Exploring the Role of Artificial Intelligence in Achieving a Net Zero Carbon Economy in Emerging Economies: A Combination of PLS-SEM and fsQCA Approaches to Digital Inclusion and Climate Resilience

Abstract

In this paper, we examine the role of artificial intelligence (AI) in sovereignty and carbon neutrality, emphasizing digital inclusion and climate-resilient AI strategies for emerging markets. Considering the previous studies on AI for carbon neutrality and digital inclusion for climate research along with technology policy frameworks as a guide, this paper undertakes Partial Least Squares Structural Equation Modelling (PLS-SEM) with AI strategies and carbon neutrality outcomes. At the same time, fuzzy-set Qualitative Comparative Analysis (fsQCA) is used to reveal different configurations leading to achieving climate resilience. The model covers various aspects of AI-enabled policy, including technology adoption, policy frameworks, digital literacy, and public engagement. Survey data were collected from key stakeholders in climate policy, technology sectors, and local communities using a structured survey to understand their attitudes towards negative emissions technologies from prominent experts in emerging countries like Vietnam, Italy, Malaysia, and Greece. PLS-SEM results reveal the importance of AI in developing carbon neutrality, a critical AI strategic dimension (Data analytics capability and policy support). Some aspects of the fsQCA findings present heterogeneous outcomes, highlighting complex combinations of digital inclusion, AI adoption, and climate resilience which are industry-specific. This study would further enrich the literature concerning climate strategies by exploring AI, digital inclusion, and carbon neutrality interactions. Theoretically, practical and enriching suggestions for future research are derived to help AI intelligence infuse sustainable climate actions.

1. Introduction

In the face of escalating climate change challenges, carbon neutrality has become a critical priority for governments, businesses, and communities worldwide. As emerging economies implement increasingly stringent climate protection measures amid rapid industrialization, artificial intelligence (AI) emerges as a potential game-changer in achieving sustainability goals. This study examines the complex interplay between AI adoption, digital inclusion, and climate resilience in emerging economies, focusing on how AI-driven strategies can facilitate the transition to a net zero carbon economy [1]. Intelligent management can provide novel climate resilience solutions like predictive analytics, optimization for renewable energy, and carbon control [2,3]. As AI has shown promise for emission impacts in the war against climate change, a valid question is, to what extent can AI prove itself as a breakthrough determinant of global carbon neutrality? As such, it is essential to consider how AI strategies can assist in achieving carbon neutrality with an added value for fostering digital inclusion and climate resilience in emerging economies.

AI-powered climate solutions are not just new technological advancements; they are also the most efficient. The level of digital inclusion and public engagement in diverse socio-economic contexts also influences it. Equitable access to digital technologies will be critical to ensuring that AI-driven climate moves can scale up effectively [4]. In some cases, there may not be the digital literacy nor the infrastructure necessary to use AI, such as in developing nations that face essential challenges for carbon neutrality.

Emerging economies face distinct challenges in adopting AI for climate resilience that set them apart from developed nations. While developed economies struggle primarily with legacy system integration, emerging markets contend with fundamental infrastructure gaps, limited digital literacy, and resource constraints. These nations must simultaneously build basic digital capabilities while implementing advanced AI solutions, often under severe climate vulnerability pressures. Additionally, they face unique regulatory uncertainties and institutional capacity limitations while balancing rapid economic growth demands with sustainability goals—a challenge less acute in developed economies where growth trajectories are more stable.

Our research reveals several significant findings with important policy implications:

• Multidimensional AI Impact: Through PLS-SEM analysis, we found that all dimensions of AI commitment (AI capabilities, digital information, digital skills, AI-enabled climate solutions, and climate policy readiness) significantly influence technological and infrastructure transformation. However, only specific dimensions directly affect socio-economic and policy adaptation.
• Multiple Pathways: The fsQCA results identify multiple sufficient configurations for achieving high levels of technological transformation and policy adaptation, suggesting that various combinations of AI commitment dimensions can lead to successful outcomes in different contexts.
• Critical Role of Digital Skills: Both analyses highlight the fundamental importance of digital skills and climate policy readiness across multiple successful pathways to sustainability, emphasizing the need for comprehensive capacity-building programs.
• Context-Specific Solutions: Our findings demonstrate that the effectiveness of AI-driven climate solutions varies across different economic and institutional contexts, necessitating tailored approaches rather than one-size-fits-all solutions.

These findings suggest several key policy implications:

• Investment in comprehensive AI strategies should integrate capability building, infrastructure development, and policy frameworks.
• Digital skills development programs should be prioritized as a foundation for successful AI adoption.
• Climate policy frameworks should be flexible and anticipatory to accommodate rapid technological change.
• Public–private partnerships should be fostered to create collaborative ecosystems for AI-driven climate solutions.

This study employs a mixed-methods approach combining two sophisticated analytical techniques:

• Partial Least Squares Structural Equation Modeling (PLS-SEM): Used to examine the direct effects of AI commitment dimensions on technological transformation and policy adaptation outcomes. This method is particularly suitable for exploring complex relationships in emerging research areas.
• Fuzzy-set Qualitative Comparative Analysis (fsQCA): Applied to identify multiple configurations of conditions leading to successful outcomes, acknowledging the complex, non-linear nature of sustainability transitions.

While existing research has explored AI’s potential in climate mitigation and its role in bridging the digital divide [5,6], a critical gap exists in understanding how these two domains intersect. Specifically, we lack comprehensive studies examining how AI applications can simultaneously address climate resilience and digital inclusion across developing and developed regions. This gap is particularly significant as AI-driven climate solutions require active community participation. As highlighted by recent studies [7,8], there is an urgent need to investigate how AI solutions can be co-designed with communities to ensure they not only address climate challenges but also promote digital inclusion, thereby enabling communities to become both active participants in and primary beneficiaries of climate action initiatives.

Moreover, the proposed application of PLS-SEM and fsQCA approaches to explore this linkage opens a way for new methodological practices in psychology-by-effect relationship research, leading to greater insight into the intricate interplay among these significant constructs. This work can tell us about the synergies and trade-offs between AI, digital inclusion, and climate resilience, which are necessary to understand strategic and policy approaches to ensure that new technological advances are realized with a positive effect on this more equitable and sustainable future.

