it cannot completely reflect possible asymmetry, particularly in the distribution of explanatory variables. In order to overcome the barriers, Sim and Zhou [48] introduce the QQR framework, which also extends the QR by integrating the non-parametric estimates [49]. Consequently, it is feasible to explore the potential effects of both the independent and dependent variables under diverse quantiles [18]. In light of the above, this research considers the QQR technique to capture the relationship between the two. The first step is to build a non-parametric quantile regression model, as shown below:

$$C{E}_t={\beta }^{\phi }O{P}_t+{\xi }_t^{\phi }$$

(7)

where $C{E}_t$ and $O{P}_t$ indicate CO₂ emissions and oil price at time t, respectively. Then, φ is defined as the conditional φth quantile distribution of CE_t. ${\xi }_t^{\phi }$ represents the quantile error term, in which the conditional φth quantile is 0. We designate ${\beta }^{\phi }$(⋅) as an unidentified function because there is no prior information about the relationship between $O{P}^t$ and $C{E}_t$. It can be evaluated by conducting a first-order Taylor expansion around ${OP}^{\tau }$:

$${\beta }^{\phi }\left(O{P}_t\right)\approx {\beta }^{\phi }(O{P}^{\tau })+{\beta }^{{\phi }^{\prime }}(O{P}^{\tau })\left(O{P}_t-O{P}^{\tau }\right)$$

(8)

where ${\beta }^{\phi \prime }$ represents the partial derivative of ${\beta }^{\phi }(OP)$ regarding OP, known as the marginal effect. Furthermore, φ and τ are double indexed in Equation (8), whose parameters are marked as ${\beta }^{\phi }(OP)$ and ${\beta }^{{\phi }^{\prime }}(O{P}^{\tau })$, respectively. Here, they are both functions of φ and τ, as β (φ, τ), and β₁ (φ, τ). Therefore, ${\beta }^{\phi }(OP)$ can be expressed as:

$${\beta }^{\phi }\left(O{P}_t\right)\approx {\beta }_0(\phi ,\tau )+{\beta }_1(\phi ,\tau )\left(O{P}_t-O{P}^{\tau }\right)$$

(9)

Then, the values of Equation (9) are substituted into Equation (7) to obtain:

$$C{E}_t=\underset{(*)}{\underbrace{{\beta }_0(\phi ,\tau )+{\beta }_1(\phi ,\tau )\left(O{P}_t-O{P}^{\tau }\right)}}+{\xi }_t^{\theta }$$

(10)

Given that the coefficients of β₀ and β₁ are dependent on $\phi$ and τ, the association between CE and OP in each quantile can be identified. For the purpose of estimating Equation (10), the expected counterparts ${\stackrel{\wedge }{OP}}_t$ and $\stackrel{\wedge }{P^{\tau }}$ are required to replace $O{P}_t$ and $O{P}^{\tau }$, respectively. In this regard, the local linear regression estimates for the coefficients d₀ and d₁, corresponding to ${\beta }_0$ and ${\beta }_1$, may be measured by minimising the following equation:

$${\min }_{d_0,d_1}{\displaystyle \sum _{i=1}^n{\rho }_{\phi }}\left[C{E}_t-d_0-d_1\left({\stackrel{\wedge }{OP}}_t-\stackrel{\wedge }{O{P}^{\tau }}\right)\right]\times K\left({\displaystyle \frac{F_n\left(\stackrel{\wedge }{O{P}^{\tau }}\right)-\tau }{h}}\right)$$

(11)

where ${\rho }_{\phi }$($\xi$) represents the function of quantile loss: ${\rho }_{\phi }$(ξ) = ξ ($\phi$ − P ($\xi$ < 0)), and P denotes the regular indicator. The selection of bandwidth is crucial for non-parametric techniques. A high value increases estimate bias but decreases the variance, and vice versa. In our research, we set the optimal bandwidth parameter at h = 0.05 by the work of Sim and Zhou [48].

