The Hidden Cost of Global Trade: Evidence from Plastic Waste Trade and Its Ecological Ramifications Across Major Waste-Trading Nations

Abstract

The rapid expansion of plastic waste trade has intensified environmental pressures, accelerating ecosystem degradation and climate change. We examine the long-term impacts of plastic waste imports and domestic waste production on ecological footprints and greenhouse gas emissions across 20 countries representing 70% of global plastic waste trade and 45% of world GDP. Under the Environmental Kuznets Curve (EKC) framework, we explore nonlinear interactions among economic growth, urbanization, and sustainability goals. Using a panel simultaneous equations approach, we apply Pedroni, Kao, and Westerlund cointegration tests and Fully Modified and Dynamic OLS estimators to address endogeneity and heterogeneity. Robustness checks include alternative environmental indicators and the Dumitrescu–Hurlin panel causality test. Results demonstrate a stable long-run equilibrium: plastic waste imports substantially increase ecological footprints and emissions, while progress on sustainable development goals mitigates some damage. The negative GDP squared coefficient supports the EKC hypothesis, indicating that environmental impacts rise initially with growth but decline once income exceeds a threshold. These findings highlight the need for stronger international regulations, enhanced waste management infrastructures, and circular economy strategies. Focused investment in sustainable technologies and global cooperation is essential to lower environmental costs of plastic waste trade.

1. Introduction

The global trade of plastic waste has increased again in recent years and has become a critical environmental issue. It causes widespread ecological degradation and exacerbates the negative impacts of climate change. At the heart of this issue lies the complex interplay between economic development and environmental quality—a relationship famously encapsulated by the Environmental Kuznets Curve (EKC) hypothesis. Originally postulated by Kuznets [1], the EKC posits that environmental degradation initially intensifies as economies develop but eventually declines when a certain income threshold is exceeded and societies begin prioritizing environmental sustainability [2]. Nonetheless, the dynamics of economic growth, foreign investment, and environmental regulation introduce further complexities into this narrative. Stringent environmental policies might deter investment, prompting firms to innovate in order to comply with higher standards [3], while leniency in regulations—potentially enacted to retain capital—can precipitate significant ecological harm [4].

Economic growth leads to an increase in plastic production and consumption. This, in turn, causes plastic waste to become a serious environmental problem [5]. Developed economies are investing more in efficient waste management systems to reduce problematic and unmanageable plastic waste. This suggests an EKC relationship may be present [6]. Economic progress not only increases plastic production but also drives innovations that provide sustainable alternatives and more efficient waste management processes [7].

Complementing the EKC perspective, critics have highlighted an essential blind spot: the role of international trade in redistributing environmental burdens [1]. This critique laid the foundation for the Pollution Haven Hypothesis, which contends that industries with high pollution intensities tend to migrate from countries with robust environmental regulations to those with more permissive standards [8,9]. Consequently, while developed countries generally impose strict waste management protocols, developing nations often lack the infrastructural and technological capacity to handle the resulting pollution effectively. This imbalance is further exacerbated in sectors such as electronic waste, where recycling demands advanced technological inputs available only in a limited number of countries [10].

The situation was further complicated by a seminal event in 2018 when China, previously the largest importer of plastic waste, banned such imports [11]. This policy shift not only disrupted established trade patterns but also spurred an increased transfer of plastic waste to nations with more lenient regulatory frameworks [12,13,14,15]. Against this backdrop, the Basel Convention—established in 1989 and progressively implemented from 1992—has played a pivotal role in governing the international movement of hazardous waste. In May 2018, as a result of growing evidence of plastic waste trade to countries lacking adequate infrastructure to dispose of waste, it was decided at the Basel Conference, at Norway’s suggestion, to establish an Open-Ended Working Group (OEWG) to address marine plastic litter and microplastics and develop recommendations for possible further action [16]. The 2019 amendments, which broadened the scope to explicitly include plastic waste, underscore the imperative for robust waste management and responsible disposal practices [17].

Despite these regulatory efforts, plastic pollution remains widespread. Globally, the production of single-use plastics dominates, and unmanageable waste has reached alarming levels. While 38% of total waste worldwide is unmanageable, the percentage is much higher for plastic waste (91%) [18,19,20,21,22,23]. The vast majority of plastic waste is incinerated, dumped in landfills, or exported and ends up in water systems, including oceans [16,22,24]. These practices cause significant environmental damage and pose significant public health risks in countries that import this waste [25,26,27,28].

Typically, waste management strategies can be dichotomized into domestic treatment and international export. Ironically, despite possessing advanced waste management infrastructures, many developed countries resort to exporting plastic waste—motivated largely by the twin advantages of cost savings and reduced localized environmental risks [29]. However, this exportation strategy can have deleterious effects on recipient countries, where inadequate disposal practices, such as open burning or improper landfilling, contribute to widespread environmental contamination and adverse health outcomes [30,31,32].

Another system used in waste management is the “informal waste management system” (IWMS). This method encompasses the unregulated and unregistered activities of individuals involved in waste collection, disposal, and recycling processes [33]. The gaps left by the lack of formal plastic waste management in developing countries are partially offset by the informal waste sector [34]. Specifically, the informal sector accounts for about 59% of plastic waste collected for recycling [35]. The informal waste sector reduces the amount of plastic waste that ends up in the environment, thus playing an important role in protecting public health and the environment [36]. Research has emphasized that by further integrating the informal waste sector with formal waste plastic management, it is possible to reduce the damage caused by plastic waste [37,38].

Trade in plastics is increasing rapidly around the world [24]. This increase in plastic trade raises concerns about waste. This is because all plastics eventually become waste at the end of their lifecycle [11]. However, unlike the increasing plastics trade, the trade of plastic waste has been decreasing since 2011, only increasing again after 2020 [39]. One of the reasons for the significant reduction in the amount of plastic waste in global trade flows has been the introduction of restrictions on the import of plastic waste [16]. Although the trade of plastic waste is less than the trade of plastics, it is of great importance, especially due to its environmental impact [11]. Additionally, the amount of plastic waste traded appears to be lower due to plastic dumped in oceans and seas, as well as illegal plastic flows [24,40].

A growing body of literature has scrutinized various dimensions of the plastic waste trade. For instance, research exploring China’s policy shift has delineated its transformative impact on global waste dynamics [41,42], while other studies have examined the potential of circular economy models to mitigate waste proliferation [43]. Concerns over illegal waste trade further underscore the complexity of managing plastic waste flows [40]. Moreover, international agreements such as the Basel Convention Amendments have demonstrated effectiveness in curbing the transfer of waste from high-income to low-income nations [44]. These insights collectively emphasize the need to redesign plastic production and use in terms of the circular economy, increase waste processing capacity, better monitor and regulate plastic waste trade, establish rigorous regulatory frameworks with enhanced international cooperation, increase public awareness and education, and invest significantly in sustainable waste management technologies.

This study investigates the nexus between plastic waste imports and their cumulative environmental impact, specifically examining how the inflow of plastic waste correlates with overall waste production and its ecological footprint. By integrating the EKC framework with contemporary trade and policy dynamics, the research seeks to enrich the global discourse on sustainable waste management and environmental policy.