This study makes several unique contributions to the literature:

• Develops and tests a multifaceted construct of AI commitment in climate action.
• Introduces a configurational perspective to AI and sustainability research.
• Bridges technology acceptance and sustainability theories.
• Provides empirical evidence from emerging economies.
• Offers practical insights for policymakers and practitioners.

This research examines how AI can support carbon neutrality while promoting digital inclusion and climate resilience. It provides valuable insights for policymakers, practitioners, and researchers seeking to develop robust, equitable, and appropriate AI-enhanced climate solutions. The findings are particularly relevant for emerging economies striving to balance rapid development with sustainability goals.

The paper is organized as follows: Section 2 presents a comprehensive literature review. Section 3 develops the theoretical framework and hypotheses. Section 4 details the methodology, including research design. Section 5 documents the data collection process. Section 6 outlines the data analysis techniques used. Section 7 presents the empirical data. Section 8 communicates the results and key findings. Section 9 discusses the theoretical implications. Section 10 explores the practical implications. Section 11 acknowledges limitations and provides recommendations for future research. Section 12 concludes the study.

2. Literature Review

In this study, the authors conducted a literature review to collate what is known across a wide range of areas vital to allowing artificial intelligence (AI)’s role in supporting carbon neutrality and climate resilience, emphasizing those from emerging economies, including inclusive digitalization. The review includes AI application in climate change mitigation and adaptation, digital inclusion as an enabler for climate action, regional implications of AI adoption, theoretical frameworks, research methodologies used to explore pathways for how data can accelerate technical change in the field of climate adaption, and policy considerations that may enable the appropriate use of data to prepare for future large-scale impacts.

While developed economies leverage AI to optimize existing climate infrastructure and refine carbon reduction strategies, emerging economies face fundamentally different challenges in AI deployment [6,7]. Existing research indicates that developed nations focus on enhancing the efficiency of established systems, whereas emerging economies must simultaneously build basic infrastructure while implementing advanced AI solutions. This disparity manifests in policy approaches, with developed nations emphasizing regulatory refinement while emerging economies need help with fundamental policy framework development [8]. Understanding these distinctions is crucial for bridging the technological divide and ensuring effective knowledge transfer between economic contexts.

2.1. Artificial Intelligence and Machine Learning in Helping to Address Climate Change

Recent research has recognized AI technologies’ potential to increase the pace of carbon neutrality transitions and bolster climate resilience [9]. AI, with its machine learning and deep learning capacities, is used for predictive climate modelling, optimizing renewable energy plants, managing smart grids, and improving carbon capture and storage methods [10].

Machine learning algorithms can improve climate analysis, notably in remote sensing detection and early warning of extreme weather events [11]. This highlights the imperative of using available data and simulations to advance our understanding of the climate system. Machine learning and artificial intelligence can improve research on climate change and our preparedness capacities by helping interpret remote sensing data and providing more advanced warnings about upcoming weather features. AI could lead to more accurate energy supply and demand predictions, increased energy efficiency in buildings, and fine-tuning crop yield forecasting for climate-resilient agriculture, among other things [12].

Recognizing the promise of artificial intelligence and machine learning nexus AI-ML for urban climate change adaptation and sustainable development is crucial. The application of AI and ML in urban climate change adaptation from a global perspective underscores the urgent need for place-based adaptations designed and planned to facilitate AI-ML integration into climate adaptation [13]. It also underscores the necessity for international cooperation in this field, as climate change is a global issue that demands immediate and collaborative solutions. While AI-ML can, in principle, deliver a step change that catalyzes urban climate change adaptation and sustainable development, the success of such strategies must be embedded into context-specific and participatory frameworks [14].

2.2. The Potential of AI-ML in Urban Climate Change Adoption and Sustainable Development

The role of artificial intelligence in climate change adaptation is not just a possibility but a necessity. It represents the gap at which AI has been used for climate change adaptation and focuses on how AI can support such complex decisions as part of adaptation [15]. Different AI approaches, such as supervised learning reinforcement learning, to inform adaptation measures can help us to make better decisions in difficult climate change adaptation choices and implementation based on more accurate information, while also creating effective synergies and trade-offs for diversified groups [16].

Machine learning for climate change has a variety of potential applications. Some of the most high-impact places where machine learning could be applied to fill current gaps are smart grids and forecasting for disaster management, supporting that the ML community should join in on climate change efforts [17]. ML can be used to reduce greenhouse gas emissions with smart grids and predict disasters. ML can be used for climate change and risk measurement. Assorted ML algorithms, e.g., Decision Tree (DT) and Random Forest (RF), are used in risk assessment for floods, landslides, or challenges in accessing remote sensing data. Although Decision Trees, Random Forests, and Artificial Neural Networks have proved to be efficient machine learning algorithms in risk analysis for climate change, these analyses still lack research on future scenarios or cascading hazards [18].

AI is incorporated in climate knowledge infrastructures, decision-making system synergies, and trade-offs with environmental conservation. AI shapes conservation and challenges our ethics and politics [19]. The impact of artificial intelligence in ecological conservation is reinventing data collection, policy enforcement, and decision-making. Still, it might also lead to ethical dilemmas and redistribution of power from some stakeholders to others [20].

2.3. AI for Carbon Neutrality in Diverse Regional Contexts

AI’s role in leapfrogging sustainable development in emerging economies is a significant aspect of this paper. AI opportunities for climate resilience and carbon neutrality are not one-size-fits-all; instead, they emerge with prominence in different regional contexts, highlighting the complexity and diversity of the issue [21]. AI helps reduce carbon emissions and is becoming an excellent tool for carbon neutrality with green technology advancement, which optimizes industrial output. In emerging economies, AI is identified as a technology that could support leapfrogging sustainable development with climate challenges [22]. For instance, researchers analyzed the role of AI applications in renewable integration and industrial energy efficiency in China. These effects depend on when, where, and how many resources the regions possess. Through the advancement of AI, energy structure and technological innovation have been improved, which has been shown to reduce carbon emissions significantly, especially in China [23].