4.2. The Wavelet Analysis Approach

The preceding technique involves only the relationship between the two variables at different quartiles regarding these original data. We then integrate the multi-scale wavelet decomposition with the QQR approach to scrutinise the scenario at multiple time scales. Accordingly, wavelet analysis is used to explore the time series in both the temporal and spectral domains, which allows us to examine non-stationary data by capturing the information of specified periods. Hence, the wavelet is separated into two halves, as follows:

$${{\displaystyle \int }}^{\phi }(t)dt=1$$

(12)

$${{\displaystyle \int }}^{\psi }(t)dt=0$$

(13)

where φ denotes the father wavelet, recording the low-frequency (smooth) parts. In addition, the mother wavelet is represented by ψ, which offers high-frequency sections. So, the produced wavelet can be constructed as:

$${\phi }_{j,k}(t)=2^{j/2}\phi \left(2^{i}t-k\right)$$

(14)

$${\psi }_{jk}(t)=2^{j/2}\psi \left(2^{j}t-k\right)$$

(15)

where j = 1, …, J means the scale, and k = 1, …, $2^j$ indexes the translation. The integration of the QQR approach and the wavelet analysis allows for identifying relationships between variables across various quantiles and periods, facilitating comprehensive and reliable conclusions.

5. Data

This research focuses on the period from 2010:M1 to 2022:M11, emphasising the U.S. monthly statistics. At the Copenhagen Conference in December 2009, there was a strong consensus among governments who urged short- and long-term measures to limit CO₂ emissions and combat climate change [50,51]. Meanwhile, the U.S. crude oil spill in April 2010 caused a massive environmental catastrophe, which aroused the government’s great concern about the adverse effects of oil. Therefore, we choose the spot price of West Texas Intermediate (WTI) crude oil, as the OP mentioned in this paper. As a crucial measure in the U.S. oil market, it is widely used as a reference price by both crude oil buyers and sellers, so its change is a potential factor to consider when drafting and adjusting climate policy [52]. In addition, we adhere to the current research that considers CO₂ emissions to be an indicator of environmental deterioration [30,53].

Table 1. Descriptive statistics for OP and CO₂ emissions.

	OP	CO2 Emissions
Observations	143	143
Mean	69.315	120.625
Median	66.330	124.412
Maximum	110.040	190.314
Minimum	16.700	49.406
Standard Deviation	22.349	32.406
Skewness	0.085	0.010
Kurtosis	1.836	2.250
Jarque–Bera	8.242 ***	3.359 ***

Note: *** represents significance at the 1% level.

summarises the descriptive statistics; the mean values of OP and CO₂ emissions are placed at 69.315 and 120.625, respectively. Regarding the kurtosis, both of them are less than three and follow the platykurtic distributions. The estimation of skewness indicates that they are positively skewed. The series of OP and CO₂ emissions exhibit non-normal distribution at a 1% level, according to the Jarque–Bera test. This paper employs wavelet analysis to examine the intricate information across multiple time scales rather than solely evaluating these raw data so as to refine our intrinsic research. (In this paper, we utilise the wavelet transform framework to decompose the CO₂ emissions and OP into three different frequencies of 1–2, 2–4, and 4–8, corresponding to the short-, medium-, and long-run, respectively). Figure 1 presents the decomposition results of CO₂ emissions and OP, respectively. These adjustments effectively smooth out short- to long-term trends and reduce signal noise. This enables a more comprehensive relationship assessment by capturing features across various temporal scales [54].

6. Empirical Results

The QQR approach presents the correlation between OP and CO₂ emissions in each quantile. Subsequently, by utilising wavelet analysis based on the aforementioned QQR approach, the decomposition of OP and CO₂ emission data enables us to clarify the relationships between variables across various times. Then, we estimate the coefficients β₀ (φ, τ) and β₁ (φ, τ), which fluctuate depending on different oil market conditions and emission levels [54]. It is pertinent to mention that the correlation between the two variables is not uniform from short-term to long-term, depending on oil market circumstances and emissions levels.

