This part of the study reviews empirical studies on green energy, technological diffusion, green innovation, green finance, globalization, and environmental policies on environmental sustainability in developed and emerging countries.

Green energy plays a crucial role in boosting energy efficiency and lowering harmful emissions, making it a central pillar of sustainable development efforts. A growing body of research underscores its environmental benefits. For example, Sharma et al. (2024) report that over the last decade, the use of renewable energy in OECD countries has helped cut carbon emissions by around 15%. Imran et al. (2024) further show that in South Asia, green energy adoption not only reduces ecological stress but also supports ongoing economic development. Similarly, Maka and Alabid (2022) explore how innovations in solar technologies are advancing sustainability goals, particularly by lowering greenhouse gas emissions and improving overall energy use. Taken together, these findings highlight the vital role of renewable energy in shaping a more sustainable and resilient environmental future.

Technological diffusion (TD) supports environmental sustainability by enhancing how resources are managed and helping to cut pollution levels (Truong, 2022; Xia et al., 2022). Research by Chen and Hao, (2022) and Wang et al. (2022) shows that TD contributes to corporate sustainability, particularly in companies subject to strict environmental regulations. Khan et al. (2020) also emphasize its importance in reducing emissions, especially within developed countries.

Green innovation (GI) is another key pillar in the push for sustainability. It helps limit environmental damage while improving how efficiently resources are used. Liu and Liang (2024) explored this dynamic across countries with varying income levels. They found that innovation—tracked through environmental patent filings—led to a marked decline in fossil fuel use, underlining the strong role innovation plays in supporting environmental goals in both advanced and developing economies.

Green finance, particularly measured through domestic credit, has become a key instrument in driving sustainable development by channeling capital into environmentally responsible projects. Multiple recent studies highlight its potential to address ecological challenges while also supporting economic growth. For instance, Xing et al. (2024) investigated green finance practices in 30 emerging economies and found that allocating domestic credit to green initiatives significantly reduced carbon emissions and encouraged the use of renewable energy, pointing to the importance of well-designed financial policies.

In a similar vein, Xiao et al. (2024) showed that expanding credit access for green investments spurred energy transitions and industrial modernization in China’s pilot regions. Their work also pointed out that cities with different levels of resources experienced varying outcomes, emphasizing the need for context-specific financial strategies to fully leverage green finance. Additionally, Fu et al. (2023) stressed that credit availability for low-carbon ventures plays a central role in fostering sustainable economic growth, and they called for stronger regulatory oversight to ensure that green finance is directed toward genuinely impactful environmental efforts.

Recent studies have explored the complex relationship between globalization (GB) and sustainable development. Diachenko et al. (2024), using the KOF Globalization Index, examined how GB influences economic progress across multiple countries. Their findings suggest that globalization enhances economic inclusion and facilitates the spread of technology, both of which can support sustainability goals. However, the study also highlighted significant challenges, particularly the uneven distribution of benefits and the environmental costs linked to increased trade and industrial expansion.

Building on this, Oluwagbade and Ibidapo (2024) focused specifically on Nigeria. Their research examined how GB affects economic growth (EG) and sustainability outcomes in the local context. They found that while globalization has the potential to open up economic opportunities, it may also pose risks to environmental quality if not paired with appropriate policy frameworks. This underscores the need for balancing global integration with strong environmental governance.

Amin et al. (2025) find that in BRICS economies, globalization and technological diffusion increase CO₂ emissions, but the combined use of green finance, renewable energy, and innovation significantly offsets this impact. Green finance is shown to be essential for directing capital toward clean technologies. Similarly, Zhu et al. (2025) show that in China, the integration of green finance with digital innovation improves carbon efficiency and reduces environmental degradation, with the digital economy amplifying green finance’s environmental benefits in globalized contexts.

