The Impact of World Uncertainty, Environmental Policy Stringency, and Technological Innovation on Environmental Sustainability: Evidence from High-Income Countries

Abstract

Industrialization and economic growth in high-income countries have considerably increased consumption-based CO₂ emissions (CCO₂), adversely affecting environmental sustainability and contributing to climate change. This study assesses the impacts of Environmental Policy Stringency (EPS), World Uncertainty (WU), and Technological Innovation (TI) on environmental sustainability, aiming to guide the development of balanced policies that foster growth. Utilizing panel data from 1990 to 2021 across high-income countries, we employed the Method of Moments Quantile Regression (MMQR) to capture the varying effects of these factors at different levels of CCO₂ emissions. Our findings reveal that WU consistently and significantly reduces CCO₂ emissions across all quantiles, while the effects of EPS are minimal and largely insignificant. Similarly, TI demonstrates a weak and statistically non-significant impact, indicating that technological advancements alone are insufficient for meaningful improvements in environmental sustainability. Importantly, renewable energy consumption (REC) significantly lowers CCO₂ emissions, while economic growth (GDP) has a strong tendency to increase emissions, particularly at lower quantiles. These insights highlight the necessity for high-income countries to adopt comprehensive fiscal and environmental policies to mitigate emissions and enhance sustainability, with a focus on prioritizing renewable energy, encouraging innovation, and strengthening policy measures to achieve long-term ecological objectives.

1. Introduction

In recent years, energy demand has surged in both high- and middle-income countries, driven by many emerging economies advancing from low to middle-income status. However, many developing nations still heavily rely on fossil fuels, creating challenges in meeting essential energy needs while pursuing long-term sustainability in recent years, energy demand has increased significantly in both high- and middle-income countries. This surge is largely due to many emerging economies progressing from low to middle-income. However, numerous developing nations still rely heavily on fossil fuels. This reliance challenges meeting essential energy needs while striving for long-term sustainability [1]. The continued growth of the global economy stimulates business expansion in numerous countries, fostering new opportunities and market development [2]. Addressing climate change has become increasingly urgent, and the Paris Agreement establishes a clear framework for coordinated global action [3]. Under this agreement, each nation outlines its initiatives to reduce greenhouse gas emissions, with wealthier countries taking on a larger share of the responsibility to support economically disadvantaged developing nations. This approach encourages collaborative yet differentiated efforts, acknowledging the necessity of assistance in the transition to sustainable practices across the globe [4]. The media frequently reminds us of the serious consequences of climate change and the urgent need for action. As a result, many individuals, organizations, and governments are taking proactive steps to promote sustainability [5]. One crucial aspect of addressing the negative impacts of climate change is aligning with the goals of the Paris Agreement. Countries worldwide strive to peak global greenhouse gas emissions as soon as possible. The main objective of the Paris Agreement is to limit global warming to below 2 degrees Celsius by 2050 [6]. Paris Agreement has encouraged the UN to promote global economic alignment, creating a model for economic growth, social prosperity, and sustainability that will benefit all parties involved [7,8]. By 2050, it is expected that two-thirds of the world’s energy will come from renewable sources [9].

Much empirical evidence demonstrates the impact of global uncertainty, stringent environmental regulations, and technological advancements on ecological sustainability, particularly in high-income countries. Global uncertainty, shaped by political, economic, and social events, significantly impacts carbon emissions by influencing decision-making and long-term planning. The World Uncertainty Index (WUI) quantifies this unpredictability by analyzing the frequency of the term “uncertain” in country reports from The Economist Intelligence Unit, scaled by a factor of 1,000,000. A higher WUI indicates heightened uncertainty and its potential effects on emissions. Similarly, environmental policy stringency, an important measure for comparing the strictness of environmental policies, both domestically and globally, is the Environmental Policy Stringency (EPS) index. Stringency refers to the extent to which environmental regulations impose a cost, either explicitly or implicitly, on activities that pollute or harm the environment. The ranking is derived from the strictness of 13 different environmental policy tools, most of which are associated with air pollution and climate change. Technological innovation refers to the development and application of new or improved tools, methods, procedures, and technologies that lead to significant breakthroughs across various fields. It involves leveraging resources, knowledge, and expertise to create inventive solutions that tackle challenges, enhance productivity, advance society, and deliver value [10]. However, these effects have largely been overlooked in previous research. This study investigates the influences of global uncertainty, strict environmental regulations, technological progress, and other key factors in high-income countries from 1990 to 2021.

In reality, energy demand, production, and economic factors are key drivers of production activities, which result in carbon emissions. Hence, the CCO₂ approach calculates the carbon emissions generated by consumption activities in the economy and attributes the responsibility to policymakers to mitigate the CO₂ emissions to consumption demand [11]. The relationship between environmental sustainability and economic uncertainty has attracted considerable attention in academic and practical fields. Several studies mentioned that technological innovation could play a critical role in reducing the level of carbon emissions [12]. Other studies suggest that strict environmental laws and regulations can play a significant role in promoting ecological sustainability [13,14]. Recently, some studies found that world uncertainty significantly affects the level of carbon emissions [15,16,17]. Most of these studies focused on carbon emissions to capture ecological pollution. They suggested limited studies that focused on the nexus between world uncertainty, and environmental policy stringency on consumption-based carbon emissions (CCO₂). For example, [18] examined the effect of environmental policy stringency on CCO₂ in the case of OECD countries. They suggested that the policymakers of these countries should restructure their environmental regulations to reduce the level of CCO₂. Li et al. (2023) focused on BRICS economies to explore the nexus between environmental policy stringency and CCO₂ and found a positive connection between environmental policy stringency and CCO₂. Another study further evaluated the role of environmental policy stringency, examining how such regulations can serve as a predictive factor for CO₂ emissions. It highlighted the extent to which stringent policies influence emission trends and their effectiveness in promoting environmental sustainability [19]. Our analysis emphasizes consumer-based CO₂ emissions (CCO₂) in light of the growing emphasis on the environmental impacts associated with consumption. This approach is in line with global sustainability objectives, which advocate for a reduction in emissions tied to consumption rather than solely those originating from production. This shift carries significant policy implications, urging governments to enhance regulations that address the entire consumption cycle rather than concentrating exclusively on production-related emissions. Furthermore, it directly engages individuals in efforts to minimize their carbon footprints. High-income countries frequently tend to outsource a substantial portion of their production-related emissions through international trade. This outsourcing results in an underestimation of their actual environmental impact when only production-based emissions are considered. By centering our analysis on consumption-based carbon dioxide (CCO₂) emissions, this study elucidates the broader environmental footprint associated with consumption patterns, thereby providing a more accurate and comprehensive assessment of these nations’ contributions to global emissions.

Furthermore, high-income countries are characterized by significant heterogeneity in economic structure, geographic attributes, and gross domestic product (GDP) levels. These variances are integral to our analysis, yielding nuanced insights into the environmental responsibilities of these nations. This perspective is vital for formulating equitable policies that hold high-income countries accountable for the emissions they generate, regardless of the production location. The emphasis on high-income countries is warranted due to their disproportionate influence on global trade and consumption dynamics. Although distinctions exist concerning economic structures, geographic characteristics, GDP levels, production capabilities, and income distributions among these nations, our analysis seeks to provide aggregated insights that advance global sustainability objectives. Future research could benefit from further categorizing these countries to achieve more detailed insights regarding their distinct contributions and challenges.

