Decarbonizing agriculture: The impact of trade and renewable energy on CO₂ emissions

Öztürk, Nil Sirel Article Decarbonizing agriculture: The impact of trade and renewable energy on CO₂ emissions Economies Provided in Cooperation with: MDPI – Multidisciplinary Digital Publishing Institute, Basel Suggested Citation: Öztürk, Nil Sirel (2025) : Decarbonizing agriculture: The impact of trade and renewable energy on CO₂ emissions, Economies, ISSN 2227-7099, MDPI, Basel, Vol. 13, Iss. 6, pp. 1-17, https://doi.org/10.3390/economies13060162 This Version is available at: https://hdl.handle.net/10419/329442 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Academic Editor: Sanzidur Rahman Received: 23 April 2025 Revised: 27 May 2025 Accepted: 2 June 2025 Published: 6 June 2025 Citation: Sirel Öztürk, N. (2025). Decarbonizing Agriculture: The Impact of Trade and Renewable Energy on CO2Emissions. Economies, 13(6), 162. https://doi.org/10.3390/ economies13060162 Copyright: © 2025 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/ licenses/by/4.0/). Article Decarbonizing Agriculture: The Impact of Trade and Renewable Energy on CO2Emissions Nil Sirel Öztürk Department of Customs Management, Ke¸san Yusuf Çapraz School of Applied Sciences, Trakya University, Ke¸san, Edirne 22880, Turkey; [email protected] Abstract: This study investigates the environmental effects of agricultural trade, renewable energy use, and economic growth in a panel of 14 selected countries for the period 2000–2021. Per capita CO 2 emissions are modeled as the dependent variable using a second-generation panel data method, the Augmented Mean Group (AMG) estimator, which accounts for cross-sectional dependence and slope heterogeneity. The analysis reveals that the share of renewable energy in total energy consumption significantly reduces carbon emissions, emphasizing the role of green energy policies in environmental improvement. In contrast, economic growth is found to increase emissions, indicating the validity of only the initial phase of the Environmental Kuznets Curve (EKC) hypothesis. Additionally, agricultural imports—and in certain cases, exports—exert upward pressure on emissions, likely due to logistics and production-related externalities embedded in the trade process. Group-specific results highlight distinct dynamics across countries: while renewable energy adoption plays a stronger role in emission mitigation in developing economies, trade composition and production technology drive environmental outcomes in developed ones. The findings underscore the need to redesign trade and energy strategies with explicit consideration of environmental externalities to align with long-term sustainability objectives. Keywords: agricultural trade; renewable energy; CO2emissions JEL Classification: Q56; F18; O44 1. Introduction and Literature Review Global economic growth has accelerated since the second half of the 20th century, largely driven by the expansion of international trade. However, this growth has also imposed significant costs on environmental sustainability. In particular, the rise in environmental awareness since the 1990s has led to a substantial body of theoretical and empirical research examining the environmental implications of economic activity. Within this context, the relationship between economic growth, international trade, and environmental quality has become a central focus in shaping sustainable development policies (Stern,2004; Antweiler et al.,2001). At the core of these discussions lies the ecological economics perspective, which conceptualizes the economy as a subsystem of the natural environment and argues that economic activity must operate within ecological limits. According to this approach, conventional growth models exert irreversible pressures on nature and, in the long term, pose risks to both environmental and social well-being (Costanza et al.,1997;Daly,1997). Ecological economics advocates for a systemic evaluation of the environmental costs associated with energy consumption, production structures, and international trade. Economies 2025,13, 162 https://doi.org/10.3390/economies13060162 Economies 2025,13, 162 2 of 17 One of the most widely cited approaches in the environmental economics literature for explaining the relationship between economic activity and environmental degradation is the Environmental Kuznets Curve (EKC) hypothesis. The EKC posits an inverted U-shaped relationship between per capita income and environmental degradation. At early stages of economic growth, environmental harm increases; however, beyond a certain income threshold, this trend is expected to reverse due to greater environmental awareness, the adoption of cleaner technologies, and the implementation of regulatory