The potential of AI to transform the energy sector and accelerate carbon neutrality is a crucial aspect of this paper. AI’s role in optimizing processes and services at their core, thereby reducing emissions, is significant. This perspective also guides future research and technological innovation. AI’s application in smart energy supply and consumption and its contribution to cybersecurity for green energy output is noteworthy. The potential for AI to revolutionize the energy sector and expedite carbon neutrality through more innovative, analytics-based decisions is a driving force behind AI-driven business model innovations. Importantly, this AI development could cost-effectively enable carbon neutrality in small medium enterprises SMEs, highlighting the role of enabling technologies and strategies in adopting energy-efficient practices and transitioning to renewable energy ideas [24]. Green AI, or environmentally sustainable AI, explores ways to reduce the carbon footprint, including tracking, hyperparameter tuning, and benchmarking, while highlighting the growth phase of Green AI efforts [25]. AI-based strategies such as the STIRPAT model, GRU neural network, and transfer learning are used to analyze carbon emissions and become carbon neutral. AI-powered analysis of time-series data with LSTM models is used to achieve maximum reduction in carbon emissions using an end-to-end architectural framework [26,27,28]. In Europe, the European Green Deal and its ambitious plans for digital transformation have prompted efforts to include AI within climate action strategies. European countries use AI to promote their pathway to carbon neutrality, and policy frameworks and public–private partnerships (PPPs) have been discussed [28].

2.4. Literature Gaps

The prevailing literature on artificial intelligence (AI) and digital tools in mitigating carbon neutrality (CN) entails a lack of synthesis about its impact, including how it varies across economic contexts and geographic boundaries. There are research gaps in multifaceted areas, such as the following:

• Exploring the potential of AI: While a growing body of work examines corporate actions towards carbon neutrality, many existing studies still rely on non-digital methods. However, recent research suggests that AI and digital technologies have the potential to contribute significantly to carbon neutrality. For instance, a longitudinal case study from the automotive industry identified four categories in which AI can control emissions: strategic trade-offs, organizational challenges, and overall business model efficiency [29]. These findings underscore the need for more systematic investigations into how AI can be effectively integrated into carbon neutrality strategies.
• Complex interactions due to economic factors: AI has demonstrated uneven effectiveness in generating potential reductions in ecological footprints and carbon emissions under different economic contexts. Research based on panel data in 67 countries showed that the impact of AI is underscored by heavier industrial and radically open economic activities [30]. However, there is less understanding of the finer details about how divergent economic structures may enable or disable AI from facilitating energy transitions and emission reductions. This represents a fundamental hole in our understanding of how AI interventions could lead to different outcomes based on regional economic differences.
• Differences in AI implementation by region—the international scene: Regional factors also seem to play a role in the efficacy of AI-led plans for net zero carbon. For instance, research has shown that digital economy changes affect carbon emissions in different regions differently: the benefits are most significant for more technologically advanced areas, such as central China, compared to less developed regions [31]. This brings questions about the scale and versatility of AI solutions in global geography and socio-economic backgrounds.
• Nonlinear relationships & heterogeneity: Much of the extant literature tends to imply linear relationships between AI deployment and environmental consequences, often overlooking non-linear dynamics. A systematic review highlighted that multiple studies did not discuss the relationships between environmental indicators and artificial intelligence, which resulted in reductive assessments of their effectiveness [30,32]. To understand how different conditions affect the potency of AI applications to help us achieve a carbon-neutral status, researchers must conduct research that accepts these complexities.
• Incorporation of stakeholder views: The other key gap includes stakeholder views and data accessibility to AI-based CN planning [33]. Still, the high-quality data that AI models need to perform well is lacking in many areas worldwide—potentially stalling plans to reach net zero. There need to be other measures of incentivizing AI application development at regional and hyperlocal levels and through research strategies to increase data availability and stakeholder participation.

This overarching idea is that while there is an acknowledgement of the possible contributions of AI and digital tools to support carbon neutrality efforts in previous sections, there are still significant associated research challenges [34,35]. The challenges are broad and varied, from an incomplete exploration of what AI can do, complex interactions with economic factors, geographically varying implementation outcomes, and non-linear relationships between variables to specific issues related to stakeholder engagement and data availability. These gaps are crucial to address when developing AI-based strategies for carbon neutrality across various contexts. This research examines the benefits of AI-based climate strategies in carbon neutrality through PLS-SEM and fsQCA techniques. It highlights the study’s importance for making climate policy, applying AI for environmental control, and promoting digital literacy.

Our research addresses critical gaps in the existing literature by integrating three previously disconnected theoretical domains: technology adoption theory, digital inclusion theory, and sustainability transition theory. While traditional technology adoption models focus primarily on individual and organizational acceptance, we extend this framework to incorporate sustainability metrics and societal-level impacts in emerging markets. Similarly, we expand digital inclusion theory beyond its conventional focus on socio-economic outcomes to encompass environmental sustainability and collective benefits. Our framework uniquely bridges these domains by establishing linkages between AI capabilities, digital inclusion, and carbon neutrality outcomes. This integration addresses significant literature gaps in technology integration, contextual understanding, implementation pathways, and outcome measurement. By developing this comprehensive theoretical framework, we provide new insights into how AI-driven solutions can effectively support climate action while ensuring digital inclusion in emerging economies.

3. Theoretical Framework Components and Hypothesis Formulation

3.1. Overview of the Framework

Another set of methodologies to be fitted into the theoretical foundation was AI Adoption and Execution, with the vision of obtaining a net zero carbon economy, which includes the Technology-Organization-Environment (TOE) framework and Life Cycle Assessment (LCA) enablers alongside sustainability regulations. This holistic view can help authorities to gain insights into the complex dynamics among technological innovations, market forces, and organizational factors for sustainable supply chain practices [36,37].

3.2. TOE (Technology-Organization-Environment) Framework

The TOE framework, a fundamental theory that studies the factors driving technology adoption in organizations, is of paramount importance. It helps us understand the complex interplay of features that influence technology adoption. Based on this framework, organizations can prioritize relevant variables influencing AI adoption after tailoring it to specific contexts, such as SMEs. This is operationalized using input from qualitative data stakeholders, which forms a human-based standard for technology-integrated approaches to sustainable issues [38,39]. The TOE framework is relevant and essential to achieving net zero emissions across several sections.

Energy efficiency, carbon capture, electrification, and zero-carbon hydrogen are vital technologies to decarbonize industries. Critical technologies for top-emitting industries, such as cement, steel, and chemicals, are also crucial [40,41]. Building Information Modelling (BIM) and Digital Twins (DT) may provide a real-time introduction. These with BIM would support the decision-making task for the whole life cycle, as it includes a net zero carbon buildings model and acts as a computational representation capturing essential decision variables [42]. The achievement of net zero emissions, especially in hydrocarbon-endowed economies, requires Circular Carbon Economy (CCE) frameworks based on renewable energy and carbon capture and storage (CCS) [43].

