Industrialization and Inclusive Growth on Business Environmental Quality: Evidence From Developing Countries

Abstract

The contributive effect of industrialization (INDU), urbanization (URB), trade openness (TOR), energy use (ENR), net inflow (FDI), and inclusive growth (INGP) on environmental quality remains a critical global concern, particularly in emerging economies where rapid industrial expansion and urbanization exacerbate environmental degradation. Despite global initiatives to improve the environmental quality of emerging economies, they continue to experience increasing emissions due to fossil fuel dependence, lax environmental policies, and carbon-intensive FDI inflows. Most extant studies focus on developed economies, ignoring a critical gap in understanding how these drivers influence environmental quality in emerging regions. This study addresses this gap using the Environmental Kuznets Curve (EKC), Pollution Haven Hypothesis (PHH), and Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) frameworks. Applying the Panel Autoregressive Distributed Lag (ARDL) model (PARDL) model to panel data (1990–2023) from BRICS, MINT, SAARC, Central Asia, and East and Pacific Asia, we analyze both the short- and long-run effects of these drivers. The results show that INDU and URB significantly degrade environmental quality, increasing CO₂ emissions by 82.4% and 86.0% and GHG emissions by 87.3% and 77.1%, respectively, thus supporting the STIRPAT model, acknowledging INDU and URB as drivers of environmental degradation. TOR and FDI exacerbate environmental degradation due to lax regulations, supporting PHH. This study validates the EKC hypothesis, with emissions declining at higher income levels. While the influences are consistent across blocs, their magnitudes vary. This study advocates green industrialization, stringent regulations, and sustainable energy policies. These results provide insights for policymakers to balance development and environmental sustainability.

JEL Classification: O14, Q56, F18, Q53, C33, Q43.

Introduction

The intricate link between human survival, business activities, and environmental variability is globally acknowledged as a driver of climate change. The Industrial Revolution of 1750 marked a pivotal moment in this nexus, triggering a surge in carbon dioxide (CO₂) emissions. From 204 metric tons of CO₂ equivalent (MtCO₂) in 1850, during the pre-industrial era, to 37,100 MtCO₂ in 2022, post-industrial, representing an increase of over 18,086.27% (World Resources Institute, 2023). Similarly, greenhouse gas (GHGs) concentrations and other climate-forcing agents, including cooling aerosols, escalated to 472 parts per million (ppm) in 2022, representing a 50 ppm increase compared to a decade ago and 192 ppm above pre-industrial levels (Lindsey, 2020). The Intergovernmental Panel on Climate Change (IPCC) identifies this level as a critical threshold that should not be exceeded. Thus, surpassing it threatens global temperature targets established under the 2015 Paris Climate Agreement, which seeks to limit global warming to 1.5–2°C above pre-industrial levels (UNFCCC, 2015).

Despite global commitments such as the Kyoto Protocol (1997), EU Emissions Trading System (EUETS), and the Paris Agreement (2015), sustained global efforts to mitigate the catastrophic ecological consequences of human and industrial activity remain inadequate and a central concern for scholar, policymakers, and environmentalists in the 21st century (Udo et al., 2024; Udo, Idamoyibo, et al., 2025). Studies by Udoh et al. (2023), Inim et al. (2024), Abner et al. (2023), and D. Khan and Ullah (2019) consistently acknowledged fossil fuel–based energy use (ENR) as the dominant contributor to environmental degradation, particularly in emerging economic blocs such as South Asian Association for Regional Cooperation (SAARC), Brazil, Russia, India, China, and South Africa (BRICS), Central Asia, Europe, East and Pacific Asia, and Mexico, Indonesia, Nigeria, and Turkey (MINT). Fossil fuels account for approximately 75% to 85% of CO₂ emissions in industrialized and emerging economies. Rapid industrialization, coupled with the widening energy demand-supply gap, exacerbates climate change through ocean acidification, global warming, and air and water pollution, leading to substantial public health and ecological costs.

In response to these hiatuses, the United Nations Sustainable Development Goals (SDGs) have prioritized environmental quality (ENQ) through Goals 13 (Climate Action), 14 (Life Below Water), and 15 (Life on Land). As part of the global agenda to achieve carbon neutrality by 2030, despite persistent reliance on fossil fuels for industrialization, inclusive growth and limited access to green technology, laxity in environmental regulation, and the relocation of carbon-intensive industries from countries with stricter standards (Udo et al., 2024; Umar et al., 2021) emerging economies like SAARC, BRICS, MINT, Central Asia, and Europe, have adopted green technologies and energy sources to mitigate climate change concerns (Abner et al., 2023; Udo et al., 2024; C. Wang, Zhao, et al., 2020).

This study adopts a comprehensive framework to assess the influence of industrialization (INDU), urbanization (URB), trade openness (TOR), energy use (ENR), net inflow (FDI), and inclusive growth (INGP) on environmental quality (ENQ), proxied by carbon dioxide (CO₂) and greenhouse gas (GHG) emissions across five major emerging blocks from 1990 to 2024. As such, Figure 1 revealed significant regional differences in environmental and economic development trajectories. As such, URB surged across these regions: from 25% in 1990 to over 36% in 2023 in SAARC countries, 68% to 73% in Europe and Central Asia, 34% to 63% in East and Pacific Asia, 190% to 271% in MINT countries, and 251% to over 332% in BRICS countries. While these trends reveal increasing migration toward industrial centers, they also reveal the environmental costs of rapid industrial expansion, particularly in energy-intensive regions such as East and Pacific Asia and BRICS.

Figure 1.

Sustainable development indicators (1990–2023).

While global agreements like the 2015 Paris Climate Agreement, Kyoto Protocol (1997), and the European Union Emissions Trading System (EUETS) spurred interest in sustainable development, empirical evidence across countries and regions remains mixed and context-dependent. Studies (Eyuboglu & Uzar, 2020; M. Khan, Rehan, & Khan, 2020; Samuel et al., 2021; Udoh et al., 2024) revealed that INDU and INGP affect ecological quality inversely across countries such as Turkey, Brazil, Nigeria, and China. And regions such as North and Central Asia, Latin America, Sub-Saharan Africa, North Africa, and the Caribbean and Central Asia. The divergence in the study can be attributed to methodological heterogeneity, data limitations, and regional variations. While most studies on INGP-INDU proxied ENQ through CO₂, because of the direct link to industrial activities, energy consumption, and INGP. Studies by Udo et al. (2024), Inim et al. (2024), and Udoh et al. (2024) argue that CO₂ emissions alone cannot capture the full spectrum of ecological impacts; thus, the inclusion of GHG emissions alongside would provide a more comprehensive assessment of ENQ. This study incorporates CO₂ and other GHG emissions to provide a more holistic understanding of ENQ in emerging economies, which is essential for formulating effective industrialization and inclusive growth strategies.

