Toward a circular Kuznets curve: integrating circular economy and life cycle assessment into industrial waste modeling

Decoupling economic growth from industrial waste generation remains a central challenge in sustainability transitions. While the Environmental Kuznets Curve (EKC) suggests that environmental pressures may decline after an income threshold, our analysis of Saudi Arabia's Industrial Solid Waste (ISW) from 2012–2024 shows a statistically robust N-shaped trajectory: ISW rises with GDP, stabilizes, and then rebounds at higher income levels. Tapio elasticity results further indicate persistent expansive coupling, confirming that economic expansion alone does not alleviate ISW pressures. A structured uncertainty assessment evaluates the effects of data gaps, interpolation, and model sensitivity to structural breaks. To address the observed rebound, we develop a Circular Economy-adjusted EKC (CE-EKC) framework that links recycling, industrial symbiosis, EPR mechanisms, and landfill taxation with LCA-based indicators capable of shifting EKC turning points and flattening rebound slopes. The findings underscore the need for coordinated CE policy packages to align industrial growth with long-term sustainability objectives under Vision 2030.

Graphical Abstract

Graphical Abstract. Conceptual illustration of the Circular Economy–adjusted Environmental Kuznets Curve (CE-EKC), showing stage-specific circular economy interventions (pre-GDP, pre-peak, peak, and post-peak) to control industrial solid waste growth and mitigate rebound effects along economic development trajectories.

1 Introduction

The stability of the global climate is increasingly threatened by accelerating resource use, rising temperatures, and persistent environmental degradation (Wang et al., 2024). These pressures are especially pronounced in rapidly industrializing economies such as Saudi Arabia, where large-scale economic expansion and infrastructure growth continue under Vision 2030. Under Vision 2030, the Kingdom is pursuing economic diversification and industrial growth, trends that simultaneously elevate resource demand and increase the complexity of national waste management systems.

Recent estimates suggest that Saudi Arabia generates nearly 110 million tons of total waste annually, yet data associated with Industrial Solid Waste (ISW) remain incomplete and inconsistent across sectors (Radwan et al., 2024; Saudi Arabia Country Commercial Guide, 2024). Earlier figures indicated that ISW amounted to 358 kilotons in 2018 (Saudi Arabia Country Commercial Guide, 2024; Jayadevappa and Chhatre, 2000; Almansour and Akrami, 2024), but fragmented reporting systems, variations in classification practices, and the absence of unified industrial waste inventories imply that actual volumes may be considerably higher. Available assessments identify the oil and petrochemical industries as the dominant contributors to ISW, followed by manufacturing and construction. These sectors produce diverse and often hazardous waste streams, such as chemical residues, industrial sludge, spent catalysts, brines, and contaminated solids, all of which pose significant environmental and operational challenges (Caviglia-Harris et al., 2009). Despite the magnitude of these waste flows, recycling levels remain low, estimated at around 18%, due largely to uneven regulatory oversight, underdeveloped treatment infrastructure, and the lack of standardized reporting and monitoring frameworks (Aldhafeeri and Alhazmi, 2022; Anis, 2020).

ISW differs substantially from municipal waste due to its hazardous nature, complex composition, and direct link to industrial processes. Management practices across sectors vary widely, reflecting different regulatory requirements, technological capacities, and environmental risks. The absence of a centralized reporting platform limits the ability to track industrial waste streams, evaluate policy implementation, or measure progress toward national sustainability goals. As Saudi Arabia aims to divert 94% of all waste from landfills by 2035, a deeper understanding of the structural, economic, and institutional factors shaping ISW generation is essential for designing effective mitigation strategies (Jayadevappa and Chhatre, 2000; Caviglia-Harris et al., 2009; Mao et al., 2020; Kanwal et al., 2024; Jordán, 2025).

A central conceptual framework for examining how economic expansion influences environmental pressures is the Environmental Kuznets Curve (EKC) hypothesis. The EKC posits that environmental degradation increases during early industrialization but may stabilize or decline once economies reach higher income levels due to technological modernization, stricter regulation, and structural transformation (Jordán, 2025; Ota, 2017). While this framework has been widely applied to air quality and emissions, findings across countries and indicators remain inconsistent. Several rapidly industrializing economies, most notably China, have achieved improvements in selected environmental domains, yet pressures such as waste generation, resource extraction, and carbon dioxide (CO₂) emissions continue to rise with income (Warsame et al., 2025). For ISW, the evidence base is especially limited, and existing research suggests that industrial waste may not follow the same economic environmental pathways observed for pollution or emissions. These uncertainties underscore the need for context-specific assessments of the income waste relationship, particularly in economies where industrial production and resource use remain central to national development.

While the EKC framework highlights potential non-linear links between income and environmental degradation, it does not fully account for material flows, waste generation mechanisms, or technological pathways. This gap has led policymakers and researchers to increasingly emphasize the role of the Circular Economy (CE) in reducing resource consumption and minimizing environmental burdens (Xu et al., 2025). CE principles advocate extending material lifecycles through reuse, recycling, remanufacturing, and industrial symbiosis, thereby reducing upstream resource extraction and downstream waste disposal (Tao et al., 2021; Kanwal et al., 2023; Aldegheishem, 2024; Alajmi, 2016; Wang et al., 2015). CE thus offers a structural approach that can shift economic systems away from linear “take–make–dispose” models and toward regenerative production cycles. However, despite the growing prominence of CE in global policy discourse, its application to ISW, particularly in resource- and energy-intensive economies, remains insufficiently explored (Kumar et al., 2024; Gyamfi et al., 2021).

Complementing CE principles, Life Cycle Assessment (LCA) provides a systematic framework for quantifying environmental impacts across the entire lifecycle of materials and processes. LCA enables the identification of emission hotspots, waste-intensive stages, and environmental trade-offs associated with different treatment options. Yet, the integration of LCA indicators into macro-level assessments of waste income dynamics remains limited. Few studies examine how LCA-derived metrics, such as waste intensity, embodied emissions, or material circularity, might influence or reinterpret EKC trajectories for industrial waste. This lack of integration limits the ability to evaluate how structural reforms, technological innovation, and circularity interventions may reshape long-term ISW trends.

Saudi Arabia presents a highly relevant case for investigating these linkages. The Kingdom's industrial development plan includes substantial expansions in petrochemicals, metals, construction materials, and advanced manufacturing sectors that generate diverse and often hazardous waste streams. Although Vision 2030 emphasizes sustainability, CE adoption, and innovation-driven industrial growth, the operational frameworks needed to support these goals, such as unified reporting systems, cross-sectoral waste inventories, and CE-based performance metrics, remain underdeveloped (Saudi Arabia Country Commercial Guide, 2024; Wang et al., 2015; GESALO, 2023; Banerjee, 2022). Existing policies articulate ambitious targets for landfill diversion and hazardous waste management, yet sector-level implementation remains uneven. These conditions highlight the need for analytical frameworks capable of linking economic expansion, industrial structure, regulatory evolution, and CE adoption to the dynamics of ISW generation (Tao et al., 2021; GESALO, 2023).

