Environmental degradation has emerged as a significant concern in recent decades, particularly for developing nations. Sustainable development and environmental protection are frequently at odds with one another as these countries pursue rapid industrialization, urbanization, and economic expansion (Anser et al., 2025; Chang et al., 2024). Public health, food security, and long-term financial viability are all gravely threatened by the environmental degradation that has become apparent through polluted air and water, deforestation, declining biodiversity, and rising carbon dioxide (CO₂) emissions (Leddin, 2024; Richardson, 2010).

The growing population, industrial production, and GDP of emerging economies, such as Turkey, China, India, Brazil, South Africa, and Indonesia, ensure that these countries will continue to shape global development (Halim & Moudud-Ul-Huq, 2024; Wang and Xu, 2025). Nevertheless, elevated levels of environmental stress often accompany their rapid development rates. The energy systems, industries, and land-use practices of many of these nations are highly dependent on fossil fuels, which leads to increased emissions of greenhouse gases, soil erosion, and the depletion of natural resources (Priya et al., 2023; Zheng X et al., 2023).

The ability of emerging economies to reduce environmental degradation is hindered by institutional restrictions, limited technical capabilities, and weak regulatory frameworks, in contrast to industrialized economies (Zhang et al., 2024). There is still an unsustainable pattern of growth in many areas, with a focus on immediate financial benefits rather than environmental resilience. According to the “pollution haven hypothesis” (Cole, 2004), emerging economies with lax environmental regulations may exacerbate local environmental conditions by attracting polluting companies from countries with more stringent standards. Furthermore, inequality in wealth and social stratification in developing nations may limit citizens’ ability to participate in environmental policymaking, which in turn increases vulnerability to environmental threats and perpetuates social injustice (Green and Healy, 2022; Islam, 2024).

However, remedies to the environmental issue in emerging economies do exist. Environmental innovation, green financing, renewable energy investments, and international environmental cooperation are among the recent efforts that have stepped up to support sustainable development (Raman et al., 2025; Tang et al., 2024). Other multilateral accords, like the Paris Climate Accord, have encouraged emerging economies to set climate goals and adopt low-carbon development plans. Environmental degradation must be addressed effectively through a comprehensive policy framework that incorporates economic complexity, technical innovation, and institutional change. The amount of development, however, remains uneven, highlighting the need for this framework (Olarewaju et al., 2025). Environmental degradation is common in development driven by fossil fuels (Jiang and Hassan, 2025). Individuals, communities, and countries must begin transitioning to renewable energy sources to build robust and equitable economic systems and save the Earth.

From 1990 to 2024, the predicted data indicate that CO₂ emissions from rising economies are expected to decrease gradually (Figure 1). This demonstrates a gradual but noticeable shift toward an emphasis on environmental sustainability. Emissions of carbon dioxide from emerging economies have been falling steadily from 1990 to 2024. Although there is considerable variation among countries and their policies, this change indicates the progressive implementation of eco-friendly practices and energy transition programs. The reaction of emerging economies to climate change is reflected in the overall drop. To accomplish long-term environmental objectives and carbon neutrality targets, it is necessary to implement stronger policy interventions, make more sustainable investments, and garner international support. Nevertheless, the modest rate of decrease shows that progress has been inconsistent.

However, it remains unclear how economic complexity relates to environmental deterioration (Neagu, 2020). Increasing industrial activity and resource use are two ways in which economic complexity may exacerbate environmental problems. Although some studies suggest that economic complexity reduces emissions and increases production, others indicate the opposite (Tabash et al., 2024; Wang et al., 2023). Further, Economies with higher complexity have greater capabilities in terms of human capital, infrastructure, and institutional quality, which are necessary for developing and implementing green technologies. These economies often have a more diversified and technologically advanced industrial base, allowing them to engage more easily in complex innovation processes (Hidalgo and Hausmann, 2009). This demonstrates how complexity reflects a country’s productive knowledge, which is essential for innovation. However, further research is needed into the relationship between economic complexity and environmental effects, particularly in emerging economies where the economy is evolving rapidly (Boleti et al., 2021; Neagu, 2020).

In terms of the relationship between the ECI and CO₂ emissions per capita, Figure 2 illustrates this relationship hypothetically. The ECI measures the degree of development in an economy’s export structure, while CO₂ emissions per capita serve as a measure of environmental degradation. This graphically represents the simulated patterns, which show that economic complexity is steadily rising while emissions follow an inverted U-shaped pattern.

