In the last 3 decades, rapid global climate change necessitates a global transition to environmentally sustainable energy sources, thereby contributing to reducing carbon emissions. Utilizing renewable energy sources has the potential to decrease global carbon emissions. Several long-term policy instruments are crucial for promoting synergy with renewable energy sources to attain sustained long-term growth (Obekpa and Alola, 2023;Yue et al., 2022). There is a growing agreement that cleaner economic productivity may be achieved through energy efficiency and the effective use of energy forms; this propels the global ambition to achieve carbon neutrality by 2050. Over the next five years, the International Energy Agency (2021) forecasts significant growth in nuclear energy usage, notwithstanding ongoing discussions about its designation as a green investment (International Energy Agency, 2021). Addressing climate change is inherently connected to advancing cost-effective, low-carbon energy technologies that can be implemented globally. Implementing a technology involves and interacts with numerous mechanisms, including economies of scale, research and development, and organizational learning, all of which can contribute to reducing technology costs. Reduced costs create new deployment opportunities, resulting in a beneficial feedback loop or multiplier effect (Jessika Trancik et al., 2015). Nuclear energy is a prime example of this process among low-carbon electricity technologies. Scholars and policy analysts have taken an interest in nuclear energy, considering these developments. There is an opportunity for global energy systems to reduce their environmental impact by using low-carbon energy sources and technology. Despite environmentalists' longstanding opposition, some have suggested reviewing nuclear energy as a clean option to fight global warming (Hassan et al., 2023(Hassan et al., , 2022)). Many clean energy sources can now be used due to technological advances and lower costs, despite transportation costs, growing commodity prices, and supply chain issues. Despite their popularity, renewable energy sources are being promoted to alleviate the climate catastrophe and boost economic growth. To accelerate energy innovation for decarbonization, public investment in research, development, and demonstration (RD&D) (Meckling et al., 2022;Simionescu, 2023). The budget allocation for research and development (R&D) in renewable energy, energy efficiency, and nuclear energy is critical for achieving sustainable development goals 7, 9, and 13. This will accelerate progress in energy innovation, support the shift to cleaner energy sources, and assist in mitigating climate change (Jin et al., 2023a). Sustainable development cannot be achieved without addressing the energy sector, which must simultaneously reduce harmful emissions while increasing access to energy for more individuals. There are risks associated with every technology that might affect people and the planet. Although low-carbon energy sources do not emit carbon dioxide when they are in use, they do produce emissions and trash throughout their lifecycle, from building to production to decommissioning. Therefore, it is critical to compare potential energy technologies to determine how well they mesh with sustainable development objectives (World Nuclear Association, 2014). The potential impact of energy sector R&D is becoming more recognized considering rising worries about climate change and global warming. Government support for energy sector R&D is critical for the discovery of low-carbon technology that can maintain a steady supply of energy, lessen reliance on fossil fuels, and mitigate the financial burden of growing nonrenewable energy prices (Danish and Ulucak, 2022). Artificial intelligence (AI) is a pivotal force driving industrial and technical innovation, significantly impacting advancements in energy conservation and reducing carbon emissions. Enhanced comprehension of the impact of AI on energy transition and carbon emissions may facilitate its application in achieving carbon neutrality (Wang et al., 2024;Zhou et al., 2024). Energy production is only one of several industries that brought Quantile Regression (MM-QR) for empirical estimation. MM-QR utilizes the method of moments to develop more robust estimators compared to traditional quantile regression, facilitating enhanced inference in complex, non-linear data structures where ordinary least squares (OLS) methods may be inadequate. MM-QR contributes methodologically by offering quantile-specific insights while addressing endogeneity and heteroscedasticity, thereby enhancing the understanding of relationships within complex data structures. This tool is essential in empirical research, especially in disciplines where it is crucial to capture distributional heterogeneity and address complex data challenges. The article organizes the remainder of the section: Section 2 provides information