Addressing these challenges requires combining insights from environmental economics, industrial ecology, and sustainability policy. The EKC provides a theoretical foundation for exploring potential non-linearities in the income ISW relationship. CE offers structural approaches for reducing material throughput and enhancing resource circularity. LCA provides methodological tools for evaluating environmental impacts across industrial value chains. Despite the relevance of these approaches, the literature lacks studies that synthesize EKC, CE, and LCA perspectives to explain ISW trends in industrializing, resource-intensive economies.

This study contributes to this gap by developing an integrated conceptual foundation for understanding the economic and structural determinants of ISW generation in Saudi Arabia. It is motivated by three core observations:

• ISW data in the Kingdom remain fragmented, inconsistent, and insufficiently standardized, limiting the capacity to assess temporal trends and evaluate policy outcomes;

• the income–ISW relationship has not been systematically examined within the EKC framework, despite the country's rapid industrialization and evolving regulatory landscape;

• CE strategies and LCA-based insights are increasingly emphasized in national policy discourse, yet their potential role in shaping long-term ISW dynamics is not well understood.

By examining Saudi Arabia as a case study, this research contributes to international discussions on the EKC, industrial waste governance, and CE–driven sustainability transitions. The analysis clarifies the structural and institutional factors influencing ISW dynamics and supports the development of policy approaches consistent with Vision 2030 (Alajmi, 2016; Banerjee, 2022; Lv et al., 2021). These insights are applicable to other rapidly developing economies seeking to reconcile industrial growth with resource efficiency and environmental protection.

We therefore adopt a combined analytical frame: EKC regressions to map the long-run GDP–ISW shape, complemented by decoupling indicators (e.g., ISW/GDP elasticity, ISW intensity trends) to monitor short-term policy effectiveness. This integration allows us to judge whether observed “flattening” is genuine absolute decoupling or merely a temporary slowdown preceding an N-shaped resurgence.

2 Literature and conceptual background

2.1 Natural context and industrial development

Saudi Arabia faces severe water scarcity, exacerbated by rising temperatures and increased evaporation rates. This condition threatens agriculture, industry, and domestic water supply, necessitating energy-intensive desalination, which further exacerbates energy demand and carbon emissions. Additionally, the country is highly vulnerable to natural disasters, including floods, sand and dust storms, and droughts, which disrupt economic activities, damage infrastructure, and negatively impact public health. Saudi Arabia's dependence on fossil-fuel-based energy production contributes significantly to air pollution and CO₂ emissions, worsening urban air quality and accelerating environmental degradation. Coastal regions also face industrial pollution pressures, threatening marine biodiversity and fisheries. The Energy, Water, and ISW sectors are at the core of this environmental challenge. The rapid economic expansion has driven a surge in energy and water consumption, accompanied by a 201% increase in ISW generation over the past decade (2012–2022).

Figure 1 illustrates Saudi Arabia's economic and environmental growth trends from 2012 to 2022, emphasizing the uneven distribution of industrial impacts across different provinces. While national GDP has grown by 52%, energy and water consumption have increased by 39 and 52%, respectively. However, ISW generation has surged by 201%, indicating a more rapid escalation of waste compared to economic and resource consumption trends. The stark differences among provinces underscore the need for region-specific CE interventions. For instance, Jizan's extreme water consumption increase (+405%) demands urgent water-efficient industrial practices, while Najran and Al-Hudud ash Shamaliyah's rapid ISW growth (>200%) necessitates targeted waste valorization and recycling strategies (Kanwal et al., 2024). The observed trends align with the first phase of the EKC, where economic growth drives higher waste levels (Jordán, 2025). However, data suggest ISW trends are not following a stabilizing trajectory, raising concerns that the country may be shifting toward an N-shaped EKC, where waste accumulation accelerates beyond an environmental tipping point.

Figure 1

Figure 1. Provincial growth rates in Saudi Arabia for GDP (+52%), industrial energy use (+39%), freshwater withdrawals (+52%), and ISW generation (+201%) over 2012–2022. The maps reveal substantial regional heterogeneity: Jizan records exceptionally high water-consumption growth, while Najran and Al-Hudud ash Shamaliyah exhibit ISW increases exceeding 200%. In contrast, several western and northern provinces show comparatively modest growth across all indicators. This spatial divergence highlights where industrial expansion and resource pressures are most concentrated, reinforcing the need for regionally targeted CE interventions and explaining why national ISW trends exhibit persistent expansive coupling. Data sources: MoMRAH, GASTAT (see Supplementary Tables 1–5).

2.2 Decoupling and EKC theory: limitations and extensions

Decoupling analysis evaluates whether environmental pressure grows more slowly than (relative decoupling) or declines in absolute terms despite GDP growth (absolute decoupling) (Kanwal et al., 2023). Saudi Arabia's recent trajectory shows relative decoupling for energy and water in some provinces, but ISW pressure has grown faster than income, indicating negative decoupling. Decoupling indicators, however, are essentially short-run intensity measures; they reveal pace, not structural inflection points.

Material-circulation thinking in CE industrial symbiosis networks, by-product exchanges, and design for recyclability adds a process perspective absent from traditional EKC models. Yet CE metrics are typically operational (e.g., recycling rate, % of secondary raw material), while EKC coefficients capture long-run correlations. Relying on either lens alone is limiting: EKC ignores time-dependent policy shocks and technology adoption; decoupling ignores latent non-linearities and rebound risks.

Practically, we operationalize this by: (i) estimating ln(ISW) on ln(GDP) polynomials, (ii) computing ISW intensity (ISW/GDP) and elasticity (Δln ISW/Δln GDP) for robustness (Section 3), and (iii) mapping CE instruments to these metrics in our policy discussion (Sections 4 and 5). The CE-EKC framework thus links concrete levers (EPR, landfill levies, industrial symbiosis) to measurable outcomes (shift/flatten the EKC; move from relative to absolute decoupling).

2.3 CE, LCA, and sectoral applications

The CE model represents a fundamental shift from traditional linear production, emphasizing resource efficiency, closed-loop material flows, and waste minimization (Mannan et al., 2018). This transition supports Saudi Arabia's Vision 2030 and the National Environment Strategy, aligning with efforts to reduce ISW, lower carbon emissions, and enhance industrial sustainability. By embedding CE principles into production and consumption systems, industries can minimize resource extraction, reduce pollution, and improve material circulation (Ghisellini et al., 2016). A key mechanism for optimizing CE-driven sustainability is LCA, which systematically evaluates environmental impacts across all stages of a product's life cycle (Kanwal et al., 2025b). When integrated with CE frameworks, LCA provides quantifiable insights into material efficiency, energy use, and emissions reductions, enabling policymakers and industries to identify the most effective waste reduction strategies. Applying LCA methodologies to ISW management in Saudi Arabia may guide policy decisions on recycling infrastructure investments, industrial waste recovery, and product design improvements (Kanwal et al., 2025a).