In addition, organizational strategies and business models should embrace circular economy principles that ethically support raw material extraction, minimize manufacturing emissions, and ensure battery management for electric vehicles (EVs) at their end-of-life [44]. SMEs can implement robust frameworks comprising greenhouse gas accounting, stakeholder pressure, and continuous improvement as pathways to net zero targets [45]. For instance, in sectors such as food and beverage, which have a history of being carbon-rich, it is possible to create Sustainable Business Models (SBMs) that use Environmental Sustainability Factors (ESFs) to effectively meet the given reduction targets for pollution while simultaneously delivering net zero value to customers [46].

On the one hand, we need environmental and social policies beyond strategic commitments to carbon pricing and government support for specific R&D or archaic emissions standards [40,43]. Policies should be geared towards a just transition for workers and left-behind communities while maintaining the space for human and economic development in low- and middle-income countries. It is recommended that coordinated policies are crucial to driving financing for innovative circular businesses and technologies and changes in behaviour towards a new way of living.

Transitioning towards a net zero carbon economy necessitates a well-rounded solution rooted in technological innovations, organizational strategies, and sound environmental policy. These are the key technologies (as well as circular economy principles) that will be essential. Moreover, new business models must evolve, comprising overarching continuous improvement and stakeholder engagement frameworks. Supporting and protecting innovation, a just transition from current development models and global leadership in sustainable development requires strategic policies and coordinated efforts worldwide.

The TOE model is relevant to the technological infrastructure transformation (TIT) in the context of moving to a net zero carbon economy. This broad and multi-sectored framework offers a comprehensive approach to examining how technological innovation is implemented in organizations. TOE relative to each aspect of TIT in the context of Renewable Energy Transition (RET) [47,48], Energy Efficiency Optimization (EEO) [40], Green Transportation Systems (GTSs) [49,50], Carbon Capture and Storage Implementation (CCSI) [51], Green Building and Infrastructure (GBI) [48], Industrial Decarbonization (ID) [40], Climate-Resilient Infrastructure (CRI) [52], and Supply Chain Decarbonization (SCD) [40].

TIT is the subject of relationships with a wide variety of other TOE factors, which come under the purview of adoption of renewable energy, green building practices, industrial decarbonization, climate-resilient infrastructure, etc., as well as transformation of technological innovation blueprints around cleaner technologies like carbon capture and storage mandates on supply chain decarbonization. Collectively, these elements represent multiple entry points towards a sustainable, low-carbon future calling for integrated and coordinated actions across sectors, technologies, etc.

3.3. LCA (Life Cycle Assessment) Adoption

Adopting LCA enablers is necessary and a gateway to achieving supply chain management strategies for net zero [53,54]. These enablers, which include the deployment of carbon capture and digital assessment tools, promise to revolutionize the application of sustainability practices across different industries [55,56]. The theoretical framework facilitates identifying and prioritizing these enablers and aligns strategy with a demand-led perspective. It offers practical business solutions that managers can secure by tapping insights and collaboratively implementing innovation actions with internal and external stakeholders.

Adopting life cycle assessment (LCA) frameworks is critical to realising a net zero carbon economy, especially in construction. LCA helps determine how buildings achieve their environmental footprint throughout the life cycle of the building, from resource extraction to recycling or landfilling. In this synthesis, we survey current research on LCA frameworks and their role in supporting net zero carbon goals. LCA usually occurs late in the design process, having a limited impact on reducing whole-life carbon emissions. Early integration with building information modelling (BIM) and life cycle costing could also improve its utility [57].

Attributional LCA (ALCA) and Consequential LCA (CLCA) provide distinct perspectives. CLCA records revealed that volume reactions and front loads embodied impacts that may be presented as greater than those of ALCA, so careful LCA method choice is required [43]. Traditionally, energy-centric assessments are inadequate. Integrated approaches combining LCA with multi-criteria decision analysis (MCDA) have been proposed to evaluate more significant impact categories, such as human health and water conservation [58]. One approach to quantifying buildings’ whole-life cost and energy involves material banks. LCA helps optimize material selection with system design, fusing attributes from architectural and engineering studies to evaluate operational versus embodied impacts at nearly zero-energy buildings (NZEB) [59].

Reusing existing structures (and using bio-based materials) is a great way to reduce embodied carbon and can be considered a practical approach to achieving net zero carbon buildings [60]. LCA frameworks are vital to achieving a building sector carbon economy with near-zero emissions. LCA must be addressed earlier in the design process; far-ranging and integrated evaluation frameworks should be adopted, and the life cycle of impacts other than the environment is crucial. Further measures to assist in delivering on net zero carbon aims include dealing with emissions and utilizing digital technologies and biological resources, coupled with retrofitting strategies.

Socio-economic policy adaptation (SEPA) is vital to facilitating a just transition to a sustainable net zero economy with the interlinking strategies, including Circular Economy Adoption (CEA) [61,62], Sustainable Agriculture Practices (SAP) [63], Carbon Pricing Mechanisms (CPM) [64], Green Finance and Investment (GFI) [64], Net Zero Policy Framework (NPF) [61], and Public Awareness and Behavior Change (PABC) [62], as well as integrating Nature-Based Solutions Integration (NBSI) [63]. In various ways, SEPA is connected with the net zero economy and LCA (life-cycle assessment, the foundation used for evaluating environmental impacts). Researchers can accelerate the evolutionary trajectory into this sustainable net zero economy by cultivating commonalities amongst these strategies, from policy frameworks to public engagement—well-supported policy adaptations to the changing climate and socio-economics. So, SEPA has more significant importance when transitioning from a deficit fossil energy economy into a sustainable capital formation net zero economy.

3.4. Hypothesis Development

The discussion of the theoretical framework asserts that the framework for the current research will be exploring a multifaceted, complex relationship between AI engagement factors along with their outcomes and net zero carbon intentions (TIT and SEPA). So, here, the researchers consider the cognitive–affective–conative model, where thinking, feeling, and executing activities concerning AI engagement to achieve a net zero carbon economy will be analyzed. Figure 1 illustrates all the hypotheses, and further descriptions of the constructs and propositions constructed to develop the hypothesized model are depicted below.

