Sustainability in High-Income Countries: Urbanization, Renewables, and Ecological Footprints

Abstract

Environmental sustainability remains a critical challenge in the face of global economic development. This study explored the complex interactions among renewable energy consumption, urbanization, trade openness, and economic development, focusing on their effects on environmental quality in 34 high-income European and Asian economies from 1970 to 2022. Using linear Bayesian regression and the Vector Error Correction Model (VECM), the analysis examined short- and long-term impacts to uncover nuanced relationships. Results demonstrated that economic development contributed to environmental degradation over the long term while mitigating it in the short term. Renewable energy consumption supported economic growth but showed limited efficacy in reducing ecological footprints across different time frames. Urbanization and trade openness emerged as significant drivers of long-term environmental degradation, emphasizing the need for targeted policy interventions. This study examined the link among economic progress and environmental sustainability, and identified key areas for improvement in urban planning, renewable energy, and trade policies. The findings provide a framework for policymakers to balance development with environmental preservation.

1. Introduction

Environmental degradation and increasing energy consumption have become contentious issues in both developed and developing economies, as they are closely linked to emerging climate changes [1]. Renewable energy and technological innovation drive economic development while reducing greenhouse gas emissions [2]. The excessive use of traditional energy resources has detrimental effects on environmental quality and human health [3], leading to a growing global focus on renewable green energy resources. In 2023, foreign investments in renewable resources exceeded US$ 214 billion in regions such as Europe, Asia, and America, which are heavily reliant on bio fossil fuels such as hydro, solar, and wind energy [4].

Nonrenewable energy consumption plays a crucial role in driving economic growth. However, it simultaneously inhibits the adoption of renewable energy sources, thereby limiting the performance of clean energy alternatives [5]. Conversely, both human development and renewable energy consumption contribute positively to economic growth, particularly in emerging economies [6]. Moreover, financial development, increased utilization of renewable energy, and greater trade openness have been identified as key factors in mitigating greenhouse gas emissions [7]. Additionally, a strong bidirectional relationship exists between renewable energy consumption and economic development, highlighting their mutual influence [8]. This relationship has significant implications for sectors such as cooling, heating, and transportation, particularly in European countries [9]. Moreover, the interplay between renewable energy consumption, migration, and ecological footprint has been shown to significantly affect the latter [10]. Lastly, a country’s development in areas like life expectancy, education, and income, along with increasing renewable energy consumption, are effective in reducing carbon pollution and mitigating the impacts of climate change [11]. Together, these factors support effective climate change strategies, promoting both environmental sustainability and social progress.

Instantaneously, the world has witnessed an unprecedented surge in urbanization, with a substantial share of the global population transitioning from rural to urban areas. Between 1980 and 2011, the proportion of urban dwellers increased significantly from 39.1% to 52.0%. While scholars contend that urbanization fosters economic growth and enhances quality of life, evidence suggests that it also escalates energy demand and poses significant challenges to environmental sustainability.

Contribution in environmental taxation and development in Information and Communication Technology (ICT) enhanced environmental sustainability in European Union (EU) countries. However, investments in research and development (R&D) and higher income per capita have been associated with environmental deterioration [12]. In Association of Southeast Asian Nations (ASEAN) countries, economic development, trade, and reliance on non-renewable energy significantly exacerbate ecological footprint, while urbanization is linked to increased non-renewable energy consumption [13]. Similarly, in Nigeria, urbanization and energy consumption are major drivers of carbon emissions, whereas trade has a mitigating effect, underscoring the importance of policies aimed at addressing energy poverty and promoting sustainable development [14]. In South Africa, higher economic development has been found to reduce energy intensity, suggesting improved energy efficiency over time [15].

Urbanization and energy consumption have been recognized as primary contributors to the substantial rise in greenhouse gas emissions and financial development, necessitating a re-evaluation of the relationship between methodological approaches and emission levels over the past three decades [16]. Numerous studies [17,18,19] explored the causal link among economic development and renewable energy consumption, particularly within the framework of environmental policies. However, other studies such as [20,21] failed to address the comprehensive relationship and distributional impacts of other critical variables—for example environmental degradation, urbanization, trade openness, and economic growth.

The current study aims to address existing gaps in the literature by examining the causal relationships between urbanization, renewable energy consumption, trade openness, and environmental sustainability. Specifically, it investigates how high-income countries in Europe and Asia tackle ecological degradation, with a focus on their strategies for promoting renewable energy. We thus explore how these countries balance economic growth and environmental sustainability, contributing to a better understanding of the approaches used to mitigate environmental challenges. By examining the interplay between urbanization, trade openness, renewable energy consumption, and economic development, this research aims to provide a deeper understanding of the pathways toward sustainable development. The findings are expected to offer valuable insights for environmental sustainability policymakers, highlighting effective strategies to mitigate environmental degradation while fostering economic growth.

Notably, ecological footprints are intricately tied to human activities, including urbanization, economic development, energy consumption, and trade openness, further emphasizing the urgency of sustainable policy interventions [22]. As economies grow, the pressure on environmental quality often intensifies, raising important questions about the role of renewable energy in mitigating environmental degradation. While renewable energy is widely seen as a key solution for reducing ecological footprints, the short-term and long-term impacts on economic development remain unclear. Additionally, factors such as urbanization and trade liberalization are often linked to environmental pressures, yet their influence over different time horizons has not been fully explored. By examining these variables across high-income European and Asian economies, this study aims to deepen our understanding of how economic and environmental factors interact, providing insights into the pathways toward more sustainable development.

The paper is organized as follows: Section 2 provides relevant literature review, followed by the methods and models. Subsequent sections present results analysis and interpretation, Section 5 discusses the findings, and Section 6 provides a conclusion and policy recommendations.

2. Literature Review

Environmental Quality and Economic Growth: Economic growth exerts a substantial influence on carbon emissions, with Chinese and Asian emerging economies as a notable driver of carbon emissions [23,24]. The Gross Domestic Product (GDP) assumes a pivotal role in fostering economic growth by facilitating industrial development and productivity within the manufacturing sectors, consequently contributing to overall economic advancement. However, heightened production and manufacturing activities are accompanied by adverse environmental consequences. Sharma [25] conducted an examination across economies with varying income levels, revealing that GDP per capita, exchange transparency, and energy significantly impact carbon emissions. Notably, urbanization exhibited a negative correlation with pollution levels. The study further identified statistically significant determinants of carbon emissions, emphasizing the impact of GDP per capita and per-capita energy consumption on a global panel. The relationship between energy consumption and GDP demonstrated a sustained beneficial connection [26].

