Artificial Intelligence, energy transitions, and the pathway to net-zero carbon emissions in OECD Countries

Artificial Intelligence, energy transitions, and the pathway to net-zero carbon emissions in OECD Countries | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Artificial Intelligence, energy transitions, and the pathway to net-zero carbon emissions in OECD Countries Jiexian Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8153017/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Existing research highlights that artificial intelligence can enhance energy security and support the pursuit of net-zero carbon emission goals. Yet, scholarly attention to its role in shaping the relationship between nuclear energy use and carbon dioxide emissions remains limited. This study addresses that gap by offering one of the earliest empirical investigations linking artificial intelligence, nuclear energy consumption, and CO₂ emissions across 17 Organization for Economic Co-operation and Development (OECD) countries from 1994 to 2020. This study also examines whether artificial intelligence can help to form an environmental Kuznets curve (EKC) in OECD countries. To achieve this objective, we design a novel analytical framework that examines the influence of artificial intelligence and nuclear energy on the dynamics of the Environmental Kuznets Curve (EKC). Empirical analysis indicates that nuclear energy contributes to reduced carbon emissions. Additionally, artificial intelligence has a negligible impact on CO 2 emissions. Furthermore, there is unidirectional causality between artificial intelligence and CO 2 emissions, as well as between nuclear energy and CO 2 emissions. To summarize, nuclear energy consumption contributes to the formation of EKC, whereas artificial intelligence does not appear to have a similar effect. Based on these results, some economic policy implications are discussed. Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Environmental social sciences Artificial intelligence Nuclear energy Carbon neutrality OECD countries CS-ARDL estimator & DH-causality Figures Figure 1 Figure 2 Figure 3 1. Introduction The gradual but noticeable increase in the Earth's average temperature is characterized by global warming. The immense production of greenhouse gas emissions is mainly responsible for the rapid expansion over the past fifty years. Carbon emissions from fossil fuels hit a new high worldwide in 2024. More than 40 billion metric tons of carbon dioxide (CO 2 ) are released into the atmosphere yearly, increasing the likelihood of severe climate change and worsening the state of the planet (World Meteorological Organization, 2024 ). Energy systems play a crucial role in the operation of economies and the welfare of communities. However, energy conversion and use are the primary factors contributing to global warming. The energy sector, mainly dependent on fossil fuels, accounts for 34 percent of anthropogenic greenhouse gas (GHG) emissions, totaling 20 gigatons (Gt) of GHG worldwide. To meet the goals outlined in the Paris Agreement, it is crucial to reduce global CO 2 emissions by 50% by 2030. Our objective is to decrease reliance on fossil fuels for electricity generation, heating and cooling, and the operation of industries and transportation. This can be achieved by accelerating the transition to sustainable and cost-effective energy solutions for everyone (IPCC, 2023 ). Unless we achieve global net-zero CO 2 emissions, the rise in world temperatures will continue, resulting in increasingly severe consequences, as seen in 2023. The energy sector encompasses various activities related to the generation, conversion, transportation, and distribution of energy in multiple forms. Energy generation from different sources is categorized into three primary categories: fossil fuels, renewable energy, and nuclear power. Fossil fuels, such as oil, natural gas, and coal, are non-renewable energy sources that emerged from buried organic matter millions of years ago. These are currently the primary energy sources globally; however, their utilization hurts the environment, human health, and climate due to greenhouse gas (GHG) emissions (Akpolat and Bakırtaş, 2024 ; Bergougui, 2024 ). Renewable energy encompasses sources such as hydro, solar, and wind energy. Renewables are considered a clean energy source that reduces pollution, improves environmental quality, alleviates energy poverty, and contributes to human development (Danish and Ulucak, 2021; Zhang et al., 2023 ). Nuclear energy is another form of energy and a source of electricity. Nuclear energy has come to prominence as a viable alternative energy source and a pragmatic solution in the quest for cleaner and more sustainable options to conventional fossil fuels. (Huang et al., 2024 ). The demand for primary energy worldwide has risen by 50% since 1990. The world will face numerous energy-related challenges, both currently and in the future. By 2035, energy demand is anticipated to exceed current levels significantly. All nations must acknowledge the imminent rise in global energy demands. A growing dialogue has emerged among policymakers regarding the importance of nuclear and renewable energy as viable alternatives to fossil-fuel-based power generation, particularly coal. This discourse primarily examines the economic viability of nuclear energy in relation to its alternatives, including wind and solar energy. The role of nuclear energy in achieving the Sustainable Development Goals (SDGs) is increasingly debated among policymakers, as recently observed at the 26th Conference of the Parties to the United Nations Framework Convention on Climate Change. The integration of renewable energy, nuclear energy, and modifications in consumer behavior may facilitate the attainment of the zero-emission goal established for 2050 by the Intergovernmental Panel on Climate Change (IPCC, 2022). Four hundred forty-two nuclear power reactors operate worldwide, providing 393 GWe of reliable, low-carbon electricity. Nuclear energy accounts for 11% of total global electricity generation and one-third of the low-carbon electricity produced worldwide (Mathew, 2022 ). Nuclear energy has considerable potential to reduce CO₂ emissions because of its low-carbon nature. However, it faces significant challenges that have limited its wider adoption and effectiveness in combating climate change. The construction of nuclear power plants requires substantial capital investment and high upfront costs related to safety regulations and waste disposal (Iurshina et al., 2019 ). The construction of a nuclear power plant typically takes 5 to 10 years and costs several billion dollars. As a result, nuclear energy is often less economical than other sources, such as renewables. Furthermore, the regulatory requirements, extended planning processes, and lengthy construction times contribute to the overall costs, making this technology less appealing for countries with limited energy budgets. Nuclear power plants are costly but relatively inexpensive, according to the World Nuclear Association ( 2023 ). Based on the above situation, the role of nuclear energy and CO 2 emissions is complex, as it is observed that nuclear energy behaves as clean energy that reduces carbon emissions (Hassan et al., 2020 ; Mehboob et al., 2024 ; Pata and Kartal, 2023 ; Simionescu and Plopeanu, 2023 ; Teng et al., 2023 ), and contributes to sustainable development (Zheng et al., 2024 ). However, opposing views are discussed in the literature that nuclear energy is not beneficial to the environment (Danish et al., 2022; Voumik et al., 2022 ) and harms environmental quality by contributing to carbon emissions (Mahmood et al., 2020 ; Soto and Martinez-Cobas, 2024 ). Advancements influence the connection between nuclear energy and environmental pollution in nuclear power plant technology (Çakar et al., 2022 ). Due to asymmetric effects, nuclear energy protects the health of the natural environment (Bandyopadhyay et al., 2022 ). The use of nuclear energy remains a contentious issue, with diverse perspectives presenting both support and opposition. Advocates of nuclear energy contend that it serves as a clean and efficient energy source, distinguished by low greenhouse gas emissions. They emphasize its relatively low environmental impact compared to conventional energy sources, highlighting its role in mitigating climate change. Additionally, nuclear power is regarded as a highly efficient electricity generation, capable of producing substantial energy output with a relatively small ecological footprint. Conversely, critics raise concerns regarding the generation of radioactive waste, which presents long-term environmental and safety risks due to its prolonged decay, often spanning thousands of years. Furthermore, nuclear energy is frequently debated regarding its renewability, as it relies on finite uranium resources. Additionally, some opponents argue that nuclear technology poses potential proliferation risks, given its possible association with the development of nuclear weapons (Soto and Martinez-Cobas, 2024 ). This highlights the need for further research into the consequences and causes of nuclear energy use and consumption. Apart from varying opinions expressing advocating for or against nuclear energy and CO 2 emissions nexus, several vital variables have been used in the nuclear energy-CO 2 emissions relationship, for instance(Tauseef et al., 2023 ) technological advancements;(Danish et al., 2021c ) foreign direct investment (FDI)(Kartal, 2022 ) fossil fuels and renewable energy;(Hassan et al., 2022 ) transportation infrastructure;(Jin et al., 2023 ) green energy, energy efficiency, research, and development (R&D);(Liu et al., 2023 ) technology and green finance;(Danish et al., 2022; Hassan et al., 2023 ) globalization and economic complexity index and(Mehboob et al., 2024 ) environmental taxes and trade globalization. The current investigation examines the relationship between artificial intelligence, nuclear energy, and CO 2 emissions in selected OECD countries that utilize significant nuclear energy and have advanced artificial intelligence (AI) integration. The economic benefits of AI-enhanced nuclear operations are increasingly recognized for their role in promoting cost-effective and sustainable clean energy solutions. Integrating AI in nuclear waste management enhances navigation of complex regulatory frameworks, reduces environmental impacts, and improves public health protection (Christopher Selvam et al., 2025 ). Policy frameworks progressively acknowledge the synergistic relationship between AI and nuclear energy as crucial factors in addressing climate change challenges. AI-driven technologies have the potential to reduce energy consumption and carbon emissions by an estimated 8% to 19% by 2050. Furthermore, aligning energy policies with low-carbon power generation strategies could lead to a 40% reduction in energy consumption and a 90% decrease in carbon emissions compared to business-as-usual projections for 2050 (Ding et al., 2024 ). The study makes a significant contribution to the existing body of knowledge. (i) The paper begins by outlining a framework for future research into the nuclear energy-CO 2 emissions nexus, where artificial intelligence (AI) is considered a possible driver of CO 2 emissions. The current literature has largely overlooked the impact of AI and nuclear energy on carbon emissions in OECD countries. (ii) According to the nuclear energy examined environmental Kuznets curve (EKC) hypothesis, neither the availability nor the study of AI's environmental effects has been considered for OECD nations. This investigation addresses critical knowledge gaps regarding the role of AI in policy measures designed to achieve net-zero carbon emissions. (iii) The CS-ARDL (Cross-Sectional Autoregressive Distributed Lag) method is employed to analyze the long-term effects of the underlying variable. Previous studies have employed panel data methods, revealing consistent findings regarding the relationship among these variables. The CS-ARDL model is an advanced econometric tool that addresses various challenges in panel data analysis, particularly when cross-sectional dependence is present. The unique features of CS-ARDL, particularly its ability to handle cross-sectional dependence, heterogeneity, and mixed integration orders, make it a powerful and flexible tool for identifying long-term relationships in panel data. This method is more effective in situations involving global shocks or panel members who are interdependent than other long-run panel estimators. This is especially true in fields like energy economics and environmental studies. The organization of this paper is as follows: Section 2 discusses the relationships among artificial intelligence, nuclear energy, and CO 2 emissions. Section 3 outlines the methodology employed in the study, covering data sources, model specifications, and analytical techniques used. Section 4 presents the empirical results, while Section 5 provides a detailed discussion of the findings. Section 6 defines the study's limitations and offers guidance for future research, ultimately concluding the paper with implications for policy and recommendations. 