The objectives of this study are twofold. First, it provides empirical evidence on the long-term impacts of plastic waste importation on ecological degradation and greenhouse gas emissions, elucidating the mechanisms through which international trade exacerbates environmental degradation. Second, it contributes to the theoretical debate by testing the validity of the EKC hypothesis and the Pollution Haven Hypothesis in the context of global plastic waste flows. The findings suggest that while economic growth initially aggravates environmental degradation, a turning point exists beyond which further development—coupled with robust environmental policies—can lead to a decline in ecological harm. However, in the absence of proactive measures, the relentless inflow of plastic waste continues to undermine environmental sustainability, particularly in nations ill-equipped to manage such burdens.

In summary, the global plastic waste trade poses a formidable challenge to environmental sustainability, intersecting with issues of economic growth, technological capacity, and regulatory disparity. The multi-layered dynamics captured in this study underscore the intricate relationships between national development trajectories and international waste management practices. As such, effective policy interventions are urgently needed to shift global waste management practices toward more sustainable and equitable outcomes. This research not only advances our understanding of the environmental impacts of plastic waste trade but also provides a critical empirical foundation for the formulation of policies that can curb environmental degradation and promote sustainable development on a global scale.

The remainder of this paper is structured as follows: Section 2 delineates the data sources and methodological framework, including the application of advanced econometric techniques such as panel cointegration tests (Pedroni, Kao, Westerlund) and long-run estimators (FMOLS, DOLS) to address endogeneity and cross-sectional heterogeneity. Section 3 presents empirical results, discussing the long-term equilibrium among key variables—economic growth, plastic waste imports, domestic plastic waste generation, urbanization, and sustainable development goals—and their implications for environmental sustainability. Finally, Section 4 concludes with a synthesis of the findings, offering actionable policy recommendations to mitigate environmental risks and promote sustainable waste management practices globally.

2. Materials and Methods

2.1. Data Specification

The influences of economic expansion, global trade, and urban development on the environment have been extensively explored in academic discourse, particularly within the framework of the EKC [45,46,47]. This study thus investigates the effects of plastic waste import (PWI), domestic plastic waste generation (WPC), economic growth (GDP), urbanization (URBAN), and sustainable development goal (SDG) attainment on ecological impacts. In alignment with recent scholarly contributions [27,48], particular emphasis is placed on how plastic waste imports contribute to ecological footprint (EFP) and greenhouse gas emissions (GHG), serving as critical indicators of environmental decline. The research utilizes annual data spanning 2000 to 2022 sourced from the Global Footprint Network (GFN), the Emissions Database for Global Atmospheric Research (EDGAR), the World Bank, UN Comtrade, the United Nations (UN), and the EPI-Yale databases. Due to data constraints and the lack of harmonized and complete cross-country environmental and trade data, the study concludes its analysis at the year 2022. Key variables examined include ecological footprint, greenhouse gas emissions, gross domestic product, plastic waste importation, plastic waste production, urbanization, and sustainable development goals. Detailed definitions, units of measurement, and data sources for these variables are presented in

Table 1. Definitions and sources of variables.

Variables	Definitions and Units	Data Source
EFP	Ecological Footprint (global hectare)	Global Footprint Network
GHG	Greenhouse Gases Emission (Gg)	EDGAR
GDP	Gross Domestic Product (Current USD)	World Bank
PWI	Plastic Waste Import (metric tonnes)	UN ComTrade
WPC	Plastic Waste Generation (Index)	EPI-Yale
URBAN	Urbanization (% of Total Population)	World Bank
SDG	Sustainable Development Goals (Index)	UN

.

The GDP, PWI, WPC, URBAN, and SDG variables were chosen based on both theoretical relevance and empirical precedent in the environmental economics and sustainability literature. GDP and its squared term are included to empirically test the EKC hypothesis, which posits a nonlinear relationship between income and environmental degradation—a widely examined framework in environmental policy analysis. PWI is the central focus of this study, as we aim to quantify the environmental burden transferred across borders through the global plastic waste trade. It directly relates to the hypotheses concerning pollution havens and trade-induced ecological degradation. WPC complements PWI, allowing for us to disentangle the impacts of internal versus imported waste on environmental outcomes. URBAN typically comprises centers of consumption and waste generation. URBAN is a well-established determinant of environmental pressure and is statistically significant in prior panel studies examining sustainability, especially in the context of infrastructure capacity and population density. The SDG composite index captures broader institutional, environmental, and social efforts toward sustainability. Its inclusion allows for us to assess whether progress on global development goals moderates the environmental impact of waste and growth.

This study examines 20 countries that collectively represent around 70% of global plastic waste trade and 45% of the world’s gross domestic product, serving as a comprehensive reference for assessing policy implications related to the environmental consequences of plastic waste imports (

Table A1. Country list.

Austria	Belgium	Canada	Czechia
Denmark	France	Germany	India
Indonesia	Lithuania	Malaysia	Netherlands
Pakistan	Poland	Slovenia	Sweden
Thailand	Türkiye	USA	Vietnam

). The selection of these countries is based on their significant role in the international plastic waste market, as they are among the largest participants in plastic waste trade within the global economy. In this context, this study tests the hypothesis that plastic waste imports contribute adversely to environmental quality.

2.2. Methodology and Empirical Model

This study examines the environmental consequences of PWI, emphasizing its role in climate change within the broader context of global plastic waste trade. The primary dependent variable is the EFP, while the control variables include GDP, its squared term (GDP²), WPC, URBAN, and SDG. The key research variable, central to this research, is PWI. Additionally, in the robustness analysis, GHG serves as the dependent variable to further assess environmental impacts.

An econometric model is developed based on Equations (1)–(5) to analyze the environmental effects of plastic waste import using a set of selected variables.

$$ln{\left(efp\right)}_{it}={\beta }_0+{\beta }_{1}ln{\left(gdp\right)}_{it}+{\beta }_{2}ln{{\left(gdp\right)}^2}_{it}+{\epsilon }_{it}$$

(1)

$$ln{\left(efp\right)}_{it}={\beta }_0+{\beta }_{1}ln{\left(gdp\right)}_{it}+{\beta }_{2}ln{{\left(gdp\right)}^2}_{it}+{\beta }_{3}ln{\left(pwi\right)}_{it}+{\epsilon }_{it}$$

(2)

$$ln{\left(efp\right)}_{it}={\beta }_0+{\beta }_{1}ln{\left(gdp\right)}_{it}+{\beta }_{2}ln{{\left(gdp\right)}^2}_{it}+{\beta }_{3}ln{\left(pwi\right)}_{it}+{\beta }_{4}ln{\left(wpc\right)}_{it}+{\epsilon }_{it}$$

(3)

$$ln{\left(efp\right)}_{it}={\beta }_0+{\beta }_{1}ln{\left(gdp\right)}_{it}+{\beta }_{2}ln{{\left(gdp\right)}^2}_{it}+{\beta }_{3}ln{\left(pwi\right)}_{it}+{\beta }_{4}ln{\left(wpc\right)}_{it}+{\beta }_{5}ln{\left(urban\right)}_{it}+{\epsilon }_{it}$$

