From artificial intelligence to renewable energy transition: pathways through green innovation and institutional readiness

1 Introduction

Climate change has intensified the urgency of transforming global energy systems, as rising temperatures, extreme weather events, biodiversity loss, and growing pressure on food, water, and health systems have made carbon-intensive development increasingly unsustainable (Wang H. J. et al., 2025; Zhao et al., 2024). Because the energy sector remains a major source of greenhouse gas emissions, the renewable energy transition has become central to decarbonization strategies, long-term sustainable development agendas, and advancing the Sustainable Development Goals (SDGs), especially SDG seven on affordable and clean energy. Advancing SDG seven requires countries to broaden access to modern energy services while simultaneously raising the renewable share and improving energy-system efficiency, reliability, and sustainability. In policy and research debates alike, solar, wind, hydro, biomass, and other clean-energy sources are promoted not only for their emissions-reduction potential but also for their contribution to energy security, resilience, reduced dependence on volatile fossil-fuel markets, and progress toward SDG 7 (Rahmani et al., 2026). Yet the renewable energy transition is not simply substituting one energy source for another. Rather, it requires a deeper restructuring of generation systems, storage technologies, transmission infrastructures, demand-side management, and the institutional coordination needed to manage increasingly interconnected energy networks (Saadi et al., 2026). This complexity is especially evident in renewable systems dominated by solar and wind, where intermittency and forecasting uncertainty create persistent challenges for supply–demand balancing, storage planning, and grid stability (Zhao et al., 2024). Consequently, progress toward renewable energy transition and SDG seven is determined not solely by expanding renewable capacity but also on developing enabling capabilities that reduce uncertainty, improve operational efficiency, and strengthen system intelligence across the energy sector (Wang Q. et al., 2025). These requirements are particularly pressing in emerging economies, where renewable deployment often advances alongside uneven digital infrastructure, limited innovation capacity, and heterogeneous institutional readiness. In such settings, renewable energy transition becomes not only an environmental imperative but also a technological and structural transformation that calls for new tools capable of managing complexity, supporting cleaner development pathways, and supporting progress toward SDG 7.

Against this backdrop, artificial intelligence (AI) has emerged as a promising enabling technology for supporting renewable energy transition and progress toward SDG seven b y making energy systems more adaptive, data-driven, reliable, and operationally efficient (Rasheed et al., 2025; Herman et al., 2026). Recent literature shows that AI can improve forecasting accuracy, optimize dispatch and storage, support predictive maintenance, enhance grid reliability, and strengthen decision-making across renewable energy infrastructure, thereby helping energy systems cope with variability and system complexity (Rebelatto et al., 2024; Ukoba et al., 2024). Empirical work has likewise associated AI with improved renewable-energy development and more intelligent energy transition processes in both advanced and large emerging economies, suggesting that digital intelligence may play a meaningful role in reshaping energy structures over time (Fang et al., 2025; Wang, 2025; Zhang et al., 2024). Even so, the literature remains inconclusive. Much of the existing work remains technology-centered, focusing on isolated engineering applications such as forecasting models, optimization algorithms, or maintenance systems rather than examining the broader macro-level processes through which AI may support structural energy transformation (Saadi et al., 2026). Moreover, AI’s transition effects do not appear to be uniformly positive across time horizons. Some studies indicate that AI’s complexity, cost, and sectoral deployment patterns may delay its renewable-energy benefits, while others suggest that AI-related computation and infrastructure can create short-run energy and environmental burdens before longer-run gains emerge (Lee and Yan, 2024; Zhao et al., 2024). These limitations point to a deeper gap in the literature: although AI is widely recognized as a potentially transformative enabler of renewable energy transition, prior research has not sufficiently explained the channels through which AI-generated capabilities are translated into durable renewable-energy progress, particularly in emerging economies where institutional support, digital readiness, and innovation systems remain uneven.

A growing strand of scholarship suggests that green technological innovation (GTI) may constitute one of the most plausible channels through which AI is associated with renewable energy transition, because AI not only improves operational performance but also strengthens the generation, diffusion, and application of environmentally oriented knowledge (Zhou et al., 2024). By enhancing data analytics, automation, predictive capabilities, and knowledge-processing efficiency, AI can improve production efficiency, reduce resource waste, and support the development of cleaner technological solutions, thereby contributing to greener innovation (Zhou et al., 2024). Existing evidence further indicates that AI can be associated with higher green technological innovation and green total-factor productivity, especially when regulatory and institutional conditions are sufficiently supportive, implying that digital intelligence can reinforce broader green development through innovation-related mechanisms. At the same time, GTI is widely regarded as a major determinant of renewable energy transition because it improves the efficiency, feasibility, and diffusion of renewable energy technologies, lowers technical and cost barriers, strengthens carbon emissions reduction, and supports cleaner energy restructuring (Khan et al., 2022). This logic is consistent with recent evidence linking AI to renewable-energy innovation and with studies showing that AI-enabled innovation in environmental monitoring can support the transition toward renewables by improving the conditions under which cleaner energy systems emerge and expand (Yin H. T. et al., 2023). However, the literature still leaves two important gaps. First, many studies examine either the AI–GTI relationship or the green technological innovation–renewable energy transition relationship separately, without integrating them into a unified mediating framework. Second, much less attention has been given to the contextual factors that may reinforce or weaken this channel, even though AI-driven green innovation is unlikely to deliver its full effectiveness automatically. In particular, AI-based innovation may require secure digital environments to protect data integrity, reduce operational risk, and sustain cumulative experimentation, while translating GTI into the renewable energy transition may depend on broader sustainability-oriented institutional conditions that facilitate commercialization, coordination, and scale-up. These omissions are especially important in emerging economies, where cybersecurity readiness and ESG performance vary markedly and may therefore clarify if AI-based innovation can be effectively converted into renewable-energy transition outcomes.

Against this backdrop, the present article investigates how AI contributes to renewable energy transition in 23 emerging economies over the period 2015–2023, with GTI as the central transmission mechanism and cybersecurity and national ESG performance as key conditioning factors. The focus on emerging economies is particularly important because these countries are undergoing simultaneous technological, structural, and institutional transformations while facing uneven digital infrastructure, heterogeneous innovation capacity, varying cybersecurity readiness, and differing sustainability-oriented governance conditions. This diversity makes the AI–green technological innovation–renewable energy transition nexus more context-dependent than in advanced economies, where digital, institutional, and innovation-related capabilities are often more mature. The selected period is also relevant because it captures the recent acceleration in digital transformation, renewable energy deployment, and sustainability pressures, and coincides with the availability of comparable cybersecurity data from 2015 onward. Methodologically, the study applies a moderated mediation framework using Hayes’s PROCESS macro, enabling the analysis to move beyond direct-effect estimation and examine both the pathway through which AI affects renewable energy transition and the conditions under which this pathway strengthens or weakens.

This study makes several distinct theoretical and methodological contributions to the growing literature on artificial intelligence, green technological innovation, cybersecurity, ESG performance, and renewable energy transition. Its novelty does not rest on demonstrating another direct association between AI and renewable energy outcomes, since this relationship has already received substantial attention. Instead, the main contribution is explaining how AI capability translates into renewable energy transition and why the effectiveness of this translation varies across national contexts. Existing studies have largely emphasized direct effects, heterogeneous impacts, threshold relationships, or broad institutional contingencies. This study extends the literature by developing and empirically testing a sequential two-stage conditional process in which green technological innovation serves as the transmission mechanism, cybersecurity conditions the transformation of AI capability into green innovation, and national ESG performance conditions the subsequent transformation of green innovation into renewable energy transition. This framework therefore shifts the analytical focus from whether AI matters to the more theoretically informative questions of how, at what stage, and under what complementary conditions AI produces renewable energy outcomes. First, the study advances the conceptualization of national AI capability relative to existing macro-level approaches. Huang et al. (2026), for example, examine multiple dimensions of national AI vibrancy, including research and development, responsible AI, education, diversity, governance, public opinion, economic conditions, and infrastructure, and evaluate their associations with renewable energy consumption. Their framework captures the broader ecosystem surrounding AI development. The present study addresses a different construct by conceptualizing AI as a productive and innovation-oriented technological capability based on four dimensions directly connected to the generation, deployment, development, and commercialization of AI: AI-related patents, industrial robots, scholarly publications, and venture-capital investment. These dimensions represent technological invention, embodied automation, scientific knowledge creation, and commercialization finance, respectively. This distinction matters because the study focuses not on the general institutional environment surrounding AI, but on a country’s ability to create and deploy AI-related technological resources that can subsequently affect green innovation and energy transformation. Methodologically, the population-adjusted composite index constructed through logarithmic transformation, normalization, and principal component analysis also reduces the likelihood that measured AI capability simply reflects differences in country scale. Second, the study contributes to the AI–energy transition literature by identifying green technological innovation as an explicit transmission mechanism rather than treating innovation as an implicit explanation for a significant AI coefficient. Sun et al. (2026) demonstrate that AI affects renewable energy consumption and that its effects vary across AI technologies, infrastructure conditions, income levels, renewable energy penetration, and institutional transparency. Their contribution focuses primarily on multidimensional effects, heterogeneity, quantiles, and threshold conditions. This study instead asks what technological process connects AI capability to the renewable energy transition. It theorizes that AI improves data processing, experimentation, knowledge recombination, automation, and technological problem-solving, thereby stimulating environmentally oriented innovation, which subsequently improves the efficiency, affordability, integration, and diffusion of renewable technologies. By formally modeling GTI as a mediator, the study integrates two relationships often investigated separately—the AI–green innovation relationship and the green innovation–renewable energy relationship—into a unified causal-process framework. The theoretical contribution therefore lies in specifying how AI-related capabilities operate through innovation to facilitate energy-system transformation. Third, the study introduces cybersecurity as a theoretically distinct digital-security boundary condition. This represents an important extension because closely related research generally emphasizes governance quality, institutional transparency, digital infrastructure, or responsible AI without explicitly examining cybersecurity as a moderator of AI’s environmental effectiveness. Huang et al. (2026) incorporate responsible AI, infrastructure, and policy and governance within a broader AI-vibrancy framework, while Chen et al. (2026) emphasize ESG dimensions and governance, and Sun et al. (2026) examine institutional transparency. The present study separates cybersecurity from these broader constructs and conceptualizes it as a productive complementary capability based on cyber resilience, data integrity, digital trust, knowledge protection, and system continuity. This distinction matters because AI-enabled renewable energy systems increasingly rely on interconnected platforms, real-time information, automated control, smart grids, and digitally coordinated infrastructure. AI’s benefits can therefore weaken when data and digital systems are vulnerable to disruption or manipulation. By testing cybersecurity as both a first-stage moderator of the AI–GTI relationship and a moderator of AI’s direct contribution to renewable energy transition, the study extends digital sustainability theory by showing that secure digital environments may shape whether AI capabilities can be transformed into tangible environmental benefits. Fourth, the study develops a more differentiated role for ESG performance. Chen et al. (2026) examine environmental and social performance as transmission mechanisms and governance as a nonlinear threshold condition. In contrast, the present study conceptualizes overall sovereign ESG performance as a second-stage institutional moderator that influences whether green technological innovation can move from invention to financing, commercialization, diffusion, and deployment in the energy system. Cybersecurity and ESG performance are therefore not treated as interchangeable institutional variables. They operate at different stages of the process: cybersecurity protects the digital and knowledge-generation stage, whereas ESG performance supports the institutional and implementation stage. This stage-specific theorization more precisely explains how different national conditions shape the AI–renewable energy pathway. Finally, the study differs from Wang Q. et al. (2026), whose central outcome is energy poverty and who treat renewable energy transition, governance, income, poverty, and resource dependence as conditions shaping AI’s distributive effects. The present analysis instead treats renewable energy transition itself as the outcome and focuses on the increasing share of renewables in final energy consumption. The study therefore addresses the structural transformation dimension of SDG seven rather than its energy-access dimension. Taken together, these distinctions establish the study’s incremental contribution: it develops a sequential two-stage conditional process theory that explains not merely whether AI promotes renewable energy transition, but how AI generates green technological innovation, how cybersecurity strengthens that innovation-generating process, and how ESG conditions determine whether green innovation ultimately becomes renewable energy deployment. Methodologically, combining mediation, first- and second-stage moderation, conditional indirect effects, and complementary two-step System GMM validation further distinguishes the study from reduced-form, fixed-effects, quantile, heterogeneity, and threshold approaches and provides a more integrated framework for understanding AI-enabled renewable energy transition in emerging economies.

The remainder of this paper is organized into five sections. Section 2 develops the theoretical basis and derives the research hypotheses. Section 3 describes the data sources, variable construction, and empirical methods. Section 4 reports the empirical results and discusses them in detail. Section 5 concludes by summarizing the principal findings and their implications.

2 Theory and hypothesis development

2.1 AI and renewable energy transition

Artificial Intelligence is progressively viewed as a foundational technology in the renewable energy transition because it enhances energy systems’ capacity to adapt, learn, and operate more efficiently amid growing complexity (Vinuesa et al., 2020; Rabbi, 2025; Wang, 2025). This linkage can be understood through theoretical perspectives that emphasize innovation-led structural change, sustainability-oriented modernization, and system reconfiguration toward cleaner development pathways (Wang Q. et al., 2024). Within this broad logic, AI serves as a general-purpose digital technology that can enhance information processing, automation, optimization, and decision-making across renewable energy systems (Ukoba et al., 2024; Zhang et al., 2024). These capabilities are especially relevant for renewable energy because the transition away from fossil fuels requires not only new energy sources but also smarter infrastructure, more responsive grids, and greater operational flexibility (Abdessadak et al., 2024). Earlier sustainability research suggested that AI could contribute meaningfully to environmental goals when deployed to improve efficiency, reduce waste, and support sustainable system management (Vinuesa et al., 2020). More recent reviews reinforce this theoretical claim by showing that AI contributes to the renewable energy transition through improved forecasting, smart grid management, predictive maintenance, optimization of storage and dispatch, and more resilient energy infrastructure (Rebelatto et al., 2024; Ukoba et al., 2024). Accordingly, the current body of knowledge increasingly treats AI not as a peripheral digital tool but as a strategic enabler that shapes the technical and organizational conditions under which renewable energy systems become scalable, reliable, and economically viable (Zhao et al., 2024). At the same time, this literature does not treat AI as unambiguously beneficial across all phases, since the computational infrastructure behind AI can itself be energy-intensive, especially in the early stages of deployment (Lee and Yan, 2024; Vinuesa et al., 2020). This has led to a more balanced interpretation: AI may initially impose resource and energy burdens but later produce cleaner, more transition-supportive outcomes once its applications become embedded in renewable energy systems and broader low-carbon strategies (Wang et al., 2026). This view is consistent with Alsobeh and Alkurdi (2026), who argue that sustainable AI deployment requires balancing sustainability benefits, environmental costs, and economic resilience through multi-objective optimization.