2. Artificial intelligence, nuclear energy, and CO emissions: Interconnectivity Nuclear power is a low-carbon energy source that significantly contributes to a low-carbon economy and the development of green energy. The current technological innovations create conditions that make nuclear energy even more affordable and attractive. These advancements include improvements in large reactors, the development of innovative technologies for advanced fuel and small modular reactors, enhancements in engineering that extend the operational lives of existing reactors, and advancements in materials and waste management. Fast-breeder reactors are already beyond commercial technology and offer additional advantages, such as producing more fuel than they consume while efficiently destroying nuclear waste, compared to existing commercial reactor technologies (Mathew, 2022 ). Nuclear energy is recognized for its ability to provide low-carbon energy. Unlike fossil fuels, nuclear power plants produce minimal CO 2 emissions during operation, making them crucial in achieving climate goals. By integrating nuclear energy into energy grids, we have the potential to replace carbon-intensive sources such as coal and gas, which could significantly reduce global emissions. However, there is also a concern that both existing and future nuclear facilities could pose a risk of severe environmental disasters if they were to be targeted by a terrorist attack (Toth and Rogner, 2006 ). The critics of nuclear energy argue that the financial burden of investing in nuclear plants is significant and that developing nations struggle with construction, given their current capabilities (Çakar et al., 2022 ). Nuclear energy proponents argue that, although the initial installation costs of nuclear power plants are significant, their operating costs are comparatively low when measured against those of fossil fuels (Usman et al., 2022 ). Theoretical explanations suggest that nuclear energy has benefits and drawbacks concerning environmental quality (Ozcan et al., 2024 ). AI has a broader role in reducing CO 2 emissions beyond nuclear energy. For instance, it facilitates carbon capture technologies, improves energy efficiency in industrial processes, and supports the transition to smart grids. However, AI’s energy-intensive nature, especially for training large models, presents a challenge, underscoring the need for sustainable practices within AI development. Integrating AI in nuclear energy systems can significantly enhance efficiency and capacity, reducing substantial emissions. AI-driven predictive analytics can optimize reactor performance and safety, ensuring nuclear facilities operate at optimal output levels while maintaining a reliable, low-carbon energy supply. Additionally, developing integrated assessment models (IAMs) can support policymakers in formulating strategies that effectively incorporate AI and nuclear technologies to advance climate mitigation efforts. As AI adoption within the nuclear sector expands, the need for workforce transformation becomes increasingly critical. The convergence of AI and nuclear energy presents a potential pathway toward net-zero emissions if challenges such as AI’s substantial energy requirements and societal concerns regarding nuclear power are effectively addressed. A deeper examination of these interdependencies may reveal further opportunities to leverage their combined capabilities for sustainable development. The adoption of AI in nuclear energy systems is expanding, comprising applications aimed at enhancing operational efficiency, safety, and energy distribution. AI-driven solutions, including predictive maintenance, facilitate the early identification of potential system failures, which enhances reactor safety and reliability. AI-based models evaluate the patterns of energy demand, optimizing the performance of nuclear reactors and promoting the integration of nuclear energy with renewables, thereby contributing to an adequate energy supply. The rapid advancement of artificial intelligence (AI) technology in recent decades has led to both new opportunities and challenges regarding enhancing the safety and economic aspects of nuclear reactors (Chen et al., 2024a ; Huang et al., 2023 ). Evidence suggests that countries leveraging nuclear power and AI technologies demonstrate accelerated progress in emissions reduction. However, success depends on careful policy design, technological innovation, and sustained investment in both sectors. Nuclear power provides stable, carbon-free baseload power essential for energy-intensive AI data centers (Johnson, 2021 ). The role of AI is significant, as it serves as a catalyst for improving safety measures within the nuclear sector (Chen et al., 2024b ). The framework between AI, nuclear energy, and carbon emissions is given in Fig. 1 . 3. Material and Method 3.1. Econometric strategy Considering the recent work (Alam, 2013 ; Danish et al., 2021a; Liu et al., 2023 ), we extended these studies by including the key factor of artificial intelligence into the empirical model. According to the objective of the study, the following econometric equation is developed: Ln (co 2 ) it = β 0 + β 1 ln(gdp) it + β 2 ln(gdp^ 2 ) it + β 3 ln(NEC) it + β 4 ln(AI) it + µ it (1) CO 2 represents carbon dioxide emissions measured in terms of carbon emissions per capita. GDP is the gross domestic product, GDP 2 is the square of the gross domestic product, NEC is nuclear energy consumption, and AI is artificial intelligence. i and t are cross-sections and times, where ‘ µ’ is an error term. The expected sign of coefficient β2 is negative, whereas the anticipated value of β1 is positive. If β1 > 0 and β2 < 0 in this scenario, then the widely recognized EKC hypothesis is present. The first proposal is a square model that examines the relationship between CO 2 emissions and income, incorporating control variables to validate the existence of an inverted U-shaped pattern in this relationship. The environmental Kuznets curve (EKC) hypothesis posits that initially, pollution increases with rising per capita income; however, ultimately, higher income levels lead to a decline in pollution (Dinda, 2004 ; Grossman and Krueger, 1995 ). Through its ability to generate low-carbon electricity, nuclear energy has garnered worldwide attention as a highly effective means of reducing greenhouse gas emissions. It offers a promising approach to mitigating greenhouse gas and carbon emissions. Nuclear energy reduces expenses for countries reliant on fossil fuel imports, addressing their current account deficits while mitigating energy dependence and enhancing security concerns. Nuclear energy is a clean energy source essential for meeting sustainable development goals and ensuring a secure energy supply, promoting economic growth by alleviating energy supply challenges (Danish et al., 2021b). Nuclear energy improves energy supply, cleans the environment, reduces CO 2 emissions, and promotes sustainable development (Hassan et al., 2020 ; Lau et al., 2020 ). Conversely, nuclear energy contributes to pollution through waste production, including the use of indigenous resources for nuclear power plants and the disposal of radioactive waste. This, in turn, has severe and irreversible consequences for both humanity and the environment (Bélaïd and Youssef, 2017 ; Mahmood et al., 2020 ). The contribution of nuclear energy to CO 2 emissions remains ambiguous. The decision-making process is, in this context, more complex. The expected outcome of nuclear energy is still uncertain. Recently, the literature has focused on the environmental impact of AI. Another essential variable utilized in the study is AI, which exhibits both immediate and extensive effects on the environment. New AI models specifically designed for training purposes can be energy-intensive, leading to an increase in energy demand for running these models, which in turn may result in higher carbon emissions. The growing demand for data storage units highlights the indirect environmental impact of the artificial intelligence sector. The economic activities associated with the artificial intelligence sector led to resource depletion, increased energy consumption, and the generation of electronic waste. The implementation of advanced manufacturing processes enhanced by AI could result in a significant reduction in energy consumption, waste, and carbon emissions, potentially achieving a decrease of 30–50%. AI influences the environment through the increasing demand for data storage units, digital services, cloud computing, and data centers. Another aspect of the environmental impact of AI includes resource depletion, increased energy demand, and electronic waste, all of which are linked to AI operations. AI-driven manufacturing has the capacity to reduce energy consumption, waste, and carbon emissions by 30–50% (Chen et al., 2023 ). The AI execution in various sectors extends to multiple application domains, including computer vision, robotics, natural language processing, and machinery, thereby enhancing energy efficiency and reducing carbon emissions across the building, transportation, and industrial sectors (Ding et al., 2024 ). AI plays a crucial role in the development of green technology innovation and the optimization of employment skill structures, which contribute to lowering carbon emissions and reducing carbon footprints (Liu, 2023 ; Rasheed et al., 2024 ; Wang et al., 2024 ). The impact of AI is unclear; therefore, it may have either a positive or negative effect on carbon emissions mitigation. 3.2. Empirical estimation procedure To achieve its objective, this study employed the Cross-Sectionally Augmented Autoregressive Distributed Lag (CS-ARDL) estimator for empirical estimation, which counteracts cross-country spillovers and allows for varied responses. The CS-ARDL methodology offers several advantages that make it particularly suitable for examining the relationship between nuclear energy, artificial intelligence, and carbon emissions. First, the CS-ARDL addresses the CSD issue arising from global economic interconnections and shared environmental challenges. This feature is essential due to the inherent interconnections of environmental policies among nations. Second, the CS-ARDL methodology can handle either stationary variables or those that exhibit unit roots, thereby eliminating the stringent necessity for pre-testing the integration orders of variables. This feature holds significant importance when data series frequently display varying statistical characteristics. Third, the CS-ARDL methodology demonstrates resilience even when working with smaller sample sizes, which is often a limitation in the field of energy policy, where dependable data may be scarce. Fourth, CS-ARDL produces both short-run dynamics and long-run relationships simultaneously, enabling the distinction between immediate policy impacts and their lasting effects over time. To assess long-run and short-run relationships, the estimated equation is: In the above equation, CO 2 represents carbon dioxide emissions, Y denotes income, NEC signifies nuclear energy consumption, and AI stands for artificial intelligence. The term ε represents the speed of adjustment toward equilibrium. Lagged differences capture short-run dynamics Δ . In energy and environmental studies, cross-sectional dependence is inevitable due to the interdependence of environmental resources and global emissions. The CS-ARDL accurately recognizes these spillovers. The model's ability to provide unit-specific estimates is crucial for developing tailored energy and environmental policies for local conditions. This method allows for an in-depth analysis of the routes economies follow in transitioning to cleaner energy systems and an assessment of the pace and uniformity of these changes. The CS-ARDL methodology is applicable to panel data with varying lengths and sizes, thereby increasing its usefulness for studies at global, regional, or sectoral levels. 3.3. Data This paper covers the annual dataset for 17 selected OECD countries (Belgium, Canada, the Czech Republic, Finland, France, Germany, Hungary, Japan, Mexico, the Netherlands, Slovakia, Slovenia, Spain, Sweden, Switzerland, the United Kingdom, and the United States) from 1994 to 2020. The dependent variable is per capita CO 2 emissions (million tons of CO 2 ). Data about CO 2 emissions is derived from the British Petroleum Statistical Review (BP, 2021 ). GDP (Gross Domestic Product), AI (Artificial Intelligence), and nuclear energy consumption are independent variables that explain CO 2 emissions. AI is measured as the number of registered AI-related patents (Triad family registered patents). AI data is taken from OECD databases. Data regarding nuclear energy is sourced from the Energy Information Administration (EIA) database. Nuclear energy is quantified in British Thermal Units (BTUs), indicating the energy generated or utilized. Income is ultimately assessed based on GDP per capita, using data from the World Development Indicators, a World Bank database. 