policies (Grossman & Krueger,1995;Dinda,2004). Nevertheless, the universal validity of this hypothesis across countries and sectors remains subject to debate. Another factor that adds complexity to the relationship between trade, growth, and the environment is the direction and nature of trade’s environmental impacts. While conventional theory suggests that trade can improve environmental efficiency through the optimal allocation of production factors, mechanisms such as the “pollution haven” and “scale effect” imply potential negative consequences (Antweiler et al.,2001). Analyzing the environmental effects of trade without sectoral distinctions may obscure these impacts. In particular, agricultural trade can contribute significantly to carbon emissions during both production and transportation phases (Verburg et al.,2011). According to IEA (2021), emissions from preand post-production processes in agrifood systems—including transportation and logistics—amounted to 5.8 Gt CO 2 e in 2019, representing approximately 35% of total agrifood system emissions (FAO Global,2022). Although agriculture is often perceived as an environmentally friendly sector, the expansion of global supply chains and the international transportation of agricultural products have significantly increased CO 2 emissions, particularly from logistics. The carbon footprint of global agricultural trade highlights the necessity for developing countries to align their trade strategies with sustainable development goals (Kastner et al.,2012). Therefore, research on the environmental impacts of agricultural trade provides valuable insights not only for economic analysis but also for environmental policymaking. On the other hand, the use of renewable energy has recently emerged as one of the most critical policy instruments for mitigating the environmental costs of economic growth. Increasing the share of renewables in total energy consumption plays a pivotal role in reducing CO 2 emissions (IEA,2021). In this context, it is evident that countries’ growth strategies and trade policies must be considered in close integration with energy transition efforts. This study analyzes the environmental effects of agricultural trade (imports and exports), renewable energy use, and economic growth using a panel dataset covering 14 countries from 2000 to 2021. Its main contribution lies in simultaneously addressing both the classical growth–environment relationship and the trade dimension through the agricultural sector. To account for issues specific to panel data, such as cross-sectional dependence and slope heterogeneity, the Augmented Mean Group (AMG) estimator is employed, enabling a consideration of country-specific dynamics. Accordingly, the study aims to offer a more comprehensive perspective on the theoretical and empirical links between environmental outcomes and international trade in agricultural products. The role of the agricultural sector in carbon emissions has attracted increasing academic interest in recent years. In particular, the effects of dynamics such as international trade, production structures, and energy use on agriculture-related greenhouse gas emissions have been examined in a multidimensional manner across various regions. In this context, the findings of this study align with the recent literature, which highlights both the mitigating impact of renewable energy use on emissions and the influential role of agricultural trade in shaping environmental outcomes. Economies 2025,13, 162 3 of 17 The impact of agricultural trade on carbon emissions has been empirically evaluated in numerous studies. For instance, W. Wang et al. (2024) find that trade openness in agriculture contributes to emission reductions, shaped through channels such as scale effects, technological advancement, and structural transformation. Similarly, G. Li et al. (2024) show that trade liberalization reduces emission intensity, supported by technology diffusion and shifts in industrial composition. This line of evidence complements the present study’s focus on the environmental effects of agricultural trade and renewable energy. The environmental impacts of agricultural trade have been examined from multiple perspectives. In a recent review of the past two decades of research, P. Wang et al. (2023) argue that the environmental effects of trade liberalization are shaped by channels such as scale, structural transformation, transportation, and technological change, often resulting in negative outcomes. Suggested policy responses include improvements in factor allocation, policy reforms, technological innovation, and the development of compensatory mechanisms. This theoretical perspective echoes the empirical observations made in the present study concerning the