Incorporating CE-LCA integration into Saudi Arabia's economic and environmental policies can accelerate the transition toward an inverted-U EKC model, where waste generation and emissions decline with economic growth. While the EKC hypothesis suggests that environmental degradation naturally decreases as economies mature and adopt cleaner technologies, CE offers an initiative-taking, cost-effective approach to achieving these improvements earlier. By embedding CE principles into industrial processes, supported by LCA-driven environmental assessments, Saudi Arabia can prevent unnecessary resource depletion, reduce waste volumes, and promote long-term sustainability (Zhang C. et al., 2025).

However, the “closed loop” nature of CE does not inherently guarantee environmental benefits, as some recycling and recovery processes may have high energy consumption or generate secondary pollutants (Gyamfi et al., 2021). Therefore, comprehensive environmental assessment tools, such as LCA, are essential for evaluating different CE strategies. LCA-based evaluations allow industries to compare various waste treatment methods, assess trade-offs, and determine the most effective pathways for sustainability. This approach can inform regulatory policies and industry standards, ensuring that Saudi Arabia's CE transition is both environmentally and economically viable (Mannan and Al-Ghamdi, 2022).

The combination of LCA and CE can result in more in-depth analysis and a better understanding of economic, social, and environmental sustainability, helping policymakers find better alternatives by comparing all key environmental impacts. This can support Saudi Arabia's Vision 2030 and the National Environment Strategy (Saudi Arabia Country Commercial Guide, 2024). Table 1 summarizes the integration of CE strategies and LCA across key industrial sectors in Saudi Arabia, highlighting their benefits, challenges, and potential applications for sustainable waste management and resource efficiency.

Table 1

Table 1. Integration of CE strategies and LCA in key industrial sectors of Saudi Arabia (Shaukat, 2023; Alqahtani and Afy-Shararah, 2025; Alsaud et al., 2025; Almutairi, 2025; Rahman et al., 2022; Hamieh et al., 2022).

2.4 Research gaps and methodological innovation

2.4.1 Conceptual and empirical gaps in the ISW literature

Although the EKC has become a widely used framework for exploring the environmental implications of economic growth, most applications remain limited in scope, focusing on aggregate indicators like CO₂ emissions or Municipal Solid Waste (MSW), and largely targeting high-income, policy-mature countries (Zhang C. et al., 2025). In particular, the dynamics of ISW, a distinct and often more hazardous stream, have received limited empirical attention. The few existing studies on waste-based EKC behavior disproportionately reflect conditions in Europe, North America, and China, where advanced waste governance, regulatory instruments, and CE practices are already embedded (Lau et al., 2025). As a result, the trajectory of ISW in resource-based, rapidly industrializing economies remains poorly understood.

Existing studies show that research on ISW remains fragmented, with most work concentrating on overall waste quantities, sector-specific material flows, or carbon footprint assessments (Dri et al., 2018; Islam et al., 2022; Nia and Shafei, 2025). In contrast, there is limited empirical evidence on how hazardous and non-hazardous ISW streams evolve as economies grow or as regulatory frameworks mature (Hashim et al., 2025; Wu et al., 2025). Current literature also provides little insight into whether ISW follows the same turning-point dynamics documented in some MSW or pollution-based EKC studies (Alajmi, 2016), or whether ISW trajectories differ due to factors such as fossil-intensive industrial structures, high material throughput, and delayed policy enforcement (Zhang S. et al., 2025; Zhang C. et al., 2025). This gap underscores the need for a focused examination of ISW behavior within an EKC–CE framework and highlights the importance of understanding how economic development and policy transitions jointly influence industrial waste generation.

2.4.2 Statistical and methodological innovation of the present study

A second critical limitation lies in the methodological compartmentalization of environmental-economic tools. Traditional EKC models are rarely integrated with policy-sensitive metrics such as Tapio decoupling elasticity or LCA-based evaluation of waste interventions. As a result, literature lacks a unified framework to examine both structural economic relationships and the efficacy of CE levers in altering waste trajectories. Moreover, most existing EKC studies treat CE instruments (e.g., EPR, landfill taxation) as qualitative background rather than quantifiable variables within the modeling structure.

To address these limitations, the present study makes two key contributions. First, it empirically extends the EKC framework to ISW within a resource-intensive, oil-exporting economy, Saudi Arabia, during a time of ambitious sustainability reform. This represents a novel empirical context where CE strategies are emerging but not yet institutionalized. With a high per capita waste generation rate (~1.7 kg/person/day), low industrial recycling rates (often <50%), and continued reliance on landfilling (>50% in many zones), the country offers a premature CE testbed for understanding how policy interventions might shape EKC trajectories. Sectoral characteristics, such as the dominance of petrochemical and construction-based waste, and the recent initiation of CE pilots (e.g., in Jubail), further differentiate this context from those studied in existing literature.

Second, the study develops and applies an integrated CE-EKC framework, combining non-linear polynomial regression for long-run income–waste dynamics, Tapio elasticity for regional decoupling analysis, and LCA-informed CE levers to evaluate the potential of instruments like EPR, landfill taxation, and industrial symbiosis to “bend” or flatten the EKC curve. This methodological integration allows for a more policy-relevant and temporally dynamic interpretation of the waste–income relationship, addressing both structural drivers and instrument-based corrections.

2.4.3 Positioning the Saudi case within the global EKC–CE landscape

To highlight the international relevance of the case and reinforce the empirical novelty, Table 2 compares Saudi Arabia's industrial waste generation patterns, CE policy maturity, and waste treatment indicators with those of OECD economies and other regions commonly analyzed in EKC literature. This comparative framing makes explicit the differences in waste intensity, regulatory evolution, and treatment infrastructure that justify Saudi Arabia as a distinctive and under-explored case within the global EKC debate.

Table 2

Table 2. Comparative metrics on industrial and municipal waste management: Saudi Arabia vs. OECD and global benchmarks.

3 Methodology

3.1 Data sources, variable definitions, and pre-processing procedures

We assembled annual data (2012–2024) for four key series from official Saudi sources:

• ISW: total hazardous and non-hazardous industrial residues (kt yr⁻¹) sourced from MoMRAH statistical yearbooks and provincial environmental reports, cross-checked against GASTAT environmental bulletins and SIRC disclosures. Two missing observations (2015 and 2019) were filled using linear interpolation. This method was selected because the ISW series exhibits a smooth, monotonic year-to-year growth pattern, making linear interpolation an assumption-light and transparent option. More complex approaches, such as spline interpolation, polynomial fitting, or model-based imputation, would require stronger assumptions and risk introducing artificial curvature that could distort EKC estimates or Tapio elasticities. Robustness checks confirm that excluding the interpolated years does not alter coefficient signs or significance. Uncertainty bounds around the interpolated values are reported in Supplementary Table 7.

• Gross Domestic Product (GDP): real GDP in constant 2015 SAR (billion) from SAMA national accounts, verified against World Bank WDI. Nominal figures are deflated to 2015 using official SAMA price indices. For econometric estimation, GDP is maintained in constant 2015 SAR; conversions to USD appearing in the Results section are performed solely for interpretive and international comparability purposes.

• Industrial Energy Consumption (EC): total industrial energy use (TWh) from GASTAT energy statistics and Ministry of Energy/SEEC reports.

• Industrial Water Consumption (WC): freshwater withdrawals by industry (million m3) from MEWA and GASTAT.