4. Results The analysis begins by examining cross-sectional dependence (CSD), as ignoring it may lead to biased estimates. This study employs the CSD test proposed by Pesaran ( 2004 ) to evaluate the CSD in panel data by examining residual correlations across units. The CSD test results in Table 1 illustrate significant CSD among the sample countries. This suggests that shocks or policies implemented in one sample country may have an impact on others. Furthermore, the Pesaran and Yamagata ( 2008 ) test examines heterogeneity. This test determines if the effects of independent variables, represented by the slope coefficients, are consistent across units. According to the study's findings, the results (Table 1 ) suggest that heterogeneity is absent in the model. Table 1 Results of CSD test and slop homogeneity test Variable CD-Test Statistic P-value Ln CO 2 14.629*** 0.000 Ln gdpc 10.876*** 0.000 Ln gdpc2 19.198*** 0.000 Ln AI 1.038** 0.029 Ln NEC 14.253*** 0.000 Slope Homogeneity Test Delta 0.839*** 0.000 Adjusted Delta 0.036*** 0.000 *** & ** means a 1% & 5% level of significance. The next step involves employing a unit root test, which ensures that econometric models are accurately specified, thereby preventing misleading outcomes and facilitating robust statistical inference. For non-stationary variables, differencing techniques or a cointegration framework must be applied or implemented for analysis. This study employs the CIPS and CADF panel data unit root test, as proposed by Pesaran ( 2007 ). The results in Table 2 indicate that the data series is non-stationary at the level, becoming stationary after the first difference. Table 2 Unit root test results CIPS unit root test CADF unit root test Variable level 1st Diff level 1st Diff Ln CO2 -3.263 -5.921*** -2.273 -6.077*** Ln gdpc -1.130 -4.377*** -2.298 -7.226*** Ln gdpc2 -4.072 -5.868*** -0.105 -5.478*** Ln AI -3.052 -7.432*** -1.354 -5.573*** Ln NEC -0.935 -5.154*** -1.589 -5.364*** *** means a 1% level of significance. Afterward, a cointegration test is employed. In particular, the Westerlund ( 2007 ) cointegration test determines whether there is a long-term relationship between two or more variables in a panel dataset comprising data from multiple entities over time. Table 3 presents the cointegration results, demonstrating that both group and panel statistics are statistically significant. A long-term relationship exists between the variables for most entities in the panel data. Table 3 Results of co-integration test Statistic Value Z-value Prob. Gt -4.253 -2.155 0.000 Ga -18.189 -1.135 0.000 Pt -13.111 -2.398 0.024 Pa -11.224 -0.134 0.009 The CS-ARDL presents long and short-run estimation results in Table 4 . From the results, it is evident that an increase in GDP per capita results in an increase in emissions in the long and short run, highlighting the positive relationship between economic growth and carbon emissions. Further, the coefficient of the GDP square is negative and significant both in the long and short run. The negative coefficient of lngdpc2 in the short and long run supports the EKC hypothesis, suggesting that emissions initially rise with economic growth but decline as income levels increase beyond a threshold. The coefficient of nuclear energy estimate is negatively significant, suggesting that an increase in nuclear energy consumption reduces CO 2 emissions over time, but the effect is marginally significant. In the short term, nuclear energy also has a negligible impact on carbon emissions. Overall, nuclear energy consumption does not appear to have a strong relationship with emissions in the short term or the long term; however, it does exhibit some effect in the long term. In both the short and long run, the impact of artificial intelligence on carbon emissions is not statistically significant. However, the sign of the coefficient of AI is harmful in the long run. The insignificant effect of AI on carbon emissions, both in the short term and long term within OECD countries, suggests that AI alone is insufficient to achieve significant decarbonization. This underscores the necessity for enhanced integration with complementary technologies, robust infrastructure, and sustainable long-term strategies. This suggests a prudent approach to investing in AI for emissions reduction, emphasizing the need for realistic expectations and a range of solutions. This highlights the importance of tackling implementation challenges and focusing on scalable, sector-specific applications. Table 4 Long run and short run results Long Run Est. Short Run Est. Variables Coef. Std.Err P>[z] Coef. Std.Err P>[z] Ln (CO 2 ) t−1 -0.4782 * 0.1316 0.000 Δ Ln (CO 2 ) t−1 0.5217 * 0.1316 0.000 Δ Ln gdpc 4.9514 ** 1.9987 0.013 Δ Ln gdpc 2.2445 ** 0.9498 0.018 Δ Ln gdpc 2 -0.2191 ** 0.0952 0.021 Δ Ln gdpc 2 -0.1037 ** 0.0455 0.023 Δ Ln ai -0.1340 0.1131 0.236 Δ Ln ai 0.0146 0.0262 0.575 Δ Ln nec -0.9694 *** 0.5580 0.082 Δ Ln nec -0.1068 0.1340 0.425 Note: *, ** & *** are significant at 1%, 5% and 10% respectively The NCA calculates the number of explanatory variables required to achieve a particular outcome, such as CO 2 emissions, by analyzing effect size and scatter plots. The first step is to examine the scatterplot of the sample data, which will demonstrate the possible crucial conditions for the x-axis independent variable in relation to the ecological footprint result. There appears to be a necessary condition, as the space above the data region is in the top left quadrant of the scatterplot (see Fig. 2 ). The DH causality results in Table 5 show a two-way causality between lngdp and CO 2 emissions. This could result from economic activity and, in some cases, show how energy-intensive development drives GDP growth. Nuclear energy produces CO 2 emissions, establishing a distinct correlation between nuclear energy and carbon emissions. The consumption of nuclear energy serves as a significant predictor of carbon emissions. There may be shifts in emissions levels in areas that use nuclear energy due to shifting energy consumption patterns or the replacement of fossil fuels. We observe a clear, unidirectional causality in which AI influences CO 2 emissions. The advancement of artificial intelligence is expected to forecast carbon emissions. This suggests that AI may indirectly impact energy efficiency, grid optimization, and industrial processes, ultimately affecting CO 2 levels. The limited influence of CO 2 emissions on AI development suggests that technological advancements and policy objectives, rather than emission metrics, primarily drive AI initiatives. We observe a bidirectional causality between income and nuclear energy, as well as between artificial intelligence and income. Economic growth forecasts indicate that nuclear energy consumption will play a significant role in stimulating economic activity by providing dependable, cost-effective power while reducing reliance on energy imports. On the other hand, economic growth propels advancements in AI because developed nations are increasing their investments in research and development, technological innovation, and energy sector development. The advancement of AI indicates potential GDP growth, implying that its integration fosters improvements in productivity, innovation, and industrial efficiency. Nuclear energy also anticipates the development of AI, possibly because nuclear energy supports the energy requirements for AI infrastructure, such as data centers. The findings suggest that integrating nuclear energy and AI into economic planning can align growth with environmental sustainability. Table 5 Dumitrescu & Hurlin (2012) Granger non-causality test results W-bar Z-bar p-value lngdpc granger cause lnco 2 3.9159 *** 8.5013 [0.0000] lnco 2 granger cause lngdpc 3.7511 *** 8.0207 [0.0000] lnnec granger cause lnco 2 5.3659 *** 12.7287 [0.0000] lnco 2 granger cause lnnec 1.3937 1.1479 [0.2510] lnAI granger cause lnco 2 4.3870 *** 9.8748 [0.0000] lnco 2 granger cause lnAI 1.4875 1.4212 [0.1553] lngdpc granger cause lnnec 2.7891 *** 5.2160 [0.0000] lnnec granger cause lngdpc 3.7452 *** 8.0035 [0.0000] lngdpc granger cause lnAI 3.4206 *** 7.0573 [0.0000] lnAI granger cause lngdpc 2.0379 *** 3.0259 [0.0025] lnAI granger cause lnnec 2.4516 *** 4.2320 [0.0000] lnnec granger cause lnAI 2.7715 *** 5.1647 [0.0000] Note: *** shows level of significance at 1%. 5. Discussions The results from the CS-ARDL estimation reveal that nuclear energy use plays a significant role in lowering carbon emissions. The results are consistent with the Environmental Kuznets Curve (EKC) Hypothesis, suggesting that as economies expand and grow, they shift from reliance on fossil fuels to cleaner energy alternatives, ultimately leading to a decrease in environmental degradation over time. The findings are also in line with the Energy Substitution Theory, which states that substituting nuclear power and other low-carbon energy sources for fossil fuels can reduce environmental damage. Considering the substantial amount of power it generates, nuclear power could be an effective strategy for the sample nations to cut their carbon emissions, according to the study's empirical estimate. Suppose we take advantage of technological developments and transition to cleaner energy sources. In that case, we can reduce our environmental impact without compromising economic growth, according to empirical estimates that support the Decoupling theory. According to the study, nuclear power generation is an integral part of this decoupling because it offers a low-carbon alternative to traditional energy sources, and emissions have been steadily declining. Nuclear energy is crucial in providing stable base-load power, strengthening energy security, and mitigating uncertainties associated with solar and wind energy, thereby effectively supporting renewable energy sources. Additionally, nuclear energy reduces reliance on unstable fossil fuel imports, thereby enhancing energy self-reliance. Nuclear power is gaining significance in the energy mix since its integration may provide a reliable baseline supply, encouraging decarbonization. Investment in nuclear energy drives the advancement of state-of-the-art reactors, small modular reactors (SMRs), and waste management solutions, creating export opportunities and establishing global leadership in clean energy technology. Nuclear energy's ongoing significance in reducing CO 2 emissions is attributed to its ability to replace high-carbon energy sources with low-carbon, reliable power over an extended period. Additionally, the long- and short-term results highlight the importance of adopting a long-term perspective when evaluating the environmental benefits of nuclear energy. Nuclear energy offers OECD countries a realistic and financially viable solution to their carbon emission problem. In the short term, several OECD nations depend on fossil fuels as their primary energy source. In the short term, the use of nuclear energy may not result in an immediate reduction in emissions if it acts as a complement to, rather than a replacement for, fossil fuel-based sources. The transition to nuclear energy requires improvements to energy grids to accommodate this new source, which may delay the immediate impact on emissions. Nations within the OECD that adopt nuclear energy witness notable decreases in carbon emissions, aiding in fulfilling their climate obligations (such as those outlined in the Paris Agreement). The empirical estimation of the CS-ARDL model reveals that AI has an insignificant impact on reducing carbon emissions. From a theoretical standpoint, the notable relationship between artificial intelligence and carbon dioxide emissions can be elucidated by the fact that innovations driven by AI can enhance energy efficiency. However, this frequently results in heightened energy demand, which counteracts potential reductions in emissions. Technological advancements alone do not automatically reduce carbon emissions unless coupled with stringent energy policies. Further, Unequal adoption across industries may be the reason for AI's limited impact on emissions reduction. The use of artificial intelligence in smart grids, energy management, and predictive maintenance is still in its early stages in the OECD countries. The diffusion of low-carbon technology powered by AI is slow and has not yet reached a level where it can substantially reduce emissions. The OECD countries must reinforce regulations that encourage the adoption of AI in energy-intensive sectors to improve AI's impact on emissions reduction. Integrating AI with smart grids, demand forecasting, and optimizing renewable energy sources is essential for maximizing environmental benefits. On a concluding note, the study suggests that AI's impact on emissions reduction in OECD countries is minimal, primarily due to inefficient adoption in high-emission sectors, high prices, and regulatory constraints. AI must systematically integrate with renewable energy policy, sectoral reform, and long-term sustainability initiatives to realize its complete potential. The results are summarized in Fig. 3 . 