environmental impact of agricultural imports. There is growing evidence that the environmental impacts of agricultural trade vary depending on factors such as regional characteristics and policy thresholds. Rong et al. (2023) find that in China, agricultural trade openness can reduce emissions only when environmental regulation surpasses a certain threshold; below that level, the effects are reversed. This pattern is consistent with the findings of the present study, where agricultural imports exhibit positive effects on emissions in some countries and negative effects in others. Moreover, the threshold-based panel model used by Rong et al. offers a plausible explanation for the heterogeneous coefficients identified in our second-generation panel analysis. The spatial and sectoral dimensions of carbon transfers driven by agricultural trade are receiving increasing attention in the literature. analyze how agricultural carbon emissions are redistributed across Chinese provinces through trade, and how this redistribution influences policy accountability. Similarly, Liu et al. (2024) model international carbon flows in agricultural trade using network-based structures, proposing both productionand consumption-oriented strategies. In this context, the present study’s use of country-specific coefficients through the AMG estimator aligns methodologically with this strand of the literature by addressing cross-country heterogeneity in carbon outcomes. The internalization of environmental externalities into trade policies has gained prominence with the emergence of instruments such as the Carbon Border Adjustment Mechanism (CBAM), which are relevant for both developed and developing countries. Bux et al. (2024) and Bassi et al. (2024) discuss the potential of the CBAM to prevent carbon leakage, as well as its uneven impacts on developing economies. Fournier Gabela et al. (2024) propose a CBAM framework specifically tailored to the agricultural sector, analyzing how such a policy could be made more feasible. In this regard, the policy implications of the present study resonate with current debates on balancing the emission-increasing effects of trade in developing countries through mechanisms like the CBAM. The role of green technological innovation in reducing agricultural emissions has been emphasized in numerous recent studies. Rong et al. (2023) find that green innovation exerts both direct and spatially mediated mitigating effects on agricultural carbon emission intensity. L. Zhang and Cai (2024) identify an inverted U-shaped relationship, suggesting that maintaining technology at an optimal level can yield both environmental and productivity benefits. Qayyum et al. (2023) and Huang and Ke (2024) highlight regional disparities in the adoption of green innovation and reveal the indirect effects of digitalization and organizational learning on agricultural emissions. The present study’s finding of a significant Economies 2025,13, 162 4 of 17 negative relationship between renewable energy use and CO 2 emissions is in line with these conclusions. Carbon pricing, environmental taxation, and carbon sequestration are also widely discussed in the literature as direct mitigation strategies. Iyke-Ofoedu et al. (2024) emphasize the impact of environmental taxes on carbon sequestration in South Africa, while Gong and Huo (2024) argue that carbon taxes alone are insufficient for reducing agricultural greenhouse gas emissions and must be complemented with sequestration strategies. Kausar et al. (2024) identify a multidimensional relationship between agricultural production and environmental taxation, suggesting that tax-based solutions are more sustainable than direct production constraints. These studies provide a conceptual foundation for the policy recommendations made in the conclusion of the present research regarding energy transition and environmentally conscious trade strategies. The relationship between agricultural production and carbon emissions also raises the issue of balancing productivity and environmental sustainability. (X. Zhang et al.,2024) report a simultaneous increase in productivity and emissions, highlighting the need for a balanced application of green technologies Accorsi et al. (2023) suggest that innovative production techniques and digital infrastructure can support this balance in a sustainable way. In this context, the present study also finds that while agricultural trade contributes to rising emissions, the energy transition may help offset this impact. International agricultural trade is a key driver of not only domestic but also crossborder greenhouse gas transfers. (Adenauer et al.,2025) show that global agricultural emissions spread through network structures, making improvements in