The empirical literature shows that AI supports the renewable energy transition through interconnected operational mechanisms spanning forecasting, optimization, integration, and resilience (Zhao et al., 2024). Forecasting is one of the most important channels because renewable energy sources such as wind and solar are intermittent, so efficient integration requires accurate anticipation of generation patterns, electricity demand, and storage needs (Ukoba et al., 2024). AI-powered algorithms improve predictions of weather conditions, output variability, and load fluctuations, thereby enabling energy systems to integrate larger shares of renewable electricity with lower balancing costs and reduced curtailment risks (Fang et al., 2025; Zhang et al., 2024). AI also enhances operational optimization by supporting dispatch decisions, coordinating distributed energy resources, managing storage systems, and identifying more efficient electricity allocations within increasingly complex smart-grid environments (Rebelatto et al., 2024). This optimization role is critical because it addresses practical barriers such as intermittency, congestion, and technical inefficiencies that often slow the diffusion of renewable energy technologies (Abdessadak et al., 2024; Zhao et al., 2024). Predictive maintenance is another important mechanism through which AI supports renewable transition, as machine-learning tools can detect faults, anticipate equipment failures, and improve the life-cycle management of solar, wind, and hybrid renewable systems, thereby reducing downtime and improving cost-effectiveness (Ukoba et al., 2024; Rebelatto et al., 2024). AI also strengthens system resilience by enabling faster, more accurate responses to demand shocks, technical disturbances, and external disruptions in energy systems with high shares of variable renewable sources (Wang B. et al., 2024). Several empirical studies directly support AI’s positive influence on renewable energy development and the transition to cleaner energy. Fang et al. (2025) show that AI contributes to the transition to renewable energy across U.S. states, while Wang (2025) reports a positive association between AI and energy transition in OECD countries by improving the quality and intelligence of energy development. In China, Zhang et al. (2024) document AI’s growing role in renewable energy development, indicating that digital intelligence is becoming increasingly embedded in national transition strategies. Other studies further link AI to broader clean-energy and environmental improvements by showing that it enhances green-economy efficiency, environmental performance, and sustainability-oriented energy outcomes (Gu and Javed, 2025; Slimani et al., 2025). AI has also been linked to high-quality energy development and cleaner energy structures through its positive effects on resource efficiency, technological modernization, and environmentally oriented transformation (Xia and Ahmad, 2025; Yang et al., 2024). Taken together, this body of work suggests that AI improves the technical, operational, and strategic foundations of renewable transition by making renewable energy systems more predictable, flexible, resilient, and scalable (Wang, 2025; Zhao et al., 2024).

The literature indicates that AI’s contribution to the renewable energy transition is stronger when it is embedded in supportive policy and structural environments and when its efficiency gains are allowed to emerge over time (Fang et al., 2025). Policy context matters because regulatory uncertainty can undermine the transition benefits of digital technologies and reduce the effectiveness of AI applications in energy systems (Fang et al., 2025; Wang, 2025). Structural conditions also matter, as AI tends to produce stronger environmental and energy effects where energy structures, resource efficiency, and sustainability-oriented transformation are already progressing (Gu and Javed, 2025; Xia and Ahmad, 2025). This implies that AI can be viewed as a catalyst whose effectiveness depends on how well it is integrated into broader transition ecosystems characterized by supportive institutions, innovation capacity, and renewable-oriented development pathways (Zhang et al., 2024). At the same time, the literature remains cautious about overstating AI’s benefits because digital infrastructure and computation may generate short-run environmental costs before long-run transition gains become evident (Lee and Yan, 2024). Even so, the dominant conclusion remains favorable, as studies on renewable energy systems and environmental performance consistently show that AI helps reduce uncertainty, manage intermittency, improve efficiency, and strengthen system intelligence in ways that support renewable expansion (Rebelatto et al., 2024; Ukoba et al., 2024; Zhao et al., 2024). The breadth of evidence across regional contexts further suggests that AI’s contribution is sufficiently consistent to support a general expectation of a positive transition effect (Fang et al., 2025; Zhang et al., 2024). Therefore, the study proposes the following hypothesis.

H1 AI is positively associated with renewable energy transition.

2.2 The mediating role of GTI

AI has emerged as an important enabler of GTI by enhancing firms’ and economies’ ability to create, process, and deploy knowledge to support environmentally oriented technological change (Saiyed et al., 2025). By enhancing data analytics, intelligent automation, predictive capability, and decision accuracy, AI can strengthen production efficiency, reduce resource waste, and support the development of cleaner technological solutions (Zhou et al., 2024). This view is reinforced by evidence showing that AI contributes positively to green technological innovation as well as green total-factor productivity, especially when environmental regulation and institutional conditions are sufficiently supportive (Yin K. et al., 2023). More broadly, AI-induced green innovation appears more effective when supported by transformative policy frameworks that improve the efficiency and environmental contribution of innovation systems (Chun and Hwang, 2025). Related studies also indicate that AI can remove conventional barriers to innovation, promote greener product design, and generate spillover effects that improve productivity while lowering emissions, thereby making green innovation more feasible and impactful (Truong and Papagiannidis, 2022; Lee et al., 2022). Comparative evidence from European regions further suggests that AI’s innovation effects are stronger where green-tech socialization and environmental policy support are more developed, confirming that AI can drive green technological progress rather than merely serving as a complementary production tool (Cicerone et al., 2023).

GTI, in turn, is widely regarded in the literature as a key determinant of the renewable energy transition because it enhances the efficiency, feasibility, and diffusion of cleaner energy technologies (Tabrizian, 2019). Empirical evidence shows that technological innovation is positively associated with renewable energy development and changes in energy structures over time, suggesting that innovation is closely linked to the energy transition rather than merely a passive consequence (Khan et al., 2022). This conclusion is strengthened by international evidence indicating that AI promotes innovation in renewable energy and, through this channel, improves the production and use of renewable energy across countries (Wang H. J. et al., 2025). Zhao et al. (2025) provide especially relevant support by showing that AI software development promotes the transition toward renewables and that this relationship tends to operate through stronger innovation performance in environmental monitoring. Similarly, Radulescu et al. (2026) show that AI and GTI jointly shape greener energy efficiency outcomes across developed and developing countries. Taken together, these findings suggest that GTI facilitates renewable energy transition by lowering technical barriers, improving energy efficiency, strengthening environmental performance, and supporting the adoption of renewable technologies.

Taken together, these strands of research provide a clear basis for viewing GTI as a mediating mechanism through which AI affects the renewable energy transition. The literature shows that AI strengthens the conditions for greener technological progress, while green innovation itself supports renewable energy development, efficiency improvements, and broader structural transformation toward cleaner energy systems (Zhou et al., 2024). Yin H. T. et al. (2023) make this indirect relationship particularly explicit, identifying innovation performance in environmental monitoring as a core mechanism linking AI software development to the renewable energy transition. Behera et al. (2025) reach a similar conclusion by showing that AI-driven green innovation plays a meaningful role in strengthening the renewable energy transition in practice. The same argument is supported by evidence that innovation is a major driver of renewable energy expansion and that combining AI with green technological progress contributes to greener energy outcomes across countries (Khan et al., 2022; Radulescu et al., 2026). In this sense, the literature suggests not only that AI matters for renewable energy transition, but that one of the main reasons it matters is its ability to foster environmentally oriented innovation that makes renewable energy systems more efficient, scalable, and sustainable. Accordingly, we assume that.

H2 AI is positively associated with renewable energy transition through green technological innovation.

2.3 The moderating role of cybersecurity

Cybersecurity is increasingly recognized as a critical enabler of effective AI use across organizational systems, digital operations, and innovation processes (Alrfai et al., 2023; Jada et al., 2024; Kaur et al., 2023). In this context, cybersecurity can be conceptualized as an important boundary condition that shapes whether AI effectively translates into GTI. Although AI enhances firms’ analytical capacity, predictive accuracy, automation, and problem-solving ability, the benefits of innovation depend heavily on the security and reliability of the surrounding digital environment. Prior evidence suggests that AI can strengthen enterprise green innovation by improving knowledge processing, facilitating R&D efficiency, and supporting environmentally oriented decision-making (Zhao et al., 2025). Yet firms are unlikely to fully realize these gains when they operate in insecure digital environments. Alrfai et al. (2023) show that cybersecurity positively moderates AI’s effect on accounting information systems efficiency, indicating that AI’s value strengthens when embedded in a secure digital setting. This moderation logic is directly relevant to GTI because AI-driven green solutions depend on large-scale data processing, intelligent optimization, interconnected information systems, and continuous digital experimentation. In contemporary economies, technology and security have become deeply interdependent, especially as digitalized activities increasingly expose strategic interests to cyber threats (Dube and Mohanty, 2023). Thus, cybersecurity should not be treated merely as a defensive mechanism; rather, it is a complementary organizational capability that helps firms extract stronger innovative returns from AI investments.

From a capability-based perspective, cybersecurity complements the linkage between AI and green technological innovation by reducing the risks associated with digital experimentation and strengthening the organizational conditions necessary for innovation (Alrfai et al., 2023; Singh et al., 2025). AI-based green innovation often requires firms to rely on large datasets, interconnected platforms, automated routines, and digitally coordinated experimentation to identify cleaner production opportunities, optimize energy use, reduce waste, redesign production processes, and improve environmental performance. However, these same features increase the risk of cyberattacks, data breaches, system disruptions, and the manipulation of strategic information. In insecure environments, managers may be less willing to deploy AI in sensitive or strategic functions, and firms may hesitate to intensify experimentation with innovative green technologies because the perceived risks can outweigh the expected gains. By contrast, stronger cybersecurity enables more reliable AI deployment, protects strategic data, and secures knowledge assets, thereby increasing the expected return from AI-driven innovation efforts. This argument is consistent with Xu (2025), who shows that cybersecurity governance promotes corporate innovation, and with Singh et al. (2025), who argue that cybersecurity resilience supports innovation ecosystems and sustainable business excellence. Likewise, Al-Somali et al. (2024) demonstrate that organizational cybersecurity systems and cybersecurity resilience contribute positively to sustainable business performance, highlighting the strategic value of cyber protection beyond pure technical defense. Cybersecurity also reinforces the core dimensions of information security: confidentiality, integrity, and availability (Kshetri, 2017). Confidentiality restricts access to information to authorized users, integrity preserves the accuracy and reliability of data throughout storage, processing, and transmission, and availability ensures that information systems and digital resources remain accessible when required. These dimensions are especially important for GTI because AI-based sustainability initiatives rely on secure environmental data, uninterrupted digital processes, and protected knowledge-sharing routines. This argument is further supported by AlSobeh et al. (2025), who show that trustworthy distributed AI systems require the integration of trust, compliance, security threat detection, provenance tracking, and institutional policy enforcement into the learning architecture itself.

Cybersecurity also strengthens the AI–GTI relationship through digital trust, strategic continuity, and innovation resilience (Chen et al., 2025; Saveljeva et al., 2025; Alshammari and Al-Mamary, 2025). GTI is not only a technical outcome but also an organizational process that requires managers, employees, and collaborators to trust digital tools, share information, and engage in sustained experimentation. When firms embed AI systems in secure digital environments, they are more likely to trust data integrity, the reliability of algorithmic outputs, and the safety of digitally mediated decisions. This strengthens organizational acceptance of AI and reduces resistance to innovation-oriented digital transformation. Chen et al. (2025) show that digital trust boosts openness and innovation, while Saveljeva et al. (2025) emphasize that digital trust rests on confidence in secure technologies, processes, and interactions. Similarly, Alshammari and Al-Mamary (2025) find that cybersecurity awareness and trust are key drivers of adoption in AI-powered environments, suggesting that cyber-secure settings enhance users’ willingness to engage with intelligent systems. In addition, cyber insecurity can disrupt innovation trajectories by diverting managerial attention and financial resources toward threat response, recovery, and system repair rather than green R&D and process transformation. Strong cybersecurity, by contrast, supports business continuity, safeguards digital assets, and maintains the stable environment necessary for cumulative AI-based learning and experimentation. Jada et al. (2024) and Kaur et al. (2023) further demonstrate that AI and cybersecurity are increasingly interlinked, with secure digital infrastructures becoming essential to the productive use of AI in advanced organizational contexts. Therefore, cybersecurity should be viewed as a reinforcing condition that amplifies AI’s role in generating innovation.

Cybersecurity also reinforces AI’s strategic contribution to both GTI and the broader renewable energy transition (Ekechukwu and Simpa, 2024; Abbasi, 2025; Anwar and Ali, 2026). As renewable energy systems become increasingly digitized, AI is being used to improve energy forecasting, optimize grid management, monitor equipment performance, enhance storage efficiency, and support the integration of decentralized clean energy sources. Nevertheless, the effectiveness of these AI-based applications depends on the security and resilience of the digital infrastructure in which they operate. Cyberattacks, data manipulation, and system disruptions can undermine the reliability of AI-enabled energy systems, weaken investor confidence, disrupt clean energy operations, and slow the adoption of intelligent renewable technologies. In this regard, Ekechukwu and Simpa (2024) argue that cybersecurity is strategically essential for protecting renewable energy investment because secure digital environments are necessary to maintain operational stability and sustain long-term green transformation. Similarly, Abbasi (2025) shows that the success of the energy transition increasingly depends on effective cybersecurity strategies, especially in smart grids and digitally managed energy systems, where vulnerability management is crucial to system reliability and the continuity of innovation. Anwar and Ali (2026) further reinforce this argument, emphasizing that cybersecurity is a fundamental requirement for AI-based renewable energy networks, as intelligent clean energy systems can function efficiently only when supported by secure, trusted, and resilient digital architectures. This secure-architecture logic is also supported by Shatnawi et al. (2026), who develop a secure-by-design framework for agentic AI and argue that dependable AI deployment requires safeguards against adversarial information flows, confidentiality and integrity risks, and unsafe tool-mediated actions.

Therefore, cybersecurity is expected to strengthen the positive association between AI and GTI and support the renewable energy transition by reducing operational risk, protecting data integrity, ensuring continuity of digital energy systems, and enhancing trust in AI-enabled green solutions. Therefore, we suggest the following two hypotheses.

H3 Cybersecurity strengthens AI’s effect for green technological innovation, indicating that the contribution of AI to green technological innovation is more pronounced in environments with stronger cybersecurity.

H4 Cybersecurity strengthens AI’s effect on renewable energy transition, suggesting that AI’s influence on advancing renewable energy adoption is greater when cybersecurity capabilities are more developed.

2.4 The moderating role of national ESG performance

Various theories, including green development theory, Porter’s hypothesis, and endogenous growth theory, provide a useful basis for explaining how ESG performance shapes the relationship between GTI and the renewable energy transition (Sun et al., 2025). Green development theory views sustainable transformation as a process that must integrate environmental protection, social progress, and sound governance into the broader development model. In this sense, ESG performance reflects the extent to which sustainability principles are embedded in the economic and institutional system. The Porter hypothesis further suggests that sustainability-oriented pressures, regulations, and institutional expectations can stimulate innovation and encourage firms and economies to generate environmentally beneficial outcomes through technological upgrading. Endogenous growth theory also supports this argument by emphasizing that technological progress, innovation, and knowledge accumulation drive long-term structural transformation, especially when supported by favorable institutional conditions. This theoretical reasoning is consistent with recent empirical evidence showing that ESG performance promotes GTI by reducing financing constraints, improving expectations, and supporting more efficient green transformation processes (Gao et al., 2025; Ma et al., 2025). Accordingly, ESG performance should not be viewed merely as a parallel sustainability indicator but rather as a broader enabling context that can influence whether GTI translates effectively into a renewable energy transition.

Building on this reasoning, ESG performance can be conceptualized as a moderating condition that influences the extent to which GTI contributes to the renewable energy transition. GTI alone does not necessarily guarantee meaningful progress in renewable energy transition, because innovation must also be financed, implemented, coordinated, and diffused within a supportive sustainability-oriented environment. In this respect, ESG performance is especially relevant because it captures environmental commitment, social alignment with sustainability goals, and governance capacity within a unified framework (Whitelock, 2019). This makes it more likely that innovation efforts can be converted into practical renewable energy outcomes through stronger policy credibility, improved information environments, and better alignment among investors, firms, and public institutions. Sun et al. (2025) argue that ESG-related policies and disclosure practices improve market expectations and support the renewable transformation, while Gao et al. (2025) show that ESG performance strengthens green innovation dynamics and yields broader knowledge spillovers. Likewise, Lu and Li (2024) find that ESG ratings are associated with stronger low-carbon investment in renewable energy companies, and Taghizadeh-Hesary and Rasoulinezhad (2025) show that ESG investments, together with smart technologies, are associated with renewable energy transition. More recently, Akadiri and Özkan (2026) highlight that ESG-related conditions remain important for clean energy markets and investment dynamics, particularly in uncertain contexts. Overall, these arguments suggest that ESG performance strengthens GTI’s positive contribution to the renewable energy transition by creating a more favorable sustainability, investment, and governance context for the commercialization, scaling, and practical adoption of green solutions. Accordingly, the following hypothesis is proposed.