Khan et al. (2024), through a systematic review, emphasize green finance as a key instrument for managing environmental risks linked to globalization. They highlight trends such as green finance performance indices, fintech’s role in scaling green investment, and the importance of resilience frameworks for protecting green capital. Liu X. et al. (2025) further argue that embedding green finance within carbon neutrality strategies helps align globalization with sustainability, promoting cross-border green technology diffusion, sustainable industrial transitions, and stronger environmental governance.

Environmental policies are crucial in guiding nations toward sustainable development. A study conducted by Ambreen et al. (2025) studied the impact of eco-policies and laws on promoting ecological health in Pakistan. They highlighted that rigorous EP, along with community involvement and ecological education programs, markedly enhance sustainability results. Political instability and corruption were recognized as impediments to successful policy execution. OECD (2025a) emphasized the necessity of synchronizing environmental policy with financial mechanisms to address the funding shortfall for sustainable development. They emphasized that creative policy frameworks are crucial for resource mobilization and the attainment of global sustainability objectives. Alatrash et al. (2025) found that EP had a minimal but positive impact on reducing consumption-based CO₂ emissions (CCO2). While the effects were not as pronounced as other factors like GE consumption, EP contributed to fostering compliance with environmental regulations.

Despite the growing body of empirical work on green finance, energy transitions, innovation, and globalization, several critical gaps remain in the existing literature that this study seeks to address.

First, prior research tends to focus narrowly on individual dimensions of sustainability—such as CO₂ emissions or energy use—rather than adopting a comprehensive ecological indicator. For instance, research by Yasir Mehboob et al. (2024), Mehboob et al. (2024a), and similar recent studies primarily assess carbon emissions but overlook broader environmental pressures such as land use, biodiversity loss, and resource consumption. This study fills that gap by using the ecological footprint as a holistic measure of environmental deterioration, providing a broader perspective on sustainability outcomes.

Second, most studies focus on single-country contexts (e.g., China or Nigeria) or small groups such as BRICS, without considering the structurally different policy environments of advanced economies. The OECD region, despite being a leader in environmental governance, remains underexplored as a unified sample in terms of how the “Triple Green Strategy” interacts with globalization and policy efforts to affect environmental outcomes. This study addresses that gap by examining 34 OECD countries over 3 decades, offering a comparative macro-panel analysis rarely undertaken in earlier work.

Third, while several studies emphasize the role of green finance, the operationalization of this concept remains problematic. Most literature uses broad credit indicators, without distinguishing green credit from conventional lending, which dilutes causal inferences (e.g., Fu et al., 2023; Xing et al., 2024). This study acknowledges this limitation but proposes the use of macro-financial proxies in combination with policy recommendations for more refined future measures.

Fourth, green innovation is frequently proxied by patent counts, but without assessing whether such innovations are commercially viable or environmentally impactful in practice. As highlighted by Khan et al. (2024), the lack of commercialization metrics may overstate innovation’s ecological benefits. This study addresses this issue by recognizing GI’s limitations and recommending future studies to incorporate innovation effectiveness indicators such as adoption rates or green R&D expenditure.

Fifth, many studies overlook temporal heterogeneity, especially the structural breaks introduced by major global events such as the 2008 financial crisis and the 2020 COVID-19 pandemic. This research bridges that gap by segmenting the analysis into pre-crisis, post-crisis, and pandemic periods, allowing for a more nuanced understanding of how the effectiveness of green strategies fluctuates across economic cycles.

Lastly, existing literature often lacks a strong theoretical integration across disciplines. While some studies touch on aspects of ecological modernization or the environmental Kuznets curve, few embed them into a systematic empirical framework. This study incorporates both, offering a cohesive theoretical base to explain the nonlinear and policy-mediated nature of green-economic-environmental linkages.

Together, these gaps highlight the need for more integrated, dynamic, and context-sensitive investigations—an area where this study makes a novel and timely contribution.

This work analyzes the relationships between GE, GI, GF, EP, GB, and TD with ED in 34 OECD countries over the period 1990–2022. The dataset is compiled from reputable sources, including QoG, GFN, the OECD database, and WDI (see Table 1). This period is selected based on the availability of comprehensive data for all variables, ensuring consistency and minimizing potential biases from missing observations. In cases where certain data points were unavailable, the average imputation method was employed, a widely accepted approach in sustainability research to maintain data continuity without distorting the overall trend.