Within the context of China, the associations among GDP, population, industrialization, and carbon emissions were scrutinized by [27]. Ref. [28] concluded that certain factors are co-integrated with carbon emissions. However, a dearth of literature addresses the precision of valuation, particularly in situations involving non-normal dispersion and variability among emissions from different ‘cities’. Furthermore, per capita GDP, population, and the state of industrialization were identified as contributors to the escalation of carbon emissions. Ref. [29] investigated a two-way causality among energy consumption, carbon emissions, and economic growth, while [30] confirmed cointegration in the association between economic growth and carbon emissions in high-income European and Central Asian countries, illustrating a positive correlation. Ref. [31] assessed the significant relationship among carbon emission, decentralization and economic growth. Collectively, these studies underscore the intricate dynamics and interconnectedness of economic growth, environmental factors, and carbon emissions, particularly in the context of distinct regions and income levels. Ref. [32] employed the Bayesian Model Averaging to determine the ecological footprint in high-income countries of Europe.

Environmental Quality and Energy Consumption: The investigation conducted by [33] scrutinizing the nexus between carbon emissions and renewable/nonrenewable energy sources across 10 high-energy countries in sub-Saharan Africa. The study underscores the pivotal role of green energies in monitoring carbon emissions and elucidates the associated adverse effects. Ref. [34] investigated the correlation between renewable energy consumption and economic growth, affirming a validated relationship between economic growth and both conventional inputs and those intertwined with energy. The study further highlights that renewable energy contributes significantly to economic production, accounting for 57%. Analytical approaches were employed to explore sustainable energy consumption and regulatory measures targeting greenhouse gas pollution, revealing two dimensions: green energy utilization and its intersection with economic activity and carbon pollution. Notably, the research discerns that carbon emissions are mitigated by renewable energy; however, the compounding factors of population and economic growth have resulted in more substantial reductions in the ecological footprint.

Several studies examined the role of renewable energy sources in diverse facets such as economic development, alleviation of hunger, industrial facilities, environmental pollution, and air equilibrium [1,35,36,37]. These studies utilized a range of statistical and econometric analyses across different time periods, consistently demonstrating the positive influence of green energy on economic activities. Collectively, these findings reinforce the pivotal role of renewable energy in reducing greenhouse gas emissions, in contrast to the positive correlation observed between nonrenewable energy consumption and increased carbon emissions. Building on the existing literature, this study hypothesizes a negative relationship between carbon dioxide emissions and renewable energy consumption in high-income European and Central Asian countries. For instance, Mata et al. [38] analyzed the relationship between renewable energy consumption, urban density, and CO₂ emissions using the CS-ARDL model across 30 high-income countries. Additionally, other studies [39,40] examined the connections between financial development, renewable energy consumption, and technological innovation by using PQ-ARDL and MMQR models.

Environmental Quality and Urbanization: Global urbanization, particularly in developing nations, has raised increasing concerns regarding its environmental consequences. Numerous empirical studies have sought to understand the impact of urbanization on climate dynamics across various regions. For instance, ref. [41] demonstrated a positive relationship between urbanization and carbon emissions through multilevel panel-based empirical research. In a subsequent study, ref. [42] examined the relationship between urbanization, energy consumption, renewable energy, economic growth, and carbon emissions in Middle Eastern and North African countries using a panel autoregressive model. These countries were stratified by income (moderate, large, and low), with the results indicating that urbanization positively impacts environmental sustainability in moderate and large-income countries.

In contrast, studies such as [25,43,44] employed panel data from 69 countries, categorized into low, middle, and high-income groups, and found that urbanization negatively affects CO₂ emissions. Focusing on the Middle East and North Africa, refs. [42,45] identified a significant contribution of urbanization to its growth, driven by increased energy consumption and CO₂ emissions. These findings emphasized the complex long-term and short-term interactions between urbanization and environmental factors. Additionally, the study noted that urbanization tends to increase reliance on biofuels, which simultaneously contribute to rising emissions.

The Generalized Method of Moments (GMM) approach was used in [46] to examine the dynamic relationship between population, age structure, urbanization, electricity consumption, and carbon dioxide emissions. Focusing on BRICS countries, the analysis shed light on the factors influencing carbon emissions in these rapidly developing economies. Similarly, ref. [22] employed a bivariate approach, emphasizing the critical role of urbanization in driving carbon emissions. In another study, ref. [47] analyzed urbanization in ASEAN countries, highlighting its negative effects on environmental quality and examining the short-run causal relationship between urbanization and carbon emissions.

Together, these studies offer a comprehensive understanding of the complex interplay between urbanization and environmental factors, revealing both positive and negative outcomes that are highly dependent on contextual variables.

Environmental Quality and Trade: The research conducted by Ghazouani and Maktouf [48] offers a comprehensive analysis of the intricate relationship between trade-related carbon dioxide emissions and economic growth. Their findings consistently indicate that active engagement in international trade acts as a driving force for global economic prosperity. Empirical evidence from these studies strongly underscores the positive correlation between trade accessibility and economic expansion. Building on this research, Zahonogo [49] conducted an extensive investigation covering the period from 1980 to 2012, examining the impact of trade openness on economic growth across 42 sub-Saharan African nations. The econometric results of this study provide robust support for the hypothesis that foreign trade significantly contributes to higher levels of economic development. Further reinforcing this perspective, ref. [50] argue that unrestricted trade in Asia not only accelerates economic development but also creates substantial opportunities for human advancement. This aligns with the widely accepted view that economic growth is deeply interconnected with broader human progress. Collectively, these studies underscore the critical role of trade in driving both economic and human development, offering valuable insights for policymakers and scholars alike.

In a more specific context, ref. [51] undertook a modeling and calibration exercise utilizing Autoregressive Distributed Lag (ARDL) and Vector Error Correction Model (VECM) frameworks for China. The outcomes of their analysis reveal a consistent linkage between trade and economic growth. Furthermore, refs. [51,52] found that the escalating environmental degradation exerts a positive impact on both renewable energy resources and trade dynamics. As illustrated in the conceptual framework (Figure 1), this interplay highlights the intricate relationships between economic growth, environmental considerations, and trade dynamics, emphasizing the depth and complexity of these global economic interactions.