H5 ESG performance strengthens the positive effect of green technological innovation on renewable energy transition, such that the contribution of green technological innovation to renewable energy transition increases as ESG performance is greater.

2.5 Moderated mediation effects

The foregoing discussion indicates that the AI-renewable-energy-transition linkage is better understood as an indirect process whose strength depends on contextual conditions, rather than as simple linear mediation. GTI is the principal mechanism by which AI contributes to the renewable energy transition, but the magnitude of this indirect effect depends on the contextual conditions under which the mediating pathway operates. In conditional process terms, an indirect effect becomes contingent when one or more component paths are moderated by relevant boundary conditions. In the present framework, this conditionality arises because cybersecurity shapes the AI–green innovation relationship, while ESG performance shapes the green innovation–renewable energy transition relationship (Alrfai et al., 2023; Sun et al., 2025; Xu, 2025). Thus, AI’s indirect influence on renewable energy transition through green innovation should not be expected to remain constant across contexts; rather, it varies with the digital-security environment and the sustainability-oriented institutional setting in which the process unfolds.

Prior research indicates that AI’s innovation-enhancing role depends on secure, reliable digital infrastructure that enables data-intensive experimentation, intelligent automation, and coordinated problem-solving with lower operational risk (Alrfai et al., 2023; Jada and Mayayise, 2024; Kaur et al., 2023). Cybersecurity therefore strengthens the first segment of the indirect mechanism by protecting digital systems, securing information flows, supporting trust in AI-enabled processes, and preserving the continuity needed for innovation-oriented experimentation (Al-Somali et al., 2024; Chen et al., 2025; Singh et al., 2025). The second source of conditionality concerns the path from green innovation to renewable energy transition. Although green innovation provides the technological basis for cleaner energy systems, its contribution to renewable transition depends on whether the broader sustainability environment supports commercialization, financing, diffusion, and implementation. In this respect, ESG performance reflects the environmental, social, and governance conditions that facilitate the translation of green technological advances into practical renewable energy outcomes (Sun et al., 2025). Empirical evidence shows that ESG-related conditions improve green innovation performance, strengthen low-carbon investment, and support the transformation to renewable energy (Gao et al., 2025; Lu and Li, 2024; Ma et al., 2025; Taghizadeh-Hesary and Rasoulinezhad, 2025; Akadiri and Özkan, 2026). Taken together, these arguments indicate that the conditional impact of AI through green innovation relies on both the cybersecurity environment at the innovation stage and the ESG environment at the transition stage.

As illustrated in Figure 1, the proposed model reflects a two-stage moderated mediation structure. The first-stage moderated mediation arises because cybersecurity conditions AI’s influence on green innovation; accordingly, AI’s indirect contribution to renewable energy transition operating through green innovation becomes stronger when cybersecurity reinforces the first link in the pathway (Alrfai et al., 2023). The second-stage moderated mediation arises because ESG performance conditions the effect of green innovation on renewable energy transition; accordingly, the same indirect effect becomes stronger when ESG performance reinforces the second link in the pathway (Sun et al., 2025; Gao et al., 2025). In this sense, Figure 1 does not merely depict a mediation model with two separate moderators; it shows that the mediating role of green innovation is itself conditional at two analytically distinct stages. Cybersecurity shapes the extent to which AI can drive green technological progress, whereas ESG performance shapes the extent to which that progress can translate into a renewable energy transition. Therefore, AI’s indirect contribution to the renewable energy transition through green innovation is expected to vary systematically with each moderator’s level, consistent with the moderated mediation logic embedded in the conceptual framework. Based on this conditional indirect reasoning, the study hypothesizes the following.

H6 The conditional AI’s influence on the renewable energy transition through green innovation is stronger when cybersecurity is higher.

H7 The conditional AI’s influence on the renewable energy transition through green innovation is stronger when ESG performance is higher.

3 Empirical strategy

3.1 Sample selection and panel structure

This article aims to demonstrate how AI contributes to the renewable energy transition through the mediating role of GTI, and to assess the moderating roles of cybersecurity and ESG performance in shaping this relationship. More specifically, the analysis examines whether AI is directly associated with the renewable energy transition, whether this effect is transmitted through GTI, whether cybersecurity strengthens the links between AI and both GTI and the renewable energy transition, and whether ESG performance reinforces GTI’s effect on the renewable energy transition. To address these relationships, the study adopts a moderated mediation framework based on the Hayes PROCESS macro. The empirical analysis uses a balanced panel of 23 MSCI-classified emerging markets covering 2015–2023. The country sample was identified using the Morgan Stanley Capital International (MSCI) Emerging Markets classification at the end of the study period. This classification was selected because it provides a transparent and internationally recognized framework for distinguishing developed, emerging, Frontier, and standalone markets based primarily on market accessibility, investability, size, liquidity, and economic-development considerations. Accordingly, the term “emerging markets” used in this study refers specifically to the MSCI classification and should not be interpreted as indicating that all included countries are classified identically by the International Monetary Fund, the World Bank, FTSE Russell, or other international organizations. This distinction matters because countries including the Czech Republic, Greece, Korea, Poland, Kuwait, Qatar, and the United Arab Emirates may be treated differently under classifications based on income level, macroeconomic development, or other financial-market criteria. The initial country universe included the 24 markets in the MSCI Emerging Markets classification at the end of 2023. Taiwan was excluded because the World Bank-based sovereign ESG indicators and some other macroeconomic variables required to construct the study variables were not consistently available on a comparable basis throughout 2015–2023. The final sample therefore comprises 23 MSCI-classified emerging markets. We determined country inclusion before the empirical analysis based exclusively on the formal MSCI classification and the availability of comparable data for the variables included in the model, rather than on observed values of artificial intelligence, green technological innovation, renewable energy transition, cybersecurity, or ESG performance. The country-year structure covers 23 countries over 9 years, from 2015 to 2023, yielding up to 207 country-year observations. The initial country-year frame included all countries and years, although the raw data collected from the different sources contained some intermittent variable-level missing observations. We treated these missing values using the country-specific interpolation procedure described below. The resulting analytical dataset is therefore a completed balanced panel of 207 country-year observations, with nine annual observations for each country. In the dynamic System GMM estimations, using the first lag of the dependent variable reduces the effective estimation sample to 184 observations because the first annual observation is lost for each of the 23 countries.

3.2 Variable measurement

Renewable energy transition (RET) is the dependent variable, measured by the share of final energy use supplied by renewable sources, including solar, wind, geothermal, hydropower, bioenergy, and marine energy. Expressed as a percentage, this indicator captures the relative contribution of renewable sources to the energy ultimately consumed by households, firms, industries, transport systems, and other end users. Higher values indicate stronger renewable-energy penetration, a cleaner national energy structure, and greater progress toward Sustainable Development Goal 7. This indicator is appropriate because RET fundamentally involves a change in the composition of the energy system, reflected in the increasing proportion of final energy demand supplied by renewable sources. This share-based measurement approach aligns with the indicators and conceptualizations used in the cited energy-transition literature. For instance, York and Bell (2019) examine changes in the percentage of energy consumption supplied by different sources and emphasize that renewable-energy growth constitutes a genuine transition only when it changes the energy mix and replaces established fossil-fuel sources. Hrnčić et al. (2021) similarly assess energy-transition pathways through the progressive integration and growing share of renewable energy sources, culminating in scenarios in which renewables supply the entire energy system. Igliński et al. (2024) also evaluate regional energy transformation by considering the expanding role of renewables and their rising proportion in the energy mix, emphasizing the importance of integrating available renewable-energy potential into the regional system. Thus, although these studies use different geographical settings and analytical procedures, they use closely comparable share-based indicators to represent RET advancement. The indicator is particularly suitable for comparing emerging economies because it standardizes renewable energy use relative to total final energy consumption, reducing distortions from differences in population, economic size, industrial structure, and overall energy demand. The variable is sourced from Our World in Data (OWID), where it is reported as the “share of final energy use that comes from renewable sources” and defined as renewable energy’s percentage share of total final energy consumption.

The key independent variable is national AI capability, operationalized through a composite AI Index (AII) that integrates four complementary dimensions: AI-related inventive activity, intelligent automation deployment, scientific knowledge production, and financial support for commercialization. These dimensions are captured through annual AI patent filings, the number of industrial robots in use, yearly academic publications related to AI, and AI-focused venture capital investment, respectively. The underlying data come from Our World in Data and the International Federation of Robotics, and OECD databases. A composite measure is appropriate because national AI capability is inherently multidimensional and cannot be adequately captured by a single proxy; rather, it reflects countries’ combined ability to generate inventions, deploy intelligent technologies, produce knowledge, attract capital, and commercialize applications. AI-related patents capture codified inventive output and the capacity of firms, universities, and research institutions to convert AI knowledge into applicable technologies, consistent with patent-based measures of AI development and innovation capability (Wang B. et al., 2024). Industrial robots reflect the embodied diffusion of intelligent automation in production systems and indicate the integration of sensing, data processing, automated decision-making, and machine-based technologies, as recognized in prior studies of AI-related automation and technological progress (Li et al., 2023; Wu et al., 2025; Urrea and Kern, 2025). AI publications represent scientific output, research infrastructure, human capital, and participation in knowledge networks, while venture-capital investment captures finance for AI start-ups, technology scaling, and commercialization. Because these indicators are originally expressed as absolute national quantities, we convert them into values per million inhabitants using population data from the World Development Indicators to prevent the index from mechanically reflecting population size or economic scale, particularly in large economies such as China, India, and Korea. This adjustment improves cross-country comparability and enables the index to capture the relative intensity of AI activity. Missing observations are treated through country-specific interpolation, after which the population-adjusted indicators are transformed using ln (1 + x) to reduce skewness and the influence of extreme values while retaining zero observations. The transformed series are then normalized through the min–max procedure and entered into PCA to construct the population-adjusted AI Index used in the primary estimations. For measurement sensitivity, an alternative index based on the original absolute indicators is constructed using the same logarithmic transformation, normalization, and PCA procedures. This multidimensional approach aligns with literature that treats AI as a transformative national capability shaped by invention, research, automation, and investment dynamics (Zhou et al., 2024), and it provides a more comprehensive measure than any single indicator used independently.

Before normalization, we treated missing observations using a country-specific interpolation procedure to preserve the panel’s temporal structure and cross-country heterogeneity. Because these variables are expressed in different units and scales, they were subsequently normalized using the min–max transformation as shown in Equation 1:where X j i t denotes the observed value of indicator j for country i n year, and min ⁡ X j and max X j refer to the minimum and maximum values of indicator j in the sample. Since all four indicators are positively associated with national AI capability, no reverse coding is required. After normalization, Principal Component Analysis (PCA) is applied to extract the common variation among these four indicators and construct the composite index. PCA is particularly suitable in this context because it reduces dimensionality, minimizes multicollinearity, and preserves as much information as possible from the original variables. The suitability of the data for PCA is verified using the Kaiser-Meyer-Olkin (KMO) measure and Bartlett sphericity test, and only components with eigenvalues above one are retained. The PCA results indicate that the first PC explains 66.75% of the total variance, while the second PC accounts for 26.18%, bringing the cumulative explained variance to 92.93%. Because the eigenvalues of the first two components are greater than one (2.6830 and 1.0522, respectively), they are retained in constructing the AI Index. This result suggests that the four indicators share a strong common structure and that the final index captures most of the information contained in the original data. The factor loadings further indicate that AI scholarly publications (0.9598), industrial robots (0.9601), and AI patent applications (0.8983) load strongly on the first component, suggesting that this dimension primarily reflects the knowledge production, innovation, and automation aspects of national AI capability. By contrast, AI VC investments load most heavily on the second component (0.9811), suggesting that the financial and commercialization dimension of AI development constitutes an additional and distinct source of variation. Moreover, the diagnostic results likewise indicate that the PCA procedure is appropriate. Bartlett’s test yields a statistically significant result (χ² = 702.182, p < 0.001), indicating that the correlation structure among the indicators is adequate for PCA. The KMO statistic equals 0.5667, indicating acceptable sampling adequacy for proceeding with PCA. The composite AII is then calculated as an eigenvalue-weighted combination of the retained component scores as shown in Equation 2:where A I I i t denotes the AI index for country i in year t, S r i t​ represents the score of the rth retained principal component, θ r is the eigenvalue associated with that component, and p is the number of retained components. This formulation ensures that the components explaining more variance receive greater weight in the final index. To improve clarity, the resulting composite score can then be rescaled using the goal-post transformation as shown in Equation 3:where the index ranges from 0 to 1, where higher values mean higher levels of AI capabilities. This final index, therefore, provides an integrated measure of national AI capability by combining the invention, automation, scientific, and investment dimensions of AI into a single comparable indicator. Substantively, a higher AII value reflects a stronger AI ecosystem, greater innovative and technological readiness, and a larger capacity to deploy intelligent systems in ways that may support green innovation and be positively associated with the renewable energy transition. This measurement choice also aligns with a growing literature that treats AI not merely as a narrow technological input, but as a broader innovation and transformation capability with implications for energy systems, environmental performance, and structural change (Zhao et al., 2024).

Z j i t = X j i t − min ⁡ X j max X j − min ⁡ X j (1)

A I I i t = ∑ r = 1 p S r i t θ r ∑ r = 1 P θ r (2)

A I I i t * = A I I i t − min ⁡ A I I max A I I − min ⁡ A I I (3)

Table 1 also shows that the correlation coefficients with AII* are all positive, with the strongest associations observed for AI scholarly publications (0.7671) and industrial robots (0.6950), followed by AI patent applications (0.6206). The positive correlation coefficients indicate that all selected indicators are aligned with the composite AI index, implying that higher values of patents, industrial robots, scholarly publications, and AI-related investment are associated with higher national AI capability. The strongest associations are observed for AI scholarly publications and industrial robots, suggesting that these dimensions contribute most closely to the variation captured by the normalized AI index. Overall, these results validate using PCA to construct a multidimensional measure of national AI capability and support the reliability of the resulting AII and its normalized form, AII*.

TABLE 1. PCA results for the AI index (AII).

AII* denotes the normalized AI, Index. we retained principal components with eigenvalues greater than one for index construction.

GTI is incorporated in this study as the mediating variable because it reflects a country’s capacity to generate and diffuse environmentally oriented technological knowledge that supports cleaner production systems and sustainable energy transformation. It represents the primary channel through which AI can translate into stronger outcomes in the renewable energy transition. At the macro level, AI does not automatically improve renewable energy transition; rather, its contribution is more likely to materialize when it stimulates the development, adoption, and diffusion of greener technologies. In this sense, GTI captures the extent to which technological progress aligns with environmental objectives, particularly those related to reducing emissions, enhancing energy efficiency, and supporting the deployment of renewable energy systems. Consistent with this logic, the literature has used several indicators to capture innovation and green innovation, including R&D expenditure, patent applications, scientific publications, and patents related to environmental technologies (Gill, 2013; Ahmad et al., 2023). To operationalize this mediating variable, GTI is measured as the percentage of patents on environmental technologies among all technologies. This indicator captures the extent to which a country’s inventive activity is directed toward environmentally beneficial technologies rather than conventional technological development. In this regard, patents on environmental technologies provide a meaningful proxy not only for the intensity of green innovation but also for its sustainability orientation, as they indicate whether national innovation systems are producing technologies compatible with long-term ecological and energy-transition objectives (Omri and Kahia, 2026). This variable is measured using data from the OECD database, which provides internationally comparable information on patents related to environmental technologies across countries and over time. The expected effect of GTI on renewable energy transition is positive. Higher GTI is expected to strengthen the renewable energy transition by improving the efficiency, feasibility, and diffusion of renewable energy technologies, reducing technical and cost barriers, and supporting cleaner energy restructuring (Bai and Lin, 2023; Dogan et al., 2025). Prior research suggests that technological innovation actively drives renewable energy development rather than merely accompanying it (Khan et al., 2022).