Another association is between green innovation and carbon dioxide (CO₂) emissions, which are central to current discussions about sustainable development and mitigating the effects of climate change (Li et al., 2023). An increasingly popular strategy for attaining environmental efficiency and separating economic growth from environmental deterioration is green innovation, which refers to the creation and adoption of new goods, processes, and behaviours that reduce the negative impact on the environment (Aliani et al., 2024; Dash et al., 2024; Hoa et al., 2024). While discussing CO₂ emissions from a theoretical perspective, it is essential to note that green innovation serves as a tool for both prevention and correction (Chang et al., 2023). To reduce emissions at the source, it promotes the development of cleaner industrial technology and low-carbon alternatives. By enhancing carbon capture, reuse, and recycling technologies and optimizing current operations for greater energy efficiency, it serves as a corrective tool (Min et al., 2022; Saxena et al., 2024).

The Green Innovation Index has shown a consistent rise from the early 1990s to 2024 (Figure 3). The trend suggests that rising economies are increasingly prioritizing environmentally friendly practices in industry, research into sustainable technologies, and clean energy solutions. This era of initial modest growth (the 1990s to the early 2000s) is consistent with the historical fact that many emerging economies put economic development ahead of environmental concerns. During this time, innovation growth was relatively slow. Green innovation, finance, energy, and development are all profoundly impacted when developing nations adopt renewable energy (Hoa et al., 2024; Nawaz et al., 2025; Xuan et al., 2024).

The relationship between FinTech and environmental sustainability, specifically regarding CO₂ emissions, is a relatively new field of study (Osei-Assibey Bonsu et al., 2025; Rabbani and Kiran, 2025). Mobile banking, blockchain, crowdfunding, digital payments, and robot-advisory platforms are all examples of financial technology (FinTech). This field can improve the efficiency, inclusivity, and sustainability of economic systems, which in turn could change environmental outcomes (Javaid et al., 2022). According to environmental economists, FinTech has the potential to significantly contribute to reducing CO₂ emissions through various direct and indirect mechanisms (Wang et al., 2024). Included in this category are efforts to increase the availability of green financing, facilitate the measurement of carbon emissions in real-time, pave the way for managing climate risk, and encourage individuals to adjust their purchasing habits to reduce their carbon footprint (Hasan et al., 2024).

The study’s overarching goal is to determine and measure the extent to which these interrelated variables, ECI, GIN, FT, and CO₂ emissions, exist in emerging economies. To achieve this, the study will use the Method of Moments Quantile Regression (MMQR) model to 1(a) examine how ECI, GIN, FT, and CO₂ emissions affect environmental degradation in emerging economies; (b) To compare the impact of ECI, GIN, FT, and CO₂ emissions overall across various quantiles of both environmental and economic performance; (c) Utilize quantile regression to examine the nonlinear effects and impact heterogeneity across emerging market sectors and varying degrees of development; and (d) to guide policymakers in these countries toward more rapid adoption of ECI, GIN, FT, and CO₂ emissions to ensure their economic growth in the long run by suggesting environmentally friendly policies and allocating appropriate subsidies. Only the group of emerging economies, as defined by the World Bank and IMF, was included; due to their structural changes, varying ECI, GIN, and FT, which provide a clear analysis of the impact of CO₂ emissions.

Investigating this association will enhance the existing information repository by enabling policymakers to develop more targeted interventions through a comprehensive, quantile-specific analysis of the effects of green initiatives on economic and environmental indicators in emerging nations. For this investigation, the MMQR (Machado and Santos Silva, 2019) is employed to examine the relationship between ECI, GIN, FT, and CO₂ emissions. Because it facilitates the evaluation of heterogeneous effects across multiple quantiles of the environmental degradation distribution, the MMQR technique is particularly well-suited for this analysis.

The MMQR approach, in conjunction with Fully Modified Ordinary Least Squares (FMOLS) and Dynamic Ordinary Least Squares (DOLS), was applied to ensure methodological rigor, as MMQR can capture distributional heterogeneity across the conditional distribution of CO₂ emissions. Furthermore, it enables an examination of how ECI, GIN, and FT exert differential impacts at lower, median, and higher emission quantiles, a heterogeneity that is significant in the context of developing regions. Moreover, the MMQR is robust to outliers and skewed data distributions, which are characteristic of environmental and financial indicators.

