ICT-environmental policy synergies for green trade and emissions reduction: an SDGs nexus analyses in OECD countries

Abstract

The Sustainable Development Goals (SDGs) represent a universal call to action, yet progress remains uneven across goals and regions, underscoring the need for policies that advance multiple targets simultaneously. This study examines how Information and Communication Technologies (ICT) infrastructure (SDG-9) affects green trade development, and how this effect, in turn, translates into reduced CO₂ emissions (SDG-13). We argue that green trade development operates as the mediating mechanism linking digital infrastructure to environmental outcomes, and we test whether stringent environmental regulations (SDG-12) moderate, specifically strengthen, the ICT–green trade relationship. Using Pooled Mean Group (PMG) and fixed effects estimation with Driscoll-Kraay Standard Errors on a panel of OECD countries from 2002 to 2021, we find that ICT penetration significantly enhances green trade development, and this effect is amplified in the presence of more stringent environmental regulations. In turn, higher green trade development is associated with lower CO₂ emissions, confirming its mediating role between ICT infrastructure and environmental performance. Together, these findings support a pathway of ‘digital ecological modernization’, in which ICT infrastructure investments (SDG-9), reinforced by environmental regulations (SDG-12), drives green trade development as a mechanism for achieving emissions reduction (SDG-13). The results suggest that coordinating digital and environmental policy can generate multiplier effects across interconnected SDGs.

1 Introduction

Information and Communication Technologies (ICT) are widely recognized as transformative tools for sustainable development, capable of catalysing progress across the sustainable development goals (Qureshi, 2023). This transformative potential, however, is not without its contradictions. A central debate within the ICT field highlights the dual nature of digital tools, which can be instruments of liberation and human enhancement on one hand, and instruments of oppression and control on the other (Masiero, 2024). On the side of oppression, a growing body of evidence shows that ICTs can exacerbate inequalities (Nemer, 2022; Akbar et al., 2025), prolong conflicts (Iazzolino, 2021), and worsen the plight of vulnerable populations, such as refugees (Bock, Haque, and McMahon, 2020; Weitzberg, 2020). Conversely, other studies highlight ICT’s liberating potential, demonstrating its role in humanitarian work (Akbari and Masiero, 2023), enterprise capability (Ferede, Negash, and Meso, 2024), economic growth (Behera, Haldar, and Sethi, 2024), women’s empowerment (Tasnima and Syed, 2024), and sustainable agriculture (Mushi, Serugendo, and Burgi, 2023), among others. Within this dialectic, one application of the critical lens has received comparatively little attention (Walsham 2017):has noted that despite environment and climate change being major issues for development, limited work has been carried out by ICT researchers in monitoring and managing these impacts. A gap echoed at the 2023 IFIP Joint Working Conference, where ICT’s application to climate change and sustainability was flagged as an emerging frontier rather than an established research stream (Brooks et al., 2023; Hovorka and Auerbach, 2023).

This paper addresses this gap through an examination of how ICT infrastructure, entangled with environmental regulations, drives sustainable development via green trade development, an understudied pathway connecting digital and environmental policy domains, and one of practical relevance to the OECD context, where digital infrastructure is mature but its trade-environment linkages remain unexamined.

Three Sustainable Development Goals structure the analysis that follows and are defined here once: SDG-9 (Industry, Innovation and Infrastructure) frames ICT infrastructure as the independent variable; SDG-12 (Responsible Consumption and Production) frames green trade development as the mediating/dependent construct of interest; and SDG-13 (Climate Action) frames CO₂ emissions reduction as the ultimate outcome.

The primary driver of climate change is CO₂ emissions from energy, transportation, construction and other sectors of the economy, effects that are expected to intensify in the coming years. Global CO₂ emissions reached 37.55 billion metric tons in 2024, a 62.6% increase since 1995 (Statista, 2024), intensifying extreme weather events like heatwaves, floods, and droughts, causing significant infrastructure damage and agricultural losses (Mirza, 2003; Seneviratne et al., 2021). This alarming trend highlights the urgent need for climate mitigation measures, consistent with the United Nations Sustainable Development Goal SDG-13’s call to reduce CO₂ emissions and strengthen environmental resilience (UNFCCC, 2015).

In response, countries are introducing stringent environmental policies that aim to mitigate the adverse effects of the existing ‘brown’ economy while stimulating the growth of a green economic system, consistent with SDG-12’s emphasis on sustainable natural resource management. Such policies typically combine penalties on environmentally harmful products with incentives for sustainable production. Implementation of stringent environmental policies positively influences green innovation (Liu, Wang, and Wu, 2021), boosts export flows, and competitiveness in international trade (Costantini and Mazzanti, 2012).

The economic imperative for green transition is clear: a single month with average temperatures exceeding 30 °C can reduce exports by 3% (Schenker and Osberghaus, 2025). Rising environmental awareness and regulatory pressure have accordingly pushed countries to invest in green R&D and eco-friendly production, and to compete for position in the global green market (Shah et al., 2021). This shift operationalizes SDG 12’s emphasis on Sustainable Consumption and Production (SCP) patterns to decouple economic growth from environmental harm (ESCAP, 2018).

The crucial role of ICT infrastructure such as, cellular subscribers, broadband connections, and internet users in economic expansion (Liu and Wan, 2023; Hussain et al., 2021), healthcare (Vavilis, Petković, and Zannone, 2012), education, information accessibility (Azam et al., 2022), and carbon emissions (Li et al., 2023) is well established in the literature. ICT facilitates rapid information dissemination about green products, enhances market visibility through digital platforms, supports e-commerce strategies tailored to eco-friendly goods, and enables real-time monitoring of supply chain transparency, positioning it as the SDG-9 infrastructure layer of the framework introduced above. This role is consistent with ICT’s established function in promoting sustainable industrialization and resilient digital infrastructure (Monaco, 2024). The United Nations Environment Programme (UNEP) estimates that global annual investments worth 2% of world GDP, approximately $1.3 trillion, will be necessary until 2050 to achieve an environmentally sustainable economic system, underscoring the scale of coordinated investment the SDG-9–SDG-12–SDG-13 pathway is meant to support.