(4)

$$ln{\left(efp\right)}_{it}={\beta }_0+{\beta }_{1}ln{\left(gdp\right)}_{it}+{\beta }_{2}ln{{\left(gdp\right)}^2}_{it}+{\beta }_{3}ln{\left(pwi\right)}_{it}+{\beta }_{4}ln{\left(wpc\right)}_{it}+{\beta }_{5}ln{\left(urban\right)}_{it}+{\beta }_{6}ln{\left(sdg\right)}_{it}+{\epsilon }_{it}$$

(5)

In this framework, i and t denote the countries and time periods, respectively. The stochastic error term is represented by ε, while the coefficients ${\beta }_1$, ${\beta }_2$, ${\beta }_3$, ${\beta }_4$, and ${\beta }_5$ capture the long-term elasticity of the variables within the equations. Furthermore, the models formulated for robustness analysis are presented below.

$$ln{\left(ghg\right)}_{it}={\beta }_0+{\beta }_{1}ln{\left(gdp\right)}_{it}+{\beta }_{2}ln{{\left(gdp\right)}^2}_{it}+{\epsilon }_{it}$$

(6)

$$ln{\left(ghg\right)}_{it}={\beta }_0+{\beta }_{1}ln{\left(gdp\right)}_{it}+{\beta }_{2}ln{{\left(gdp\right)}^2}_{it}+{\beta }_{3}ln{\left(pwi\right)}_{it}+{\epsilon }_{it}$$

(7)

$$ln{\left(ghg\right)}_{it}={\beta }_0+{\beta }_{1}ln{\left(gdp\right)}_{it}+{\beta }_{2}ln{{\left(gdp\right)}^2}_{it}+{\beta }_{3}ln{\left(pwi\right)}_{it}+{\epsilon }_{it}$$

(8)

$$ln{\left(ghg\right)}_{it}={\beta }_0+{\beta }_{1}ln{\left(gdp\right)}_{it}+{\beta }_{2}ln{{\left(gdp\right)}^2}_{it}+{\beta }_{3}ln{\left(pwi\right)}_{it}+{\beta }_{4}ln{\left(wpc\right)}_{it}+{\beta }_{5}ln{\left(urban\right)}_{it}+{\epsilon }_{it}$$

(9)

$$ln{\left(ghg\right)}_{it}={\beta }_0+{\beta }_{1}ln{\left(gdp\right)}_{it}+{\beta }_{2}ln{{\left(gdp\right)}^2}_{it}+{\beta }_{3}ln{\left(pwi\right)}_{it}+{\beta }_{4}ln{\left(wpc\right)}_{it}+{\beta }_{5}ln{\left(urban\right)}_{it}+{\beta }_{6}ln{\left(sdg\right)}_{it}+{\epsilon }_{it}$$

(10)

2.3. Panel Cointegration Test and Estimation of Long-Term Coefficients

A group of 20 countries dominates 70% of global plastic waste trade and contributes 45% of the world’s gross domestic product, underscoring economic and developmental disparities that may lead to heterogeneity. For panel cointegration analysis to be reliable, it is crucial to allow for heterogeneity among panel units [49]. Pedroni [50] introduced a cointegration test that accommodates such heterogeneity across cross-sections. His residual-based model [50,51,52] considers variations in slope coefficients, individual effects, and linear trends. Expanding on Engle and Granger’s [53] two-step method, Pedroni integrated into the Augmented Dickey–Fuller (ADF) and Phillips–Perron (PP) tests. The Pedroni cointegration test is based on the following regression model (11):

$$Y_{it}={\alpha }_i+{\delta }_{i}t+{\theta }_t+{\beta }_{1i}{x}_{1it}+\cdots +{\beta }_{mi}{x}_{mit}+{\epsilon }_{it}$$

(11)

In this context, t, i, and m denote time, cross-sectional units, and the number of explanatory variables, respectively. Additionally, ${\alpha }_i$, ${\delta }_{i}t$, and ${\theta }_t$ represent unit-specific effects, individual trends, and common time effects. The coefficient ${\beta }_{mi}$ varies across units, reflecting panel heterogeneity.

Pedroni [50] developed seven panel cointegration tests to examine residual stationarity, consisting of four within-dimension and three between-dimension methods to address heterogeneity. These tests assess regression residuals for long-run equilibrium. The heterogeneous panel and group mean statistics applied in panel cointegration analysis are derived from the models in Equations (12)–(18):

$${\mathrm{H}}_{\mathrm{v}}={\left({{\displaystyle \sum }}_{\mathrm{i}=1}^{\mathrm{N}}{{\displaystyle \sum }}_{\mathrm{t}=1}^{\mathrm{T}}{\hat{\mathrm{L}}}_{11\mathrm{i}}^{-2}{\hat{\mathrm{e}}}_{\mathrm{it}-1}^2\right)}^{-1}$$

(12)

$${\mathrm{H}}_{\mathrm{p}}={\left({{\displaystyle \sum }}_{\mathrm{i}=1}^{\mathrm{N}}{{\displaystyle \sum }}_{\mathrm{t}=1}^{\mathrm{T}}{\hat{\mathrm{L}}}_{11\mathrm{i}}^{-2}{\hat{\mathrm{e}}}_{\mathrm{it}-1}^2\right)}^{-1}{{\displaystyle \sum }}_{\mathrm{i}=1}^{\mathrm{N}}{{\displaystyle \sum }}_{\mathrm{t}=1}^{\mathrm{T}}{\hat{\mathrm{L}}}_{11\mathrm{i}}^{-2}\left({\hat{\mathrm{e}}}_{\mathrm{it}-1}{\mathrm{\Delta }\hat{\mathrm{e}}}_{\mathrm{it}}-{\widehat{\lambda }}_{\mathrm{i}}\right)$$

(13)

$${\mathrm{H}}_{\mathrm{t}}={\left({\hat{\mathrm{\sigma }}}^2{{\displaystyle \sum }}_{\mathrm{i}=1}^{\mathrm{N}}{{\displaystyle \sum }}_{\mathrm{t}=1}^{\mathrm{T}}{\hat{\mathrm{L}}}_{11\mathrm{i}}^{-2}{\hat{\mathrm{e}}}_{\mathrm{it}-1}^2\right)}^{-1/2}{{\displaystyle \sum }}_{\mathrm{i}=1}^{\mathrm{N}}{{\displaystyle \sum }}_{\mathrm{t}=1}^{\mathrm{T}}{\hat{\mathrm{L}}}_{11\mathrm{i}}^{-2}\left({\hat{\mathrm{e}}}_{\mathrm{it}-1}{\mathrm{\Delta }\hat{\mathrm{e}}}_{\mathrm{it}}-{\hat{\mathrm{\lambda }}}_{\mathrm{i}}\right)$$

