This study investigates how climate finance influences CO 2 emissions, emphasizing the pivotal role of sustainable development as a transmission pathway. To account for heterogeneity in economic level, policy frameworks, energy dependence, and environmental governance, the study uses a full sample of 73 countries from 1990 to 2023, and differentiates between 37 OECD and 36 non-OECD countries. Empirical results of both Common Correlated Effects Pooled (CCEP) and Common Correlated Effects Mean Group (CCEMG) estimators show that both climate finance and sustainable development significantly reduce CO 2 emissions. This result is confirmed for both OECD and non-OECD countries. Additionally, renewable energy enhances environmental quality since it significantly lowers CO 2 emissions, while total energy consumption increases it. Finally, the results confirm the Environmental Kuznets Curve (EKC) theory since GDPG increases CO 2 emissions while the GDPG squared decreases it. These findings offer actionable insights for policymakers aiming to enhance the effectiveness of climate finance in fostering sustainable, low-carbon development. 1. Introduction Recent studies highlight the important role of climate finance in reducing CO 2 emissions and improving environmental quality and sustainability [ 9, 10, 11]. Climate finance, involving public and private resources for mitigation and adaptation, supports investments in clean energy, sustainable infrastructure, and resilience [ 12]. Empirical evidence shows that higher climate finance reduces emissions and promotes renewable energy and environmental governance. For example, Ref. [ 13] find that it enhances sustainability through green innovation, while Ref. [ 14] show that its effect is strengthened by green technological innovation. Similarly, Ref. [ 15] report that green finance in China reduces CO 2 emissions via technological and structural channels. Overall, the literature confirms that climate finance is a key driver of low-carbon transition and environmental improvement. While most of study performed a linear approach, few works applied a nonlinear framework. More recently, Ref. [ 16] show that the impact of green finance on CO 2 emissions is nonlinear and heterogeneous across developing countries. Their findings indicate that it reduces emissions more effectively in higher-income economies, while its effect is weaker in lower-income countries due to institutional and structural constraints. For the Asian context, Ref. [ 17] examines the nonlinear dynamics and threshold effects of green finance on carbon emissions in the Asia-Pacific region and finds that its environmental impact is not linear. Their results show that green finance reduces CO 2 emissions only after surpassing certain threshold levels, highlighting the presence of nonlinear and regime-dependent effects. This suggests that the effectiveness of green finance varies across development stages and institutional contexts. There is a dynamic and reciprocal relationship between climate finance, sustainable development, and CO 2 emissions [ 22]. The role of climate finance is to supply the necessary capital to stimulate green innovation, renewable energy, and sustainable infrastructure. These investments stimulate sustainable development, by incorporating environmental principles within the development process of economies and society. As a consequence, sustainable development leads to better environmental quality with lower emissions, promoting clean production and building resilience. All these factors develop a virtuous cycle, with climate finance driving sustainability, sustainability promoting environmental protection, and a healthier environment driving more future climate finance, which follows in the logic of the Environmental Kuznets Curve (EKC) and sustainable development theories. Despite the positive role of climate finance and sustainable development in improving environmental outcomes, the literature highlights several constraints that limit their effectiveness [ 23, 24], including unequal access to finance, weak governance, inefficient allocation, and limited transparency, particularly in developing economies. Moreover, concerns such as “greenwashing,” institutional weaknesses, and technological gaps further weaken the environmental impact of these policies. Although existing studies have examined climate finance and sustainable development separately, there is still limited integrated empirical evidence on how both jointly influence CO 2 emissions, especially in developing-country contexts. In addition, there is a lack of comparative cross-country evidence between OECD and non-OECD countries on the links between climate finance, sustainable development, and CO 2 emissions. This is important given differences in income levels, institutions, and energy structures, which may shape policy effectiveness. These gaps motivate the present study, which addresses the following research question: How do climate finance affect CO 2 emissions directly, and indirectly through sustainable development? This study examines the impact of climate finance on CO 2 emissions, with a focus on the role of sustainable development as a key transmission channel. By analyzing the interaction between climate finance and sustainable development, it explores how financial and non-financial factors jointly shape environmental outcomes, offering policy insights on strengthening their combined effect on environmental protection. The empirical analysis is based on a panel of 73 countries over the period 1990–2023. To account for heterogeneity, the sample is further divided into 37 OECD and 36 non-OECD countries, reflecting differences in income levels, environmental governance, energy dependence, and policy priorities. Overall, empirical findings indicate that sustainable development and climate finance both significantly lower CO 2 emissions. This outcome is confirmed for both OECD and non-OECD countries. Renewable energy improves environmental quality since it significantly reduces CO 2 emissions, whereas total energy consumption increases them. Finally, the results also support the Environmental Kuznets Curve (EKC) theory, since GDPG