3. Methods

Utilizing a distributional relations approach, this study examines the effects of renewable energy consumption, economic growth, trade, and urbanization on environmental quality, using data spanning from 1970 to 2022. The selection of these variables is theoretically grounded in their significance to the study’s objective understanding of the intricate interactions between economic growth, renewable energy consumption, urbanization, and trade liberalization, and their implications for environmental sustainability. These variables were chosen for their ability to capture critical dynamics influencing both economic development and environmental outcomes, making their relationships central to the research inquiry.

Economic growth, as measured by GDP, is examined within the framework of the well-known Environmental Kuznets Curve (EKC) hypothesis [53], which posits that CO₂ emissions initially rise with economic expansion but eventually decline once a certain level of growth is attained. However, the empirical relationship between economic growth and environmental efficiency remains inconclusive in the literature. Similar to capital and labor, energy serves as a fundamental input for production activities. Increased energy consumption can either contribute to environmental degradation through fossil fuel combustion or enhance sustainability through the adoption of renewable energy sources. Given the global shift toward renewable energy to mitigate CO₂ emissions and climate change [54,55], we have incorporated renewable energy consumption into our model.

Urbanization also plays a critical role in environmental dynamics [56], as it drives industrial production, transportation, and service sector expansion, all of which intensify energy demand. Therefore, urbanization must be carefully assessed, particularly in regions where a significant proportion of the population resides in urban areas.

The impact of trade on environmental quality can be either negative or positive. On the one hand, trade may exacerbate pollution by fostering specialization in environmentally harmful industries, increasing hazardous waste production, and expanding transportation-related emissions [57]. On the other hand, trade can facilitate the transfer of capital and environmentally friendly technologies between countries, leading to improvements in environmental quality [58]. Developed nations, in particular, have the resources to invest in and disseminate green technologies.

To assess these dynamics, we have incorporated trade into our model to evaluate its effect on environmental efficiency. Based on these theoretical foundations, the following model has been constructed for empirical analysis.

3.1. Model Erection

This study employs the stochastic influence of regression framework to analyze the relationships between economic growth, renewable energy consumption, urbanization, and trade. The econometric terminologies presented in this section are derived from the regression models applied in this study. These formulations represent the estimated relationships among the variables, as inferred from the empirical analysis using the Vector Error Correction Model (VECM) and Bayesian regression. The constants and functional forms are determined based on statistical estimation procedures, ensuring the robustness of the model’s analytical capabilities. This methodological approach enhances the reliability of the findings for parameter uncertainty inherent in Bayesian inference. The model equation is specified as follows:

CO₂ = f (GDP_PCit, REC_it, URB_it,TR_it)…

(1)

This study utilizes secondary data obtained from multiple sources. The dependent variable is carbon emissions (CO₂), while the independent variables include renewable energy consumption (REC), urbanization (URB), trade (TR), and gross domestic product per capita (GDPp). All variables are transformed using the natural logarithm per capita method for consistency and comparability.

The variables are represented as follows: CO₂ emissions (measured in metric tons per capita), urbanization (percentage of the total population living in urban areas), renewable energy consumption (percentage of total final energy consumption), trade (percentage of GDP), and real GDP per capita, which serves as a proxy for economic growth.

The panel data set spans the period from 1970 to 2022 and is sourced from the World Development Indicators (WDI), available online through the World Bank’s database.

3.2. Bayesian Regression

In the Bayesian regression model, prior distributions are carefully selected to ensure methodological rigor and mitigate potential biases. This study employs a weakly informative prior, which provides regularization while allowing the data to primarily inform the results. Specifically, a Normal (0,2) prior is assigned to regression coefficients to maintain reasonable parameter estimates, while the error variance follows a Half-Cauchy (0,5) distribution to accommodate variability. For hierarchical structures, a Normal (μ, τ²) prior captures country-specific deviations. Additionally, GDP elasticity and environmental impact coefficients are modeled using Normal (0,2) priors to prevent extreme values [59]. These choices minimize subjective bias, stabilize estimates, and align with best practices in Bayesian econometrics, ensuring robust and interpretable inferences in environmental sustainability research [60]. Considering the Bayesian linear issue of regression, where we define the mean of the predictor vector’s conditional distribution:

$$y_{i={x^T}_{i }}\beta +{\epsilon }_i$$

(2)

where a vector and the random variables distributed are independent and identically normal:

$${\epsilon }_i ~N(0,{\sigma }^2)$$

(3)

This conforms to the following possible function:

$$\rho (y|X, \beta ,{\sigma }^2)\propto ({{\sigma }^2)}^{{\displaystyle \frac{-n}{2}}}\mathrm{e}\mathrm{x}\mathrm{p}({\displaystyle \frac{1}{2{\sigma }^2}}{(y-X\beta )}^2(y-X\beta )$$

(4)

Using the Moore–Penrose pseudoinverse, the typical least square solution estimates the vector coefficient:

$$\widehat{\beta }={{(X}^{T}X)}^{-1}{X}^{T}y$$

(5)

3.3. Vector Error Correction Model

We have employed the Vector Error Correction Model (VECM) to analyze the long-term and short-term dynamics among the variables, given that the variables in the Vector Autoregression (VAR) framework exhibit cointegration. The VECM is specified as follows: Conventional VECM for Cointegration series

$$\mathsf{\Delta }{y}_t={\mathsf{\beta }}_0+{\sum }_{i=1}^n{\mathsf{\beta }}_i\mathsf{\Delta }{Y}_{t-i} + {\sum }_{i=0}^n{\mathsf{\delta }}_i\mathsf{\Delta }{X}_{t-i}+\phi Z_{t-i}+{\mu }_t$$

(6)

Z is the ECT and is the OLS residual from the following long-run cointegration regression:

$$y_t={\beta }_0+{\beta }_1{x}_1 +{\epsilon }_t$$

(7)

and defined as

$$z_{t-1}={ECT}_{t-1}=y_{t-1}-{\beta }_0-{\beta }_1{x}_{t-1}$$

(8)

The error correction term (ECT) plays a crucial role in capturing the adjustment process toward long-run equilibrium. It indicates how deviations from the equilibrium in the previous period influence the short-run dynamics of the dependent variable. The coefficient of the ECT, denoted as $\phi$ phi, represents the speed of adjustment, measuring the rate at which the dependent variable (y) returns to equilibrium following a change in the independent variables (x). A statistically significant and negative $\phi$ phi confirms the existence of a long-run relationship, ensuring that short-term fluctuations correct toward equilibrium over time.