All series are natural-log transformed to stabilize variance and interpret coefficients as elasticities (Table 3). Provisional data for 2025–2026 appear in figures for context but are excluded from baseline regressions (2012–2024). A detailed variable dictionary and processing steps are provided in Supplementary Table 1; raw time series are plotted in Supplementary Figure 1.

Table 3

Table 3. Descriptive statistics, units, sources, and processing steps for study variables in Saudi Arabia.

3.2 EKC estimation strategy and decoupling metrics

To examine the income–ISW relationship, we estimate quadratic and cubic polynomial EKC models of ln(ISW) on ln(GDP) over 2012–2024. Neither the interpolated ISW values nor the provisional forward-estimates for 2025–2026 influence the econometric results; the EKC regressions and Tapio elasticity calculations are based exclusively on observed data for 2012–2024. The baseline specifications are:

Quadratic: $ln(IS{W}_t)={\beta }_0+{\beta }_{1}ln(GD{P}_t)+{\beta }_2{\left[ln(GD{P}_t)\right]}^2+{\epsilon }_t$

Cubic: $ln(IS{W}_t)={\beta }_0+{\beta }_{1}ln(GD{P}_t)+{\beta }_2{\left[ln(GD{P}_t)\right]}^2+{\beta }_3{\left[ln(GD{P}_t)\right]}^3+{\epsilon }_t$

Model suitability is evaluated based on adjusted R², Ramsey RESET tests, and sign/stability patterns of coefficients.

Since all variables, including GDP, are expressed in natural logarithms, the estimated EKC turning points are obtained in logged GDP units. These are then converted back into real economic units using $GD{P}_{TP}=e^{ĝ}$, where ĝ denotes the turning point in log terms. All turning-point values reported in the Results section are expressed in constant 2015 SAR.

It is important to emphasize that this EKC specification is correlational and should be interpreted as a reduced-form association between ln(ISW) and ln(GDP); the model does not identify structural causal mechanisms. Causal effects would require quasi-experimental or structural modeling designs, which are beyond the scope of this study. Accordingly, any discussion of potential drivers around the turning point is offered as interpretive context consistent with the observed statistical pattern, not as causal mechanisms identified by the model.

Because annual environmental and macroeconomic series often exhibit trending behavior, we first assess non-stationarity using Augmented Dickey–Fuller (ADF), Phillips–Perron (PP), and KPSS tests for ln(ISW) and ln(GDP). These diagnostics confirm whether variables are stationary in levels or whether regressions must be interpreted as trend-based structural approximations rather than long-run equilibrium relationships. Consistent with EKC literature using short annual series, we interpret polynomial estimates as reduced-form approximations rather than cointegrating vectors. To acknowledge this limitation, turning-point interpretation is supplemented with sensitivity checks described below.

We also evaluate the potential presence of structural breaks, given major policy and economic events during the study period, including the launch of Vision 2030 (2016), energy-price reforms, COVID-19 disruptions, and changes in industrial reporting. We apply Zivot–Andrews tests for unit roots with endogenous breaks and supplement this by inspecting breakpoints using Bai–Perron multiple-break procedures. While the short sample restricts extensive breakpoint modeling, acknowledging and testing for these shifts improves robustness and avoids overstating the stability of EKC coefficients.

To capture dynamic relationships, we examine whether GDP effects on ISW exhibit lag structure, as waste generation often responds gradually to industrial expansion and policy enforcement. Due to sample-size constraints, we retain the static EKC form but conduct supplementary checks including: (i) adding 1-year lagged ln(GDP), (ii) estimating a simple ARDL(1,1) structure, and (iii) comparing HAC-robust OLS to Newey–West (lag = 1). These checks assess whether short-run adjustments influence sign patterns or turning-point estimates while keeping model parsimony.

A key concern in EKC studies is uncertainty around turning points, which may be sensitive to sample length, data quality, polynomial degree, and interpolation. We therefore compute turning-point confidence regions by applying bootstrap resampling of the cubic model and checking whether alternative specifications (quadratic only, cubic only, with or without interpolated years, adding EC/WC controls) materially shift the inferred inflection points. This prevents over-interpretation of a single polynomial curve and acknowledges parameter uncertainty inherent in small-sample regressions.

Parameters are estimated by OLS in Stata 17 with HC3 heteroskedasticity-robust standard errors (to account for small-sample variance heterogeneity) and Newey–West HAC SEs (lag = 1) to correct for autocorrelation. Diagnostic tests include: Breusch–Godfrey LM and Durbin–Watson for serial correlation; Breusch–Pagan/White for heteroskedasticity; Ramsey RESET for functional form; and Cook's D (> 4/n) for influential points. We conduct five sensitivity checks: (i) dropping interpolated ISW years (2015, 2019), (ii) using only the quadratic form, (iii) adding ln (EC) and ln (WC), (iv) applying HAC SEs, and (v) jackknifing the strongest influence year. Full robustness results appear in Table 5.

To characterize short-run GDP–ISW dynamics, we compute the Tapio decoupling elasticity using year-on-year log differences. The elasticity classifies annual ISW–GDP relationships as weak (<0.8), strong (0.8–1.2), or expansive (>1.2) coupling. Because annual elasticities may also be sensitive to structural changes and reporting variability, we interpret them alongside the EKC estimates to provide a more robust account of short-term fluctuation and long-term trajectory.

3.3 Uncertainty identification, management, and analytical implications

Uncertainty in annual ISW–GDP analyses arises from several sources, including data gaps, reporting variability, forward-estimate values, model specification choices, and small-sample limitations. Following established frameworks in sociotechnical systems analysis and digitalized process-system risk assessment, such as Expert Judgment and Uncertainty in Sociotechnical Systems Analysis and Uncertainty Modeling in Risk Assessment of Digitalized Process Systems (Yazdi et al., 2022; Zarei et al., 2024), we distinguish between epistemic uncertainty (stemming from incomplete or imperfect information) and aleatory uncertainty (stemming from inherent variability in economic–environmental systems). These frameworks emphasize explicit identification of uncertainty sources, careful differentiation between uncertainty types, and structured assessment of how uncertainty propagates through quantitative models.

Data and measurement uncertainty: gaps in the official ISW record (2015 and 2019) introduce epistemic uncertainty. These values were interpolated using a linear method justified by the monotonic structure of the series; uncertainty bounds (±5% and ±10%) are provided in Supplementary Table 7 to quantify plausible variability. Reporting noise in annual environmental datasets is addressed through log-transformation and HAC-robust inference.

Model specification uncertainty: polynomial EKC models are sensitive to functional-form assumptions. We address this through parallel estimation of quadratic and cubic forms, lag inclusion tests, structural-break diagnostics, and jackknife procedures. Turning-point uncertainty is quantified using bootstrap resampling, producing confidence regions rather than relying on single-point estimates.

Propagation into elasticity metrics: Tapio elasticities may be influenced by year-to-year fluctuations. To mitigate this, we interpret elasticity categories in conjunction with EKC trends and acknowledge classification uncertainty near threshold values (0.8 and 1.2).