production technologies and consumption strategies effective at both local and global scales. In this context, the country-specific effects identified through the AMG model in this study may reflect such structural interdependencies. Moreover, technological transformation not only reduces emissions but also generates transnational spillover effects. A limited but growing number of studies have examined the relationship between agricultural trade and carbon emissions in national or regional contexts. For instance, Turan (2025) analyzes the long-run impact of agricultural exports on CO 2 emissions in Türkiye using ARDL models and finds that export-led growth can reduce emissions, particularly when supported by renewable energy adoption. In a more detailed regional study, Q. Li and Zhang (2024) investigate the redistribution of agricultural carbon emissions across Chinese provinces and show that both import and export activities can contribute to emission reduction through structural transformation and technology diffusion. These findings highlight the significance of national conditions, production models, and trade structures in determining the environmental outcomes of agricultural trade. Several studies suggest that the environmental effectiveness of renewable energy in India may be constrained by structural and policy limitations. Singh et al. (2023) highlight the country’s heavy reliance on biomass and the uneven development of renewable sectors, which may undermine emission reductions. Similarly, Dubey et al. (2023) note that challenges such as grid instability, policy uncertainty, and fluctuating solar tariffs affect the environmental outcomes of renewable energy deployment. These insights are relevant in understanding why renewable energy use in India may not yet yield consistent emissionreducing effects. Finally, a variety of methodological approaches have been employed in recent studies, including panel ARDL, AMG, GMM, panel NARDL, spatial Durbin models, and QARDL techniques (e.g., W. Wang et al.,2024;Kausar et al.,2024;L. Zhang & Cai,2024). By using the AMG estimator, which accounts for slope heterogeneity and cross-sectional dependence, this study also contributes methodologically to the existing literature. Economies 2025,13, 162 5 of 17 In this study, agricultural trade is examined through the disaggregated effects of imports and exports, measured in current USD, while renewable energy use is defined as the share of renewables in total energy consumption. Detailed definitions, measurement units, and data sources for all variables are provided in Table 1. Table 1. Variables used in the study. Variable Code Description Unit Source CO2Emission CO2emissions per capita Metric tons (tons/capita) WB ln_GDPCapita Real GDP per capita USD ln_AgriIM Agricultural imports Billion USD ln_AgriEX Agricultural exports Billion USD RenewableE Share of renewables in total energy eating Percentage (%) Note: Variables preceded by “ln” indicate that the natural logarithm of the original values has been taken. In this context, recent approaches and empirical findings in the literature largely align with the results of this study, emphasizing the need to examine the complex relationship between agricultural trade and environmental sustainability from a multidimensional perspective. The literature suggests that the interactions among energy transition, technology policies, and trade regulations are particularly important for advancing carbon-neutral agricultural strategies. 2. Analysis and Findings 2.1. Country and Variable Selection This study analyzes a group of 14 countries that includes both developed and developing economies: the United States, the United Kingdom, Australia, Canada, Germany, France, Denmark, China, India, Indonesia, Russia, Brazil, Türkiye, and Mexico. These countries are not only key players in global agricultural trade but also exhibit diverse characteristics in terms of energy consumption, greenhouse gas emissions, and economic growth dynamics. This diversity enables a comparative analysis of the relationship between agricultural trade and carbon emissions across countries at different stages of development. Country selection also considered data availability and the consistency of the observation period. The analysis covers the years 2000 to 2021, a timeframe chosen for offering a sufficiently long observation window while also encompassing both the preand post-Paris Agreement periods. This allows for the indirect observation of the effects of international environmental regimes and green development policies. The variables used in this study are selected from key economic and sectoral indicators commonly employed in the literature to assess environmental impacts. The dependent variable is