The study incorporates two moderating variables that represent the digital security environment and the sustainability-oriented institutional context in which AI and GTI operate: cybersecurity and national ESG performance. Their inclusion is consistent with the study’s conceptual framework, which argues that AI’s contribution to GTI and GTI’s subsequent contribution to the renewable energy transition depend on contextual conditions rather than emerging uniformly across countries. Cybersecurity reflects the extent to which a country provides a secure and resilient digital ecosystem that protects information systems, networks, software, and data from cyber threats, while national ESG performance captures the broader environmental, social, and governance conditions that shape sustainable development. In this sense, both moderators represent enabling conditions that may strengthen or weaken the indirect pathway linking AI to renewable energy transition through GTI. Cybersecurity is proxied by the International Telecommunication Union’s (ITU) Global Cybersecurity Index (GCI). The GCI evaluates national cybersecurity commitment and preparedness across five main pillars: legal, technical, and organizational measures, together with capacity development and cooperation, with higher scores indicating stronger cybersecurity readiness. Because the ITU releases GCI scores for selected assessment years rather than for every year, annual observations are not directly available for the entire 2015–2023 study period. To create a consistent annual panel, we used the available official country-level GCI scores as reference points and estimated values for the intervening years using a country-specific linear interpolation procedure. We applied this procedure separately to each country to preserve its temporal pattern and cross-country heterogeneity in cybersecurity development. The resulting series was then rescaled to a 0–1 range for empirical analysis, with higher values representing stronger cybersecurity readiness. Although this approach enables a consistent annual panel, the interpolated observations may not fully capture abrupt year-to-year changes in national cybersecurity policies or capabilities; this potential measurement uncertainty is therefore recognized as a study limitation. Cybersecurity is used in the present analysis because AI-driven innovation and renewable energy systems increasingly depend on interconnected digital platforms, automated processes, real-time information flows, and intelligent infrastructures. Stronger cybersecurity can consequently reduce operational risks, protect data integrity, reinforce digital trust, and ensure the continuity of AI-enabled innovation and energy processes, leading us to expect a positive moderating effect of cybersecurity.

The second moderating variable, national ESG performance, is measured through a composite sovereign ESG index constructed using the World Bank’s Sovereign ESG Data Framework, following a multidimensional procedure that preserves the environmental, social, and governance structure of national sustainability performance. As reported in Table 2, the sovereign ESG framework used in this study comprises 56 indicators across 16 categories and is organized into three sub-indices: environmental, social, and governance. The environmental dimension includes 23 indicators grouped into five categories: emissions and pollution; natural capital endowment and management; energy use and security; environmental and climate risk and resilience; and food security. The social dimension comprises 20 indicators across six categories: education and skills, employment, demography, health and nutrition, poverty and inequality, together with access to services. The governance dimension comprises 13 indicators across five categories: human rights, government effectiveness, stability and rule of law, the economic environment, and gender. In addition, Supplementary Table A reports the country-level mean values of the environmental, social, governance, and overall ESG scores for the 23 emerging countries over 2015–2023, illustrating substantial cross-country heterogeneity in sustainability performance. These differences further support the relevance of ESG performance as a contextual moderator in the present analysis.

TABLE 2. Sovereign national ESG framework.

Category weights are obtained by summing the weights of all indicators included within each category, whereas each individual indicator weight is estimated using the entropy weighting method. The sovereign ESG data framework is adapted from the World Bank (https://esgdata.worldbank.org/data/framework?lang=en).

The construction of the sovereign ESG index follows a sequential procedure designed to ensure comparability across indicators, reduce weighting subjectivity, and preserve the multidimensional nature of national sustainability performance. Because the selected indicators differ in units, scales, and directional orientation, the first step is preprocessing and normalization. Missing values were treated using a country-specific interpolation procedure. Each indicator is then transformed through min–max normalization. For indicators negatively associated with ESG performance, the transformation is reversed so that a larger normalized value always indicates better sustainability performance. After normalization, the entropy weight method estimates indicator weights, assigning larger weights to indicators with greater variation because they contain more discriminatory information. The entropy value of each indicator is computed in Equation 4:where q = N × T is the total number of country-year observations and p k i t​ is the proportion associated with the normalized indicator k. The information utility of each indicator is then defined in Equation 5:

e k = − 1 ln q ∑ i = 1 N ∑ t = 1 T p k i t ln ⁡ p k i t (4)

d k = 1 − e k (5)

Based on this utility measure, the final entropy-based weight is obtained in Equation 6:

w k = d k ∑ k = 1 k d k (6)

This weighting scheme ensures that the weights are normalized and sum to one across all included indicators. Finally, the sovereign ESG score for each country-year observation is computed as the weighted sum of all normalized indicators as shown in Equation 7:where E S G i t denotes the composite national ESG score for country i in year t, w k​ is the entropy-based weight of indicator k, and z k i t​ is its normalized value. This procedure produces both the overall national ESG score and the three component indices corresponding to the ESG dimensions. In the context of the present study, ESG performance is expected to positively moderate the linkage between GTI and the renewable energy transition, because stronger environmental commitment, better social conditions, and more effective governance should facilitate the financing, coordination, diffusion, and implementation of renewable-energy-related innovations. Accordingly, countries with higher ESG performance are expected to convert GTI into renewable energy transition outcomes more effectively than countries with weaker sustainability-oriented institutional settings.

E S G i t = ∑ k = 1 K w k z k i t (7)

The model also included four control variables that may independently affect renewable energy transition. GDP growth (annual %) is introduced as a measure of economic growth, since economic expansion can increase investment capacity and infrastructure development, although it may also raise overall energy demand. Trade (% of GDP) is included to capture external openness, which can facilitate access to cleaner technologies, capital goods, and knowledge spillovers. Green bond issuances, measured in US dollars, are employed as a green-finance proxy because they reflect how strongly financial markets direct capital toward environmentally sustainable activities, particularly renewable energy projects and low-carbon infrastructure. R&D expenditure (% of GDP) is used to measure innovation effort and technological capacity, as stronger research activity can support technological progress and improve renewable energy deployment. GDP growth, trade, and R&D expenditure are collected from the World Development Indicators (WDI), whereas green bond issuances are obtained from the International Monetary Fund (IMF). Overall, their expected effects on renewable energy transition are generally positive, although the impacts of economic growth and trade may vary across structural contexts. Recent studies suggest that technological upgrading, innovation capacity, and supportive financial conditions can strengthen renewable energy development and transition outcomes (Bai and Lin, 2023; Dogan et al., 2025; Taghizadeh-Hesary and Rasoulinezhad, 2025).

3.3 Research methods

This research adopts the conditional process methodology proposed by Hayes to assess the direct effects, mediated pathways, and moderation-dependent associations between AI, GTI, cybersecurity, ESG performance, and the renewable energy transition. In line with the conceptual model and hypotheses, the empirical strategy is implemented in separate stages. First, the analysis assesses AI’s direct influence on the shift toward renewable energy. Second, Hayes’ PROCESS Model four is applied to evaluate whether GTI mediates the link between AI and the renewable energy transition. Third, Hayes’ PROCESS Model eight examines the moderating role of cybersecurity, which is hypothesized to condition both AI’s influence on GTI and its direct contribution to the renewable energy transition. Finally, Hayes’ PROCESS Model 14 tests the second-stage moderated mediation effect, in which ESG performance conditions the GTI-renewable-energy-transition relationship, thereby conditioning AI’s indirect effect on the renewable energy transition via GTI. Estimating these stages separately clarifies the distinct mechanisms and conditional effects embedded in the proposed framework.

The direct effect model is first specified as shown in Equation 8:where R E T i t denotes the renewable energy transition, A I I i t is the index of AI, C V k i t represents the vector of control variables, and ε i t is the error term. α 1 captures the overall impact of AI on RET.

R E T i t = α 0 + α 1 A I I i t + ∑ α k C V k i t + ε i t (8)

To test the simple mediation effect of GTI, corresponding to PROCESS Model 4, the following equations are estimated:

G T I i t = β 0 + β 1 A I I i t + ∑ β k C V k i t + ε 1 i t (9)

R E T i t = γ 0 + γ 1 A I I i t + γ 2 G T I i t + ∑ γ k C V k i t + ε 2 i t (10)

The indirect effect (I.E.,) is then computed as:

I E = β 1 γ 2 (11)

In Equation 9, β 1 measures AI’s impact on GTI. In Equation 10, γ 2 indicates the impact of GTI on renewable energy transition, while γ 1 is the direct impact of AI on RET after accounting for GTI. A statistically significant indirect effect in Equation 11 indicates that GTI mediates the relationship between AI and RET.

To test the moderating role of cybersecurity and the corresponding first-stage moderated mediation effect. In this specification, the mediator equation is given by:and the outcome equation is written as:

G T I i t = i M + α 1 A I I i t + α 2 C Y B S i t + α 3 A I I i t × C Y B S i t + ∑ α k C V k i t + e M i t (12)

R E T i t = i y + c 1 ′ A I I i t + c 2 ′ C Y B S i t + c 3 ′ A I I i t × C Y B S i t + b 1 G T I i t + ∑ b k C V k i t + e Y i t (13)

The corresponding conditional indirect effect under cybersecurity is:

I E C Y B S = α 1 + α 3 C Y B S i t b 1 (14)

In Equation 12, α 1 captures the impact of AI on GTI, α 2 denotes the direct impact of cybersecurity on GTI, and α 3 measures the interaction effect between AI and cybersecurity. Thus, α 3 is the key moderation parameter for the AI → GTI path. In Equation 13, c’₁ represents the direct effect of AI on RET, c’₂ denotes the direct impact of cybersecurity on RET, and c’₃ captures the interaction effect between AI and cybersecurity on RET. Accordingly, c’₃ is the moderation coefficient for the AI → RET path. The coefficient b 1 measures the impact of GTI on RET. Equation 14 indicates that the conditional effect of AI on RET through GTI fluctuates with the degree of cybersecurity.

To test the second-stage moderated mediation effect associated with ESG performance. The mediator equation remains:while the outcome equation becomes:

G T I i t = i M + α 1 A I I i t + ∑ α k C V k i t + e M i t (15)

R E T i t = i y + c 1 ′ A I I i t + b 1 G T I i t + b 2 E S G i t + b 3 G T I i t × E S G i t + ∑ b k C V k i t + e Y i t (16)

The conditional indirect effect under ESG performance is therefore expressed as:

I E E S G = α 1 b 1 + b 3 E S G i t (17)

In Equation 15, α 1 captures AI’s impact on GTI. In Equation 16, b 1 measures the effect of GTI on renewable energy transition at the ESG reference value, b 2 denotes the direct effect of ESG performance on RET, and b 3​ captures the interaction effect between GTI and ESG performance. Thus, b 3 is the key moderation parameter for the GTI-RET path, while c' continues to represent the direct impact of AI on RET after accounting for the mediator. In applying Equation 17, the conditional effect of GTI on RET is computed as b1 + b3 (ESG - 0.420), where 0.420 is the 50th-percentile ESG reference value used in the conditional-effect table. The conditional indirect effect of AI on RET through GTI is therefore calculated as a1 x [b1 + b3 (ESG - 0.420)]. A positive b3 means that stronger ESG conditions reinforce GTI’s contribution to RET.

Accordingly, the coefficients displayed in Figure 2 can be explained as follows. The parameters a1, a2, and a3 refer to the first-stage process linking AI and cybersecurity to GTI. The parameters c'1, c'2, and c'3 correspond to the direct and moderated direct effects of AI and cybersecurity on renewable energy transition. Finally, the parameters b1, b2, and b3 describe the second-stage process through which GTI and ESG performance influence renewable energy transition. We assess the significance of the indirect and conditional indirect effects reported in Equations 11, 14 and 17 using bias-corrected bootstrap confidence intervals, as recommended in Hayes’s conditional process methodology.

To strengthen the robustness of the empirical analysis, the study also employs a two-step system-GMM estimation approach alongside Hayes’s PROCESS macro, providing an alternative dynamic panel approach to corroborate the mediation and moderated mediation findings from the PROCESS framework. Hayes’s PROCESS macro remains the primary analytical method because it enables the direct estimation of mediation, moderation, and conditional indirect effects through bootstrapped confidence intervals. Nevertheless, applying this approach to panel data can present methodological challenges associated with dynamic effects, unobserved country-level heterogeneity, simultaneous determination, and reverse-causality concerns, serial correlation, heteroskedasticity, and possible endogeneity. These concerns are relevant in the present study because the renewable energy transition may persist over time, while artificial intelligence, green technological innovation, cybersecurity, and ESG performance may be jointly determined. For instance, while artificial intelligence can contribute to accelerating the shift toward renewable energy, countries with more developed renewable energy infrastructures may simultaneously possess greater capacity to implement AI technologies, foster environmentally oriented innovation, enhance cybersecurity preparedness, and establish stronger ESG-related institutional frameworks. This potential bidirectional relationship, together with dynamic persistence and country-specific unobserved factors, makes the System GMM estimator particularly suitable for the empirical analysis. The method integrates equations specified in first differences with equations in levels and employs lagged observations of potentially endogenous variables as internal instruments. Relative to Difference GMM, System GMM can provide greater estimation efficiency in settings where the variables exhibit substantial persistence and lagged levels provide weak instruments for the differenced equations. The two-step estimator is adopted because of its higher asymptotic efficiency, with finite-sample-corrected robust standard errors applied to address the tendency of conventional two-step standard errors to be downward biased. Furthermore, the potential problem of instrument proliferation is mitigated by collapsing the instrument matrix and limiting the number of included lags. Time-specific dummy variables are incorporated to account for common temporal shocks that affect all countries simultaneously.

The dynamic model employed to examine the overall effect of artificial intelligence on the renewable energy transition is specified as follows:where R E T i t represents the renewable energy transition, while its lagged value is R E T i t − 1​, AI​ signifies artificial intelligence, and CV represents a set of control variables comprising GDP growth, trade, green bond issuances, and R&D expenditure. μ i describes unobserved heterogeneity across countries, λ t​ accounts for common shocks occurring across countries during the study period, and ε​ represents the idiosyncratic error component. In this equation, c represents the total impact of artificial intelligence on the renewable energy transition. If c is positive and statistically significant, this would indicate that artificial intelligence contributes to renewable energy transition even after accounting for the past level of renewable energy transition and possible endogeneity.

R E T i t = α 0 + ρ R E T i t − 1 + c A I i t + ∑ α k C V i t + μ i + λ t + ε i t (18)

To assess the mediating mechanism, the System GMM analysis then estimates the effect of artificial intelligence for green technological innovation. This relationship is specified as follows:where G T I i t refers to green technological innovation, and G T I i t − 1​ captures the persistence of green technological innovation over time. The coefficient a captures the dynamic impact of artificial intelligence on green technological innovation. A positive and significant value of a would indicate that artificial intelligence promotes green technological innovation, thereby validating the first link in the proposed mediation pathway. This step is important because the theoretical framework assumes that artificial intelligence not only directly affects the renewable energy transition but also operates through its capacity to strengthen environmentally oriented technological innovation.

G T I i t = δ 0 + ∅ G T I i t − 1 + a A I i t + ∑ α k C V i t + μ i + λ t + μ i t (19)

The next equation examines whether green technological innovation contributes to renewable energy transition after artificial intelligence has been included in the model. The equation is specified as follows:where b captures green technological innovation’s influence on renewable energy transition, while c′ characterises the remaining direct impact of artificial intelligence once considering the mediator. The interaction mechanism is reinforced when artificial intelligence meaningfully influences the renewable energy transition in Equation 18, green technological innovation significantly affects the renewable energy transition in Equation 20, and artificial intelligence significantly affects green technological innovation in Equation 19. If c′ continues substantial but is minor than c, the evidence points to restricted mediation. If c′ loses significance after green technological innovation is included, the evidence would suggest full mediation. In this way, System GMM provides a dynamic robustness test of the mediation logic estimated through Hayes’s PROCESS macro.