Forward-estimate uncertainty: provisional values for 2025–2026 are used strictly for conceptual visualization and are excluded from all econometric estimations. Thus, they do not propagate into parameter estimates, turning points, or elasticity classifications.

Overall, uncertainty analysis indicates that while measurement and specification uncertainties exist as in most short annual environmental datasets, they do not alter the qualitative conclusions: the cubic EKC pattern is robust, and all short-run dynamics remain in the expansive coupling regime. Figure 2 synthesizes the uncertainty pathway across the analytical pipeline, linking measurement variability in the ISW dataset to interpolation assumptions, model-specification sensitivity, and statistical uncertainty in the econometric findings.

Figure 2

Figure 2. Illustrates the propagation of uncertainty across the analytical pipeline, from measurement error in the ISW dataset to interpolation assumptions, specification sensitivity in the cubic EKC model, and statistical uncertainty in the econometric findings.

4 Results

4.1 Baseline EKC results: quadratic and cubic polynomial models

Table 4 reports on the baseline regressions. In the quadratic specification, ln GDP is positive and the (ln GDP)² term is negative but not statistically significant; the model explains 99.5% of the variation (Adj. R² = 0.9954). In the cubic specification, ln GDP > 0, (ln GDP)² <0, and (ln GDP)³ > 0, and all three coefficients are significant at the 1% level, raising Adj. R² to 0.9992. This sign pattern is consistent with an emerging N-shaped. Without intervention, ISW rises with income, flattens, and then increases again at higher GDP levels. Although the cubic model coefficients are significant at the 1% level, the quadratic form's squared term fails to achieve significance (p > 0.10). Given the short annual series (n = 13) and aggregate national data, we interpret the N-shape result as an indicative trend rather than definitive proof, and caution that statistical power is limited.

Table 4

Table 4. Baseline EKC regressions for ISW (dependent variable: ln(ISW)). Saudi Arabia, 2012–2024.

4.2 Diagnostic tests and robustness assessments

To ensure the EKC pattern is not driven by specific years or modeling choices, we re-estimated the models under five alternative specifications (Table 5). Excluding the interpolated ISW years, altering the polynomial order, adding ln EC and ln WC, applying HAC errors, or dropping the single most influential year leaves the qualitative N-shape of the cubic model unchanged (Adj. R² ≥ 0.999 in all cubic variants). Breusch-Godfrey p-values exceed 0.21 in the key models, indicating no residual autocorrelation; Breusch-Pagan tests are non-significant (heteroskedasticity not detected), except in the EC/WC model, where perfect collinearity yields a zero p-value. Ramsey RESET p-values are ≈0.05 in the baseline and HAC models, but indicate misspecification for the quadratic-only form, as expected. Overall, the rise–flatten–rise trajectory is robust to reasonable perturbations, though the limited sample size still warrants cautious interpretation.

Table 5

Table 5. Robustness checks for the EKC specification (dependent variable: ln(ISW)).

While the EKC trajectory is empirically robust, a deeper understanding of decoupling dynamics is needed to contextualize these trends within the broader sustainability transition framework.

4.3 Tapio decoupling elasticity: trends and classification outcomes

To complement the long-run EKC modeling presented in Sections 3.3 and 3.4, we compute the short-run Tapio elasticity between ISW and GDP over the 2013–2022 period. Tapio elasticity is defined as the ratio of the percentage change in ISW to the percentage change in GDP, calculated via year-on-year log differences. This approach enables classification of each year's coupling status into three empirically grounded categories: weak coupling (elasticity <0.8), strong coupling (0.8 ≤ elasticity ≤ 1.2), and expensive coupling (elasticity > 1.2), consistent with the decoupling scale used in Figure 4.

Table 6 reports the elasticity values and their corresponding classifications. Notably, all observed years fall into the “expensive coupling” category, with elasticity values ranging from 1.21 to 3.31. This indicates that ISW has consistently grown faster than GDP across the decade, reflecting the material- and energy-intensive nature of Saudi Arabia's industrial growth model. The absence of weak or strong decoupling suggests a lack of systemic efficiency improvements or regulatory constraints in industrial waste generation. These findings are visually reinforced in Figure 4, where all elasticity bars lie above the 1.2 threshold, underscoring persistent over-proportional ISW growth.

Table 6

Table 6. Tapio elasticity of ISW relative to GDP and coupling type (2013–2022).

This empirical pattern aligns with the cubic EKC shape observed in Section 3.3, where ISW initially rises, stabilizes, and then rebounds with economic expansion. The Tapio results add nuance by capturing short-run dynamics and reinforcing the need for targeted CE policies to “bend” the elasticity curve downward. Without intervention, the current trajectory implies continued environmental degradation per unit of economic output. The Tapio metrics thus serve both as a diagnostic and as a performance baseline for future CE tracking.

It is important to acknowledge that Tapio elasticity classifications can involve interpretive uncertainty, particularly when values fall close to category boundaries (e.g., around 0.8 or 1.2). Minor fluctuations in annual ISW or GDP data arising from reporting variability, rounding, or interpolation may shift a year's status between adjacent categories. Accordingly, Tapio outcomes should be interpreted as indicative trend signals rather than strict categorical states.

4.4 CE implications and the empirical emergence of an N-shaped EKC

Empirical evidence from industrialized economies demonstrates a strong positive correlation between GDP growth, energy consumption, and CO₂ emissions, often resulting in an N-shaped EKC trajectory for environmental pressures (Virdi, 2021). In our analysis of Saudi Arabia's ISW, the cubic regression model confirms this pattern: all three polynomial terms are statistically significant at the 1% level, and the fitted curve in Figure 3 clearly exhibits a “rise–flatten–rise” sequence. Specifically, ISW generation initially increases alongside GDP up to ~USD 850 billion, then stabilizes between USD 900–1,050 billion, before rebounding beyond USD 1,100 billion. In contrast, the quadratic (inverted-U) specification suggests a trough near USD 1,000 billion, but its squared term lacks statistical significance (p > 0.10), limiting its explanatory power. Confidence intervals derived using HC3 robust standard errors (not shown) further validate the cubic model's resilience against sampling variation and alternative specifications. Figure 3 complements the trends observed in Table 5, where Tapio elasticity values exceeding 1.5 across 2013–2022 reflect a consistently rising ISW trajectory over the GDP range, reinforcing the classification of “expensive coupling” and underscoring the absence of meaningful decoupling in the Saudi context.

Figure 3

Figure 3. Estimated Environmental Kuznets Curves (EKCs) for Saudi Arabia's ISW relative to GDP (2012–2024), comparing quadratic (inverted-U, red) and cubic (N-shaped, green) specifications. The quadratic curve suggests a turning point near USD 1,000 billion, but its squared term is not statistically significant. The cubic EKC is significant across all terms and displays a distinct rise–flatten–rise pattern: ISW increases up to about USD 850 billion, stabilizes between USD 900–1,050 billion, and then rebounds beyond USD 1,100 billion. Data: MoMRAH, GASTAT. Note: GDP turning points are derived from the log-transformed EKC model and then converted back into raw USD values for interpretation.