per capita carbon dioxide (CO 2 ) emissions. Although not exclusive to the agricultural sector, this variable is widely used as a macro-level indicator of environmental impact and serves as a proxy for overall national greenhouse gas trends. The independent variables are defined as follows. Agricultural imports reflect the degree of trade openness in the agricultural sector and the global logistics activity within the food supply chain. Agricultural exports represent the environmental burden associated with production-based trade and are relevant in the context of potential carbon leakage. Per capita GDP is included to examine the environmental impact of economic growth, particularly in relation to the Environmental Kuznets Curve (EKC) hypothesis. Renewable energy use refers to the share of renewables in total energy consumption and serves as a key indicator for assessing the environmental effects of sustainable energy transitions. The selection of variables in this study is broadly informed by the IPAT identity (I = P × A × T) and its stochastic extension, the STIRPAT model. These frameworks Economies 2025,13, 162 6 of 17 conceptualize environmental impact (I) as a function of population (P), affluence (A), and technology (T). Accordingly, per capita GDP is used as a proxy for affluence, while renewable energy share represents the technological component. Trade-related variables capture the production and structural aspects of agricultural systems that influence emissions. The inclusion of these indicators allows for a macro-level interpretation of drivers of carbon emissions, consistent with environmental economic theory. Regarding data transformation, logarithmic forms were applied selectively based on distributional characteristics and interpretability. Specifically, variables with high skewness and wide magnitude ranges (such as GDP per capita and CO 2 emissions) were log-transformed to ensure linearity and reduce heteroskedasticity. Variables expressed in percentage form or bounded between 0 and 100 (such as renewable energy share) were retained in level form to preserve their scale interpretability and avoid distortion. This transformation strategy aligns with common practices in empirical environmental studies. Detailed information on these variables is presented in Table 1. 2.2. Cross-Sectional Dependence (CD Test) The CD test developed by Pesaran (2005) was applied to examine cross-sectional dependence in the panel dataset. This test is designed to detect the existence of dependence among cross-sectional units—such as countries in a panel—due to common shocks or shared dynamics. It is particularly useful in datasets with a large time dimension and is widely applied to examine whether observations are correlated across units. The hypotheses for the test are defined as follows (Pesaran,2015): H0 (Null Hypothesis): There is weak cross-sectional dependence among the variables; for example, a country’s import levels are independent of those in other countries. H1 (Alternative Hypothesis): There is strong cross-sectional dependence; that is, import behavior across countries is mutually influenced. The statistical formulation of the test is based on the average pairwise correlation coefficients among panel units and is calculated as follows: CD =√N √2T N−1 ∑ i=1 N ∑ j=i+1 ˆ Pij In the equation, Nrepresents the number of countries in the panel, and Tdenotes the time dimension. The term ˆ Pij refers to the correlation coefficient between the variables of country iand country j. The p-value obtained from the CD test determines whether the null hypothesis (H 0 ) can be rejected at conventional significance levels (e.g., 1%, 5%, or 10%). If the p-value is below 0.10, H 0 is rejected, indicating the presence of strong cross-sectional dependence among the variables (Pesaran,2005). The results of the CD test are presented in Table 2. As shown in Table 2, the CD and CDw + statistics are statistically significant (p< 0.01) for most of the variables. This indicates the presence of cross-sectional dependence among countries, particularly in variables such as agricultural trade (ln_AgriIM, ln_AgriEX), per capita income (ln_GDPCapita), and CO 2 emissions. Although the CDw and CD* tests are not significant for all variables, the consistent significance of the CDw + test—which has greater statistical power—suggests that conventional first-generation panel techniques may be inadequate. In this context, external shocks, global economic integration, and common environmental policies may contribute to the formation of shared dependence structures across countries. Economies 2025,13, 162 7 of 17 Table 2. CD test results. Variable CD p-Value CDw p-Value CDw+p-Value CD * p-Value ln_AgriIM 36.69 (0.000) *** 0.20 (0.839) 350.22 (0.000) *** −2.20 (0.028) ** ln_GDPCapita 39.31 (0.000) *** 0.17 (0.865) 375.17 (0.000) *** 1.51 (0.132) ln_AgriEX 34.66 (0.000) *** −0.20 (0.839) 330.43 (0.000) *** 0.41 (0.683) CO2Emissions 1.66 (0.097) 1.02 (0.309) 312.34 (0.000) *** 5.65 (0.000) *** RenewableE 3.09 (0.002) ** 7.09 (0.000) *** 260.30 (0.000) *** −0.33 (0.739) Note: ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively. CDw: Weighted cross-sectional dependence test, CDw + : Enhanced test with stronger statistical power, CD: Bias-corrected statistic based on (Pesaran,2015). 