3.4. Robustness Checks

To certify the consistency of our results, multiple robustness tests were conducted. Johansen Fisher Panel Co-Integration was used to confirm the long-run equilibrium associations between variables. Correlation analysis evaluated the strength and direction of relations. Normality tests ensured that residuals followed a suitable distribution, while heteroscedasticity tests checked for variance stability. To identify multi-collinearity, Variance Inflation Factor (VIF) analysis was employed. These diagnostic tests strengthen the validity of our Vector Error Correction Model (VECM) estimates, confirming that outcomes are not driven by causal econometric issues. This approach aligns with best practices in panel econometric modeling [61].

3.5. Panel Unit Root Tests

For all selected variables, panel unit root tests have been conducted to ensure the robustness of the data and prevent the issue of imitation regression. The primary motivation for employing panel unit root tests is to address the low-power problem associated with the Augmented Dickey–Fuller (ADF) test, which can affect the reliability of estimates, particularly when the time-series and panel sample sizes are below 50 [62]. Panel unit root tests offer higher statistical power and follow a standard asymptotic distribution, making them a more reliable approach for assessing stationarity. As highlighted by [63], these tests provide more effective results in panel data settings. Prior studies on energy consumption have extensively applied these techniques to validate stationarity before proceeding with further econometric analysis.

The unit root test equation is formulated as follows:

$${\mathsf{\Delta }\mathrm{y}}_{\mathrm{it}} = \mathsf{\Delta }{\mathsf{Ø}}_{\mathrm{it}}+{\mathsf{\beta }}_{\mathrm{i}}{\mathrm{x}}_{\mathrm{it}-1}+{\mathsf{\rho }}_{\mathrm{i}}\mathsf{{\rm T}}{\sum }_{j-1}^n{\theta }_{\mathrm{ij}}\Delta {\mathrm{x}}_{\mathrm{it}-\mathrm{j}} + {\mathsf{\epsilon }}_{\mathrm{it}}$$

(9)

In the unit root test equation, Ø_it, X_it, ρ_i, Δ, Τ, and ε_it represent the intercept, the analyzed variables, the autoregressive coefficient, the difference operator, the time period, and the error term, respectively. The second-generation unit root test is widely preferred in the literature, as first-generation methods often lead to misleading conclusions due to cross-sectional dependence issues.

Table 1. Summary of Variables, Measures, and Sources.

Variables	Abbreviations	Measures	Sources
Carbon Dioxide/Ecology Footsteps	CO2	CO2 emissions (metric tons per capita)	World Bank and Global Footprint Network
Renewable Energy	REC	Percent energy consumption in total	World Bank
Gross Domestic Product Per Capita	GDP	GDP per capita growth annual percent	World bank
Urbanization	URB	Urban Population (share of Total population)	World Bank
Trade Openness	TR	Trade (as GDP percent)	World Bank

presents the measure definitions and sources of the selected variables, ensuring transparency and replicability of the data set used in this study. CO₂ refers to carbon dioxide emissions measured in metric tons per capita. REC represents renewable energy consumption as a percentage of total energy consumption. GDP indicates the gross domestic product per capita growth in annual percentage, which means the average economic output per person in a country or region per year. URB denotes the urban population as a share of the total population. TR stands for trade openness, meaning exports plus imports as percent of GDP.

4. Empirical Analysis and Interpretation

Table 2. Descriptive Statistics.

	CO2	GDPPC	URB	REC	TR
Number of observations	1666	1666	1666	1666	1666
Mean	7.564	25,990.530	37.972	7.991	71.822
Standard deviation	0.395	1.550	0.0798	0.586	0.690
Minimum	1.336	1686.922	17.972	0.334	9.099
Maximum	40.590	111,968.200	98.041	77.345	408.362
Skewness	−0.846	−1.900	−0.687	0.626	−1.540
Std. error skewness	0.060	0.060	0.060	0.060	0.060
Kurtosis	−0.189	1.890	0.301	−1.020	4.090
Std. error kurtosis	0.120	0.120	0.120	0.120	0.120
25th percentile	0.652	3.950	1.800	0.000	1.560
50th percentile	0.862	4.370	1.860	0.238	1.800
75th percentile	0.999	4.590	1.900	0.966	2.000

Notes: CO₂: Carbon Dioxide, GDP_PC: Gross Domestic Product per capita, URB: Urbanization, REC: Renewable Energy Consumption, TR: Trade Openness.

presents descriptive statistics for the variables in the study. The average carbon dioxide (CO₂) emissions per capita stand at 7.564 metric tons, with a minimum value of 1.336 and a maximum of 40.590. The mean GDP per capita is 25,990.530, ranging from a low of 1686.922 to a high of 111,968.300. Renewable energy consumption has an average value of 7.991%, with a minimum of 0.334% and a maximum of 77.345%. The urban population’s mean percentage is 37.972%, fluctuating between 17.972% and 98.041%. Lastly, trade as a percentage of GDP averages 71.822%, with values ranging from a minimum of 9.099% to a peak of 408.362%. These summary statistics provide an overview of the data set’s variability and distribution.

The correlation coefficient indicates the pairwise associations between the dependent variable, i.e., environmental degradation, and the independent variables, i.e., economic growth, renewable energy consumption, urbanization, and trade (

Table 3. Correlation coefficients: pairwise associations between the dependent variable (environmental degradation) and the independent variables (economic growth, renewable energy consumption, urbanization, and trade).

		CO2	GDPPC	URB	TR	REC
LN-CO2	Pearson’s r	-
	p-value	-
	95% CI Upper	-
	95% CI Lower	-
GDPPC	Pearson’s r	0.452 *	-
	p-value	<0.001	-
	95% CI Upper	0.489	-
	95% CI Lower	0.413	-
LN-URB	Pearson’s r	0.386 *	0.474 *	-
	p-value	<0.001	<0.001	-
	95% CI Upper	0.426	0.510	-
	95% CI Lower	0.345	0.436	-
LN-TR	Pearson’s r	0.391 *	0.901 *	0.358 *	-
	p-value	<0.001	<0.001	<0.001	-
	95% CI Upper	0.431	0.91	0.399	-
	95% CI Lower	0.349	0.892	0.315	-
LN-REC	Pearson’s r	0.255 *	0.328 *	0.151 *	0.326 *	-
	p-value	<0.001	<0.001	<0.001	<0.001	-
	95% CI Upper	0.299	0.37	0.198	0.369	-
	95% CI Lower	0.209	0.285	0.104	0.283	-

Notes: CO₂: Carbon Dioxide, GDP_PC: Gross Domestic Product per capita, URB: Urbanization, TR: Trade Openness, REC: Renewable Energy Consumption. * p < 0.01.

