Rewiring Sustainability: How Digital Transformation and Fintech Innovation Reshape Environmental Trajectories in the Industry 4.0 Era

Abstract

This study investigates the long-run impact of digital transformation and fintech innovation on environmental sustainability across OECD countries from 1999 to 2024. Drawing on a novel empirical framework that integrates panel fully modified ordinary least squares, the system-generalized method of moments, and machine learning estimators, the analysis captures both linear and nonlinear dynamics while addressing heterogeneity, endogeneity, and structural complexity. Environmental sustainability is measured by per capita CO₂ emissions, while digital transformation and fintech innovation are proxied by secure internet servers and G06Q patent applications, respectively. The findings reveal that both digital infrastructure maturity and fintech-driven innovation significantly reduce carbon emissions, suggesting that technologically advanced digital ecosystems serve as effective instruments for climate mitigation. Robustness checks via the system-generalized method of moments confirm the stability of these relationships, while machine learning models—Random Forest and XGBoost—highlight digital variables as top predictors of emissions reduction. The convergence of results across estimation methods underscores the reliability of the digital–environmental nexus. Policy implications emphasize the need to embed sustainability metrics into digital strategies, promote green fintech regulation, and prepare labor markets for Industry 4.0 transitions. These findings position digital and fintech innovation not merely as enablers of economic growth, but as structural levers for achieving environmentally sustainable development in high-income economies.

1. Introduction

The accelerating convergence of digital technology and environmental policy is redefining the architecture of sustainable development across advanced economies. As global challenges intensify—ranging from carbon lock-ins to decarbonization inertia—governments and industries are increasingly turning to digital systems and fintech innovation as structural levers for environmental reform. In the context of OECD countries, where technological capacity and institutional maturity offer fertile ground for transformation, digitalization is no longer a peripheral force but a central driver in the pursuit of climate-resilient growth. Recent research underscores the potential of digital infrastructure and fintech innovation to reshape energy systems, industrial processes, and behavioral dynamics (Sun et al. [1]; Hossain et al. [2]; Hou et al. [3]). Yet despite mounting interest, the long-term structural impact of digital systems on environmental sustainability remains underexplored, especially in macroeconomic settings characterized by complexity, heterogeneity, and institutional asymmetries.

To fully appreciate the practical significance of digital transformation in achieving environmental sustainability, it is essential to consider concrete policy and corporate practices within OECD countries. For instance, Denmark’s “Digital Hub Denmark” initiative has notably integrated digital platforms and fintech innovations to systematically track carbon emissions across multiple industries, enabling precise real-time monitoring and facilitating regulatory compliance. Similarly, Germany’s implementation of blockchain-based green finance solutions, such as the Green Assets Wallet, illustrates how fintech innovation concretely promotes investment transparency and accelerates funding towards renewable energy projects. In the corporate realm, France’s multinational Schneider Electric leverages sophisticated digital systems—encompassing IoT-enabled smart grids and AI-driven energy management solutions—to significantly enhance resource efficiency and achieve measurable carbon reductions in manufacturing and logistics operations. These targeted examples elucidate how specific digital initiatives not only align with broader macroeconomic strategies, but also deliver tangible environmental outcomes, validating digital transformation as an integral, practical tool for sustainable development. Such cases underscore the indispensable role of robust institutional frameworks, adaptive regulatory environments, and proactive corporate engagement in actualizing the theoretical environmental benefits attributed to digitalization.

While the environmental benefits of digitalization are widely theorized, ranging from optimization of resource flows to the dematerialization of economic activity, empirical insights into these relationships are fragmented and inconclusive. A persistent knowledge gap exists at the intersection of digital infrastructure, fintech innovation, and macro-level sustainability outcomes. Most extant studies rely on short-run, single-country analyses or adopt narrow proxies that overlook the systemic nature of digital change. Moreover, prevailing econometric approaches often fail to accommodate the endogenous feedback loops and structural heterogeneity that typify OECD economies. These limitations obscure our understanding of whether and how digital transformation catalyzes long-term environmental improvements in practice. In particular, the distinction between foundational digital capacity (e.g., secure internet infrastructure) and frontier innovation (e.g., G06Q-class fintech patents) has rarely been made explicit in empirical work. This analytical omission is consequential: while both dimensions are presumed to be beneficial, their mechanisms of impact may diverge substantially and interact with policy, trade, and labor market dynamics in nontrivial ways. Closing this gap is critical for designing evidence-based digital strategies aligned with sustainability goals.

This study addresses these challenges by investigating the impact of digital transformation and fintech technological innovation on environmental sustainability across OECD countries between 1999 and 2024. It introduces a novel empirical framework that integrates fully modified ordinary least squares (FMOLS), system-generalized method of moments (GMM), and machine learning estimators—thereby capturing both linear and nonlinear dynamics while controlling for endogeneity, heterogeneity, and structural complexity. Environmental sustainability is proxied by per capita CO₂ emissions, a theoretically grounded and policy-relevant indicator in climate economics. Digital transformation is measured through the density of secure internet servers, reflecting infrastructural readiness, while fintech innovation is proxied by G06Q patent filings, capturing the evolution of algorithmic finance and data-centric economic coordination. This dual-indicator design enables a multidimensional exploration of the digital–environmental nexus, disentangling infrastructure-driven and innovation-led mechanisms. The methodological architecture is equally innovative: by combining econometric and machine learning approaches, the study not only estimates causal relationships but also ranks variable importance and uncovers nonlinear effects—capabilities often overlooked in conventional environmental macroeconomics.

The results offer compelling evidence that both digital infrastructure maturity and fintech-driven innovation exert significant and persistent downward pressure on CO₂ emissions across the OECD. These effects remain robust across multiple estimation techniques and are confirmed by ensemble machine learning models, which identify digital variables among the most influential predictors of emissions reduction. By distinguishing between types of digitalization and embedding them within a rigorous macro-panel framework, the study advances a more granular and generalizable understanding of digital sustainability pathways. The findings contribute theoretically by reconceptualizing the role of digital systems in environmental governance, not merely as tools for efficiency gains but as synergistic, structurally embedded enablers of sustainability transformation. Building upon the notion of technological complementarity, this study posits that foundational digital infrastructure (e.g., secure internet servers) and frontier fintech innovations (e.g., G06Q-class patents) interact to produce amplified environmental effects. These interactions facilitate the deployment of real-time emissions tracking, algorithmic sustainability assessments, and decentralized green finance mechanisms, thereby generating systemic efficiencies and scaling the governance of carbon-intensive activities. Simultaneously, the framework incorporates institutional mediation as a critical lens, recognizing that the environmental impacts of digital–financial architectures are contingent upon regulatory capacity, governance integrity, and policy coherence. Strong institutional environments not only accelerate the uptake of clean technologies through credible incentives and oversight but also mitigate rebound effects and market failures that may otherwise dilute sustainability gains. Together, these mechanisms position digital and fintech ecosystems as co-evolving instruments of decarbonization—whose efficacy depends not only on their technical sophistication but also on the institutional scaffolding within which they are embedded.

In sum, this research makes four key contributions. First, it empirically validates the sustainable decarbonization potential of digital systems in technologically advanced economies, addressing a major blind spot in current sustainability science. Second, it introduces a dual-measure framework that distinguishes between infrastructure and innovation, enabling a more nuanced interpretation of digital impacts. Third, by incorporating machine learning alongside econometric methods, the study bridges causal inference with predictive analytics, offering a blueprint for future interdisciplinary research. Finally, it provides actionable insights for policymakers seeking to harmonize digital and environmental agendas in the face of mounting climate urgency. As OECD countries continue to navigate the twin transitions of digitalization and decarbonization, the evidence presented here affirms that environmental sustainability is not a byproduct, but a plausible outcome of intentional, well-governed technological change. Moreover, this study aims to address two primary research questions: (1) How do digital transformation and fintech innovation influence long-term environmental sustainability in OECD economies? (2) What are the distinct impacts of foundational digital infrastructure compared to frontier fintech innovations on CO₂ emissions per capita? Correspondingly, the hypotheses guiding this investigation are: H1: Enhanced digital infrastructure (proxied by secure internet servers) is associated with a significant reduction in CO₂ emissions per capita. H2: Higher fintech innovation activity (proxied by G06Q patent applications) contributes significantly to reductions in CO₂ emissions per capita”.

2. Literature Review

The intellectual trajectory at the intersection of technological modernization and environmental sustainability has undergone profound conceptual evolution over the past three decades. Foundational work grounded in ecological modernization theory emphasized that innovation, reflexive governance, and market-based mechanisms could effectively decouple economic growth from environmental degradation (Hasan and Tucci [4]; York and Venkataraman [5]; Van den Bergh et al. [6]; Horbach et al. [7]; Saboori and Sulaiman [8]). This theoretical optimism underpinned early policy frameworks, particularly in high-income economies, focused on regulatory incentives and technological leapfrogging. However, the digital revolution has introduced new structural complexities into the sustainability discourse. Initially viewed as a catalyst for productivity (Ghosal, [9]; Bertrand and Capron, [10]), digital transformation is now increasingly recognized as a systemic force capable of restructuring entire production and governance architectures (Guandalini [11]; Horcea-Milcu [12]). This recognition has led to the emergence of the “digital–environmental nexus,” which posits that data-intensive systems, such as real-time monitoring platforms, algorithmic control networks, and automated sustainability dashboards, can enhance resource efficiency, shift consumption patterns, and facilitate greener financial intermediation (Feroz et al. [13]; Raihan, [14]). In parallel, fintech innovation has been reframed as a transformative institutional layer that enables more inclusive access to green capital, scalable carbon pricing instruments, and enhanced environmental accountability (Muhammad et al. [15]; Tu, [16]; Uddin et al. [17]).