The timeframe from 1990 to 2022 holds special relevance for studying environmental sustainability, especially within the context of OECD countries. This three-decade span captures several landmark global policy milestones, such as the Kyoto Protocol, the Paris Agreement, and the launch of the European Green Deal—all of which significantly shaped environmental agendas across developed nations (Choi, 2024; UNFCCC, 2024). Beyond policy, this period also witnessed major leaps in green innovation, including the rise of renewable energy, rapid digitalization, and the growing role of green finance (Ristanović et al., 2024). Covering such an extended timeframe enables evaluating not only the immediate effects of environmental initiatives but also their deeper, long-term structural impacts, providing a richer, more nuanced picture of how sustainability efforts have evolved and taken hold over time.

With 1,122 data points for each variable, the dataset offers a strong basis for analyzing what drives sustainability, helping to ensure reliable statistical results and reduce issues like inconsistent variance or missing key factors. Focusing on OECD countries adds further value, given their active roles in enforcing environmental regulations, adopting cutting-edge green technologies, and using financial tools to support sustainable growth. This enables the study to generate policy-relevant insights applicable to both developed and developing economies, facilitating a broader understanding of global sustainability dynamics.

Table 2 provides the descriptive statistics for the 34 OECD countries, reflecting the distribution and variability of key variables. The mean values indicate that ED averages 1.767, while EG stands at 10.235, showing stable economic patterns. Figure 1 illustrates the fluctuations in ED across selected OECD countries throughout the study period. GE and GI have means of 2.395 and 2.951, respectively, with GI displaying substantial variation, as highlighted by its high standard deviation of 4.927. TD and GF exhibit moderate mean values of 2.597 and 4.317, though TD shows significant dispersion with a standard deviation of 3.583. EP presents a negative mean (−0.114), indicating variations in policy effectiveness among the selected countries. GB remains relatively stable with an average of 4.335 and low dispersion (standard deviation of 0.122). The lowest values highlight extreme fluctuations, particularly in GI, TD, and EP, suggesting that some economies experience severe downturns in these variables. The statistically significant skewness values confirm deviations from normality, necessitating advanced econometric techniques to ensure robust modeling.

Figure 2 presents Q-Q plots assessing the normality of the variables used in the study. The deviation of data points from the diagonal line indicates that most variables exhibit non-normal distributions, with noticeable skewness and heavy tails in some cases. The log transformations applied to certain variables, such as TD, EP, and GF, improve normality but do not eliminate deviations. These results suggest that robust econometric techniques, such as panel unit root tests and estimators accommodating non-normality and heteroskedasticity, are necessary to ensure reliable inference in the study.

Table 3 presents the correlation analysis, illustrating the relationships among the study variables. ED shows a strong positive correlation with EG (0.571), indicating that economic growth is associated with higher environmental degradation. GE and GI exhibit weak correlations with ED, with GE having a slight positive effect (0.096) and GI showing a marginal negative association (−0.050), suggesting that green energy alone may not significantly curb degradation, while green innovation could play a role in mitigation. TD and GF display positively inclined correlations with ED (0.111 and 0.282, respectively), implying that technological diffusion and green finance influence ecological outcomes. EP has a notable negative correlation with ED (−0.242), reinforcing the role of environmental policies in reducing degradation. GB maintains a positive link with ED (0.176), hinting at financial sector dynamics contributing to ecological strain. Notably, GI and EP show a strong correlation (0.519), indicating that innovative policies complement environmental regulations. The significant correlations among multiple variables highlight complex interactions, necessitating advanced econometric modeling to capture the underlying causal mechanisms.