raises CO 2 emissions while GDP squared lowers them. This study provides several contributions to the existing literature. First, we provide a unified empirical framework that jointly examines climate finance, sustainable development, and CO 2 emissions while explicitly accounting for cross-country heterogeneity through the OECD versus non-OECD distinction, which remains underexplored in an integrated setting. Second, we employ second-generation panel techniques (CCEP and CCEMG) that address cross-sectional dependence and slope heterogeneity, thereby offering more reliable and comparable estimates than conventional approaches widely used in prior studies. Third, we extend the literature by offering new evidence based on an updated and comprehensive global dataset, allowing for more robust policy-relevant insights. Finally, the study provides actionable insights for policymakers regarding climate finance opportunities to be targeted towards sustainability and environmental performance improvement, particularly in developing economies. 2. Related Literature and Hypotheses Development The underlying theoretical framework linking climate finance, sustainable development and CO 2 emissions begin at the crossroads between environmental economics and sustainable development theory. Climate finance is the financial support from public and private sources to support climate mitigation and adaptation projects to develop renewable energy technology and climate-resilient infrastructure [ 13]. Climate finance directly improves the environment by reducing emissions, while also indirectly enhancing the environment through promoting sustainable development for environmentally sustainable economic growth that is also socially equitable. The sustainable development theory states that long-term progress is necessary to achieve a balance between economic, social, and environmental goals. In this context, climate finance is an enabling factor for the integration of green technologies, the enhancement of institutional frameworks, and the governance of environmental protection [ 21]. The Environmental Kuznets Curve (EKC) hypothesis [ 25] provides an additional theoretical perspective by arguing that environmental deterioration increases with income initially, but declines where income levels lead to the adoption of cleaner technologies and stiffer regulations. Climate finance accelerates this process, enabling developing countries to skip the highly polluting levels of economic growth and take up sustainable pathways earlier. These theoretical perspectives indicate that climate finance and sustainable development are two sides of the same coin in driving an improvement in the environment. Climate finance offers the resources, while sustainable development offers the policies and institutional frameworks to ensure that economic growth will deliver environmental improvement as an objective outcome. Nevertheless, this theoretical alignment may be overly optimistic. The EKC remains empirically inconclusive, especially in developing countries where structural and institutional constraints may delay the turning point. Moreover, climate finance does not automatically lead to environmental improvements; its effectiveness depends on governance quality, regulatory enforcement, and absorptive capacity. Weak institutions and misallocation of funds can limit its impact, making the relationship between climate finance and sustainable development conditional rather than automatic. Climate finance has a direct and indirect effect on CO 2 emissions by encouraging more environmentally friendly and long-lasting business practices [ 14, 26]. First, it provides the necessary funds to climate change mitigation and adaptation projects in renewable energy, energy efficiency, and reforestation. These strategies reduce pollution and greenhouse gas emissions, leading to an improvement in the air, water, and soil. For instance, giving necessary funds to solar and wind energy projects makes us less reliant on fossil fuels, which lowers carbon intensity and improves environmental quality [ 15, 27]. Second, climate finance indirectly decreases CO 2 emissions by encouraging new technologies and the building of green infrastructure. Financial flows aimed at clean technologies motivate businesses and governments to implement eco-friendly manufacturing methods and sustainable urban development. This process also has spillover effects that make ecological resilience stronger [ 28, 29]. Third, climate finance helps institutions and governments by encouraging policy frameworks that prioritize sustainability and accountability. Countries get help with implementing environmental rules and monitoring systems through international funding programs like the Green Climate Fund (GCF) and the Global Environment Facility (GEF). Climate finance helps protect ecosystems and biodiversity by making people less vulnerable to climate shocks and helping developing countries adapt. This not only helps the environment right now, but it also makes it stronger in the long-run [ 30]. However, the impact of climate finance on CO 2 emissions is not automatic. Its effectiveness depends on governance quality, institutional capacity, and efficient allocation. Weak frameworks and misallocation can limit its impact, making emission reductions conditional on country-specific contexts. Recent empirical studies consider climate finance as one of the most important factors for achieving environmental sustainability. The relationship between climate finance and sustainable development operates through several key mechanisms. Climate finance supports investment in renewable energy and low-carbon technologies, promotes energy efficiency improvements, and facilitates technological transfer and innovation. It can also strengthen institutional capacity and environmental governance, improving the effectiveness of sustainability policies. Through these channels, climate finance contributes to environmental improvement and broader sustainable development outcomes. For instance, Ref. [ 31] found that air quality and energy efficiency are positively impacted and with an increase in climate-related investments, innovation