Against this background, the paper’s problem statement is as follows: although ICT infrastructure, environmental regulation, and green trade are each independently linked to sustainable development outcomes, their joint pathway, from digital infrastructure (SDG-9), through regulation-moderated green trade (SDG-12), to emissions reduction (SDG-13) has not been examined empirically in the OECD context (Walsham, 2017; Raihan, 2023).This study accordingly examines the role of ICT infrastructure (SDG-9) and stringent environmental regulations (SDG-12) in green trade development and its transformative impact on reducing CO2 emissions (SDG-13) in the OECD countries. Specifically, it asks the following research questions: (1) What is the impact of ICT infrastructure on OECD nation’s green trade development? (2) How do stringent environmental regulations moderate the relationship between ICT infrastructure and green trade development? (3) Does the development of green trade promote environmental sustainability in OECD countries by reducing CO₂ emissions?

Figures 1, 2 depict longitudinal trend in ICT measures (such as, total internet users (IUT), fixed broadband subscribers (FBS), and mobile cellular subscribers (MCS) and green trade volume across OECD nations from 2002 to 2021.

FIGURE 1

FIGURE 2

Figure 3 illustrates the average carbon emissions trend across OECD countries (2001–2020), revealing a general decline from 2007 onward. This downward trajectory is interrupted by brief increases during 2009–2010 and 2014–2016.

FIGURE 3

Figure 4 presents the study’s logical framework, illustrating how progress in one SDG advances others, consistent with the SDG-9 → SDG-12 → SDG-13 pathway defined above. It shows that ICT infrastructure development (SDG-9) boosts green trade development (SDG-12), especially when supported by stringent environmental regulations (SDG-12). In turn, increased green trade directly reduces CO₂ emissions (SDG-13).

FIGURE 4

We make four contributions. First, green trade development research has largely examined consequences—green economic growth (Ahmed et al., 2022; Xu, 2022), green technology (Liu, Lei, and Zhou, 2022), consumption-based CO₂ emissions (Bai et al., 2023), and natural resource consumption (Huang and Zhao, 2022), rather than antecedents. We introduce a comprehensive Green Trade Development Index (GTDI) and, to our knowledge, provide one of the first analyses of its antecedents in OECD countries, Second, the growing body of literature on the ICT-environment nexus has predominantly examined this relationship through the lens of energy consumption (Raihan, 2023); we instead investigates the underexplored linkage between ICT infrastructure and green trade development, specifically analysing the moderating role of environmental regulations in this association. Third, we test whether GTDI is negatively associated with carbon emissions, bearing on the factors that mitigate CO2 emissions, which—if supported by the results—would be consistent with SDG 13’s principle of mainstreaming climate action into national planning. Fourth, methodologically, the Pooled Mean Group (PMG) estimator is employed to examine the long-run relationship between ICT and green trade, while a fixed effects model with Driscoll-Kraay standard errors is used to capture the contemporaneous impact of ICT, capturing both dynamic and immediate effects within a single design.

The rest of the study is arranged as follows. Section “literature review, theory, and hypotheses development” presents the relevant literature and develops the study’s hypotheses. Section “Research methodology” introduces the data, variables, econometric models, and empirical strategy employed in this research. The results of diagnostic tests, descriptive statistics and regression analyses are provided in section “Empirical results”. Finally, section “Conclusion” offers concluding remarks, discusses research implication, and outlines the future research agenda.

2 Literature review, theory, and hypotheses development

2.1 Theoretical underpinning

In the field of ICT, theoretical frameworks are not merely academic exercises; they provide the essential lenses for understanding how and why technology leads to—or fails to lead to—meaningful developmental change. A strong theoretical foundation moves research beyond simply identifying correlations and allows us to propose and test plausible mechanisms of action, thereby contributing to broader debates on technology’s role in development. Guided by this principle, this study is grounded in Affordance Theory. Originally developed in ecological psychology, and later adapted to information systems research (Sein et al., 2019) this theory moves beyond technological determinism (the idea that technology alone causes outcomes) and social constructivism (the idea that only society shapes technology). Instead, it focuses on the possibilities for action that emerge from the relationship between a goal-oriented user and the material features of a technology (Thapa and Sein, 2018).

In simple terms, an “affordance” is what a user can do with a technology. For example, a computer’s affordance for a writer might be creating documents, while for a graphic designer, it's creating digital art. Similarly, a mobile phone can afford communication, information access, and entertainment, but the actualized affordance depends on the user’s intent and the context of their environment (Wang, 2025).

We theorize that ICT infrastructure provides key affordances for green trade development, such as enabling global market access, supply chain transparency, and operational optimization. Consequently, actors (e.g., government, businesses) may actualize these affordances to some extent even in the absence of specific policies, motivated by existing market demands or corporate social responsibility. However, stringent environmental regulations serve as a critical catalytic factor that systematically enhances this process. Such regulations are hypothesized to re-shape the actor-technology relationship by sharpening the perception of ICT’s environmental affordances and increasing the incentive to actualize them. By transforming sustainability from a peripheral concern into a core strategic imperative, regulations steer the use of general-purpose digital tools toward specific, pro-environmental outcomes.

This theoretical logic connects directly to the study’s three empirical constructs and sets up the hypotheses that follow. ICT infrastructure is the material technology in this framework: it is measured through internet users, fixed broadband subscribers, and mobile cellular subscribers, and it represents the affordances that actors can perceive and act on. Green trade development, measured through the study’s Green Trade Development Index (GTDI), is the actualized outcome of that affordance-seeking behavior. Environmental regulation stringency is the catalytic condition theorized to shift ICT’s affordances from latent to actualized. Each of the following sections builds on this: Section 2.2 explains how ICT infrastructure leads to green trade development (H1); Section 2.3 explains how environmental regulation strengthens that relationship (Hypothesis 1); and Section 2.4 extends the argument to environmental outcomes (H2).

2.2 ICT and green trade development

Early research on international trade highlighted its negative environmental impacts, including resource overexploitation and pollution, particularly in countries with weak environmental regulations (Wen et al., 2018; Brander and Scott Taylor, 1997). However, over the past 15 years, the focus has shifted toward green economic development, driven by mounting environmental concerns. Green growth theory posits that economic productivity must align with ecological sustainability (Xu, 2022; Kharb, Shri, and Saini, 2025). This approach emphasizes the perseverance of biodiversity and natural resources (such as, water, oil, gas, timber, and coal) to ensure long-term economic and eco-system sustainability. Green development perspective also holds a significant place in the discourse on formulating policies within international economic and development institutions (Hickel and Kallis, 2020; Keyser, Adeoluwa, and Fourie, 2020). While, green trade development is considered as one of the most reliable channels to promote green economy (Ahmed et al., 2022).