Most studies in this area focused on developed economies, which benefit from advanced data infrastructure, stronger research funding, and institutional support for environmental policy. Consequently, global environmental policy discourse is largely shaped by developed countries, whose resources and influence drive international agreements, while developing economies, where two-thirds of global warming originates (Gielen et al., 2019; Udo et al., 2024; Udo, Chiyem, et al., 2025; Udo, Ekwunife, et al., 2025; Udo, Idamoyibo, et al., 2025; Udo, Okoh, et al., 2025), remain underrepresented. This imbalance reveals the need for inclusive and region-specific policy frameworks that reflect the realities of emerging economies. Udo et al. (2024), Inim et al. (2024), and Udoh et al. (2023) emphasize that growth achieved at the detriment of the environment can lead to enduring sustainability issues, which undermine the foundation of INGP.

Given these challenges, this study contributes to the literature by focusing on emerging economic blocs and offering a robust comparative analysis of ENQ determinants and emission reduction strategies at both regional and bloc levels. These economies face unique challenges such as higher levels of poverty, lax regulatory frameworks, and greater vulnerability to climate change impacts, which differ from those in developed nations. Addressing these environmental issues in developing economies is essential to achieving global environmental goals. This study adds to the literature in the following ways:

1. Providing a comprehensive comparative assessment of the environmental drivers across five major emerging economic blocs, BRICS, MINT, SAARC, Central Asia & Europe, and East & Pacific Asia, addressing the fragmentation in previous studies and enabling direct comparison of the magnitude and direction of environmental impacts across heterogeneous regions.

2. Assessing the joint influence of INDU, URB, INGP, TOR, FDI, and ENR on ENQ, capturing the structural interactions among these factors, and offering a nuanced understanding of sustainability dynamics in emerging economies.

3. Adopting the Panel Autoregressive Distributed Lag (PARDL) model, to accommodate cross-sectional dependence, mixed integration orders, and heterogeneity common challenges in panel data from developing countries. This model provides simultaneous estimation of both short-run dynamics and long-run equilibrium relationships, marking a significant improvement over prior bloc-specific study. To address potential endogeneity issues, this model effectively captures the dynamic link, including the lagged effects of these variables.

4. This study employs both CO₂ and total GHG emissions to measure ENQ, responding to recent scholarly calls for broader, more inclusive sustainability metrics that capture diverse environmental pressures.

By focusing on developing economies, through an integrated theoretical and empirical framework, this study fills a critical gap in global environmental research. The results of this study have substantial policy implications, emphasizing the need for green technology adoption, strengthened environmental regulation, and international climate finance to support sustainable industrialization. By aligning industrialization with environmental sustainability, developing economies can contribute to global efforts to mitigate climate change, while pursuing inclusive growth. This paper is structured as follows: Section “Literature Review” presents the empirical literature, Section “Methodology” details the methodology, Sections “Empirical Estimation” and “Panel ARDL Estimation” present the results and discussion, and Section “Conclusion and Policy Implications” presents the policy implications and conclusions.

Literature Review

Theoretical Review

This study integrates three complementary theoretical perspectives: the Environmental Kuznets Curve (EKC), the Pollution Haven Hypothesis (PHH), and the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) models into a unified analytical framework to assess their interconnected influence on environmental quality. These theories are interlocked to provide a multidimensional understanding of how economic and business development, globalization, and structural drivers such as industrialization and urbanization collectively shape environmental quality.

EKC: Initially proposed by Griffin and Schiffel (1972) and refined by Grossman and Krueger (1991), posits that the nexus between inclusive economic and business growth and environmental degradation follows an inverted U-shaped trajectory. The EKC hypothesis implies that in the initial stages of development, industrial expansion, energy consumption, and urbanization degrade environmental quality due to fossil fuel consumption. However, as income levels increase along with technological innovation, environmental awareness and stringent environmental regulations, industrial expansion, globalization, and urbanization improve environmental quality improves (Udo et al., 2024; Udo, Idamoyibo, et al., 2025; Udo, Okoh, et al., 2025). Empirical findings of (Enemuo et al., 2025; Inim et al., 2024; Islam, 2021; Udo et al., 2024; Udo, Chiyem, et al., 2025; Udo, Ekwunife, et al., 2025; Udo, Idamoyibo, et al., 2025; Udo, Okoh, et al., 2025) affirmed this pattern across various contexts, revealing that initial growth correlates with an increase in CO₂ and GHG emissions, followed by a decreases in CO₂ and GHG emissions as the economy evolves to enforce stringent environmental regulations and encourage the adoption of green energy sources (REN). However, the EKC turning point across countries and regions varies due to institutional quality, regulatory frameworks, and public awareness.

This study proposed that: H₁: There is an inverted U-shaped nexus between income growth (INGP) and environmental quality in developing countries.

PHH: The PHH framework developed by Copeland and Taylor (1994) linked environmental degradation to globalization, particularly through trade openness (TOR) and foreign direct investment (FDI). The PHH framework posits that countries with feebler environmental regulations attract carbon-intensive industries from economies with stricter regulations. This dynamic is evident in emerging economies, where FDI inflows and trade liberalization increase CO₂ and GHG emissions due to regulatory and institutional laxity (Emmanuel et al., 2023; Enemuo et al., 2025; Inim et al., 2024; Steinbach, 2019; Udoh et al., 2024; Udo et al., 2025e; Udo, Ekwunife, et al., 2025b; Udo, Okoh, et al., 2025d). However, Afridi et al. (2019); Udo, Chiyem, et al. (2025a); Udo, Idamoyibo, et al. (2025c) revealed that the environmental effects of globalization are anchors on industry composition, technology transfer, and domestic regulatory strength.

This study proposes H₂: Trade liberalization and FDI inflows contribute to environmental degradation in emerging economies.