6. Conclusion This study examines the role of artificial intelligence and nuclear energy in achieving net-zero carbon emission targets in the 17 OECD economies from 1990 to 2020. The study employed the CS-ARLD estimation and DH-causality models to investigate the long-run impact of the underlying variables in the study. The derived key findings can be summarized as follows: i) Nuclear energy contributes to carbon emissions in the long run but does not seem to do so in the short run. ii) The results evidence an insignificant impact of artificial intelligence on carbon emissions both in the long- and short-run. Both nuclear energy and artificial intelligence contribute to the formation of EKC in the long and short run within OECD economies. The DH-causality reveals unidirectional causality from nuclear energy toward carbon emissions and from artificial carbon emissions. Finally, bidirectional causality is found between artificial intelligence and nuclear energy. The study's results present significant policy implications. Considering the beneficial impact of nuclear energy on reducing carbon emissions, it is proposed that OECD countries can optimize their potential to achieve net-zero carbon emissions in the long term by incorporating nuclear energy with complementary technologies and implementing robust governance frameworks. The governments of these OECD countries need to allocate resources towards constructing and modernizing nuclear power plants. These facilities provide a dependable, low-carbon energy source that aligns with long-term net-zero emission goals. Long-term energy strategies must emphasize regulatory and policy stability to attract private investment in nuclear energy projects, which generally involve substantial initial costs and lengthy payback periods. Additionally, it is crucial to launch public education campaigns to address misconceptions and emphasize the benefits of nuclear energy as a reliable and low-emission energy source, particularly in relation to long-term climate goals. Given the limited role of AI and carbon emissions, policymakers must develop strategies that connect AI's potential with its practical applications in achieving carbon neutrality. Policymakers should advocate for the incorporation of AI technologies in the nuclear energy sector that play a significant role in reducing carbon emissions. Allocate resources toward AI-driven smart grids to enhance energy distribution efficiency and facilitate the integration of nuclear energy. Utilize artificial intelligence to oversee nuclear energy systems in real-time, forecast demand, and diminish dependence on fossil fuels. Governments should promote private sector investments and innovations in AI for climate-related applications and offer tax incentives to private enterprises that develop AI technologies to reduce emissions. Establish collaborations to enhance the application of AI technologies in integrating nuclear energy and developing sustainable urban environments. Governments of OECD countries should issue green bonds to finance extensive AI initiatives that support net-zero carbon emission objectives. Policymakers should leverage AI as a powerful tool for achieving net-zero emissions over time by enhancing AI integration, investing in infrastructure, and promoting collaboration across various sectors. Policies that ensure the responsible and effective deployment of AI in line with climate goals must support these initiatives. The study offers some limitations. It primarily examines the role of AI in reducing carbon emissions, providing a broad overview rather than focusing on specific sector industries. The influence of AI is likely to differ among the manufacturing, transportation, and energy production sectors. Future investigations should delve into the impact of AI on emissions reduction across specific sectors, including transportation, energy, and agriculture, to yield more precise policy recommendations. The availability and consistency of AI-related data among OECD nations present significant challenges. Variations in AI adoption rates, policy frameworks, and technological advancements could affect the study's generalizability. The analysis focuses on empirical estimations, excluding policy measures that may affect AI's contribution to emissions reduction. Examining the influence of government policies would yield a deeper understanding of AI’s efficacy in reducing emissions. 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Nuclear Engineering and Technology 56, 3983–3992. https://doi.org/10.1016/j.net.2024.04.046 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 10 Jan, 2026 Reviews received at journal 06 Jan, 2026 Reviews received at journal 25 Dec, 2025 Reviews received at journal 17 Dec, 2025 Reviewers agreed at journal 14 Dec, 2025 Reviewers agreed at journal 12 Dec, 2025 Reviewers agreed at journal 12 Dec, 2025 Reviewers invited by journal 12 Dec, 2025 Editor assigned by journal 11 Dec, 2025 Editor invited by journal 10 Dec, 2025 Submission checks completed at journal 06 Dec, 2025 First submitted to journal 01 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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1","display":"","copyAsset":false,"role":"figure","size":187555,"visible":true,"origin":"","legend":"\u003cp\u003eThe framework of the study (Author’s calculation)\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8153017/v1/1f59f818a630f50ed378107f.png"},{"id":98441232,"identity":"563cf67e-932a-4db8-a4b1-8c60bcbef94b","added_by":"auto","created_at":"2025-12-17 17:05:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":208008,"visible":true,"origin":"","legend":"\u003cp\u003eScatterplots illustrating the nine essential conditions for the estimation\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8153017/v1/0622e2ef509132bcb817bc06.png"},{"id":98441151,"identity":"7fe6aaa9-f08a-4755-b3e1-3b4e55e82de4","added_by":"auto","created_at":"2025-12-17 17:04:59","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":183391,"visible":true,"origin":"","legend":"\u003cp\u003eSummary of findings\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8153017/v1/0b852498a6bc171f9a1bdec9.png"},{"id":98445862,"identity":"247f625a-f6e9-4792-bfcd-452884bc4ff2","added_by":"auto","created_at":"2025-12-17 17:22:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1424210,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8153017/v1/9da78071-b093-41c2-9c70-16b61e91750e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Artificial Intelligence, energy transitions, and the pathway to net-zero carbon emissions in OECD Countries","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe gradual but noticeable increase in the Earth's average temperature is characterized by global warming. The immense production of greenhouse gas emissions is mainly responsible for the rapid expansion over the past fifty years. Carbon emissions from fossil fuels hit a new high worldwide in 2024. More than 40\u0026nbsp;billion metric tons of carbon dioxide (CO\u003csub\u003e2\u003c/sub\u003e) are released into the atmosphere yearly, increasing the likelihood of severe climate change and worsening the state of the planet (World Meteorological Organization, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Energy systems play a crucial role in the operation of economies and the welfare of communities. However, energy conversion and use are the primary factors contributing to global warming. The energy sector, mainly dependent on fossil fuels, accounts for 34 percent of anthropogenic greenhouse gas (GHG) emissions, totaling 20 gigatons (Gt) of GHG worldwide. To meet the goals outlined in the Paris Agreement, it is crucial to reduce global CO\u003csub\u003e2\u003c/sub\u003e emissions by 50% by 2030. Our objective is to decrease reliance on fossil fuels for electricity generation, heating and cooling, and the operation of industries and transportation. This can be achieved by accelerating the transition to sustainable and cost-effective energy solutions for everyone (IPCC, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Unless we achieve global net-zero CO\u003csub\u003e2\u003c/sub\u003e emissions, the rise in world temperatures will continue, resulting in increasingly severe consequences, as seen in 2023.\u003c/p\u003e \u003cp\u003eThe energy sector encompasses various activities related to the generation, conversion, transportation, and distribution of energy in multiple forms. Energy generation from different sources is categorized into three primary categories: fossil fuels, renewable energy, and nuclear power. Fossil fuels, such as oil, natural gas, and coal, are non-renewable energy sources that emerged from buried organic matter millions of years ago. These are currently the primary energy sources globally; however, their utilization hurts the environment, human health, and climate due to greenhouse gas (GHG) emissions (Akpolat and Bakırtaş, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Bergougui, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Renewable energy encompasses sources such as hydro, solar, and wind energy. Renewables are considered a clean energy source that reduces pollution, improves environmental quality, alleviates energy poverty, and contributes to human development (Danish and Ulucak, 2021; Zhang et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Nuclear energy is another form of energy and a source of electricity. Nuclear energy has come to prominence as a viable alternative energy source and a pragmatic solution in the quest for cleaner and more sustainable options to conventional fossil fuels. (Huang et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The demand for primary energy worldwide has risen by 50% since 1990. The world will face numerous energy-related challenges, both currently and in the future. By 2035, energy demand is anticipated to exceed current levels significantly. All nations must acknowledge the imminent rise in global energy demands. A growing dialogue has emerged among policymakers regarding the importance of nuclear and renewable energy as viable alternatives to fossil-fuel-based power generation, particularly coal. This discourse primarily examines the economic viability of nuclear energy in relation to its alternatives, including wind and solar energy.\u003c/p\u003e \u003cp\u003eThe role of nuclear energy in achieving the Sustainable Development Goals (SDGs) is increasingly debated among policymakers, as recently observed at the 26th Conference of the Parties to the United Nations Framework Convention on Climate Change. The integration of renewable energy, nuclear energy, and modifications in consumer behavior may facilitate the attainment of the zero-emission goal established for 2050 by the Intergovernmental Panel on Climate Change (IPCC, 2022). Four hundred forty-two nuclear power reactors operate worldwide, providing 393 GWe of reliable, low-carbon electricity. Nuclear energy accounts for 11% of total global electricity generation and one-third of the low-carbon electricity produced worldwide (Mathew, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Nuclear energy has considerable potential to reduce CO₂ emissions because of its low-carbon nature. However, it faces significant challenges that have limited its wider adoption and effectiveness in combating climate change. The construction of nuclear power plants requires substantial capital investment and high upfront costs related to safety regulations and waste disposal (Iurshina et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The construction of a nuclear power plant typically takes 5 to 10 years and costs several billion dollars. As a result, nuclear energy is often less economical than other sources, such as renewables. Furthermore, the regulatory requirements, extended planning processes, and lengthy construction times contribute to the overall costs, making this technology less appealing for countries with limited energy budgets. Nuclear power plants are costly but relatively inexpensive, according to the World Nuclear Association (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBased on the above situation, the role of nuclear energy and CO\u003csub\u003e2\u003c/sub\u003e emissions is complex, as it is observed that nuclear energy behaves as clean energy that reduces carbon emissions (Hassan et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Mehboob et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Pata and Kartal, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Simionescu and Plopeanu, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Teng et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and contributes to sustainable development (Zheng et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, opposing views are discussed in the literature that nuclear energy is not beneficial to the environment (Danish et al., 2022; Voumik et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and harms environmental quality by contributing to carbon emissions (Mahmood et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Soto and Martinez-Cobas, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Advancements influence the connection between nuclear energy and environmental pollution in nuclear power plant technology (\u0026Ccedil;akar et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Due to asymmetric effects, nuclear energy protects the health of the natural environment (Bandyopadhyay et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The use of nuclear energy remains a contentious issue, with diverse perspectives presenting both support and opposition. Advocates of nuclear energy contend that it serves as a clean and efficient energy source, distinguished by low greenhouse gas emissions. They emphasize its relatively low environmental impact compared to conventional energy sources, highlighting its role in mitigating climate change. Additionally, nuclear power is regarded as a highly efficient electricity generation, capable of producing substantial energy output with a relatively small ecological footprint. Conversely, critics raise concerns regarding the generation of radioactive waste, which presents long-term environmental and safety risks due to its prolonged decay, often spanning thousands of years. Furthermore, nuclear energy is frequently debated regarding its renewability, as it relies on finite uranium resources. Additionally, some opponents argue that nuclear technology poses potential proliferation risks, given its possible association with the development of nuclear weapons (Soto and Martinez-Cobas, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This highlights the need for further research into the consequences and causes of nuclear energy use and consumption.