and adoption of clean technologies. Similarly, Ref. [ 32] stated that the use of economically renewable energy and policies designed for sustainable development decreases the dependence on fossil fuels and enhances the relationship between financial inflows and environmental sustainability. More recently, Ref. [ 33] examine whether green finance reduces carbon emissions and whether environmental expenditure enhances this effect. They used cross-country panel data on green finance, public environmental spending, and CO 2 emissions. They applied panel econometric models with interaction terms to capture moderating effects. The results indicate that green finance significantly lowers emissions, and its effectiveness is notably stronger when supported by higher environmental expenditure, underscoring the role of complementary public policies. However, this optimistic view should be treated with caution. The empirical evidence remains mixed, as the effectiveness of climate finance depends heavily on institutional quality, policy coherence, and the efficient allocation of resources. In some cases, financial flows may be insufficient, poorly targeted, or concentrated in less impactful sectors, limiting their contribution to emission reduction. Moreover, many studies rely on aggregate data and may not fully capture country-specific constraints or implementation gaps. Therefore, the contribution of climate finance to environmental sustainability is not uniform but conditional on supportive governance structures and complementary public policies. The adoption of sustainable practices directly and indirectly impacts CO 2 emissions, and plays a key role in the climate finance—CO 2 emissions relationship. Sustainable development directly lowers CO 2 emissions by incorporating ecological protection into the economic and social policies of a country. Protecting the environment by prioritizing cleaner production technologies, investing in renewable energy, and the efficient use of natural resources reduces carbon emissions. Promotion of responsible consumption, strong environmental governance, green innovation and sustainable development aligns economic growth within ecosystem boundaries. Countries with sustainable development planning and institutional frameworks experience lower environmental degradation. Sustainable policies, like governance transparency, renewable energy policy frameworks, and environmental regulations, allow climate finance to target promising green initiatives. This synergy of climate finance to green initiatives ensures the availability and effective use of critical resources to achieve ecological outcomes. However, unsustainable development, weak institutional frameworks, and corruption, alongside prioritizing short-term economic goals, can hinder the positive impacts of climate finance. A significant part of research emphasizes how sustainable development affects CO 2 emissions. For instance, Ref. [ 19] reported that combining financial and environmental policies results in an ecological improvement, while Ref. [ 7] showed that sustainability-oriented governance enhances environmental performance by encouraging innovation and resource efficiency. More recently, Ref. [ 34] examined how climate finance contributes to achieving Sustainable Development Goal 13. They analyzed panel data from 171 countries (2013–2023) using a double machine learning approach. They found that climate finance significantly reduces greenhouse gas emissions by promoting renewable energy use, improving energy efficiency, and enhancing carbon sinks. The results also show heterogeneous effects, with loan-based and mitigation-focused finance being more effective than grants and adaptation finance. Importantly, strong national governance enhances the effectiveness of climate finance, indicating that institutional quality is key to maximizing environmental outcomes. Based on the development above, we formulate the two following hypotheses: H1.Climate finance is expected to exert a negative and significant effect on CO 2 emissions. H2.Sustainable development is hypothesized to be negatively associated with CO 2 emissions. Figure 1 gives a conceptual framework on the transmission channel between climate finance, sustainable development and CO 2 emission. The literature on this topic reports that there are notable differences between OECD and non-OECD nations in how well climate finance tends to lower CO 2 emissions and enhance environmental quality. Higher technological capability, improved financial management, and stronger institutional quality in developed economies increase the effectiveness of climate finance mechanisms. However, the impact of global climate finance on CO 2 emissions is less effective in developing nations due to their limited access and weak regulatory frameworks. The advantages of climate finance are also greater in countries with strong institutional frameworks and transparency initiatives, underscoring the importance of governance in guaranteeing that funds are translated into environmental benefits [ 35]. Even though several studies have examined the linkage between climate finance, sustainable development, and CO 2 emissions is increasing, there are still important gaps in the literature [ 35, 36, 37]. First, the majority of prior research has separately investigated these relationships, concentrating either on how climate finance directly affects environmental outcomes or on how sustainable development lowers CO 2 emissions, without combining the three aspects into a single framework. Second, the role of sustainable development in climate finance and CO 2 emissions is less explored. Third, although several studies have examined the effectiveness of climate finance on global or regional dimensions, the comparative analyses between OECD and non-OECD countries remain scarce. Given the substantial differences between developed and developing countries in terms of institutional quality, financial capacity, and environmental governance, splinting these two groups becomes crucial [ 38, 39]. By filling in these gaps, the current study adds to the body of literature by offering a thorough cross-country analysis that