The employment of Modified M-Quantile Regression (MMQR) and Dumitrescu-Hurlin panel causality tests are not inherently innovative; however, these methods remain relatively underutilized in the specific context of analyzing the roles of global uncertainty and the stringency of environmental policies on CCO₂ emissions within high-income countries. MMQR has been selected for its capacity to capture the heterogeneous effects of explanatory variables across different quantiles of CCO₂ emissions, an advantage that traditional mean-based methodologies often overlook. However, the reliability of results when analyzing high-income nations with integrated economic systems may be influenced by the limitations of MMQR, particularly its susceptibility to cross-sectional dependence. This study aims to provide a balanced methodological discussion by acknowledging these constraints and suggesting potential solutions for the future, such as employing robust error corrections or utilizing cross-sectionally enhanced regression models. The Dumitrescu-Hurlin panel causality test serves to uncover directional relationships between variables, thereby enriching the robustness of our findings by revealing causality patterns that contextualize our results. By meticulously explicating the benefits of these methodologies and contrasting them with those traditionally employed in prior research, this study aims to demonstrate how our methodological approach enhances understanding and provides nuanced insights into the research problem.

Traditional mean-based regression techniques aggregate effects across the entire dataset, potentially masking variations at distinct levels of CCO₂ emissions. In contrast, MMQR empowers us to capture quantile-specific effects, unveiling nuanced relationships among world uncertainty (WU), environmental policy stringency (EPS), technological innovation (TI), and CCO₂ emissions across lower, median, and higher emission levels. This quantile-specific approach provides empirical evidence that could inform more effective and tailored policies aimed at addressing disparities in emission reduction strategies among high-income countries.

While earlier studies predominantly focused on production-based emissions, our emphasis on consumption-based emissions offers a complementary perspective that accounts for the environmental ramifications embedded in trade and consumption behaviors. By integrating world uncertainty (WU) and environmental policy stringency (EPS) variables, frequently overlooked in the extant literature, our research accentuates how these factors interact with technological innovation (TI) to influence emissions trajectories. For instance, our findings could assist policymakers in formulating adaptive strategies to mitigate the impacts of uncertainty or enhance the efficacy of environmental policies based on quantile-specific insights.

The structure of the study is delineated as follows: Section 2 encompasses the literature review, while Section 3 and Section 4 articulate the methodology and present the findings. The study concludes with a comprehensive discussion of the implications of these findings.

2. Literature Review

2.1. World Uncertainty and Environmental Sustainability

The world is currently confronted with various challenges amid these turbulent times. A key factor contributing to the heightened uncertainty is the ongoing conflict between Russia and Ukraine. The emergence of new virus strains, coupled with varying COVID-19 vaccination rates, further complicates the situation. Additionally, inflationary pressures and challenges in maintaining fiscal discipline present further obstacles. Global dynamics are also shaped by trade disputes, supply chain disruptions, economic stimulus measures, and issues within the Chinese real estate market. Moreover, the increasing frequency of climate-related catastrophic events, which lead to supply chain interruptions and rising freight costs, adds even greater complexity to the global landscape [20]. Recovery efforts across various economies continue to be impeded by the lingering effects of the COVID-19 pandemic. Additionally, Russia’s invasion of Ukraine, which commenced on 24 February 2022, has compounded these challenges. This conflict has become a critical issue, representing the most significant military confrontation in Europe since World War II and garnering considerable global attention. Its ramifications extend far beyond regional borders, posing substantial risks to the global economy and necessitating an immediate, coordinated international response [21].

The finance industry is uniquely positioned to drive significant change because of its crucial role in the global economy and its capacity to promote sustainable economic practices worldwide [22]. The COVID-19 pandemic, which began in 2019, has exacerbated existing challenges by diverting government attention from long-term sustainability goals. Significant resources were allocated to economic recovery efforts, jeopardizing the timely achievement of the Sustainable Development Goals (SDGs). As a result, the implementation of these goals has been delayed by several years [17]. In the current situation, investments in sustainability from both the government and private sectors have become increasingly essential, as they are crucial for ensuring a sustainable future for international environmental and socioeconomic initiatives [23].

From 1985 to 2017, a comprehensive empirical study was conducted to investigate the relationship between energy consumption in the UK and economic policy uncertainty. The study found a significant link between these two factors. Researchers used an autoregressive distributed lag model to analyze the data, revealing important insights regarding the effects of economic policy uncertainty on energy use. The findings emphasize the critical role of economic policy uncertainty in influencing changes in energy consumption. Specifically, the short-term effects of this uncertainty are more pronounced than its long-term impacts. Increased levels of economic policy uncertainty are associated with a slower rate of short-term growth in CO₂ emissions. Conversely, in the long term, this uncertainty leads to an increase in CO₂ emissions. Additionally, a unidirectional causal relationship was identified through paired Granger causality analysis, indicating that fluctuations in CO₂ emissions affect economic policy uncertainty. These correlational patterns were also observed for other factors examined in the study [24]. The research examines the effects of uncertainties related to healthcare, tax, and trade policies on environmental sustainability, specifically focusing on CO₂ emissions in the United States from 2000 to 2021. Utilizing models such as ARDL, MTNARDL, and NARDL, the study analyzes both symmetric and asymmetric effects over short-term and long-term periods. The findings highlight the varying influence of policy uncertainties on carbon emissions, providing valuable insights into the relationship between policy dynamics and environmental outcomes [25].

This study investigates the relationship between CO₂ emissions intensity at the firm level in China and economic policy uncertainty. To explore this relationship, the authors developed provincial economic policy uncertainty indices, enabling them to examine the influence of this uncertainty on emissions intensity among firms. The empirical findings reveal a strong correlation, indicating that economic policy uncertainty at the provincial level significantly affects the emissions produced by firms. Notably, the study identifies several mediating factors, including energy intensity, innovation, and the proportion of fossil fuels consumed. These findings contribute to a better understanding of the complex interactions between environmental impacts and economic policy uncertainty, particularly in the context of Chinese enterprises [26].

This innovative study is the first to quantify the relationship between Economic Policy Uncertainty (EPU) and carbon emissions, proposing two main pathways through which EPU may impact emissions: changes in environmental governance and effects on business performance. Businesses may respond to EPU by reducing their efforts to cut emissions, while inconsistent emission outcomes could arise. Lower emissions may result from suboptimal performance, whereas higher emissions might occur due to a shift to cheaper but more environmentally harmful fuels. The empirical findings indicate that EPU in the United States not only generates uncertainty regarding carbon emissions but can also contribute to an increase in emissions under certain conditions. This groundbreaking research highlights the initial effort to quantify the link between carbon emissions and economic policy uncertainty, suggesting that firms may scale back their efforts to mitigate emissions as a response to EPU, leading to varying performance in emissions that may stem from reduced operational efficiency or a shift to more cost-effective but environmentally damaging fuels [27]. This study examines the impact of Economic Policy Uncertainty (EPU) on CO₂ emissions and economic growth in the ten largest carbon-emitting countries from 1990 to 2015. The findings, derived from the PMG-ARDL model, show a strong relationship between CO₂ emissions and the World Uncertainty Index (WUI) in both the short and long term. The empirical analysis indicates that a 1% increase in the World Uncertainty Index (WUI) is associated with a significant reduction in CO₂ emissions, suggesting a positive long-run elasticity. By highlighting the importance of addressing economic policy uncertainty as a means to achieve sustainable environmental outcomes, this study contributes valuable insights to the existing body of research on the topic [28]. CO₂ emissions increased by 0.12% due to economic policy uncertainty (EPU). Additionally, a 1% rise in the World Uncertainty Index (WUI) is associated with a 0.11% increase in CO₂ emissions in the short term. These findings underscore the critical importance of addressing EPU to mitigate carbon emissions and promote sustainable environmental practices.