about the profile of sample countries. Afterward, use the econometric tools and data in Section 3, then explain and evaluate the empirical findings in Section 3. In the last section, the paper presents its findings. From 1990 to around 2010, coal and other fossil fuels played a significant role in the continuous emissions rise. However, global CO2 emissions have stabilized over the past decade due to improved energy efficiency and the increasing use of renewable energy sources. This trend is especially evident in the United States and the European Union. The COVID-19 epidemic caused a short-lived decrease in emissions in 2020, but when economies resumed in 2021, they quickly recovered, highlighting the ongoing dependence on fossil fuels in numerous regions worldwide (Hannah Ritchie and Max Roser, 2020). In 2022, OECD nations substantially contributed to global CO₂ emissions, mainly from energy-related sources such as coal, oil, and natural gas. Global emissions from the energy sector have continued to rise, particularly due to coal-fired electricity generation. Despite certain regions achieving progress in emissions reductions via the implementation of renewable energy, global emissions have nonetheless surged to an unprecedented level, exceeding 36.8 billion metric tons. Countries within the OECD, especially in Europe and North America, experienced diverse trends. Conversely, the United States experienced a modest rise in emissions attributed to increased demand in the building sector during severe weather events, despite the growth of clean energy sources such as wind and solar (IEA, 2022). Figure 1 shows annual per capita CO2 emissions worldwide in 2024. The utilization of nuclear energy has shown a consistent upward trend since the early 1960s. With a 300-gigawatt electrical (GWe) capacity, nuclear energy currently contributes 25% to the OECD countries' electricity consumption. It is crucial for maintaining the energy security of the OECD. Nuclear energy constitutes the most significant proportion of clean energy production among OECD countries.
The Paris Agreement of 2015, according to the OECD database, seeks to restrict global warming to below 2°C relative to pre-industrial levels; projections indicate a rise of 3-6 degrees Celsius in the global average temperature by the century's end. Nuclear power plants, expected to supply low-carbon electricity to many countries for the forthcoming decades, will experience significant disruptions in their operations (Nuclear Energy Agency, 2022). As significant economies and notable greenhouse gas emitters, the OECD countries bear a considerable responsibility by investing significantly in energy innovation via research and development (R&D). This study seeks to investigate and propose effective policy measures concerning the role of R&D in the nuclear energy sector to mitigate pollution. Applications aimed at boosting workplace productivity, particularly in the finance and manufacturing sectors, drove notable growth in the adoption of AI in OECD countries in 2022. A significant number of companies have implemented AI technologies for various functions, including production tracking, image recognition, and natural language processing; however, the extent of adoption has shown considerable variation. Large firms typically spearhead AI integration, particularly for tasks aimed at optimizing operations and enhancing output quality. Nonetheless, the general adoption rate stayed comparatively low, influenced in part by the expenses linked to AI implementation and the insufficient availability of skilled personnel required for its effective operation (Stijn, 2023). The trend for the number of artificial intelligence-related patents in OECD countries comparing the data of 1995 and 2020 is shown in Figure 2. Theoretical background and literature review Policies must promote their adoption, ensure safe operation, and facilitate integration with other low-carbon technologies by enabling nuclear energy and AI to substantially aid in achieving net-zero objectives. Environmental economics shapes the design of incentives and penalties, ensuring the economic viability and competitiveness of nuclear and AI-driven systems within the energy market. This outline presents a well-organized framework for examining the effects of manifest. This finding highlights the critical need for stringent environmental policies to sustain the beneficial effects of innovation, a principle that also extends to nuclear energy. In the absence of sufficient policy backing, the potential environmental advantages of nuclear and other low-carbon technologies might remain unfulfilled (Wang and Zhu, 2020). In summary, the existing literature consistently highlights the potential of nuclear energy to reduce CO2 emissions, particularly when bolstered by focused policy, sufficient investment in research and development, and harmonious integration with renewable energy sources. Material and Methods:

The study's objective is to empirically investigate the nuclear energy, AI, and CO2 emissions nexus. For cross-sectional or panel data analysis (e.g., across OECD countries over multiple years), the model can be specified as follows:

(1)

In the above equation ( 1). To improve the energy mix's carbon efficiency and decrease the demand for fossil fuel backup energy, AI algorithms can dynamically modulate nuclear production in reaction to changes in renewable generation (IEA, 2022). Emissions reductions are proportional to the degree to which AI enhances the integration of renewables and nuclear energy. A regression model's interaction term allows one to examine AI's moderating function. For example, we can construct an empirical model in the following manner:

(2)

In this model, the interaction term (LnAIitxLnNRDDit) would help capture the moderating effect of AI on the relationship between nuclear energy and CO₂ emissions. A negative and statistically significant coefficient for this term would suggest that AI enhances the emissions reduction impact of nuclear energy. For empirical estimation, this study used the Method of Moment Quantile MM-QR method proposed by Sim and Zhou, (2015). Statistics traditionally use the MM-QR estimation technique to find estimates for model parameters by equating sample moments, such as sample means or variances, to their theoretical counterparts. In quantile regression, the MM-QR estimates the quantiles of the response variable based on the predictors. This gives a more complete picture of how the variables are connected than mean regression. The MoM approach allows one to obtain quantile estimates without needing to make strict assumptions about the underlying distribution of residuals, making it suitable for heteroscedastic or non-normally distributed data. This method provides a comprehensive perspective on the influence of predictors on the distribution of a response variable, extending beyond the mean. This is important in situations where the mean does not sufficiently reflect the data's distribution or when outliers have a substantial impact on mean-based estimates. This method enhances robustness against outliers and model misspecification, rendering it especially beneficial in domains such as finance and economics, where these challenges commonly occur. This method exhibits reduced sensitivity to outliers compared to traditional mean-based regression, as it emphasizes quantiles rather than the entire distribution. The MM-QR method addresses nonnormal distributions, effectively accommodating diverse error structures in the data, such as skewed or heavy-tailed distributions. MM-QR offers a comprehensive view of the conditional distribution of the response variable, which is essential for analyzing variability and skewness in data. MM-QR estimates parameters by addressing moment conditions, generally employing optimization methods. In this process, the error between the sample moments and theoretical moments is minimized. After estimating, interpret the regression coefficients as effects on the specified quantile of the response variable. These interpretations can provide insights that differ across quantiles and reveal trends not visible in mean regression. For quantile τ, the MMQR equation can be represented as follows:

Where Qτ (.) : The conditional quantile function of CO₂ emissions at quantile τ. X is a Vector of independent variables, which includes nuclear energy budgeting, AI investment, and interaction variable (AI*NRDD). bτ Vector of coefficients specific to quantile τ that reflect the impact of each independent variable on CO₂ emissions, and ϵ(τ) is the error term associated with quantile τ. The equation can be expanded to explicitly include NRDD and AI, as follows:

Where, NRDD Nuclear energy budgeting (investment or budget allocation for nuclear technology), AI number of registered AI-related patents, potentially supporting optimization and efficiency in nuclear energy. AI*NRDD is the interaction between Artificial intelligence and nuclear energy budgeting that may influence CO₂ emissions.
The MM-QR models are especially valuable in environmental and energy economics for capturing heterogeneous impacts across different levels of emissions, allowing for a more nuanced policy and investment approach. Finally, perform diagnostic checks to validate the model. This may include testing the robustness of the moment conditions, checking for model misspecification, and comparing the quantile regression estimates with other estimation methods. This paper covers the annual dataset for selected 20 OECD countries (refer to  3. The first step in panel data analysis is to examine cross-sectional dependence (CD). Refraining from ignoring cross-sectional reliance can lead to biased estimates and incorrect conclusions. Therefore, it is advisable to use techniques such as fixed effects with crosssectional averages, factor models, or spatial econometric models to address this dependency issue effectively. This study employs the CD test for this purpose by Pesaran, (2015). The results in Table 2 reject the null hypothesis, indicating that CD exists. Due to this dependence, shared shocks can influence parameter estimates, potentially resulting in biased outcomes in models that assume unit independence. In international economics, cross-sectional dependence often suggests that global factors-such as commodity prices, financial crises, or technological spillovers-impact multiple countries simultaneously. This understanding informs the selection of appropriate models and policy recommendations. Once it is proven that the dataset is cross-sectionally dependent, the next step is to use panel unit root tests. These give essential information about the data's structure and help analysts choose the best modeling methods for accurate results. For this purpose, we utilize the CIPS and CADF unit root tests by Pesaran, (2007). The findings in Table 3 indicate a noteworthy test statistic (typically at a 5% significance level), enabling the rejection of the null hypothesis, which suggests that the series is stationary at its first difference. A nonsignificant statistic indicates that the series is non-stationary. Adopting artificial intelligence (AI) positively correlates with CO₂ emissions across all quantiles (see Table 4). Generally, an increase in AI adoption leads to an increase in CO2 emissions, irrespective of the examined emission level (low, medium, or high quantiles). This finding suggests that, although AI can improve efficiency and reduce emissions in certain contexts, its deployment might also lead to environmental costs that could outweigh these benefits. A positive coefficient at lower quantiles suggests that, even in nations with relatively low emissions, including Denmark, Switzerland, Norway, Finland, Sweden, Portugal, Hungary, and Austria, an increase in AI adoption is associated with higher CO2