Figure 2a depicts the raw data results, which show the compound influence that stems from OP to CO₂ emissions. The strong positive impact is tracked in the initial to medium estimates (0.00–0.35) of OP, spanning all the quantiles of CO₂ emissions. For the rest of the graph, a negative association can be established between these two variables. The mitigating effect is pronounced in the area of intermediate OP and the low quantiles (0.10–0.35) of CO₂ emissions, reaching the highest point (coefficient β₁ = 2.5). We subsequently employ a wavelet decomposition method to analyse the short-, medium-, and long-term scenarios. Consequently, Figure 2b–d illustrates the correlation between these two variables following this process.

Figure 2b illustrates the impact of OP on CO₂ emissions in the short term. The variation of these two variables differs across quantiles. Multiple factors have influenced the development of this impact. First, the widespread exploitation of natural resources boosts the economy through increased rental levels. However, such measures may also decrease supply, consequently driving up the price of natural resources (such as OP). Therefore, it lays the groundwork for reducing oil use and promoting environmental sustainability. In addition, COVID-19 and the Russia–Ukraine conflict are still lingering, heightening geopolitical risks and seriously threatening oil supplies, transportation and consumption [55,56]. The disruption in crude oil production has led to a surge in oil prices and upheaval in the U.S. economy. The growing OP witnessed a massive lure for households and companies to invest in alternative solutions that offer higher energy efficiency and lower environmental impact. However, the high-price environment of oil may give rise to an increase in coal usage (the most polluting energy) because of the energy-dominated economic structure of the U.S., thus resulting in the intensification of CO₂ emissions. Hence, in the short term, an increase in oil prices does not necessarily reduce carbon dioxide emissions. The results are inconsistent with the view of Malik et al. [24], who propose that exposure to increased OP can temporarily alleviate CO₂ emissions.

Concerning the medium term (Figure 2c), the results we observe are also mixed. It is pertinent to mention that OP positively correlates with CO₂ emissions across high quantiles. This demonstrates that CO₂ emissions are more vulnerable to positively responding to the bullish oil market because the high OP levels have witnessed enormous enthusiasm for oil corporations to invest more in exploration and production, which runs against urgent aspirations to decarbonise the economy and promote environmental sustainability. The negative effect is concentrated at low to medium quantiles (0.00–0.70) of OP, indicating the positive role in mitigating the CO₂ emissions agenda. With the shale oil expansion and drive for energy independence, the U.S. has established itself as one of the largest oil producers. The development of shale oil has contributed to lower expenditure for industrial, commercial and residential, but at the cost of increasing CO₂ emissions [10]. In addition, in response to President Biden’s commitments to combat climate change and achieve zero emissions, the U.S. government has rolled out climate policy instruments such as imposing carbon taxes [57]. Introducing such measures upsets the conditions or balance of competitive markets by raising the OP. As a result, increasing economic activity comes with embracing sufficient alternatives to replace oil over time. Likewise, investors and consumers will be saddled with the burden of environmental costs for the foreseeable future as levies and attempts to reduce emissions intensify.