In recent years, advancements in technology, especially in ICT, have significantly impacted the environmental, economic and social development (Rothe, 2020). From economic perspective, ICT developments have a significantly positive impact on the growth of international trade in, financial, transportation, travel and insurance sector (Nath and Liu, 2017). ICT tools enable corporations to access larger markets, expand their customer base, boost sales and increase profits. They also facilitate the acquisition of new ideas and skills, while compelling firms to stay abreast of competitive market dynamics (Freund and Weinhold, 2004; Kodama, 2013). Vemuri and Shahid (2009) analysed a panel of 64 countries to assess the impact of ICT on trade volume, finding a significantly positive relationship between ICT adoption and international trade growth. Similarly, Yushkova (2014) demonstrated that internet usage among business communities in both importing and exporting countries enhances bilateral export flows. The adoption and efficient use of ICT facilitate e-commerce growth and enhances bilateral trade (Xing, 2018; Ozcan, 2018).

From environmental perspective research highlights ICT’s potential to promote environmental sustainability (Asongu, Le Roux, and Biekpe, 2018; Zhao, Hafeez, and Faisal, 2022; Chatti and Majeed, 2022; Wang et al., 2024a; Manu and Asongu, 2025). Although, the pursuit of conventional economic growth, often characterized by excessive electricity consumption, presents a fundamental challenge to environmental sustainability. ICT can be strategically leveraged to decouple prosperity from ecological impact by enabling energy-efficient systems and fostering sustainable socioeconomic development (Saud et al., 2023). ICT serve as a crucial enabler for three key sustainable development drivers: (1) enhanced resource efficiency, (2) improved education systems, and (3) optimized business operations. These capabilities position ICT as a critical success factor for achieving the United Nations Sustainable Development Goals (Tjoa and Simon, 2016). Moreover, digital technologies also enhance local green total factor productivity (Lin et al., 2025). This aligns with modern growth theories, which posit a reciprocal relationship between technological innovation and environmental development (Berkhout, 2005; Majerník et al., 2023; Ahmad et al., 2023). Specifically, ICT-driven frameworks promote environmental development through diverse mechanisms, such as paperless communication (e.g., email), the use of virtual collaboration tools (e.g., teleconferencing and e-banking) to curb physical travel, smart waste management, and AI-powered e-marketing platforms that replace resource-heavy traditional campaigns while enhancing global reach.

Two things become clear when these literatures are read together. One line of research shows that ICT boosts trade in general (Vemuri and Shahid, 2009; Yushkova, 2014; Ozcan, 2018; Xing, 2018). Another shows that ICT supports environmental sustainability in general (Asongu, Le Roux, and Biekpe, 2018; Chatti and Majeed, 2022; Manu and Asongu, 2025; Wang et al., 2024a). Neither asks whether ICT’s affordances are actually put to use for green trade specifically—that is, trade in environmentally sound goods and services. This is the gap this study addresses. Building on the affordance mechanism described above, ICT infrastructure is theorized to lower the cost of entering green export markets in three ways: it makes foreign green-standard requirements visible to exporters (an information affordance), it connects sellers of green goods with buyers abroad (a market-access affordance), and it lets firms verify supply-chain claims that eco-conscious buyers demand (a transparency affordance). Together, these three mechanisms point specifically to green trade development, not to trade or sustainability outcomes in general. Based on this reasoning, and the theoretical logic developed in Section 2.1, we put forward the following hypothesis:

2.3 ICT, environmental regulations and green trade development

Following the 1992 Rio climate treaty, signed by 154 nations at the UN Conference on Environment and Development, countries redirected their focus toward environmental policies for a green economy (Adams, 2008). This landmark agreement established a framework for international cooperation on sustainable development, prompting nations to reconsider traditional economic models. As environmental concerns gained prominence, governments began implementing specific regulatory measures to balance ecological protection with economic growth (Zhang and Wen, 2008). While, the role of ICT remains contingent on government regulations addressing these challenges (Nejati and Shah, 2023). Environmental Regulations (ER) cover various measures such as emission standards, technology standards, carbon pricing, subsidies, tax incentives, environmental impact assessments, and waste management standards, all aimed at sustainable environmental and economic growth. ER stimulates green technology development (Shang et al., 2022) which improves a country’s international trade performance (Lin, Liu, and Zhang, 2009). In China’s case, contrary to the conventional assumption that stringent environmental regulations hinder economic growth, these measures have demonstrated a significantly positive impact on foreign trade performance (Wang, Zhang, and Zeng, 2016). While environmental protection expenditures show limited effects on sector-level employment in Swedish firms (Amjadi et al., 2025), such policies consistently boost demand for green goods, strengthen competitiveness, and expand export markets while driving green innovation (Bellelli and Xu 2024)These findings imply that countries should restructure industrial frameworks to comply with environmental standards for green trade development. Moreover, rising global demand for eco-certification also compels producers to adopt sustainable practices (Zhang et al., 2012). This aligns with the Porter Hypothesis, which posits that well-designed environmental regulations can spur innovation, elevate product value, and deliver economic-environmental synergies (Porter, 1996).

Positioned within the affordance framework of Section 2.1, this literature indicates the mechanism by which regulation moderates the ICT–green trade relationship. Regulatory instruments (emission standards, carbon pricing, eco-certification requirements) do not create ICT’s affordances for market access or supply-chain transparency; those affordances already exist as features of the technology. What regulation does, as per (Sein et al., 2019; Thapa and Sein, 2018) raise the salience and payoff of actualizing them: emission standards and carbon pricing (Shang et al., 2022) increase the cost of not using ICT for green-market positioning; eco-certification demand (Zhang et al., 2012). Increases the value of ICT’s transparency affordance specifically for firms seeking export credibility; and the resulting innovation incentives (Porter, 1996) reinforce firms’ motivation to actualize ICT affordances toward green rather than conventional trade. Based on the conclusions, we propose our second hypothesis as follows;