3.4.1. AI Commitment

In emerging markets, where digital inclusion has the highest value proposition, artificial intelligence (AI) is leading the transformation to a net zero carbon economy [65]. Therefore, companies across industries can use AI to streamline processes to minimize carbon emissions and improve sustainability [66]. As in the example, AI can detect patterns of how energy will be used and distribute the resources as efficiently as possible to prevent/minimize waste or emissions [67]. Also, the advent and coupling of AI to agriculture, transport, etc., helps reduce carbon footprints via operational efficiencies, leading to increased decision-making consistency [68]. It is also vital to deal with the negative side of AI’s energy consumption, as an increase in data processing demand for use cases defined through AI can mean more greenhouse gases emitted when not handled sustainably [69]. As a result, striking this equilibrium in marrying AI capabilities with sustainability is imperative to building climate resilience and catalysing a net zero economy in emerging markets [70,71]. This AI and sustainability nexus is significant for its environmental benefits and how it drives economic and social development by demonstrating AI as a critical enabler toward sustainable global goals [72]. In this context, AI commitment is conceptualized as a multidimensional construct comprising five dimensions: AI Capabilities (AIC), Digital Infrastructure (DI), Digital Skills (DS), AI-Enabled Climate Solutions (AICS), and Climate Policy Readiness (CPR).

It would not be wrong to say that AI commitment is a multi-dimensional construct that captures all the essential aspects needed for properly utilizing artificial intelligence. This gives a unique and highly effective multidimensional snapshot of how an organization’s total AI commitment, the five dimensions—AI Capabilities (AIC), Digital Infrastructure (DI), Digital Skills (DS), AI-Enabled Climate Solutions (AICS) and climate policy readiness 2050—pair to collectively influence how their efforts in one area will likely affect success upstream. This means that the relationships between climate-related challenges and organization performance across different dimensions are crucial to understanding how a deep commitment to AI can enhance organizations’ performance. By developing strong AIC, investing in DI, growing DS, operationalizing AICS, and ensuring CPR is implemented, organizations can harness this synergy to achieve a family of effects, including real-world business objectives and societal goals.

3.4.2. The Interaction of AI Commitment Dimensions

AI Capabilities (AIC)

AI capabilities refer to how well an organization can build, integrate, and leverage AI technologies. Effectively committing to AI means further building this capability, which will require substantial investment in R&D [73]. This is imperative for growth and staying ahead of the competition. AIC-led organizations are better positioned to leverage AI for operational efficiency and business-oriented decision-making, which can improve indices across many sectors and climate solutions.

Digital Infrastructure (DI)

At the heart of any AI initiative is its digital infrastructure. Of course, any sizable commitment to AI demands a robust digital infrastructure consisting of cloud computing, storage, and networking [74]. The infrastructure is in place to collect, process, and analyze massive amounts of data required to train these AI models [75]. Organizations can ensure the smooth operation and efficiency of their AI systems to intervene against climate challenges by investing heavily in DI, thereby effectively implementing AI-enabled climate- change-addressing solutions.

Digital Skills (DS)

This requires digital skills to maximize what AI technologies can deliver. Focusing on building digital skills among the workforce helps prepare employees with the right capabilities to work productively alongside AI systems [73,74]. Companies like IBM are piloting the “SkillsBuild” initiative to help prepare workers today for the types of positions that will be most in demand tomorrow [75]. This increases the person’s employability and makes the organization more capable of leveraging AI towards its goal.

AI-Enabled Climate Solutions (AICS)

AI-enabled climate solutions are a distinct use of related AI technologies meant to ease environmental difficulties [71,74]. AI-centered corporations are no exception, and AICS strives to manifest itself in imaginative ways for development, such as by utilizing predictive assessment for resource allocation or energy control through smart appliances [73,75]. AI can make a massive impact through sustainability while staying aligned with climate policy aims.

Climate Policy Readiness (CPR)

In describing the readiness concept developed by GBI, climate policy readiness refers to how well an organization has readied its operational procedures and policies for changes in the environmental regulations and laws a company must legally support [76]. Total commitment to AI means embedding the organization’s strategies with climate policies and ensuring that the AI efforts encourage sustainability [77,78]. Proactive engagement with CPR will enable organizations to do more than ever merely comply; they use these same AI technologies to get ahead on compliance and demonstrate their environment credentials, providing them with the visibility and operational resiliency they need in a changing regulatory world.

The cognitive–affective–conative model is a theoretical framework designed to explain and describe the nature of attitudes and how they influence behaviour. This model has dramatically impacted many fields related to psychology and SDGs [79]. According to this model, attitudes toward AI commitment consist of three components.

• Cognitive Component: This is theoretical about an object or idea. It is the logical, intellectual side of a mindset. For instance, it is essential to take the time to carefully learn what an artificial intelligence system can and cannot do.
• Affective Component: This is identifiable based on the emotions, feelings, and moods associated with an object or concept. It is the emotional side or evaluative portion of an attitude.
• Conative Component: This includes behavioural intentions, behaviours, or capabilities related to the object or concept. It leads to the probability of performing SDG activity, which will depend on cognitive beliefs and affective feelings about a net zero carbon economy. For example, AI technologies can be implemented for net zero carbon emissions in business operations.

This model can explain how knowledge and understanding of AI (cognitive) impact attitudes toward its use (affective), which in turn determines behavioural intentions regarding the application of AI for climate solutions (conative). It offers a structured approach to help analyze the relationship between AI engagement factors and their solutions in delivering a net zero carbon economy.

AI Commitment and Technological Infrastructure Transformation

Studies on AI’s impact on technological innovation and transformation are gaining attention as organizations and governments seek to leverage AI for sustainable development [80,81]. AI capabilities have been shown to drive technological innovations across various sectors [82]. The relationships between AI commitment and technological infrastructure are based on sub-elements like AI Capabilities (AIC), Digital Infrastructure (DI), Digital Skills (DS), AI Enabled climate Solutions (AICS), and Climate Policy Readiness (CPR). AI solutions that integrate increasingly powerful algorithms with detailed analysis of vast datasets can produce more precise predictions essential for establishing successful strategies to combat climate change, fuelling a commitment to the role of AI [83]. So, organizations using AI can commit to limiting carbon emissions [84]. Therefore, more investment in AI capacities can yield better predictive modelling and drive more effective climate solutions [85]. So, hypothesis 1 represents that AI capabilities have a substantial effect on TIT.

H₁: 

AI Capabilities (AIC) have a positive effect on Technological Infrastructure Transformation (TIT).