incorporates these factors and looks at how climate finance and sustainable development affect CO 2 emissions in both OECD and non-OECD economies. This study fills these gaps by offering a comparative analysis of the connection between CO 2 emissions, sustainable development, and climate finance. Contrary to prior studies, the current study combines these three factors to reveal the transmission mechanism through which climate finance and sustainable development influence CO 2 emission. The study uses a cross-country dataset of 73 countries, 37 OECD and 36 non-OECD, to capture heterogeneity across various levels of institutional capacity, economic development, and environmental policy performance. The OECD and non-OECD classification is grounded in institutional and development differences that may shape the effectiveness of climate finance. OECD countries generally have stronger institutions, more developed financial systems, and stricter environmental regulations, while non-OECD countries often face institutional and financial constraints. These differences justify potential heterogeneity in how climate finance translates into environmental outcomes. Additionally, the study employs econometric methods based on CCEP and CCEMG estimators to offer empirical support for the joint effect of climate finance and sustainable development on CO 2 emission. This paper helps to fill the gap between theory and practice by providing fresh perspectives and policy-relevant ramifications for improving the alignment of financial mechanisms, sustainability plans, and environmental preservation objectives in both developed and developing countries. 3.1. The Sample To investigate the relationship between climate finance, sustainable development, and CO 2 emissions, this study uses a final sample of 73 countries from 1990 to 2023. Additionally, to capture the heterogeneity interactions between climate finance, sustainable development and environmental factors across different stages of economic progress, the full sample is split into 37 OECD and 36 non-OECD countries. This classification makes it possible to compare advanced economies with developing countries. This decomposition offers a deeper understanding of how these factors influence the transmission channel between climate finance, sustainable development, and CO 2. It also reflects strong differences in energy dependence, policy priorities, and institutional maturity. To ensure consistency and cross-country comparability of the data, the majority of the variables used in this analysis are retrieved from the World Bank’s World Development Indicators (WDI) database. The countries used in this study are listed in Appendix A. 3.2. Empirical Approach and Model Specification This paper applies a rigorous second-generation panel econometric framework in order to investigate the long-run relationship between climate finance, sustainable development, and CO 2 emissions across OECD and non-OECD countries. This methodological option is particularly fitting, considering the structure of large transnational and temporal datasets that inevitably produce strong cross-sectional dependencies due to common global shocks, such as coordinated climate policies and global technological diffusion, along with slope heterogeneity arising from differences in economic development, institutional capacity, and environmental governance. Traditional first-generation panel techniques would produce biased and inconsistent estimates in this context. To ensure valid inference, the empirical analysis proceeds in several steps. First, tests for slope homogeneity and cross-sectional dependence are conducted to diagnose the underlying panel structure and provide justification for the use of second-generation methods. Second, CADF and CIPS panel unit root tests are applied in order to determine the order of integration while explicitly accounting for cross-sectional dependence. Third, the existence of a long-run equilibrium relationship is checked using the robust cointegration procedures that include the Bootstrap CCE-augmented Westerlund test and the factor-augmented test by [ 40], which are conducted by incorporating Common Correlated Effects to control for unobserved common factors. When cointegration is established, the long-run coefficients are estimated by applying the Common Correlated Effects Pooled (CCEP) and the Common Correlated Effects Mean Group estimators (CCEMG). These two econometric approaches allow for homogeneous and heterogeneous long-run responses across countries. They provide a robust framework for macro-panel analysis by effectively addressing cross-sectional dependence through the inclusion of cross-sectional averages that proxy unobserved common factors. Both approaches mitigate omitted variable bias linked to global shocks and interdependencies, ensuring consistent and reliable estimates, particularly in panels characterized by strong economic and environmental linkages. The empirical strategy can be summarized in the following diagram ( Figure 2) which outlines the sequential steps of the second-generation panel econometric framework. To explore the relationship between climate finance, sustainable development, and CO 2 emissions, we use the carbon dioxide (CO 2) emissions excluding LULUCF per capita (t CO 2e/capita) to measure environmental quality. Following [ 46], we used the natural logarithm of Aid (ODA) commitments to countries and regions as a proxy of climate finance. ODA commitments are often used as a proxy for climate finance because a growing share of development aid supports mitigation, adaptation, and low-carbon projects. ODA provides a standardized and widely available measure of public financial flows relevant to climate objectives. To measure sustainable development, we used the Sustainable Development Index (SDI) which measures the ecological efficiency of human development, recognizing that development must be achieved within planetary boundaries. It was created to update the Human Development Index (HDI) for the ecological realities of the Anthropocene. It is computed as the ratio between 2 components: (1) development index, based on the Human Development Index (life