2.4 Green trade development and environmental degradation

Scholars have extensively analysed the relationship between international trade and environmental quality, with studies demonstrating both positive and negative linkages (Neary, 2006; Jayadevappa and Chhatre, 2000; Halicioglu and Ketenci, 2016; Frankel, 2009). This complex association varies according to trade openness (Ali et al., 2020), natural resource depletion, environmental laws (Baek and Koo, 2009), capital vs. labour intensive products (Cole and Elliott, 2003), and trade diversification (Wang et al., 2024b). Three dominant theories explain these dynamics: the pollution haven hypothesis posits that industries migrate to regions with lax environmental standards (Copeland and Scott Taylor, 1995); the pollution halo effect maintains that FDI from environmentally strict nations elevates host country practices (Copeland and Taylor, 2004); and the race to the bottom (or top) theory concerns regulatory competition-countries lower their environmental standards to attract investments and promote business activities (or vice versa)- (Fischel, 1981). While researchers have thoroughly examined these conventional trade-environment connections, studies on the impact of ICT on environment remain scarce (Andersson and Hatakka, 2023). Recent findings show that green trade boosts economic growth (Ahmed et al., 2022) and encourages local green technology adoption (Liu, Lei, and Zhou, 2022), while green innovation mediates between sustainability and growth (Ullah et al., 2025). However, expanded green trade may elevate consumption-based emissions in developed economies (Bai et al., 2023), even as it moderates resource-growth relationships (Xu, 2022) and enhances resource efficiency (Huang and Zhao, 2022). The impact of digital transformation on CO2 emissions depends on a number of factors such as, demand expansion and carbon reduction coefficients (Chen et al., 2025). Crucially, green trade reduces technology costs and generates employment (Liu, Lei, and Zhou, 2022). Furthermore, trade policies drive economic diversification and reduce vulnerability to external shocks. They support SDGs and green economy transition, balancing economic resilience with environmental sustainability (Sarangi, 2019). This trade liberalization of green products helps to achieve win-win-win relationship for trade, the environment, and the economy (Yu, 2007). Unlike H1 and Hypothesis 1, H2 concerns the downstream consequence of green trade rather than ICT’s affordances directly, and is accordingly grounded in the pollution-halo logic introduced above (Copeland and Taylor, 2004) rather than in Affordance Theory. The mechanism runs as follows: green trade development raises the share of environmentally sound goods and eco-certified production in an economy’s trade mix (Zhang et al., 2012); and the resulting technology adoption and cost reduction (Liu, Lei, and Zhou, 2022) lower the emissions intensity of the developed economies. Bases on aforementioned evidences we propose following hypothesis;

3 Research methodology

3.1 Data collection

The sample for the current research consists of 37 OECD countries from 2002 to 2021. The data on ICT components (individuals using the internet, fixed broadband subscription, and mobile phone subscription) was accessed from World Development Indicators (WDI) of the World Bank. Data on two green trade measures-total trade in environmental goods, and comparative advantage in environmental goods-was retrieved from the International Monetary Fund (IMF), while information on international collaboration in development of environment-related technologies was fetched from the OECD database. A complete detail of the data sources is provided in Table 1.

3.2 Variables measurement

3.2.1 Dependent variables

Green trade development serves as the primary dependent variable in this study. While prior research has proposed various proxies to measure green trade development (Huang and Zhao, 2022; Bai et al., 2023; Xu, 2022; Can, Ben Jebli, and Brusselaers, 2022; Liu, Lei, and Zhou, 2022), these approaches adopt a narrow definition, focusing either exclusively on environmental goods exports (Huang and Zhao, 2022; Xu, 2022) or aggregating exports and imports (Can, Ben Jebli, and Brusselaers, 2022; Bai et al., 2023; Liu, Lei, and Zhou, 2022).

To address this limitation, we argue that green trade development should be evaluated holistically, accounting for both trade volumes and a country’s long-term environmental commitments. Accordingly, we develop a novel composite index for OECD countries, integrating three key components: (1) total trade volume in environmental goods (imports plus exports), (2) revealed comparative advantage in green trade, and (3) international collaborative efforts in environmental technology development, thereby capturing both the scale and sophistication of a nation’s green trade development. The element of comparative advantage measures a country’s relative advantage or disadvantage in the trade of environmental goods. A positive (negative) value specifies relative advantage (disadvantage). The third component of our index (international collaborations in environment-related technology development) is included because technology transfer and cross-border environmental innovation facilitate the development, production, and exchange of environmental goods, thereby supporting green trade development.

Principal Component Analysis (PCA) is employed to develop a singular index of green trade development. PCA follows a series of steps to reduce dimensionality in the dataset while preserving the variance as much as possible. At first, it standardized the data by subtracting the mean of each variable included in the analysis. Then, it computes the covariance matrix of the standardized data. Afterwards, it computes the eigenvalues of eigenvectors and sorts them in the descending order. Finally, it chooses the largest eigenvectors and transforms the data to a new dimensional space using main principal components. Results of PCA have been reported in Supplementary Appendixs III, IV. The first component had an eigenvalue of 2.13, explaining 71.23% of the total variance in the three variables. The remaining two components accounted for 25.09% and 3.68% of the variance, respectively. Based on the Kaiser criterion (eigenvalue >1), only the first component was retained. The first principal component exhibited positive loadings for all three variables (TGT = 0.652, COMGT = 0.432, and INTCOL = 0.623), indicating that it captures the common variation among these indicators. Therefore, the first principal component was used as the composite index in subsequent analyses.

Moreover, as a robustness check, the green trade index was reconstructed using an alternative weighting scheme. Specifically, an equally weighted index was developed by assigning identical weights to each of the constituent indicators after standardization. This approach ensures that the contribution of each component to the composite index is treated uniformly and allows us to examine whether the empirical findings are sensitive to the weighting procedure adopted in the baseline PCA-based index.

Environmental degradation is the second dependent variable of this study. We use country’s annual CO2 emissions as a proxy for environmental degradation.

3.2.2 Moderating variable

Environmental regulations of a country play an integral role in promoting green trade development by ensuring that manufacturing process, products, and supply chain mechanism meet certain environmental criteria. This undoubtedly enhances a country’s competitiveness in the global trade of environment friendly products and services. We employ the OECD environmental policy stringency index as a moderating variable between ICT and green trade.

3.2.3 Explanatory variable

ICT infrastructure serves as the primary explanatory variable in this study. The literature suggests various measures of ICT infrastructure within a country, which can be categorized into traditional infrastructure (e.g., internet users, mobile phone users, number of broadband connections) and modern infrastructure (e.g., big data centres, cloud platforms, e-commerce transactions volume, and the number of listed intelligent manufacturing companies) (Sheng, Zhu, and Chen, 2024). We employed traditional ICT infrastructure indicators–such as individuals using the internet, broadband connections, and cellular connections-in our study and constructed a comprehensive ICT index using PCA. Details of other control variables are provided in Table 1.