Collecting and sharing data at scale—necessary for AI systems to analyze climate data effectively and enable policy decisions—requires a solid digital infrastructure. From this micro-level perspective of AI climate use cases, deploying such technologies at scale within climate policy frameworks is contingent on a general capacity for creating robust digital infrastructures [86]. AI will only become a practical climate policy tool in countries with good digital infrastructure [87]. To provide the data, the whole digital infrastructure will be better able to collect and share, and algorithmized information processing about climate AI decision-making will be used. So, hypothesis 2 depicts that data infrastructure has a strong effect on TIT.

H₂: 

Digital infrastructure (DI) has a positive effect on Technological Infrastructure Transformation (TIT).

Skilled personnel must interpret AI-generated insights and integrate them into actionable climate policies, increasing overall commitment to AI solutions [87,88]. Higher levels of digital skills in the workforce will enhance the effectiveness of AI implementations in climate initiatives [88,89]. It takes people to process the insights and turn them into actionable climate policy. This builds broader acceptance of AI solutions [90]. The third hypothesis is that more profound digital skills among the workforce will strengthen climate AI initiative implementations.

H₃: 

Digital skills (DS) have a positive effect on Technological Infrastructure Transformation (TIT).

AI technologies will have the ability to streamline resource utilization and enable better monitoring solutions for the environment [91]. This will produce positive metrics in deliverables in climate strategies as one sector errs on the side of not having recourse to AI-enhanced solutions [92]. It will forecast that more AI-powered solutions being part of a climate strategy would mean fewer greenhouse gas emissions—making the it faster to restore the environment [93,94]. This method of environmental management is more efficient because it allows limited resources to be used without waste, making it effective in monitoring [95]. So, the fourth hypothesis depicts that AI-enabled climate solutions have a profound relationship with TIT.

H₄: 

AI-Enabled Climate Solutions (AICS) have a positive effect on Technological Infrastructure Transformation (TIT).

AI technologies will have the ability to streamline resource utilization and enable better monitoring solutions for the environment [95]. This will produce positive metrics in deliverables in climate strategies as one sector errs on the side of not having recourse to AI-enhanced solutions [96]. It will forecast that more AI-powered solutions being part of a climate strategy would mean less greenhouse gas emissions—making it faster to restore the environment [97]. This method of environmental management is more efficient because it allows limited resources to be used without waste, making them effective in monitoring. So, researchers here assume that climate policy readiness (CPR) has a profound relationship with TIT.

H₅: 

Climate Policy Readiness (CPR) has a positive effect on Technological Infrastructure Transformation (TIT).

AI Commitment and Socio-Economic Policy Adaptation

SEPA is significantly affected by AI commitment elements—AI Capabilities (AIC), Digital Infrastructure (DI), Digital Skills (DS), AI-Enabled Climate Solutions (AICS), and Climate Policy Readiness (CPR). These are powerful tools in the hands of policymakers that will help them with data analytics, prediction, and decision support. This can allow for better, quicker, and more evidence-based policy flexibility [98,99].

AIC is about analytics and deriving actionable insights from large datasets for policymakers. These functional competencies boost understanding of socio-economic dynamics, enabling context-specific policy interventions addressing existing and forthcoming challenges [100]. So, here, H₆ depicts a strong relationship between AI capabilities and SEPA.

H₆: 

AI Capabilities (AIC) have a positive effect on Socio-Economic Policy Adaptation (SEPA).

A strong digital infrastructure ensures that the tools and platforms are in place to use these technologies, which helps advance economic growth and adaptation strategies. This infrastructure undergirds the rollout of AI applications driving policy outcomes [101]. A few decades ago, such escalation would paralyze communication and collaboration across the stakeholders in a value chain, leaving policy adaptation to socioeconomic shifts in disarray. So, researchers assume a significant relationship exists between digital infrastructure and SEPA.

H₇: 

Digital Infrastructure (DI) has a positive effect on Socio-Economic Policy Adaptation (SEPA).

Improving digital skills can make the workforce more capable of dealing with new technologies and increase labour market adaptability [102]. This is important to reduce the disruption caused by any technology change and to help workers on their way to new jobs. Digital skills and AI skills training programs provide individuals with a foundation to engage in the economy, leading to increased social equity and more adaptive policymaking refinements [103]. So, this study assumes that digital skills significantly affect SEPA.

H₈: 

Digital Skills (DS) have a positive effect on Socio-Economic Policy Adaptation (SEPA).

By detecting vulnerable communities and sectors through AI-enabled climate solutions, policymakers can make targeted interventions to address socio-economic needs concerning climate change [93]. AICS adds to these by promoting sustainable economic practices, which imply the development of clean technologies and sound business models that can deliver broader socio-economic impact, resulting in resilient policy frameworks [101]. So, this study assumes that AI-enabled climate solutions have a robust relationship with SEPA.

H₉: 

AI-Enabled Climate Solutions (AICS) have a positive effect on Socio-Economic Policy Adaptation (SEPA).

Climate policy readiness (CPR) offers a structured entry point for embedding climate rationale in socio-economic pursuits. This preparation facilitates more rapid and efficient responses to the new environmental problems confronting us because of climate change. Articulated climate policy can incentivize businesses and localities to embrace clean technologies, spur economic development and address environmental issues [102,103]. So, researchers here assume that climatic policy readiness relates significantly to SEPA.

H₁₀: 

Climate Policy Readiness (CPR) has a positive effect on Socio-Economic Policy Adaptation (SEPA).

4. Methodology

The nature of this study and the research constructs considered suggest that Partial Least Squares Structural Equation Modeling (PLS-SEM), plausibly combined with Fuzzy-set Qualitative Comparative Analysis (fsQCA), can be used to investigate the influence of AI engagement on net zero carbon economy outcomes.

PLS-SEM would be highly appropriate for this research, given that it is more exploratory and theory-building by nature. Moreover, that would appear to be the case here, as the authors explored a relatively new area of AI involvement and its effects. Even large models with multiple constructs and relationship paths are not a problem. It does well with small sample sizes and is distribution-free. It is easily adaptable to reflectively measured constructs, which might be required depending on how you conceptualize AI engagement and its dimensions.

Although fsQCA is not a regular statistical model, it would be of interest to apply it given that it allows non-recursive relations between conditions and (in the findings case) possible solutions—as multiple configurations of AI engagement dimensions leading to high levels in TIT and SEPA may prevail. It is suitable for complex configurational relationships. Furthermore, it might be used along with PLS-SEM to provide a richer exploration.