R E T i t = α 1 + ρ R E T i t − 1 + c ′ A I i t + b G T I i t + ∑ α k C V i t + μ i + λ t + ε i t (20)

To test whether cybersecurity strengthens the artificial intelligence–green technological innovation channel, the mediator equation is augmented with including cybersecurity and its interaction with artificial intelligence:where C Y B S i t denotes cybersecurity, while A I × C Y B S​ represents the interaction between artificial intelligence and cybersecurity. The coefficient α 3 is the main parameter for testing the first-stage moderation effect. A positive and statistically significant α 3 would show that cybersecurity strengthens the positive effect of artificial intelligence on green technological innovation. This means that artificial intelligence is expected to yield stronger outcomes in green technological innovation in countries with higher cybersecurity readiness. To reduce multicollinearity and facilitate understanding of the interaction term, artificial intelligence and cybersecurity are mean-centered before constructing the interaction variable.

G T I i t = δ 0 + ∅ G T I i t − 1 + α 1 A I i t + α 2 C Y B S i t + α 3 A I × C Y B S + ∑ α k C V i t + μ i + λ t + μ i t (21)

To examine the moderating role of cybersecurity in the direct artificial intelligence–renewable energy transition relationship, the outcome equation is extended in Equation 22 as follows:where c 3 ′ captures whether cybersecurity changes the strength of the direct relationship between artificial intelligence and renewable energy transition. A positive and statistically significant c 3 ′ would show that artificial intelligence contributes more strongly to renewable energy transition when cybersecurity is higher. This equation matters because cybersecurity is expected not only to reinforce the artificial intelligence–green technological innovation pathway but also to strengthen artificial intelligence’s direct contribution to the renewable energy transition.

R E T i t = γ 0 + ρ R E T i t − 1 + c 1 ′ A I i t + b G T I i t + c 2 ′ C Y B S i t + c 3 ′ A I × C Y B S + ∑ γ k C V i t + μ i + λ t + ε i t (22)

To include the second-stage moderation effect of national ESG performance, the renewable energy transition equation is further specified by adding ESG performance and its interaction with green technological innovation:where E S G i t denotes national ESG performance, while G T I × E S G represents the interaction between green technological innovation and ESG performance. The factor b 3 is the central coefficient for the second-stage moderation impact. A statistically significant positive b 3 would indicate that ESG performance strengthens the impact of green technological innovation on renewable energy transition. In other words, economies with stronger ESG performance are expected to translate green technological innovation into renewable energy transition outcomes more effectively. This equation is necessary to test the second-stage moderated mediation logic because H7 assumes that the indirect effect of artificial intelligence on the renewable energy transition through green technological innovation becomes stronger when ESG performance is higher.

R E T i t = γ 0 + ρ R E T i t − 1 + c ″ A I i t + b 1 G T I i t + b 2 E S G i t + b 3 G T I × E S G + ∑ θ k C V i t + μ i + λ t + ε i t (23)

We evaluate the first-stage moderated mediation mechanism by combining the conditional artificial intelligence–green technological innovation effect in Equation 21 with the green technological innovation–renewable energy transition effect in Equation 20. This means the indirect effect of artificial intelligence on renewable energy transition through green technological innovation varies with the level of cybersecurity. The second-stage moderated mediation mechanism combines the artificial intelligence–green technological innovation effect in Equation 19 with the conditional green technological innovation–renewable energy transition effect in Equation 23. This means the indirect effect of artificial intelligence on renewable energy transition through green technological innovation also varies with national ESG performance. Therefore, the System GMM robustness framework now captures both conditional mechanisms: cybersecurity as the first-stage moderator and ESG performance as the second-stage moderator.

To make the System GMM implementation more transparent, we specify the instrument strategy explicitly. In all dynamic specifications, we treat the lagged dependent variable as endogenous because current renewable energy transition and green technological innovation likely depend on their own past values. We treat artificial intelligence, green technological innovation, cybersecurity, national ESG performance, and the interaction terms as potentially endogenous or predetermined, since these variables may be jointly determined with renewable energy transition or may respond to previous changes in the energy system. We therefore use their lagged values as internal instruments. The control variables account for macroeconomic, trade-related, financial, and innovation-related conditions that may also shape the renewable energy transition. To reduce the risk of instrument proliferation, the instrument matrix is collapsed and the lag depth is restricted. The validity of the dynamic specifications is assessed using the Arellano–Bond AR (1) and AR (2) tests, the Hansen test of overidentifying restrictions, and the Wald test of joint significance. A significant AR (1) statistic is expected in first-differenced residuals, whereas an insignificant AR (2) statistic indicates the absence of second-order serial correlation. An insignificant Hansen test provides no evidence against the validity of the internal instruments.

Nevertheless, retaining Hayes’s PROCESS macro as the principal analytical framework is appropriate because the research assesses a conditional process model incorporating mediation, moderation of the first-stage relationship, moderation of the direct effect, moderation of the second-stage relationship, and conditional indirect effects. Hayes’s PROCESS macro estimates these relationships transparently and provides bootstrapped confidence intervals for indirect and conditional indirect effects. However, the PROCESS framework is not specifically designed to account for dynamic dependence, the longitudinal structure of country-level observations, unobserved country-specific heterogeneity, common temporal shocks, simultaneity, reverse causality, or possible endogeneity. Accordingly, the findings generated through PROCESS are supplemented with estimates from the two-step System GMM approach. This complementary methodology enables Hayes’s PROCESS macro to identify and assess the hypothesized theoretical pathways directly, while the System GMM estimates provide an additional robustness check by determining whether the direct effect, mediating mechanism, first-stage moderated mediation, moderation of the direct relationship, and second-stage moderated mediation remain consistent after addressing key dynamic panel-data challenges. The mediation, moderation, and moderated-mediation analyses were conducted using Hayes’s PROCESS macro version 4.2 in IBM SPSS Statistics 27. Bias-corrected bootstrap confidence intervals based on 5,000 bootstrap replications and a 95% confidence level were used to assess the significance of the indirect and conditional indirect effects. We conducted two-step System GMM robustness estimations in Stata 14. The System GMM specifications applied finite-sample-corrected robust standard errors, collapsed instruments, restricted lag depth, and year effects to reduce instrument proliferation and account for common temporal shocks.

4 Results and discussion

4.1 Descriptive statistics and correlations

Table 3 presents the descriptive statistics for the study variables. RET, measured as the share of final energy use supplied by renewable sources, averages 15.39% (SD = 12.24), with values ranging from 0.010% to 50.000%. This large dispersal indicates major differences in renewable energy penetration across the sampled emerging markets. The AI Index (AII) averages 0.055 and ranges from 0 to 1, reflecting pronounced cross-country variation in national AI capability. GTI averages 32.19%, with a range of 11.49%–100%, indicating that the environmental orientation of national inventive activity differs considerably across countries. Cybersecurity averages 0.732, while national ESG performance averages 0.45 with a smaller standard deviation of 0.058. These patterns suggest that the sample contains greater heterogeneity in technological and energy-transition outcomes than in the overall sovereign ESG index. The control variables also display meaningful variation. GDP growth ranges from −10.93% to 13.36%, capturing both contractionary and expansionary episodes during 2015–2023. Trade openness averages 79.44% of GDP but reaches 199.05%, showing that several sampled markets are highly integrated into international trade. Green bond issuances are strongly dispersed, as indicated by a mean of 4.37, a 12.60 standard deviation, together with a maximum of 95.98. R&D expenditure averages 1% of GDP and varies from 0.06% to 4.94%. This heterogeneity supports including macroeconomic, financial, and innovation-related controls, as countries differ substantially in growth conditions, international exposure, green financing depth, and formal research effort. The number of available observations varies across GTI, trade, and green bond issuances because Table 3 reports the available-level observations from the descriptive-statistics database before the complete-case diagnostics used in the correlation and VIF analyses.

TABLE 3. Descriptive statistics of the key variables.

RET, renewable energy transition; AII, artificial intelligence index; GTI, green technological innovation; CYBS, cybersecurity; ESG, environmental, social, and governance; R&D = research and development.

Table 4 reports Pearson correlations between RET and the explanatory variables. RET is positively and significantly associated with AII (r = 0.699), GTI (r = 0.714), cybersecurity (r = 0.395), green bond issuances (r = 0.508), and R&D expenditure (r = 0.259), while it is negatively related to trade openness (r = −0.250). The particularly strong RET-GTI association is consistent with the study’s mediation logic, which holds that environmentally oriented technological innovation is a central pathway through which AI contributes to the renewable energy transition. It should not, however, be interpreted as evidence of multicollinearity because RET is the dependent variable, whereas multicollinearity concerns arise from linear dependence among the regressors included jointly in the same equation. Turning to the explanatory variables, the largest correlation is 0.640 between AII and GTI. This positive association is theoretically reasonable because stronger AI capability can support environmental innovation, knowledge processing, and technology diffusion, yet it remains below the conventional 0.80 threshold. AII is also positively associated with green bond issuances (r = 0.57) and R&D expenditure (r = 0.36), while its correlations with ESG performance (r = −0.03) and cybersecurity (r = 0.12) are weak. GTI is moderately associated with cybersecurity (r = 0.58) and green bond issuances (r = 0.42), but its relationships with R&D expenditure (r = 0.18) and ESG performance (r = −0.07) are small. These patterns support construct differentiation: AII captures multidimensional AI capability, GTI reflects environmentally directed inventive output, R&D expenditure represents general research inputs, and ESG performance captures the broader sustainability-oriented institutional setting. Importantly, no explanatory variable exceeds 0.80 in correlation with the others.

TABLE 4. Pearson correlation matrix.

*, , and * represents significance levels of 10%, 5%, and 1% respectively.

To supplement the bivariate evidence, Table 5 reports variance-inflation factors for the core renewable-energy-transition specification, which includes AII, GTI, ESG performance, GDP growth, trade, green bond issuances, and R&D expenditure. RET is not included in the VIF calculation because VIF is a diagnostic of linear dependence among explanatory variables, not between the dependent variable and the regressors. VIF values range from 1.123 for GDP growth to 2.299 for AII, with a mean VIF of 1.583. All values are well below the commonly applied threshold of 5, while the corresponding tolerance statistics range from 0.435 to 0.891 and remain above 0.20. The variables directly implicated in the reviewer’s concern also display low VIFs: 2.299 for AII, 1.729 for GTI, 1.592 for R&D expenditure, and 1.254 for ESG performance. These results indicate that the explanatory variables do not create problematic linear dependence when included jointly. However, these diagnostics should be understood as tests of empirical multicollinearity, not as evidence that all conceptual proximity among constructs has been removed. For this reason, we interpret the ESG moderation results cautiously. This study uses national ESG performance as a broad, sustainability-oriented institutional moderator, not as an alternative measure of renewable energy transition. In addition, the ESG index used in the analysis does not include the World Bank indicator “Renewable energy consumption (% of total final energy consumption),” which corresponds closely to the RET outcome. Within the environmental dimension, the energy use and security category includes electricity production from coal sources (% of total), energy imports, net (% of energy use), energy intensity level of primary energy, energy use, fossil fuel energy consumption, and renewable electricity output. Therefore, although some energy-related proximity remains, the ESG variable is conceptually and empirically broader than RET and is retained as a contextual moderator reflecting the sustainability-oriented institutional environment in which green technological innovation is converted into renewable energy-transition outcomes.

TABLE 5. Variance-inflation-factor diagnostics.

Values below five and tolerance values above 0.20 indicate that multicollinearity is not a material concern.

4.2 Process macro analysis

The study estimates the proposed conditional process framework using Hayes’s PROCESS approach, which enables the simultaneous examination of mediation, moderation, and their combined effects. Model adequacy is assessed through the multiple correlation coefficient (R) and the coefficient of determination (R ²) to evaluate the strength of association and the proportion of explained variance, while the overall F-statistic is used to verify the joint significance of the regressors. The mediating role of GTI is evaluated using bootstrap confidence intervals for the indirect effect, yielding more reliable inference than conventional methods, whereas moderated mediation effects related to cybersecurity and ESG performance are examined using the index of moderated mediation and its corresponding bootstrap confidence interval. Statistical significance is determined at the 5% level, such that a p-value below 0.05 indicates that the model provides a meaningful and robust explanation of the relationships under investigation.

To investigate the direct and indirect effects of artificial intelligence on the renewable energy transition, the study applies the simple mediation procedure specified in Hayes’s PROCESS Model 4. This model simultaneously evaluates AI’s direct effect on the renewable energy transition and the indirect effect transmitted through GTI. With respect to the direct effect, the findings reported in Table 6 indicate that AI exhibits a positive and statistically significant association with the renewable energy transition. The estimated overall effect equals 1.027, with an associated standard error of 0.2893, while the 95% confidence interval [0.456, 1.598] excludes zero, confirming the statistical significance of the overall effect. Furthermore, AI’s direct effect on the renewable energy transition, denoted by C′1 in Figure 3, is positive and significant in statistical terms (C′1 = 0.586; SE = 0.1651; p = 0.000; 95% CI [0.260, 0.912]). Because the confidence interval fails to cross zero, these findings provide support for H1 and suggest that AI is directly associated with the renewable energy transition across the sampled emerging economies. From a substantive perspective, the finding implies that stronger national AI capabilities are associated with greater advancement in renewable energy deployment and the broader shift toward cleaner energy systems. This direct association may arise through several operational and system-level mechanisms. AI improves renewable energy forecasting, enhances the management of intermittency, optimizes electricity dispatch and storage, supports predictive maintenance of renewable infrastructure, improves demand-side management, and strengthens smart-grid coordination. These functions are particularly important because renewable sources such as solar and wind are variable and require more intelligent, flexible, and data-driven energy systems. Therefore, AI appears to enhance the technical and managerial capacity of energy systems to integrate and expand renewable energy sources more effectively. This finding is consistent with Fang et al. (2025), Wang (2025), and Zhang et al. (2024), who show that AI contributes to renewable energy development and cleaner energy transition. It also supports Rebelatto et al. (2024), Ukoba et al. (2024), and Zhao et al. (2024), who emphasize the role of AI in smart grids, forecasting, optimization, storage management, and predictive maintenance. However, the result differs from more cautious studies, which suggest that AI may generate short-term energy and environmental burdens due to the high energy requirements of digital infrastructure and computation (Lee and Yan, 2024).

TABLE 6. Direct and indirect associations between AI and renewable energy transition.

AII = AI, index; GTI, green technological innovation; RET, renewable energy transition; X = independent variable; M = mediating variable; Y = dependent variable; Coeff = coefficient; S.E., standard error; CI, confidence interval; a1 = effect of AI, on GTI; b1 = effect of GTI, on RET; c’1 = AI’s direct effect on RET, after considering the effect of GTI.