STIRPAT: in assessing the collective effects of population (P), affluence (A), and technology (T) on environmental degradation. The STIRPAT model, an econometric extension of the IPAT identity proposed by Dietz and Rosa (1994), provides a flexible framework for capturing the contributions of industrialization (INDU), urbanization (URB), and energy use (ERN) on environmental degradation in developing economies, while also allowing for context-specific effects of technological change (Akbostanci et al., 2011; Peng et al., 2022; Udo et al., 2024; Udoh et al., 2024; Udo et al., 2025b). The role of technology in this context remains controversial; Udo et al. (2025c); Samuel et al. (2023) argue that technological advancements can mitigate environmental degradation, while others argue that it may exacerbate environmental degradation if not properly deployed and regulated (Salari et al., 2021; Udo et al., 2023).

This study proposed that: H₃: Population growth, affluence, and technological change exert significant though context-specific influence on environmental quality.

Integration of the EKC, PHH, and STIRPAT Frameworks

While each theory explains distinct aspects of the development–environment nexus, their integration provides a comprehensive and interlocking explanation of environmental dynamics in emerging economies: The STIRPAT model, a foundational structure, recognizes population, affluence, and technology as the underlying anthropogenic pressures that drive emissions. Within this framework, INDU and URB represent the technological and demographic forces influencing environmental quality. The EKC hypothesis is incorporated as a dynamic extension of the affluence component of STIRPAT. EKC posits that an increase in INGP decreases environmental quality before improving at higher development levels. Thus, EKC operationalizes the non-linear trajectory of affluence’s influence on environmental quality. The PHH framework acknowledges the influence of international economic integration through TOR and FDI as an external modifier of the STIRPAT and EKC dynamics. This framework posits that globalization influences the relocation of carbon-intensive industries to emerging economies with regulatory and institutional laxity, thereby distorting the EKC turning point.

The integration of the EKC–PHH–STIRPAT frameworks supports the study’s hypotheses:

• H₁: An inverted U-shaped nexus exists between INGP and environmental quality.

• H₂: Trade liberalization and FDI inflows contribute to environmental degradation in emerging economies.

• H₃: Population growth, affluence, and technological change exert a significant, context-specific influence on environmental quality.

This theoretical integration is represented in Figure 2. Figure 2 reveals how domestic factors (STIRPAT), developmental dynamics (EKC), and globalization channels (PHH) interact to influence environmental quality, proxied by CO₂ and GHG emissions. This framework directly informs the model specification in Section “Methodology”, where these interrelationships are operationalized through variables such as INDU, URB, INGP, TOR, FDI, and ERN. By empirically testing these linkages, this study translates its theoretical synthesis into a structured analytical model that contributes to the discourse on UN SDG 2030 carbon neutrality objective and inclusive environmental policy within emerging economies.

Figure 2.

Integrated research framework combining EKC, PHH, and STIRPAT models.

Figure 2 shows how the STIRPAT model’s core drivers (Population/Affluence/Technology proxied by URB, INGP, INDU) form the base. The EKC hypothesis introduces the non-linear income-environment pathway, while the PHH incorporates the global economic drivers (FDI, TOR) that interact with and potentially exacerbate the domestic pressures.

Empirical Review

The empirical literature on INDU, INGP, URB, ERN, and environmental sustainability in emerging economies blocs such as BRICS, MINT, and SAARC reveals significant variation in results. This variation is attributed to differences in industrial growth trajectories, economic structures, energy mixes, urbanization patterns, environmental regulations, and development stages.

Within the BRICS bloc, the environmental effects of INDU and URB differ markedly. Q. Wang and Zhang (2020) reported that rapid industrial growth in China and India, driven by fossil fuel dependence, significantly deteriorates environmental quality. In contrast, the slower pace of industrial growth in Brazil and Russia led to a non-significant environmental effect. This divergence reveals that variation in industrial growth trajectories yields diverse environmental effects even within similar economic alliances.

In regions such as Central Asia, MINT, East Asia, and Nigeria, studies by Adebayo et al. (2020; Udo et al., 2024) acknowledged URB, INDU, and fossil fuel-based ERN as key drivers of environmental degradation. Although fossil fuel consumption exacerbates environmental degradation, it simultaneously contributes to productivity and industrial growth (Prince et al., 2021; Samuel et al., 2021; Udo et al., 2025), highlighting the inherent growth–environment trade-off that characterizes emerging economies.

In contrast, studies focusing on the Eurozone and the Middle East present mixed results. Magazzino (2016, 2017), employing the vector autoregression (VAR) model in Turkey, reported an inverse nexus between CO₂ emissions and REN consumption. In Middle Eastern countries, such as Kuwait, Oman, and Qatar, Magazzino (2016) reported a unidirectional causality between ERN and income levels, indicating that environmental quality in resource-dependent economies is largely shaped by their energy structures and natural resource endowments. These results confirm that economic growth, renewable energy adoption, and environmental quality are deeply context-specific and influenced by each country’s resource profile and policy orientation.

For emerging economies such as Turkey and Iran, the environmental effects of INDU and URB are predominantly negative. Azam (2016) revealed that without effective environmental regulations, inclusive growth in these economies results in environmental deterioration. Contemporary studies by Udo, Chiyem, et al. (2025), Udo, Ekwunife, et al. (2025), Udo, Idamoyibo, et al. (2025), Udo, Okoh, et al. (2025), Enemuo et al. (2025), F. Wang, Wang, et al. (2020), Samuel et al. (2021), Udo et al. (2024), Inim et al. (2024), and Udoh et al. (2024) revealed that GHG and CO₂ emissions in rapidly industrializing and densely populated economies such as those in the MINT, BRICS, Bangladesh, and Nigeria could potentially double in the coming decades. This projection is due to heavy dependence on fossil fuels, accelerated urbanization, population growth, and unsustainable resource utilization. Thus, underscoring the need for nuanced environmental policies, particularly in emerging economic blocs that are most vulnerable to climate change.

Complementary studies across Asia and MENA regions (Magazzino & Cerulli, 2019; Zafar et al., 2020) also emphasize the need for localized and context-sensitive environmental policies, as structural, demographic, and governance variations determine how growth affects sustainability outcomes. To capture these complex linkages, contemporary studies employed advanced methods, such as Dynamic ARDL, Fuzzy Cognitive Maps, and machine learning. Magazzino et al. (2021) report that biomass energy significantly improved environmental quality in Germany from 1990 and 2018, notwithstanding the environmental pressure from economic expansion.