expectancy, education, and income), and an (2) ecological impact index, which captures the extent to which consumption-based CO 2 emissions and material use exceed fair shares of planetary boundaries. This approach reflects both social progress and environmental pressure, with further methodological details provided in Hickel (2020) [ 47]. The SDI formula can be described as follows: SDI = Development index Ecological Impact Index As the control variable, we include in the econometric model total final energy consumption (TFEC). TFEC is defined as the total energy consumed by end-users across all sectors, excluding non-energy uses, and is derived from energy balance statistics. It is measured in terajoules (TJ). We also include renewable energy consumption (REN). REN refers to the share of total final energy consumption that comes from renewable sources. It is measured as a percentage of total final energy consumption (%). As a macroeconomic factor, we include the annual growth of gross domestic product (GDPG). We estimate the following econometric model given in Equation (1): C O 2 i , t = α 0 + α 1 C F i , t + α 2 S D I i , t + α 3 R E N i t + α 4 G D P G i t + α 5 G D P G S Q i , t + α 6 T F E C i t + ε i , t (1) All variable definitions and measurements are given in . 3.3. Exploratory Data Analysis Figure 3 indicates that despite the short-term fluctuations connected with economic and energy-market conditions, there has been a clear long-term decline in CO 2 emissions per capita over the 1990–2023 period. This downward trend would thus suggest gradual improvement in energy efficiency, the increasing use of renewables, and the hardening of environmental policies. It provides initial evidence that climate finance and sustainable development efforts may be contributing to lower emissions over time. Figure 4Figure 5 together suggest that both climate finance and sustainable development are negatively related to CO 2 emissions, but the intensities of these relationships vary across different country groups. For the full sample, Figure 4 indicates that higher levels of climate finance and better sustainable development performance generally result in lower CO 2 emissions, consistent with expectations that financial and developmental advancement provide the impetus for environmental improvement. Figure 5 disaggregates countries by their development status and presents more pronounced patterns for OECD economies, which have clearer and more consistent relationships. Greater dispersion among non-OECD countries indicates broader variation in policy environments and in the effectiveness of both climate finance and sustainable development initiatives. Together, these figures indicate substantial cross-country differences and provide a rationale for treating OECD and non-OECD country groups separately in the empirical analysis. After providing an explanatory analysis of the data used in this study, the following step consists of checking for potential multicollinearity problems. The results of the pairwise correlation matrix and the Variance Inflation Factor (VIF) diagnostics are reported in . The statistics displayed in show that the level of pairwise correlation among the explanatory variables remains relatively weak to moderate, with the highest correlation coefficient remaining well below the conventional threshold associated with severe multicollinearity. In addition, the VIF values are all close to 1, with a mean VIF of 1.05, which is substantially below the commonly accepted critical values reported in the literature. Hence, we confirm that there is no serious multicollinearity issue in this study, and the estimated models appear to be well specified. 4. Discussion of the Empirical Findings presents the slope homogeneity diagnostics. The test statistics are statistically significant at the 1% level, implying the rejection of the null hypothesis of slope homogeneity. This means that the coefficients are heterogeneous across countries and confirming that countries do not share a common long-run structure. 4.1. Slope Homogeneity and Cross-Sectional Dependence reports the results for the cross-sectional dependence test and the exponent of cross-sectional dependence. The CD statistics are highly significant for all variables, thus strongly rejecting the null of cross-sectional independence. This implies the presence of strong cross-sectional dependence in the series, indicating that countries within this sample are connected through common shocks, spillovers, or global factors. Additionally, the range of α ^ values between 0.3999 and 0.9110 confirms that the dependence is moderate to strong for the variables under consideration. These results given in confirm the appropriateness of applying second-generation panel econometric techniques that take into account both cross-sectional dependence and slope heterogeneity explicitly. Specifically, the CADF and CIPS unit root tests are applicable since they consider cross-sectional dependence in the testing procedure. Similarly, the Bootstrap CCE-augmented Westerlund cointegration test and the factor-augmented cointegration test of [ 40] are well-founded because both tests rely on CCE augmentation to control for unobserved common factors and cross-sectional dependence, thus confirming the methodological choices made in the empirical analysis. 4.2. Panel Unit Root Tests Following the confirmation of slope heterogeneity and strong cross-sectional dependence, below presents the results for the second-generation panel unit root tests that explicitly account for cross-sectional dependence, namely the CADF and CIPS tests. The obtained results show a clear pattern: all series are non-stationary in levels but become stationary after first differencing, confirming they are integrated of order one, I(1). More precisely, while CADF statistics at levels are insignificant for all series, first-difference CADF values are strongly significant at the 1% level for all series. The result presented in is also corroborated by the CIPS test: the level statistics fail to reject the null of non-stationarity for any variable, while the first-difference CIPS statistics are all significant at conventional levels, which suggest stationarity after differencing. Overall, the combined CADF and CIPS results indicate that all variables follow an I(1) process, a necessary condition for conducting panel cointegration analysis in the next section. Second-generation Panel unit root tests. Second-generation Panel unit root tests. Pesaran (2007) [ 42] CADF Test CIPS Test Level First Difference Level First Difference CO 2−1.210 2.976 *** −1.210 −2.12 ** CF −1.567 0.956 *** −0.977 −2.05 * SD 5.773 −10.621 *** −1.996 −2.08 ** GDPC 4.447 −16.045 *** −1.050 −2.21 *** GDPCSQ 9.442 −13.099 *** −0.409 −2.34 *** REN 38.316 −12.111 *** −1.097 −2.10 ** TFEC −1.333 4.409 *** −2.03 * −2.33 * ***, ** and * indicate level of significance at 1%, 5%, and 10%. 