2.2. Environmental Policy Stringency and Environmental Sustainability

The current literature lacks sufficient information to determine the effects of strict environmental laws on adopting renewable energy [13]. While some countries have made progress with their environmental regulations, the overall framework set by the OECD remains inadequate for addressing larger environmental challenges. Policymakers must critically evaluate existing policies and implement necessary reforms that balance penalties for pollution with financial incentives designed to lower the costs of transitioning to green energy. By doing this, they can develop a more comprehensive and effective strategy for promoting environmental sustainability [29]. Research has demonstrated that implementing Environmental Policy Stringency (EPS) effectively reduces both the overall ecological footprint and per capita carbon emissions. Despite these positive outcomes, many OECD countries have not fully utilized EPS to achieve significant environmental improvements, highlighting the need for stronger commitments and more rigorous enforcement of environmental policies to maximize their benefits in reaching sustainability goals. Although efforts to move toward a more sustainable ecological trajectory are underway, carbon intensity remains persistently high. Furthermore, stricter environmental regulations have positively influenced the production of renewable energy, particularly in the areas of eco-innovation, wind energy, and solar energy. The policy implications derived from these findings can provide valuable guidance for the adoption and effective implementation of environmental policies in OECD countries, allowing them to enhance their renewable energy sectors while simultaneously advancing their sustainability objectives [14].

2.3. Technological Innovation and Environmental Sustainability

Technological advancements in areas such as big data, computing, healthcare, and mobile technologies have significantly improved economic conditions and stimulated growth. However, there continues to be substantial debate among scholars regarding the impact of these advancements on the environment. While many industries have greatly benefited from technological innovations, the overall effect on sustainability remains uncertain. This ambiguity emphasizes the need for further investigation into the complex relationship between technology and environmental outcomes, aiming to better understand how to leverage these advancements for sustainable development [30,31]. Many studies indicate that technological innovation is vital for tackling energy and environmental challenges, significantly contributing to global efforts against climate change [32]. This innovation is regarded as crucial in tackling climate change and enhancing environmental sustainability. By advancing technology, countries can better adopt eco-friendly practices that effectively lower their carbon footprints [32]. Technological advancements are essential for developing sustainable energy solutions, optimizing the use of renewable resources, and improving overall environmental quality. These innovations foster more efficient energy production, distribution, and consumption, contributing to a cleaner and more sustainable future [33]. Promoting the advancement of renewable energy technology can significantly enhance a nation’s environmental performance by reducing greenhouse gas emissions, transitioning to cleaner energy sources, and fostering sustainability. This shift not only addresses environmental challenges but also encourages economic growth and job creation within the renewable energy sector. However, the relationship between technological innovation and sustainable development varies by region; while technological progress is essential for the long-term success of many countries, research indicates that its effectiveness in promoting sustainable development can differ widely. For instance, studies show that technological innovation may not sufficiently advance sustainable development in lower-income countries, where challenges such as limited resources, inadequate infrastructure, and lack of access to advanced technologies can hinder its effectiveness. This underscores the importance of tailored approaches that consider regional contexts and capacities to maximize the benefits of innovation for sustainable development [34].

2.4. Literature Gap

The influence of global uncertainty, stringent environmental regulations, and technological innovation on environmental outcomes has yielded mixed results. Despite extensive research into the interplay among environmental sustainability, economic growth, and technological innovation, inconsistencies persist due to variations in study variables, timeframes, geographic focus, sample selection, and estimation methods, which can lead to differing conclusions. This highlights the need for a more nuanced understanding of these complex relationships and a standardized approach in future research to achieve more consistent findings. Furthermore, the ongoing debate surrounding the impact of policy stringency and technological advancement on environmental sustainability underscores the necessity for additional studies. This research is among the first to comprehensively evaluate the combined effects of technological innovation, environmental policy stringency, and global uncertainty on CCO₂ emissions in high-income countries. By investigating these factors, this study aims to address a critical gap in the literature, offering new empirical insights into how rigorous environmental policies and global uncertainties shape sustainable outcomes. This analysis is particularly timely as countries increasingly seek innovative strategies to meet their sustainability targets amid growing global uncertainties.

3. Methodology

3.1. Data Source

This study examines the relationship between World Uncertainty (WU), Environmental Policy Stringency (EPS), Technological Innovation (TI), and Environmental Sustainability (ES) in 18 high-income countries: Australia, Austria, Canada, Chile, Denmark, Finland, France, Germany, Greece, Netherlands, New Zealand, Norway, Romania, Spain, Sweden, Switzerland, the United Kingdom, and the United States, from 1990 to 2021. This time frame was selected because it offers consistent and reliable data for the key variables of interest over an extended period, allowing for a comprehensive analysis of both short-term and long-term trends. Additionally, the 1990–2021 period facilitates an examination of the evolving impacts of global political and economic volatility, as well as the increasing stringency of environmental regulations. The decision to focus on high-income nations stems from their advanced economies, greater capacity for technological innovation, and more developed environmental regulations, all of which provide an excellent context for studying the interactions among these factors and their effects on CO₂ emissions. Moreover, the availability and reliability of data in these countries enable a thorough exploration of the proposed relationships. World Uncertainty (WU) is measured using the World Uncertainty Index, which captures global economic and political uncertainties. At the same time, Environmental Policy Stringency (EPS) is indicated by an index that measures the strictness of environmental regulations. The number of patent applications is a proxy for Technological Innovation (TI), and CCO₂ emissions indicate environmental sustainability. Additional independent variables include Renewable Energy Consumption (REC), which reflects the proportion of renewable energy in total energy consumption, and GDP per capita, measured in constant 2015 US dollars to represent economic growth. A detailed description of these variables and their sources is presented in

Table 1. Data source and definition of variables.

Variable	Indicator	Definition and Measurement	Source
CCO2	CCO2 Emissions	Per capita consumption-based CO2 emissions, measured in metric tons. This indicator reflects the amount of CO2 emissions attributed to consumption activities within a country.	OWID
GDP	Economic growth	Gross Domestic Product (GDP) per capita, measured in constant 2015 US dollars. This indicator serves as a proxy for the level of economic activity in a country.	WDI-2024
RE	Renewable energy	The share of renewable energy in total energy consumption, expressed as a percentage. This includes energy from sources such as wind, solar, and hydropower.	WDI-2024
WU	World Uncertainty	World Uncertainty Index derived from the Economist Intelligence Unit reports. This index measures the degree of global uncertainty based on economic and political factors.	WDI-2024
TI	Technological innovation	Number of patent applications filed annually. This serves as a proxy for the level of innovative activity, which can influence environmental and economic outcomes.	WDI-2024
ESP	Environmental Policy Stringency	Index of environmental regulation strictness. This index measures the intensity of government policies aimed at controlling environmental pollution and promoting sustainability.	WDI-2024

.

3.2. Model Specification and Theoretical Underpinning

To explore the relationship among WU, ESP, technological innovation, REC, GDP, and CCO₂ emissions in high-income countries, an econometric model was constructed:

$${\mathrm{C}\mathrm{C}\mathrm{O}}_2 \mathrm{i}\mathrm{t}=\mathsf{\alpha }0+\mathsf{\beta }1\mathrm{W}\mathrm{U}\mathrm{i}\mathrm{t}+\mathsf{\beta }2\mathrm{E}\mathrm{S}\mathrm{P}\mathrm{i}\mathrm{t}+\mathsf{\beta }3\mathrm{T}\mathrm{I}\mathrm{i}\mathrm{t}+\mathsf{\beta }4\mathrm{R}\mathrm{E}\mathrm{C}\mathrm{i}\mathrm{t}+\mathsf{\beta }5\mathrm{G}\mathrm{D}\mathrm{P}\mathrm{i}\mathrm{t}+\mathsf{\mu }\mathrm{i}\mathrm{t}$$