A hierarchical component model (HCM) within PLS-SEM is suggested for AI engagement. This approach allowed the researchers in this study to investigate the individual impacts of AI engagement dimensions and their collective impact, i.e., higher-order constructs. Finally, given the likely non-linear and complex relationships in this area, the authors complement PLS-SEM analysis with fsQCA. This amalgamation might allow for a far more fine-grained understanding of the extent to which AI engagement factors shape technology innovation transformation and socioeconomic policy adaptation.

The PLS-SEM methodology significantly enhances our research by identifying direct and indirect relationships between AI dimensions and climate resilience outcomes while quantifying these causal relationships’ strengths. This approach enables us to rigorously test the hypothesized structural relationships in our theoretical model, providing robust validation of measurement constructs for AI commitment dimensions, thereby establishing a solid empirical foundation for understanding how AI initiatives contribute to climate resilience in emerging economies.

The fsQCA methodology enriches our understanding by revealing complex configurational patterns in AI adoption and climate resilience while identifying multiple viable pathways emerging economies can follow to achieve their sustainability goals. This approach excels at capturing non-linear relationships and context dependencies, acknowledging that different combinations of factors may lead to successful outcomes based on local conditions and capabilities. Importantly, it accounts for equifinality in achieving climate resilience. It recognizes that various combinations of AI capabilities, digital skills, and policy frameworks can lead to equally effective results, thereby providing valuable insights for policymakers and practitioners in diverse emerging market contexts.

5. Data Collection

This paper aims to answer the research questions, identify gaps in current approaches, and study the role of AI in promoting a net zero carbon economy in an emerging economy, namely Vietnam, Italy, Malaysia, and Greece. With a dearth of studies on this topic from emerging economies, we employed a combination of methods to collect data for this study. Given the knowledge gaps identified concerning the practical potential of the role of AI in achieving a climate-resilient, low-carbon economy in an emerging economy, it was essential to collect our data from primary sources, like stakeholders. Much of our data analysis was based on studies and reports concerning the situation in Vietnam Italy, Malaysia, and Greece to address the data gap. In addition, due to the radical nature of the questions addressed in this study, we required indigenous knowledge; therefore, discussions with relevant stakeholders in the form of interviews provided a deeper understanding of technical and contextual knowledge.

Therefore, the research used a qualitative approach to data collection through semi-structured interviews and a quantitative approach through survey data collection. These provided the primary data for the research objectives, while existing reports, literature, and policy papers were reviewed for the literature. Multiple data collection methods were needed to gather indigenous knowledge, responsive narratives, and secondary data on the nuances of the research aims. A combination of data collection methods was employed to explore the nexus role of AI in digital inclusion for climate resilience in the long term.

Here, the data collection method for this study was a combination of unstructured interviews (open-ended questions) and a survey. In unstructured, in-depth interviews, participants answered open-ended questions and were expected to show all relevant and essential information. Open interviews are expected to enable information to be tapped that other data collection methods cannot reveal. Therefore, selecting this data collection method was appropriate. Expectations were that the interviews would yield various responses and that the results would be seen during the interview when the questions were asked to receive the required information.

From a mix of urban centers (60%), secondary cities (25%), and rural areas (15%), our geographic distribution strategy was well balanced, allowing us to include respondents from different contexts of development. The strategic sampling design not only ensured that the responses reflected diverse examples of sustainable solutions based on AI across multiple economic and cultural contexts, which is regarded as one of its definite strengths in furthering the understanding of climate action strategies, but it enabled direct comparison amongst the best practices and challenges working under different regulatory frameworks whilst also revealing how emerging economies use their unique circumstances to leapfrog towards action in sustainability through AI solution development approaches depending upon institutional framework functions.

There were three dimensions of quality assurance measures for robust data collection—validation protocols—30 participants for the pilot study (n = 30) per country, reviewed by an expert panel for each country and then translated into the local language and back-verifying the translation of the questionnaire. Response quality was maintained by screening for expertise (only granting access to experts on flexible work), inserting random attention checks throughout the survey, and conducting follow-up verification for 10% of individual responses. Our methodology was reinforced with statistical rigor (non-response bias analysis, power analysis for sample size adequacy, and verification of representative sampling), securing our findings to be reliable and valid.

This comprehensive sampling strategy ensures that our findings provide meaningful insights into how emerging economies can effectively leverage AI for climate action while accounting for their unique contextual challenges and opportunities.

6. Data Analysis Techniques

Data analysis techniques are vital in interpreting the collected data, and they are also crucial in generating the results and, eventually, the data findings. Thus, we utilized PLS-SEM and fsQCA techniques, as they are known for analyzing complex relationships among the variables. Both utilized techniques have been considered valid for investigating AI’s role in a net zero carbon economy, and the advanced PLS-SEM and fsQCA tools have been used to model the theoretical framework used in this paper. R version 4.2.1 was also utilized with the assistance of software to test the relationships postulated in the hypotheses. ModeQ, consistency and parsimony, and the PLS-SEM’s model fit and predictive power were validated. R values will range from 0.35 to 0.70, and the value of Q will be 0 or higher. In addition, for fsQCA, the consistency of model fit, will be between 0 and 1. It is also worth mentioning that an integrated analysis technique combining qualitative and quantitative analysis with clustering analysis can generate a deeper understanding of the potential relationships between both models.

Data validity is assessed based on many criteria. Specifically, many processes and tests, such as instrument measurements, control variable constraints, collateral variables, and temporal reactions, will be used as a referencing source and tested to examine the accuracy of the results. The consistency of R shows that the PLS-SEM postulates reliability between 0.35 and 0.70, and Q < 1. However, in the literature reviewed, the R exercise did not follow this stance strictly. However, this is possible, as this approach is aimed at meeting our research aims. Even though theories used the higher values of R to fit a minimum gate, the studies argued that those using R-squared executed an acceptable position value of between 0.35 and 0.68. It is worth mentioning that triangularity was followed in this study. However, there was no compelling reason why R-squared should be used as an entry point. The structural equation argument strengthens the position taken in this research. This conceptualization eliminates the arbitrary distinction between reflective and conceptual approaches.