Robustness checks, including the exclusion of interpolated ISW years, the addition of industrial energy and water controls, and a jackknife removal of high-influence observations, demonstrate that the N-shape persists under diverse model configurations. These empirical results confirm that, over the study period, Saudi Arabia's ISW trajectory follows a statistically robust N-shaped EKC pattern, with a clear rebound phase occurring beyond the initial stabilization point. All EKC turning points are estimated in constant 2015 SAR and subsequently converted into USD for interpretive clarity, consistent with Figure 3.

5 Discussion

This section interprets the EKC findings for Saudi Arabia's industrial waste, linking them to CE transition strategies and deriving targeted policy measures to advance sustainable ISW management in line with Vision 2030 objectives.

5.1 Interpreting the N-shaped EKC for ISW

Our baseline cubic EKC estimates (Table 2) reveal a classic rise–flatten–rise pattern in ln ISW with respect to ln GDP: the coefficient on ln GDP is positive (844.118, p < 0.01), the squared term is negative (−122.491, p < 0.01), and the cubic term is positive (5.935, p < 0.01). This N-shape persists across all robustness checks (Table 3), with adjusted R² ≥ 0.999 in each cubic variant and no evidence of serial correlation or heteroskedasticity (Breusch–Godfrey p > 0.21; Breusch–Pagan p > 0.05 in most models). The statistical significance of the cubic term, even after dropping interpolated years, adding ln EC and ln WC, applying HAC standard errors, or a jackknife removal, confirms that ISW generation in Saudi Arabia does not simply plateau at higher incomes but rebounds, signaling a second inflection beyond the conventional inverted-U turning point.

This trajectory contrasts with pollutants such as SO₂ or NO_x, for which many studies observe steady post-peak declines. In the case of ISW, income-driven stabilization appears temporarily: as industrial activity expands and resource-intensive production persists, waste generation accelerates again. Without targeted CE interventions, the upward rebound implies increasing pressure on landfill systems, material extraction, and overall environmental risk. These findings, therefore, suggest that any observed stabilization in ISW is not self-correcting but contingent on structural policy action.

Figure 4 illustrates the EKC transition by comparing the trajectories of an N-shaped and an inverted-U EKC in the context of Saudi Arabia's industrial development, highlighting the influence of CE strategies on long-term pollution–growth dynamics. Initially, economic growth is coupled with rising industrial waste and emissions, reinforcing an “expensive coupling” phase driven by fossil fuel dependency, resource-intensive manufacturing, and linear waste disposal. The empirical N-shape observed in Section 4 underscores that income-driven stabilization is temporary unless reinforced by CE interventions. However, CE adoption can decouple economic growth from waste accumulation, transitioning Saudi Arabia from “expensive coupling” to “strong decoupling.” This structural shift in material circulation modifies the EKC trajectory, ensuring that GDP growth is maintained while industrial waste and emissions decline (Warsame et al., 2025; Sadaoui et al., 2025).

Figure 4

Figure 4. Conceptual trajectories of the EKC for Saudi Arabia's ISW, illustrating inverted-U and N-shaped forms overlaid with CE strategies that influence long-run pollution-growth dynamics. The diagram highlights how CE interventions such as renewable energy substitution, resource efficiency, eco-design, industrial symbiosis, and waste-to-resource systems can moderate the rebound portion of an N-shaped EKC. By shifting the curve downward or flattening the upper segment, these strategies illustrate how targeted CE measures can help redirect ISW trends toward a sustainable decoupling pathway. This figure contextualizes the empirical N-shaped EKC observed in Saudi Arabia and clarifies why CE-oriented measures are required to suppress the rebound phase.

Achieving such a shift requires system-level reforms, including expanded waste-to-resource capacity, CE industrial parks with integrated recycling and symbiosis networks, and targeted technological upgrades in high-intensity industrial regions. For non-recyclable fractions, selectively deployed WTE systems can support Saudi Arabia's broader emissions reduction objectives while reducing pressure on landfills. Collectively, these CE-driven measures provide a pathway to suppress the rebound phase identified in the empirical N-shaped EKC and enable long-term environmental alignment with Vision 2030.

5.2 Regional and cluster-adjusted EKC dynamics

To capture province-level heterogeneity in the ISW–GDP relationship, we extend the baseline cubic EKC model by interacting each polynomial term with regional cluster dummies. The provinces are grouped into three categories: High-Intensity (H) regions, comprising Riyadh, Makkah, and the Eastern Province; Emerging (E) regions, including Jizan, Najran, and Al Hudud ash Shamaliyah; and Other (O) regions, which encompass the remaining ten provinces and serve as the reference group in the model.

We estimate:

$\begin{array}{l}\ln \left(IS{W}_{i,t}\right)={\beta }_0+{\displaystyle \sum _{k=1}^3[{\beta }_k\ln {\left(GD{P}_{i,t}\right)}^k+{\gamma }_{kH}\ln {\left(GD{P}_{i,t}\right)}^k{D}_{H,i}}\\ +{\gamma }_{kE}\ln {\left(GD{P}_{i,t}\right)}^k{D}_{E,i}]+{\epsilon }_{i,t}\end{array}$

where D_{H, i} and D_{E, I} indicate membership in the High-Intensity and Emerging clusters.

• High-Intensity: rebound coefficient β3 + γ3H = 7.12 (p < 0.01)

• Emerging: β3 + γ3E = 5.48 (p < 0.05)

• Other: β3 = 4.85 (p < 0.10)

Model diagnostics confirm no residual autocorrelation (Breusch–Godfrey p > 0.20), no heteroskedasticity (Breusch–Pagan p > 0.05), and high explanatory power (Adj R² ≥ 0.997) across clusters. These differences demonstrate that high-intensity provinces face the strongest waste rebound and thus require the most stringent CE measures. We recommend a zonal CE strategy:

• High-Intensity zones (Riyadh, Makkah, Eastern): mandate EPR and develop industrial symbiosis parks. Deploy large-scale WTE to process residual non-recyclables.

• Emerging zones (Jizan, Najran, HS): focus on capacity building technology transfer, workforce training in resource-efficient practices, and small-scale recycling facilities to pre-empt the N-shape rebound.

• Other provinces: roll out standardized CE tools (digital waste-tracking systems, material passports) without extensive customization, leveraging economies of scale.

Across all regions, solar-powered recycling units and modular WTE installations can reduce additional carbon footprints (Gómez et al., 2023). Aligning these zonal plans with the Middle East Green Initiative will facilitate shared governance and accelerate CE adoption through technology dissemination. By anchoring policies to quantified EKC heterogeneity, Saudi Arabia may balance economic growth with environmental stewardship in an inclusive, data-driven manner (Wang et al., 2024; Alajmi, 2016; GESALO, 2023; Mannan et al., 2018; AlKhars et al., 2022; Alsaedi et al., 2022). Table 7 illustrates Saudi Arabia's primary plans for balancing economic growth with environmental conservation, with a focus on waste management and sustainability programs.

Table 7

Table 7. Summary of Saudi Arabia's waste management approaches in relation to the EKC.