3.3 Empirical model

This study pursues four key objectives that align with the United Nations Sustainable Development Goals framework: (1) constructing a comprehensive green trade development index (GTDI) to operationalize SDG 12’s (Responsible Consumption and Production) emphasis on sustainable trade; (2) analysing how ICT infrastructure development (SDG 9: Industry, Innovation and Infrastructure) influences green trade development; (3) investigating how environmental regulations (SDG 12’s policy frameworks) moderate the ICT-GTDI relationship; and (4) assessing the GTDI’s role in facilitating carbon emissions reduction (SDG 13: Climate Action). To empirically test these relationships, we develop three analytical models that capture both direct and moderated pathways among these SDG-aligned variables.

In Equation 1, GTDI_it is green trade development for country i at time t. We have employed three different measures such as, total trade in environmental goods, comparative advantage in environmental goods, and international collaboration in development of environment-related technologies and also used principal component analysis to calculate a single green trade index. measures the information communications technologies penetration in country i at time t. We used three commonly used measures of ICT and developed an index using principal component analysis. GDP, Rnew, and Labor are control variables to account for the economic growth, renewable energy production, and employed labour force of the sampled countries. Yearst is the year fixed effects to control for time-vary common shocks. Countryi is the country fixed effects to control for country level time-invariant unobserved factors. In Equation 2, we have interacted the ICT with environmental regulations (ER) of the country to examine the moderating role of ER between GTDI and ICT, while rest of the model specification is same.

In Equation 3, CO_2i,t is our dependent variables that measures the level of carbon emissions by country i at time t. GTDI_i,t is the green trade index; FDI_i,t is the amount of foreign direct investment in the country; EF_i,t is the level of economic freedom in the country.

It is important to note that the empirical framework adopted in this study represents an interconnected sustainability system rather than a simple linear causal chain. The three models are specified to examine the sequential relationships among ICT, environmental regulations, green trade development, and CO₂ emissions while recognizing that these relationships may be interdependent. In this framework, the GTDI is conceptualized as a mediating mechanism through which ICT contributes to environmental sustainability by facilitating the production, competitiveness, and trade of environmental goods. At the same time, we acknowledge that potential feedback effects and reverse causality may exist among the variables. For example, improvements in green trade development may encourage further investment in ICT infrastructure, while lower carbon emissions may reinforce environmental policies and green innovation.

3.4 Basic diagnostics

Panel data could inherently possess several issues such as, multicolinearity, heteroscedasticity, cross sectional dependence, serial correlation, and endogeneity. Therefore, before conducting regression analysis on panel data, it is imperative to perform certain diagnostic tests to ensure that our results valid and reliable. Present study has performed a series of diagnostic and robustness analyses to ensure the reliability of regression results.

3.4.1 Multicolinearity

The presence of multicolinearity in panel data reduces the precision of regression analysis by inflating the standard errors and producing unstable coefficients. We perform the Variance Inflation Factor (VIF) analysis to ensure that our models do not have an issue of multicolinearity. Supplementary Appendix I shows that the VIF values of all variables are well below 5, indicating absence of multicolinearity (Kim, 2019).

3.4.2 Heteroscedasticity, serial correlation and cross-sectional dependence

The issue of heteroscedasticity, serial correlation and cross-sectional dependence could seriously undermine the reliability of the significance of the predictors and lead to incorrect conclusions. Supplementary Appendix II, panel A, B and C show the results of modified Wald statistics, Wooldridge test, and Pesaran test correspondingly. Significant (p < 0.01) probability values in panels A, B and C indicates the presence of heteroscedasticity, serial correlation and cross-sectional dependence in our panel data. Therefore, we use Fixed-effects model with Driscoll-Kraay standard errors to ensure our findings are robust to these issues.

3.4.3 Endogeneity

Endogeneity arises when an explanatory variable is correlated with the error term, leading to biased and inconsistent parameter estimates and potentially undermining the validity of the regression results. To examine the presence of endogeneity, the Durbin-Wu-Hausman (DWH) test was conducted for all three models by treating each explanatory variable individually as potentially endogenous. The results of both the Durbin and Wu-Hausman tests were statistically insignificant in all cases, indicating a failure to reject the null hypothesis of exogeneity. Therefore, the findings suggest that none of the explanatory variables are correlated with the error term, implying that all regressors can be treated as exogenous and that the estimated coefficients are unlikely to suffer from endogeneity bias.

3.5 Empirical strategy

The present study employs the Pooled Mean Group (PMG) estimator developed by (Pesaran, Shin, and Smith, 1999) because the dataset consists of a macro panel of countries observed over a relatively long time period. The PMG estimator is particularly suitable for panels with moderate to large time dimensions as it allows the short run dynamics, adjustment speeds, and intercepts to vary across countries while imposing homogeneity on the long run coefficients. This framework is appropriate because countries may respond differently in the short run due to differences in institutional and economic conditions, yet exhibit similar long run relationships. Moreover, the existence of cointegration among the variables further justifies the application of the PMG approach. To ensure the appropriateness of the homogeneity restriction on long run coefficients, the Hausman test is employed to compare the PMG and MG. Moreover, the PMG approach is particularly appropriate for our analysis because it effectively handles panel data with mixed stationary properties, accommodating both I (0) and I (1) variables. Crucially, it allows for country-specific short-run dynamics while assuming a common long-run relationship - a theoretically justified assumption for green trade development patterns that tend to converge over time due to global environmental agreements and technology diffusion. While the error correction mechanism (ECT) provides insights into the speed of adjustment towards long-run equilibrium following trade policy changes or economic shocks. Additionally, it accommodates the lagged dependent variables as regressors, avoiding inconsistencies inherent in static panel approaches (Manu and Asongu, 2025). The PMG estimator’s ability to differentiate between short-run adjustments and long-run equilibrium relationships allows us to precisely investigate not just if ICT infrastructure and environmental regulations impact green trade development, but how and over what time horizon these effects materialize. Specifically, it enables us to determine whether these relationships are transient or permanent and to quantify the speed of adaptation to policy shocks.