This study draws on the foundations of ecological modernization theory and the environmental Kuznets curve (EKC) hypothesis to establish a theoretical connection between economic development, green strategies, globalization, and environmental outcomes. Ecological modernization posits that environmental degradation can be mitigated through technological innovation, institutional reform, and market-based mechanisms such as green finance and renewable energy. In parallel, the EKC framework suggests a nonlinear relationship between economic development and ecological impact, where degradation initially upsurges with evolution but declines as income and environmental regulation strengthen.

Guided by these perspectives, this research conceptualizes environmental deterioration (ED) as a function of multiple interconnected drivers, particularly those representing sustainable growth and global integration. Accordingly, the baseline functional form is expressed as in Equations 1, 2: E D = f E G , G E , G I , T D , G F , E P , G B (1)

Where ED, EG, GE, GI, TD, GF, EP, and GB stand for ecological deterioration, economic growth, green energy, green innovation, technological diffusion, green finance, ecological policies, and globalization, respectively.

The following is the log-linear transformation form of Equation 1: l n E D i , t = β 0 + β 1 ⁡ ln ⁡ E G i , t + β 2 l n G E i , t + β 3 l n G I i , t + β 4 l n T D i , t + β 5 ⁡ ln ⁡ G F i , t + β 6 ⁡ ln ⁡ E P i , t + β 7 ⁡ ln ⁡ G B i , t + ε i , t (2)

Log-transforming variables standardize magnitudes, simplifying regression analysis and capturing nonlinear economic effects on ED. This approach manages variability while retaining key variables in their original form for clarity, ensuring easier interpretation of factors like EG, GE, GI, TD, GF, EP, and GB on sustainability.

The coefficients β₁ through β₇ represent the elasticity estimates, indicating both the strength and direction of the relationship between each explanatory variable and environmental deterioration. The term β₀ denotes the intercept of the model, while ε_it captures the stochastic error component. In this panel dataset, t denotes the time dimension spanning from 1990 to 2022, and i refers to the cross-sectional units, specifically the 34 OECD countries included in the analysis.

Grounded in the empirical findings and aligned with SDG objectives, this study offers targeted and operational policy recommendations that extend beyond general directives and address the diverse institutional realities of OECD countries.

First, the significant role of green innovation in mitigating environmental deterioration—especially pre-crisis—highlights the need for structured innovation frameworks. OECD governments should adopt targeted subsidies for environmental patents and promote industry-university-research collaborations, similar to Germany’s Fraunhofer model, which has effectively bridged applied research and green technology commercialization. Lessons from Sweden and Finland, which emphasize mission-driven innovation funding, should be replicated through tailored national innovation strategies that align R&D with climate goals.

Second, while green finance showed mixed impacts, the observed bidirectional causality with environmental outcomes underscores the need for explicit green financial taxonomies and regulatory incentives. Countries like France and the Netherlands have advanced green bond frameworks and sustainability-linked lending benchmarks, which can serve as blueprints for others. Policies should focus on de-risking green investments, mandating environmental disclosures, and mobilizing private sector capital toward climate-aligned sectors.

Third, the limited yet occasionally significant impact of ecological policies calls for enhancing not only policy stringency but also implementation efficiency. Successful models like Denmark’s environmental performance contracts and Japan’s voluntary corporate compliance schemes can be adapted to suit other OECD economies with different governance capacities. Enforcement must be matched with monitoring infrastructure and transparent compliance metrics to ensure policy effectiveness.

Fourth, the environmental burden of technological diffusion—especially post-crisis and during the pandemic—suggests a need for greener digital transitions. Policy frameworks should support low-energy digital technologies and circular electronics value chains, drawing from the EU’s digital sustainability regulations.

Finally, green energy, while expected to reduce ED, showed mixed effects across periods. This signals a need for phased renewable integration strategies that are coupled with grid modernization, storage infrastructure, and fossil phase-out timelines, exemplified by Spain’s just transition framework and Canada’s clean energy incentives.

In sum, the study advocates for differentiated, evidence-backed policies rooted in successful OECD practices. These measures should be dynamically aligned with SDG targets, particularly Goals 7 (affordable and clean energy), 9 (industry, innovation, and infrastructure), 12 (responsible consumption and production), and 13 (climate action). By combining policy stringency with innovation pathways and financial alignment, OECD countries can lead a credible and scalable ecological transition.