The mediation analysis further investigates whether GTI operates as a pathway through which artificial intelligence influences the renewable energy transition. Table 6 and illustrated in Figure 3, the first link in the proposed indirect pathway is AI’s effect on GTI, denoted by a₁. The estimated coefficient for a₁ is positive and significant in statistical terms (a₁ = 0.334; SE = 0.1480; p = 0.025; 95% CI [0.042, 0.626]). Because zero is not contained within the confidence interval, the findings provide evidence that AI has a significant positive effect on GTI. This result suggests that countries with stronger AI capabilities possess greater potential to develop, adopt, and disseminate technologies that support environmental sustainability. This linkage can be clarified through AI’s ability to enhance data processing, improve research and development, ameliorate intelligent automation, identify inefficiencies in production and energy systems, and support cleaner product and process innovation. AI may additionally enhance the ability of firms and national innovation systems to process large environmental and technological datasets, detect opportunities for resource savings, and design technologies that reduce emissions and improve energy efficiency. Accordingly, AI can be viewed as an upstream digital capability capable of enhances the technological and knowledge foundations required for GTI. This result aligns with the findings of Zhou et al. (2024), who demonstrate that AI can promote advances in green technology while supporting energy efficiency and reducing carbon emissions. It also confirms Yin K. et al. (2023), who find that the development of AI strengthens green technology innovation and supports technological change toward a renewable energy transition. Similarly, the result supports Saiyed et al. (2025), who conclude that AI enhances green innovation capacity by improving knowledge creation and knowledge deployment for sustainable development. Thus, the positive and significant a1 coefficient confirms that AI is not only a technical tool for managing existing energy systems but also a strategic innovation capability that supports cleaner technological development.

The second link in the proposed mediation mechanism captures the effect of green technological innovation on the renewable energy transition, denoted by b₁. The results presented in Table 6 show that b₁ is positive and significant in statistical terms (b₁ = 1.320; SE = 0.3440; 95% CI [0.641, 1.999]). Because the confidence interval remains entirely above zero, the findings provide strong evidence that green technological innovation makes a significant contribution to advancing the renewable energy transition. This means that economies with superior levels of environmentally oriented technological innovation are better positioned to expand renewable energy generation and move toward cleaner energy systems. The relationship can be explained through several channels. GTI improves the efficiency, reliability, affordability, and scalability of renewable energy technologies. It can reduce technical barriers to renewable energy adoption, lower production and installation costs, improve storage and grid-integration technologies, enhance energy efficiency, and support the diffusion of cleaner technologies across sectors. It also strengthens energy systems’ capacity to replace fossil-fuel-based technologies with cleaner alternatives. Therefore, renewable energy transition is determined not solely by investment and policy commitment but also on the availability of environmental technologies that make renewable energy systems more viable and competitive. This finding confirms the argument developed in previous literature that GTI is a key driver of renewable energy transition. It is consistent with Khan et al. (2022), who show that technological innovation promotes renewable energy development, and with Tabrizian (2019), who emphasizes the importance of technological innovation for the diffusion of renewable energy in developing countries. It also supports Radulescu et al. (2026), who show that GTI contributes to greener energy outcomes in both developed and developing countries.

Moreover, the indirect-effect estimates presented in Table 6 provide evidence that GTI functions as a significant mediator in the association linking AI with renewable energy transition. The indirect effect, calculated as a₁ × b₁, is positive and significant in statistical terms, yielding an estimate of 0.441, an associated bootstrap standard error of 0.174, together with a 95% bootstrap confidence interval of [0.157, 0.835]. Because the confidence interval fails to encompass zero, the mediated effect is statistically distinguishable from zero. These findings support H2 and demonstrate that GTI represents an important transmission mechanism through which AI contributes to the renewable energy transition. As depicted in Figure 3, this mediation pathway operates through two statistically significant links: AI enhances GTI (a₁ = 0.334), which subsequently promotes the renewable energy transition (b₁ = 1.320). The meaning of this result is that AI contributes to the renewable energy transition not only by directly improving the operation of energy systems but also by building the technological and innovation capabilities needed for cleaner energy transformation. Nevertheless, the continued statistical significance of AI’s direct effect on the renewable energy transition indicates that GTI does not fully account for this relationship. The mediation can therefore be characterized as partial, suggesting that AI advances the renewable energy transition through two complementary mechanisms: a direct operational mechanism and an indirect pathway mediated by green technological innovation. This result is consistent with Yin H. T. et al. (2023), who identify innovation performance as an important pathway linking AI to renewable energy transition, and with Behera et al. (2025), who show that AI-driven green innovation supports renewable energy transition. It also confirms Wang H. J. et al. (2025), who find that AI contributes to renewable energy innovation across countries. Taken together, the results offer substantial empirical evidence supporting the study’s core proposition that artificial intelligence is directly associated with the transition toward renewable energy, while GTI serves as a significant transmission channel through which AI capabilities translate into cleaner energy outcomes.

To test H3, H4, and H6, Hayes’s PROCESS Model eight was employed because cybersecurity is hypothesized to moderate both AI’s effect on GTI and AI’s direct effect on renewable energy transition. Table 7 reports the mediator equation, examining the effects of AI, cybersecurity, and their interaction on GTI. It indicates that AI is positively and significantly associated with GTI (Coeff = 0.188, p = 0.000), with a 95% confidence interval spanning [0.091, 0.285], indicating a direct positive AI-GTI association. This indicates that greater AI capability is linked to stronger country capacity to generate and diffuse environmentally oriented technologies. Cybersecurity is also positively and significantly associated with GTI (Coeff = 0.299, p = 0.000), with a 95% confidence interval spanning [0.271, 0.327], indicating that countries with stronger cybersecurity conditions tend to achieve higher GTI. This result suggests that secure digital infrastructures are not only protective systems but also enabling conditions for innovation, because they are linked to improved data reliability, reduced digital risks, and increased confidence in the use of intelligent technologies for green research and development. Moreover, the interaction of AI with cybersecurity is positive and significant (Coeff = 0.650, p = 0.000), with a 95% confidence interval [0.399, 0.901]. This indicates that cybersecurity significantly conditions the linkage between AI and GTI. Put differently, the positive AI-GTI association becomes stronger as cybersecurity rises. The model is also statistically robust, with R = 0.944, R ² = 0.891, and F = 211.364 (p = 0.000), indicating that it explains a substantial proportion of the variation in GTI.

TABLE 7. Conditional effect of AI on GTI (first-stage moderated mediation effect).

AII = AI, index; GTI, green technological innovation; RET, renewable energy transition; CYBS, cybersecurity; X = independent variable; M = mediating variable; Y = dependent variable; W = moderating variable; XW, interaction of AI, with cybersecurity; Coeff = coefficient; S.E., standard error; CI, confidence interval; R²-chng = change in R-squared; df = degrees of freedom.

Moreover, the highest-order unconditional-interaction test confirms the significance of the moderating effect of cybersecurity. The interaction of AI with cybersecurity produces an additional explanatory contribution to the model (R²-chng = 0.027, F = 44.835, p = 0.000), confirming that including the interaction term significantly improves prediction of GTI. This means that the influence of AI on GTI depends on the level of cybersecurity. Figure 4 clearly illustrates this conditional effect because the slope from AI to GTI becomes steeper as cybersecurity increases. The conditional effects presented in Table 8 support this pattern. Specifically, when cybersecurity is low (CYBS = 0.268), the AI-GTI association is positive and significant (Effect = 0.362). This association becomes stronger at a medium level of cybersecurity (CYBS = 0.511), where the conditional effect increases to 0.520 (p = 0.000), with a 95% confidence interval [0.435, 0.605]. The effect reaches its maximum when cybersecurity is high (CYBS = 0.761), with a conditional effect of 0.683 (p = 0.000) and a 95% confidence interval [0.549, 0.817]. These results indicate that AI is positively associated with GTI across all levels of cybersecurity, while this association becomes progressively stronger as cybersecurity improves. Thus, cybersecurity functions as an amplifying condition under which the AI-GTI relationship becomes stronger.

TABLE 8. AI conditional effects across different values of cybersecurity.

CYBS, cybersecurity; Effect = conditional effect of AI, on GTI, at the specified level of cybersecurity; S.E., standard error.

These findings support H3, which suggests that cybersecurity strengthens the linkage between AI and GTI. The results indicate that AI can stimulate GTI even under low cybersecurity conditions, but its contribution is considerably greater in more secure digital environments. This pattern reflects that AI-driven GTI depends heavily on reliable data, secure digital platforms, protected knowledge assets, and uninterrupted digital experimentation. When cybersecurity is weak, firms and institutions may face risks of data breaches, system disruptions, manipulation of digital information, and reduced trust in AI-generated outputs. These risks can limit the willingness and ability of economic actors to use AI in environmentally oriented research, cleaner production design, energy-efficiency solutions, and green product development. Conversely, stronger cybersecurity improves data integrity, system availability, confidentiality, and digital trust, thereby enabling the more effective use of AI for green technological innovation. This finding is consistent with the literature arguing that cybersecurity is a complementary capability that enhances the value of AI in digital systems (Alrfai et al., 2023; Jada et al., 2024; Kaur et al., 2023). It also supports studies showing that cybersecurity governance and resilience promote innovation by reducing operational risk and protecting strategic knowledge assets (Singh et al., 2025). In the context of emerging economies, these results are especially important because AI capabilities may do not automatically result in GTI unless they are supported by secure, trusted digital infrastructure. Overall, the results in Tables 7, 8 and Figure 4 confirm that cybersecurity moderates the linkage between AI and GTI.

Table 9 reports the conditional AI’s influence on RET at different values of cybersecurity. The results show that AI is positively and significantly associated with the renewable energy transition (Coeff = 0.391, p = 0.003), with a 95% confidence interval spanning [0.134, 0.648], supporting a direct AI-RET association. Cybersecurity also has a positive and significant effect (Coeff = 0.217, p = 0.000), with a 95% confidence interval spanning [0.148, 0.286], indicating that more secure digital environments are associated with stronger outcomes in the renewable energy transition. More importantly, the interaction involving AI and cybersecurity is positive (Coeff = 0.869, p = 0.000), with a 95% confidence interval [0.536, 1.202]. This indicates that cybersecurity significantly conditions the linkage between AI and renewable energy transition. In other words, the AI-RET association depends on the level of cybersecurity. Moreover, the highest-order unconditional-interaction test confirms the significance of the moderating effect, with R²-chng = 0.013, F = 37.143, p = 0.000, indicating that including the interaction of AI with cybersecurity significantly improves predictions of the renewable energy transition. Figure 5 provides visual support for this finding because the slope from AI to renewable energy transition becomes steeper as cybersecurity increases. This implies that AI is more strongly associated with renewable energy transition in countries with higher cybersecurity readiness. The result, therefore, supports H4, which suggests that cybersecurity strengthens the positive linkage between AI and renewable energy transition.

TABLE 9. Conditional effect of AI on renewable energy transition.

AII = AI, index; GTI, green technological innovation; RET, renewable energy transition; CYBS, cybersecurity; XW, interaction of AI, with cybersecurity; Coeff = coefficient; S.E., standard error; CI, confidence interval; R²-chng = change in R-squared; df = degrees of freedom.

The conditional effects in Table 10; Figure 5 clarify this moderating effect. Specifically, when cybersecurity is low (CYBS = 0.268), AI positively influences renewable energy transition (Effect = 0.624, p = 0.000), with a 95% confidence interval [0.512, 0.736]. This implies that AI still contributes to renewable energy transition under weak cybersecurity conditions, although the effect remains comparatively modest in magnitude. At a medium level of cybersecurity (CYBS = 0.511), the effect becomes stronger (Effect = 0.835, p = 0.000), with a 95% confidence interval [0.713, 0.957]. The effect is highest when cybersecurity is high (CYBS = 0.761), where the influence of AI on the renewable energy transition increases to 1.052 (p = 0.000), with a 95% confidence interval [0.878, 1.226]. These estimates reveal a clear increasing pattern: the effect of AI increases from 0.624 at low cybersecurity to 0.835 at medium cybersecurity and 1.052 at high cybersecurity. This confirms that cybersecurity amplifies the contribution of AI to renewable energy transition. Substantively, this finding means that AI can generate stronger renewable energy outcomes when it operates within secure, trusted, and resilient digital infrastructures. AI-based renewable energy systems depend on real-time data, digital communication, smart grids, automated monitoring, storage optimization, forecasting tools, and digitally coordinated energy flows. When cybersecurity is weak, these systems are more vulnerable to data breaches, cyberattacks, information manipulation, and operational disruptions, which can erode trust in AI-enabled energy solutions and undermine their effectiveness. By contrast, strong cybersecurity protects data integrity, ensures system availability, strengthens digital trust, and improves the resilience of renewable energy infrastructure. This result aligns with recent literature, which argues that cybersecurity is a key enabling condition for AI-based energy systems. It supports Ekechukwu and Simpa (2024), Abbasi (2025), and Anwar and Ali (2026), who emphasize that cybersecurity is essential to protect investments in renewable energy, secure smart grids, and ensure the reliable operation of AI-based renewable energy networks. It also extends prior studies showing that AI supports the renewable energy transition through forecasting, optimization, predictive maintenance, and smart-grid management (Rebelatto et al., 2024; Ukoba et al., 2024; Zhao et al., 2024) by demonstrating that these benefits are stronger when cybersecurity is higher.

TABLE 10. AI conditional effects across cybersecurity levels.

CYBS, cybersecurity; Effect = conditional effect of AI, on RET, at the specified level of cybersecurity; S.E., standard error.

Table 11 presents the AI conditional indirect effects on RET through GTI across different levels of cybersecurity. The indirect effect is positive and significant across all three cybersecurity levels, as none of the bootstrap confidence intervals include zero. When cybersecurity is low (CYBS = 0.268), AI’s indirect influence on RET through GTI is positive (Effect = 0.190, BootSE = 0.039, bootstrap CI [0.116, 0.270]). This indicates that even in countries with weaker cybersecurity conditions, AI can still contribute indirectly to renewable energy transition by stimulating GTI. However, the magnitude of this indirect effect remains relatively limited, suggesting that insecure digital environments constrain the extent to which AI-generated capabilities are translated into green innovation and, ultimately, renewable energy outcomes. At a medium level of cybersecurity (CYBS = 0.511), the impact becomes stronger (Effect = 0.274, BootSE = 0.053, bootstrap CI [0.173, 0.380]). This means that as cybersecurity improves, the innovation-based transmission mechanism becomes more effective. In other words, stronger cybersecurity enables AI to generate greater benefits for the renewable energy transition through GTI. At the highest level of cybersecurity (CYBS = 0.761), the conditional indirect effect reaches its highest value (Effect = 0.359, BootSE = 0.071, bootstrap CI [0.228, 0.505]), confirming that the mediating role of GTI is most pronounced in countries with stronger cybersecurity readiness. The moderated mediation index is also positive and significant (Index = 0.342, BootSE = 0.082, bootstrap CI [0.199, 0.521]), confirming that cybersecurity significantly moderates AI’s indirect influence on the renewable energy transition via GTI. Specifically, a one-unit increase in cybersecurity is associated with a 0.342-unit increase in the indirect effect. Therefore, we conclude that AI’s indirect influence on renewable energy transition increases as cybersecurity improves. Substantively, this means that cybersecurity strengthens the green innovation pathway by protecting data integrity, improving the reliability of AI-enabled systems, reducing digital risks, and increasing trust in intelligent technologies used for environmental research, cleaner production, smart energy management, and renewable energy deployment. These findings support H6, which proposes that AI’s indirect influence on renewable energy transition through GTI is stronger when cybersecurity is higher. This result confirms the theoretical argument that AI-driven green innovation requires secure digital infrastructures, protected information flows, and reliable digital experimentation environments. It also aligns with studies showing that cybersecurity reinforces the productive use of AI in digital systems and supports innovation by reducing operational uncertainty and protecting strategic knowledge assets (Xu, 2025). In the context of renewable energy transition, the result also supports Ekechukwu and Simpa (2024), Abbasi (2025), and Anwar and Ali (2026), who emphasize that cybersecurity is essential for securing smart grids, protecting renewable energy investments, and ensuring the reliable operation of AI-based renewable energy networks. Overall, Table 11 confirms that AI makes a stronger contribution to the renewable energy transition through GTI when embedded in secure, trusted, and resilient cybersecurity environments.

TABLE 11. AI conditional indirect effects on renewable energy transition across different levels of CYBS.

CYBS, cybersecurity; Effect = conditional inAI’s direct effect on RET, through GTI., The moderated-mediation index assesses whether the indirect effect differs significantly across levels of cybersecurity.