However, despite this growing literature on these nexuses, empirical literature remains fragmented, particularly in its treatment of cross-bloc comparisons and methodological robustness in emerging economies. Empirical literature reveals three primary gaps that this study addresses to enhance understanding of the sustainability–development nexus in emerging economic blocs:

• Fragmented Bloc-Specific Analyses: Extant studies focus narrowly on individual blocs such as BRICS (Q. Wang & Zhang, 2020) or specific regions like Central Asia (Adebayo et al., 2020), ignoring a unified framework for cross-bloc comparison. Consequently, the differential influence of industrialization, energy consumption, and globalization across BRICS, MINT, SAARC, Central Asia, and East and Pacific Asia remains underexplored. This study bridges this gap by employing a comparative, multi-bloc analytical framework that systematically examines environmental dynamics across emerging economies.

• Methodological Limitations in Capturing Short- and Long-Run Dynamics: Prior empirical studies relied on static panel estimations or time-series models that either ignored cross-sectional dependence or failed to distinguish between short-run and long-run effects. The adoption of the Pooled Autoregressive Distributed Lag (PARDL) model in this study addresses these limitations by accommodating variable integration orders, controlling for heterogeneity across countries, and simultaneously estimating the short- and long-term nexus between industrialization, urbanization, energy use, and environmental quality.

• Incomplete Proxy for Environmental Quality: Extant studies rely solely on CO₂ emissions as a measure of environmental degradation (Azam, 2016; Magazzino, 2017), ignoring other greenhouse gases and broader sustainability dimensions. This study extends the analytical scope by incorporating both CO₂ and GHG emissions as dependent variables, providing a more comprehensive assessment of environmental quality.

This study expands the frontiers of extant studies by integrating multiple emerging economic blocs within a single analytical framework, adopting a dynamic econometric approach to capture both short- and long-run effects, and employing comprehensive environmental proxies. By focusing on key variables INDU, URB, INGP, TOR, FDI, and ERN across diverse regional blocs, it offers a nuanced understanding of how structural transformation, globalization, and energy utilization collectively shape environmental outcomes. This approach not only fills methodological and contextual gaps in the existing literature but also contributes valuable policy insights toward achieving the UN SDG 2030 carbon neutrality goals in developing regions.

Methodology

Model

This study models the effects of ERN, INDU, URB, INGP, TOR, and FDI on environmental quality using three key theoretical frameworks: the EKC, PHH, and STIRPAT models. The EKC explores the U-shaped link between income growth and environmental degradation; PHH assesses the role of FDI and TOR in turning emerging economies into pollution havens, and STIRPAT examines INDU and URB’s contribution to environmental degradation. Based on these theoretical underpinnings, a panel model is employed to analyze these factors across emerging economic blocs. The linear model is expressed as follows:

$$\mathrm{CO}{2}_{\mathrm{it}}=f(\mathrm{IND}{U}_{\mathrm{it}}+\mathrm{ING}{P}_{\mathrm{it}}+\mathrm{UR}{B}_{\mathrm{it}}+\mathrm{EN}{R}_{\mathrm{it}}+\mathrm{TO}{R}_{\mathrm{it}}+\mathrm{FD}{I}_{\mathrm{it}})$$

(1)

$$\mathrm{GH}{G}_{\mathrm{it}}=f(\mathrm{IND}{U}_{\mathrm{it}}+\mathrm{ING}{P}_{\mathrm{it}}+\mathrm{UR}{B}_{\mathrm{it}}+\mathrm{EN}{R}_{\mathrm{it}}+\mathrm{TO}{R}_{\mathrm{it}}+\mathrm{FD}{I}_{\mathrm{it}})$$

(2)

To enhance the resourcefulness of our model estimation, the series in equations (1) and (2) are transformed using a natural log and expressed as:

$$\begin{array}{c}\multicolumn{1}{c}{\mathrm{InCO}{2}_{\mathrm{it}}={\alpha }_0+{\alpha }_{1i}\mathrm{InIND}{U}_{\mathrm{it}}+{\alpha }_{2i}\mathrm{InING}{P}_{\mathrm{it}}+{\alpha }_{3i}\mathrm{InING}{P^2}_{\mathrm{it}}}\\ \multicolumn{1}{c}{+{\alpha }_{4i}\mathrm{InUR}{B}_{\mathrm{it}}+{\alpha }_{5i}\mathrm{InEN}{R}_{\mathrm{ti}}+{\alpha }_{6i}\mathrm{InTO}{R}_{\mathrm{ti}}}\\ \multicolumn{1}{c}{+{\alpha }_{7i}\mathrm{InFD}{I}_{\mathrm{it}}+{\mu }_{\mathrm{it}}}\end{array}$$

(3)

$$\begin{array}{c}\multicolumn{1}{c}{\mathrm{InGH}{G}_{\mathrm{it}}={\alpha }_0+{\alpha }_{1i}\mathrm{InIND}{U}_{\mathrm{it}}+{\alpha }_{2i}\mathrm{InING}{P}_{\mathrm{it}}+{\alpha }_{3i}\mathrm{InING}{P^2}_{\mathrm{it}}}\\ \multicolumn{1}{c}{+\mathrm{In}{\alpha }_{4i}\mathrm{UR}{B}_{\mathrm{it}}+{\alpha }_{5i}\mathrm{InEN}{R}_{\mathrm{ti}}+{\alpha }_{6i}\mathrm{InTO}{R}_{\mathrm{ti}}}\\ \multicolumn{1}{c}{+{\alpha }_{7i}\mathrm{InFD}{I}_{\mathrm{it}}+{\mu }_{\mathrm{it}}}\end{array}$$

(4)

where t is the time period (1990–2023); i is the cross-section (countries and economic blocs), μ = the residual term; $a_{1-7}=$  long-run coefficients in natural logarithms.

Cross-sectional Dependence (CD)

Given the interconnectedness of the selected countries through globalization, geographic proximity, and economic, sociocultural, and political activities, there is a logical emergence of CD. To account for this, we employ the Pesaran-CD test, Breusch-Pagan LM (Breusch & Pagan, 1980), and the Pesaran-scaled LM test. These are expressed as follows:

$$\mathrm{CD}=\frac{\sqrt{2T}}{N(N-1)}\left(\sum _{i=1}^{N-1}\sum _{j=i+1}^N{\varpi }_{\mathrm{ij}}\right)$$

(5)

Where N = the number of cross-sectional units; T = time dimension and ${\varpi }_{\mathrm{ij}}$  = pairwise correlation. The time-series stationarity properties of the series are tested using second-generation models, including the cross-sectional augmented IPS (CIPS) and cross-sectional Augmented Dickey-Fuller (ADF; CADF), to determine the stochastic trend and effectively address CD and panel heterogeneity.