(14)

$${\mathrm{H}}_{\mathrm{t}}^{*}={\left({\hat{\mathrm{s}}}^{*2}{{\displaystyle \sum }}_{\mathrm{i}=1}^{\mathrm{N}}{{\displaystyle \sum }}_{\mathrm{t}=1}^{\mathrm{T}}{\hat{\mathrm{L}}}_{111}^{-2}{\hat{\mathrm{e}}}_{\mathrm{it}-1}^{*2}\right)}^{-1/2}{{\displaystyle \sum }}_{\mathrm{i}=1}^{\mathrm{N}}{{\displaystyle \sum }}_{\mathrm{t}=1}^{\mathrm{T}}{\hat{\mathrm{L}}}_{11\mathrm{i}}^{-2}{\hat{\mathrm{e}}}_{\mathrm{it}-1}^{*}{\mathrm{\Delta }\hat{\mathrm{e}}}_{\mathrm{it}}^{*}$$

(15)

$${\tilde{\mathrm{H}}}_{\mathrm{p}}={{\displaystyle \sum }}_{\mathrm{i}=1}^{\mathrm{N}}{\left({{\displaystyle \sum }}_{\mathrm{t}=1}^{\mathrm{T}}{\hat{\mathrm{e}}}_{\mathrm{it}-1}^2\right)}^{-1}{{\displaystyle \sum }}_{\mathrm{t}=1}^{\mathrm{T}}\left({\hat{\mathrm{e}}}_{\mathrm{it}-1}{\mathrm{\Delta }\hat{\mathrm{e}}}_{\mathrm{it}}-{\hat{\mathrm{\lambda }}}_{\mathrm{i}}\right)$$

(16)

$${\tilde{\mathrm{H}}}_{\mathrm{t}}={{\displaystyle \sum }}_{\mathrm{i}=1}^{\mathrm{N}}{\left({\hat{\mathrm{\sigma }}}^2{{\displaystyle \sum }}_{\mathrm{t}=1}^{\mathrm{T}}{\hat{\mathrm{e}}}_{\mathrm{it}-1}^2\right)}^{-1/2}{{\displaystyle \sum }}_{\mathrm{t}=1}^{\mathrm{T}}\left({\hat{\mathrm{e}}}_{\mathrm{it}-1}{\mathrm{\Delta }\hat{\mathrm{e}}}_{\mathrm{it}}-{\hat{\mathrm{\lambda }}}_{\mathrm{i}}\right)$$

(17)

$${\tilde{\mathrm{H}}}_{\mathrm{t}}^{*}={{\displaystyle \sum }}_{\mathrm{i}=1}^{\mathrm{N}}{\left({{\displaystyle \sum }}_{\mathrm{t}=1}^{\mathrm{T}}{\hat{\mathrm{s}}}_{\mathrm{i}}^2{\hat{\mathrm{e}}}_{\mathrm{it}-1}^{*2}\right)}^{-1/2}{{\displaystyle \sum }}_{\mathrm{t}=1}^{\mathrm{T}}\left({\hat{\mathrm{e}}}_{\mathrm{it}-1}{\mathrm{\Delta }\hat{\mathrm{e}}}_{\mathrm{it}}\right)$$

(18)

The Kao [54] test incorporates cross-sectional intercepts and assumes homogeneous coefficients for first-stage regressors. The bivariate representation of the Kao test is formulated as follows (19)–(22):

$$y_{it}={\alpha }_i+{\delta }_{it}+{\beta }_{1i}{x}_{1it}+{\beta }_{2i}{x}_{2it}+\cdots +{\beta }_{Mi}{x}_{Mit}+e_{it}$$

(19)

$$y_{it}={\alpha }_i+\beta X_{it}+e_{it}$$

(20)

$$y_{it}=y_{it-1}+u_{i,t}$$

(21)

$$x_{it}=x_{it-1}+e_{i,t}$$

(22)

The Westerlund [55] panel cointegration test is employed to account for cross-sectional dependence. The statistical measures for this test are derived from the models presented below (23)–(27):

$${\alpha }_i\left(L\right)∆y_{it}={\delta }_{1i}+{\delta }_{2i}t+{\alpha }_i\left(y_{it-1}-{\beta }_i{x}_{it-1}\right)+{\lambda }_i{\left(L\right)}^{\prime }{v}_{it}+e_{it}$$

(23)

$$G_t={\displaystyle \frac{1}{N}}{{\displaystyle \sum }}_{i=1}^N{\displaystyle \frac{{\alpha }_i^{\prime }}{SE\left({\alpha }_i^{\prime }\right)}}$$

(24)

$$G_a={\displaystyle \frac{1}{N}}{{\displaystyle \sum }}_{i=1}^N{\displaystyle \frac{T{\alpha }_i^{\prime }}{{\alpha }_i^{\prime }\left(1\right)}}$$

(25)

$$P_t={\displaystyle \frac{{\alpha }^{\prime }}{SE\left({\alpha }^{\prime }\right)}}$$

(26)

$$P_a=T{\alpha }^{\prime }$$

(27)

The $G_t$ and $G_a$ statistics examine cointegration relationships at the individual country level, whereas $P_t$ and $P_a$ assess cointegration across the entire panel. Westerlund’s [55] test utilizes these four statistics to determine panel-wide and group-specific cointegration. Specifically, $G_t$ and $G_a$ identify overall cointegration presence, while $P_t$ and $P_a$ evaluate whether at least one cross-sectional unit exhibits cointegration within the dataset. These measures ensure a comprehensive assessment of long-run relationships, capturing both country-specific and panel-wide integration dynamics in the given econometric framework.

Ordinary Least Squares (OLS) can produce biased estimates in cointegration analysis [56]. To address this, this study employs advanced techniques such as panel FMOLS [57,58] and DOLS [59,60,61], which correct for serial correlation and endogeneity, ensuring reliable long-term relationship estimates (28):

$${\widehat{\beta }}_{FMOLS}={\displaystyle \frac{1}{N}}{{\displaystyle \sum }}_{i=1}^N{\left({{\displaystyle \sum }}_{i=1}^T{\left(x_{i,t}-{\overline{x}}_i\right)}^2\right)}^{-1}\left({{\displaystyle \sum }}_{i=1}^T\left(x_{i,t}-{\overline{x}}_i\right)y_{i,t}^{*}-T{\widehat{\gamma }}_i\right)$$

(28)

$y_{i,t}^{*}$ denotes the modified value of $y_{i,t}$ after resolving endogeneity concerns, while ${\widehat{\gamma }}_i$ accounts for serial correlation arising from heterogeneity within the panel. FMOLS corrects for endogeneity by adjusting for both serial correlation and the correlation between the regressors and the error term through non-parametric transformations. This involves using estimates of the long-run covariance matrices to transform the regressors, thus providing unbiased and consistent estimates of the long-run relationships even in the presence of endogenous regressors.