After confirming cybersecurity’s conditional role in strengthening the first-stage pathway, the analysis turns to H7, which examines the second-stage moderated mediation effect. Specifically, this stage tests whether national ESG performance strengthens GTI’s impact on the renewable energy transition and, consequently, whether AI’s indirect effect through GTI becomes stronger under higher ESG conditions. Table 12 shows that when renewable energy transition is the dependent variable, national ESG performance has a positive and statistically significant effect (Coeff = 0.594, S.E. = 0.1673, p = 0.000), with a 95% confidence interval [0.264, 0.924]. More importantly, the interaction between GTI and ESG performance is positive and statistically significant (Coeff = 1.823, S.E. = 0.1288, p = 0.000), with a 95% confidence interval [1.569, 2.077]. This confirms that ESG performance significantly changes the strength of the association between GTI and renewable energy transition. Because the ESG moderator is evaluated around the 50th-percentile reference value used in Table 13, the conditional effect of GTI on RET is computed as 0.497 + 1.823 x (ESG - 0.420). Accordingly, the impact of GTI on renewable energy transition is not fixed; rather, it strengthens as ESG performance rises. This provides empirical support for H5, which proposes that ESG performance strengthens GTI’s positive contribution to the renewable energy transition. This interaction can be explained through several channels. The environmental dimension of ESG performance may strengthen GTI’s effect by increasing regulatory pressure, environmental accountability, and national commitment to low-carbon development. Under such conditions, green patents and environmental technologies are more likely to be connected to renewable energy deployment, cleaner production systems, and energy-efficiency improvements. The social dimension may also matter because renewable energy transition requires public acceptance, human capital, social inclusion, and access to services that facilitate the adoption of new energy technologies. Similarly, the governance dimension can improve policy credibility, regulatory quality, coordination, investor confidence, and the effectiveness of public-private partnerships. These conditions reduce uncertainty and make it easier for GTI to move from invention to implementation. This result is consistent with the literature review, which argues that ESG performance should not be treated merely as a background sustainability indicator but as an enabling context that determines whether innovation can translate into renewable energy outcomes. It supports Sun et al. (2025), who argue that ESG-related policies and disclosure practices improve market expectations and support renewable transformation. It also aligns with Gao et al. (2025) and Ma et al. (2025), who show that ESG performance promotes green innovation by reducing financing constraints and improving transformation efficiency.

TABLE 12. Conditional effect of GTI on renewable energy transition (second-stage moderated mediation effect).

AII = AI, index; GTI, green technological innovation; RET, renewable energy transition; ESG, environmental, social, and governance; MV, interaction between green technological innovation and ESG, performance. The ESG-conditioned GTI, effect is calculated as b1 + b3 (ESG, 0.420), where 0.420 is the 50th-percentile ESG, reference value.

TABLE 13. Conditional effects of GTI on renewable energy transition across different levels of ESG.

ESG, environmental, social, and governance; Effect = conditional effect of GTI, on RET, at the specified level of ESG, performance, calculated as b1 + b3 (ESG, 0.420).

Table 13; Figure 6 more clearly illustrate this moderating effect by showing the conditional effect of GTI on RET across different levels of ESG performance. The conditional effects are calculated from the coefficients in Table 12 using b1 + b3 (ESG - 0.420), where 0.420 is the 50th-percentile ESG reference value. When ESG performance is low (ESG = 0.320), GTI exerts a positive and statistically significant effect on renewable energy transition (Effect = 0.315, S.E. = 0.0230, p = 0.000), with a 95% confidence interval [0.270, 0.360]. This indicates that GTI still contributes to renewable energy transition even in countries with weaker ESG conditions, although the effect size remains relatively limited. At a medium level of ESG performance (ESG = 0.420), the effect becomes stronger (Effect = 0.497, S.E. = 0.0160, p = 0.000), with a 95% confidence interval [0.465, 0.529]. At the highest level of ESG performance (ESG = 0.574), the effect reaches its strongest value (Effect = 0.778, S.E. = 0.0210, p = 0.000), with a 95% confidence interval [0.737, 0.819]. These estimates reveal a clear increasing pattern: the effect increases from 0.315 at low ESG performance to 0.497 at medium ESG performance and 0.778 at high ESG performance. Figure 6 visually corroborates this pattern by indicating that the slope connecting GTI to renewable energy transition becomes steeper as ESG performance increases. Hence, countries with stronger ESG systems are better able to transform environmental technologies into renewable energy generation, cleaner energy infrastructure, and broader transition outcomes.

Table 14 presents the AI conditional indirect effects on RET through GTI at different ESG performance levels. The indirect effect is calculated as a1 x [b1 + b3 (ESG - 0.420)], using the AI-GTI path reported in Table 12 and the ESG-conditioned GTI-RET path reported in Table 13. The indirect impact is positive across all three ESG levels, and none of the bootstrap confidence intervals include zero. At a low level of ESG performance (ESG = 0.320), the conditional indirect effect is positive and significant (Effect = 0.105, BootSE = 0.037), with a bootstrap confidence interval [0.035, 0.181]. At the middle level of ESG performance (ESG = 0.420), the indirect impact increases to 0.166 (BootSE = 0.055), with a bootstrap confidence interval [0.056, 0.276]. At the highest level of ESG performance (ESG = 0.574), the indirect impact reaches its strongest value (Effect = 0.260, BootSE = 0.085), with a bootstrap confidence interval [0.090, 0.428]. This upward progression indicates that the GTI pathway becomes more effective as ESG performance improves. In other words, AI is more capable of generating renewable energy transition outcomes through GTI when the sustainability-oriented institutional environment is stronger.

TABLE 14. AI conditional indirect effects on renewable energy transition across different levels of ESG.

ESG, environmental, social, and governance; Effect = conditional indirect effect of AI, on RET, through GTI., The moderated-mediation index tests whether the indirect effect changes significantly across ESG, performance levels.

The formal test of moderated mediation further confirms that ESG performance significantly conditions the indirect pathway, as the index is positive and statistically significant (Index = 0.609, BootSE = 0.136), with a bootstrap confidence interval [0.287, 0.931]. The coefficient 0.609 means that a one-unit increase in the ESG index is associated with a 0.609-unit increase in AI’s indirect effect on renewable energy transition through GTI. Substantively, this result indicates that ESG performance strengthens the transmission mechanism through which AI contributes to the renewable energy transition via GTI. This occurs because ESG performance provides the broader environmental, social, and governance conditions needed to convert AI-enabled green innovation into practical renewable energy outcomes. The environmental dimension can increase pressure for clean energy deployment, strengthen climate-related accountability, and encourage the adoption of environmental technologies, thereby making GTI more likely to support renewable energy transition (Sun et al., 2025). The social dimension can improve public acceptance of renewable energy projects, support human capital development, and facilitate inclusive participation in the clean-energy transformation, thereby reducing resistance to the diffusion of renewable technologies (Taghizadeh-Hesary and Rasoulinezhad, 2025). The governance dimension can enhance regulatory quality, policy credibility, institutional coordination, and investor confidence, thereby facilitating the movement of green technologies from invention to commercialization and large-scale diffusion (Gao et al., 2025; Ma et al., 2025). Stronger ESG performance can also reduce financing constraints and improve expectations for low-carbon investment, enabling AI-driven green innovation to be more effectively translated into renewable energy infrastructure and deployment (Lu and Li, 2024). Therefore, the results in Table 14 support H7, confirming that AI contributes more to the renewable energy transition through GTI when national ESG performance is higher.

4.3 Robustness check using two-step system-GMM estimation approach

The two-step system-GMM estimation approach is used to complement Hayes’s PROCESS macro analysis by examining whether the main mediation, moderation, and conditional-process findings remain stable under an alternative dynamic panel-data strategy. Hayes’s PROCESS macro remains the primary framework because it tests direct effects, mediation effects, moderation effects, and conditional-process mechanisms within a coherent conditional-process structure. However, because the present study uses country-year panel data for emerging economies, additional econometric issues may arise, particularly dynamic persistence, unobserved heterogeneity across countries, simultaneous determination, and reverse-causality concerns, heteroskedasticity, and serial correlation. System GMM is appropriate because it incorporates lagged dependent variables, combines equations in levels and first differences, and uses internal instruments based on lagged variables. This helps reduce potential bias arising from the possibility that artificial intelligence, green technological innovation, cybersecurity, ESG performance, and renewable energy transition may influence each other over time. Findings reported in Tables 15–18 provide evidence supporting the appropriateness of the dynamic model specification. The coefficients of the lagged dependent variables are positive and statistically significant at conventional levels across the renewable energy transition and green technological innovation equations, indicating that both processes are persistent over time. The coefficient of RET (t−1) is positive and significant in statistical terms in both the total-effect model and the mediation model, and the coefficient of GTI (t−1) is also positive and significant in statistical terms at conventional levels in the mediator equation, suggesting that previous levels of renewable energy transition and green technological innovation strongly shape current outcomes, which is consistent with the cumulative nature of energy transformation and innovation development. The diagnostic tests further support the validity of the system-GMM estimates. AR (1) is significant, whereas AR (2) is not significant, indicating that the models are not affected by second-order serial correlation. Moreover, the Hansen tests provide no evidence against instrument validity, while the Wald tests demonstrate that the model specifications are jointly statistically significant. Therefore, System GMM provides a rigorous robustness assessment that complements the Hayes PROCESS analysis by showing that the hypothesized relationships remain valid even after controlling for relevant dynamic panel-data effects.

TABLE 15. Two-step system-GMM estimates of the direct and mediation effects of artificial intelligence on renewable energy transition.

RET, renewable energy transition; GTI, green technological innovation; AI, artificial intelligence; R&D = research and development. Standard errors appear in parentheses. The estimations are based on two-step System GMM., to limit instrument proliferation, the instrument matrix is collapsed and the lag depth is restricted. The specifications include country and year effects. AR (1), AR (2), Hansen, and Wald test p-values are reported to assess first-order serial correlation, second-order serial correlation, instrument validity, and joint model significance, respectively. *, , and * indicate significance at the 1%, 5%, and 10% levels, respectively.

The results in Table 15 first confirm the positive direct contribution of artificial intelligence to renewable energy transition. In the total-effect model, the coefficient on artificial intelligence is positive and significant (0.184), indicating that stronger national AI capability contributes to a stronger renewable energy transition after accounting for past renewable energy transition, country effects, year effects, and potential endogeneity. This finding supports the expectation that AI can accelerate renewable energy transition by improving energy-system forecasting, storage management, dispatch optimization, predictive maintenance, grid reliability, and data-driven decision-making. In substantive terms, AI helps renewable energy systems become more adaptable and operationally efficient, particularly in contexts where renewable sources such as solar and wind create forecasting uncertainty, intermittency, and supply–demand balancing challenges. This result aligns with Rebelatto et al. (2024), Ukoba et al. (2024), and Zhao et al. (2024), who emphasize AI’s role in renewable-energy forecasting, smart-grid management, storage optimization, and system resilience. It also aligns with Fang et al. (2025), Wang (2025), and Zhang et al. (2024), who show that AI contributes to renewable energy development and more intelligent energy transition processes by strengthening the technical and operational foundations of cleaner energy systems. The significance of the AI coefficient under System GMM is important because it indicates that the positive AI–renewable energy transition relationship is not only a static association captured by the PROCESS macro, but also a dynamic relationship that remains robust when persistence and possible reverse causality are considered. Thus, the evidence confirms that AI should be understood not merely as an isolated technical instrument, but rather as a broader macro-level technological capability that supports the structural transformation of energy systems.

The mediation results show that green technological innovation is an important channel through which artificial intelligence contributes to renewable energy transitionIn Table 15 AI is positively and significantly associated with green technological innovation in the mediator Equation (0.173), confirming that AI strengthens environmentally oriented innovation capacity. Green technological innovation, in turn, exerts a positive effect that is statistically significant on renewable energy transition in the outcome Equation (0.154). These two results confirm the two essential paths of the mediation mechanism. Table 16 summarizes this pathway by reporting a positive indirect effect of 0.027, calculated as the product of the AI–GTI path and the GTI–RET path. The mediation is partial because artificial intelligence’s direct effect on renewable energy transition remains statistically significant after green technological innovation is introduced into the model. However, the AI coefficient declines from 0.184* in the total-effect model to 0.118 in the mediation model, indicating that part of AI’s contribution to renewable energy transition operates through green technological innovation, while another part remains direct. This result is theoretically meaningful because AI supports renewable energy transition through two interconnected pathways. The first is a direct energy-system mechanism, where AI expands forecasting, grid intelligence, storage planning, monitoring, and operational efficiency. The second is an innovation mechanism, where AI enhances data analytics, intelligent automation, knowledge-processing capacity, and technological experimentation, thereby stimulating green technological innovation. This argument is consistent with Zhou et al. (2024), who conclude that AI may enhance production efficiency, reduce resource waste, and support cleaner technological solutions. It also supports Khan et al. (2022), Yin H. T. et al. (2023), Behera et al. (2025), and Radulescu et al. (2026), who highlight the importance of green technological innovation in renewable energy development, cleaner energy restructuring, and translating AI capabilities into sustainability-oriented outcomes.

TABLE 16. Two-step system-GMM estimates of the indirect effect of artificial intelligence on renewable energy transition through green technological innovation.

RET, renewable energy transition; GTI, green technological innovation; AI, artificial intelligence. The a-path refers to the effect of AI, on GTI, while the b-path refers to the effect of GTI, on RET, after controlling for AI., The indirect effect equals the product of the a-path and b-path coefficients. The estimations are based on two-step System GMM., to limit instrument proliferation, the instrument matrix is collapsed and the lag depth is restricted. AR (1), AR (2), Hansen, and Wald test p-values are reported to assess serial correlation, instrument validity, and joint model significance. *** indicates the significance at the 1% level.

The first moderation results in Table 17 show that cybersecurity strengthens the relationship between artificial intelligence and green technological innovation. In the green technological innovation equation, AI remains positive and significant at conventional levels (0.166), while cybersecurity also has a positive and significant coefficient (0.137). More importantly, the interaction term AI × CYBS is positive and statistically significant (0.064), indicating that AI’s effect on green technological innovation strengthens as cybersecurity rises. This finding accords with the argument that AI-driven green innovation depends on secure digital environments, reliable data systems, protected knowledge assets, and trusted information flows. AI-based green innovation usually requires large datasets, interconnected digital platforms, automated routines, algorithmic experimentation, and continuous knowledge exchange. When cybersecurity is weak, these processes may be exposed to cyberattacks, data breaches, system disruptions, and manipulation of strategic information, which can reduce trust in AI applications and weaken the willingness of firms and institutions to use AI for environmentally oriented innovation. By contrast, stronger cybersecurity improves the reliability of AI systems, protects data integrity, strengthens digital trust, and supports the continuity of innovation processes. This confirms Alrfai et al. (2023), who show that cybersecurity strengthens the value of AI in digital systems, and Xu (2025) and Singh et al. (2025), who emphasize that cybersecurity governance and resilience support innovation ecosystems. The finding also aligns with Kshetri (2017), who identifies confidentiality, integrity, and availability as the core dimensions of information security, and with Chen et al. (2025), Saveljeva et al. (2025), and Alshammari and Al-Mamary (2025), who link digital trust and cybersecurity awareness to openness, adoption, and innovation. Therefore, the System GMM results show that AI is more effective in generating green technological innovation when it operates within a secure and trusted digital environment.

TABLE 17. Two-step system-GMM estimates for the moderating roles of cybersecurity and national ESG performance.