Panel ARDL Model

The panel ARDL model was adopted for its ability to capture both long- and short-term equilibrium and disequilibrium links among variables, and accommodate variables integrated at diverse levels (I(1) and I(0)). PARDL’s inclusion of lagged effects captures the dynamic impacts on environmental quality more accurately, addressing the limitations of studies that focus solely on short-term effects. Its flexibility makes it ideal for analyzing the diverse economic structures and environmental policies of emerging economies, enabling a robust exploration of the complex links between growth, industrialization, urbanization, and sustainability. The model equation is as follows:

$$\Delta \mathrm{CO}{2}_{\mathrm{it}}=a_1+\sum _{k=1}^p{\gamma }_{\mathrm{ij}}\mathrm{CO}{2}_{\mathrm{it}-j}+\sum _{k=0}^q{\phi }_{\mathrm{ij}}{\theta }_{\mathrm{it}-j}+{\mu }_{\mathrm{it}}$$

(6)

$$\Delta \mathrm{GH}{G}_{\mathrm{it}}=a_1+\sum _{k=1}^p{\omega }_{\mathrm{ij}}\mathrm{GH}{G}_{\mathrm{it}-j}+\sum _{k=0}^q{\chi }_{\mathrm{ij}}{\tau }_{\mathrm{it}-j}+{\mu }_{\mathrm{it}}$$

(7)

Where: i, t, and j = cross-sectional units; Φandτ = exogenous variables of ΔINDU, ΔURB, ΔENR, ΔTOR, and ΔFDI; p and q are the optimal lag orders; μ_it = error term.

The F-statistic value from the bound test is used to determine the long-run nexus among the variables. The decision rule is as follows: H₀: No co-integration and H₁: Co-Integration if the F-statistic exceeds the upper critical value, reject (H0); if it is below the lower critical value, accept (H0). Inconclusive results are obtained when the F-statistic falls between the upper and lower critical values. For equations (8) and (9), the short-run dynamics are denoted by π, ψ, ϙ, and v; the long-run dynamics are captured by γ, θ, ω, andτ for equations (6) and (7). The ECM is expressed as follows:

$$\begin{array}{c}\multicolumn{1}{c}{\Delta \mathrm{InCO}{2}_{\mathrm{it}}={\partial }_1+\sum _{k=1}^p{\pi }_{\mathrm{ij}}\Delta \mathrm{InCO}{2}_{\mathrm{it}-j}+\sum _{k=0}^q{\lambda }_{\mathrm{ij}}\Delta \mathrm{In}{\psi }_{\mathrm{it}-j}}\\ \multicolumn{1}{c}{+{\eta }_{\mathrm{it}}\mathrm{EC}{T}_{t-1}+{\mu }_{\mathrm{it}}}\end{array}$$

(8)

$$\begin{array}{c}\multicolumn{1}{c}{\Delta \mathrm{GH}{G}_{\mathrm{it}}={\rho }_1+\sum _{k=1}^p{\Delta }_{\mathrm{ij}}\Delta \mathrm{InGH}{G}_{\mathrm{it}-j}+\sum _{k=0}^q{\varsigma }_{\mathrm{ij}}\Delta \nu I{n}_{\mathrm{it}-j}}\\ \multicolumn{1}{c}{+{\xi }_{\mathrm{it}}\mathrm{EC}{T}_{t-1}+{\mu }_{\mathrm{it}}}\end{array}$$

(9)

The coefficient of (∞) specifies the speed of convergence to the long-run equilibrium from short-run divergence due to shocks in the system. ηandξ are expected to be negative and significant after an external shock.

Source of Data

This study uses data covering 27 developing economies, grouped into regional and individual economic blocs, sourced from the World Bank Index from 1990 to 2023. Table 1 details the data sources, variable descriptions, and country grouping.

Table 1.

Data and Variables Description.

Indicators	Description
CO2	Carbon emissions (Metric tons per capita)
GHG	Greenhouse gas (Kiloton (kt) of CO2 equivalent)
INDU	Industry value added (% of GDP)
TOR	Trade (% of GDP)
FDI	Net inflow (% of GDP)
URB	Urbanization (% population)
INGP	GDP per capita (Inclusive Growth) ($)
INGP2	Square of GDP per capita (Inclusive Growth) ($)
ENR	Total energy use in CO2 emission (kilotons)
Source of Data: WDI (2024)
Countries: Brazil, Russia, India, China, South Africa, Uzbekistan, Tajikistan, Turkey, Iran, Ukraine, Mongolia, Brunei, Malaysia, Indonesia, Philippines, Thailand, Vietnam, Cambodia, Mexico, Indonesia, Nigeria, Turkey, Bangladesh, Bhutan, Nepal, Pakistan, Sri Lanka
Economic blocs	Countries
BRICS	Brazil, Russia, India, China, South Africa (Five major emerging economies)
Central Asia and Europe	Uzbekistan, Tajikistan, Turkey, Iran, Ukraine
East and Pacific Asia	Mongolia, Brunei, Malaysia, Indonesia, Philippines, Thailand, Vietnam, Cambodia
MINT	Mexico, Indonesia, Nigeria, and Turkey (Four emerging economies taking over from BRICS)
SAARC	Bangladesh, Bhutan, Nepal, Pakistan, Sri Lanka

Source. Authors' estimations based on the data described in the methodology section (2024).

Empirical Estimation

The descriptive statistics in Table 2 indicate a generally consistent distribution across variables, with no extreme outliers. The mean and median values are closely aligned, indicating a balanced dataset. The negative skewness values of the CO₂ and GHG emissions indicate that most countries, such as Uzbekistan and Bhutan, have lower emissions.

Table 2.

Descriptive Statistics.

Variables	Mean	Median	Std.Dev	Skewness	Kurtosis	JarqueBera	Prob.
InCO2	12.96	14.47	3.21	−0.49	1.42	20.58	.000
InGHG	15.55	15.92	0.84	−0.26	1.62	14.01	.000
InINDU	3.46	3.17	0.74	0.20	1.24	20.98	.000
InTOR	4.43	4.27	0.68	−0.01	1.83	9.59	.008
InFDI	5.53	2.59	0.82	0.14	2.23	0.86	.672
InURB	4.52	4.24	0.89	0.131	1.41	18.23	.000
InINGP	8.99	9.34	1.33	−0.96	2.96	26.27	.000
InENR	7.78	8.08	1.09	−0.42	2.09	8.127	.017

Source. Authors' estimations based on the data described in the methodology section (2024).