While the current work offers meaningful perceptions into the ecological impacts of green strategies and globalization across 34 OECD countries from 1990 to 2022, several limitations warrant careful consideration.

First, the analysis is restricted to OECD countries, which represent a relatively homogeneous group of high-income economies. This limits the generalizability of the findings to developing countries, where institutional capacities, resource endowments, and economic structures differ significantly. Moreover, the study does not consider within-OECD heterogeneity, such as differences in income levels, industrial structures, or natural resource dependency, which may influence how green policies and globalization affect environmental outcomes. Future research should disaggregate OECD samples or expand to include non-OECD nations for broader applicability.

Second, the measurement of green innovation (GI) relies solely on the number of environmental technology patents. While this indicator captures formal innovation output, it does not reflect the commercial application or real-world impact of these innovations. This may lead to an overestimation of GI’s effectiveness in improving environmental outcomes. Incorporating alternative indicators, such as patent citations, innovation adoption rates, or environmental R&D expenditures, could enhance the robustness of future analyses.

Third, the proxy used for green finance (GF)—domestic credit to the private sector as a share of GDP—does not distinguish between green and non-green financial flows. This dilutes the precision of the measure and may obscure the specific role of financial mechanisms in supporting sustainability transitions. More targeted indicators, such as the volume of green bonds issued, green loans, or ESG-aligned capital flows, would better capture the essence of green finance, although such data are not yet uniformly available across countries and time.

Finally, while the model controls for key econometric concerns and includes robustness checks, some variables may suffer from measurement limitations or temporal lag effects, especially during the pandemic era when structural disruptions were highly nonlinear. The short time frame for that sub-period also constrains the reliability of dynamic estimates.

Addressing these limitations in future research—through refined indicators, expanded geographical coverage, and greater temporal granularity—will strengthen the analytical depth and policy relevance of studies in this domain.

Building on the limitations identified, several directions emerge for future research to deepen and refine understanding of the environmental impacts of green strategies and globalization, particularly within diverse national contexts.

First, future studies should aim to expand the geographical scope beyond OECD countries to include developing and emerging economies. This would enable comparative analysis across varying institutional capacities, environmental vulnerabilities, and economic structures, thereby enhancing the global relevance of policy insights. Including low- and middle-income countries could also reveal differentiated pathways for sustainable development under resource and regulatory constraints.

Second, there is a clear need to capture intra-group heterogeneity among OECD nations. Future research could stratify countries by income levels, natural resource endowments, or industrial composition to assess whether the effects of green innovation, finance, and policy differ significantly across sub-groups. This stratified approach would yield more context-sensitive recommendations and help identify best practices adaptable to specific national profiles.

Third, refinement of indicator selection is critical. The current reliance on environmental patents as a proxy for green innovation should be complemented with metrics that reflect innovation impact and adoption, such as patent citations, technology diffusion rates, or sector-level innovation outcomes. Similarly, replacing or augmenting the green finance measure with explicitly green instruments—such as green bond issuance, sustainable loan portfolios, or ESG-based financial indices—would provide a more accurate picture of financial contributions to environmental goals.

Fourth, future analyses should incorporate nonlinear effects and threshold dynamics, particularly around policy stringency, trade openness, and digital expansion. Advanced techniques such as panel threshold regression or machine learning models can uncover tipping points or diminishing returns in environmental responses to policy or economic variables.

Finally, the temporal scope should be extended in future updates to capture post-pandemic trajectories and emerging structural shifts in the global green economy. As green recovery packages, digital transitions, and climate targets intensify, updated data will be crucial for evaluating the long-term efficacy of current strategies and informing adaptive policymaking.

In sum, future research should pursue greater inclusivity, precision, and methodological innovation to fully capture the evolving complexity of ecological sustainability in a globalized and transitioning world.