2.3. Slope Homogeneity (Heterogeneity) Test The slope homogeneity test developed by Pesaran and Yamagata (2008), also known as the delta test, is used to determine whether the regression coefficients across panel units are similar. This method tests the validity of a common coefficient across the entire panel. The core idea is to statistically assess how much each unit’s individual coefficient deviates from the overall average. In doing so, it helps determine whether parameter heterogeneity should be considered in panel data modeling. The delta test is expressed through the following formulas: 1. Standard Delta Test: ∆=qN 21 N∑N i=1ˆ βi−β 2. Augmented Delta Test (Delta Tilde): ∼ ∆=√N1 N∑N i=1 ˆ βi−β σi In these formulas, Nrepresents the number of panel units, ˆ βi , is the estimated slope coefficient for unit i , β is the average slope coefficient across all units, and σi denotes the standard error of the estimated coefficient for unit i. The results of the slope homogeneity test are presented in Table 3. Table 3. Slope homogeneity test results. Test Type Statistic p-Value Delta Test 11.466 (0.000) *** Adjusted Delta Test 13.445 (0.000) *** Note: Under the null hypothesis of slope homogeneity, both the Delta and Adjusted Delta tests are asymptotically normally distributed. The p-values indicate significance at the 1% level (*** p< 0.01). The slope homogeneity test results presented in Table 3(Pesaran & Yamagata,2008) are highly significant based on both the Delta and Adjusted Delta statistics (p< 0.01). This leads to the rejection of the null hypothesis, which assumes that slope coefficients are homogeneous across all countries. The findings suggest that the impact of the independent variables in the model—such as agricultural imports, exports, per capita income, and the share of renewable energy—on the dependent variable (CO 2 emissions) differs across countries, indicating the presence of slope heterogeneity. 2.4. Unit Root Test The Augmented Dickey–Fuller test developed by Pesaran (2007) offers a unit root testing approach that accounts for cross-sectional dependence in panel data settings. Known as the Cross-sectionally Augmented Dickey–Fuller (CADF) test, this method improves the reliability and realism of unit root testing by incorporating interdependencies among countries or panel unit factors often neglected in traditional panel unit root tests. As a result, it provides a more robust assessment of stationarity, particularly for time series influenced by common shocks or similar trends across countries. Economies 2025,13, 162 8 of 17 The standard form of the Augmented Dickey–Fuller (ADF) test for panel data is expressed as follows: ∆yit =αi+βiyit +∑pi k=1γik∆yit−k+ϵit In this formulation, Y it , represents the observation for unit iat time t, αi , is the individual fixed effect, βi , is the coefficient of the lagged level term (the speed of adjustment), γik, are the coefficients of the lagged differences, and ϵit, is the error term. Pesaran’s CADF test incorporates cross-sectional dependence and is specified as follows: ∆yit =αi+βiyit +∑pi k=1γik∆yit−k+ϵit +δyt−1+ϵit Here, y t−1 , denotes the cross-sectional mean at time t − 1, which captures common factors across units in the panel. The results of the unit root test are presented in Table 4. Table 4. Pesaran CADF unit root test results. Variable Stationarity Level t-Bar Critical Value (5%) Z[t-Bar] p-Value Conclusion ln_AgriIM Level [I(0)] −2.309 −2.250 −2.079 0.019 ** Stationary ln_GDPCapita First Difference [I(1)] −2.606 −2.250 −3.225 0.001 *** Stationary ln_AgriEX First Difference [I(1)] −2.972 −2.250 −4.635 0.000 *** Stationary CO2Emissions First Difference [I(1)] −2.474 −2.250 −2.715 0.003 *** Stationary Renewable Energy First Difference [I(1)] −3.250 −2.250 −5.709 0.000 ** Stationary Note: ***, and ** denote statistical significance at the 1%, and 5% levels, respectively. According to the results of the panel CADF test developed by Pesaran (2007), only the variable ln_AgriIM is stationary at level [I(0)], while the remaining variables become stationary at their first differences [I(1)]. This indicates that the variables exhibit different orders of integration, which limits the applicability of conventional panel models assuming fixed coefficients. Therefore, second-generation panel estimation methods—such as Panel ARDL structures, AMG, or CCE estimators—are more appropriate, as they allow for a combination of I(0) and I(1) variables. 