In contrast, highly industrialized countries such as China and India contribute to environmental degradation, supporting the EKC hypothesis. The positive skewness in INDU and URB indicates that countries with rapid industrial and urban growth experience greater environmental depletion, which aligns with STIRPAT and PHH theories. TOR and FDI have mixed effects on environmental quality, with TOR potentially exacerbating global warming through carbon-intensive imports, aligning with the STIRPAT hypothesis.

The kurtosis values across variables were platykurtic (<3). Diagnostic tests, including the Pesaran, scaled LM, and CD tests in Table 3, show no CD, indicating that shocks in one country can affect others through interconnectedness, globalization, and shared sustainable development goals. The unit root results in Table 4 confirm series stationarity at both the I(0) and I(1) levels, validating that the variables do not exhibit long-term trends, suitability for time-series analysis, or the use of the PARDL model. The cointegration results in Table 5 show that the series meets the Gauss–Markov conditions for unbiased estimation. To determine the best model between the Pooled Mean Group (PMG) and Mean Group (MG) estimator for this study, the Hausman test was conducted to check for long-run homogeneity across the economic blocs.

Table 3.

CD Test Results.

Variables	Breusch-Pagan LM	Pesaran scaled LM	Pesaran CD
InCO2	136.05**	28.18**	4.30**
InGHG	246.10**	52.79**	5.28**
InINDU	81.142**	15.90**	3.77**
InTOR	208.18**	44.31**	14.27**
InFDI	240.41**	51.52**	5.51**
InURB	336.59**	73.02**	18.34**
InINGP	308.89**	66.83**	17.57**
InENR	132.52**	27.39**	27.29**

Source. Authors' estimations based on the data described in the methodology section (2024).

**

5% significance level.

Table 4.

Second-generation Unit Root Results.

Variables	CIPS		CADF
	Level 1(0)	First Difference	Level 1(0)	First Difference
InCO2	−6.015**	−9.711**	−2.029**	−7.446**
InGHG	−6.015**	−7.002**	−6.536**	−8.017**
InINDU	−4.013**	−2.111*	−1.129	−6.285**
InTOR	−5.519**	−8.040**	−3.161	−6.589**
InFDI	−1.051	−6.471**	−6.921**	−7.313**
InURB	−5.023**	−6.872**	−5.940**	−6.933**
InINGP	−6.231**	−7.542**	−3.321	−6.452**
InENR	−7.210**	−9.4017**	−7.326**	−9.520**

Source. Authors' estimations based on the data described in the methodology section (2024).

*

and ** indicate significance at the 5% and 1% levels, respectively.

Table 5.

Pedroni’s Cointegrating Panel Results.

Test	Statistic	Prob.	Weighted Statistic	Prob.
Panel v-Statistic	−0.020	.508	−3.617	.999
rho-Statistic	0.678	.751	1.032	.849
PP-Statistic	−4.538**	.000	−5.167**	.000
ADF-Statistic	−6.871**	.000	−8.173**	.000
Group rho-Statistic	2.467	.993
PP-Statistic	−2.861**	.002
ADF-Statistic	−8.446**	.000

Source. Authors' estimations based on the data described in the methodology section (2024).

**

5% significance.

Hausman Test Results

From the Hausam test results in Table 6, the p-value of .4518 for CO₂ emissions, which is greater than .05, implies that the PMG estimator is preferred for its capacity to assume long-run homogeneity, while allowing for short-run heterogeneity. Similarly, for GHG emissions, the p-value of .3481 again supported the PMG model over the MG. The PMG estimator is more efficient when countries share a common long-run relationship but differ in their short-run dynamics (Hausman, 1978). The long-run coefficients of INDU (0.824 for CO₂, 0.873 for GHG) are consistent across blocs, with short-run adjustment variation validating the PMG estimator. This reinforces the generalizability of the study results on INDU’s environmental impact of INDU in emerging economies.

Table 6.

Hausman Test.

Indicators	Estimator	Statistics	p-Value	Estimator	Statistics	p-Value
CO2 emissions	MG or PMG	5.8912	.4518	PMG or DFE	0.014	.126
GHGs	MG or PMG	3.7826	.3481	PMG or DFE	0.108	.240

Source. Authors' estimations based on the data described in the methodology section (2024).

Bound Test Results

The results in Table 7 show the F-statistics for CO₂ and GHG emissions exceeding 1(1) critical values at 0.05%, indicating long-term cointegration. Based on this, the error correction Model (ECM) was estimated, and the results in Table 8 confirm convergence from short-run deviations to long-run symmetry.

Table 7.

Long-run Bound Test Results.

Variables	Economic blocs	F-stat	I (0)	I (1)
		Test statistics (5%)
CO2	Panel	20.862	2.193	4.193**
	BRICS	6.863
	Central Asia and Europe	49.087
	East and Pacific Asia	7.824
	MINT	5.276
	SAARC	5.930
GHGs	Panel	15.684	2.910	4.193**
	BRICS	8.8084
	Central Asia and Europe	49.268
	East and Pacific Asia	9.7161
	MINT	7.3888
	SAARC	10.537

Source. Authors' estimations based on the data described in the methodology section (2024).

Significance level: **p < .05.

Table 8.

Short Run Error Correction Estimation (ECM) Model.

DP CointEq(–1)	Panel	BRICS	Central Asia and Europe	East and Pacific Asia	MINT	SAARC
InCO2	−0.9077** (−8.0940)	−0.78288** (−7.5814)	−0.8270** (−14.940)	−0.396** (−5.929)	−0.527** (−21.269)−	0.337** (−7.6752)
InGHGs	−0.8801** (−8.1505)	−0.7396** (−7.2972)	−0.6795** (−4.4883)	−0.754** (−21.949)	−0.7096** (−7.0013)−	0.7051** (−10.280)

Note. Dependent variables (DP).

Source. Author's Computations (2024), Significance level: **p < .05.

Error Correction and Long-run Dynamics Result

The results from the ECM in Table 8 provide valuable insights and reveal that the variables are rightly signed “negative and significant,” indicating an annualized and varying convergence to the long-run equilibrium from short-run deviations. This implies that INDU, URB, INGP, FDI, TOR, and ERN exert immediate but correctable pressure on environmental quality over time. However, the varying speeds of convergence across economic blocs, such as slower adjustments in East and Pacific Asia and SAARC, highlight divergent capacities to manage environmental degradation. This aligns with the EKC and PHH hypotheses, indicating that while some regions face growth-driven degradation, others attract carbon-intensive industries due to lax regulations. The STIRPAT model emphasizes the need for policies promoting green technology and robust environmental regulations for sustainable development. This approach is critical to achieving sustainable development in diverse economic contexts. Table 9 is intentionally presented under the “Panel ARDL Estimation” section because it reports the corresponding estimation results.