(1)

where CCO₂ is the dependent variable, representing environmental sustainability (ES). The independent variables, or regressors, include WU, ESP, TI, REC, and GDP. The constant term is indicated by α₀, while t denotes the period, and i represents the countries being studied. The stochastic error term is denoted by μ. Additionally, β₁, β₂, β₃, β₄, and β₅ are the coefficients that need to be calculated. To address heteroscedasticity and ensure accurate and reliable results, all series were transformed into natural logarithms. Therefore, the revised model is expressed as:

$$\mathrm{l}\mathrm{n}{\mathrm{C}\mathrm{C}\mathrm{O}}_2 \mathrm{i}\mathrm{t}=\mathsf{\alpha }0+\mathsf{\beta }1\mathrm{l}\mathrm{n}\mathrm{W}\mathrm{U}\mathrm{i}\mathrm{t}+\mathsf{\beta }2\mathrm{l}\mathrm{n}\mathrm{E}\mathrm{S}\mathrm{P}\mathrm{i}\mathrm{t}+\mathsf{\beta }3\mathrm{l}\mathrm{n}\mathrm{T}\mathrm{I}\mathrm{i}\mathrm{t}+\mathsf{\beta }4\mathrm{R}\mathrm{l}\mathrm{n}\mathrm{E}\mathrm{C}\mathrm{i}\mathrm{t}+\mathsf{\beta }5\mathrm{l}\mathrm{n}\mathrm{G}\mathrm{D}\mathrm{P}\mathrm{i}\mathrm{t}+\mathsf{\mu }\mathrm{i}\mathrm{t}$$

(2)

In this study, the variables lnCCO₂, lnWU, lnESP, lnTI, lnREC, and lnGDP refer to the logarithmic transformations of CCO₂ emissions, world uncertainty, environmental stringency policies, technological innovation, renewable energy consumption, and gross domestic product, respectively. Numerous previous investigations have theoretically utilized various indicators as proxies for environmental sustainability.

3.3. Econometric Strategy

Before assessing relationships among WU, ESP, technological innovation, REC, GDP, and CCO₂ emissions in high-income countries, we used some pre-tests. Given the economic interconnections among these countries, there may be cross-sectional dependencies between them, and ignoring these dependencies in regression analysis can lead to biased results and incorrect interpretations. To address this issue, the first step was to apply [35] CD statistical test to evaluate whether there is a cross-sectional correlation among the residuals. The test is represented as follows:

$$\mathrm{C}\mathrm{D}={\left[{\displaystyle \frac{2\mathrm{T}}{\mathrm{N}(\mathrm{N}-1)}}\right]}^{{\displaystyle \raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$2$}\right.}}\sum _{\mathrm{i}=1}^{\mathrm{N}-1}\sum _{\mathrm{j}=\mathrm{I}+1}^{\mathrm{N}}{\widehat{\mathsf{\rho }}}_{\mathrm{i}\mathrm{j}}$$

(3)

In the equation mentioned earlier, ${\widehat{\mathsf{\rho }}}_{\mathrm{i}\mathrm{j}}$ represents the pairwise correlation coefficient, while T refers to the period and N denotes the cross-sectional units. Additionally, overlooking results from the study may be skewed due to variations in the slope parameters. The study evaluated this assumption using the [36] technique. This method includes two statistical measures, which are formulated as follows:

$$\widetilde{∆}=\sqrt{\mathrm{N}}\left({\displaystyle \frac{{\mathrm{N}}^{-1}\tilde{\mathrm{S}}-\mathrm{k}}{\sqrt{2\mathrm{k}}}}\right)$$

(4)

$${\widetilde{∆}}_{\mathrm{a}\mathrm{d}\mathrm{j}}=\sqrt{\mathrm{N}}\left({\displaystyle \frac{{\mathrm{N}}^{-1}\mathrm{S}-\mathrm{E}\left({\tilde{\mathrm{z}}}_{\mathrm{i}\mathrm{T}}\right)}{\sqrt{\mathrm{V}\mathrm{a}\mathrm{r}\left({\tilde{\mathrm{z}}}_{\mathrm{i}\mathrm{T}}\right)}}}\right)$$

(5)

In this context, ($\widetilde{∆}$ and ${\widetilde{∆}}_{\mathrm{a}\mathrm{d}\mathrm{j}})$ correspond to the test statistic of Swamy (1970) and its modified version, respectively. The CADF and CIPS approaches, which are excellent for handling cross-sectional dependence, were then used to examine the unit root characteristics of the series. The CADF test can be stated as follows, according to [36]:

$$\Delta y_{it}={\alpha }_i+b_i{y}_{i,t-1}+c_i{\overline{y}}_{t-1}+{\displaystyle \sum _{j=0}^p{d}_{ij}\Delta {\overline{y}}_{t-j}+}{\displaystyle \sum _{j=1}^p{\delta }_{ij}\Delta y_{i,t-j}+}{e}_{it}$$

(6)

where ${\overline{\mathrm{y}}}_{\mathrm{t}-\mathrm{j}}$ and $∆{\overline{\mathrm{y}}}_{\mathrm{t}-\mathrm{j}}$ represent the cross-sectional means. The CIPS is calculated from the CADF using the following formula:

$$CIPS=N^{-1}{\displaystyle \sum _{l=1}^{N}CAD{F}_l}$$

(7)

The null hypothesis in these tests asserts that the series being examined does not exhibit stationarity. Therefore, rejecting this hypothesis indicates that the variables are indeed stationary. Before estimating the elasticities of the covariates, it was important to establish whether the series exhibits a long-run co-integration relationship. Following the methodology of Gyamfi et al. (2022) [34], slope heterogeneity assessments were conducted, along with the Westerlund (2007) test, which considers cross-sectional dependence and heterogeneity [37]. This comprehensive approach is crucial for your research, particularly given the focus on the intricate dynamics of environmental sustainability and economic factors in high-income countries. By employing robust testing methods, you can strengthen your findings and provide valuable insights into the literature.

$$\Delta z_{it}={\delta }_i^{\prime }{d}_i+{\theta }_i\left(z_{i(t-1)}+{\pi }_i^{\prime }\right)+{\displaystyle \sum _{j=1}^m{\theta }_{ij}\Delta z_{i(t-1)}+}{\displaystyle \sum _{j=1}^m{\phi }_{ij}\Delta y_{i(1-j)}+}{\omega }_{it}$$

(8)

In the equation presented above, ${\mathsf{\theta }}_{\mathrm{i}}$ represents the speed of adjustment toward the equilibrium relationship. This test produces two statistics, which are defined as follows:

$${\mathrm{G}}_{\mathsf{\tau }}={\displaystyle \frac{1}{\mathrm{N}}}\sum _{\mathrm{i}=1}^{\mathrm{N}}{\displaystyle \frac{{\mathsf{\theta }}_{\mathrm{i}}}{\mathrm{S}\mathrm{E}\left({\widehat{\mathsf{\theta }}}_{\mathrm{i}}\right)}}$$

(9)

$${\mathrm{G}}_{\mathsf{\alpha }}={\displaystyle \frac{1}{\mathrm{N}}}\sum _{\mathrm{i}=1}^{\mathrm{N}}{\displaystyle \frac{{\mathrm{T}\mathsf{\theta }}_{\mathrm{i}}}{{\mathsf{\theta }}_{\mathrm{i}}^{\prime }\left(1\right)}}$$

(10)

$${\mathrm{P}}_{\mathsf{\tau }}={\displaystyle \frac{{\widehat{\mathsf{\theta }}}_{\mathrm{i}}}{\mathrm{S}\mathrm{E}\left({\widehat{\mathsf{\theta }}}_{\mathrm{i}}\right)}}$$

(11)

$${\mathrm{P}}_{\mathsf{\alpha }}=\mathrm{T}{\widehat{\mathsf{\theta }}}_{\mathrm{i}}$$

(12)

In this analysis, Ga and Gt represent the group mean statistics, while Pa and Pt refer to the panel mean statistics. After confirming that the series are co-integrated, we calculated the elasticities of the regressors using a quantile regression approach. This technique enables us to examine the relationship between the independent and dependent variables at different quantiles, providing a more detailed understanding of the dynamics involved across various levels of the dependent variable.