The long-term scenario is illustrated in Figure 2d. A positive connection can be observed within the quantiles (0.00–0.15) of OP, while the remaining part is predominantly characterised by a negative impact of OP on CO₂ emissions, although the effect fluctuates around zero. Hence, as a consequence of our findings, we underline the overall negative correlation in this time scale. The conclusion aligns with Rasheed et al. [58], who posits that the increased OP prompts a transition toward alternative energy sources and mitigates CO₂ emissions. Higher and more volatile OP will catalyse individual and global initiatives to decarbonise, which is vital in reaching sustainable goals. First, consumers prove to be sparked by the surging OP to abandon conventional cars in favour of other kinds of transportation, accelerating the take-up of electric vehicles and the shift away from oil. The surge in OP has stimulated a robust rally in energy stocks and significantly contributed to the inflationary pressures impacting producer costs. Meanwhile, it also stimulates the usage of energy-efficient items, which lessens overall energy consumption and CO₂ emissions. This initiative will significantly drive down the long-term oil demand and would be crucial to an environmentally sustainable strategy. In addition, higher OP restricts oil consumption and induces research and development (R&D) in renewable energy, hastening the tipping point where renewables become a valuable option. As the world’s largest oil producer, the U.S. benefits from the high oil price and pushes economic growth. According to the EKC hypothesis, nations convey that they will invest more in R&D when they increase their income, resulting in a technological transition that decreases conventional energy usage and improves environmental quality [24,59].

We can find the short to medium-term compound effects from the results. The CO₂ emissions are particularly vulnerable to positively responding to the bullish oil market in the medium term. The primary reasons are attributable to the substitution effect and income effect resulting from the alteration in OP. We also find that high oil prices promote environmental sustainability in the long run, as CO₂ emissions have a deep-rooted negative response to OP. The findings are endorsed by the theoretical mechanisms between energy prices and emissions, which indicate the role of OP in promoting CO₂ emissions mitigation.

Furthermore, pursuing environmental sustainability can impact oil prices, so we then consider the potential correlation. Figure 3a depicts the response of OP to CO₂ emissions in different quantiles, which are dominated by the negative relationship. This impact is apparent at the low quantiles (0.20–0.35) of emission, in conjunction with the quantiles (0.00–0.65) of OP. This result, however, is excluded in the area of higher quantiles (0.65–1.00). The positive link is also available at the initiate quantiles (0.05–0.20) of CO₂ emissions, with the low to medium estimations (0.20–0.60) of OP. Considering the multifold tendencies between these two variables, the effects may vary over different time spans. We then analyse the specific influence caused by CO₂ emissions on OP from various horizons.

From the short to medium term (Figure 3b,c), the impact proves to be minor (coefficient β₁ < 1) in general, and we can capture the positive correlation that stems from CO₂ emissions toward OP. The phenomenon can be explained from the investment perspective, even though the U.S. is an energy-independent nation, partly because of the shale oil revolution. However, major oil companies respond to environmental concerns and carbon emission constraints by phasing out or reducing new fossil energy projects. Therefore, the investment with high expenses in the oil fields has been greatly choked off, which reduces the oil supply to a certain extent and contributes to the increased OP. For the remaining regions and long-term scenarios (Figure 3d), the overall negative impacts of CO₂ emissions on OP can be monitored, which is pronounced in the bearish oil market. The Biden administration pledges to decarbonise the U.S. economy by 2050, which incentivises the development of low-carbon technologies. These efforts sow the seeds for an increase in efficiency and renewable consumption. In addition, the agenda for green transition attracts more investors to embrace renewable energy [60]. A combination of these factors will decrease the demand and create a headwind for the performance of OP, which may be more aggressive in a bear market because of concerns about rising enthusiasm for oil. These findings confirm the link between CO₂ emissions and OP.

Following the empirical estimates, we additionally provide comparative evidence using the QQR method and quantile regression (QR) to examine the robustness of the results. It is crucial to highlight that the QQR methodology operates without parameters, which implies it lacks an intrinsic capability to conduct significance tests for coefficients, thereby limiting the direct validation of quantitative analysis rationality [49]. To evaluate the sturdiness of this novel technique, including its wavelet-enhanced version, we contrast the QR model against the QQR approach or its wavelet-based variant. For the sake of comparison, we derive the average of the QQR parameters, ensuring a consistent basis for comparing the models. Then, we present the comparative results of QQR and QR in Figure 4 and Figure 5 by exhibiting the regression outputs of QR and the τ-averaged QQR. Considering the potential noise effects, the estimates of the two methods are not the same. Furthermore, the findings reveal that the total effect coefficient of the QQR approach fluctuates significantly, which allows it to capture better the effect of the oil market [54]. Despite these variations, the QR model demonstrates a largely similar pattern to both the QQR method and its wavelet-enhanced counterpart. This observation emphasises the solidity of the quantitative evaluations performed with the QQR or wavelet-based QQR techniques, which supports the validity of our findings [61].