However, recent critiques have challenged the unilateral optimism of ecological modernization by introducing critical frameworks that highlight systemic contradictions. Scholars advancing degrowth, rebound, and digital sobriety perspectives have warned that digitalization, while capable of dematerializing some forms of production, may simultaneously generate new environmental externalities. These include the proliferation of energy-intensive infrastructures such as AI training facilities, blockchain networks, and data center clusters, all of which impose significant and often underreported carbon costs. The rebound effect, where efficiency gains lead to increased aggregate consumption, further complicates the assumption that digital innovation necessarily reduces net emissions. These concerns are particularly salient in the context of high-tech OECD economies, where digital maturity can simultaneously enable both decarbonization and digital overreach. Within this contested terrain, two parallel research strands have emerged. One focuses on foundational digital capacity, e.g., broadband infrastructure, secure server density, and cybersecurity readiness, as enablers of systemic efficiency; the other investigates frontier innovation, including algorithmic finance, blockchain applications, and AI-driven optimization, as the next frontier of ecological modernization. Attempts to integrate these paradigms can be seen in models such as the “twin transition” framework (Bianchini et al. [18]; Kovacic et al. [19]; Burinskienė and Nalivaikė, [20]) and the “smart sustainability” paradigm (Kar et al. [21]; Gomez-Trujillo and Gonzalez-Perez, [22]), yet these efforts remain largely normative and are rarely tested in comparative macroeconomic settings.

Empirical research on the digital–environmental nexus has proliferated, albeit unevenly, across methodological domains. The dominant tradition remains econometric, with fixed-effects panel models employed to detect associations between ICT variables and CO₂ emissions (Ostadzad, [23]; Li et al. [24]). Dynamic panel techniques, including system-GMM (Ullah et al. [25]; Karimi et al. [26]), have been widely adopted to address endogeneity and serial correlation in energy–environment regressions (Kuziboev et al. [27]; Ganda, [28]). More recently, cointegrated panel models—especially FMOLS and DOLS—have emerged as robust tools for uncovering long-run digital impacts (Olorogun, [29]; Shaari et al. [30]). Yet, these econometric approaches remain constrained by linearity assumptions and limited sensitivity to nonlinear feedback loops. To address these limitations, machine learning models such as Random Forests and XGBoost have gained traction for their ability to model high-dimensional, nonlinear interactions (Malashin et al. [31]; Chen et al. [32]). However, few studies have integrated ML with causally identified estimators, leading to a methodological bifurcation between prediction and inference. Natural experiments and instrumental variable approaches—common in environmental policy analysis—remain rare in this domain, largely due to data limitations and the systemic nature of digital transitions. Likewise, spatial econometrics and difference-in-differences frameworks have yet to be widely mobilized to identify heterogeneous treatment effects of digital or fintech shocks across time and space (Nenavath, [33]; Wei et al. [34]; Li and Niu, [35]). As a result, the field is characterized by a growing methodological pluralism but also a notable fragmentation, where robust causal inference and structural modeling remain the exception rather than the rule.

Despite its momentum, the literature remains beset by several structural blind spots. First, most empirical studies adopt proxy variables, such as internet penetration or mobile subscriptions, that fail to capture the layered architecture of digital systems (Wang and Xu, [36]; Quaglione et al. [37]; Edquist and Bergmark, [38]). This leads to a conflation of digital maturity with technological innovation, ignoring the qualitative divergence between infrastructural readiness and algorithmic complexity. Second, existing indicators of environmental sustainability are often narrow (e.g., energy intensity), neglecting systemic outcomes such as emissions per capita, ecological footprint, or planetary boundaries compliance (Vanham et al. [39]; Desing et al. [40]). Third, temporal scopes are limited: few studies span more than two decades, and even fewer incorporate lag effects or feedback loops critical for assessing long-run impacts. Fourth, spatial biases persist, with much of the evidence deriving from either single-country (e.g., China, India) or global-pooled models that obscure regional heterogeneity. Within the OECD, institutional variations in green policy regimes, digital governance, and fintech regulation remain underexamined, limiting external validity and policy relevance (He et al. [41]; Gao et al. [42]). Finally, there is limited theoretical integration between the digital and environmental domains. Most studies are empirically driven and fail to engage with the normative dimensions of sustainability or the techno-political economy of digital ecosystems. These omissions not only limit explanatory power but also constrain the formulation of coherent digital sustainability strategies capable of informing public policy at scale.

Addressing critical gaps identified within the existing literature, this study reconceptualizes digital transformation and fintech innovation as distinct yet interrelated structural forces that collectively shape long-term environmental outcomes. Specifically, it differentiates between foundational digital infrastructure, proxied by the density of secure internet servers, and frontier fintech innovation, measured through the volume of G06Q-class patent applications. By adopting this dual analytical framework, the study responds directly to contemporary critiques calling for more nuanced examinations beyond techno-optimistic narratives, thereby providing a granular understanding of the diverse pathways through which digital ecosystems influence environmental sustainability (Péréa et al. [43]; Marcos, [44]).

Integrating methodologically robust approaches, including FMOLS, system-GMM, and machine learning techniques, this research effectively bridges causal inference with predictive analytics, thereby accommodating cross-sectional heterogeneity, endogeneity concerns, and nonlinear dynamics inherent to macroeconomic datasets. The empirical analysis leverages a comprehensive panel dataset spanning OECD nations, rigorously examining the impact of digital–financial systems on per capita CO₂ emissions, while carefully controlling for renewable energy consumption, carbon pricing policies, and Industry 4.0 employment dynamics. Consequently, this investigation generates actionable insights, underscoring the role of digital–financial architectures not merely as facilitators, but as pivotal structural levers in the pursuit of decarbonization and climate-aligned economic development within advanced economies. Explicitly, two core hypotheses are formulated and tested to clarify these critical causal relationships: Hypothesis 1 (H1): Enhanced digital infrastructure, as measured by secure internet server density, significantly reduces per capita CO₂ emissions across OECD countries. Hypothesis 2 (H2): Increased fintech innovation activity, indicated by the volume of G06Q-class patent applications, significantly contributes to reductions in per capita CO₂ emissions within OECD economies. By clearly delineating these hypotheses, this study provides a robust theoretical and empirical foundation for understanding how digitally advanced infrastructures and innovative fintech practices intersect to facilitate meaningful environmental improvements, thereby enriching the discourse on sustainable transitions in the Industry 4.0 era.

3. Variables and Method

3.1. Variables

Dependent variable: In contemporary environmental economics and sustainability science, identifying a scientifically valid and policy-relevant indicator for environmental degradation remains a foundational task. Among the range of available metrics, carbon dioxide emissions per capita (in metric tons) have gained prominence as the most empirically consistent and theoretically grounded proxy for assessing environmental sustainability, particularly across advanced economies such as those within the OECD. This variable captures not only the aggregate scale of emissions but also adjusts for population size, thereby enabling equitable cross-national comparisons that reflect the true intensity of anthropogenic pressure on the planet’s climate systems. Rooted in the dynamics of fossil fuel combustion, industrial production, and consumption behavior, per capita CO₂ emissions offer a direct and measurable expression of the structural drivers of climate change. The Intergovernmental Panel on Climate Change in 2023 underscores the analytical importance of per capita emissions in its Sixth Assessment Report, particularly in relation to climate equity, accountability, and differentiated responsibilities among nations. Recent high-impact studies, such as those by Rockström et al. [45] and Friedlingstein et al. [46], further reinforce the validity of this metric, highlighting its role as a composite reflection of unsustainable energy use, economic structures, and institutional practices. By integrating micro-level behavioral impacts with macro-level environmental outcomes, this measure facilitates a multidimensional evaluation of sustainability trajectories. Given its empirical robustness, theoretical relevance, and normative significance, carbon dioxide emissions per capita are thus employed as the dependent variable in this analysis to evaluate environmental sustainability performance.

Independent variable: To capture the structural and technological underpinnings of digital transformation and fintech innovation within OECD economies, this study integrates two theoretically robust and empirically validated indicators as independent variables. The first, Secure Internet Servers per million people, as reported by the World Bank’s World Development Indicators, serves as a proxy for digital infrastructure maturity, and it is also supported by Huang et al. [47]. This metric reflects a country’s institutional and technological capacity to facilitate encrypted communication, secure data exchanges, and trustworthy digital services, which are prerequisites for digitally mediated governance in both economic and environmental domains. Recent empirical work underscores the positive association between secure server density and broader metrics of digital capability, including cybersecurity resilience, financial sector modernization, and regulatory effectiveness (Du and Wang, [48]). The second proxy, annual patent applications in IPC class G06Q, captures innovation intensity within the domain of digital data processing technologies for business and finance. This class, tracked by WIPO and the OECD Patent Database, encompasses advancements in algorithmic finance, intelligent payment systems, and data-driven economic coordination. G06Q patent activity has been recognized as a leading indicator of fintech sophistication and innovation-led financial infrastructure development (Chen et al. [49]). Together, these indicators offer a multidimensional lens on digital advancement, spanning foundational infrastructure and frontier innovation. Their inclusion enables a rigorous assessment of how digital evolution shapes environmental sustainability in technologically advanced economies.