). Based on the pairwise correlations’ findings, carbon dioxide emission shows a positive and statistically meaningful association with independent variables. It illustrates that rising renewable energy consumption, growing urbanization, and increasing trade significantly impact economic growth, inversely hitting CO₂ emissions. All variables have a significant positive effect that is matched with environmental Kuznets Curve hypothesis and green growth theories (all p < 0.05).

The panel unit root test results in

Table 4. Panel Unit Root Results.

Variables	Im, Pesaran, and Shin W-Stat				ADF–Fisher Chi-Square				Results
	Level		First Difference		Level		First Difference
	T-Stat	p-Value	T-Stat	p-Value	T-Stat	p-Value	T-Stat	p-Value
CO2	−0.247	0.402	−28.724	0.000	69.674	0.421	752.430	0.000	I(1)
GDPPC	1.668	0.952	−15.709	0.000	167.588	0.000	600.788	0.000	I(1)
REC	−4.200	0.000	−24.352	0.000	137.294	0.000	621.494	0.000	I(1)
URB	2.061	0.980	4.722	1.000	90.940	0.033	39.331	0.000	I(1)
TR	−1.450	0.074	−17.104	0.000	125.657	0.000	491.547	0.000	I(1)

Notes: CO₂: Carbon Dioxide, GDP_PC: Gross Domestic Product per capita, REC: Renewable Energy Consumption, URB: Urbanization, TR: Trade Openness.

indicate that renewable energy consumption (REC) is stationary at level I(I) across all tests, including Pearson, Shin W-stat, and ADF Fisher. This confirms the absence of a unit root in REC, ensuring its stability over time. The consistency of results across multiple unit root tests strengthens the reliability of this finding, supporting its inclusion in further econometric modeling without the need for differencing.

The unit root test results indicate that GDP per capita, carbon emissions (CO₂), urbanization (URB), and trade (TR) are non-stationary at level I(0) but become stationary after first differencing at I(I), confirming the need for further co-integration analysis to determine long-term relationships. The application of the Vector Error Correction Model (VECM) requires ensuring that none of the variables are integrated at I(I) to avoid misleading results. The null hypothesis of the Im, Pesaran, and Shin (IPS) and ADF–Fisher Chi-square tests assumes non-stationarity, reinforcing the need for first differencing. The findings suggest that GDP positively influences economic growth, while CO₂, REC, and TR exhibit a negative impact on environmental quality, aligning with the expected role of renewable energy in mitigating environmental degradation.

This section of empirical findings falls in parallel with [64]. The conclusions of the Harris–Tzavalis that is a unit root test, validate that both the variables that are economic growth and environmental quality are at a same level of stationary I(I) and I(I). Environmental degradation, renewable energy consumption, urbanization, and trade are stationary at I(I), while no unit root is at I(I).

Table 5. Lag Identification Criteria.

Lag	Log L	LR	FPE	AIC	SC	HQ
0	−2532.032	NA	2.62 × 10−5	3.639931	3.658726	3.646959
1	7717.844	20,411.52	1.12 × 10−11	−11.02991	−10.91714	−10.98774
2	9764.962	4061.928	6.13 × 10−13	−13.93108	−13.72434	−13.85378
3	9949.503	364.8459	4.88 × 10−13	−14.15998	−13.85926 *	−14.04754 *
4	9987.775	75.39077	4.78 × 10−13 *	−14.17902 *	−13.78433	−14.03144
5	9998.039	20.14464	4.89 × 10−13	−14.15787	−13.66922	−13.97516
6	10,029.07	60.68815 *	4.84 × 10−13	−14.16653	−13.5839	−13.94868
7	10,034.56	10.69043	4.98 × 10−13	−14.13854	−13.46193	−13.88555
8	10,049.95	29.87997	5.05 × 10−13	−14.12475	−13.35417	−13.83663

Notes: * Indicates lag order selected by the criterion, LR: sequential modified LR test statistic (each test at 5% level), FPE: Final prediction error, AIC: Akaike information criterion, SC: Schwarz information criterion, HQ: Hannan–Quinn information criterion.

explains the lag identification, which illustrates a distributed-lag model, a dynamic model that affects the regressor x on y over time rather than all at once. In the simple case of one explanatory variable and a linear relationship, lag is needed to make the equation. The concept of ‘fit’ plays a crucial role in middle-range theories across various disciplines, referring to the alignment between two or more factors. Recent studies have explored how the interplay between internal and external variables influences performance, emphasizing that a well-matched relationship among these factors can enhance efficiency and outcomes. This approach provides a structured framework for analyzing complex interactions, making it a fundamental aspect of contemporary research in organizational, economic, and environmental study [65].

In this research, a key methodological decision involved selecting an appropriate research design to test the fit between variables. To enhance the effectiveness of the study, we examined whether identifying proper lag structures within the fit relationship improves model accuracy. The findings indicate that determining suitable lag structures is crucial for robust research design, particularly when analyzing models that incorporate the concept of fit, as it significantly impacts the validity and reliability of the results. Ref. [66] described these coefficient tables as standardized means to convert the norm into 0 and variance into 1 so that the whole data exhibits proper explanation. As shown, PGD, URB are significant at lag three, REC and TR are significant at lag 4, whereas trade openness has a negative beta, which is not significant.

Table 6. Johansen Fisher Panel Co-Integration Test Results.

Hypothesized	Fisher Stat. *		Fisher Stat. *
No. of CE(s)	(From Trace Test)	Prob.	(From Max-Eigen Test)	Prob.
None	1262.	0.000	757.6	0.000
At most 1	785.1	0.000	468.2	0.000
At most 2	415.7	0.000	265.7	0.000
At most 3	233.3	0.000	172.1	0.000
At most 4	173.6	0.000	173.6	0.000

Notes: * Probabilities are computed using asymptotic Chi-square distribution.

provides the results of the Johansen Fisher Panel Co-Integration Test confirms a long-run relationship between the variables, while the short-run error correction equation highlights their short-term association. In the long run, REC, TR, and GDPPC exhibit a negative relationship with CO₂ emissions, whereas URB has a positive impact. In the short run, REC initially has a positive effect, though its long-term influence turns negative. The model indicates that deviations from long-run equilibrium are corrected at a slow speed of 0.00016. Additionally, a 1% increase in REC leads to a 0.38% rise in the short run, while a 1% increase in GDP per capita results in a 0.017% reduction in CO₂ emissions.

Table 7. Vector Error Correction Model (VECM): Long-run and Short-run Estimation.