This study employs the advanced Method of Moments Quantile Regression (MMQR) approach as proposed by Machado and Santos Silva (2019) [38] to assess the relationships among the selected variables. The MMQR technique transcends the traditional impact of moving averages by accounting for individual effects, enabling the identification of conditional heterogeneous covariance effects. This method is particularly effective for panel data that includes endogenous explanatory variables and individual effects, demonstrating significantly enhanced performance in such contexts [39]. The Method of Moments Quantile Regression (MMQR) integrates fixed effects within its framework, effectively addressing unobserved heterogeneity in panel data. This characteristic allows for individual influences to affect the overall distribution, facilitating the examination of conditional heterogeneous covariance effects among the factors that impact the dependent variable. MMQR is highly regarded for its robustness in non-linear models and highlights the significance of location-based asymmetry in the dependent variable. As a result, the estimated parameters may vary based on the position of the dependent variable [40]. Despite its advantages, the MMQR method does have some drawbacks. Notably, if cross-sectional dependence is not sufficiently addressed, its implementation can result in biased outcomes. This concern is particularly pertinent in high-income countries, where shared environmental regulations and interconnected economic systems can influence the variables under consideration. To address this issue, future research could employ cross-sectionally augmented methods, such as the Cross-Sectionally Augmented Autoregressive Distributed Lag (CS-ARDL) model. Furthermore, incorporating robust standard errors into the estimation process can help account for these dependencies and improve the accuracy of the findings. We will integrate these aspects into our methodological framework to enhance the robustness of our study. The parameter estimation will be conducted within the quantile regression framework, as outlined in Equation (13).

$${\mathrm{Q}}_{\mathrm{y}}\left(\mathsf{\tau }\mid {\mathrm{X}}_{\mathrm{i}\mathrm{t}}\right)=\left({\mathsf{\gamma }}_{\mathrm{i}}+{\mathsf{\beta }}_{\mathrm{i}}\left(\mathrm{q}\left(\mathsf{\tau }\right)\right)+{\mathrm{X}}_{\mathrm{i}\mathrm{t}}^{\prime }\mathsf{\alpha }+{\mathrm{Q}}_{\mathrm{i}\mathrm{t}}^{\prime }{\mathsf{\delta }}_{\mathrm{q}}\left(\mathsf{\tau }\right)\right.$$

(13)

In Equation (13), the vector ${\mathrm{X}}_{\mathrm{i}\mathrm{t}}^{\prime }$ represents the explanatory variables. The left-hand side of the equation displays the conditional quantile distribution of CCO₂ emissions. Unlike traditional LS-fixed effects models, this method does not include an intercept term for individual effects. As noted, according to [38], the variables are treated as time-independent, and variability among the units is anticipated to differ. Once the solution is obtained, the (τ)To determine the sample quantiles, the following expression must be minimized:

$$\underset{\mathrm{q}}{\mathrm{m}\mathrm{i}\mathrm{n}}\sum _{\mathrm{i}}\sum _{\mathrm{t}}{\mathsf{\vartheta }}_{\mathsf{\tau }}\left({\mathrm{Z}}_{\mathrm{i}\mathrm{t}}-\left({\mathsf{\beta }}_{\mathrm{i}}+{\mathrm{Q}}_{\mathrm{i}\mathrm{t}}^{\prime }{\mathsf{\delta }}_{\mathrm{q}}\right)\mathrm{q}\right)$$

(14)

where ${\mathsf{\vartheta }}_{\mathsf{\tau }}$ is the check function.

$${\mathrm{Y}}_{\mathrm{i}\mathrm{t}}={\mathrm{w}}_{\mathrm{i}}+\sum _{\mathrm{m}=1}^{\mathrm{M}}{{\mathsf{\alpha }}_{\mathrm{i}}}^{\left(\mathrm{m}\right)}{\mathrm{Y}}_{\mathrm{i}\mathrm{t}-\mathrm{m}}+\sum _{\mathrm{m}=1}^{\mathrm{M}}{{\mathsf{\delta }}_{\mathrm{i}}}^{\left(\mathrm{m}\right)}{\mathrm{X}}_{\mathrm{i}\mathrm{t}-\mathrm{m}}+{\mathsf{\epsilon }}_{\mathrm{i}\mathrm{t}}$$

(15)

In this formulation, ${\mathrm{w}}_{\mathrm{i}}$ represents the constant term, $\mathrm{M}$ denotes the number of lags, and ${{\mathsf{\alpha }}_{\mathrm{i}}}^{\left(\mathrm{m}\right)}$ is the autoregressive parameter. The variables ${\mathrm{Y}}_{\mathrm{i}\mathrm{t}}$ and ${\mathrm{X}}_{\mathrm{i}\mathrm{t}}$ are the response and predictor variables, respectively, for the country $\mathrm{i}$ in time $\mathrm{t}$.

To evaluate the robustness of the MM-QR estimations, we utilized several alternative methodologies, including Dynamic Ordinary Least Squares (D-OLS), Fully Modified Ordinary Least Squares (FMOLS), and Fixed Effects Ordinary Least Squares (FE-OLS).

Finally, this study utilizes the Dumitrescu and Hurlin approach to investigate and establish the short-run causal relationships among the variables [41]. The DH test of causality consists of two statistics, which are expressed as follows:

$${\mathrm{W}}_{\mathrm{N},\mathrm{T}}^{\mathrm{H}\mathrm{N}\mathrm{C}}={\mathrm{N}}^{-1}\sum _{\mathrm{i}=1}^{\mathrm{N}}{\mathrm{W}}_{\mathrm{i},\mathrm{t}}$$

(16)

$${\mathrm{Z}}_{\mathrm{N},\mathrm{T}}^{\mathrm{H}\mathrm{N}\mathrm{C}}={\displaystyle \frac{\frac{1}{\sqrt{\mathrm{N}}}\left[\sum _{\mathrm{i}=1}^{\mathrm{N}}{\mathrm{W}}_{\mathrm{i},\mathrm{t}}-\sum _{\mathrm{i}=1}^{\mathrm{N}}\mathrm{E}\left({\mathrm{W}}_{\mathrm{i},\mathrm{t}}\right)\right]}{\sqrt{\frac{1}{\mathrm{N}}\sum _{\mathrm{i}=1}^{\mathrm{N}}\mathrm{V}\mathrm{a}\mathrm{r}\left({\mathrm{W}}_{\mathrm{i},\mathrm{t}}\right)}}}$$

(17)

In this context, ${\mathrm{W}}_{\mathrm{N},\mathrm{T}}^{\mathrm{H}\mathrm{N}\mathrm{C}}$ is the WIstaisc, ${\mathrm{Z}}_{\mathrm{N},\mathrm{T}}^{\mathrm{H}\mathrm{N}\mathrm{C}}$ is the $\mathrm{Z}$-bar statistic, I and $\mathrm{V}\mathrm{a}\mathrm{r}\left({\mathrm{W}}_{\mathrm{i},\mathrm{t}}\right)$ and $\mathrm{E}\left({\mathrm{W}}_{\mathrm{i},\mathrm{t}}\right)$ which represent the variance and expectations of the W-statistic, respectively. The foundation of this test assumes no causal relationship between the series. Consequently, failing to confirm this assumption indicates the presence of causal relationships within the series. Figure A1 illustrates the framework of the current investigation.