emissions. This could arise from the essential infrastructure and energy needs required for implementing AI technologies in settings that typically show reduced emissions. For average emitters (Belgium, Netherlands, Spain, France, Italy, UK, Australia, and Türkiye), a positive coefficient at the median level (e.g., 50th percentile) suggests a link between AI adoption and increased emissions. This indicates that the efficiencies gained through AI may not adequately offset the emissions generated by data processing, storage, and computational requirements. At upper quantiles (e.g., 75th or 90th percentile), a positive coefficient indicates that even significant emitters (Canada, Germany, Japan, and the USA) see an increase in CO₂ emissions with the rise of AI adoption. Industries with significant emissions, including energy, manufacturing, and logistics, could experience a heightened environmental impact from AI due to the substantial computing power and energy consumption required for extensive AI operations. emissions across quantiles suggests that variations in funding for nuclear energy technology do not significantly impact CO emissions at different levels of the emissions distribution (see Table 4). This finding may arise from multiple factors about nuclear energy investments and the broader energy production and emissions context. An insignificant relationship across quantiles in quantile regression means this lack of effect is the same for all emissions levels, from lower to higher quantiles. This consistency indicates that irrespective of a country's status as a low or high CO₂ emitter, increases in nuclear energy funding are not presently associated with emissions reductions, reinforcing the notion that nuclear budgeting may exert limited or delayed direct effects on CO₂ emissions across various emission levels. There is a negative coefficient for AI's moderating role in the relationship between funding for nuclear energy technology and CO2 emissions across all quantiles. This suggests that AI may help lower the CO2 emissions from nuclear energy projects (see Table 4). AI is a mitigating factor, diminishing the positive relationship between nuclear energy investment and emissions across all levels. The application of AI in nuclear energy development and operations indicates that its efficiencies and optimizations could lead to decreased emissions throughout the project lifecycle. At lower emission levels (10th or 25th percentile), AI may have a negative moderating effect. This means that in places with moderate emissions, AI may reduce the initial CO2 footprint of nuclear energy projects. Figuring out this may necessitate the optimization of construction and logistical procedures, reducing waste, and improvement of resource efficiency. The median quantile, or 50th percentile, for average emitters, shows that AI has a moderating effect that greatly reduces emissions related to nuclear energy projects. This is achieved through AI-driven efficiency, such as predictive maintenance, better energy management, and automation routine tasks. There is a negative coefficient for the moderating effect of AI at high quantiles, like the 75th or 90th percentile. This means that adding AI may have a big effect on situations with high emission levels. Artificial intelligence has the potential to reduce the carbon footprint associated with extensive nuclear initiatives, enhance operational efficiency, and optimize the integration of nuclear facilities into the energy grid. In quantile regression analysis, plotting the regression results across different quantiles provides a robust check of how the relationship between the independent and dependent variables changes as the outcome variable's distribution changes. Through the graphic representation of these results, we can understand the stability and variation of coefficients, which helps confirm the robustness of the findings. In summary, quantile regression plots serve as an important tool for assessing robustness by demonstrating the variability (or consistency) of coefficient estimates across different quantiles.
Consistent coefficients indicate reliability, whereas fluctuations provide understanding of distributional diversity. This visual representation bolsters confidence in the results, especially when dealing with intricate data that may include outliers, non-normal distributions, or endogeneity concerns. By validating the relationship across quantiles, quantile regression plots act as a significant instrument for evaluating the robustness and depth of empirical findings. Quantile regression plots (see Figure 4) exhibit robustness to outliers and non-normal data distributions. If the plot indicates that the coefficients keep their significance and direction across quantiles, this resilience to outliers further corroborates the stability and validity of the relationship. It provides confidence that the results are not unduly influenced by extreme values or deviations from normality. This approach has been used to estimate Eqs 3 & 4 to check the robustness of the quantitative regression analysis results obtained by the MM-QR method. ( 1) and ( 2). Moreover, we conduct a robustness check by applying the alternative method of the augmented Anderson-Hsiao estimator AAH estimator by (Chudik and Pesaran, 2022) and the Panel -Corrected Standard Errors (PCSE) method by (Beck et al., 1995) for comparison purposes. To this end, we include ln (CO2)it-1 in Eqs. ( 1) and (2) to estimate the parameters. Here, we should indicate that as the cross-sectional dependence exists in our dataset, using conventional panel data estimators like fixed effect, random effect, or pooled OLS to estimate the static panel data models can lead to biased and inconsistent results. For this reason, we used the AAH regression method which not only considers the cross-sectional dependence but also solves autocorrelation and reduces the endogeneity issue. The robust analysis findings are reported in Table 5 and confirm our results about the sign of the coefficient of NRDD, AI, and the interaction effect of AI and NRDD. The issue of global warming is likely to persist, even beyond the established deadline for reaching carbon neutrality and the 1.5°C temperature increase threshold. Despite their knowledge of major greenhouse gases, scientists and policymakers struggle to accurately capture CO2 emissions dynamics about their pathways (GHGs) (Magazzino et al., 2024). In this regard, we identified gaps in the literature regarding the analysis of the interactions of AI and nuclear energy technology budgeting (composite variable) with CO2 emissions. Thus, this study employed a robust econometric approach in evaluating the present relationships between AI and nuclear energy technology budgeting (composite variable) with CO2 emissions. On the methodological side, we introduce a novel approach by introducing moderating AI in the relationship between nuclear energy technology budgeting and CO2 emissions. For empirical estimates, the study relies on the MM-QR method. Here, we were able to demonstrate that nuclear energy technology budgeting does influence all CO2 emissions. We also observed that AI could contribute to CO2 emissions directly. However, novel findings related to the interaction effect of AI and nuclear energy technology budget, the findings reveal that nuclear energy technology budgeting reduces carbon emissions through AI. Policymakers may need to introduce incentives for renewable energy use in AIrelated operations or promote the development of more energy-efficient AI algorithms. Governments could conduct thorough environmental impact assessments for AI initiatives, especially in industries that are heavy emitters. Efforts to develop AI solutions specifically targeted at reducing emissions and enhancing energy efficiency should be prioritized to counteract the possible environmental costs of AI deployment. Policymakers and stakeholders need to consider the short-term emissions increase associated with nuclear energy development against its long-term potential for low-emission energy production. Clear strategies can help bridge the gap until the emissions-reducing benefits of nuclear

AI-related patents. Data on NRDD arrives from the International Energy Agency. AI data is taken from OECD databases. The trend in artificial intelligence, nuclear energy RD&D, and CO2 emissions in sample OECD countries is shown in Figure