	Long-Run	Short-Run
	Lag Selection 1	Lag Selection 1–4
CO2	1.000000	−0.087508	−0.05097	−0.027087	0.032261
		(0.02600)	(0.02601)	(0.03742)	(0.03759)
		[−3.36580]	[−1.95986]	[−0.72379]	[0.85823]
REC	−11.72971	0.077962	0.052808	0.042867	0.092802
	(9.80438)	(0.05428)	(0.05429)	(0.07813)	(0.07847)
	[−1.19638]	[1.43641]	[0.97268]	[0.54869]	[1.18260]
TR	−21.6441	0.096245	0.051418	0.234767	−0.003933
	(18.3325)	(0.03978)	(0.03980)	(0.05727)	(0.05752)
	[−1.18064]	[2.41921]	[1.29205]	[4.09958]	[−0.06837]
URB	37.59649	0.000128	−5.10 × 10−6	−1.33 × 10−5	−8.18 × 10−5
	(77.5396)	(0.00013)	(0.00013)	(0.00018)	(0.00018)
	[0.48487]	[1.00776]	[−0.04025]	[−0.07306]	[−0.44671]
GDPPC	−27.69498	0.156463	−0.04475	0.458123	0.017027
	(8.53673)	(0.07511)	(0.07513)	(0.10812)	(0.10860)
	[−3.24421]	[2.08310]	[−0.59561]	[4.23729]	[0.15679]
R-squared	0.434155	0.414080	0.074447	0.950545	0.087640
Adj.R-squared	0.426094	0.390034	0.061261	0.949841	0.074642

Notes: CO₂: Carbon Dioxide, REC: Renewable Energy Consumption, TR: Trade Openness, URB: Urbanization, GDP_PC: Gross Domestic Product per capita.

presents the long-run and short-run analysis of the vector error correction model, with CO₂ as the dependent variable. In the long run, the coefficient signs are reversed, and all variables significantly impact CO₂ emissions, holding other factors constant. The coefficients are statistically significant at the 0.05% level, confirming cointegration in the model. In the short-run estimation, GDP per capita and trade are significant at lag four, while renewable energy consumption and urbanization are significant at lag three.

Table 8. Tests results of correlation, normality, and heteroskedasticity.

VEC S Correlation LM Test			VEC R Normality Test				VEC R Heteroskedasticity Tests
							Joint Test:
Lags	LM-Stat	Prob	Component	Jarque–Bera	Df	Prob.	Chi-sq	df	Prob.
1	59.60281	0.0831	1	58,425.41	2	0.7541	1954.603	630	0.0787
2	56.06569	0.0924	2	53,351.63	2	0.0943
			3	424,635.3	2	0.0862
			4	891,393.8	2	0.0769
			5	916,766.8	2	0.8761
Probs from chi-square with 25 df			Joint	2,344,573.	10	0.3776

Notes: VEC: Vector Error Correction, LM-Stat: Lagrange Multiplier (LM) test statistic for autocorrelation, df: degrees of freedom.

shows the findings of different tests for correlation, normality, and heteroskedasticity. There is no serial correlation in the model; that is why we have moved towards the Johnson integration test to check the normality of data. This indicates the presence of multicollinearity issues within the data set, leading to non-normally distributed model residuals. Also, we found heteroskedasticity, as the probability value is less than 0.05, which suggests that this is a concern for the overall model.

Table 9. Bayesian Linear Regression.

Model Comparison
Models	P(M)	P(M|Data)	BFM	BF10	R2
Null model	0.0625	2.57 × 10−6	3.86 × 10−5	1	0
PGDPC + URB + REC	0.0625	0.702	35.39	2.73 × 105	0.256
PGDPC + URB + TR + REC	0.0625	0.298	6.36	1.16 × 105	0.256
PGDPC + URB	0.0625	1.12 × 10−6	1.68 × 10−5	4.36 × 1099	0.243
PGDPC + URB + TR	0.0625	4.16 × 10−7	6.24 × 10−6	1.62 × 1099	0.243
URB + TR + REC	0.0625	2.66 × 10−9	3.99 × 10−8	1.03 × 1097	0.238
URB + TR	0.0625	3.90 × 10−16	5.85 × 10−15	1.52 × 1090	0.223
PGDPC + TR + REC	0.0625	4.47 × 10−18	6.71 × 10−17	1.74 × 1088	0.219
PGDPC + REC	0.0625	1.18 × 10−18	1.77 × 10−17	4.59 × 1087	0.217
PGDPC + TR	0.0625	8.20 × 10−24	1.23 × 10−22	3.19 × 1082	0.206
PGDPC	0.0625	4.84 × 10−24	7.27 × 10−23	1.88 × 1082	0.204

Notes: REC: Renewable Energy Consumption, URB: Urbanization, TR: Trade Openness, BF: Bayes factor.

outlines the Bayesian approach to linear regression, which employs probability distributions rather than point estimates for parameter estimation. In this framework, the response variable, CO₂, is not represented by a single estimated value but is instead assumed to follow a probability distribution, typically a normal distribution. The Bayesian methodology does not seek a single “best” estimate for model parameters but instead derives their posterior distribution based on observed data and prior assumptions. Both the response variable and model parameters are treated as random variables with probability distributions. The posterior probability distribution of the model parameters, denoted as P(β∣y,X) P(β|Y, X), is determined by the likelihood function P(Y∣β,X)P(y|β, X), weighted by the prior probability distribution of the parameters and normalized by a constant factor. Priors, represented as P(M)P(M), serve as initial estimates of the model parameters, allowing their incorporation into the analysis. This contrasts with the frequentist approach, which assumes that all parameter information is derived solely from observed data. In cases where prior information is unavailable, non-informative priors, such as normal distributions, can be utilized to mitigate potential biases and ensure objectivity in parameter estimation.

Since all prior values are equal, the analysis progresses to the posterior distribution, which represents the range of possible model parameters based on both the observed data and prior assumptions. Bayesian regression thus allows for the quantification of uncertainty in model estimation, where a smaller data set results in a more dispersed posterior distribution. While considerable emphasis is often placed on selecting an appropriate prior distribution, the likelihood function of the statistical model typically holds greater significance. As stated by [58], the Bayes factor model (BFm) provides a measure of model comparison relative to the null hypothesis. Among the evaluated models, the first and second models emerge as the most suitable alternatives for comparison with the linear model.

Table 10. Collinearity and Variance Influence Factor (Dependent Variable: CO₂).