\u003c/p\u003e \u003cp\u003eApart from varying opinions expressing advocating for or against nuclear energy and CO\u003csub\u003e2\u003c/sub\u003e emissions nexus, several vital variables have been used in the nuclear energy-CO\u003csub\u003e2\u003c/sub\u003e emissions relationship, for instance(Tauseef et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) technological advancements;(Danish et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021c\u003c/span\u003e) foreign direct investment (FDI)(Kartal, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) fossil fuels and renewable energy;(Hassan et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) transportation infrastructure;(Jin et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) green energy, energy efficiency, research, and development (R\u0026amp;D);(Liu et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) technology and green finance;(Danish et al., 2022; Hassan et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) globalization and economic complexity index and(Mehboob et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) environmental taxes and trade globalization. The current investigation examines the relationship between artificial intelligence, nuclear energy, and CO\u003csub\u003e2\u003c/sub\u003e emissions in selected OECD countries that utilize significant nuclear energy and have advanced artificial intelligence (AI) integration. The economic benefits of AI-enhanced nuclear operations are increasingly recognized for their role in promoting cost-effective and sustainable clean energy solutions. Integrating AI in nuclear waste management enhances navigation of complex regulatory frameworks, reduces environmental impacts, and improves public health protection (Christopher Selvam et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Policy frameworks progressively acknowledge the synergistic relationship between AI and nuclear energy as crucial factors in addressing climate change challenges. AI-driven technologies have the potential to reduce energy consumption and carbon emissions by an estimated 8% to 19% by 2050. Furthermore, aligning energy policies with low-carbon power generation strategies could lead to a 40% reduction in energy consumption and a 90% decrease in carbon emissions compared to business-as-usual projections for 2050 (Ding et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe study makes a significant contribution to the existing body of knowledge. (i) The paper begins by outlining a framework for future research into the nuclear energy-CO\u003csub\u003e2\u003c/sub\u003e emissions nexus, where artificial intelligence (AI) is considered a possible driver of CO\u003csub\u003e2\u003c/sub\u003e emissions. The current literature has largely overlooked the impact of AI and nuclear energy on carbon emissions in OECD countries. (ii) According to the nuclear energy examined environmental Kuznets curve (EKC) hypothesis, neither the availability nor the study of AI's environmental effects has been considered for OECD nations. This investigation addresses critical knowledge gaps regarding the role of AI in policy measures designed to achieve net-zero carbon emissions. (iii) The CS-ARDL (Cross-Sectional Autoregressive Distributed Lag) method is employed to analyze the long-term effects of the underlying variable. Previous studies have employed panel data methods, revealing consistent findings regarding the relationship among these variables. The CS-ARDL model is an advanced econometric tool that addresses various challenges in panel data analysis, particularly when cross-sectional dependence is present. The unique features of CS-ARDL, particularly its ability to handle cross-sectional dependence, heterogeneity, and mixed integration orders, make it a powerful and flexible tool for identifying long-term relationships in panel data. This method is more effective in situations involving global shocks or panel members who are interdependent than other long-run panel estimators. This is especially true in fields like energy economics and environmental studies.\u003c/p\u003e \u003cp\u003eThe organization of this paper is as follows: Section 2 discusses the relationships among artificial intelligence, nuclear energy, and CO\u003csub\u003e2\u003c/sub\u003e emissions. Section 3 outlines the methodology employed in the study, covering data sources, model specifications, and analytical techniques used. Section 4 presents the empirical results, while Section 5 provides a detailed discussion of the findings. Section 6 defines the study's limitations and offers guidance for future research, ultimately concluding the paper with implications for policy and recommendations.\u003c/p\u003e"},{"header":"2. Artificial intelligence, nuclear energy, and CO emissions: Interconnectivity","content":"\u003cp\u003eNuclear power is a low-carbon energy source that significantly contributes to a low-carbon economy and the development of green energy. The current technological innovations create conditions that make nuclear energy even more affordable and attractive. These advancements include improvements in large reactors, the development of innovative technologies for advanced fuel and small modular reactors, enhancements in engineering that extend the operational lives of existing reactors, and advancements in materials and waste management. Fast-breeder reactors are already beyond commercial technology and offer additional advantages, such as producing more fuel than they consume while efficiently destroying nuclear waste, compared to existing commercial reactor technologies (Mathew, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Nuclear energy is recognized for its ability to provide low-carbon energy. Unlike fossil fuels, nuclear power plants produce minimal CO\u003csub\u003e2\u003c/sub\u003e emissions during operation, making them crucial in achieving climate goals. By integrating nuclear energy into energy grids, we have the potential to replace carbon-intensive sources such as coal and gas, which could significantly reduce global emissions. However, there is also a concern that both existing and future nuclear facilities could pose a risk of severe environmental disasters if they were to be targeted by a terrorist attack (Toth and Rogner, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). The critics of nuclear energy argue that the financial burden of investing in nuclear plants is significant and that developing nations struggle with construction, given their current capabilities (\u0026Ccedil;akar et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Nuclear energy proponents argue that, although the initial installation costs of nuclear power plants are significant, their operating costs are comparatively low when measured against those of fossil fuels (Usman et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Theoretical explanations suggest that nuclear energy has benefits and drawbacks concerning environmental quality (Ozcan et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAI has a broader role in reducing CO\u003csub\u003e2\u003c/sub\u003e emissions beyond nuclear energy. For instance, it facilitates carbon capture technologies, improves energy efficiency in industrial processes, and supports the transition to smart grids. However, AI\u0026rsquo;s energy-intensive nature, especially for training large models, presents a challenge, underscoring the need for sustainable practices within AI development. Integrating AI in nuclear energy systems can significantly enhance efficiency and capacity, reducing substantial emissions. AI-driven predictive analytics can optimize reactor performance and safety, ensuring nuclear facilities operate at optimal output levels while maintaining a reliable, low-carbon energy supply. Additionally, developing integrated assessment models (IAMs) can support policymakers in formulating strategies that effectively incorporate AI and nuclear technologies to advance climate mitigation efforts. As AI adoption within the nuclear sector expands, the need for workforce transformation becomes increasingly critical. The convergence of AI and nuclear energy presents a potential pathway toward net-zero emissions if challenges such as AI\u0026rsquo;s substantial energy requirements and societal concerns regarding nuclear power are effectively addressed. A deeper examination of these interdependencies may reveal further opportunities to leverage their combined capabilities for sustainable development.\u003c/p\u003e \u003cp\u003eThe adoption of AI in nuclear energy systems is expanding, comprising applications aimed at enhancing operational efficiency, safety, and energy distribution. AI-driven solutions, including predictive maintenance, facilitate the early identification of potential system failures, which enhances reactor safety and reliability. AI-based models evaluate the patterns of energy demand, optimizing the performance of nuclear reactors and promoting the integration of nuclear energy with renewables, thereby contributing to an adequate energy supply. The rapid advancement of artificial intelligence (AI) technology in recent decades has led to both new opportunities and challenges regarding enhancing the safety and economic aspects of nuclear reactors (Chen et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e; Huang et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Evidence suggests that countries leveraging nuclear power and AI technologies demonstrate accelerated progress in emissions reduction. However, success depends on careful policy design, technological innovation, and sustained investment in both sectors. Nuclear power provides stable, carbon-free baseload power essential for energy-intensive AI data centers (Johnson, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The role of AI is significant, as it serves as a catalyst for improving safety measures within the nuclear sector (Chen et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024b\u003c/span\u003e). The framework between AI, nuclear energy, and carbon emissions is given in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"3. Material and Method","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1. Econometric strategy\u003c/h2\u003e\n \u003cp\u003eConsidering the recent work (Alam, \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e; Danish et al., 2021a; Liu et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e), we extended these studies by including the key factor of artificial intelligence into the empirical model. According to the objective of the study, the following econometric equation is developed:\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eLn (co\u003c/em\u003e \u003csub\u003e\u0026nbsp;\u003cem\u003e2\u003c/em\u003e\u0026nbsp;\u003c/sub\u003e \u003cem\u003e)\u003c/em\u003e \u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e \u003cem\u003e= \u0026beta;\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;\u0026beta;\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e \u003cem\u003eln(gdp)\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e \u003cem\u003e+ \u0026beta;\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e \u003cem\u003eln(gdp^\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e)\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e \u003cem\u003e+ \u0026beta;\u003c/em\u003e\u003csub\u003e\u003cem\u003e3\u003c/em\u003e\u003c/sub\u003e \u003cem\u003eln(NEC)\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e \u003cem\u003e+ \u0026beta;\u003c/em\u003e\u003csub\u003e\u003cem\u003e4\u003c/em\u003e\u003c/sub\u003e \u003cem\u003eln(AI)\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e \u003cem\u003e+ \u0026micro;\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e \u003cem\u003e(1)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eCO\u003c/em\u003e \u003csub\u003e\u0026nbsp;\u003cem\u003e2\u003c/em\u003e\u0026nbsp;\u003c/sub\u003e \u003cem\u003erepresents\u003c/em\u003e carbon dioxide emissions measured in terms of carbon emissions per capita. GDP \u003cem\u003eis the\u003c/em\u003e gross domestic product, GDP\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e is the square of the gross domestic product, \u003cem\u003eNEC\u003c/em\u003e is nuclear energy consumption, and \u003cem\u003eAI\u003c/em\u003e is artificial intelligence. \u003cem\u003ei\u003c/em\u003e and t are cross-sections and times, where \u0026lsquo;\u003cem\u003e\u0026micro;\u0026rsquo; is\u003c/em\u003e an error term. The expected sign of coefficient \u003cem\u003e\u0026beta;2\u003c/em\u003e is negative, whereas the anticipated value of \u0026beta;1 is positive. If \u003cem\u003e\u0026beta;1\u0026thinsp;\u0026gt;\u0026thinsp;0\u003c/em\u003e and \u003cem\u003e\u0026beta;2\u0026thinsp;\u0026lt;\u0026thinsp;0\u003c/em\u003e in this scenario, then the widely recognized \u003cem\u003eEKC\u003c/em\u003e hypothesis is present. The first proposal is a square model that examines the relationship between