7. Conclusion

7.1. Main Findings

This paper examines the correlation between OP and CO₂ emissions, aiming to determine whether oil is a stumbling block to environmental sustainability from the price perspective. We employ the wavelet-based quantile on-quantile regression method to detect the bilateral relationship between these variables across various temporal scales. The CO₂ emissions are more susceptible to positively responding to the bullish oil market in the intermediate term. Likewise, the overall negative impacts of CO₂ emissions on OP can be captured, particularly in bearish conditions. Therefore, oil is a mixed blessing for promoting environmental sustainability from a price perspective in the short–medium run, depending on which category dominates, because of the multiple factors caused by the substitution and the income effects. Nevertheless, it also reveals that the negative response of CO₂ emissions to OP is long-lasting. The findings are endorsed by the theoretical mechanisms of energy prices and emissions, which highlight the role of oil in facilitating CO₂ emissions mitigation and promoting environmental sustainability.

Compared with previous studies, in the short–medium term, our results show that rising OP can lead to increases and decreases in CO₂ emissions, depending on the quantiles of OP and emission levels. This finding is partially consistent with the work of Mohamued et al. [6], Malik et al. [24], and Okwanya et al. [34], who argue that higher oil prices can temporarily reduce CO₂ emissions due to reduced consumption of fossil fuels. Nevertheless, our analysis also reveals that in certain quantiles, particularly during periods of high OP, CO₂ emissions may increase because of the substitution effect, where consumers and industries shift to more polluting energy sources such as coal. This aligns with the observations of Lu et al. [7], who note that OP spikes can disrupt energy markets and increase reliance on cheaper, dirtier fuels. The mixed effect is consistent with Barrales-Ruiz and Neudörfer [5], who emphasise that increases in OP driven by demand-side factors can spur fossil fuel production, thereby intensifying emissions, whereas rising OP attributable to supply-side factors could lead to reduced emissions. However, our study goes further by showing that this effect is not uniform across all quantiles, as lower quantiles of oil prices may still exhibit a negative relationship with emissions due to reduced consumption. In the long term, our findings support the conclusions of Rasheed et al. [58] and Islam and Sohag [35], who argue that higher oil prices can drive a transition toward alternative energy sources, thereby mitigating CO₂ emissions. Our results indicate that, in the long run, higher oil prices act as a catalyst for renewable energy adoption and energy efficiency improvements, which are crucial for achieving environmental sustainability.

While our findings align with some aspects of previous studies, they also highlight important differences, particularly due to our consideration of timescale and quantile effects. This underscores the importance of considering non-linear and dynamic relationships and varying market conditions and time frequencies when analysing the impact of oil prices on environmental sustainability. Integrating our findings with previous studies provides a more comprehensive understanding of the relationship between OP and CO₂ emissions. Our results highlight the need for targeted policies addressing the dual challenges of energy security and environmental sustainability, particularly in fluctuating oil prices. This nuanced perspective offers valuable insights for navigating towards a sustainable energy future.

7.2. Policy Implications

The findings of our study hold significant practical implications for policymakers, industries, and consumers, particularly in the context of the global low-carbon transition. For governments, this study underscores the importance of designing targeted policies that reflect the environmental costs of oil consumption. In the short run, imposing oil taxes or carbon levies can effectively curb oil demand and encourage energy conservation. These taxes should be structured to reflect the social cost of carbon, ensuring that higher OP levels translate into reduced consumption and lower emissions. Additionally, governments could implement demand-side regulations, such as stricter energy efficiency standards or carbon pricing mechanisms, to make the oil less competitive compared with cleaner alternatives. In the long run, however, supply-side measures are equally critical. Governments should phase out subsidies for fossil fuel exploration and development, redirecting these funds toward renewable energy projects and green technology innovation. Combining short-term demand reduction with long-term supply constraints, this dual approach can create a sustainable pathway for reducing CO₂ emissions.