3.6 Robustness test

As our panel data suffer from heteroskedasticity, serial correlation, and cross-sectional dependence, fixed effects regression was estimated using Driscoll–Kraay standard errors as a secondary approach to examine the contemporaneous association between ICT, GTD and Co_2. The fixed effects estimator, controls for unobserved time-invariant heterogeneity and omitted variable bias through country-specific intercepts, while Driscoll–Kraay standard errors ensure robustness against heteroskedasticity, within-panel serial correlation and cross-sectional dependence. This dual-method approach not only reinforces the validity of our findings across distinct econometric assumptions but also enhances the credibility of our conclusions regarding ICT’s role in sustainable development.

4 Empirical results

4.1 Descriptive statistics

Table 2 presents the descriptive statistics of our variables. The mean value of GTDI is 0.0072 with a standard deviation of 1.00. Internet users, broad band connections and cellular connections show a large spread in minimum and maximum values, indicating that ICT penetration in OECD countries is at different levels of development. The average of economic freedom is 71.01 which suggest that in OECD countries individuals are ‘mostly free¹’ to work, produce, consume and invest their capital.

4.2 Panel unit root results

Given the presence of cross-sectional dependence, the study employs the Pesaran (2007) Cross-sectionally Augmented Im, Pesaran, and Shin (CIPS) panel unit root test. The CIPS test augments the conventional ADF regressions with cross-sectional averages to account for unobserved common factors and cross-sectional dependence among countries. Table 3 reports the results of the Pesaran CIPS unit root test. The null hypothesis is that the variables are non-stationary, while the alternative hypothesis states that the variables are stationary. The reported values reveal that some variables are stationary at level, whereas other variables-such as, ICTindex, ICTER, CO₂, FBS, MCS, COMGT, Rnew, and GDP-are stationary at first difference.

4.3 Co-integration results

After confirming the stationarity of our variables at either level or first difference, we proceeded to cointegration analysis to evaluate long-term relationships among the series. Given the presence of significant cross-sectional dependence among countries, the Westerlund (2007) error-correction-based cointegration test is considered the primary source of inference because it is more robust to cross-sectional dependence and parameter heterogeneity. Table 4 shows the results of the Westerlund test, where the significant values for models 1, 2 and, 3 supports the acceptance of the alternative hypothesis of integration among variables. Furthermore we also employed (Pedroni, 2004) co-integration test. Pedroni (2004) developed seven different statistical tests for heterogeneous panels. The null hypothesis is that there is no co-integration, while alternative hypothesis suggests the presence of co-integration among the variables. Table 5 presents the results for each of our three econometric models. Of the seven statistical tests, four are significant, indicating the presence of co-integration in all models.

4.4 Regression results

Table 6, Model (1 &2) presents the PMG estimation for our first econometric model. It shows that ICTindex has a positive and significant effect on our both green trade proxies, GTDI and GTDI2. This finding aligns with affordance theory, which posits that ICT provides key action potentials such as, the affordances for global market access, supply chain transparency, and operational optimization, that enable green trade development. The ECT (model 1) is −0.607 (p < 0.01), indicating about 60.7% of the disequilibrium is corrected annually, confirming H1. It implies that ICT boosts green trade development by making eco-friendly business practices easier and cheaper. Precisely, digital tools help countries find greener suppliers/customers, and connect with global sustainability markets more efficiently (Alshatter and Mubarak, 2024). The long-run effect of GDP on GTDI is also significant (0.00551, p < 0.10). However, renewable energy and labour force are insignificant. In both models, short-run coefficients are insignificant. One plausible reading is that the effects of ICT and other factors on green trade materialize over time rather than immediately, consistent with the time typically required to build infrastructure and adjust trade patterns; however, the null short-run result is also consistent with limited short-run variation in the panel or insufficient statistical power at that horizon, and the data here cannot distinguish between these explanations.

In model 3-5 we separately run PMG model to examine the effects of ICT infrastructure on individual components of green trade development index. In long-run, ICTindex, GDP, and renewable energy (Rnew) all have positive and significant effects on total green trade development component, in contrast to the composite GTDI model above where GDP was only marginally significant and Rnew was not significant at all—a divergence worth noting explicitly, since it means Rnew’s role depends on which component of green trade is examined. Specifically, 1-unit increase in ICTindex is associated with a 0.229-unit increase in TGT. The stronger effect on TGT compared to the composite index suggests that ICT’s affordances for market access and logistics optimization are most readily actualized for increasing the volume of green trade, which is a direct, quantitative outcome.

In the short run, both ICTindex and GDP show significant positive effects (0.171 and 1.138, respectively). The significant short-run effects for TGT might reflect that total green trade can react more quickly to changes in ICT and economic growth, perhaps because of existing trade networks and immediate demand. Furthermore, ICTindex has a negative effect (−0.0198, p < 0.10) on COMGT, suggesting that higher ICT development might reduce the comparative advantage in green trade development. This could be because ICT facilitates trade in other sectors more than green goods, or because it increases competition.

In the context of international collaborations (INTCOL), both ICTindex and GDP have positive effects (0.950, p < 0.05 and 1.582, p < 0.10, respectively). This suggests that ICT development and economic growth foster international collaborations in environmental technology. However, in the short run, all variables are insignificant. This is consistent with international collaborations taking time to form and not responding immediately to changes in ICT or GDP. Labor force is consistently insignificant across models. This may indicate that labor force size is not a key driver of green trade in this sample, though we cannot rule out that the variable is not well suited to capturing the relevant channel (e.g., labor quality or skill composition may matter more than raw size, but this study does not test that directly).

In summary, the PMG results confirm a significant long-run association between ICT infrastructure and green trade development, where digital adoption is associated with higher levels of sustainable trade and international eco-innovation collaborations. However, short-run effects are largely insignificant, which we interpret cautiously as possibly reflecting implementation lags, while acknowledging the panel’s short-run power limitations noted above. The rapid error correction (28%–64% annually) indicates that deviations from the long-run ICT–green trade relationship are resolved relatively quickly, a pattern relevant to policy discussions framed around SDG-9 and SDG-12, though the coefficient itself measures statistical adjustment speed, not SDG attainment.

Table 7 presents the results of contemporaneous relationship between ICT and two green trade development proxies using fixed effects with Driscoll-Kraay Standard Errors estimates. Model-1 establishes that ICT infrastructure significantly enhances the green trade development index (p < 0.01). This relationship persists when controlling for Rnew, Labor and GDP, demonstrating that digital transformation systematically is associated with sustainable trade development. The positive ICT-GTDI relationship operates through two channels the data allow us to distinguish: (1) facilitating green productivity and (2) enabling cross-border green trade (Sheng, Zhu, and Chen, 2024).