CO\u003csub\u003e2\u003c/sub\u003e emissions and income, incorporating control variables to validate the existence of an inverted U-shaped pattern in this relationship. The environmental Kuznets curve (EKC) hypothesis posits that initially, pollution increases with rising per capita income; however, ultimately, higher income levels lead to a decline in pollution (Dinda, \u003cspan class=\"CitationRef\"\u003e2004\u003c/span\u003e; Grossman and Krueger, \u003cspan class=\"CitationRef\"\u003e1995\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThrough its ability to generate low-carbon electricity, nuclear energy has garnered worldwide attention as a highly effective means of reducing greenhouse gas emissions. It offers a promising approach to mitigating greenhouse gas and carbon emissions. Nuclear energy reduces expenses for countries reliant on fossil fuel imports, addressing their current account deficits while mitigating energy dependence and enhancing security concerns. Nuclear energy is a clean energy source essential for meeting sustainable development goals and ensuring a secure energy supply, promoting economic growth by alleviating energy supply challenges (Danish et al., 2021b). Nuclear energy improves energy supply, cleans the environment, reduces CO\u003csub\u003e2\u003c/sub\u003e emissions, and promotes sustainable development (Hassan et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Lau et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). Conversely, nuclear energy contributes to pollution through waste production, including the use of indigenous resources for nuclear power plants and the disposal of radioactive waste. This, in turn, has severe and irreversible consequences for both humanity and the environment (B\u0026eacute;la\u0026iuml;d and Youssef, \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e; Mahmood et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). The contribution of nuclear energy to CO\u003csub\u003e2\u003c/sub\u003e emissions remains ambiguous. The decision-making process is, in this context, more complex. The expected outcome of nuclear energy is still uncertain.\u003c/p\u003e\n \u003cp\u003eRecently, the literature has focused on the environmental impact of AI. Another essential variable utilized in the study is AI, which exhibits both immediate and extensive effects on the environment. New AI models specifically designed for training purposes can be energy-intensive, leading to an increase in energy demand for running these models, which in turn may result in higher carbon emissions. The growing demand for data storage units highlights the indirect environmental impact of the artificial intelligence sector. The economic activities associated with the artificial intelligence sector led to resource depletion, increased energy consumption, and the generation of electronic waste. The implementation of advanced manufacturing processes enhanced by AI could result in a significant reduction in energy consumption, waste, and carbon emissions, potentially achieving a decrease of 30\u0026ndash;50%. AI influences the environment through the increasing demand for data storage units, digital services, cloud computing, and data centers. Another aspect of the environmental impact of AI includes resource depletion, increased energy demand, and electronic waste, all of which are linked to AI operations. AI-driven manufacturing has the capacity to reduce energy consumption, waste, and carbon emissions by 30\u0026ndash;50% (Chen et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). The AI execution in various sectors extends to multiple application domains, including computer vision, robotics, natural language processing, and machinery, thereby enhancing energy efficiency and reducing carbon emissions across the building, transportation, and industrial sectors (Ding et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). AI plays a crucial role in the development of green technology innovation and the optimization of employment skill structures, which contribute to lowering carbon emissions and reducing carbon footprints (Liu, \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e; Rasheed et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e; Wang et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). The impact of AI is unclear; therefore, it may have either a positive or negative effect on carbon emissions mitigation.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2. Empirical estimation procedure\u003c/h2\u003e\n \u003cp\u003eTo achieve its objective, this study employed the Cross-Sectionally Augmented Autoregressive Distributed Lag (CS-ARDL) estimator for empirical estimation, which counteracts cross-country spillovers and allows for varied responses. The CS-ARDL methodology offers several advantages that make it particularly suitable for examining the relationship between nuclear energy, artificial intelligence, and carbon emissions. First, the CS-ARDL addresses the CSD issue arising from global economic interconnections and shared environmental challenges. This feature is essential due to the inherent interconnections of environmental policies among nations. Second, the CS-ARDL methodology can handle either stationary variables or those that exhibit unit roots, thereby eliminating the stringent necessity for pre-testing the integration orders of variables. This feature holds significant importance when data series frequently display varying statistical characteristics. Third, the CS-ARDL methodology demonstrates resilience even when working with smaller sample sizes, which is often a limitation in the field of energy policy, where dependable data may be scarce. Fourth, CS-ARDL produces both short-run dynamics and long-run relationships simultaneously, enabling the distinction between immediate policy impacts and their lasting effects over time.\u003c/p\u003e\n \u003cp\u003eTo assess long-run and short-run relationships, the estimated equation is:\u003c/p\u003e\n \u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\u003cimg 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\"\u003e\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eIn the above equation, \u003cem\u003eCO\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e represents carbon dioxide emissions, \u003cem\u003eY\u003c/em\u003e denotes income, \u003cem\u003eNEC\u003c/em\u003e signifies nuclear energy consumption, \u003cem\u003eand AI stands for\u003c/em\u003e artificial intelligence. The term \u0026epsilon; represents the speed of adjustment toward equilibrium. Lagged differences capture short-run dynamics \u003cem\u003e\u0026Delta;\u003c/em\u003e. In energy and environmental studies, cross-sectional dependence is inevitable due to the interdependence of environmental resources and global emissions. The \u003cem\u003eCS-ARDL\u003c/em\u003e accurately recognizes these spillovers. The model\u0026apos;s ability to provide unit-specific estimates is crucial for developing tailored energy and environmental policies for local conditions. This method allows for an in-depth analysis of the routes economies follow in transitioning to cleaner energy systems and an assessment of the pace and uniformity of these changes. The \u003cem\u003eCS-ARDL\u003c/em\u003e methodology is applicable to panel data with varying lengths and sizes, thereby increasing its usefulness for studies at global, regional, or sectoral levels.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3. Data\u003c/h2\u003e\n \u003cp\u003eThis paper covers the annual dataset for 17 selected OECD countries (Belgium, Canada, the Czech Republic, Finland, France, Germany, Hungary, Japan, Mexico, the Netherlands, Slovakia, Slovenia, Spain, Sweden, Switzerland, the United Kingdom, and the United States) from 1994 to 2020. The dependent variable is per capita CO\u003csub\u003e2\u003c/sub\u003e emissions (million tons of CO\u003csub\u003e2\u003c/sub\u003e). Data about CO\u003csub\u003e2\u003c/sub\u003e emissions is derived from the British Petroleum Statistical Review (BP, \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). GDP (Gross Domestic Product), AI (Artificial Intelligence), and nuclear energy consumption are independent variables that explain CO\u003csub\u003e2\u003c/sub\u003e emissions. AI is measured as the number of registered AI-related patents (Triad family registered patents). AI data is taken from OECD databases. Data regarding nuclear energy is sourced from the Energy Information Administration (EIA) database. Nuclear energy is quantified in British Thermal Units (BTUs), indicating the energy generated or utilized. Income is ultimately assessed based on GDP per capita, using data from the World Development Indicators, a World Bank database.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Results","content":"\u003cp\u003eThe analysis begins by examining cross-sectional dependence (CSD), as ignoring it may lead to biased estimates. This study employs the CSD test proposed by Pesaran (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) to evaluate the CSD in panel data by examining residual correlations across units.\u003c/p\u003e \u003cp\u003eThe CSD test results in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrate significant CSD among the sample countries. This suggests that shocks or policies implemented in one sample country may have an impact on others. Furthermore, the Pesaran and Yamagata (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) test examines heterogeneity. This test determines if the effects of independent variables, represented by the slope coefficients, are consistent across units. According to the study's findings, the results (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) suggest that heterogeneity is absent in the model.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of CSD test and slop homogeneity test\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCD-Test Statistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLn CO\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.629***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLn gdpc\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.876***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLn gdpc2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.198***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLn AI\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.038**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLn NEC\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.253***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSlope Homogeneity Test\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDelta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.839***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdjusted Delta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.036***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e*** \u0026amp; ** means a 1% \u0026amp; 5% level of significance.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe next step involves employing a unit root test, which ensures that econometric models are accurately specified, thereby preventing misleading outcomes and facilitating robust statistical inference. For non-stationary variables, differencing techniques or a cointegration framework must be applied or implemented for analysis. This study employs the CIPS and CADF panel data unit root test, as proposed by Pesaran (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). The results in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e indicate that the data series is non-stationary at the level, becoming stationary after the first difference.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnit root test results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eCIPS unit root test\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eCADF unit root test\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003elevel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1st Diff\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003elevel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1st Diff\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLn CO2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-3.263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-5.921***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-6.077***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLn gdpc\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-4.377***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-7.226***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLn gdpc2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-4.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-5.868***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-5.478***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLn AI\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-3.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-7.432***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-5.573***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLn NEC\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.935\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-5.154***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.589\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-5.364***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e*** means a 1% level of significance.