The Dumitrescu-Hurlin (DH) causality test, designed specifically for heterogeneous panel data, was utilized in this study to assess the direction and significance of causal relationships among the variables. Understanding these relationships is vital for guiding government policy decisions, particularly regarding the short-term causal connections among the involved components. These features collectively enable a more comprehensive examination of causal interactions across heterogeneous panels [42]. The DH causality test also offers significant flexibility regarding the temporal dimension of the data, as it does not impose specific constraints or requirements. This flexibility allows it to be applied to datasets covering various periods, including time series, panel, and cross-sectional data. As a result, researchers can investigate causal relationships across a wide range of temporal contexts without being limited by the test’s inherent restrictions. Therefore, the DH causality test is a valuable tool for empirical research in this field, facilitating a thorough exploration of causal links.

4. Empirical Findings

Table 2. Descriptive statistic results.

	CCO2	GDP	REC	TI	WU	ESP
Mean	2.398246	10.48107	2.463188	8.051643	9.640453	0.568063
Median	2.463395	10.57643	2.653242	7.686621	9.549077	0.797507
Maximum	3.120246	11.37508	4.056989	12.59584	10.23485	1.587192
Minimum	0.824099	8.590503	−0.510826	5.017280	9.172021	−2.207275
Std. Dev.	0.426164	0.460352	1.006006	1.619726	0.311819	0.731979
Skewness	−0.859224	−1.339767	−0.906911	0.919990	0.540318	−1.439009
Kurtosis	3.952349	5.735257	3.197244	3.641560	2.058442	5.040023
Jarque-Bera	84.75983	321.9428	73.09616	83.37851	45.10914	273.2644

presents the descriptive statistics of the variables examined in this study: CCO₂, GDP, REC, TI, WU, and ESP. The mean values for these variables are 2.398246, 10.48107, 2.463188, 8.051643, 9.640453, and 0.568063, respectively. The descriptive statistics table further details the standard deviation, median, minimum, and maximum values for each variable. Following the descriptive analysis, it is essential to assess the stationarity of the variables included in the analysis. Accordingly, two-panel unit root tests—the Covariate-Augmented Dickey-Fuller (CADF) test and the Cross-sectional Im-Pesaran-Shin (CIPS) test—were employed. The outcomes from both tests, illustrated in

Table A1. CDS and CIPS and CADF Test Outcomes.

Variables	CDS	p-Value	CIPS and I(1)	CADF and I(1)
CCO2	27.38 a	0.000	−5.741 a	−4.106 a
GDP	60.17 a	0.000	−3.795 a	−3.418 a
REC	37.75 a	0.000	−5.589 a	−3.973 a
TI	8.50 a	0.000	−4.838 a	−3.779 a
WU	64.93 a	0.000	−4.610 a	−4.012 a
ESP	51.1 a	0.000	−5.671 a	−4.374 a

^a means significant at the 1% level.

, indicate that all variables are integrated at the first difference level, affirming their stationarity.

Furthermore, to explore potential correlations within the panel data, the Cross-Sectional Dependence (CD) test was conducted. The results, also shown in Table A1, confirm the presence of significant cross-sectional dependence among the variables, underscoring the interconnectedness of the high-income countries analyzed in this study.

The long-term interactions among the selected variables in this study, as indicated by the Westerlund cointegration results shown in

Table A3. Westerlund Cointegration Outcomes.

Statistics	Value	Z-Value	p-Value	Robust p-Value
GT	−2.745	2.213	0.014	0.013
Ga	−10.416	0.703	0.759	0.017
Pt	−12.16	3.602	0.000	0.007
Pa	−10.703	1.385	0.083	0.02

, underscore the necessity for further empirical analyses to validate our findings. This research employs the advanced MM-QR approach to examine the relationship between the response variable, energy efficiency, and its determinants. The results obtained from the MM-QR approach are summarized in

Table 3. The Outcomes of MMQR.

Model I 1990–2021	Quantiles
	$0.10$	$0.20$	$0.30$	$0.40$	$0.50$	$0.60$	$0.70$	$0.80$	$0.90$
GDP	0.861 a	0.794 a	0.735 b	0.678 a	0.631 a	0.582 a	0.534 a	0.491 a	0.440 a
REC	−0.078	−0.091 b	−0.102 a	−0.113 a	−0.122 a	−0.131 a	−0.140 a	−0.148 a	−0.158 a
TI	0.519	0.048	0.044	0.041	0.038	0.036	0.033	0.030	0.027
WU	−0.416 a	−0.415 a	−0.373 a	−0.334 a	−0.301 a	−0.267 a	−0.233 a	−0.204 a	−0.168 a
ESP	0.023	0.021	0.020	0.019	0.018	0.017	0.170	0.016	0.015
Model II   2000–2021
	$0.10$	$0.20$	$0.30$	$0.40$	$0.50$	$0.60$	$0.70$	$0.80$	$0.90$
GDP	0.77 b	0.759 a	0.751 a	0.743 a	0.737 a	0.728 a	0.721 a	0.714 a	0.706 a
REC	−0.092 c	−0.109 a	−0.122 a	−0.133 a	−0.143 a	−0.156 a	−0.168 a	−0.178 a	−0.19 a
TI	0.148	0.133	0.122	0.112	0.103	0.092	0.082	0.073	0.063
WU	−0.382 a	−0.361 a	−0.345 a	−0.331 a	−0.319 a	−0.303 a	−0.289 a	−0.275 a	−0.261 a
ESP	0.033	0.027	0.023	0.019	0.016	0.011	0.007	0.004	0.000

^a,b,c means significant at the “1% and 5%” levels.

.

The analysis investigates the impact of various factors on CCO₂ emissions (CCO₂) in high-income countries, revealing that Gross Domestic Product (GDP) has a positive and significant relationship with emissions, with coefficients ranging from 0.861 at the 10th quantile to 0.440 at the 90th quantile, indicating that economic growth leads to increased emissions due to higher production and energy consumption. In contrast, Renewable Energy Consumption (REC) shows a negative and significant relationship with emissions, with coefficients ranging from −0.078 at the 10th quantile to −0.158 at the 90th quantile, suggesting that adopting renewable energy sources effectively reduces emissions, particularly in higher energy-use economies. Technological Innovation (TI) demonstrates a positive but statistically insignificant effect across all quantiles, with coefficients from 0.519 to 0.027, implying that while promising, technological advancements have not significantly impacted emissions reduction. Similarly, Environmental Policy Stringency (EPS) exhibits a positive but insignificant relationship, with coefficients ranging from 0.023 to 0.015, indicating that current policies may lack necessary enforcement and robustness. Lastly, World Uncertainty (WU) has a negative and significant relationship with CO₂ emissions across all quantiles, with coefficients from −0.416 at the 10th quantile to −0.168 at the 90th quantile, suggesting that periods of global uncertainty reduce industrial activity and energy demand, leading to lower emissions. In summary, the study emphasizes that while GDP drives emissions in high-income countries, renewable energy is crucial for mitigating environmental impact. Additionally, the limited effectiveness of technological innovation and environmental policies highlights the need for stronger measures to enhance sustainability in these economies. To affirm the findings of MMQR, we have tested different data period from 2000–2021, the coefficients of GDP, REC, TI, WU, and ESP remain constant.

Table A2. Testing for slope heterogeneity.

$\widehat{∆}$	p-Value	$-{\widehat{∆}}_{\mathbf{A}\mathbf{d}\mathbf{j}}$	p-Value
9.466 a	0.000	10.759 a	0.000

^a means significant at the 1% level.

presents the results of the slope heterogeneity test, which is used to ascertain whether the relationship between the independent variables and CCO₂ emissions is consistent across quantiles. The results reveal significant evidence of heterogeneity in the slope, as both the unadjusted change in the slope ($\widehat{∆}$ = 9.466a, p-value = 0.000) and the adjusted change in the slope (−${\widehat{∆}}_{\mathrm{A}\mathrm{d}\mathrm{j}}$ = 10.759a, p-value = 0.000) are statistically significant at the 1% level. This suggests that the impact of global uncertainty, environmental policy stringency, technological innovation, and GDP per capita on CCO₂ emissions varies across different segments of the data. The findings highlight the need for tailored policies and interventions, as the effect of these factors is not homogeneous across all economic contexts. Checking for long-term relationships between variables, the Westerlund cointegration test (Table A3) findings suggest that the cointegration linkage among the tested variables is valid.