Control variable: To rigorously investigate the structural determinants of environmental sustainability within advanced economies, particularly those in the OECD, it is essential to incorporate a theoretically grounded and multidimensionally informed set of control variables. This study adopts five critical covariates, each rooted in contemporary research spanning ecological macroeconomics, digital–industrial transformation, and low-carbon development paradigms. First, renewable energy consumption is introduced as a core proxy for decarbonization potential embedded in a nation’s energy structure. Empirical evidence suggests that a systematic transition toward renewables significantly reduces carbon emissions through substitution effects, while concurrently enhancing energy resilience and fostering green innovation spillovers (Abban et al. [50]). In parallel, green capital formation, measured as the ratio of environmental infrastructure investment to GDP, captures the extent to which economies embed ecological externalities within long-term productive capital, thus reflecting structural readiness for sustainable growth (Ai et al. [51]). Additionally, carbon pricing coverage, defined as the proportion of total emissions subjected to market-based pricing mechanisms, introduces a policy-sensitive dimension that reflects the institutional commitment to internalizing environmental costs. Prior studies have identified its role in constraining emissions intensity and catalyzing low-carbon investment under robust regulatory regimes (Steinebach et al. [52]). On the technological frontier, high-tech export intensity serves as a proxy for industrial upgrading and knowledge-based competitiveness. A greater share of advanced, low-emission manufacturing output is often correlated with improved environmental outcomes, particularly in digitally enabled and innovation-driven trade structures. Finally, Industry 4.0-intensive employment, which denotes the share of the workforce engaged in AI, automation, and data-centric industries, captures labor market adaptation to low-carbon transformation. It reflects both production-side adoption of smart, cleaner technologies and the human capital reconfiguration necessary for sustainable industrial ecosystems. Together, these five variables constitute a robust and policy-relevant framework to control for the complex structural, technological, and institutional heterogeneity that shapes environmental performance in digitally advanced economies. Detailed definitions and data sources for each variable are presented in

Table 1. Results of variable description.

Variable	Form	Definition	Source
Environmental sustainability	$\mathrm{s}\mathrm{u}\mathrm{s}$	CO2 emissions per capita (metric tons) in log	World Bank WDI; Global Carbon Atlas
Digital transformation	$\mathrm{d}\mathrm{i}\mathrm{g}$	Secure Internet Servers per million people in log	World Bank WDI
Fintech technological innovation	$\mathrm{f}\mathrm{i}\mathrm{n}$	Annual patent applications (IPC class G06Q) in log	WIPO Statistics; OECD Patent Database
Renewable energy	$\mathrm{r}\mathrm{e}\mathrm{e}$	Renewable energy consumption (% of total energy use)	IEA Renewables; World Bank WDI
Green capital formation	$\mathrm{g}\mathrm{c}\mathrm{f}$	Environmental fixed capital formation/GDP (%)	OECD Environment Statistics + IEA Green Investment Tracker
Carbon pricing	$\mathrm{c}\mathrm{a}\mathrm{r}$	Carbon-priced emissions/total emissions (%)	OECD Effective Carbon Rates + World Bank Carbon Pricing Dashboard
High-tech export	$\mathrm{h}\mathrm{t}\mathrm{e}$	High-tech exports/total manufactured exports (%)	World Bank WDI + OECD STAN Database
Industry 4.0-intensive employment	$\mathrm{i}\mathrm{i}\mathrm{e}$	Employment in AI, automation, and digital sectors/total employment (%)	Eurostat ICT Employment + OECD Digital Economy Database

.

3.2. Method

Analyzing the complex interplay between digital transformation, fintech innovation, and environmental sustainability across OECD countries necessitates an econometric framework that carefully integrates foundational theories from ecological modernization, digital governance, and innovation economics. Drawing from ecological modernization theory, digital transformation is conceptualized as a systemic enabler, improving resource efficiency and facilitating sustainable production and consumption through enhanced information dissemination and operational optimization. Concurrently, fintech innovation is framed within the context of green finance theory, serving as an institutional catalyst that efficiently mobilizes and allocates financial resources toward environmentally sustainable practices. These theoretical foundations establish clear causal mechanisms underpinning our empirical hypotheses, explicitly connecting digitalization’s capacity to optimize resource allocation and fintech’s role in fostering green investments.

Given the institutional, technological, and policy asymmetries characterizing macroeconomic environments within the OECD, conventional panel data models, which often assume parameter homogeneity across units, may obscure critical country-specific dynamics, leading to biased estimates and compromised policy interpretations. Recent methodological insights (e.g., Pesaran, [53]) underscore that neglecting slope heterogeneity can distort convergence diagnostics and obscure heterogeneous adjustment trajectories, a concern empirically validated by Desbordes et al. [54]. To overcome these challenges, this study adopts a heterogeneous dynamic panel approach that accommodates cross-sectional variation in short-run responses while maintaining the integrity of long-run relationships. Furthermore, to mitigate issues of endogeneity and serial correlation commonly encountered in policy and technological diffusion models, we employ the fully modified ordinary least squares (FMOLS) technique. This method, which is robustly validated in cointegrated panel settings, provides consistent and asymptotically efficient long-run parameter estimates. Thus, the chosen methodological configuration effectively captures the nuanced theoretical expectations regarding how digital infrastructure and fintech innovation distinctly impact environmental performance, ensuring a rigorous and theoretically coherent analytical framework. The structural form of our baseline econometric model is presented in Equation (1).

$${\mathrm{s}\mathrm{u}\mathrm{s}}_{\mathrm{i},\mathrm{t}}={\mathrm{a}}_0+{\mathrm{a}}_1{\mathrm{d}\mathrm{i}\mathrm{g}}_{\mathrm{i},\mathrm{t}}+{\mathrm{a}}_2{\mathrm{f}\mathrm{i}\mathrm{n}}_{\mathrm{i},\mathrm{t}}+{\mathrm{a}}_3{\mathrm{r}\mathrm{e}\mathrm{e}}_{\mathrm{i},\mathrm{t}}+{\mathrm{a}}_4{\mathrm{g}\mathrm{c}\mathrm{f}}_{\mathrm{i},\mathrm{t}}+{\mathrm{a}}_5{\mathrm{c}\mathrm{a}\mathrm{r}}_{\mathrm{i},\mathrm{t}}+{\mathrm{a}}_6{\mathrm{h}\mathrm{t}\mathrm{e}}_{\mathrm{i},\mathrm{t}}+{\mathrm{a}}_7{\mathrm{i}\mathrm{i}\mathrm{e}}_{\mathrm{i},\mathrm{t}}+{ϵ}_{\mathrm{i},\mathrm{t}}$$

(1)

In Equation (1), the subscript $\mathrm{i}$ denotes the cross-sectional unit, referring to individual OECD member states, while $\mathrm{t}$ represents the time dimension, corresponding to the annual observations spanning the study period. The term ${\mathrm{a}}_0$ captures the model’s constant intercept, reflecting baseline environmental sustainability in the absence of explanatory effects. The vector of parameters $[{\mathrm{a}}_1,{\mathrm{a}}_7]$ corresponds to the slope coefficients associated with the independent variables, capturing their marginal effects on the dependent outcome. The stochastic error term ${ϵ}_{\mathrm{i},\mathrm{t}}$ is assumed to be white noise, satisfying classical properties of zero mean, constant variance, and no autocorrelation, thereby allowing for consistent and unbiased estimation. This specification forms the basis for the dynamic panel estimators employed in subsequent sections, where slope heterogeneity, cross-sectional dependence, and endogeneity are accounted for to ensure econometric robustness.

To ensure the validity of subsequent regression analyses and avoid spurious statistical inference, the stationarity properties of the panel data series were first examined. This study applies widely accepted first-generation panel unit root tests, including the LLC and IPS procedures, to evaluate whether the variables exhibit unit root behavior. While the LLC test assumes homogeneity in the autoregressive coefficient across cross-sectional units, the IPS test relaxes this assumption and permits individual unit heterogeneity, thereby enhancing its applicability in structurally diverse macroeconomic contexts such as the OECD. The IPS framework initiates the analysis by estimating ADF regressions independently for each cross-sectional entity, allowing for both intercept and deterministic trend components. It then combines the individual t-statistics to construct an overall panel test statistic, which is particularly effective in panels with relatively small time dimensions (T) and moderate cross-sectional units (N), which are conditions reflective of many environmental and economic datasets. This methodological flexibility enables the IPS test to account for serial correlation and dynamic heterogeneity more effectively than traditional time-series approaches. The stationarity diagnostics derived from these procedures provide the necessary econometric foundation for subsequent model estimation. The underlying regression equations for these tests are specified as follows.

$${\Delta \mathrm{Y}}_{\mathrm{i},\mathrm{t}}={\beta }_{\mathrm{i}}+{\rho }_{\mathrm{i}}{\mathrm{Y}}_{\mathrm{i},\mathrm{t}-1}+\gamma {\mathrm{X}}_{\mathrm{i},\mathrm{t}}+{\sum }_{\mathrm{k}=1}^{\mathrm{n}}{\gamma }_{\mathrm{i},\mathrm{t}-\mathrm{k}}+{\epsilon }_{\mathrm{i},\mathrm{t}}+{\mu }_{\mathrm{i},\mathrm{t}}.$$

(2)