4.3. Cointegration Analysis Since the analysis confirms slope heterogeneity and strong cross-sectional dependence across countries, it is methodologically relevant to split the analysis into three samples: the full sample, OECD countries, and non-OECD countries. Such differentiation is relevant because of the quite substantial differences in the institutional, economic, and environmental structures between advanced economies and developing/emerging ones. While mature climate policies, higher technological capacity, established carbon pricing frameworks, and generally stronger governance systems are more typical for OECD countries, the major binding constraints that most non-OECD countries face relate to structural factors, lower institutional effectiveness, and high dependency on external climate finance. These structural differences imply that both the adjustment dynamics and the long-run relationships between climate finance, sustainable development, economic variables and CO 2 emissions may be quite different across groups. The splitting of the sample can thus provide a more accurate capture of such differential long-run mechanisms. reports the results of the [ 40] factor-augmented cointegration test, which further controls for unobserved common factors. The results, which have consistently long-run relations to exchange, exist between the set of explanatory variables of model 1 and CO 2 emissions across all groups of countries. For the whole sample, we find that the trace statistic strongly rejects the null of no cointegration in the first step, while it fails to reject subsequent hypotheses which suggests one cointegrating vector. The same pattern is also echoed for OECD countries, in which the null of no cointegration is rejected, but maintaining all higher-order hypothesis, thus confirming a single long-run equilibrium relationship within more advanced economies. For non-OECD countries, the test also rejects the null of no cointegration, and the trace statistic for the second hypothesis (r ≤ 1) marginally exceeds the critical value, suggesting the presence of two cointegrating relationships. This suggests more complicated long-run dynamics in developing and emerging countries due, presumably, to their substantially greater structural heterogeneity, multiple adjustment channels and stronger linkages with common global shocks. On the other hand, presents the Bootstrap CCE-augmented Westerlund ECM Panel Cointegration Test, which considers cross-sectional dependence through CCE augmentation. The full-sample test statistics indicate strong evidence of cointegration; specifically, two out of four statistics yield robust p-values below 5%. This supports the idea of a long-run equilibrium relationship when all countries are pooled together. The cointegration evidence is even stronger for the OECD subsample since the robust p-values of all four statistics are below 5%, indicating that long-run environmental and economic adjustments are more stable and coherent across advanced economies. On the contrary, it presents a mixed pattern in the non-OECD group; specifically, while two test statistics report significant cointegration, the other two are insignificant. This seems to suggest that although there is a long-run relationship in developing countries, it is less uniform, influenced by the heterogeneity of institutional, policy, and financial conditions. Also, the CD tests illustrate strong and significant cross-sectional dependence across all samples, confirming that countries are affected by common global shocks. This, in turn, justifies using the CCE-augmented Westerlund test designed to account for cross-sectional dependence and supports splitting the sample into OECD and non-OECD groups, since the strength and transmission of these common shocks vary across country types. Overall, the results displayed in suggest that the variables share a common trend and help in supporting the use of long-run estimators like CCEP and CCEMG for further analysis. 4.4. Homogeneous and Heterogeneous Long-Run Estimates The statistical results of the significant CD test show that the sample countries are impacted by common global shocks, which cause cross-sectional dependence in the data. Because this cross-sectional dependence produces biased results, first-generation panel estimators are inappropriate. Since second-generation estimators like CCEP and CCEMG successfully account for these common factors, their use is therefore appropriate. These estimators enhance the results’ credibility and robustness by taking this dependence into account, guaranteeing that the long-term relationships found are not influenced by invisible global co-movements. From , we note that both the CCEP and CCEMG estimators validate the robustness of the results. Although the estimated coefficients obtained from these estimators are broadly consistent across specifications, this consistency should not be interpreted mechanically. Rather, given that the two estimators rely on different assumptions regarding slope homogeneity, the similarity of the results provides additional evidence that the identified long-run relationships are stable and not driven by a specific estimation technique or restrictive specification assumption. The findings displayed in show consistent long-term relationships between the explanatory variables and CO 2 emissions across the full sample, OECD, and non-OECD countries. All models show a negative and significant