Complementing MMQR with FMOLS and DOLS allows for the examination of long-run equilibrium relationships among the variables. The FMOLS corrects for the endogeneity and serial correlation inherent in non-stationary panel data. Therefore, it can provide an efficient and unbiased estimate of cointegrating relationships. DOLS, on the other hand, incorporates leads and lags of the differenced regressors to account for dynamic adjustments, addressing small-sample bias and ensuring robustness of the long-run estimates. Employing both FMOLS and DOLS validates the stability of the long-run coefficients, thereby enhancing confidence in the empirical findings. Hence, offering a comprehensive econometric strategy.

The following sections will serve as a synopsis of the reviewed literature. The technique goes on to detail the data and econometric methodology. Finally, the study will review the results, provide policy suggestions, outline limitations, and identify potential avenues for further research.

Table 2 presents the descriptive statistics that are examined in the empirical analysis. The mean values for ECI, GIN, FT, and CO₂ emissions are 2.287, 11.962,10.664, and 8.421, respectively. The skewness value indicates that GIN and (CO₂ emissions) exhibit positive skewness. In contrast, ECI and FT exhibit negative and left-skewed distributions. The kurtosis value suggests that the values of ECI, GIN, and (CO₂ emissions) are greater than three; on the other hand, the values of GIN and FT are less than 3, and statistically non-normally distributed. The results of the Jarque-Bera test demonstrated that the variables deviate from a normal distribution, offering valuable insights into their behavior. As a result, we used the novel MMQR approach to evaluate these relationships.

The slope heterogeneity test results, as demonstrated in Table 3, indicate considerable variance in the relationships between emerging economies. Both the Δ ^ S C H statistics (7.381, p-value = 0.002) and Δ ^ A S C H statistic (5.206, p-value = 0.004) reject the null hypothesis of slope Heterogeneity at the 1% level. This highlights the fact that crucial variables, such as economic complexity (ECI), green Innovation (GIN), FinTech (FT), and environmental degradation (CO₂ emissions), differ significantly across countries.

Table 4 presents the Cross-Sectional Dependence (CSD) test estimates. Supporting second-generation unit root tests, such as the CADF, CIPS, and the CD test, which account for cross-sectional dependence. The variables stabilize, and all are integrated of order 1 (I) at the 1% significance level. Based on the results, there is CSD evidence in the dataset, which suggests the use of the unit root tests of the second-generation CADF and CIPS to examine the stationary characteristics of the variables under investigation when both SCH and CSD are present. Moreover, according to the outcomes of (CSD), (CADF), and (CIPS) Tests, the Westerlund cointegration test is also recommended.

Table 5 summarizes the statistical results regarding the existence of co-integration in the evaluation. The test results confirm the existence of cointegration among the variables, although the statistical significance is excluded. The findings showed that the existence of co-integration was accepted as the alternative hypothesis, while the null hypothesis was rejected. This analysis lends credence to the study’s hypothetical testing by establishing a long-run relationship between the variables. Additionally, the results revealed a long-term correlation between the variables ECI, GIN, FT, and CO₂ emissions.

The relationship between ECI, GIN, and FT on CO₂ emissions across various quantiles in emerging economies is shown in Table 6, which displays the results of the Method of Moments Quantile Regression (MMQR). Quantile regression provides a comprehensive understanding of how these variables impact emissions differently at various levels, enabling the identification of the exact emission intensities that require prompt intervention.

There is a clear negative correlation between (ECI) and CO₂ emissions, which becomes stronger at higher quantiles (−0.4392 at the Q90). (ECI) is correlated with the adoption of cleaner technology in countries with higher emissions. Nevertheless, policies should be implemented to encourage green growth strategies during the initial phases of development, as the decreasing negative impact at lower quantiles (−0.0734 at Q10) suggests that in areas with low emissions, (ECI) may not directly lead to substantial reductions in emissions.

Moreover, a significant negative relationship exists between (GIN) and CO₂ emissions, and this relationship becomes stronger as the emission quantiles increase (−0.3647 at Q90). In cases where emissions are high, this highlights the importance of (GIN) in reducing emissions. Additionally, there is a strong negative correlation between CO₂ emissions and (FT) at all quantiles, with the negative correlation increasing with higher CO₂ emission levels (−0.2392 at the Q90). This suggests that fintech solutions will play a crucial role in reducing emissions in areas with high levels of pollution.