In the context of the IPS test, the null hypothesis (${\mathrm{H}}_0$) posits the presence of a unit root across all cross-sectional units, thereby indicating non-stationarity. The alternative hypothesis (${\mathrm{H}}_1$) allows for heterogeneity in stationarity properties, asserting that at least a subset of the series exhibits stationarity. This structure is particularly appropriate for cross-national panel datasets such as those encompassing OECD countries, where economic and technological asymmetries are prevalent. Formally, for each cross-sectional unit $\mathrm{i}=\mathrm{1,2},\dots ,\mathrm{N}$ and time period $\mathrm{t}=\mathrm{1,2},\dots ,\mathrm{T}$, the test estimates an ADF regression of the form:

$${\check{\mathrm{t}}}_{\mathrm{N}\mathrm{T}}{\sum }_{\mathrm{i}=1}^{\mathrm{N}}{\mathrm{t}}_{\mathrm{i},\mathrm{t}}({\rho }_{\mathrm{i}}·{\gamma }_{\mathrm{i}})$$

(3)

The standardized panel statistic ${\check{\mathrm{t}}}_{\mathrm{N}\mathrm{T}}$, often referred to as the t-bar statistic, serves as the aggregate measure derived from averaging individual ADF test statistics across cross-sectional units. Under the null hypothesis of non-stationarity, this statistic follows a standard normal distribution, enabling conventional inference procedures. One of the distinguishing advantages of the IPS test framework lies in its ability to retain statistical power even when the number of time periods (T) is limited, which is a characteristic particularly beneficial for panels such as those involving OECD economies over medium temporal spans. Moreover, when cross-sectional dependence is suspected, potentially arising from common global shocks or synchronized policy responses, the IPS test can be augmented through a cross-sectionally demeaned specification. This adjustment eliminates the influence of time-varying common factors embedded in the error structure by subtracting the cross-sectional mean from each observation. Such demeaning enhances the reliability of unit root inference in the presence of unobservable global components, thereby improving the robustness of stationarity diagnostics in macro-panel settings. This methodological refinement is especially pertinent in environmental and financial datasets, where international co-movement and structural spillovers are pervasive.

Given the presence of statistically significant cross-sectional dependence (CSD) in Models 1, 2, and 3, as established by the Pesaran [53] test, it becomes imperative to adopt estimation techniques that remain valid under such conditions. To address this, the Westerlund [55] error-correction-based cointegration test is employed. Unlike traditional residual-based panel cointegration tests, Westerlund’s approach directly evaluates the existence of long-run equilibrium relationships by testing whether the error-correction term in a conditional dynamic model significantly deviates from zero. This specification allows for heterogeneous dynamics and avoids reliance on potentially biased residual-based inference in the presence of CSD. The key advantage of the Westerlund framework lies in its robustness to cross-sectional dependence, owing to its structural orientation that does not impose restrictions on unobserved common factors. As demonstrated in recent empirical applications (e.g., Chudik and Pesaran, [56]; Everaert and De Groote, [57]; Juodis, [58]), the test accommodates both individual-specific intercepts and slope coefficients, as well as short-run dynamics, deterministic components, and weakly exogenous regressors. When CSD is confirmed, the standard asymptotic distribution of the test statistic becomes unreliable. In such cases, Westerlund’s bootstrapped version of the test provides corrected critical values and valid inference, thereby ensuring robustness in the presence of contemporaneous correlation across units. Accordingly, the cointegration analysis proceeds by implementing the Westerlund group-mean and panel-mean statistics, both under bootstrapped and asymptotic settings, depending on the extent of detected cross-sectional dependence. The corresponding model structure and test criteria are formally defined in the equations below.

$$\begin{gathered} {\Delta \mathrm{Z}}_{\mathrm{i},\mathrm{t}}=({\gamma }_{\mathrm{i},\mathrm{t}}·{\mathrm{d}}_{\mathrm{i},\mathrm{t}})+{\mathrm{a}}_{\mathrm{i}}({\mathrm{Z}}_{\mathrm{i},\mathrm{t}-1}-{\beta }_{\mathrm{i}}{\mathrm{X}}_{\mathrm{i},\mathrm{t}-1})+\sum {\mathrm{a}}_{\mathrm{i},\mathrm{j}}{\rho }_{\mathrm{i},\mathrm{t}}=\Delta {\mathrm{Z}}_{\mathrm{i},(\mathrm{t}-\mathrm{j})}+ \\ \sum {\theta }_{\mathrm{i},\mathrm{j}}{\rho }_{\mathrm{i},\mathrm{j}}\Delta {\chi }_{\mathrm{i},(\mathrm{t}-\mathrm{j})}+{ϵ}_{\mathrm{i},\mathrm{t}}. \end{gathered}$$

(4)

To assess the long-run equilibrium relationships among variables in the presence of panel heterogeneity and cross-sectional dependence, this study complements the Westerlund [55] framework with the cointegration methodology advanced by Pedroni [59]. In particular, Pedroni’s heterogeneous panel cointegration test is adopted to provide further empirical validation of cointegrating dynamics across OECD economies. This test allows for individual-specific intercepts, slope coefficients, and deterministic components, thereby offering flexibility to account for structural asymmetries inherent in advanced economies. The test specification incorporates a deterministic component ${\mathrm{d}}_{\mathrm{i},\mathrm{t}}$, a parameter vector $\delta \prime$, and an error term ${\mathrm{a}}_{\mathrm{i}}$, capturing both fixed effects and idiosyncratic disturbances. Pedroni’s approach distinguishes between within-dimension (panel) and between-dimension (group) test statistics, including the panel-v, panel-ρ, panel-PP, and panel-ADF statistics, as well as their group analogs. The between-dimension framework averages individual autoregressive coefficients obtained from unit root tests on the residuals of each cross-sectional unit, enabling detection of heterogeneous cointegration relationships across countries. Among these, the one-sided panel-v statistic is particularly informative, as it rejects the null of no cointegration at significantly large positive values, while the other statistics do so at large negative values. Importantly, all test statistics asymptotically follow a standard normal distribution, enabling straightforward statistical inference. By accommodating heterogeneous dynamics and allowing for temporal dependence across units, Pedroni’s methodology enhances the robustness of long-run relationship assessments, especially in settings where structural policies, innovation capacities, and digitalization trajectories differ markedly across national contexts. The relevant estimable forms are presented in Equations (5) through (9).

The panel $\mathrm{v}$-statistic is shown as follows:

$${\mathrm{L}}^2{\mathrm{S}}^{{\displaystyle \frac{3}{2}}}{\mathrm{Z}}_{\check{\mathrm{V}},\mathrm{S},\mathrm{L}}={\mathrm{L}}^2{\mathrm{S}}^{{\displaystyle \frac{3}{2}}}{\mathrm{Z}}_{\check{\mathrm{V}},\mathrm{S},\mathrm{L}}{\displaystyle \frac{1}{{\sum }_{\mathrm{j}-1}^{\mathrm{S}}·\sum _{\mathrm{t}-1}^{\mathrm{L}}{\mathrm{C}}_{11\mathrm{j}}^{\frac{1}{2}}·{\check{\mathrm{u}}}_{\mathrm{j},\mathrm{t}-1}^2}}.$$

(5)

The panel $\rho$-statistic is shown as follows:

$$\mathrm{L}{\mathrm{S}}^{{\displaystyle \frac{1}{2}}}{\mathrm{Z}}_{\check{\rho },\mathrm{S},\mathrm{L}-1}=\mathrm{L}{\mathrm{S}}^{{\displaystyle \frac{1}{2}}}\left({\displaystyle \frac{1}{{\sum }_{\mathrm{j}-1}^{\mathrm{S}}·\sum _{\mathrm{t}-1}^{\mathrm{L}}{\mathrm{C}}_{11\mathrm{j}}^{\frac{1}{2}}·{\check{\mathrm{u}}}_{\mathrm{j},\mathrm{t}-1}^2}}\right){\sum }_{\mathrm{j}-1}^{\mathrm{S}}·{\sum }_{\mathrm{t}-1}^{\mathrm{L}}{\mathrm{C}}_{11\mathrm{j}}^{{\displaystyle \frac{1}{2}}}·{\check{\mathrm{u}}}_{\mathrm{j},\mathrm{t}-1}^2({\check{\mathrm{u}}}_{\mathrm{j},\mathrm{t}-1}^2\Delta {\mu }_{\mathrm{j},\mathrm{t}}-{\vartheta }_{\mathrm{j}}).$$

(6)

The panel PP $\mathrm{t}$-statistic is shown as follows:

$${\mathrm{Z}}_{\check{\mathrm{t}},\mathrm{S},\mathrm{L}}={\displaystyle \frac{1}{{\check{\tau }}_{\mathrm{L},\mathrm{M}}^2{\sum }_{\mathrm{j}-1}^{\mathrm{S}}·\sum _{\mathrm{t}-1}^{\mathrm{L}}{\mathrm{C}}_{11\mathrm{j}}^{\frac{1}{2}}·{\check{\mathrm{u}}}_{\mathrm{j},\mathrm{t}-1}^2}}{\sum }_{\mathrm{j}-1}^{\mathrm{S}}·{\sum }_{\mathrm{t}-1}^{\mathrm{L}}{\mathrm{C}}_{11\mathrm{j}}^{{\displaystyle \frac{1}{2}}}·{\check{\mathrm{u}}}_{\mathrm{j},\mathrm{t}-1}^2({\check{\mathrm{u}}}_{\mathrm{j},\mathrm{t}-1}^{\ast }\Delta {\mu }_{\mathrm{j},\mathrm{t}}-{\vartheta }_{\mathrm{j}}).$$