CYBS, cybersecurity; ESG, environmental, social, and governance; RET, renewable energy transition; GTI, green technological innovation; AI, artificial intelligence; R&D = research and development. Standard errors appear in parentheses. The estimations are based on two-step System GMM., The interaction term AI × CYBS, tests whether cybersecurity strengthens the AI–GTI, pathway and the direct AI–RET, pathway. The interaction term GTI × ESG, tests whether national ESG, performance strengthens the GTI–RET, pathway. To limit instrument proliferation, the instrument matrix is collapsed, and the lag depth is restricted. The specifications include country and year effects. AR (1), AR (2), Hansen, and Wald test p-values are reported to assess serial correlation, instrument validity, and joint model significance. *, , and * indicate significance at the 1%, 5%, and 10% levels, respectively. *, , and * indicate significance at the 1%, 5%, and 10% levels, respectively.

The second cybersecurity-related result shows that cybersecurity also strengthens the direct contribution of artificial intelligence to renewable energy transition. In Table 17, the renewable energy transition equation reports a positive and significant AI coefficient at conventional levels (0.149), while cybersecurity also has a positive and significant effect (0.124). The interaction term AI × CYBS is positive and significant (0.057), showing that the transition-enhancing effect of AI becomes stronger when cybersecurity readiness is higher. This finding is highly relevant because renewable energy systems are increasingly digitalized, interconnected, and dependent on AI-enabled tools. Smart grids, storage optimization systems, automated monitoring, predictive maintenance, energy forecasting, and real-time dispatch all require secure digital infrastructures. If these systems are exposed to cyber threats, data manipulation, or operational disruptions, the ability of AI to support renewable energy transition may be weakened. Strong cybersecurity, by contrast, protects digital energy infrastructure, reduces operational risks, maintains system continuity, and increases confidence in AI-supported renewable applications. This explains why cybersecurity amplifies the direct AI–renewable energy transition relationship. This result aligns with Ekechukwu and Simpa (2024), who argue that cybersecurity is strategically important for protecting renewable energy investment and sustaining green transformation. It also supports Abbasi (2025), who shows that energy transition increasingly depends on cybersecurity strategies in smart grids and digitally managed systems, and Anwar and Ali (2026), who emphasize that AI-based renewable energy networks require secure, resilient, and trusted digital architectures. Accordingly, cybersecurity plays a dual enabling role: it strengthens AI’s contribution to green technological innovation and also enhances AI’s direct contribution to renewable energy transition.

Table 17 also confirms the moderating role of national ESG performance. In the model examining the green technological innovation–renewable energy transition relationship, green technological innovation exerts a positive effect that is statistically significant on renewable energy transition (0.168), while national ESG performance also has a positive and statistically significant coefficient at conventional levels (0.151). The interaction term GTI × ESG is positive and significant (0.069), confirming that ESG performance strengthens the effect of green technological innovation on renewable energy transition. This result indicates that green technological innovation makes a stronger contribution to renewable energy transition when the national ESG environment is more favorable. The finding is consistent with the theoretical argument that innovation alone does not automatically lead to renewable energy transition unless it is supported by institutional, financial, and sustainability-oriented conditions that facilitate commercialization, diffusion, coordination, and implementation. Strong ESG performance can improve policy credibility, reduce financing constraints, strengthen environmental commitment, improve governance capacity, and create better conditions for scaling green technologies. This is consistent with Sun et al. (2025), who argue that ESG-related policies and disclosure practices improve market expectations and support renewable transformation. It also aligns with Gao et al. (2025) and Ma et al. (2025), who demonstrate that ESG performance fosters green innovation by alleviating financial constraints and enhancing the efficiency of technological transformation. Similarly, Lu and Li (2024) link ESG ratings to stronger low-carbon investment in renewable energy firms, while Taghizadeh-Hesary and Rasoulinezhad (2025) show that ESG investment, together with smart technologies, can accelerate renewable energy transition. Akadiri and Özkan (2026) further emphasize the importance of ESG-related conditions for clean energy markets and investment dynamics. Therefore, the System GMM results confirm that ESG performance operates as an enabling institutional environment that strengthens the conversion of green technological innovation into renewable energy transition outcomes.

The conditional indirect logic summarized in Table 18 further reinforces the robustness of the full conceptual framework. The results show that the indirect pathway from artificial intelligence to renewable energy transition through green technological innovation becomes stronger under favorable cybersecurity and ESG conditions. The cybersecurity-related mechanism is supported because AI significantly increases green technological innovation, green technological innovation significantly increases renewable energy transition, and the AI × CYBS interaction significantly strengthens the AI–GTI path. This means that cybersecurity reinforces the first stage of the innovation pathway by making AI-based experimentation, data processing, and knowledge creation more secure and reliable. The ESG-related mechanism is supported because AI significantly increases green technological innovation, green technological innovation significantly increases renewable energy transition, and the GTI × ESG interaction significantly strengthens the GTI–RET path. This means that ESG performance reinforces the second stage of the pathway by creating a more supportive environment for financing, implementing, and scaling green technologies. These results are consistent with the two-stage conditional process logic developed in the main article, where cybersecurity shapes the extent to which AI can drive green technological progress, while ESG performance shapes the extent to which that progress can be transformed into renewable energy transition. This evidence also supports the argument that AI-enabled renewable energy transition is not driven by AI capability alone. Rather, it depends on the combination of digital intelligence, green innovation capacity, cyber resilience, and ESG-oriented institutional readiness. In emerging economies, where digital infrastructure, innovation systems, cybersecurity readiness, and sustainability governance conditions vary substantially, this conditional perspective is particularly important because it explains why similar AI capabilities may generate stronger renewable transition outcomes in some national contexts than in others.

TABLE 18. Summary of hypotheses tested using the two-step System GMM robustness framework.

H6 and H7 are deduced from the significance and direction of the component paths rather than recalculated as bootstrapped conditional indirect effects. The decision column indicates whether each hypothesis is supported, partially supported, or not supported under the two-step System GMM, robustness framework. *, , and * indicate significance at the 1%, 5%, and 10% levels, respectively.

Overall, the System GMM results in Tables 15–18 strongly reinforce the main findings obtained from Hayes’s PROCESS macro. Both approaches lead to the same substantive conclusion: artificial intelligence accelerates renewable energy transition directly and indirectly through green technological innovation, while cybersecurity and national ESG performance strengthen the conditions under which this pathway becomes more effective. The PROCESS macro provides the primary conditional-process evidence by estimating direct, mediation, moderation, and conditional indirect effects within the proposed theoretical framework. The System GMM analysis complements this evidence by showing that the same relationships hold after introducing dynamic persistence, internal instruments, country effects, year effects, and diagnostic tests for serial correlation and instrument validity. The consistency between the two approaches strengthens confidence in the empirical results. The positive direct AI effect confirms that artificial intelligence contributes to renewable energy transition beyond its innovation channel. The partial mediation result confirms that green technological innovation transmits an important part of AI’s effect, but does not fully absorb it. The positive cybersecurity interaction shows that secure digital environments are essential for translating AI into green innovation and renewable energy outcomes. The positive ESG interaction shows that sustainability-oriented institutional conditions are necessary for converting green technological innovation into actual renewable energy progress. Taken together, the robustness results provide a coherent empirical message: emerging economies can gain greater renewable energy benefits from AI when AI deployment is combined with green technological innovation, cybersecurity readiness, and ESG-oriented institutional development.

5 Conclusion and implications

This study investigates whether, how, and under what national circumstances AI is associated with the transition toward renewable energy across 23 emerging economies over 2015–2023. It responds to the growing need to understand AI beyond its conventional treatment as an engineering tool for forecasting renewable output, optimizing electricity dispatch and storage, supporting predictive maintenance, and coordinating smart grids. Instead, the study conceptualized AI as a multidimensional national capability embedded in technological invention, embodied automation, scientific knowledge production, and commercialization finance. This broader view matters because a country’s ability to benefit from AI is determined not solely by possessing individual digital technologies but also by the strength of the wider ecosystem through which knowledge is generated, financed, deployed, and transformed into practical applications. GTI was therefore positioned as the central mechanism linking national AI capability to environmentally oriented technologies and renewable energy outcomes. Cybersecurity was incorporated as a first-stage boundary condition affecting the AI–GTI relationship and as a moderator of the direct AI-renewable energy transition association, whereas national ESG performance was introduced as a second-stage condition shaping the extent to which GTI is linked to renewable energy transition. This integrated framework is especially relevant to emerging economies, where growing energy demand and continued fossil-fuel dependence coexist with uneven digital readiness, heterogeneous innovation systems, cybersecurity vulnerabilities, financing constraints, and varying sustainability-oriented institutional quality. Methodologically, Hayes’s PROCESS macro was retained as the primary empirical framework because it allows direct effects, mediation, first-stage moderation, direct moderation, second-stage moderation, and conditional indirect effects to be assessed within a coherent conditional process structure using bootstrapped confidence intervals. Furthermore, the study incorporates the two-step system-GMM estimation approach as an additional robustness technique to examine whether the mediation and moderated mediation findings identified through the PROCESS analysis remain consistent. This alternative dynamic panel methodology is particularly suitable for accounting for persistent relationships over time, unobserved cross-country differences, simultaneous determination, bidirectional causality, and possible endogeneity among the variables. This combined strategy preserves the theoretical clarity of the conditional process model while testing whether its central relationships remain valid under a panel estimator designed to address dynamic and endogeneity-related concerns. The study consequently contributes to the literature by reconceptualizing AI as a national transformative capability, identifying GTI as the mechanism connecting AI to renewable energy transition, and establishing cybersecurity and ESG performance as distinct enabling conditions operating at different stages of this pathway. By combining mechanism-based analysis with dynamic panel validation, the study also offers a more methodologically coherent basis for examining complex technology–sustainability relationships in country-level panel settings.

The empirical results support the proposed conceptual framework and yield several important conclusions. First, AI is positively associated with renewable energy transition, suggesting that stronger national AI capability is linked to greater capacity in emerging economies to expand renewable-energy generation and manage it effectively. This association is consistent with AI’s capacity to enhance forecasting, reduce uncertainty associated with intermittent renewable sources, optimize storage and dispatch, improve predictive maintenance, coordinate distributed resources, and strengthen smart-grid responsiveness. Second, GTI significantly and partially mediates this relationship. AI is therefore associated with renewable energy transition through two complementary routes: a direct operational pathway associated with improvements in the efficiency, flexibility, and reliability of energy systems and an indirect innovation pathway associated with stronger creation and diffusion of cleaner technologies. This finding suggests that AI’s environmental relevance extends beyond managing existing infrastructure to supporting research, green patenting, cleaner product and process development, resource-efficiency solutions, and technologies that make renewable energy more viable and scalable. Third, cybersecurity conditions the AI–GTI relationship and is associated with a stronger direct AI–renewable energy transition relationship. Secure data systems, protected knowledge assets, trustworthy digital platforms, and resilient infrastructure may allow firms, researchers, grid operators, and public institutions to use AI more confidently and continuously. Cybersecurity is also associated with a stronger indirect AI–GTI–renewable energy transition pathway, indicating that cyber resilience is a productive enabling capability rather than merely a defensive safeguard. Fourth, national ESG performance is associated with a stronger GTI–renewable energy transition relationship and a stronger indirect AI–GTI–renewable energy transition pathway. Green technologies are therefore more likely to move from invention to commercialization and large-scale deployment when environmental commitments are credible, social conditions support transition, governance quality is strong, and sustainable financing is accessible. The complementary two-step System GMM findings support the direct, mediating, and moderating relationships identified through PROCESS after addressing dynamic dependence, latent country-level heterogeneity, reverse causation, and potential endogeneity. Taken together, these results suggest a sequential pattern: AI is associated with technological and informational capacity; GTI helps translate that capacity into environmentally oriented solutions; cybersecurity supports the digital processes through which innovation is generated and deployed; and ESG performance provides institutional conditions that help those solutions reach the energy system. Deficiencies at any stage may therefore weaken the final transition outcome, even when national AI capability is relatively advanced. Overall, AI is neither an isolated technological input nor an automatic environmental solution; its estimated contribution depends on an integrated national ecosystem that enables green technologies to be developed, financed, diffused, and implemented securely.

These conclusions generate important policy and managerial implications for emerging economies seeking to use digital transformation as a lever for low-carbon energy development. First, policymakers should incorporate AI explicitly into national renewable energy strategies instead of pursuing digital transformation and energy-transition objectives through disconnected policy frameworks. Energy plans should identify concrete applications in renewable-generation forecasting, grid balancing, storage scheduling, demand-response management, predictive maintenance, infrastructure monitoring, and coordinating distributed energy resources. This requires investment in smart meters, sensors, interoperable energy-data platforms, secure communication networks, cloud and edge computing, and specialized human capital, together with transparent standards for data quality, algorithmic accountability, system reliability, and human oversight. Second, GTI’s mediating role suggests that AI policy should be coordinated with green innovation policy. Governments should strengthen clean-energy R&D, environmental patent support, university–industry collaboration, interdisciplinary laboratories, innovation clusters, demonstration projects, technology incubators, and commercialization programs. Targeted grants, tax incentives, green public procurement, accelerated patent procedures, and access to testing facilities can help firms apply AI to renewable storage, smart grids, cleaner production, energy efficiency, resource assessment, and environmental monitoring. Support should particularly target smaller firms and research teams with promising technologies but lacking the finance, infrastructure, or regulatory expertise needed for commercialization. Third, cybersecurity should be embedded in renewable energy systems from the design stage. Regulatory frameworks should impose secure-by-design requirements, cyber-risk assessments, encryption, access controls, continuous monitoring, vulnerability testing, independent audits, incident-reporting procedures, backup arrangements, and recovery protocols. Energy ministries, regulators, cybersecurity agencies, grid operators, technology suppliers, and research institutions should establish coordinated information-sharing and response mechanisms, while education and training policies should address shortages of specialists who can protect AI-enabled energy networks. Fourth, stronger ESG frameworks are required to convert GTI into practical transition outcomes. Environmental policy should set credible renewable targets, maintain consistent regulation, ensure transparent permitting, and enforce compliance effectively. Social policy should promote public participation, workforce reskilling, affordability, access to energy services, and a fair distribution of transition benefits. Governance reforms should improve policy continuity, procurement transparency, institutional accountability, regulatory coordination, and investor protection. Fifth, sustainable finance should be aligned with the AI–GTI pathway. Green bonds, ESG-linked lending, blended finance, concessional funding, credit guarantees, and development-bank participation can reduce financing constraints during research, demonstration, and commercialization. Credible green taxonomies and disclosure requirements are also necessary to direct capital toward genuine low-carbon technologies and limit greenwashing. Finally, policy design should reflect each country’s weakest capability: economies with advanced AI but weak cybersecurity should prioritize cyber resilience; those with strong research but limited commercialization should expand venture finance and market-creation mechanisms; and those with substantial GTI but weak deployment should improve ESG institutions, permitting, financing, and implementation capacity. Governments should also monitor whether AI-related public expenditure produces measurable innovation, security, and renewable-energy outcomes, and adjust policies when expected benefits do not materialize. International organizations can support this integrated agenda through technology transfer, shared research infrastructure, common cybersecurity standards, professional training, and climate finance focused on domestic capability formation rather than long-term technological dependence.

Several limitations should be acknowledged, while also providing opportunities for further investigation in future studies. The composite AI Index may conceal heterogeneous effects among AI-related patents, industrial robots, scholarly publications, and venture capital investment; future studies should therefore examine these dimensions separately. The sovereign ESG index may also obscure differences across its environmental, social, and governance components, while renewable energy share captures one aspect of energy transition. Country-specific interpolation may introduce measurement uncertainty, and the 23-country sample limits wider generalization. Future research should use alternative indicators, disaggregated ESG measures, broader samples, and complementary dynamic panel, instrumental-variable, or longitudinal methods to strengthen inference about these relationships.

Correction note

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Supplementary material

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Footnotes

Edited by: Richard Damania, World Bank, United States

Reviewed by: Anas AlSobeh, Utah Valley University, United States

Yuanren Zhou, Central University of Finance and Economics, China