Disaggregated analysis reveals ICT’s differential impacts across GTDI components (Model 3-5). First, ICT penetration significantly boosts total green trade volume as digital platforms expand market access for eco-friendly goods. Second, ICT strongly supports international green patent collaborations (p < 0.01), accelerating joint R&D and technology transfer for environmental innovations. However, ICT shows no significant effect on competitive advantage in green trade (COMGT), echoing the marginal (p < 0.10) negative long-run effect found in the PMG estimates above rather than contradicting it outright, suggesting that while digital tools enable trade flow, they don't automatically confer comparative advantage.

Interestingly, the short-run estimates obtained from the ARDL framework in Table 6 differ from the contemporaneous results presented in Table 7, because ARDL explicitly incorporates dynamic adjustments and lagged effects, capturing how variables respond over time rather than only within the current period. In contrast, the fixed effects with Driscoll-Kraay Standard Errors estimates contemporaneous relationships, ignoring intertemporal dynamics and adjustment processes. Moreover, ARDL accounts for error-correction mechanisms and past disequilibria, which can alter the magnitude and significance of short-run coefficients. Hence, differences arise due to the inclusion of temporal dynamics and adjustment paths in ARDL that are absent in the fixed effects specification.

Table 8 presents the association between green trade development index and individual ICT components in OECD countries, revealing positive associations that are consistent with the SDG-9/SDG-12 constructs this study operationalizes. Findings (model 1-3) confirm that all ICT components (internet usage (IUT), mobile subscriptions (MCS), and fixed broadband (FBS) exhibit significant positive long-run relationships with green trade development, validating ICT infrastructure as a factor positively associated with sustainable trade patterns in OECD economies. Crucially, short-run effects are insignificant, which may indicate structural lags in converting digital capacity into green trade outcomes, though as with the results in Table 6, this interpretation is tentative given the limits of a short-run panel test. While rapid error correction (ECT ≈ −0.6) indicates that deviations from the long-run relationship between ICT components and GTDI are corrected roughly 60% faster than is typical for the labor and GDP variables in these models.

Table 9 presents the results of fixed effects model with Driscoll-Kraay Standard Errors estimates. All three ICT measures demonstrate statistically significant (p < 0.01) positive relationships with GTDI. These results are consistent with the view that digital infrastructure development and universal connectivity are associated with sustainable trade expansion.

The interaction term between ICT development and environmental regulations (ICTER) demonstrates a multifaceted influence on green trade development outcomes in OECD economies. As presented in Table 10, ICTER exhibits a strong positive long-run association with the composite green trade development index (Model 1 & 2) and TGT (Model 3). This pattern is consistent with affordance theory: environmental regulations fundamentally reshape organizational goals, creating a context where the perceived utility of ICT’s affordances for green purposes is heightened. This is consistent with the view that digital infrastructure amplifies the effectiveness of environmental regulations (Ma et al., 2023), likely through enhanced monitoring of supply chains (e.g., blockchain carbon tracking) and streamlined compliance for green exporters. Moreover, the coefficient for ICTER (0.00517) is higher than the standalone ICT effect (0.00314) presented in Table 6, indicating that environmental governance is associated with an enhancement of ICT’s efficacy for policy design. Interestingly, ICTER significantly reduces green comparative advantage (COMGT: β = −0.0332***, Model 4), suggesting that stringent regulations, even when digitally enabled, may initially impede comparative advantage because of higher production cost associated with environmental regulations, such as carbon tax and waste management restrictions.

Notably, the rapid error correction indicates that 59% of ICTER-related disequilibrium resolves annually—faster than adjustments to labour or GDP shocks. This is consistent with digitally enabled regulatory compliance adjusting relatively quickly once initial barriers are overcome—a pattern relevant to SDG-12 policy discussions, though the ECT coefficient measures adjustment speed in the model, not SDG attainment itself.

Table 11 presents the outcome of fixed effect model with Driscoll-Kraay Standard Errors estimates. The interaction term (ICTER) shows a statistically significant positive effect on GTDI (Model 1–2), supporting H2 and demonstrating that stringent regulations are associated with an amplification of ICT’s impact on green trade. Notably, once again the coefficient for ICTER (0.136) exceeds the standalone ICT effect (0.119 in Table 7). Disaggregated analysis (Model 3–4) confirms this pattern for both total green trade development and international green patent collaborations, underscoring regulations’ role in fostering cross-border innovation.

Figure 5 illustrates the moderating role of environmental regulations in the relationship between ICT and the Green Trade Index. The upward-sloping lines indicate that higher levels of ICT are associated with greater green trade. Moreover, the steeper slope under high environmental regulations demonstrates that the positive impact of ICT on green trade is amplified as environmental regulations become more stringent. The widening gap between the predicted values at higher levels of ICT further suggests that environmental regulations strengthen the effectiveness of ICT in promoting green trade. These graphical findings are consistent with the positive and statistically significant coefficient of the interaction term (ICTER), thereby supporting the study’s moderation hypothesis.

FIGURE 5

4.4.1 Green trade and CO₂ emissions

Tables 12,13, demonstrates the climate mitigation potential of green trade development, revealing that both the composite GTDI and its total green trade (TGT) component exhibit statistically significant negative relationships with CO₂ emissions (p < 0.05 and p < 0.01, respectively), thereby supporting H3. This is consistent with the SDG-13 climate-action framework this study is situated within, though the regression result itself demonstrates a statistical association, not SDG attainment. This outcome is also consistent with the theorized environmental actualization of green trade’s affordances discussed in Section 2.1. These results highlight a dual mechanism: (1) On the supply side, increased exports of environmental goods incentivize domestic producers to adopt cleaner production methods, reducing national emissions (Pantelaiou et al., 2020); (2) On the demand side, imports of eco-friendly products (e.g., energy-efficient appliances, electric vehicles) directly lower consumption-based emissions (Zhang et al., 2024). Both mechanisms are enabled by the prior actualization of ICT’s affordances, which made these green trade flows efficient and verifiable. The consistency across aggregate (GTDI) and disaggregated (TGT) measures underscores that green trade trade is associated with a viable pathway for OECD nations to decouple economic activity from environmental harm. While the insignificant ECT in Model 2 of Table 12 (which includes FDI and EF) might be due to the inclusion of these controls affecting the dynamics.