(7)

The panel ADF $\mathrm{t}$-statistic is shown as follows:

$${\mathrm{Z}}_{\check{\mathrm{t}},\mathrm{S},\mathrm{L}}=\sqrt[2]{{\check{\sigma }}_{\mathrm{L},\mathrm{M}}^2{\sum }_{\mathrm{j}-1}^{\mathrm{S}}·{\sum }_{\mathrm{t}-1}^{\mathrm{L}}{\mathrm{C}}_{11\mathrm{j}}^{{\displaystyle \frac{1}{2}}}·{\check{\mathrm{u}}}_{\mathrm{j},\mathrm{t}-1}^2}{\sum }_{\mathrm{j}-1}^{\mathrm{S}}·{\sum }_{\mathrm{t}-1}^{\mathrm{L}}{\mathrm{C}}_{11\mathrm{j}}^{{\displaystyle \frac{1}{2}}}·{\check{\mathrm{u}}}_{\mathrm{j},\mathrm{t}-1}^2{\check{\mathrm{u}}}_{\mathrm{j},\mathrm{t}-1}^{\ast }\Delta {\mu }_{\mathrm{j},\mathrm{t}}.$$

(8)

The group $\rho$-statistic is shown as follows:

$$\mathrm{L}{\mathrm{S}}^{{\displaystyle \frac{1}{2}}}{\mathrm{Z}}_{\check{\mathrm{V}},\mathrm{S},{\mathrm{L}}^{-1}}={\mathrm{L}}^2{\mathrm{S}}^{-{\displaystyle \frac{1}{2}}}{\sum }_{\mathrm{j}-1}^{\mathrm{S}}·{\displaystyle \frac{1}{\sum _{\mathrm{t}-1}^{\mathrm{L}}·{\check{\mathrm{u}}}_{\mathrm{j},\mathrm{t}-1}^2}}{\sum }_{\mathrm{t}-1}^{\mathrm{L}}·({\check{\mathrm{u}}}_{\mathrm{j},\mathrm{t}-1}^{\ast }\Delta {\mu }_{\mathrm{j},\mathrm{t}}-{\vartheta }_{\mathrm{j}}).$$

(9)

The group PP $\mathrm{t}$-statistic is shown as follows:

$${\mathrm{S}}^{{\displaystyle \frac{1}{2}}}{\mathrm{Z}}_{\check{\mathrm{t}},\mathrm{S},{\mathrm{L}}^{-1}}={\mathrm{S}}^{-{\displaystyle \frac{1}{2}}}{\sum }_{\mathrm{j}-1}^{\mathrm{S}}·{\displaystyle \frac{1}{\sum _{\mathrm{t}-1}^{\mathrm{L}}·{\check{\mathrm{u}}}_{\mathrm{j},\mathrm{t}-1}^2}}{\sum }_{\mathrm{t}-1}^{\mathrm{L}}·({\check{\mathrm{u}}}_{\mathrm{j},\mathrm{t}-1}^{\ast }\Delta {\mu }_{\mathrm{j},\mathrm{t}}-{\vartheta }_{\mathrm{j}}).$$

(10)

The group ADF $\mathrm{t}$-statistic is shown as follows:

$${\mathrm{S}}^{{\displaystyle \frac{1}{2}}}{\mathrm{Z}}_{\check{\mathrm{t}},\mathrm{S},{\mathrm{L}}^{-1}}={\mathrm{S}}^{-{\displaystyle \frac{1}{2}}}{\sum }_{\mathrm{j}-1}^{\mathrm{S}}·\sqrt[2]{{\displaystyle \frac{1}{\sum _{\mathrm{t}-1}^{\mathrm{L}}·{\check{\mathrm{t}}}_{\mathrm{j}}^2{\check{\mathrm{u}}}_{\mathrm{j},\mathrm{t}-1}^2}}}{\sum }_{\mathrm{t}-1}^{\mathrm{L}}·({\check{\mathrm{u}}}_{\mathrm{j},\mathrm{t}-1}^{\ast }\Delta {\mu }_{\mathrm{j},\mathrm{t}}).$$

(11)

Equations (5) through (11) formally define the statistical foundations underlying the heterogeneous panel cointegration framework. From these equations, key test parameters and inference metrics are systematically derived, including the panel and group-based statistics associated with residual dynamics, autoregressive structures, and error correction mechanisms. These parameters capture both the within-unit and between-unit adjustments toward long-run equilibrium, accommodating heterogeneity across intercepts, trends, and lag structures. The derivation of each statistic reflects the model’s sensitivity to cross-sectional heterogeneity and temporal dependence, thereby offering a robust basis for evaluating the existence and strength of cointegrating relationships across the OECD sample.

$${\vartheta }_{\mathrm{j}}={\displaystyle \frac{{\sum }_{\mathrm{Z}=1}^{{\omega }_{\mathrm{j}}}(1-\frac{\mathrm{Z}}{{\omega }_{\mathrm{j}}+1}){\sum }_{\mathrm{j}=\mathrm{Z}+1}^{\mathrm{L}}·{\check{\mu }}_{\mathrm{j},\mathrm{t}}^{\ast }{\check{\mu }}_{\mathrm{j},\mathrm{t}-{\mathrm{Z}}^{\ast }}}{\mathrm{L}}}.$$

(12)

$${\check{\tau }}_{\mathrm{j}}^2={\check{\sigma }}_{\mathrm{j}}^2+2{\vartheta }_{\mathrm{j}}.$$

(13)

$${\check{\tau }}_{\mathrm{L},\mathrm{M}}^2={\displaystyle \frac{\sum _{\mathrm{t}-1}^{\mathrm{L}}·{\mathrm{C}}_{11\mathrm{j}}^{\frac{1}{2}}·{\check{\stackrel{ˇ}{\tau }}}_{\mathrm{j}}^2}{\mathrm{L}}}.$$

(14)

$${\mathrm{C}}_{11\mathrm{j}}^2={\displaystyle \frac{\sum _{\mathrm{t}-1}^{\mathrm{L}}·{\check{\stackrel{ˇ}{\sigma }}}_{\mathrm{j}}^2}{\mathrm{L}}}+{\displaystyle \frac{2\sum _{\mathrm{L}=1}^{\mathrm{L}}(1-1-\frac{{\vartheta }_{\mathrm{j}}}{{\omega }_{\mathrm{j}}+1})·{\check{\stackrel{ˇ}{\sigma }}}_{\mathrm{j},\mathrm{t}}}{\mathrm{L}}}.$$

(15)

$${\check{\sigma }}_{\mathrm{j}}={\displaystyle \frac{\sum _{\mathrm{t}=1}^{\mathrm{L}}{\check{\stackrel{ˇ}{\mu }}}_{\mathrm{j},\mathrm{t}}^2}{\mathrm{L}}}$$

(16)

$${\check{\stackrel{ˇ}{\sigma }}}_{\mathrm{j}}^2={\displaystyle \frac{\sum _{\mathrm{t}=1}^{\mathrm{L}}{\check{\stackrel{ˇ}{\mu }}}_{\mathrm{j},\mathrm{t}}^2}{\mathrm{L}}}.$$

(17)

Once cointegration among the panel variables has been empirically confirmed, the next analytical step involves estimating the long-run equilibrium relationships. Traditional estimators such as ordinary least squares (OLS) are ill-suited for this purpose, as they fail to correct for the endogeneity and serial correlation that frequently characterize cointegrated panels. In this context, dynamic ordinary least squares (DOLS) has gained recognition for enhancing the reliability of long-run parameter estimates by incorporating lead and lag differences of the regressors. However, DOLS retains a notable shortcoming: it imposes homogeneity assumptions across cross-sectional units, thereby overlooking the structural heterogeneity intrinsic to macroeconomic panels comprising diverse economies such as those in the OECD. To overcome these limitations and obtain consistent and asymptotically unbiased long-term coefficients, this study applies the FMOLS technique, as developed by Pedroni [59]. FMOLS corrects for both endogeneity and serial correlation while accommodating individual-specific slope coefficients and intercepts, which are features particularly well-suited for heterogeneous panels with country-level variation. Moreover, the estimator allows for weakly exogenous regressors and varying deterministic trends, thereby increasing its robustness in settings characterized by institutional, technological, and policy asymmetries. Recent applications in sustainability-focused macroeconometric literature further validate FMOLS as a reliable estimator for capturing long-run dynamics between structural indicators and environmental performance metrics. As formalized in Equation (18), this estimation framework facilitates a precise and context-sensitive assessment of how digital transformation, fintech innovation, and accompanying policy variables contribute to environmental sustainability over time.

$${\mathrm{Y}}_{\mathrm{i},\mathrm{t}}={\mathrm{a}}_{\mathrm{i},\mathrm{t}}+({\mathrm{Z}}_{\mathrm{i},\mathrm{t}-1}+{\epsilon }_{\mathrm{i},\mathrm{t}})\beta +{ϵ}_{\mathrm{i},\mathrm{t}}.$$

(18)

Equation (18) presents the formalized structure of the FMOLS estimator, as developed by Pedroni [59], tailored to accommodate heterogeneous panel data settings. This specification adjusts for both endogeneity and serial correlation in the cointegrated system, thereby yielding asymptotically efficient and unbiased long-run coefficient estimates across cross-sectional units with potentially divergent dynamics.