Similar to FMOLS, the panel DOLS estimator corrects for endogeneity by incorporating lagged explanatory variables, ensuring accurate estimations. The formulation of the panel DOLS estimator is presented below (29):

$${\widehat{\beta }}_{DOLS}={\displaystyle \frac{1}{N}}{{\displaystyle \sum }}_{i=1}^N{\left({{\displaystyle \sum }}_{i=1}^T{z}_{i,t}-z_{i,t}^{\prime } \right)}^{-1}\left({{\displaystyle \sum }}_{i=1}^T{z}_{i,t}\left(y_{i,t}-{\overline{y}}_i\right)\right)$$

(29)

where $z_{i,t}\left(x_{i,t}-{\overline{x}}_i,\mathrm{\Delta }{x}_{i,t-k}\mathrm{\Delta }{x}_{i,t+k}\right)$ is 2(K + 1) × 1 vector of regressors. DOLS, on the other hand, mitigates endogeneity by augmenting the cointegration equation with leads and lags of the first differences of the explanatory variables, effectively controlling for feedback effects and simultaneity bias. This parametric adjustment ensures that the error term is uncorrelated with the explanatory variables in finite samples.

2.4. Panel Causality Test

This research applies the panel causality test developed by Dumitrescu and Hurlin [62] to examine the directional linkages between variables. Causality observed in one country may extend to others, with the reliability of results strengthening as the number of observations increases (30):

$$Y_{it}={\beta }_i+{{\displaystyle \sum }}_{j-1}^J{\delta }_i^j{Y}_{i\left(t-j\right)}+{{\displaystyle \sum }}_{j-1}^J{\eta }_i^j{X}_{i\left(t-j\right)}+{\epsilon }_{it}$$

(30)

within this framework, ${\delta }_i^j$ and ${\eta }_i^j$ denote the autoregressive coefficients and elasticity, respectively, which are expected to differ across cross-sectional units. The null and alternative hypotheses are formulated as follows ((31) and (32)):

$$h_0:{\mu }_i=0 \mathrm{for} \forall \mathrm{I}$$

(31)

$$h_1: \left(\begin{array}{cc}{\mu }_i=0& for all i=1,2,3,4,\dots ,N_1\\ {\mu }_i\ne 0& for all i=N_1+1,2,3,4,\dots ,N\end{array}\right)$$

(32)

The hypotheses are tested using Wald statistics, which are computed based on the formulation presented in Equation (33):

$$W_{N,T}^{HNC}=N^{-1}{{\displaystyle \sum }}_{i=1}^N{W}_{i,T}$$

(33)

In this context, $W_{i,T}$ denotes the Wald test statistics computed for each cross-sectional unit i.

3. Empirical Findings

Table 2. Preliminary statistics.

Variables	$\mathbf{E}\mathbf{F}\mathbf{P}$	$\mathbf{G}\mathbf{H}\mathbf{G}$	$\mathbf{G}\mathbf{D}\mathbf{P}$	$\mathbf{P}\mathbf{W}\mathbf{I}$	$\mathbf{W}\mathbf{P}\mathbf{C}$	$\mathbf{U}\mathbf{R}\mathbf{B}\mathbf{A}\mathbf{N}$	$\mathbf{S}\mathbf{D}\mathbf{G}$
Mean	18.77	5.64	26.88	17.43	3.43	4.14	73.21
Maximum	21.86	8.88	30.88	20.59	4.31	4.59	85.86
Minimum	16.06	2.89	23.17	0	2.47	3.19	51.38
Std. Dev.	1.28	1.42	1.45	3.76	0.47	0.35	8.56
Skewness	0.21	0.09	0.27	−3.72	0.03	−0.93	−0.83
Kurtosis	3.21	2.89	3.37	17.39	2.58	2.79	2.88
Jarque–Bera	4.13	1.01	8.29	5029	3.47	67.51	53.42
Probability	0.13	0.6	0.02	0.00	0.18	0.00	0.00
Observations	460	460	460	460	460	460	460

presents a summary of the seven variables, with an average EFP of 18.77 and a mean PWI value of 17.43. Standard deviations indicate minimal variation in URBAN (0.35) and WPC (0.47), while PWI exhibits moderate variability (3.76). Skewness analysis shows a rightward bias in GDP (0.27) and a leftward skew in PWI (−3.72). Most variables display near-normal distributions based on kurtosis values close to 3, except for plastic waste imports (17.39), which exhibit high kurtosis. The Jarque–Bera test confirms significant non-normality (p < 0.05), emphasizing the necessity for robust or non-parametric analytical approaches.

Table 3. Correlation matrix.

Variables	$\mathbf{E}\mathbf{F}\mathbf{P}$	$\mathbf{G}\mathbf{H}\mathbf{G}$	$\mathbf{G}\mathbf{D}\mathbf{P}$	$\mathbf{P}\mathbf{W}\mathbf{I}$	$\mathbf{W}\mathbf{P}\mathbf{C}$	$\mathbf{U}\mathbf{R}\mathbf{B}\mathbf{A}\mathbf{N}$	$\mathbf{S}\mathbf{D}\mathbf{G}$
$EFP$	1
$GHG$	0.98	1
$GDP$	0.85	0.79	1
$PWI$	0.13	0.13	0.19	1
$WPC$	0.04	0.11	−0.36	−0.07	1
$URBAN$	−0.11	−0.18	0.31	0.09	−0.77	1
$SDG$	−0.39	−0.46	0.08	0.08	−0.67	0.71	1

displays the correlation matrix, revealing a positive relationship between the EFP and both PWI (0.13) and WPC (0.04), suggesting that higher plastic waste trade and generation contribute to environmental pollution. Conversely, negative correlations are observed between the ecological footprint and SDG index (−0.11) as well as URBAN (−0.39). The strong positive correlations between the GDP, EFP, GHG, PWI, and WPC underscore the substantial role of environmental degradation. These findings emphasize the intricate relationship between economic growth, urbanization, and sustainability objectives.

In panel data analysis, particularly within a globalized economy, selecting an appropriate estimator is crucial to account for cross-sectional dependence and slope heterogeneity. This study identifies significant cross-sectional dependence using the Breusch–Pagan [63] LM test, Pesaran’s scaled LM test, and Pesaran’s CD test, highlighting spillover effects among 20 countries responsible for 70% of global plastic waste trade (

Table A2. Cross-sectional dependence and homogeneity tests.

Test	Equation (1)	Equation (2)	Equation (3)	Equation (4)	Equation (5)
CDBP	3753.503 ***	3501.887 ***	2957.767 ***	3242.572 ***	2922.148 ***
CDLM	182.804 ***	169.896 ***	141.983 ***	156.594 ***	140.156 ***
CD	61.124 ***	58.916 ***	52.405 ***	56.469 ***	53.416 ***
$\widetilde{∆}$	23.791 ***	19.152 ***	15.371 ***	13.541 ***	11.551 ***
$\widetilde{∆}$adj	26.176 ***	21.651 ***	17.877 ***	16.235 ***	14.303 ***

Note: *** indicates significance at 1%.

). Additionally, slope homogeneity tests by Swamy [64] and Pesaran and Yamagata [65] confirm the presence of country-specific effects, emphasizing the need for models that accommodate heterogeneity across different economies.