Table 9.

Panel ARDL Long-run Estimates for CO₂ and GHG Emissions.

Dependent variable: CO2 emissions
Model: ARDL (3, 3, 3, 3, 3)	Coefficient
Economic blocs	Panel	BRICS	Central Asia and Europe	East and Pacific Asia	MINT	SAARC
InINDU	0.824** (9.788)	0.958** (3.0134)	0.8015** (7.917)	0.7355 (12.382)	0.530 (5.890)	0.238 (7.711)
InTOR	0.713**(9.645)	0.605** (3.568)	0.195** (12.137)	0.1065 (5.4917)	0.620 (9.557)	0.843 (7.983)
InFDI	0.984** (9.315)	0.870** (8.126)	0.197** (6.7871)	0.814** (6.557)	0.187* (2.771)	0.540** (3.718)
InURB	0.860** (7.938)	0.708** (6.432)	0.709** (5.954)	0.796** (5.932)	0.036** (6.242)	0.585** (8.757)
InINGP	0.713** (9.645)	0.510** (5.057)	0.127** (37.999)	0.557** (6.3768)	0.016** (3.807)	0.730** (8.536)
InINGP2	−0.803** (−21.532)	−0.280** (−3.969)	−0.950** (−5.289)	−0.114** (−3.440)	−0.741** (−6.265)	−0.271** (−11.395)
InENR	0.824** (9.788)	0.6215** (8.179)	0.212** (13.4778)	0.460** (5.570)	0.095 (0.307)	0.671** (6.243)
Model: ARDL (3, 3, 3, 3, 3)	Dependent variable: GHGs
InINDU	0.8731** (15.176)	2.1844** (18.0867)	0.7217** (8.1389)	0.7314** (9.7481)	0.9173** (8.2724)	0.984** (9.315)
InTOR	0.7087** (13.301)	0.7709** (16.470)	0.7384** (10.477)	0.8019** (7.0735)	0.8223** (3.418)	0.860** (7.938)
InFDI	0.8576** (6.8957)	0.3328** (5.9398)	8.7424** (27.769)	0.2308** (15.888)	0.0604** (4.050)	0.7135** (9.645)
InURB	0.7719** (7.3223)	1.3661** (7.2988)	3.1065** (0.2689)	0.4153** (6.6571)	0.0785** (4.852)	0.355** (4.365)
InINGP	0.60727** (27.5122)	3.1485** (14.6212)	0.6010** (7.4327)	0.3102** (10.964)	0.572** (3.676)	16.917** (12.332)
InINGP2	−0.706** (−7.961)	−0.509** (−9.697)	−0.553** (−7.539)	−0.035** (−5.871)	−0.246** (−16.56)	−0.486** (−8.717)
InENR	0.7035** (3.7809)	8.5625** (26.159)	0.8084** (11.470)	0.8183** (11.686)	0.4913** (8.3754)	0.951** (8.135)

Source. Authors' estimations based on the data described in the methodology section (2024).

Significance level: **p < .05, *p < .1. The non-significant coefficients (p > .1) are unmarked.

The ARDL lag model (3, 3, 3, 3, 3) was selected using the Akaike Information Criterion (AIC), which balances model fit and complexity. The AIC identifies three lags as optimal for capturing dynamics without overfitting.

Panel ARDL Estimation

Discussion of Long-run Estimates and Theoretical Implications

INDU is a major driver of environmental degradation and is significantly correlated with CO₂ and GHG emissions across all blocs. The BRICS, with a coefficient of 0.958, highlights the significant influence of industrial activities in China, India, and Brazil on CO₂ emissions. Similarly, Central Asia and Europe (0.8015), East and Pacific Asia (0.7355), MINT (0.530), and SAARC (0.238) also exhibited substantial impacts.

The higher coefficient for GHG emissions in BRICS countries (2.1844) underscores the broader environmental consequences of energy-intensive sectors. These findings support the EKC hypothesis, which suggests that industrial growth initially leads to environmental degradation before improvement occurs. Additionally, the PHH was validated, indicating that regions with lax environmental regulations, such as BRICS, attract polluting industries. The STIRPAT model further corroborates the critical role of INDU in environmental degradation. These findings are also consistent with those of (Abner et al., 2023; Anwar, Younis, & Ullah, 2020; Inim et al., 2024; Udo et al., 2024). The URB, marked by a 1% decrease in the rural population, contributes significantly to environmental pressures by an 86.0% increase in CO₂ emissions and a 77.1% increase in GHG emissions. BRICS (0.708) and East Pacific Asia (0.796) are particularly affected by rapid urban growth, which escalates energy consumption and pollution. Although the impact was less pronounced in MINT (0.036), it remained significant, reflecting the early stages of urban development in these regions.

The STIRPAT model emphasizes the need for sustainable urban planning to mitigate these effects. The effect of URB on GHG emissions is also significant across all blocs, with BRICS (1.3661) showing the highest sensitivity, indicating that urbanization is a key factor in environmental degradation in emerging economies. The STIRPAT model supports these findings and emphasizes the need for sustainable urban planning (Inim et al., 2024; Udo et al., 2024).

ENR further exacerbates environmental degradation, with CO₂ and GHG emissions rising by 82.4% and 70.3% across all blocks. In BRICS (0.6215) and East Pacific Asia (0.460), fossil fuel reliance is particularly detrimental. The minimal impact of MINT (0.095) suggests varying energy dynamics across the regions. These findings align with the STIRPAT model, highlighting the urgency of transitioning to cleaner energy sources to meet global climate goals, particularly SDGs 13 (Climate Action) and 8 (Sustainable Work and economic growth).