Model	Unstandardized Coefficients		Standardized Coefficients	T	Sig.	Correlations			Collinearity Statistics
	B	Std. Error	Beta			Zero-Order	Partial	Part	Tolerance	VIF
(Constant)	−1.596	0.214		−7.467	0
GDPPC	0.085	0.013	0.332	6.277	0	0.452	0.152	0.133	0.16	6.234
URB	1.091	0.121	0.22	9.009	0	0.386	0.216	0.191	0.75	1.334
TR	−0.015	0.028	−0.026	−0.531	0.595	0.391	−0.013	−0.011	0.18	5.545
REC	0.082	0.015	0.121	5.397	0	0.255	0.131	0.114	0.887	1.127

Notes: CO₂: Carbon Dioxide, GDP_PC: Gross Domestic Product per capita, REC: Renewable Energy Consumption, URB: Urbanization, TR: Trade Openness, VIF: variance inflation factor.

presents the coefficients and diagnostic tests for the regression model, examining how the independent variables GDP per capita (PGDP), urbanization (URB), trade openness (TR), and renewable energy consumption (REC) affect the dependent variable CO₂ emissions. The beta values indicate the strength and direction of the relationships between each independent variable and CO₂ emissions. Specifically, both GDP per capita and urbanization show positive beta values, suggesting that increases in these factors are associated with higher CO₂ emissions. In contrast, trade openness has a negative beta value, indicating that a 1% increase in CO₂ emissions is associated with a 0.015% decrease in trade openness, although this effect is statistically insignificant. Furthermore, the collinearity diagnostics, including tolerance and variance inflation factor (VIF), suggest that multicollinearity is not an issue in the model, as all VIF values are below 10.

5. Discussion

5.1. Main Study Findings

The Environmental Kuznets Curve (EKC) hypothesis posits an inverted U-shaped relationship between economic growth and environmental degradation, suggesting that environmental quality deteriorates at early development stages but improves as income levels rise. Sustainable Development Theory emphasizes the integration of economic, environmental, and social objectives. High-income countries mitigate ecological footprints through renewable energy adoption, strategic urban planning, and sustainable trade policies, aligning economic growth with de-carbonization and circular economy principles to promote long-term environmental sustainability.

Economic growth exerts a dual effect on environmental sustainability. Initially, it tends to exacerbate environmental degradation due to increased emissions. However, as economies advance and adopt cleaner technologies, the environmental impact can become more positive. Notably, in certain regions, such as ASEAN and CIVETS countries, economic growth remains predominantly associated with higher environmental degradation due to reliance on fossil fuels and industrial expansion [13,67]. This study investigates the interrelationships among economic growth, renewable energy consumption, urbanization, and trade liberalization to evaluate their combined impact on environmental sustainability. The findings reveal that increased renewable energy consumption is consistently linked to a reduction in ecological footprints and CO₂ emissions, thereby enhancing environmental sustainability across diverse economies, including OECD countries [67,68].

Empirical results indicate that economic growth has a significant long-term impact on environmental degradation. However, in the short run, economic growth demonstrates a negative association with GDP per capita at a two-period lag. The Bayesian estimator reinforces a strong negative relationship between economic growth and environmental degradation. Across various models, key variables including per capita GDP (PGDPC), urbanization (URB), trade openness (TR), and renewable energy consumption (REC) are found to significantly contribute to the intensity of carbon dioxide emissions. However, under VECM models, these coefficients indicate an inverse association, implying that as income levels rise, CO₂ efficiency improves. Therefore, economically strong nations are better equipped to control the intensification of carbon dioxide emissions amid rising urbanization, trade, and energy consumption. This underscores the importance of technological advancements in reducing CO₂ emissions across selected economies.

Urbanization is often linked to increased environmental degradation, primarily driven by higher energy consumption and emissions, particularly in economies reliant on non-renewable energy sources. As urban areas expand, energy demand rises, typically met by fossil fuels, leading to greater greenhouse gas emissions and resource depletion. Furthermore, urbanization often results in land use changes, such as deforestation and habitat loss, which exacerbate environmental damage. Trade openness affects carbon emissions in two key ways: the pollution haven effect and technology spillover. The pollution haven effect occurs when countries with lenient environmental regulations attract more polluting industries, resulting in higher emissions. Conversely, trade openness can foster technology spillovers, where the exchange of goods and ideas promotes the adoption of cleaner technologies, potentially reducing carbon emissions over time. However, its impact varies depending on a country’s energy composition and policies [13,69]. Trade liberalization has heterogeneous effects. While it often contributes to higher CO₂ emissions through expanded industrial activity and consumption, it can also facilitate the adoption of renewable energy and promote sustainable economic growth. The study finds that trade does not necessarily exacerbate environmental degradation, provided that economies prioritize renewable energy utilization [68].

The influence of trade and urbanization on ecological footprint is highly depending upon the effectiveness of policy frameworks, advancements in technology, regulatory enforcement, and infrastructure development. While well-regulated trade can simplify access to environmentally friendly technologies and promote sustainable production practices, weak regulatory oversight may lead to increased resource depletion and pollution. Similarly, planned urbanization, supported by sustainable infrastructure and stringent environmental policies, can enhance ecological efficiency, whereas uncontrolled urban expansion may exacerbate environmental degradation. Therefore, integrating sustainable urban planning with environmentally responsible trade policies is essential to achieving economic development without compromising ecological integrity. Empirical evidence suggests that renewable energy consumption significantly influences energy intensity. Trade liberalization, when coupled with renewable energy adoption, substantially reduces energy intensity in the long run. The VECM model results indicate that a 1% increase in economic development leads to a 2.696% rise in environmental degradation per annum, highlighting its negative implications for sampled economies. These findings align with the economic growth effects model [70].

Moreover, renewable energy consumption exhibits a positive and significant relationship with economic growth. Specifically, a 1% increase in renewable energy consumption is associated with a 0.7% increase in economic growth across selected countries. These results are consistent with prior studies. For instance, [71] examined data from 800 European firms and found a strong correlation between green practices (such as green energy adoption and ISO 14001 certification) and both economic and environmental performance. Their findings demonstrated that businesses adopting green practices experienced substantial improvements in environmental efficiency while simultaneously enhancing profitability. In emerging economies, the adoption of sustainable and eco-friendly practices is growing, further reinforcing environmental sustainability.