In summary, these findings are consistent with the Paris Agreement’s emphasis on trade as a tool for climate mitigation, while empirically validating the theoretical link between SDG 12 (Responsible Consumption) and SDG 13 (Climate Action) that this study’s framework proposes—a support relationship, not a proof of the SDG link itself.

5 Conclusion

The 2030 Agenda for Sustainable Development explicitly recognizes the interconnected nature of SDGs, where progress in one goal often depends on advancements in others. This study bridges critical dimensions of sustainable development by examining how ICT advancement (SDG-9), reinforced by stringent environmental regulations (SDG-12.1), fosters green trade development (SDG-12), and how such trade, in turn, mitigates CO₂ emissions (SDG-13) in OECD countries (2002–2021). By positioning this investigation within the ICT paradigm, we engage directly with the field’s central dialectic on technology’s dual potential for both empowerment and disruption. Our findings are interpreted through the lens of Affordance Theory, which elucidates how the action potentials (affordances) of ICT are actualized to achieve sustainability goals.

5.1 Key findings

Three empirical results anchor this study’s conclusions, each tied to a specific test reported in Section 4.First, we establish that ICT infrastructure development is positively and significantly associated with green trade development, both in the long run (PMG: ECT = −0.607, p < 0.01) and contemporaneously (fixed effects with Driscoll-Kraay SE: p < 0.01), providing key affordances for global market access, supply chain transparency, and operational optimization. Second, our findings show that environmental regulations significantly strengthen ICT’s positive impact on green trade development—the ICTER interaction coefficient (0.00517 in the PMG model; 0.136 in the fixed-effects model) exceeds the standalone ICT coefficient (0.00314 and 0.119, respectively) in both specifications. This aligns with affordance theory’s emphasis on context, demonstrating that regulations are a critical catalytic factor that sharpens the perception of ICT’s green affordances and increases the incentive for their actualization. This same interaction, however, significantly reduced comparative advantage in green trade in the short run (COMGT: β = −0.0332, p < 0.01), indicating the relationship is not uniformly positive across all green trade components and should not be overstated as such Third, we find that green trade development is negatively and significantly associated with CO₂ emissions in our OECD panel Read together, these three results are consistent with—though they do not on their own establish—the theorized ICT-affordance-to-emissions pathway set out in Section 2.1; the study tests statistical association across three linked models rather than observing the full causal chain directly.

5.2 Contributions

Against the gap identified in Sections 1 and 2 — that ICT-trade and ICT-environment linkages have been studied separately, and that green trade research has focused on consequences rather than antecedents—this study’s specific contribution is empirical evidence for the ICT-to-green-trade linkage itself, and for environmental regulation’s moderating role in that link, in an OECD panel. This is a narrower and more precisely bounded claim than asserting the perspectives are wholly undiscussed in the literature, and is the claim the results in Section 4 actually support. We introduce a novel composite index designed to quantify national-level green trade development (GTDI), addressing the antecedents gap noted in Section 2.2. To ensure analytical rigor, we conducted extensive diagnostic tests to verify the validity and reliability of our dataset prior to regression analysis. Empirical results from PMG and panel fixed effects with Driscoll-Kraay standard errors estimates demonstrate long-run, short-run and contemporaneous association between dependent and independent variables.

5.3 Policy implications

The study yields three findings, each with a directly linked policy implication, for policymakers. First, ICT infrastructure positively affects the green trade development index, at both the long-run and contemporaneous horizons reported above, which is consistent with the view that traditional infrastructure remains indispensable for sustainable trade alongside modern digital systems (such as, big data centres, cloud platforms, and AI-based analytics). Given this, continued investment in both conventional and digital infrastructure is a reasonable policy priority, though this study measured infrastructure stocks already in place and did not test which specific infrastructure investments would produce the largest marginal gain. Second, stringent environmental regulations amplify the ICT-green trade development relationship, as shown by the larger ICTER coefficient relative to the standalone ICT effect reported above. This finding supports pairing digital-infrastructure investment with environmental regulation rather than pursuing either in isolation, since the interaction—not either variable alone—produced the larger effect. Given the short-run comparative-advantage cost identified above (COMGT: β = −0.0332), policymakers introducing stricter environmental standards may wish to pair them with transitional support for exporters, rather than assuming digitally enabled compliance offsets these costs immediately. This also underscores a core ICT for development principle: that technology’s developmental impact is contingent on supportive governance frameworks. Affordance theory clarifies this principle: governance frameworks define the context that determines whether technological affordances are perceived and actualized for development goals. Third, the inverse relationship between green trade development and carbon emissions found in this study’s OECD panel identifies sustainable trade as a candidate pathway to net-zero targets worth further testing beyond this sample, rather than a general pathway established for all economies. These results are consistent with a ICT-green trade-emissions relationship that integrates: (1) infrastructure policy (SDG-9), (2) regulatory frameworks, and (3) green trade mechanisms (SDG-12), in the specific context of the policy question this study tests—the CO2 outcome (SDG-13).

Unequivocally, the reliability of the data used in the current research and the econometric techniques employed has meaningful implications for the policymakers in OECD countries. However, there are certain limitations that can reduce the generalizability of the empirical outcomes. This study has several limitations that provide opportunities for future research. First, although ICT is measured using a composite index based on traditional digital infrastructure indicators, future studies could incorporate more advanced measures of digital transformation, such as data centre capacity, big data infrastructure, digital payment systems, e-commerce platform sophistication, and artificial intelligence adoption. Second, environmental regulations are proxied by the OECD Environmental Policy Stringency Index, which, despite being widely used, may not fully capture regulatory enforcement effectiveness, may underrepresent command-and-control regulatory instruments, and may not specifically reflect regulations targeting green trade. Future research could therefore employ alternative or more comprehensive measures of environmental regulation. Third, future studies may investigate the effects of major global disruptions, such as the global financial crisis and the COVID-19 pandemic, using structural break or crisis-specific analyses to examine whether these events alter the observed relationships. Finally, additional heterogeneity analyses based on income groups, regional classifications, or other country-specific characteristics, as well as the application of alternative dynamic empirical approaches, could further enhance understanding of the complex relationships among ICT, environmental regulations, green trade development, and environmental sustainability.

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