For industries, the results suggest that short- to medium-term oil OP hikes should not be seen as an opportunity to increase fossil fuel investments or delay the adoption of cleaner technologies. Instead, industries should respond to OP fluctuations strategically by accelerating their transition to low-carbon alternatives. Overreliance on oil during periods of high prices risks locking capital into unsustainable practices, diverting resources away from environmentally sustainable trajectories. To mitigate this, industries should prioritise investments in energy efficiency and renewable energy technologies, which can reduce long-term exposure to OP volatility and contribute to lower CO₂ emissions.

This study highlights the role of behavioural changes in responding to OP shocks for consumers. When coupled with effective policy measures, higher oil prices can incentivise consumers to adopt energy-efficient practices, such as using public transportation, purchasing electric vehicles, or reducing energy-intensive consumption. Governments can support these shifts by providing subsidies for electric vehicles, improving public transportation infrastructure, and promoting awareness campaigns about energy conservation’s environmental and economic benefits.

Furthermore, this study emphasises that high OP alone is not sufficient to drive a significant reduction in CO₂ emissions. While higher prices can discourage oil consumption, they must be complemented by robust government interventions and long-term strategies to promote energy transition. For instance, expanding investment in research and development (R&D) for green technologies is crucial to increasing the share of renewable energy in the global energy mix. Governments should also consider implementing policies encouraging private sector participation in clean energy innovation, such as tax incentives for R&D or public-private partnerships.

7.3. Limitations and Future Directions

While this study provides valuable insights into the relationship between oil price fluctuations and environmental sustainability from the perspective of CO₂ emissions, it is not without limitations. First, the research focuses exclusively on the United States, which may limit the generalizability of the findings to other regions with different economic structures, energy policies, and environmental regulations. Second, this study primarily relies on the QQR approach, which, while robust, may not fully capture the time-varying interactions between oil prices and CO₂ emissions. Additionally, the analysis does not account for potential external shocks, such as geopolitical events or technological breakthroughs, which could significantly alter the relationship between oil prices and emissions.

To advance this field of research, future studies should address these limitations and explore new avenues that can contribute to the scientific body of knowledge. Specifically, researchers could employ alternative methodologies, such as the time-varying parameters stochastic volatility structural vector autoregression (TVP-SV-SVAR) model, to better capture the dynamic and time-varying interactions between oil prices and environmental sustainability. This approach would provide a more nuanced understanding of how oil price fluctuations impact CO₂ emissions under different economic and environmental conditions. Moreover, extending the analysis to include a broader range of countries, particularly those with varying levels of economic development and energy dependency, could offer a more comprehensive understanding of the global implications of oil price fluctuations. Comparative studies across regions could reveal how different policy frameworks, energy mixes, and technological capabilities influence the relationship between oil prices and environmental outcomes. Last, investigating the interplay between oil prices, renewable energy adoption, and technological innovation could offer deeper insights into how energy transitions can be accelerated to achieve long-term environmental sustainability goals. For instance, studies could examine how oil price volatility influences investment in renewable energy technologies or how technological advancements in energy efficiency mitigate the environmental impact of oil consumption.

By addressing these gaps, future research can provide new contributions to the scientific body of knowledge, offering actionable insights for policymakers, industries, and researchers working towards a sustainable energy future. These studies will enhance our understanding of the complex relationship between oil prices and environmental sustainability and inform strategies to mitigate the adverse effects of fossil fuel dependency in the context of global climate change.