The estimates derived from the VECM provide significant insights into the short-run dynamics governing the relationship between economic growth and CO₂ emissions. The negative and statistically significant error correction term (ECT) validates the existence of a long-run equilibrium, thereby affirming the model’s stability across all three estimators. This finding indicates that deviations from the long-run equilibrium are progressively adjusted over time, reflecting a systematic convergence process. Furthermore, the results suggest that economic growth does not exert a statistically significant influence on CO₂ emissions in the short run. This finding aligns with previous studies suggesting that structural changes in the economy and energy efficiency improvements may moderate the immediate effects of economic expansion on environmental degradation.

These findings contribute to the ongoing discourse on sustainable development by underscoring the significance of long-term policy interventions. Notably, both short-run and long-run analyses indicate that increasing CO₂ emissions can substantially influence economic growth. However, across all estimators, renewable energy consumption emerges as a pivotal factor in mitigating CO₂ emissions in the short run. Moreover, the results reveal no direct short-run relationship between urbanization and CO₂ emissions, suggesting that other mediating factors may influence this dynamic.

This study underscores the complicated relationship between clean energy adoption, economic development, and carbon emissions. While the increased utilization of renewable energy sources contributes to a reduction in CO₂ emissions, the simultaneous effects of urban population growth and economic expansion present persistent environmental challenges. These findings emphasize the need for balanced policy measures that promote sustainable economic growth while mitigating the adverse environmental impacts associated with urbanization and industrialization. Previous research [1,35,36,37] has explored the role of renewable energy in economic development, industrial productivity, and CO₂ reduction, supporting the conclusion that green energy positively influences economic activity. The findings also align with trends observed in regions experiencing rapid urbanization, where increased energy demand and emissions have been linked to extensive urban expansion [72].

5.2. Policy Recommendations

In addition to the broader theoretical implications, it is important to consider how different policy contexts might influence the relationships observed in this study. For example, carbon taxation can serve as a powerful tool to reduce emissions by providing economic incentives for companies and individuals to adopt cleaner technologies [12]. Similarly, a bidirectional relationship between energy consumption and carbon emissions, emphasizing the need for a policy framework that balances environmental sustainability with economic growth. To address this challenge, a progressive carbon tax is proposed, which internalizes environmental costs while ensuring minimal disruption to economic expansion. This approach aligns with global best practices and serves as a key incentive for industries to adopt cleaner production technologies, thereby fostering a transition toward a low-carbon economy. Furthermore, the findings indicate the existence of a long-run equilibrium between economic growth and energy consumption, underscoring the necessity of policy-driven incentives to promote renewable energy adoption.

Targeted measures on trade such as subsidies, feed-in tariffs, and financial support mechanisms can accelerate the integration of renewable energy sources into national energy portfolios, reducing dependence on fossil fuels while maintaining economic stability. To mitigate the environmental impact of urbanization, it is imperative to integrate green infrastructure policies that enhance energy efficiency and sustainability. Key strategies include tax breaks for energy-efficient buildings, the implementation of smart grid technologies to optimize energy use, and improvements in public transportation systems to reduce reliance on fossil fuels. By incorporating these measures, policymakers can foster sustainable urban development while minimizing carbon footprints and ensuring long-term environmental resilience.

To further contextualize these relationships, we can look at real-world examples of how policy frameworks have shaped outcomes. For instance, Sweden has implemented substantial investment in renewable energy infrastructure, leading to reductions in emissions despite strong economic growth [46]. On the other hand, regions with less stringent environmental regulations or weaker enforcement may experience more significant environmental degradation as urbanization and industrialization proceed unchecked. Linking these findings to such concrete case studies can provide valuable insights for policymakers looking to balance economic growth and environmental sustainability. This broader policy context highlights the need for a nuanced understanding of how different strategies can either mitigate or exacerbate the challenges posed by urbanization and trade, helping to design more effective and context-specific solutions.

Future research should extend beyond high-income economies to examine the applicability of these strategies in lower- and middle-income countries, where challenges and opportunities may differ. Exploring the role of policy stringency in driving sustainability outcomes could provide valuable insights into how governance structures impact long-term environmental goals. Finally, investigating the complex interactions between economic growth, urbanization, and trade, with a focus on their effects on biodiversity and ecosystem services, will offer a more holistic understanding of sustainable development in the face of global climate change-related challenges.

Policymakers should adopt a comprehensive approach to balancing economic progress with environmental sustainability. Investing in green technologies, promoting sustainable urban development, and ensuring environmentally responsible trade practices are essential strategies for reducing carbon emissions while maintaining economic growth [73]. High-income economies, in particular, have the opportunity to lead by example in implementing policies that foster a balance between development and ecological preservation.

5.3. Limitations

Despite offering valuable insights, this study has several limitations. First, the analysis focuses exclusively on high-income European and Asian economies, which may restrict the generalizability of the findings to lower- and middle-income countries with distinct economic structures, regulatory frameworks, and energy-consumption patterns. Expanding the scope of research to a more diverse set of countries could provide a more comprehensive understanding of these relationships. Moreover, the study primarily relies on aggregate data, which may overlook country-specific variations in policy implementation, institutional quality, and technological advancements. A more granular, country-level analysis could better capture these differences and facilitate the development of tailored policy recommendations. Meanwhile for key economic and environmental factors, it does not explicitly consider other critical variables, such as energy efficiency policies, technological innovation, and sectoral transformations in economic activity. These factors could significantly influence the long-term interactions between economic growth and environmental sustainability. Alternative methodological approaches, such as case studies or machine learning techniques, to enhance the robustness of the findings and provide deeper insights into the complex dynamics at play.

6. Conclusions

This study underscores the intricate relationships between economic growth, renewable energy consumption, urbanization, and trade liberalization in shaping environmental quality. The three primary issues explored are: (1) the role of renewable energy in both fostering economic development and reducing CO₂ emissions, (2) the continued environmental pressures exerted by urbanization and trade expansion, and (3) the need for integrated strategies that balance economic growth with environmental sustainability. The main outcome reveals that while renewable energy positively influences CO₂ reduction, urbanization and trade liberalization contribute to long-term emissions growth. However, the study primarily focuses on high-income countries, which may limit the generalizability of the findings to lower- and middle-income nations.

To mitigate environmental degradation while promoting sustainable economic growth, policymakers should adopt a comprehensive approach that encourages renewable energy adoption, sustainable urban planning, and responsible trade practices. Policies that strengthen energy efficiency and foster investment in clean technologies will be pivotal for achieving long-term decarbonization. Additionally, institutional reforms and sectoral transitions toward green technologies will enhance environmental sustainability across industries. Extending these policies to lower- and middle-income countries is essential for achieving global climate resilience and fostering inclusive, sustainable development.