Economic Growth and Environmental Quality: The Role of Inclusive Growth Inclusive Growth (INGP) and its Squared Term (INGP²)

The study results validate the EKC hypothesis across emerging economies, implying that INGDP initially increases CO₂ emissions as the economy evolves, along with income level, CO₂ decreases, and environmental quality increases, but the magnitude and turning point vary significantly by region. This effect is most pronounced in SAARC, where INGP initially increases CO₂ emissions by 73.0% (INGP) as the economy evolves along with income level, CO₂ emissions decrease by 27.1% (INGP²), and a similar pattern is observed for GHG emissions. This implies that SAARC economies experience a relatively faster transition toward sustainable development once a certain income threshold is reached. The negative and significant effect of INGP² indicates that these economies begin to prioritize environmental quality more rapidly as they evolve, due to increased public awareness, policy reforms, and gradual adoption of cleaner technologies.

In contrast, other regions exhibited diverse trajectories. The BRICS bloc shows a more gradual EKC pattern, with INGP increasing emissions by 51.0% and INGP² reducing emissions by 28.0%, indicating a slower but significant transition toward environmental sustainability at higher income levels (around $7,000–$9,000 GDP per capita). This may be attributed to their larger industrial base and greater reliance on fossil fuels. Central Asia and Europe present the most dramatic post-threshold improvement, with 12.7% initial emissions increase and a substantial 95.0% reduction from INGP², reflecting the influence of the strictness of EU environmental policies in the region.

The results from East and Pacific Asia reveal laxity in the EKC effect among the studied regions, with 55.7% emissions increase from INGP and an 11.4% reduction from INGP², indicating prolonged carbon-intensive growth patterns in manufacturing-driven economies such as China and Vietnam. The MINT bloc (Mexico, Indonesia, Nigeria, and Turkey) presents an interesting case, with an initial emission increase of 1.6% and a subsequent decline of 74.1%, implying that the MINT bloc may bypass traditional pollution-intensive development stages through technological leapfrogging.

These regional variations have critical policy implications. The effect in SAARC (0.730, −0.271) implies that early environmental investments yield rapid returns, while BRICS (0.510, −0.280) indicates the need for stringent industrial decarbonization policies. The effect in Central Asia (0.127, −0.950) reveals the effectiveness of stringent environmental regulations, whereas East Asia’s lax effects (0.557, −0.114) call for targeted industrial reform. MINT’s unique pattern (0.016, −0.741) presents opportunities for green technology transfer and sustainable development.

Impact of TOR and FDI

TOR correlated with a significant increase in CO₂ (71.3%) and GHG emissions (70.8%), particularly in regions with lax environmental regulations. BRICS, with a coefficient of 0.605, exemplifies how TRO activity exacerbates pollution. Central Asia and Europe (0.195), East and Pacific Asia (0.1065), and the MINT (0.620) also experienced significant impacts. This finding aligns with PHH, which suggests that TOR in regions with lenient environmental standards leads to increased pollution. To mitigate these adverse effects, it is crucial to integrate environmental considerations into TOR policies and to promote green trade agreements. This finding aligns with those of (Udo et al., 2024; Udoh et al., 2024). FDI inflows caused a 98.4% surge in CO₂ emissions and an 85.7% increase in GHG emissions, particularly in the carbon-intensive sectors. BRICS (0.870) and East and Pacific Asia (0.814) are the most affected, as FDI in polluting industries contributes significantly to environmental degradation. Although the impact was less pronounced in MINT (0.187) and SAARC (0.540), it remained significant.

The EKC hypothesis is supported, indicating that, while FDI initially exacerbates environmental degradation, technological advances brought about by FDI can improve environmental quality over time. This dual impact highlights the need to regulate FDI to ensure that it positively contributes to sustainable development. This finding is supported by (Balli, 2021; Udoh et al., 2024).

The results indicate that INDU, URB, INGP, FDI, TOR, and ERN significantly contribute to environmental degradation across different economic blocs. The EKC hypothesis is validated as income growth initially increases emissions, and at a certain threshold, growth reduces emissions. Policymakers must enforce stricter environmental regulations, promote cleaner technologies, and integrate environmental considerations into trade and investment policies to ease the ecological impact of rapid INDU and URB. Emphasizing sustainable industrial and urban practices, particularly in rapidly developing regions such as BRICS and East Asia, will be crucial in aligning economic growth with global climate goals, such as SDGs 13 (Climate Action) and 8 (Decent Work and Economic Growth).

Conclusion and Policy Implications

This study explored the integrated impact of INDU and INGDP on environmental quality in emerging economic blocs from 1990 to 2023. Using CO₂ and GHG emissions as environmental quality measures and the panel ARDL model to capture both short- and long-run contributive impact, the study offers nuanced insights into the drivers of environmental degradation. The findings reveal that INDU, TOR, FDI, INGP, URB, and ERN significantly contribute to environmental degradation. These results align with and validate the EKC, which posits that growth initially degrades environmental quality and can lead to improvements at higher income levels. This study also aligns with the PHH and STIRPAT models, emphasizing the complex link between economic activities and environmental outcomes. Given these findings, this study emphasizes the need for incentives for businesses to reduce their carbon footprint and increase green industrialization, environmental protection frameworks, and investments in eco-friendly technologies in emerging and advanced economies.

Policy Implications

Based on our findings, we recommend promoting green industrialization through sustainable technologies, incentivizing eco-friendly R&D, and prioritizing energy efficiency in industrial processes. To counter the Pollution Haven Hypothesis effect, emerging economic blocs must enforce stringent environmental regulations, particularly for carbon-intensive industries, and integrate environmental safeguards into trade policies while adhering to international agreements such as the Paris Agreement. We also recommend sustainable urban planning and green infrastructure investment to minimize the environmental impacts of inclusive growth and urbanization. Implementing these policy recommendations will balance inclusive growth with environmental sustainability for the current and future generations. For further studies, we recommend sector-specific environmental impacts.

Limitations and Avenues for Future Research

Despite the study’s valuable contribution to extant literature, the limitations of this study revealed insights for further scholarly inquiry. The reliance on macro-level and country-aggregated data limits the assessment of industrialization and inclusive growth effect across firms and industries. This study therefore recommends the adoption of micro-level, firm-specific, or sectoral data in further studies to capture the heterogeneous environmental effects of various economic activities across different industries and provide more granular policy recommendations. While the adoption of panel data for regional bloc analyses is informative in this study, the study ignores country-specific political, economic, and social dynamics. This study therefore recommends the adoption of time-series analyses to delve deeper into country-specific unique contextual influences.

The integration of a granular data and country-focused analyses would strengthen the evidence base policy recommendations to balance inclusive growth with the urgency of environmental sustainability. By championing green industrialization, robust environmental frameworks, and sustainable urban and energy planning, focused countries and regions will chart a development course that is transgenerational.