$${\beta }_{\mathrm{F}\mathrm{M}}=\left[{\sum }_{\mathrm{i}=1}^{\mathrm{K}}{\check{\vartheta }}_{221}^2+{\sum }_{\mathrm{T}=1}^{\mathrm{T}}{\left({\mathrm{Z}}_{\mathrm{i},\mathrm{t}}{\check{\mathrm{Z}}}_{\mathrm{t}}\right)}^2\right]·{\sum }_{\mathrm{i}=1}^{\mathrm{K}}{\check{\vartheta }}_{11\mathrm{i}}^1{\check{\vartheta }}_{22}^1\left[{\sum }_{\mathrm{T}=1}^{\mathrm{T}}\left({\mathrm{Z}}_{\mathrm{i},\mathrm{t}}{\check{\mathrm{Z}}}_{\mathrm{t}}\right){ϵ}_{\mathrm{i},\mathrm{t}}·\mathrm{T}·{\check{\gamma }}_{\mathrm{t}}\right].$$

(19)

4. Results and Discussion

4.1. Unit Root Test

To ensure the validity of subsequent econometric estimations and to avoid misleading inferences due to non-stationary behavior, the analysis begins by rigorously examining the unit root properties of each variable included in the study. Given the structural and institutional heterogeneity characterizing OECD countries, panel-based unit root diagnostics must be able to accommodate both cross-sectional independence and individual-specific stochastic trends. To this end, this study employs the LLC and IPS tests, two first-generation unit root tests widely acknowledged for their balance of statistical power and tractability. While the LLC test imposes a homogenous autoregressive structure across cross-sections, the IPS test relaxes this constraint, allowing for heterogeneity in autoregressive parameters, thereby offering a more flexible empirical framework suited to macro-panel data with diverse national trajectories. Both tests are conducted at level and first-difference forms of the series to ensure robustness across specifications. Notably, these tests are particularly useful for datasets with moderate cross-sectional dimension (N) and shorter temporal span (T), which mirrors the data structure typical in environmental–economic analyses across developed nations. Establishing stationarity is a crucial step in validating the dynamic panel model framework, as it determines whether standard inference techniques are appropriate or whether differencing or cointegration methods must be applied. The resulting test statistics and significance levels are reported in

Table 2. Results of unit root test.

Variables	IPS Test		LLC Test
	Level	1st Level	Level	1st Level
$\mathrm{s}\mathrm{u}\mathrm{s}$	−1.487 (0.137)	−5.416 *** (0.000)	−1.321 * (0.093)	−4.928 *** (0.000)
$\mathrm{d}\mathrm{i}\mathrm{g}$	−1.107 (0.181)	−4.782 *** (0.000)	−1.433 * (0.076)	−3.925 *** (0.000)
$\mathrm{f}\mathrm{i}\mathrm{n}$	−1.359 (0.155)	−5.732 *** (0.000)	−1.214 (0.117)	−4.841 *** (0.000)
$\mathrm{r}\mathrm{e}\mathrm{e}$	−0.978 (0.192)	−4.416 *** (0.000)	−1.073 * (0.138)	−3.711 *** (0.000)
$\mathrm{g}\mathrm{c}\mathrm{f}$	−1.249 (0.214)	−5.203 *** (0.000)	−1.002 (0.148)	−4.114 *** (0.000)
$\mathrm{c}\mathrm{a}\mathrm{r}$	−1.607 (0.112)	−4.943 *** (0.000)	−1.383 * (0.089)	−4.209 *** (0.000)
$\mathrm{h}\mathrm{t}\mathrm{e}$	−1.415 (0.129)	−5.154 *** (0.000)	−1.326 * (0.091)	−4.627 *** (0.000)
$\mathrm{i}\mathrm{i}\mathrm{e}$	−1.089 (0.172)	−4.881 *** (0.000)	−1.207 (0.103)	−4.021 *** (0.000)

Note: *** 1% significance level. * 10% significance level. p-value shown in parentheses.

.

The unit root test results presented in Table 2 provide a foundational diagnostic for assessing the stationarity properties of the variables included in the empirical model. As panel data methods are sensitive to the presence of unit roots, particularly in macroeconomic settings characterized by structural diversity, such as OECD member states, it is essential to validate the stochastic properties of each series. Both the LLC and IPS tests are employed to accommodate varying degrees of heterogeneity and serial correlation across units. The findings reveal that all variables are non-stationary at levels but attain stationarity upon first differencing, thereby confirming their integration of order one, I(1). This statistical outcome justifies the subsequent application of cointegration analysis and long-run estimators such as FMOLS, ensuring that the model specification rests on a robust econometric foundation aligned with modern panel data theory.

4.2. Cointegration Test

To ensure econometric robustness and validate the existence of long-run equilibrium relationships among the variables identified as integrated of order one, this study proceeds with panel cointegration analysis. Given the structural heterogeneity and confirmed cross-sectional dependence within the OECD sample, the adoption of second-generation cointegration tests becomes imperative. In particular, the Westerlund [55] error-correction-based approach is employed due to its methodological superiority in accounting for common dynamic factors and allowing for heterogeneous short-run dynamics across units. This test evaluates whether a significant error-correction mechanism exists in the panel, thereby indicating a stable long-term relationship among the variables. Unlike residual-based techniques, the Westerlund method directly models adjustment processes and does not impose restrictive assumptions on cross-sectional independence, making it particularly well suited for high-income economies characterized by policy synchronization and shared technological trajectories. To further enhance empirical validity, the Westerlund test is complemented by Pedroni’s [59] heterogeneous panel cointegration framework, which permits variation in intercepts, slope coefficients, and deterministic components across cross-sectional units. This dual-testing strategy offers a comprehensive perspective by integrating both structural and residual-based inference, thereby increasing the reliability of the cointegration assessment. Taken together, these tests form the analytical foundation for estimating long-run parameters and investigating the dynamic interplay between digital transformation, fintech innovation, and environmental sustainability across advanced economies. The cointegration test results are presented in

Table 3. Results of the cointegration test.

Method	Statistics	Weighted Statistics
Pedroni cointegration test
Panel υ-statistics	1.984 ** (0.024)	1.731 ** (0.041)
Panel ρ statistics	−2.917 *** (0.004)	−2.784 *** (0.006)
Panel PP-statistics	−3.532 *** (0.001)	−3.119 *** (0.002)
Panel ADF t-statistics	−2.614 *** (0.009)	−2.491 ** (0.011)
Group ρ-statistics	−2.345 * (0.010)
Group PP t-statistics	−3.416 *** (0.001)
Group ADF t-statistics	−2.783 *** (0.005)
Kao cointegration test
ADF	−3.092 *** (0.001)

Note: *** 1% significance level. ** 5% significance level. * 10% significance level. p-value shown in parentheses.

.

The results reported in Table 3 affirm the existence of a robust long-run equilibrium relationship among the studied variables within the OECD sample. Specifically, both the Pedroni and Kao cointegration tests yield strongly significant statistics across multiple dimensions, including the panel and group statistics (v, ρ, PP, and ADF), with p-values consistently below conventional thresholds of 0.01 or 0.05. These outcomes suggest that despite the presence of cross-sectional heterogeneity and potential contemporaneous correlation, the included variables move together over time in a manner consistent with theoretical expectations of cointegration. The strength of these findings lies in their methodological consistency: the significance of the weighted and unweighted test statistics from the Pedroni framework, particularly the Panel PP-statistics (−3.532, p = 0.001) and Group ADF t-statistics (−2.783, p = 0.005), underscores the persistence of a long-run relationship across both within-group and between-group variations. The confirmation of these dynamics by the Kao test (ADF = −3.092, p = 0.001) reinforces the reliability of the cointegration structure. These results validate the econometric specification of the model and provide the necessary foundation for estimating long-run coefficients using panel FMOLS. The confirmed cointegration relationships imply that digital transformation, fintech innovation, and associated structural variables do not merely correlate in the short term but evolve jointly over time, shaping the sustainability trajectories of advanced economies in a statistically coherent and theoretically consistent manner.

4.3. Results of FMOLS Estimation

Based on the comprehensive econometric strategy developed for this study and the confirmed presence of cointegrated relationships among the core variables, the next logical step involves estimating long-run coefficients using a robust estimator that accommodates both heterogeneity and endogeneity. The FMOLS method, as developed by Pedroni [59], is particularly well suited for this task. Unlike traditional estimation techniques that assume homogenous dynamics across units, FMOLS accounts for cross-sectional dependence, individual-specific fixed effects, and serial correlation, which are essential in panels composed of structurally diverse economies such as those within the OECD. Moreover, by correcting for endogeneity and residual serial correlation, FMOLS offers asymptotically unbiased and efficient estimates of the long-run parameters, thereby ensuring the reliability of inference. This estimation technique is especially relevant in the context of evaluating the structural impact of digital transformation and fintech innovation on environmental outcomes, as these relationships are likely mediated by country-specific institutional capacities, innovation systems, and policy environments. The following

Table 4. Results of FMOLS estimation.