First-generation unit root tests [66,67] and second-generation tests [68] (

Table A3. First- and second-generation unit root test results.

Variables	First-Generation Unit Root Test				Second-Generation Unit Root Test
	Im, Pesaran, and Shin		Levin, Lin, and Chu		PESCADF
	Intercept	Intercept and Trend	Intercept	Intercept and Trend	Intercept	Intercept and Trend
Level
$EFP$	−1.108	−1.106	−0.225	−1.142	−1.818	−2.388
$GHG$	3.778	−0.491	−0.801	2.264	−1.478	−2.251
$GDP$	3.104	0.349	−0.504	−0.553	−1.321	−1.894
$PWI$	−0.834	2.315	0.185	5.608	−1.877	−1.875
$WPC$	10.939	10.524	5.326	0.895	−1.855	−2.022
$URBAN$	6.774	8.531	2.348	0.214	−0.354	−0.799
$SDG$	7.699	−1.171	1.695	−0.526	−1.986	−2.263
First Difference
$EFP$	−20.798 ***	−15.479 ***	−20.812 ***	−13.304 ***	−3.609 ***	−3.558 ***
$GHG$	−16.122 ***	−14.089 ***	−15.552 ***	−13.533 ***	−3.222 ***	−3.271 ***
$GDP$	−9.312 ***	−11.979 ***	−11.105 ***	−13.341 ***	−2.272 ***	−2.823 ***
$PWI$	−19.768 ***	−17.685 ***	−19.601 ***	−15.955 ***	−3.244 ***	−3.449 ***
$WPC$	−6.159 ***	−3.298 ***	−4.611 ***	−2.764 ***	−3.319 ***	−4.076 ***
$URBAN$	−8.553 ***	−9.275 ***	−10.288 ***	−21.841 ***	−2.985 ***	−3.303 ***
$SDG$	−11.379 ***	−11.858 ***	−9.932 ***	−7.809 ***	−3.189 ***	−3.314 ***

Note: *** indicate significance at 1%.

) confirm that all variables are integrated of order one, I(1), supporting the application of Pedroni, Kao, and Westerlund panel cointegration tests. The Pedroni test accounts for heterogeneous fixed effects, while Westerlund’s second-generation test enhances reliability by incorporating cross-sectional dependence, ensuring a more robust assessment of long-term relationships among the variables.

Empirical analysis indicates a long-term equilibrium among the studied variables, with panel statistics demonstrating significance at the 5% level (refer to

Table 4. Panel cointegration test results.

Tests	Equation (1)	Equation (2)	Equation (3)	Equation (4)	Equation (5)
Pedroni Panel Cointegration Test
Panel PP-stat	−6.319 ***	−7.628 ***	−5.917 ***	−8.718 ***	−14.969 ***
Panel ADF-stat	−6.238 ***	−7.203 ***	−5.662 ***	−8.556 ***	−12.689 ***
Group PP-stat	−5.577 ***	−6.469 ***	−3.856 ***	−6.694 ***	−12.308 ***
Group ADF-stat	−5.948 ***	−5.888 ***	−3.759 ***	−5.577 ***	−6.345 ***
Kao Panel Cointegration Test
ADF-stat	4.547 ***	4.479 ***	3.376 ***	3.538 ***	3.759 ***
Westerlund Panel Cointegration Test
Gt	−2.857 ***	−2.905 ***	−3.045 ***	−3.936 ***	−4.025 ***
Ga	−7.865 ***	−8.904	−8.101	−3.591	−3.383
Pt	−11.818 ***	−13.071 ***	−15.819 ***	−17.786 ***	−18.366 ***
Pa	−7.771 ***	−9.902 ***	−9.764 ***	−5.413	−6.339

Note: *** denotes significance at 1%.

). Additionally, the application of the Westerlund [55] test—which accounts for cross-sectional dependence—produces significant Pt and Gt statistics, further supporting this relationship. When these findings are integrated with those of Pedroni, Kao, and Westerlund, there is consistent evidence of cointegration among the variables. This consistency emphasizes the persistent long-run interconnections between EFP, GDP, PWI, WPC, URBAN, and SDG.

After establishing a long-run association among the variables, the research proceeds to assess the long-term elasticities employing both FMOLS and DOLS methods (see

Table 5. Long-run FMOLS and DOLS estimation results.

Variables	Equation (1)		Equation (2)		Equation (3)		Equation (4)		Equation (5)
	FMOLS	DOLS	FMOLS	DOLS	FMOLS	DOLS	FMOLS	DOLS	FMOLS	DOLS
$GDP$	1.275 ***	1.655 ***	1.156 ***	1.089 ***	1.185 ***	1.185 ***	1.729 ***	1.213 ***	1.165 ***	1.289 ***
$GD{P}^2$	−0.021 ***	−0.028 ***	−0.018 ***	−0.018 ***	−0.018 **	−0.019 ***	−0.411 ***	−0.021 ***	−0.018 ***	−0.021 ***
$PWI$	-	-	0.004 ***	0.008 ***	0.004 **	0.002 **	0.079 ***	0.003 **	0.004 **	0.003 ***
$WPC$	-	-	-	-	0.291 ***	0.172 ***	0.318 ***	0.141 ***	0.209 ***	0.083 ***
$URBAN$	-	-	-	-	-	-	0.762 ***	1.261 ***	1.399 ***	1.391 ***
$SDG$	-	-	-	-	-	-	-	-	−1.545 ***	−1.493 ***
Adj R2	0.994	0.997	0.994	0.998	0.995	0.994	0.995	0.994	0.995	0.994
Obs.	440	423	440	423	440	323	440	320	440	320

Note: *** and ** denote significance at 1% and 5%, respectively.

). The estimated coefficients for GDP, GDP², PWI, WPC, URBAN, and SDG illustrate their respective impacts on EFP. The analysis reveals that while GDP exerts a positive influence on EFP, GDP² exerts a negative effect, lending support to the EKC hypothesis [69,70].

The analysis reveals that higher values of PWI are associated with an enhancement in EFP, thereby contributing to environmental harm [69]. Moreover, elevated levels of WPC and URBAN further amplify EFP across all models, a finding that is in line with earlier studies [17,71]. In addition, the SDG indicator appears to lower EFP, which lends support to the EKC hypothesis [36]. Increased urbanization intensifies environmental strain by driving higher consumption levels [71]. In summary, the growing import and production of plastic waste aggravates environmental degradation, although sustained economic growth over the long run plays a critical role in mitigating these negative impacts in the twenty nations responsible for 70% of the global plastic waste trade.

Unlike the prevalent bidirectional causality observed among most variables, the findings reveal a unidirectional or restricted causal relationship between EFP and both GDP and WPC. In contrast, PWI exhibits bidirectional causality with all examined variables (see

Table 6. Dumitrescu and Hurlin heterogeneous panel causality test results.