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAfterward, a cointegration test is employed. In particular, the Westerlund (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) cointegration test determines whether there is a long-term relationship between two or more variables in a panel dataset comprising data from multiple entities over time. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the cointegration results, demonstrating that both group and panel statistics are statistically significant. A long-term relationship exists between the variables for most entities in the panel data.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of co-integration test\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStatistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eZ-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProb.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-4.253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-2.155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-18.189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-13.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-2.398\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-11.224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe CS-ARDL presents long and short-run estimation results in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. From the results, it is evident that an increase in GDP per capita results in an increase in emissions in the long and short run, highlighting the positive relationship between economic growth and carbon emissions. Further, the coefficient of the GDP square is negative and significant both in the long and short run. The negative coefficient of lngdpc2 in the short and long run supports the EKC hypothesis, suggesting that emissions initially rise with economic growth but decline as income levels increase beyond a threshold.\u003c/p\u003e \u003cp\u003eThe coefficient of nuclear energy estimate is negatively significant, suggesting that an increase in nuclear energy consumption reduces CO\u003csub\u003e2\u003c/sub\u003e emissions over time, but the effect is marginally significant. In the short term, nuclear energy also has a negligible impact on carbon emissions. Overall, nuclear energy consumption does not appear to have a strong relationship with emissions in the short term or the long term; however, it does exhibit some effect in the long term. In both the short and long run, the impact of artificial intelligence on carbon emissions is not statistically significant. However, the sign of the coefficient of AI is harmful in the long run. The insignificant effect of AI on carbon emissions, both in the short term and long term within OECD countries, suggests that AI alone is insufficient to achieve significant decarbonization. This underscores the necessity for enhanced integration with complementary technologies, robust infrastructure, and sustainable long-term strategies. This suggests a prudent approach to investing in AI for emissions reduction, emphasizing the need for realistic expectations and a range of solutions. This highlights the importance of tackling implementation challenges and focusing on scalable, sector-specific applications.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLong run and short run results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eLong Run Est.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c11\" namest=\"c6\"\u003e \u003cp\u003eShort Run Est.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoef.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eStd.Err\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u0026gt;[z]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCoef.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eStd.Err\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eP\u0026gt;[z]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLn (CO\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e)\u003c/em\u003e \u003csub\u003e\u003cem\u003et\u0026minus;1\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.4782 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eΔ Ln (CO\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e)\u003c/em\u003e \u003csub\u003e\u003cem\u003et\u0026minus;1\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.5217 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.1316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eΔ Ln gdpc\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.9514 **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.9987\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eΔ Ln gdpc\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.2445 **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.9498\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eΔ Ln gdpc\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.2191 **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0952\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eΔ Ln gdpc\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.1037 **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eΔ Ln ai\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.1340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eΔ Ln ai\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eΔ Ln nec\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.9694 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5580\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eΔ Ln nec\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.1068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.1340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eNote: *, ** \u0026amp; *** are significant at 1%, 5% and 10% respectively\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe NCA calculates the number of explanatory variables required to achieve a particular outcome, such as CO\u003csub\u003e2\u003c/sub\u003e emissions, by analyzing effect size and scatter plots. The first step is to examine the scatterplot of the sample data, which will demonstrate the possible crucial conditions for the x-axis independent variable in relation to the ecological footprint result. There appears to be a necessary condition, as the space above the data region is in the top left quadrant of the scatterplot (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe \u003cem\u003eDH\u003c/em\u003e causality results in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e show a two-way causality between \u003cem\u003elngdp\u003c/em\u003e and \u003cem\u003eCO\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e emissions. This could result from economic activity and, in some cases, show how energy-intensive development drives \u003cem\u003eGDP\u003c/em\u003e growth. Nuclear energy produces CO\u003csub\u003e2\u003c/sub\u003e emissions, establishing a distinct correlation between nuclear energy and carbon emissions. The consumption of nuclear energy serves as a significant predictor of carbon emissions. There may be shifts in emissions levels in areas that use nuclear energy due to shifting energy consumption patterns or the replacement of fossil fuels. We observe a clear, unidirectional causality in which AI influences \u003cem\u003eCO\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e emissions. The advancement of artificial intelligence is expected to forecast carbon emissions. This suggests that \u003cem\u003eAI may indirectly impact\u003c/em\u003e energy efficiency, grid optimization, and industrial processes, ultimately affecting \u003cem\u003eCO\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e levels. The limited influence of \u003cem\u003eCO\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e emissions on \u003cem\u003eAI\u003c/em\u003e development suggests that technological advancements and policy objectives, rather than emission metrics, primarily drive \u003cem\u003eAI\u003c/em\u003e initiatives. We observe a bidirectional causality between income and nuclear energy, as well as between artificial intelligence and income.\u003c/p\u003e \u003cp\u003eEconomic growth forecasts indicate that nuclear energy consumption will play a significant role in stimulating economic activity by providing dependable, cost-effective power while reducing reliance on energy imports. On the other hand, economic growth propels advancements in AI because developed nations are increasing their investments in research and development, technological innovation, and energy sector development. The advancement of \u003cem\u003eAI\u003c/em\u003e indicates potential \u003cem\u003eGDP\u003c/em\u003e growth, implying that its integration fosters improvements in productivity, innovation, and industrial efficiency. Nuclear energy also anticipates the development of AI, possibly because nuclear energy supports the energy requirements for AI infrastructure, such as data centers. The findings suggest that integrating nuclear energy and AI into economic planning can align growth with environmental sustainability.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDumitrescu \u0026amp; Hurlin (2012) Granger non-causality test results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eW-bar\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eZ-bar\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003elngdpc granger cause lnco\u003c/b\u003e\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e3.9159 ***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e8.5013\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e[0.0000]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003elnco\u003c/b\u003e\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003e \u003cb\u003egranger cause lngdpc\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e3.7511 ***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e8.0207\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e[0.0000]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003elnnec granger cause lnco\u003c/b\u003e\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e5.3659 ***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e12.7287\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e[0.0000]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnco\u003csub\u003e2\u003c/sub\u003e granger cause lnnec\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.3937\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.1479\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[0.2510]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003elnAI granger cause lnco\u003c/b\u003e\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e4.3870 ***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e9.8748\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e[0.0000]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnco\u003csub\u003e2\u003c/sub\u003e granger cause lnAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.4875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.4212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[0.1553]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003elngdpc granger cause lnnec\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e2.7891 ***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e5.2160\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e[0.0000]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003elnnec granger cause lngdpc\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e3.7452 ***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e8.0035\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e[0.0000]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003elngdpc granger cause lnAI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e3.4206 ***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e7.0573\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e[0.0000]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003elnAI granger cause lngdpc\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e2.0379 ***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e3.0259\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e[0.0025]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003elnAI granger cause lnnec\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e2.4516 ***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e4.2320\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e[0.0000]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003elnnec granger cause lnAI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e2.7715 ***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e5.1647\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e[0.0000]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: *** shows level of significance at 1%.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"5. Discussions","content":"\u003cp\u003eThe results from the CS-ARDL estimation reveal that nuclear energy use plays a significant role in lowering carbon emissions. The results are consistent with the Environmental Kuznets Curve (EKC) Hypothesis, suggesting that as economies expand and grow, they shift from reliance on fossil fuels to cleaner energy alternatives, ultimately leading to a decrease in environmental degradation over time. The findings are also in line with the Energy Substitution Theory, which states that substituting nuclear power and other low-carbon energy sources for fossil fuels can reduce environmental damage. Considering the substantial amount of power it generates, nuclear power could be an effective strategy for the sample nations to cut their carbon emissions, according to the study's empirical estimate. Suppose we take advantage of technological developments and transition to cleaner energy sources. In that case, we can reduce our environmental impact without compromising economic growth, according to empirical estimates that support the Decoupling theory. According to the study, nuclear power generation is an integral part of this decoupling because it offers a low-carbon alternative to traditional energy sources, and emissions have been steadily declining.