The study employed the MMQR methodology to assess the relationships between the selected variables, with the findings summarized in Table 3. The results indicate that GDP has a positive association with CCO₂ emissions across various quantiles. Specifically, confirmthe significant effect of GDP ranges from 0.440% at the 0.90 quantile to 0.861% at the 0.10 quantile, highlighting its strong positive influence on the targeted outcomes. The results should be interpreted with caution due to the potential for cross-sectional dependence among high-income nations. While MMQR provides valuable insights into the varied effects of explanatory variables, the interdependencies in trade and shared economic policies may either enhance or mitigate these observed effects. Future robustness checks, such as utilizing alternative estimation techniques or assessing residuals for dependencies, may be warranted, as MMQR might not fully capture these interrelationships. This approach will facilitate a more nuanced understanding of the outcomes in practical contexts.

In contrast, renewable energy consumption (REC) shows a negative correlation with CCO₂ emissions, particularly at the 0.40 to 0.90 quantiles, where coefficients decline from −0.113% to −0.158%. This suggests that increased levels of renewable energy consumption positively affect ecological sustainability by reducing the level of CCO₂ emissions. Technological innovation (TI) demonstrates a less consistent impact, with significant results only at certain quantiles, such as 0.30, where it indicates a slight positive effect of 0.044%. Meanwhile, the World Uncertainty Index (WU) exhibits a significant negative correlation across all quantiles, showing a decrease in the dependent variable of approximately −0.168% at the 0.90 quantile and −0.416% at the 0.10 quantile. These findings collectively reinforce the negative impact of uncertainty on economic performance.

Furthermore, the results from the FE-OLS, D-OLS, and FM-OLS estimators are presented in

Table 4. The Outcomes of FE-OLS, D-, D-OLS FM-OLS.

	DOLS			FMOLS			FEOLS
Variable.	Coeff.	${\mathbf{t}}_{\mathbf{s}\mathbf{t}\mathbf{a}\mathbf{t}\mathbf{s}}$	Prob	Coeff.	${\mathbf{t}}_{\mathbf{s}\mathbf{t}\mathbf{a}\mathbf{t}\mathbf{s}}$	Prob	Coeff.	${\mathbf{t}}_{\mathbf{s}\mathbf{t}\mathbf{a}\mathbf{t}\mathbf{s}}$	Prob
GDP	0.537887 a	3.472086	0.0006	0.763676 a	8.080235	0.0000	0.641611 a	3.796563	0.0016
REC	−0.062156 a	−1.793591	0.0744	−0.095999 a	−4.047776	0.0001	−0.120568 b	−2.222127	0.0410
TI	0.056939	1.046060	0.2968	0.024981	0.737151	0.4614	0.039604	0.573862	0.5740
WU	−0.387934 a	−5.100399	0.0000	−0.396649 a	−8.338656	0.0000	−0.307395 a	−4.700424	0.0002
ESP	0.028423	0.849436	0.3967	−0.015570	−0.764185	0.4451	−0.019816	−0.974729	0.3442

^a,b means significant at the “1% and 5%” levels.

. The results confirm that GDP is a robust predictor of the dependent variable, with coefficients consistently showing significance across all models (ranging from 0.537 to 0.763). Conversely, REC consistently exhibits a negative effect across the models, with significant coefficients ranging from −0.120 to −0.096, indicating that increases in renewable energy consumption adversely affect CO₂ emissions. These findings are in line with [43,44,45,46].

However, technological innovation (TI) does not demonstrate significant coefficients across the models, suggesting a weaker direct relationship with the dependent variable. The World Uncertainty Index (WU) consistently shows a negative and significant relationship with the dependent variable, with coefficients indicating a substantial reduction in the examined outcomes, ranging from −0.307 to −0.396.

To evaluate the causal relationships among the variables, we conducted the Dumitrescu and Hurlin (2012) [41] panel causality tests, with the results summarized in

Table 5. Dumitrescu and Hurlin Panel Causality Tests.

Null Hypothesis:	W-Stat.	Zbar-Stat.	Prob.
$\mathrm{G}\mathrm{D}\mathrm{P}\ne$ CCO2	4.38983	3.80904	0.0001
${\mathrm{C}\mathrm{C}\mathrm{O}}_2\ne$$\mathrm{G}\mathrm{D}\mathrm{P}$	1.62552	−0.95968	0.3372
$\mathrm{R}\mathrm{E}\mathrm{C}\ne$ CCO2	6.40931	7.29287	0.0000
$\mathrm{C}\mathrm{C}\mathrm{O}\ne$$\mathrm{R}\mathrm{E}\mathrm{c}$	4.23028	3.53381	0.0004
$\mathrm{T}\mathrm{I}\ne$ CCO2	4.81491	4.54235	0.0000
${\mathrm{C}\mathrm{C}\mathrm{O}}_2\ne$$\mathrm{T}\mathrm{I}$	2.34665	0.28435	0.7761
$\mathrm{W}\mathrm{U}\ne$ CCO2	3.97708	3.09701	0.0020
${\mathrm{C}\mathrm{C}\mathrm{O}}_2\ne$$\mathrm{W}\mathrm{U}$	3.93829	3.03010	0.0024
$\mathrm{E}\mathrm{P}\mathrm{S}\ne$ CCO2	3.86717	2.90740	0.0036
${\mathrm{C}\mathrm{C}\mathrm{O}}_2\ne$$\mathrm{E}\mathrm{P}\mathrm{S}$	2.06446	−0.20246	0.8396

. The findings reveal bidirectional causality between GDP and carbon dioxide emissions (CCO₂), indicating that changes in GDP can lead to fluctuations in CCO₂ levels and vice versa. Additionally, there is a significant causal relationship between renewable energy consumption (REC) and CCO₂, highlighting the impact of renewable energy on emissions. Furthermore, technological innovation (TI) and water usage (WU) also show significant causation with CCO₂, suggesting that both variables influence carbon emissions within this framework. These results emphasize the interconnected nature of these variables and provide valuable insights into the complex dynamics governing their relationships. Figure 1 presents a summary of the study’s findings.

5. Findings Discussion

This study explores the impact of world uncertainty, environmental policy stringency, and technological innovation on CCO₂ emissions. It utilizes panel data from high-income countries covering the years 1990 to 2021. By applying the Method of Moments Quantile Regression (MMQR) approach, we examined the relationships among these key factors and their influence on overall environmental sustainability. The findings from the MMQR analysis provide several important insights.

This analysis reveals that world uncertainty significantly reduces CCO₂ emissions across all quantiles. This observation suggests that during periods of global uncertainty, economic and political factors may lead to a temporary decline in environmental degradation. A plausible explanation for this phenomenon is that economic activities typically slow during uncertain times, resulting in decreased energy demand and lower emissions. However, while uncertainty may serve as a temporary mitigating factor for emissions, it does not constitute a sustainable long-term solution for environmental sustainability. Such uncertainty may impede investments in renewable energy infrastructure and green technologies. Therefore, although uncertainty can reduce emissions in the short term, it does not facilitate enduring environmental improvements without strategic policy interventions. Table 3 reflects that these findings are in line with [47,48].