Dependent Variables	Independent Variables
	$\mathbf{E}\mathbf{F}\mathbf{P}$	$\mathbf{G}\mathbf{H}\mathbf{G}$	$\mathbf{G}\mathbf{D}\mathbf{P}$	$\mathbf{G}\mathbf{D}{\mathbf{P}}^2$	$\mathbf{P}\mathbf{W}\mathbf{I}$	$\mathbf{W}\mathbf{P}\mathbf{C}$	$\mathbf{U}\mathbf{R}\mathbf{B}\mathbf{A}\mathbf{N}$	$\mathbf{S}\mathbf{D}\mathbf{G}$
$EFP$	-	8.715 ***	6.593 ***	6.537 ***	7.787 ***	16.151 ***	12.615 ***	11.056 ***
$GHG$	2.574 ***	-	8.134 ***	8.193 ***	8.807 ***	62.937 ***	19.509 ***	12.141 ***
$GDP$	−0.492	−0.068	-	6.679 ***	2.921 ***	6.295 **	−1.143	−1.216
$GD{P}^2$	−0.404	−0.051	6.652 ***	-	2.996 ***	6.508 **	−1.062	−1.205
$PWI$	6.321 ***	4.032 ***	4.371 ***	4.381 ***	-	0.395	4.652 ***	2.684 ***
$WPC$	0.978	1.042	0.033	0.039	0.262	-	1.568	4.439 **
$URBAN$	8.968 ***	30.157 ***	43.771 ***	42.676 ***	10.322 ***	2.326	-	41.144 ***
$SDG$	4.042 ***	4.175 ***	10.486 ***	10.801 ***	2.329 **	11.622 ***	7.899 ***	-

Note: *** and ** denote significance at 1% and 5%, respectively.

). This study underscores the complex, nonlinear interactions between plastic waste imports, environmental deterioration, and economic expansion, providing empirical support for theoretical models such as EKC.

Table 7. Robustness check.

Variables	Equation (1)		Equation (2)		Equation (3)		Equation (4)		Equation (5)
	FMOLS	DOLS	FMOLS	DOLS	FMOLS	DOLS	FMOLS	DOLS	FMOLS	DOLS
$GDP$	0.751 ***	1.178 ***	0.569 ***	1.269 ***	1.135 ***	1.085 ***	0.669 ***	0.974 ***	0.539 ***	1.143 ***
$GD{P}^2$	−0.011 ***	−0.019 ***	−0.007 ***	−0.021 ***	−0.021 **	−0.017 ***	−0.013 ***	−0.018 ***	−0.053 *	−0.019 ***
$PWI$	-	-	0.006 ***	0.007 ***	0.001 **	0.004 **	0.002 ***	0.002 **	0.653 **	0.002 **
$WPC$	-	-	-	-	0.051 ***	0.346 ***	0.278 ***	0.256 ***	0.062 ***	0.152 ***
$URBAN$	-	-	-	-	-	-	0.672 ***	1.696 ***	1.571 ***	1.843 ***
$SDG$	-	-	-	-	-	-	-	-	−2.761 ***	−2.536 ***
Adj R2	0.993	0.992	0.993	0.992	0.996	0.994	0.995	0.994	0.995	0.994
Obs.	440	425	440	409	440	320	440	320	440	320

Note: ***, **, and * denote significance at 1%, 5%, and 10%, respectively.

presents the robustness check of the results using the FMOLS and DOLS methods, incorporating the robustness variable. For the 20 countries that account for 70% of the global plastic waste trade, the model indicates that GDP, PWI, WPC, and URBAN contribute to an increase in GHG, whereas the GDP² and SDG play a mitigating role in reducing emissions. Notably, PWI, the focal variable of this study, is identified as a significant driver of rising GHG levels. Moreover, the findings provide strong empirical support for the validity of the EKC in these countries.

4. Conclusions and Policy Recommendations

This study conducted a comprehensive empirical investigation into the environmental repercussions of the global plastic waste trade, with a focus on its influence on ecological footprint (EFP) and greenhouse gas (GHG) emissions among 20 major economies. These nations, which collectively account for 70% of global plastic waste trade and 45% of worldwide GDP, serve as a critical lens through which the interplay among plastic waste imports (PWI), domestic plastic waste production (WPC), economic growth, urbanization, and the attainment of sustainable development goals (SDG) is observed.

The analyses—employing robust panel cointegration tests (Pedroni, Kao, Westerlund), long-run estimators (FMOLS, DOLS), and Dumitrescu–Hurlin causality tests—indicate a statistically significant long-run equilibrium among the studied variables. The findings reveal that both PWI and WPC contribute positively to increases in the EFP and GHG emissions, signaling the environmental burden imposed by the continuous influx and production of plastic waste. The finding that plastic waste imports (PWI) significantly increase ecological footprint (EFP) and GHG emissions supports our call for stricter cross-border waste trade regulations, import-adjusted environmental controls, and tracking mechanisms—concrete actions aimed at mitigating the identified negative environmental impacts. Conversely, the SDG index demonstrates a mitigating effect, suggesting that adherence to sustainable development principles can partially offset these adverse impacts. The mitigating effect of the Sustainable Development Goals (SDG) index on both EFP and GHG emissions directly informs our recommendation to integrate waste trade and pollution metrics into national SDG reporting, thus reinforcing sustainability through institutional alignment.

Moreover, the econometric results support the EKC hypothesis, as reflected by the positive coefficient for GDP and a negative coefficient for its squared term (GDP²) [1,2]. This suggests that while economic expansion initially exacerbates environmental degradation, a turning point exists beyond which further growth—if aligned with effective environmental policies—leads to improved environmental outcomes. However, such a transition is not guaranteed without proactive and coordinated policy intervention, particularly in nations that have become de facto dumping grounds for global plastic waste. The validated EKC relationship between GDP and environmental impact underlines the importance of economic policy alignment, leading us to propose fiscal incentives and innovation subsidies for circular economy transitions in mid- to high-income countries approaching or surpassing the environmental turning point. Furthermore, the analysis highlights urbanization (URBAN) as a significant contributor to environmental degradation, thereby supporting the recommendation to implement urban-specific waste management strategies and infrastructure investments based on capacity needs. The statistical strength of urban expansion as a determinant of ecological harm in our models underscores the necessity of targeted urban policy interventions.

The causality analyses further reveal that the relationship between plastic waste trade and environmental degradation is bidirectional, implying that policy measures targeting PWI may have far-reaching effects across economic and urban development indicators. In essence, this research confirms that unregulated plastic waste imports aggravate environmental decline and underscores the indispensable role of effective governance and technological innovation in achieving long-term sustainability. Building directly on the causal relationships revealed by the Dumitrescu–Hurlin panel causality tests and the heterogeneous effects identified through FMOLS and DOLS estimations, it is required to target interventions for countries identified as net plastic waste importers. These include designing import-adjusted environmental tariffs tied to the ecological footprint intensity of waste-processing industries and performance-based aid mechanisms linked to improvements in sustainability indicators (e.g., SDG scores and GHG reductions).