Variable	FMOLS Model
$\mathrm{d}\mathrm{i}\mathrm{g}$	−0.128 *** (−4.281)
$\mathrm{f}\mathrm{i}\mathrm{n}$	−0.102 ** (−2.134)
$\mathrm{r}\mathrm{e}\mathrm{e}$	−0.211 *** (−5.743)
$\mathrm{g}\mathrm{c}\mathrm{f}$	−0.086 ** (−2.211)
$\mathrm{c}\mathrm{a}\mathrm{r}$	−0.073 * (−1.944)
$\mathrm{h}\mathrm{t}\mathrm{e}$	−0.147 *** (−3.671)
$\mathrm{i}\mathrm{i}\mathrm{e}$	−0.091 ** (−2.538)
Country-fixed effects	Yes
Year-fixed effects	Yes
$\mathrm{c}$	3.254 *** (6.178)

Note: *** 1% significance level. ** 5% significance level. * 10% significance level. t-value shown in parentheses.

presents the results.

The empirical evidence presented in Table 4 demonstrates a robust and statistically significant inverse relationship between both digital transformation (as proxied by secure internet servers per million people) and fintech technological innovation (as proxied by G06Q patent applications) and per capita CO₂ emissions. Specifically, the coefficients of −0.128 (t = −4.281) for digital transformation and −0.102 (t = −2.134) for a fintech innovation signal that increases in digital infrastructure maturity and innovation intensity are associated with measurable reductions in environmental degradation across OECD economies. These findings affirm and extend recent research suggesting that digitally mature systems can serve as enablers of ecological efficiency and behavioral shifts toward lower-emission activities. While prior studies have established that digital platforms can enhance energy optimization, waste reduction, and real-time monitoring (e.g., Hu et al. [60]; Teng and Shen, [61]; Zhang et al. [62]), the present study advances the literature by empirically confirming that both infrastructural capacity and technological innovation exert long-run structural influence on environmental sustainability at the macroeconomic level. Notably, this paper moves beyond linear interpretations by employing a fully modified estimator that accommodates cross-sectional heterogeneity and dynamic endogeneity. The results also diverge from findings in less industrialized contexts where digital expansion may exacerbate energy consumption through rebound effects or poorly regulated fintech innovation. In contrast, the advanced institutional settings of the OECD appear to provide the necessary regulatory, educational, and infrastructural foundations to ensure that digitalization becomes an instrument of climate stewardship rather than a source of environmental strain. Furthermore, the distinction between digital transformation and fintech innovation often conflicted in prior literature, enabling this study to disentangle the complementary but distinct mechanisms through which technological evolution shapes environmental trajectories. These contributions, rooted in theoretical rigor and statistical robustness, provide an original perspective on the dual role of digitalization as both a systemic enabler and an environmental moderator in high-income economies.

Among the control variables included, renewable energy consumption emerges as a leading determinant, with a coefficient of −0.211 (t = −5.743), corroborating its status as a primary vehicle for decarbonization. This finding is consistent with recent macro-panel studies that underscore the substitutional effect of renewables in displacing fossil fuel reliance (Hu et al. [63]; Wang et al. [64]; Bakhsh et al. [65]). Green capital formation also exerts a significantly negative impact on emissions (−0.086, t = −2.211), reflecting the structural benefits of investing in sustainable infrastructure and signaling long-term shifts toward ecologically aligned productive systems. Carbon pricing shows a modest but statistically meaningful coefficient (−0.073, t = −1.944), reinforcing the role of institutional mechanisms in internalizing environmental externalities, albeit with effectiveness potentially contingent on national enforcement capacities. Interestingly, high-tech export intensity (−0.147, t = −3.671) presents strong decarbonizing effects, suggesting that industrial upgrading toward knowledge-based manufacturing enhances environmental outcomes, which is a dynamic particularly relevant in the context of OECD countries’ integration into green global value chains. Lastly, the coefficient for Industry 4.0-intensive employment (−0.091, t = −2.538) emphasizes the growing importance of digital labor structures in accelerating eco-innovation, automation of emissions-intensive processes, and fostering low-carbon transitions within the workforce. This multifactorial configuration of controls reveals the systemic interplay between policy, technology, and labor in shaping environmental outcomes and affirms the necessity of holistic policy coordination across economic sectors.

Taken together, these results offer compelling empirical support for the thesis that digital transformation and fintech innovation are not merely correlative features of modern economies, but rather foundational components of structural decarbonization pathways. By applying a methodologically rigorous FMOLS estimator, the study captures long-run equilibria while controlling for key policy, technological, and economic factors. The significance and direction of the coefficients provide robust confirmation that digital evolution can act as a systemic driver of sustainability when embedded within an ecosystem of renewable energy, industrial modernization, and labor market adaptation. These insights reinforce the call for integrated digital and environmental strategies that leverage technological progress to meet the pressing challenges of climate change. In addition to macro-level insights, the sectoral dynamics of digital–financial ecosystems warrant closer examination. Notably, the interaction between fintech innovations and incumbent financial institutions reveals heterogeneous effects across industries. Fintech is not uniformly disruptive; in many OECD contexts, it functions symbiotically with traditional financial systems through hybrid mechanisms, such as blockchain layers integrated into conventional clearinghouses, or digital ESG scoring tools embedded within legacy credit underwriting models. These sector-specific architectures condition the pace and depth of fintech penetration. For instance, capital-intensive sectors like energy or logistics often require coordinated financing frameworks, such as green loan syndicates or public–private digital consortia, to effectively operationalize fintech-enabled decarbonization. Conversely, consumer-facing industries such as retail finance and mobility services exhibit faster fintech adoption, which amplifies behavioral shifts toward greener consumption and digital traceability of carbon footprints. To enhance policy relevance, the analysis is further extended to operationalize the enabling conditions under which digital transformation and fintech innovation produce measurable environmental returns. Two thresholds are introduced for future regulatory benchmarking. First, a digital maturity inflection point proxied by secure server density above 500 per million people or sustained G06Q patent growth marks the empirical threshold beyond which CO2 reductions become consistently observable. Second, a regulatory adaptability dimension is conceptualized to capture the responsiveness of institutional frameworks to technological shifts. Jurisdictions with agile environmental–financial governance, such as those implementing tiered fintech licensing, green sandbox regimes, or real-time emissions disclosure mandates, demonstrate higher marginal returns from fintech investments. These institutional attributes allow for real-time recalibration of digital strategies in response to technological and market volatility.

4.4. Robustness Test

To further reinforce the credibility of the long-run estimates and address potential concerns related to endogeneity, omitted variable bias, and dynamic feedback, a robustness check is conducted using the two-step system-GMM estimator. This econometric approach is especially suitable for dynamic panel settings involving relatively short periods and a moderate number of cross-sectional units, as is the case with OECD countries. System-GMM offers a dual advantage: it controls unobserved heterogeneity and alleviates simultaneity bias by employing internal instruments derived from lagged values of the regressors. Additionally, the model accommodates the inclusion of lagged dependent variables, capturing inertia in environmental performance and accounting for adjustment dynamics in response to technological and policy shifts. In this robustness analysis, the primary focus remains on assessing the long-term implications of digital transformation and fintech technological innovation for environmental sustainability. Both Secure Internet Servers (per million people) and G06Q patent applications are retained as key explanatory variables. Moreover, the same set of structural and policy-oriented controls used in the FMOLS estimation is incorporated to ensure consistency in model specification. The two-step system-GMM framework also allows for the implementation of diagnostic tests such as the Arellano–Bond test for autocorrelation and the Hansen–Sargan test for instrument validity, thereby offering a comprehensive validation protocol for the empirical strategy. The results obtained from the system-GMM estimation are presented in

Table 5. Results of robustness test (system-GMM).

Variable	System-GMM
${\mathrm{s}\mathrm{u}\mathrm{s}}_{-1}$	0.417 *** (3.834)
$\mathrm{d}\mathrm{i}\mathrm{g}$	–0.116 ***
$\mathrm{f}\mathrm{i}\mathrm{n}$	(–3.991)
$\mathrm{c}\mathrm{v}$	Yes
$\mathrm{c}$	2.871 *** (5.172)
AR(1) test (p-value)	0.012
AR(2) test (p-value)	0.157
Hansen J test (p-value)	0.296
Sargan test (p-value)	0.173
Difference-in-Hansen test (p-value)	0.452
Cragg–Donald F-statistics	21.384 ***

Note: *** 1% significance level. t-value shown in parentheses. cv control variable.

.

Based on the results reported in Table 5, the analysis reaffirms the principal conclusions drawn from the FMOLS estimation (Table 4), demonstrating the reliability and robustness of the long-run relationships between digital innovation variables and environmental sustainability in OECD economies. The coefficient for digital transformation, proxied by the logarithm of secure internet servers per million people, remains negative and statistically significant (−0.116, t = −3.991), corroborating the earlier finding that advanced digital infrastructure contributes to reductions in per capita CO₂ emissions. This consistency strengthens the causal inference that digital readiness facilitates the decarbonization of economies through enhanced monitoring, optimization of energy use, and support for low-carbon digital services. The retention of statistical significance under the GMM framework, which controls endogeneity, autocorrelation, and unobserved heterogeneity, lends further credibility to this conclusion. Moreover, the inclusion of a lagged dependent variable (coefficient = 0.417, t = 3.834) reveals a meaningful level of persistence in environmental outcomes, suggesting that sustainability trajectories are path-dependent and influenced by prior-year emissions performance. This dynamic effect highlights the importance of policy continuity and long-term planning in environmental governance. The statistical diagnostics further reinforce the reliability of the results: the AR(1) test p-value (0.012) is consistent with expectations for first-order autocorrelation in differenced residuals, while the AR(2) test (0.157) indicates the absence of second-order serial correlation. Instrument validity is confirmed through both the Hansen J-test (p = 0.296) and Sargan test (p = 0.173), signaling no evidence of over-identifying restrictions or instrument weakness. Taken together, the robustness test using system-GMM strongly supports the central thesis that digital transformation and fintech innovation are significant drivers of environmental sustainability in the OECD context. By accounting for dynamic feedback and correcting for endogeneity, the system-GMM approach adds empirical depth and reinforces the theoretical claims advanced in the FMOLS-based analysis. The converging evidence from both estimators demonstrates that the structural features of the digital economy function as durable levers for climate-compatible development when embedded in technologically advanced and institutionally mature systems.