Table 7 shows that for every 1% rise in ECI, the adverse effect of ECI on CO₂ emissions is 0.248 and 0.219, respectively, according to FMOLS and DOLS. This contradicts the previous results, as in the GIN and CO₂ emissions nexus, FMOLS and DOLS indicate that GIN has an adverse effect on environmental degradation and CO₂ emissions, resulting in decreases of −0.042 and −0.063, respectively, with a 1% rise in GIN. Moreover, the results from FMOLS and DOLS indicate a negative correlation between Financial Technology FT and CO₂ emissions, with advanced estimates suggesting that a 1% increase in FT tends to reduce CO₂ emissions by −0.018 and −0.025, respectively.

This study empirically examines the association between CO₂ emissions and other factors in developing economies, which may be better understood from the study’s results. The study examines the relationship between ECI, GIN, and FT and the dependent variable CO₂ emissions. This provides strong evidence of different effects on the conditional distribution of CO₂ emissions, green innovation, and green growth indicators using econometric methodologies, including MMQR, FMOLS, and DOLS. Several key insights are offered by the results, which contribute to the ongoing conversation on sustainable development in developing countries.

ECI mitigates environmental deterioration, particularly at the highest quantiles of CO₂ emissions (Montagna et al., 2025). This suggests that economies that are more complex and expanding are better positioned to outperform polluting companies, diversify their production patterns, and adopt cleaner technologies. At higher quantiles, ECI is negatively associated with emissions, indicating that economies with more sophisticated production structures are better equipped to mitigate environmental degradation (Christoforidis and Katrakilidis, 2025; Marić et al., 2020; Martins et al., 2021). This aligns with the theoretical perspective that complex economies enable resource efficiency and innovation in cleaner technologies, such as technological diffusion, cleaner production, and institutional upgrading (Kumar et al., 2025). On the other hand, the opposite is true for the lower quantiles, where the coefficient of ECI is positive or insignificant, implying that economic complexity may initially exacerbate environmental pressures (Saqib et al., 2023). Studies have demonstrated that more complex economies tend towards cleaner production systems over time (Montagna et al., 2025; Zeng et al., 2024). These dynamics reflect the Environmental Kuznets Curve (EKC) hypothesis, which posits that environmental degradation may increase in early stages of industrial development before declining at higher levels of economic sophistication (Almeida et al., 2024).

The statistics support the hypothesis that FinTech contributes to the funding of green innovation for the top quantiles. Because FinTech has a negligible effect at lower quantiles, infrastructure constraints and digital divides continue to limit the full potential of financial innovation (Cao et al., 2025; Hossain et al., 2024; Musa et al., 2025). Further, the study utilizes high-technology exports (as a % of manufactured exports) as a proxy for FinTech (FT), which also aligns with previous research (Meral, 2019; Navarro Zapata et al., 2023; Srholec, 2007; Wang and Huang, 2025; Yang et al., 2024). However, High-technology exports do not fully capture the FinTech ecosystem, such as digital payments and mobile banking (Chand et al., 2025; Tut, 2023).

In addition, the results of the co-integration test revealed a long-run correlation between the variables ECI, GIN, and FT, and CO₂ emissions. The slope heterogeneity test results indicate considerable variance in the relationships between emerging economies. This highlights the fact that crucial variables differ significantly across countries (Capelleras et al., 2025; Musa et al., 2025).

On the other hand, the findings show that for every 1% rise in ECI, the adverse effect on CO₂ emissions is 0.248 and 0.219, respectively, according to FMOLS and DOLS. This contradicts the previous results, as in the GIN and CO₂ emissions nexus, FMOLS and DOLS indicate that GIN has an adverse effect on CO₂ emissions, resulting in decreases of −0.042 and −0.063, respectively, with a 1% rise in GIN. Moreover, the results from FMOLS and DOLS indicate a negative correlation between FT and CO₂ emissions, with advanced estimates suggesting that a 1% increase in FT tends to reduce CO₂ emissions by 0.018 and 0.025, respectively.