Given the intricate and far-reaching implications of plastic waste trade on both environmental quality and public health, the notable policy recommendations are advanced to promote sustainable waste management and mitigate environmental degradation. First of all, strengthening international regulatory frameworks is crucial for environmental quality. The Basel Convention, along with its 2019 amendments that explicitly address plastic waste, offers a critical legal basis for regulating the cross-border movement of hazardous and non-hazardous waste [17]. Introducing the role of urbanization as a significant amplifier of environmental degradation, we recommend urban waste zoning policies and data-driven resource mapping platforms to optimize waste management capacity across rapidly expanding urban regions—an area underexplored in conventional policy frameworks. However, enforcement and compliance remain inconsistent, particularly in regions with limited monitoring capabilities. Governments and international bodies must enhance the enforcement of existing agreements by establishing robust reporting and verification mechanisms. This could involve an internationally mandated digital monitoring system, overseen by the United Nations, to ensure the transparency and traceability of plastic waste shipments. Develop and implement a certification program for waste exporters and importers, ensuring that all transactions meet stringent environmental standards: this certification should be backed by periodic audits and penalties for non-compliance, thereby dissuading the diversion of waste to regions lacking proper waste management infrastructures [8].

Another important policy recommendation is promoting the circular economy and waste reduction strategies. Transitioning from a linear “take-make-dispose” model to a circular economy is essential to minimize plastic waste generation. Upstream measures that reduce the production and use of single-use plastics can significantly alleviate the pressure on waste management systems. Introduce extended producer responsibility (EPR) schemes, eco-design standards, and incentives for industries to invest in biodegradable materials and advanced recycling technologies: such initiatives can substantially reduce waste generation and promote recycling. Establish fiscal incentives such as tax breaks or subsidies for companies that pioneer innovations in waste reduction and recycling infrastructure. Additionally, public procurement policies that favor sustainable products can drive market transformations, leading to reduced reliance on disposable plastics [21].

Enhancing domestic waste management infrastructure is another recommendation for policymakers. Low- and middle-income countries, which are often the recipients of exported plastic waste, frequently lack the infrastructure required to process and recycle this waste safely. Inadequate waste management not only exacerbates environmental degradation but also poses significant public health risks. International financial institutions and donor organizations should prioritize investments in modern waste processing facilities, particularly in regions identified as high-impact waste importers. It could create a Global Plastic Waste Infrastructure Fund, under the auspices of multilateral organizations such as the World Bank or UNEP, to provide technical and financial assistance for upgrading domestic waste management systems. This fund would support the development of state-of-the-art recycling plants, efficient incineration technologies with energy recovery, and safe landfill management practices [14]. Incineration should be strictly limited to non-recyclable residuals and only deployed in countries with robust regulatory oversight, advanced emissions control, and environmental safeguards. Furthermore, investment should focus on scalable, low-emission recycling infrastructure rather than promoting incineration technologies that are often misaligned with local capacities and sustainability goals in low- and middle-income contexts. Additionally, strengthening waste treatment capacity within net exporting countries represents a crucial—yet often overlooked—element of addressing the long-term challenges associated with global plastic waste trade. Despite possessing advanced infrastructure, many high-income nations continue to externalize significant portions of their waste to reduce domestic environmental burdens or comply with national policy objectives. This practice contributes to the unequal distribution of ecological impacts across countries. In light of this, targeted recommendations for exporting nations are revealed, such as increasing investments in domestic recycling, material recovery, and waste-to-energy technologies to lessen dependence on transboundary waste flows; introducing regulatory or economic instruments—such as export restrictions or environmental tariffs—to discourage the outsourcing of waste streams; and establishing robust, transparent waste tracking systems that integrate export data into national environmental monitoring and climate accountability frameworks.

Furthermore, it should foster technology transfer and capacity building. Advanced recycling technologies and waste management practices are currently concentrated in high-income countries. Facilitating technology transfer to developing nations is crucial to enabling them to cope with the burdens of imported waste. Establish international partnerships and public-private collaborations to facilitate the transfer of technology, knowledge, and best practices in waste management. Launch regional technology hubs designed to provide training, research, and development in advanced recycling techniques. Such hubs should be supported through North–South and South–South collaborations, ensuring that developing countries can access cutting-edge technologies while building local capacity [10].

The unique finding that progress on the SDGs mitigates ecological harm led us to propose the integration of plastic waste metrics into national SDG reporting frameworks, enabling more transparent benchmarking and international cooperation tied to quantifiable sustainability metrics. In addition, plastic waste considerations should be integrated into the climate policy agenda. The environmental impact of plastic waste extends beyond local pollution, contributing directly to GHG emissions and climate change. These implications necessitate that plastic waste management be integrated into broader climate policy frameworks. Countries should incorporate plastic waste management strategies into their Nationally Determined Contributions (NDCs) under the Paris Agreement, ensuring that waste-related emissions are accounted for in national climate plans. Implement economic instruments such as carbon pricing or import tariffs on high-emission plastic waste: this would internalize the environmental costs associated with plastic imports and incentivize both waste reduction and cleaner processing methods [12].

Moreover, environmental governance and cross-sectoral coordination have to be enhanced by policymakers. Effective environmental governance is essential to oversee the entire plastic waste lifecycle, from production through disposal. This requires a coordinated approach across various government agencies, industry stakeholders, and civil society. Establish national and regional task forces dedicated to plastic waste management, ensuring that policies are harmonized across trade, environmental protection, urban planning, and public health sectors. Create multi-stakeholder platforms that include government bodies, industry representatives, academic researchers, and non-governmental organizations. These platforms would facilitate transparent policy development, regular monitoring of enforcement, and the sharing of best practices at both the national and international levels [4].

Finally, public awareness and stimulating behavioral change should be raised by major waste-trading countries. Consumer behavior is a critical driver of plastic waste generation. Enhancing public awareness about the environmental and health implications of plastic waste is vital for reducing consumption and encouraging recycling. Governments and international organizations should launch comprehensive public awareness campaigns to educate citizens on the environmental impacts of plastic waste and to promote sustainable consumption practices. Integrate environmental education into school curricula and support community outreach programs. These initiatives should aim to shift public attitudes towards sustainability, fostering behaviors that reduce waste generation and support recycling initiatives [32].

This study highlights several important areas that warrant further investigation. First, future research should expand the panel to include smaller economies that are disproportionately affected by waste imports. Second, an analysis at the sectoral level could yield insights into the specific contributions of different industries to plastic waste generation. Third, additional research into the role of informal waste management and illicit trade in exacerbating environmental risks is essential. By addressing these research gaps, policymakers and scholars will be better equipped to design and implement globally coordinated, locally effective strategies for mitigating the environmental impacts of plastic waste trade.

In conclusion, the findings of this study underscore the urgent need for comprehensive policy interventions that address the multifaceted challenges posed by the plastic waste trade. Strengthening international regulations, promoting a circular economy, enhancing domestic waste management infrastructure, facilitating technology transfer, integrating waste management into climate policy, and improving environmental governance are critical steps toward sustainable waste management. The combined implementation of these recommendations will not only mitigate the adverse environmental effects identified but also promote a more equitable distribution of technological resources and environmental protection measures globally. It is imperative for policymakers to act decisively and collaboratively to transform current practices and secure a sustainable future for all nations.