2.5. Augmented Mean Group (AMG) Estimation Results The Augmented Mean Group (AMG) estimator, developed by Eberhardt and Bond (2009) and Eberhardt and Teal (2010), is designed to estimate long-run relationships in heterogeneous panel datasets. This approach extends the conventional Mean Group (MG) estimator by incorporating a common dynamic process that accounts for cross-sectional dependence. In a panel data model, where y it denotes the dependent variable and x it is a vector of explanatory variables, the general specification is given as follows: yit =αi+β′ ixit +uit Here, i= 1, . . . ,Ndenotes the countries, and t= 1, . . . ,Trepresents the time periods. αi captures the country-specific fixed effects, while βi is a k × 1 vector of coefficients for each country. uit denotes the error term. To account for cross-sectional dependence, the error term uit is modeled as follows: uit =λift+εit Economies 2025,13, 162 15 of 17 Agricultural exports, by contrast, do not have a significant effect in the overall model, but country-specific results offer useful insights. In Brazil and Russia, exports are associated with higher emissions, possibly due to large-scale, industrialized farming. The United States presents a contrasting case, where exports appear to reduce emissions, potentially due to advanced technologies and resource-efficient practices. Country-level results suggest that the impact of growth, trade, and energy variables differs meaningfully across national contexts. For instance, in Russia, per capita income is negatively associated with emissions—hinting at the possibility of decoupling between economic growth and environmental harm. Likewise, countries such as Türkiye, Brazil, and Mexico exhibit statistically significant emission-reducing effects from renewable energy use, indicating the effectiveness of current energy policies. These variations reinforce the conclusion that universal policy recommendations may not be effective. Instead, country-specific strategies that consider structural and sectoral dynamics are likely to yield better outcomes. The study emphasizes the value of disaggregated environmental analysis for both academic research and policy design. The analysis also demonstrates that agricultural imports and renewable energy use have predictive power over emission levels, as confirmed by Granger causality tests. This adds robustness to the empirical findings and strengthens the policy relevance of the model. Addressing the emission intensity of agricultural imports will require strategies such as carbon certification schemes, sustainable sourcing standards, and greener supply chains. In countries where renewable energy contributes to emission reduction, energy transition should be deepened through better infrastructure and efficiency gains. Where renewables are not yet delivering emission reductions, structural barriers must be addressed to unlock their potential. In conclusion, this study integrates trade, energy, and growth into a comprehensive framework to explain cross-country variation in carbon emissions. The results confirm that sustainable development requires more than economic expansion—it necessitates a fundamental rethinking of how trade and energy systems interact with the environment. Future work may benefit from testing nonlinear growth effects, analyzing green product trade, and incorporating more detailed sectoral data. Funding: This research received no external funding. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: The data used in this study are publicly available from the World Bank Open Data platform: https://data.worldbank.org, accessed on 27 May 2025, as also stated in Table 1. No new data were created. Conflicts of Interest: The author declares no conflict of interest. Appendix A Table A1. Robustness check: CCEMG estimation results. Variable Coefficient Std. Err. p-Value ln_GDPCapita 387.82 233.14 0.096 * ln_AgriIM 227.65 94.89 0.016 ** ln_AgriEX −20.02 55.44 0.718 RenewableE −30.64 17.46 0.079 * **, and * indicate statistical significance at the 5%, and 10% levels, respectively. Economies 2025,13, 162 16 of 17 References Accorsi, R., Akkerman, R., Pawar, K. S., & Sasso, M. (2023). Sustainable food supply chains—Emerging technologies and strategies toward low-carbon ecosystems. Journal of Cleaner Production,413, 137393. [CrossRef] Adenauer, M., Adjemian, M. K., Arita, S., Brorsen, W., Cooper, J., Goh, G., Karali, B., Mallory, M. L., Thompson, W., & Yu, J. 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