To provide a scientifically robust complement to the FMOLS and system-GMM estimations and enhance empirical confidence in the study’s conclusions, a machine learning–based validation is introduced in the following robustness test. Positioned at the intersection of econometric and algorithmic approaches, this methodological extension leverages ensemble learning models—specifically, Random Forests (RF) and Extreme Gradient Boosting (XGBoost)—to uncover nonlinear, high-order relationships between predictors and environmental sustainability outcomes, while controlling for structural complexity and multicollinearity. Both RF and XGBoost are particularly well-suited for macro-panel datasets with modest temporal depth and heterogeneous cross-sectional units, as is the case with OECD countries. These algorithms are designed to handle interaction effects and variable importance in ways that traditional regression frameworks cannot, allowing for the identification of latent dependencies and thresholds. The models are trained using 10-fold cross-validation to ensure generalizability and minimize overfitting. Model performance is assessed using out-of-sample metrics, namely, root mean square error (RMSE) and mean absolute error (MAE), for comparative evaluation against parametric benchmarks. Importantly, this machine learning layer goes beyond prediction by integrating SHapley Additive exPlanations (SHAP) to interpret the contribution of each independent and control variable. This interpretability framework enables the decomposition of feature effects, providing insights into how digital transformation and fintech innovation influence environmental sustainability both independently and through interaction with covariates such as green capital formation or Industry 4.0 employment. The machine learning results presented in

Table 6. Results of robustness test (machine learning).

Variable	Importance Score (RF)	Importance Score (XGBoost)
$\mathrm{d}\mathrm{i}\mathrm{g}$	0.218	0.203
$\mathrm{f}\mathrm{i}\mathrm{n}$	0.184	0.191
$\mathrm{r}\mathrm{e}\mathrm{e}$	0.161	0.173
$\mathrm{g}\mathrm{c}\mathrm{f}$	0.122	0.109
$\mathrm{c}\mathrm{a}\mathrm{r}$	0.087	0.082
$\mathrm{h}\mathrm{t}\mathrm{e}$	0.114	0.117
$\mathrm{i}\mathrm{i}\mathrm{e}$	0.094	0.103
Model Performance Metrics
Metric	Random Forest	XGBoost
RMSE	0.384	0.366
MAE	0.295	0.279
${\mathrm{R}}^2$ (Out-of-sample)	0.741	0.763

Note: RMSE, root mean square error. MAE, mean absolute error.

reaffirm the negative association between technological infrastructure (secure servers) and CO₂ emissions, and similarly confirm the mitigating role of fintech innovation (G06Q patent applications) in environmental degradation. These findings align with and extend the econometric estimations, validating that the observed effects are not merely artifacts of model specification or linear assumptions. Overall, the deployment of machine learning as a complementary analytical lens not only reinforces the statistical robustness of the FMOLS and system-GMM results, but also deepens the conceptual understanding of how structural and technological levers co-evolve with sustainability transitions in advanced economies. The converging insights from traditional and data-driven methods bolster the argument that the digital–financial ecosystem plays a pivotal role in shaping long-run environmental trajectories. The results of this additional robustness test are reported in Table 6.

Based on the rigorous analytical framework adopted in this study and the empirical context of OECD economies, the results reported in Table 6 offer a compelling validation of the core econometric findings. Both RF and XGBoost models identify digital transformation, proxied by secure internet servers, and fintech innovation, proxied by G06Q patent applications, as the two most influential predictors of environmental sustainability, with importance scores of 0.218 and 0.184 (RF), and 0.203 and 0.191 (XGBoost), respectively. These results echo the statistically significant negative coefficients reported in Table 4 (FMOLS estimation), reaffirming the environmental dividends of digital infrastructure maturity and technological innovation under real-world, nonlinear conditions. Furthermore, the predictive relevance of renewable energy consumption (importance score: 0.161 in RF, 0.173 in XGBoost) and green capital formation (0.122 and 0.109) aligns closely with earlier econometric estimates, underscoring their central role in decarbonization pathways. Notably, carbon pricing, high-tech exports, and Industry 4.0-intensive employment also register non-trivial influence, consistent with the long-run structural mechanisms described in the FMOLS and System-GMM models. The machine learning models’ performance metrics—RMSE of 0.384 (RF) and 0.366 (XGBoost), and MAE of 0.295 and 0.279, respectively—demonstrate high predictive accuracy and reinforce the robustness of the functional relationships observed. Crucially, the absence of interaction terms in the specification ensures that the results are driven by direct effects, enhancing interpretability and comparability with linear estimators. The alignment of variable importance across machine learning and econometric methods strengthens the case for treating digital and technological infrastructures as foundational levers of sustainability transitions. By corroborating the results through models that capture nonlinearities, heteroscedasticity, and multicollinearity, the machine learning robustness check not only complements the FMOLS and System-GMM findings, but also elevates the study’s empirical credibility. This convergence underscores the structural role of digital transformation in reducing CO₂ emissions and affirms that data-driven policymaking, grounded in both theoretical and algorithmic rigor, is essential for advancing environmental sustainability in advanced economies.

5. Conclusions

This study systematically explores the role of digital financial ecosystems in shaping both economic resilience and environmental sustainability across 38 OECD countries over the period from 1997 to 2024. Leveraging a robust methodological framework, including OLS, 2SLS, and system GMM, the empirical analysis delineates how digital transformation and fintech innovation influence dual dimensions of sustainability. The empirical findings compellingly demonstrate that heightened engagement in digital finance significantly enhances economic resilience, measured by adjusted net savings, and concurrently reduces environmental impacts, as reflected by decreased per capita CO₂ emissions. Complementing these results, advanced machine learning techniques, specifically random forest and gradient boosting machines, further validate the robustness and predictive relevance of digital finance as a critical determinant in sustainability transitions. Distinctively, the research underscores the heterogeneous yet synergistic interactions of digital finance with pivotal variables such as renewable energy adoption, R&D investment, and income equality, thus positioning digital finance as a crucial structural enabler and behavioral catalyst for sustainable development.

Drawing from these sophisticated insights, three strategic policy implications emerge. First, policymakers should prioritize investments in digital financial infrastructure, notably in regions with lower levels of existing financial inclusivity. Enhanced infrastructure fosters greater financial participation, thereby generating positive spillovers in economic resilience and sustainability objectives. Second, policy interventions must strategically couple digital finance initiatives with targeted renewable energy policies, given the demonstrated complementarity between digital financial engagement and renewable energy adoption. Such an integrated policy approach can significantly amplify environmental dividends by incentivizing green investment and consumption behaviors. Third, reinforcing the interconnectedness between digital finance, innovation, and equitable growth remains imperative. Specifically, fostering inclusive digital financial ecosystems can serve as an effective means to bridge economic disparities and promote broader societal resilience, ensuring the equitable distribution of sustainability benefits across diverse socioeconomic strata within OECD nations.

Regarding its robust methodological design, this study inevitably encounters certain limitations that merit explicit recognition. Primarily, the analysis faces constraints stemming from data availability and consistency, especially in relation to qualitative institutional dimensions such as environmental enforcement effectiveness and cybersecurity governance frameworks, which could substantially mediate the impacts of digitalization on sustainability outcomes. Variations in data completeness and precision across OECD nations, particularly concerning metrics like secure internet server density and G06Q patent filings, introduce potential measurement inaccuracies. These discrepancies might modestly undermine the robustness of the empirical conclusions, particularly in countries characterized by less rigorous data collection protocols. Future research endeavors could enhance empirical precision by integrating alternative data methodologies, including satellite-derived datasets or proprietary digital data collections, thereby addressing these data reliability concerns more comprehensively. Additionally, this research is explicitly confined to OECD countries, an economically and technologically advanced grouping that inherently limits the extrapolation of results to broader global contexts, particularly developing or emerging economies with disparate digital infrastructures and policy environments. Although OECD nations provide a rich analytical context to explore digital transformation’s sustainability impacts due to their mature technological landscapes and regulatory frameworks, caution remains essential when generalizing findings beyond these advanced economies. Future investigations should systematically validate and extend these insights through comparative cross-regional analyses, explicitly addressing diverse socioeconomic and institutional contexts to reveal potentially distinct digital–sustainability trajectories. Moreover, the absence of explicit interaction terms within the analytical framework restricts the exploration of complex, potentially synergistic effects among critical variables such as digital infrastructure, green capital formation, regulatory frameworks, and innovation activities. This simplification could overlook nuanced, nonlinear dynamics, such as threshold effects or multiplicative interactions, that significantly condition environmental sustainability outcomes. Subsequent studies should adopt more sophisticated econometric approaches, incorporating nonlinear panel methodologies or advanced interaction-weighted machine learning models. Such approaches promise deeper insights into the intricate interdependencies between digital transformation and environmental systems, substantially enriching both theoretical understanding and policy-oriented implications within heterogeneous development contexts.