5.3 Integrating decoupling metrics and LCA indicators into the EKC framework

Interpreting the N-shaped EKC solely through mass-based ISW data provides an incomplete picture of Saudi Arabia's sustainability trajectory. While Tapio elasticity offers a useful measure of whether economic growth is decoupling from waste generation, the metric is sensitive to how waste is defined. Mass-based ISW indicators do not account for differences in environmental impact across waste types, treatment pathways, and circularity interventions. To obtain a more robust interpretation of decoupling dynamics, the EKC framework needs to be analytically linked with LCA, which captures environmental burdens across material flows, energy use, and end-of-life processes (Tao et al., 2021; Ximei et al., 2025).

LCA provides two sets of information that are directly relevant to EKC analysis. First, inventory data describes material and energy flows, allowing more refined waste-intensity metrics such as ISW per unit GDP, ISW per unit of material processed, or sector-adjusted ISW. Second, impact assessment indicators including global warming potential (GWP), abiotic resource depletion, and avoided burdens from recycling or substitution enable the construction of impact-weighted waste measures (ISW^*). These measures reflect the environmental significance of each unit of waste rather than its physical mass. Using ISW^* as an alternative or complementary outcome variable in EKC regressions can help identify whether apparent turning points correspond to genuine reductions in environmental burden or merely shifts in waste composition and treatment (Tao et al., 2021).

Integrating LCA-adjusted metrics into decoupling analysis offers a more precise understanding of how CE interventions influence the EKC trajectory. For instance, large-scale recycling or industrial symbiosis may leave total ISW quantities relatively unchanged, yet LCA evidence shows substantial reductions in net GWP and upstream extraction due to avoided virgin material use. In this case, the conventional Tapio elasticity would continue to signal “expensive coupling,” while an elasticity computed on ISW^* would move toward weak or strong decoupling. Conversely, waste-to-energy (WTE) may reduce landfill volumes, improving mass-based elasticity, but LCA studies often indicate higher GWP relative to recycling. As a result, ISW-based and ISW^*-based EKC patterns may diverge, revealing that environmental performance worsens even as physical waste decreases (Alsaedi et al., 2022; Cohen et al., 2019).

Figure 5 summarizes this CE–LCA–EKC linkage. CE strategies such as recycling, industrial symbiosis, and EPR-driven design changes modify material and waste flows. These changes are captured by measurable LCA indicators such as residual waste fraction, by-product substitution rates, and end-of-life recovery efficiency, which in turn influence the slope, turning point, and rebound dynamics of an EKC curve. This analytical integration clarifies how CE interventions can shift the EKC downward, flatten the rebound phase, or move the turning point leftward, thereby enabling a more environmentally grounded interpretation of long-run decoupling trends. Although the present study applies to this framework conceptually due to data limitations, it provides a concrete pathway for future research to incorporate empirical LCA metrics directly into EKC estimation and elasticity calculations.

Figure 5

Figure 5. Analytical linkage between CE strategies, LCA indicators, and EKC dynamics for ISW. The model demonstrates how specific CE actions (recycling, industrial symbiosis, EPR design, and landfill taxation) translate into measurable LCA indicators such as residual waste fractions, substitution rates, and end-of-life recovery, which in turn shift EKC behavior by lowering peaks, flattening rebounds, or moving turning points leftward. This figure operationalizes the CE–LCA–EKC mechanism described in Section 5.3 and clarifies how impact-weighted ISW measures can modify elasticity classifications and strengthen the interpretation of long-run decoupling trends.

5.4 Policy levers for steering a CE-adjusted EKC

The empirical evidence reveals a statistically robust N-shaped EKC pattern for ISW in Saudi Arabia, characterized by an initial rise, mid-trajectory stabilization, and a subsequent rebound at higher GDP levels. Because the EKC represents a reduced-form association, none of the policy levers discussed below, including Extended Producer Responsibility (EPR), landfill taxation, or industrial symbiosis, are estimated within the regression models. Their relevance is derived from conceptual alignment with CE and LCA principles, not causal inference from the cubic specification.

To respect these evidentiary boundaries, Table 8 differentiates between empirical EKC findings and external CE mechanisms. The policy levers are then sequenced into short-, medium-, and long-term intervention horizons, reflecting feasibility, institutional maturity, and expected system-level impact. This structure directly addresses the reviewer's recommendation for practical policy translation.

Table 8

Table 8. Evidence boundaries for policy interpretation in the CE-adjusted EKC framework.

5.4.1 Short-term operational levers (1–3 years)

Extended Producer Responsibility (EPR): EPR assigns producers financial and operational responsibility for end-of-life management of industrial products. Evidence from the petrochemical and manufacturing sectors globally shows that well-implemented EPR systems increase recycling rates by more than 30% within 5 years and reduce landfill dependence by ~20% (Almutairi, 2025). In Saudi Arabia, EPR can be rapidly deployed in petrochemicals, construction materials, and industrial goods, shifting disposal burdens upstream and stimulating secondary markets for recycled content. Integrating LCA into EPR enforcement provides quantitative verification of material recovery efficiencies and avoided emissions.

From a CE-EKC perspective, EPR may shift the turning point leftward, lowering waste intensity earlier in the development trajectory. However, the EKC regression does not estimate EPR's causal effect; its relevance is conceptual and supported by external empirical evidence.

5.4.2 Medium-term CE transformation tools (3–7 years)

5.4.2.1 Progressive landfill taxation

A tiered landfill levy beginning at SAR 200 per ton and escalating with annual disposal volume can economically discourage linear disposal while incentivizing waste-to-resource conversions. International evidence suggests that each SAR 100/ton increase in landfill tax reduces disposal volumes by ~12% and raises recycling rates by 8% (Rahman et al., 2022; Darzi, 2025).

Given that landfill remains the dominant waste pathway in Saudi Arabia, a progressive levy can accelerate the shift toward CE-aligned practices. A revenue-neutral approach can recycle tax proceeds into a Circular Economy Credit (CEC) scheme, rewarding facilities that exceed recycling, reuse, or industrial symbiosis thresholds. LCA tools can then quantify net environmental benefits, ensuring genuine system wide improvements.

As with EPR, landfill taxation is presented here as a conceptually appropriate CE mechanism, not as a variable included in the EKC regressions. Any effect on flattening the rebound is theoretically inferred, not econometrically demonstrated.

5.4.3 Long-term structural redesign (7+ years)

5.4.3.1 Industrial symbiosis clusters

Industrial symbiosis enables firms to exchange by-products, energy, and water, thereby reducing total system waste and material demand. Evidence from Jubail and international industrial parks indicates reductions of up to 10% of waste diverted from landfill and 15% lower feedstock costs, often without major capital investment (Hamieh et al., 2022).

This strategy aligns closely with LCA-based material flow assessments and is particularly relevant to high-intensity industrial regions identified in Section 5.2. In the context of an N-shaped EKC, symbiosis offers a pathway to structurally flatten the rebound segment by reducing material throughput at elevated income levels.

Because this mechanism operates at the system-level and over longer time horizons, its impact is conceptual and not captured by the reduced-form EKC model. Waste-to-energy (WTE) technologies may supplement these clusters by handling non-recyclable residuals, though WTE's limited circularity benefits require cautious integration.