impact from climate finance, with non-OECD countries showing the largest impact, except for the CCEP estimation for the full sample. Climate finance exhibits a consistently negative effect across all model specifications, although its magnitude and significance vary. A 1% increase in climate finance leads to a reduction ranging from 1.8% to 4.9% in the dependent variable, indicating a non-negligible economic responsiveness. For the full sample, the effect is economically meaningful (around 3.1%) in the CCEMG estimation, while it remains statistically insignificant under CCEP, suggesting some sensitivity to estimator choice. In OECD countries, the impact is comparatively smaller, with elasticities between 1.8% and 2.3%, reflecting a more moderate economic effect in advanced economies. In contrast, non-OECD countries exhibit the strongest response, where a 1% increase in climate finance is associated with a reduction of approximately 4.1% to 4.9%, implying a relatively high economic magnitude. Overall, these results indicate that the economic effect of climate finance is substantially more pronounced in developing economies, where marginal increases in climate finance generate larger proportional changes in the dependent variable. This highlights that the economic effect of climate finance is substantially stronger in developing economies. Additionally, the stronger effect in non-OECD countries reflects higher dependence on external finance and greater vulnerability to climate and financial constraints. OECD countries, with more developed financial systems and existing climate policies, show smaller marginal effects. Finally, institutional and structural differences explain the heterogeneity in the results. This finding suggests that where there are greater financial constraints and mitigation needs, external climate-related aid is especially effective. Climate finance is expected to be more effective in OECD countries due to stronger institutions, developed financial systems, and cleaner energy structures [ 49]. In contrast, non-OECD countries may experience weaker effects because of institutional constraints, limited financial development, and higher fossil-fuel dependence. This outcome is consistent with the findings of [ 50, 51, 52]. These differences can be explained by variations in institutional quality, financial market development, and energy transition capacity across country groups. OECD countries generally benefit from stronger governance frameworks, more developed financial systems, and advanced energy structures, which enhance the efficient allocation and transmission of climate finance into emission reductions. In contrast, non-OECD countries often face weaker institutions, limited financial depth, and higher dependence on fossil fuels, which can constrain the effectiveness and speed with which climate finance translates into environmental improvements. The results given in also show that, for all model specifications, sustainable development has a negative and statistically significant coefficient. A 1% increase in sustainable development reduces the dependent variable by about 6.2% to 12.1%, indicating a strong economic impact. The effect is moderate in OECD countries, stronger in the full sample and largest in non-OECD countries (10.3–12.1%), highlighting a more pronounced impact in developing economies. The stronger effect in non-OECD countries reflects their greater scope for improvements in economic structure, governance, and environmental regulation. OECD countries already have more advanced and stable systems, so the marginal impact is smaller. This suggests that significant contributions to lowering CO 2 emissions come from widespread advancements in the economic, social, and environmental spheres. This implies that countries that make investments in more balanced economic systems, improved social well-being, robust environmental regulations, and improved governance structures typically see notable drops in carbon intensity. The impact is particularly more pronounced in developing nations, where advancements in sustainable development are frequently correlated with enhanced institutional capability, more accessible green technologies, and cleaner production methods. This result is comparable to that of [ 29, 53, 54]. In addition, findings in show that the impact of economic growth on CO 2 emissions is found to be positive and statistically significant. This result confirms that growing economic activity tends to increase environmental pressures and energy use in the early stages of development. A 1% increase in GDP growth raises the dependent variable by about 18.6% to 27.1%, indicating a strong economic impact. The effect is moderate in OECD countries, higher in the full sample, and largest in non-OECD countries (24.7–27.1%), suggesting a stronger impact in developing economies. Nonetheless, a pronounced U-shaped relationship is revealed by the negative and significant coefficient of the squared GDP term. Our results confirm the Environmental Kuznets Curve (EKC), with the negative and significant squared GDP term indicating a turning point where emissions decline after surpassing a certain income threshold. Economically, this turning point represents the income level at which countries shift from pollution-intensive growth to cleaner and more sustainable development paths driven by technological progress, structural transformation, and stronger environmental regulations. Importantly, this threshold has distinct policy implications across country groups: OECD countries are generally closer to or beyond the turning point, implying that additional income growth is increasingly associated with emissions reduction, supported by advanced institutions and green innovation capacity. In contrast, non-OECD countries are typically below the turning point, meaning that economic growth still tends to increase emissions, and therefore require targeted policies such as access to climate finance, technology transfer, and institutional strengthening to accelerate the transition. The EKC turning point highlights both the long-run potential for decoupling growth from emissions and the short-run challenges faced by