\u003c/p\u003e \u003cp\u003eNuclear energy is crucial in providing stable base-load power, strengthening energy security, and mitigating uncertainties associated with solar and wind energy, thereby effectively supporting renewable energy sources. Additionally, nuclear energy reduces reliance on unstable fossil fuel imports, thereby enhancing energy self-reliance. Nuclear power is gaining significance in the energy mix since its integration may provide a reliable baseline supply, encouraging decarbonization. Investment in nuclear energy drives the advancement of state-of-the-art reactors, small modular reactors (SMRs), and waste management solutions, creating export opportunities and establishing global leadership in clean energy technology. Nuclear energy's ongoing significance in reducing CO\u003csub\u003e2\u003c/sub\u003e emissions is attributed to its ability to replace high-carbon energy sources with low-carbon, reliable power over an extended period. Additionally, the long- and short-term results highlight the importance of adopting a long-term perspective when evaluating the environmental benefits of nuclear energy.\u003c/p\u003e \u003cp\u003eNuclear energy offers OECD countries a realistic and financially viable solution to their carbon emission problem. In the short term, several OECD nations depend on fossil fuels as their primary energy source. In the short term, the use of nuclear energy may not result in an immediate reduction in emissions if it acts as a complement to, rather than a replacement for, fossil fuel-based sources. The transition to nuclear energy requires improvements to energy grids to accommodate this new source, which may delay the immediate impact on emissions. Nations within the OECD that adopt nuclear energy witness notable decreases in carbon emissions, aiding in fulfilling their climate obligations (such as those outlined in the Paris Agreement).\u003c/p\u003e \u003cp\u003eThe empirical estimation of the CS-ARDL model reveals that AI has an insignificant impact on reducing carbon emissions. From a theoretical standpoint, the notable relationship between artificial intelligence and carbon dioxide emissions can be elucidated by the fact that innovations driven by AI can enhance energy efficiency. However, this frequently results in heightened energy demand, which counteracts potential reductions in emissions. Technological advancements alone do not automatically reduce carbon emissions unless coupled with stringent energy policies. Further, Unequal adoption across industries may be the reason for AI's limited impact on emissions reduction. The use of artificial intelligence in smart grids, energy management, and predictive maintenance is still in its early stages in the OECD countries. The diffusion of low-carbon technology powered by AI is slow and has not yet reached a level where it can substantially reduce emissions. The OECD countries must reinforce regulations that encourage the adoption of AI in energy-intensive sectors to improve AI's impact on emissions reduction. Integrating AI with smart grids, demand forecasting, and optimizing renewable energy sources is essential for maximizing environmental benefits. On a concluding note, the study suggests that AI's impact on emissions reduction in OECD countries is minimal, primarily due to inefficient adoption in high-emission sectors, high prices, and regulatory constraints. AI must systematically integrate with renewable energy policy, sectoral reform, and long-term sustainability initiatives to realize its complete potential. The results are summarized in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eThis study examines the role of artificial intelligence and nuclear energy in achieving net-zero carbon emission targets in the 17 OECD economies from 1990 to 2020. The study employed the CS-ARLD estimation and DH-causality models to investigate the long-run impact of the underlying variables in the study. The derived key findings can be summarized as follows: i) Nuclear energy contributes to carbon emissions in the long run but does not seem to do so in the short run. ii) The results evidence an insignificant impact of artificial intelligence on carbon emissions both in the long- and short-run. Both nuclear energy and artificial intelligence contribute to the formation of EKC in the long and short run within OECD economies. The DH-causality reveals unidirectional causality from nuclear energy toward carbon emissions and from artificial carbon emissions. Finally, bidirectional causality is found between artificial intelligence and nuclear energy. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe study's results present significant policy implications. \u0026nbsp; Considering the beneficial impact of nuclear energy on reducing carbon emissions, it is proposed that OECD countries can optimize their potential to achieve net-zero carbon emissions in the long term by incorporating nuclear energy with complementary technologies and implementing robust governance frameworks. The governments of these OECD countries need to allocate resources towards constructing and modernizing nuclear power plants. These facilities provide a dependable, low-carbon energy source that aligns with long-term net-zero emission goals. \u0026nbsp;Long-term energy strategies must emphasize regulatory and policy stability to attract private investment in nuclear energy projects, which generally involve substantial initial costs and lengthy payback periods. \u0026nbsp;Additionally, it is crucial to launch public education campaigns to address misconceptions and emphasize the benefits of nuclear energy as a reliable and low-emission energy source, particularly in relation to long-term climate goals.\u003c/p\u003e\n\u003cp\u003eGiven the limited role of AI and carbon emissions, policymakers must develop strategies that connect AI's potential with its practical applications in achieving carbon neutrality. Policymakers should advocate for the incorporation of AI technologies in the nuclear energy sector that play a significant role in reducing carbon emissions. \u0026nbsp;Allocate resources toward AI-driven smart grids to enhance energy distribution efficiency and facilitate the integration of nuclear energy. Utilize artificial intelligence to oversee nuclear energy systems in real-time, forecast demand, and diminish dependence on fossil fuels. Governments should promote private sector investments and innovations in AI for climate-related applications and offer tax incentives to private enterprises that develop AI technologies to reduce emissions. Establish collaborations to enhance the application of AI technologies in integrating nuclear energy and developing sustainable urban environments. Governments of OECD countries should issue green bonds to finance extensive AI initiatives that support net-zero carbon emission objectives. Policymakers should leverage AI as a powerful tool for achieving net-zero emissions over time by enhancing AI integration, investing in infrastructure, and promoting collaboration across various sectors. Policies that ensure the responsible and effective deployment of AI in line with climate goals must support these initiatives.\u003c/p\u003e\n\u003cp\u003eThe study offers some limitations. It primarily examines the role of AI in reducing carbon emissions, providing a broad overview rather than focusing on specific sector industries. The influence of AI is likely to differ among the manufacturing, transportation, and energy production sectors. \u0026nbsp;Future investigations should delve into the impact of AI on emissions reduction across specific sectors, including transportation, energy, and agriculture, to yield more precise policy recommendations. The availability and consistency of AI-related data among OECD nations present significant challenges. Variations in AI adoption rates, policy frameworks, and technological advancements could affect the study's generalizability. \u0026nbsp;The analysis focuses on empirical estimations, excluding policy measures that may affect AI's contribution to emissions reduction. \u0026nbsp;Examining the influence of government policies would yield a deeper understanding of AI’s efficacy in reducing emissions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding Declaration:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData will be available upon reasonable request from Authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eETHICS STATEMENTS NOT APPLICABLE\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis article does not contain any studies with human participants performed by any of the authors’\u003cbr\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAkpolat, A.G., Bakırtaş, T., 2024. 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Resources Policy 85. https://doi.org/10.1016/j.resourpol.2023.103838\u003c/li\u003e\n \u003cli\u003eZheng, S.Y., Liu, H., Guan, W., Li, B., Ullah, S., 2024. How do nuclear energy and stringent environmental policies contribute to achieving sustainable development targets? Nuclear Engineering and Technology 56, 3983\u0026ndash;3992. https://doi.org/10.1016/j.net.2024.04.046\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Artificial intelligence, Nuclear energy, Carbon neutrality, OECD countries, CS-ARDL estimator \u0026 DH-causality","lastPublishedDoi":"10.21203/rs.3.rs-8153017/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8153017/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eExisting research highlights that artificial intelligence can enhance energy security and support the pursuit of net-zero carbon emission goals. Yet, scholarly attention to its role in shaping the relationship between nuclear energy use and carbon dioxide emissions remains limited. This study addresses that gap by offering one of the earliest empirical investigations linking artificial intelligence, nuclear energy consumption, and CO₂ emissions across 17 Organization for Economic Co-operation and Development (OECD) countries from 1994 to 2020. This study also examines whether artificial intelligence can help to form an environmental Kuznets curve (EKC) in OECD countries. To achieve this objective, we design a novel analytical framework that examines the influence of artificial intelligence and nuclear energy on the dynamics of the Environmental Kuznets Curve (EKC). Empirical analysis indicates that nuclear energy contributes to reduced carbon emissions. Additionally, artificial intelligence has a negligible impact on CO\u003csub\u003e2\u003c/sub\u003e emissions. Furthermore, there is unidirectional causality between artificial intelligence and CO\u003csub\u003e2\u003c/sub\u003e emissions, as well as between nuclear energy and CO\u003csub\u003e2\u003c/sub\u003e emissions. To summarize, nuclear energy consumption contributes to the formation of EKC, whereas artificial intelligence does not appear to have a similar effect. Based on these results, some economic policy implications are discussed.\u003c/p\u003e","manuscriptTitle":"Artificial Intelligence, energy transitions, and the pathway to net-zero carbon emissions in OECD Countries","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-17 11:56:58","doi":"10.21203/rs.3.rs-8153017/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-10T17:42:19+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-07T04:08:33+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-25T19:29:48+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-17T12:23:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"69965028861520033327368224200037828096","date":"2025-12-14T05:32:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"159891041705905478816568730133966532164","date":"2025-12-12T16:10:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"172900482851636815685767296150072931118","date":"2025-12-12T06:12:57+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-12T05:27:52+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-12T04:40:26+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-12-10T10:50:42+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-06T22:16:54+00:00","index":"","fulltext":""},{"type":"submitted","content":"Humanities and Social Sciences Communications","date":"2025-12-02T00:48:24+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"d5c689c5-2770-4bd6-b1d8-dbae46a5d10b","owner":[],"postedDate":"December 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":59729946,"name":"Earth and environmental sciences/Environmental sciences"},{"id":59729947,"name":"Earth and environmental sciences/Environmental social sciences"}],"tags":[],"updatedAt":"2026-05-19T10:23:09+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-17 11:56:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8153017","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8153017","identity":"rs-8153017","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}