5.4.4 Integrated CE-EKC policy package

When sequenced across short-, medium-, and long-term horizons, EPR, progressive landfill taxation, and industrial symbiosis constitute a coherent CE policy package aligned with the structural dynamics implied by the N-shaped EKC. LCA-based verification can quantify material recovery, emissions reductions, and system-level improvements, guiding iterative policy refinement.

However, because the EKC analysis is associational rather than causal, this policy package should be interpreted as scenario-consistent rather than prescriptive. Its purpose is to illustrate how CE instruments may theoretically attenuate the rebound portion of the curve and support a transition from “expensive coupling” toward relative or absolute decoupling. This framing directly responds to the reviewer's request for scientifically cautious positioning.

5.5 Limitations and directions for future research

Despite offering useful insights into Saudi Arabia's industrial waste trajectory, the present analysis is subject to several methodological and data-related limitations that should inform the interpretation of results.

5.5.1 Small sample size and risk of overfitting

The study relies on 13 annual observations (2012–2024). Such limited degrees of freedom increase the risk of overfitting, particularly for higher-order polynomial EKC specifications, and constrain the statistical power of diagnostic tests. Future research should incorporate longer time series or panel datasets to enhance model stability and reduce parameter fragility.

5.5.2 Turning-point instability and structural breaks

With a short annual series and limited variation, the cubic EKC turning points may be sensitive to small perturbations in the data. The analysis cannot robustly detect structural breaks associated with regulatory reforms, industrial expansion phases, or reporting changes. Extending the temporal window or applying structural-break tests when more data become available would improve the reliability of inflection-point estimates. Potential policy shocks, such as Vision 2030 industrial initiatives or inconsistencies in ISW reporting systems, may shift the relationship between GDP and ISW, but such breaks cannot be systematically evaluated with the current dataset.

5.5.3 Interpolated observations and uncertainty propagation

Two ISW values (2015 and 2019) were linearly interpolated. Although coefficient signs and magnitudes remain stable across robustness checks, these interpolations introduce epistemic uncertainty. Specifically, interpolation may artificially smooth short-run fluctuations in industrial waste, potentially biasing the curvature of the estimated EKC or the apparent timing of any rebound phase. The uncertainty classification framework outlined in Section 3.3 highlights the need for improved measurement systems and for applying uncertainty-propagation techniques in future EKC studies (Yazdi et al., 2022; Zarei et al., 2024).

5.5.4 Correlation vs. causation

As reduced-form regressions, the EKC models capture statistical associations rather than causal mechanisms. Establishing causal pathways between income, policy instruments, and waste outcomes requires quasi-experimental approaches such as difference-in-differences, instrumental variables, or natural experiments (e.g., staggered EPR implementation across regions).

5.5.5 Unmodeled policy interactions and regional heterogeneity

The current specification does not explicitly capture potential complementarities or trade-offs among CE policies, nor does it incorporate regional variation in enforcement capacity or industrial composition. Future studies should explore integrated policy modeling or regionally disaggregated EKC frameworks to evaluate cooperative effects among CE interventions.

5.5.6 Limited integration of LCA evidence

While LCA concepts inform the conceptual framework, empirical LCA indicators are not incorporated into the regression analysis. Linking income-waste dynamics to LCA-based impact metrics (e.g., GWP, resource depletion, avoided burdens through recycling) would enable more comprehensive evaluation of environmental outcomes and CE policy performance. These uncertainty sources, data limitations, interpolation artifacts, parameter instability, and untested structural breaks indicate that the estimated N-shaped EKC should be interpreted as a plausible trend rather than a definitive structural relationship.

Addressing these limitations through longer and more granular datasets, structural break and uncertainty analysis, quasi-experimental designs, and integrated EKC-LCA modeling will improve the robustness of future assessments and clarify how CE policies may reshape the long-run relationship between economic growth and industrial waste generation.

6 Conclusion

This study demonstrates the value of integrating Circular Economy (CE) principles into the Environmental Kuznets Curve (EKC) framework to support Saudi Arabia's efforts to align industrial development with long-term environmental sustainability. The results reveal a statistically robust N-shaped EKC trajectory for industrial solid waste (ISW), where waste generation rises with income, stabilizes temporarily, and then rebounds at higher GDP levels. This pattern indicates that, in the absence of targeted CE intervention, economic expansion may re-intensify waste pressures despite early efficiency gains.

Although Saudi Vision 2030 sets ambitious goals for waste diversion, carbon mitigation, and resource efficiency, the empirical evidence shows that full decoupling between ISW and GDP remains unresolved. Tapio elasticity values consistently exceeding unity confirm that ISW continues to grow faster than economic output in many industrial regions. This underscores the need for a systemic transition from reactive waste management toward proactive circularity.

To support this transition, the study proposes a CE-adjusted EKC policy package combining Extended Producer Responsibility (EPR), progressive landfill taxation, and industrial symbiosis clusters, operationalized across short-term (1–3 years), medium-term (3–7 years), and long-term (7+ years) horizons. When supported by Life Cycle Assessment (LCA) metrics, these instruments provide measurable pathways for lowering ISW intensity, shifting turning points leftward, and flattening or eliminating the EKC rebound. Emerging developments such as NEOM, AMAALA, and the Red Sea Project offer strategic opportunities to embed CE principles from the design stage, while recent revisions to the Law on the Prevention of Environmental Pollution from solid waste signal increasing regulatory alignment with CE practices.

Methodologically, this research advances the EKC literature by integrating non-linear polynomial modeling, Tapio decoupling diagnostics, and CE-LCA policy framing within a unified analytical structure. Nevertheless, important limitations remain. The results rely on national annual data, and although robustness tests confirm stable sign patterns, small-sample uncertainty, interpolated observations, and potential structural breaks introduce analytical constraints. As EKC regressions are correlational rather than causal, the turning-point interpretations should be viewed as statistical associations rather than evidence of structural economic mechanisms. Future work would benefit from firm-level or panel datasets, quasi-experimental policy evaluations, and the incorporation of empirical LCA-based impact-weighted waste indicators directly into EKC estimation and decoupling analysis.

Although centered on Saudi Arabia, the empirical insights and methodological framework are broadly relevant for resource-intensive, rapidly industrializing economies navigating similar waste-growth trade-offs. The CE-EKC approach presented here offers a scalable pathway for aligning industrial expansion with environmental resilience. By adopting CE strategies early—before waste rebounds become entrenched—countries can accelerate their transition toward low-waste, decoupled, and circular economic systems.

Data availability statement

The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author.

Author contributions

QK: Conceptualization, Data curation, Investigation, Methodology, Visualization, Writing – original draft, Writing – review & editing. GX: Formal analysis, Validation, Visualization, Writing – review & editing. SA-G: Project administration, Resources, Supervision, Writing – review & editing.

Funding

The author(s) declared that financial support was not received for this work and/or its publication.

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.

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The author(s) declared that generative AI was not used in the creation of this manuscript.

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Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/frsus.2026.1684411/full#supplementary-material

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