developing economies in achieving sustainable development. This result supports the research of [ 55, 56]. The results in also show that the use of renewable energy has a negative and significant impact on all models except for the CCEMG estimation for the full sample. This suggests that increasing the use of renewable energy sources regularly helps to reduce CO 2 emissions. This finding emphasizes the critical role cleaner energy transitions play in reducing environmental degradation, both in developing nations that are increasingly incorporating renewables into their energy mix and in developed economies with well-established renewable infrastructure. By displacing carbon-intensive fossil fuels and promoting more efficient, low-emission energy systems, renewable energy adoption emerges as a key driver of long-term decarbonization. This result is in line with [ 57, 58]. Finally, the results in reveal that the impact of final energy consumption is statistically significant, mostly in the full sample, non-OECD countries, and OECD countries (CCEMG estimation). This emphasizes how higher energy demand is inherently carbon-intensive. Reliance on fossil fuels directly contributes to rising CO 2 emissions as economies grow and the energy demands of industry, transportation, and households rise. In non-OECD nations, the effect is less noticeable, possibly as a result of variations in the energy mix or lower overall energy consumption. Even in comparatively developed economies, this finding highlights the necessity of increasing energy efficiency and making a slow shift to cleaner energy sources in order to lessen the negative environmental effects of growing energy demand. 5. Concluding Remarks and Recommendations The purpose of this study was to investigate the effects of climate finance on CO 2 emissions with consideration of sustainable development as a key driver. To do this, we analyzed a dataset consisting of 73 countries covering the period from 1990 to 2023 with 37 members of the OECD and 36 non-OECD members, while accounting for variations in terms of economic maturity, policy environment, energy dependence, and environmental governance. The findings reveal that both climate finance and sustainable development significantly reduce CO 2 emissions. This result is confirmed for both OECD and non-OECD countries. Renewable energy is found to be a driver to enhance environmental quality since it significantly lowers CO 2 emissions, while total energy consumption increases it. Finally, the results also confirm the Environmental Kuznets Curve (EKC) theory, since GDPG increases CO 2 emission while the GDP squared decreases it. The results of this research provide several relevant policy implications. First, it is very useful to target climate finance towards sustainable development efforts, such as renewable energy, energy use, and green infrastructure, to maximize the reduction of carbon dioxide emissions. For OECD countries, the findings suggest that climate finance should be directed toward accelerating innovation in renewable energy, enhancing energy efficiency, and supporting advanced green infrastructure projects. Given their stronger institutional capacity and established regulatory frameworks, OECD nations are well-positioned to leverage climate finance for scaling up technological breakthroughs and reinforcing stringent environmental standards. In contrast, for non-OECD countries, climate finance should prioritize building institutional capacity, strengthening environmental governance, and supporting basic infrastructure for clean energy access. Moreover, targeted support for monitoring and accountability mechanisms is crucial to ensure transparency and effective use of funds. Finally, cross-border cooperation remains essential: OECD countries can facilitate technology transfer and share best practices, while non-OECD countries can adapt these lessons to local contexts, thereby advancing toward climate-resilient and sustainable development pathways. While the study produced some interesting results, it is important to recognize some limitations to the analysis. First, the analysis is based on country-level data, which may obscure potential regional or sectoral differences in the relationship between climate finance and sustainable development as it relates to CO 2 emissions. Second, the study is able to segregate information between OECD and non-OECD countries, but it is unable to delineate intra-group heterogeneity, such as governance quality, technology adoption, or energy mix. Third, the use of CCEP and CCEMG estimators helps mitigate endogeneity related to unobserved common factors through the inclusion of cross-sectional averages, while the incorporation of lagged regressors partially reduces simultaneity bias. However, these approaches do not fully eliminate all sources of endogeneity, particularly reverse causality and measurement errors. Finally, the limited set of variables included in the study may have impacted the observed relationships. Future research has the potential to capitalize on this study in multiple ways. First, studies at the sub-national or sectoral level could provide greater nuance into how climate finance relates to CO 2 emissions and sustainable development within that industry or region. Second, future studies should investigate intra-group diversity and examine differences in terms of governance quality, technological adoption, and energy sources within OECD and non-OECD countries. Third, the use of instrumental variables or dynamic panel methods could solve the endogeneity problem. Lastly, adding other political stability, cultural attitude, or private sector behavior variables would deepen the understanding of how climate finance influences sustainable development and environmental outcomes. Conceptual framework. Conceptual framework. Evolution of CO 2 Emissions over Time. Evolution of CO 2 Emissions over Time. Scatterplot relationships between, climate finance, sustainable development and CO 2 emissions. Scatterplot relationships between, climate finance, sustainable development and CO 2 emissions. Climate Finance, and Sustainable Development and CO 2 Emissions: Cross-Country Relati