Furthermore, the analysis indicates that environmental policy stringency has an insignificant and marginally positive effect on CCO₂ emissions. This counterintuitive outcome may reflect that stricter environmental policies often lack necessary enforcement and effectiveness in significantly curbing emissions, particularly in high-income countries. The reliance on fossil fuels in key sectors, such as industry and transportation, persists despite stringent regulations. These findings suggest that enacting rigorous policies alone is insufficient. Robust enforcement mechanisms and enhanced incentives for the transition to cleaner energy sources are crucial. Additionally, while Environmental Policy Stringency (EPS) positively influences renewable energy usage, as noted in Table 3, it does not significantly reduce CCO₂ emissions. This underscores the need for a deeper examination of the structural economic reliance on fossil fuels and further aligns with [49], which highlights the challenges in policy enforcement and the continued dependence on carbon-intensive energy systems despite regulatory measures.

Moreover, technological innovation (TI) exhibits an insignificant and limited positive impact on CCO₂ emissions. Although technological advancements are frequently proposed as solutions to environmental challenges, the findings indicate that, in high-income countries, the current pace of green technologies is inadequate to counteract emissions resulting from industrial and economic growth. While innovation has potential, its limited impact, as reflected in Table 3, may be attributed to insufficient alignment with environmental sustainability goals. This highlights the critical need for increased investments in eco-innovation and the scaling of clean energy technologies to achieve substantial emissions reductions.

In contrast, a significant reduction in CCO₂ emissions is correlated with increased REC. The results demonstrate that integrating a greater proportion of renewable energy into the overall energy mix constitutes an effective strategy for emissions reduction. Nevertheless, despite these advantages, many high-income countries continue to rely heavily on non-renewable energy sources such as coal and natural gas. This scenario underscores the necessity for stronger regulatory incentives and financial subsidies to expedite the transition from fossil fuels to renewable energy sources. Table 3 provides a detailed quantile breakdown of the relationship between renewable energy consumption and emissions, emphasizing its significance across various levels of energy use.

Finally, GDP per capita, as a measure of economic growth, is found to have a positive and significant effect on CCO₂ emissions across all quantiles. This observation reflects the environmental costs associated with industrialization and economic development; an increase in GDP typically correlates with heightened energy consumption and emissions, particularly in economies that rely on carbon-intensive industries. These findings underscore the imperative of decoupling economic growth from environmental degradation clean energy, sustainable development practices, and green investments.

In summary, the study underscores that achieving environmental sustainability in high-income countries necessitates a multifaceted approach. Policymakers must enforce stringent ecological regulations while also fostering technological innovations tailored to address environmental challenges. Expanding renewable energy consumption, promoting green investments, and decoupling economic growth from emissions are essential steps toward attaining long-term sustainability. A comprehensive policy framework that integrates regulation, innovation, and economic incentives is vital for mitigating CCO₂ emissions and fostering a sustainable future.

Results Summary Paragraph

According to the results of the MMQR research, global uncertainty significantly reduces CO₂ emissions across all quantiles. This is likely because economic activity tends to slow down during uncertain times. However, such a reduction is not sustainable without specific initiatives in place. Stricter laws alone are insufficient unless accompanied by robust enforcement and structural changes in energy systems. This is evident from the negligible and modest positive impact of environmental policy stringency (EPS) on emissions. Similarly, the minimal positive effect of technological innovation (TI) suggests that current breakthroughs in green technology are not enough to achieve substantial emissions reductions. The gap between technological breakthroughs and the legislative frameworks that support them is a potential factor in this issue. Additionally, the capacity of technological innovation to significantly lower emissions may be hindered by market failures, including insufficient incentives for widespread adoption and inadequate investment in green technologies. The perceived marginalization of technological innovation may also stem from structural challenges, such as difficulties in scaling innovations and limited access to clean technology in developing countries. It is important to acknowledge that while MMQR has identified specific dynamics within quantiles, the cross-sectional dependencies typical of high-income nations may influence the findings. To obtain more reliable results, future research should address these dependencies by employing methodologies specifically designed to account for these relationships. For example, utilizing spatial econometric models or cross-sectionally enhanced approaches could enhance the results and offer a more comprehensive understanding of the policy implications.

To effectively move away from fossil fuels, stronger incentives are necessary. In contrast, renewable energy consumption (REC) significantly reduces CO₂ emissions, underscoring the crucial role of renewable energy in emissions mitigation. Lastly, GDP per capita has a considerable positive influence on emissions, highlighting the environmental consequences of economic growth. This stresses the urgent need to decouple economic growth from emissions through clean energy and sustainable development strategies.

6. Conclusions and Policy Suggestions

High-income nations have experienced substantial economic growth in recent decades while also implementing stringent environmental regulations. This study utilizes the Method of Moments Quantile Regression (MMQR) technique to analyze the effects of technological innovation, environmental policy stringency, and global uncertainty on CCO₂ emissions from 1990 to 2021. This approach allows for an examination of how these factors influence emissions at various levels of distribution. The findings of the study show that global uncertainty significantly reduces CCO₂ emissions across all quantiles. This suggests that periods of global instability, whether economic or political, may lead to a temporary decrease in environmental degradation. It implies that high-income countries could experience short-term reductions in their environmental impact during uncertain times, although the long-term implications merit further investigation. Another important finding is that while Environmental Policy Stringency (EPS) positively influences renewable energy usage, it does not significantly reduce CCO₂ emissions. Despite the implementation of stricter regulations, many high-income countries still rely heavily on non-renewable energy sources. This indicates that current policies may not effectively achieve sustainability goals due to entrenched economic structures that depend on fossil fuels.

Regarding Technological Innovation (TI), the findings are mixed. While innovation can support environmental sustainability in certain contexts, its overall impact remains limited. Empirical evidence suggests that innovation alone is insufficient for driving substantial environmental improvements unless it is accompanied by policies that incentivize clean energy and sustainable practices.

Based on these findings, the study recommends several policy actions:

Environmental Policies: Lawmakers ought to strengthen environmental legislation by ensuring it is backed by effective enforcement mechanisms, such as carbon pricing schemes and stringent emissions reduction targets. Additionally, they should introduce specific incentives to encourage the adoption of green technologies. For example, governments could offer low-interest loans for sustainable projects, along with tax breaks or direct subsidies for businesses investing in renewable energy solutions. By providing tax incentives, funding for clean energy initiatives, and supporting research and development in renewable technologies, particularly in solar, wind, and battery storage, governments can motivate businesses to adopt environmentally friendly practices.

Utilizing Global Uncertainty: Given that global uncertainty can temporarily lower emissions, policymakers should take advantage of these opportunities to advocate for sustainable, long-term policies. During challenging economic periods, the emphasis should be on investing in green initiatives that can withstand economic fluctuations, such as energy-efficient infrastructure, enhancements to renewable energy grids, and climate-resilient urban designs. Governments should consider green stimulus plans that create immediate job opportunities in the clean energy sector, thereby promoting both environmental advantages and economic recovery.

Aligning Technology with Environmental Goals: Policies aimed at advancing environmental sustainability should be in harmony with technological advancements. Governments should prioritize eco-innovation in high-emission industries like transportation, manufacturing, and agriculture, while also providing incentives for research and development in these sectors. This could include establishing public-private partnerships to expedite the commercialization of promising innovations and offering grants or subsidies for low-carbon technologies. Furthermore, governments should implement regulations that lower barriers to the broad adoption of green technologies, such as financial incentives for early adopters and ensuring access to clean technology in underserved communities.

This study underscores the importance of a comprehensive approach to environmental sustainability that incorporates global uncertainty, policy stringency, and technological innovation. High-income countries must move beyond isolated initiatives to develop frameworks that synergize regulatory, fiscal, and innovation strategies for achieving long-term ecological sustainability. While this study provides valuable insights into the dynamics of environmental sustainability, future research should consider additional factors such as green technological innovation to further strengthen these findings. Additionally, the lack of recent data on certain variables presents limitations; future studies should revisit these results when updated data becomes available. Researchers may also explore alternative econometric techniques, such as the CS-ARDL model, to validate the robustness of these findings.