The MMQR test revealed a clear negative correlation between ECI and CO₂ emissions, which becomes stronger at higher quantiles (−0.4392 at the Q90). Economic complexity (ECI) is positively correlated with the adoption of cleaner technologies in countries with higher emissions, as indicated by this study. Nevertheless, policies should be implemented to encourage green growth strategies during the initial phases of development, as the decreasing negative impact at lower quantiles (−0.0734 at Q10) suggests that this approach may be particularly effective in areas with low emissions. ECI may not directly lead to substantial reductions in emissions. Moreover, a significant negative relationship exists between GIN and CO₂ emissions, and this relationship becomes stronger as the emission quantiles increase (−0.3647 at Q90). In cases where emissions are high, this highlights the importance of GIN in reducing emissions.

The discrepancies between MMQR and FMOLS/DOLS estimates arise because FMOLS/DOLS capture a homogeneous long-run average effect, while MMQR accounts for distributional heterogeneity, revealing that the impact of economic complexity varies across quantile groups. Although it was significantly negative, its magnitude depends on the specific quantile group to which it belongs (Fatima et al., 2024; Zhao and Arshad, 2025).

Additionally, there is a strong negative correlation between CO₂ emissions and FinTech (FT) at all quantiles, with the negative correlation increasing with higher CO₂ emission levels (−0.2392 at the Q90). It follows that fintech solutions will be crucial in reducing emissions in areas with high levels of pollution (Guo et al., 2023).

For instance, if there were no restrictions on the movement of energy-consuming equipment between nations, this could lead to increased emissions, as there would be no environmental safeguards in place to prevent environmental pollution. This study’s quantile-based findings highlight the asymmetric and non-linear character of the connections being studied. It is crucial to address the diverse capacities and limitations of emerging economies through targeted policy initiatives. The findings also reflect the idea of institutional theory, which emphasis is placed on the influence of regulations, governance, and social norms in promoting green innovation and FinTech, as well as the connection between institutions, green innovation, FinTech, and their roles in reducing CO₂ emissions (Khan et al., 2024; Li and Zhang, 2025; Ni et al., 2023). It also demonstrates that economic and technological developments do not occur in isolation but are closely tied to the regulatory environment (Glover et al., 2014). To encourage the adoption of low-carbon practices and policies by financial institutions and companies within a sustainability framework, key pillars such as carbon taxes, environmental regulations, and clean energy standards should be highlighted and addressed (Habib et al., 2025; Omri and Ben Jabeur, 2024). Furthermore, national systems operating within a compliance framework strengthen the credibility of sustainable efforts, which require the development of specific policies, methods, and mechanisms to enforce relevant laws (Alnaim and Metwally, 2024).

This study contributes to our understanding of ECI, GIN, and FT as key factors in reducing CO2 emissions in emerging economies, spanning the period from 1990 to 2024. This provides strong evidence of varying effects on CO₂ emissions, highlighting the significant differences in these variables across countries. The empirical findings offer several important policy implications. It emphasizes the importance of customizing green growth programs to specific environmental settings and quantile levels. This also includes the need for tailored policy strategies and adapting green growth initiatives to environmental conditions and quantile levels. In other words, the lower-emission countries should prioritize preventive policies, such as early investments in renewable energy, and tax incentives for eco-friendly innovation. The upper quintile countries redirect structural change away from carbon-intensive industries and toward green and technology-based ones.

Emerging economies can reduce digital inequality by making internet access affordable, enhancing digital literacy, subsidizing smartphone purchases, fostering broadband partnerships, and promoting interoperable mobile payments (Michael, 2025; Pasupuleti, 2024). Governments are encouraged to back agent banking in local shops and create regulatory sandboxes to advance inclusive FinTech; such steps make FinTech more accessible in underserved areas (Lukonga, 2018).

Ultimately, the association highlights the interconnection between economic and environmental issues, underscoring the need for policies that are both comprehensive and proactive. Strict environmental restrictions are necessary, but policymakers developing markets must also recognize that production systems, innovation ecosystems, and financial frameworks must undergo fundamental transformations to adequately address environmental concerns. However, it is impossible to tackle the problem with separate policy actions. Therefore, for a sustainable transition, governments are encouraged to coordinate technological advancements, economic innovation, and environmental preservation in a manner that considers both financial inclusion and environmental resilience. It has been demonstrated over the years that development opportunities can be created from environmental problems by utilizing an integrated policy framework. Additionally, they should support international sustainability efforts, such as the SDGs and the Paris Agreement. Emphasize expanding green funding through FinTech and work towards standardizing global carbon markets.