7. Empirical Data Collection

Taking a practical, pragmatic look, discussing the empirical evidence gathered for this study is essential. First, we defined a transparent process of the steps taken in gathering such evidence. Second, we highlighted the importance of collecting this data within the daily context of the local population in emerging economies. Third, we emphasized our tailored data efforts to suit our target participants. Our interest in the regional context and relevant phenomenology brings such potential strategies into further refinement. Our sampling design attempted to generate as representative and diverse a sample of emerging market adults as is feasible for nuanced perspectives on digital inclusion perceptions.

7.1. Sampling Strategy

The data for this study were collected from May to August 2024. A purposive sampling technique was employed to collect data from professionals and stakeholders involved in AI, digital inclusion, and climate resilience initiatives in selected emerging economies, including Vietnam, Italy, Malaysia, and Greece.

• Motivation for Emerging Economies Focus

The focus on emerging economies is strategically motivated by three critical factors that make these nations particularly relevant for studying AI-driven climate solutions:

• Carbon Intensity–Development Paradox: Emerging economies face unique challenges in balancing rapid industrialization with environmental commitments. These nations collectively account for approximately 63% of global carbon emissions while pursuing economic growth, making them critical battlegrounds for climate action.
• Digital Transformation Opportunity: Emerging economies are experiencing rapid digital transformation, with significant investments in AI and digital infrastructure. This presents a unique opportunity to study how technological leapfrogging can facilitate sustainable development without following the traditional carbon-intensive development path.
• Climate Vulnerability–Innovation Nexus: These economies often face disproportionate climate change impacts while showing remarkable innovation in adopting technological solutions, making them ideal candidates for studying AI-enabled climate resilience strategies.

• Country Selection Rationale

The fourcountries in our study were carefully selected to represent different stages of emerging market development and various approaches to AI adoption and climate action:

Vietnam

• Rapid industrialization (6.8% annual GDP growth 2016–2023).
• Strong government push for Industry 4.0.
• High climate vulnerability (ranked 6th globally).
• Strategic position in global tech supply chains.
• National AI strategy focusing on green technology.

Malaysia

• Advanced digital infrastructure among Southeast Asian nations.
• Pioneer in Islamic green finance.
• Strong regulatory framework for AI adoption.
• Smart city initiatives integrating AI and sustainability.
• Strategic location in ASEAN digital economy.

Italy

• Bridge between developed and emerging markets.
• Leading Mediterranean AI research hub.
• EU climate policy implementation experience.
• Strong manufacturing sector digital transformation.
• Innovative SME ecosystem.

Greece

• Post-crisis economic transformation.
• Renewable energy transition leader.
• EU Green Deal early adopter.
• Growing technology startup ecosystem.
• Unique island-based climate challenges.

The regional distribution matrix (

Table A1. Country Selection and Respective Key Contribution.

Region	Countries	Key Contribution to Study
Southeast Asia	Vietnam	Scale and complexity of AI adoption in climate action
Southeast Asia	Malaysia	Digital transformation in high-growth economies
Southern Europe	Italy, Greece	EU policy implementation in emerging contexts

) for countries and sectorial coverage (

Table A2. Sectoral coverage for Data collection.

Sector	Percentage	Rationale
Manufacturing/Industry	25%	Core emission-intensive sectors
Technology/Digital Services	30%	AI implementation expertise
Environmental Services	20%	Climate action perspective
Policy/Government	15%	Regulatory insight
Research/Academia	10%	Theoretical foundation

) are mentioned in Appendix A.

Out of the 450 distributed questionnaires, 400 were returned in complete form. Of the 400 respondents, 230 were male and 170 were female, ensuring a relatively balanced gender representation. Most respondents (265) were between 25 and 45 years old, representing the core working-age population engaged in the technology and sustainability sectors. The remaining 135 respondents were over 45 years old, bringing valuable experience and long-term perspectives to the study.

Regarding professional background, 220 respondents had direct experience working with AI technologies or digital inclusion, while 180 had backgrounds in environmental science, climate policy, or sustainable development. This distribution gave a balanced perspective on the research’s technological and environmental aspects.

Regarding educational background, most respondents (310) held at least a bachelor’s degree, with a significant portion having advanced degrees in computer science, environmental studies, or public policy. The remaining 90 respondents had practical experience in relevant industries without formal higher education, providing valuable insights from a non-academic perspective. The survey was conducted across various urban and rural areas in the selected countries to capture diverse perspectives on AI’s role in achieving net zero carbon economies and enhancing digital inclusion. This approach ensured that the study captured viewpoints from technology hubs and areas where digital inclusion efforts are most crucial.

This data collection approach aimed to gather comprehensive perspectives on the intersection of AI, digital inclusion, and climate resilience in emerging economies. The diverse sample, which included gender, age, professional background, and education level, was designed to capture a holistic view of the challenges and opportunities in leveraging AI to achieve net zero carbon economies while promoting digital inclusion.

7.2. Data Collection Instruments

In ensuring effective AI intervention for addressing climate resilience requirements, it is essential to determine whether the tools developed for collecting data serve the research objectives effectively, ensuring that the data collected through these instruments effectively address individual research objectives. Based on the discussion, the development of these data collection tools was carried out, offering the questionnaire, interview guides, and observational checklist, considered the three data collection instruments for achieving the identified objectives. Finally, each question of this tool was evaluated, and individual questions were collected to aid in developing the research framework.

These instruments have been used to collect data based on a survey conducted in four emerging countries in the southeast Asia-Pacific and southern European regions. The questionnaire was designed and developed in English to capture the research objective with easy comprehension. During the pilot study conducted earlier, no difficulty was reported by the respondents regarding language barriers, and it has been kept simple and easy to understand. Therefore, the pilot study was considered successful since no changes were needed to the instrument. Expert reviews were also carried out on the questionnaire. The development and design of the survey were done in a participative manner to ensure that the study was culturally sensitive and accepted. The tools were evaluated based on reliability and validity to ensure that the data collected from these instruments were reliable and valid.

Table 1. Research items.

VE_Capabilities (AIC)

I am heavily invested in the AI capabilities of my organization/country.

I am passionate about the potential of AI in addressing climate change.

I am enthusiastic about the advancements in AI capabilities in my field.

VE_Information (DI)

I pay a lot of attention to anything about digital information related to climate change.

Any new digital information about climate solutions grabs my attention.

I focus on acquiring and analyzing digital information in my work.

VE_Skills (DS)

When working on improving my digital skills, I forget everything around me.

Time flies when I am learning new digital skills related to climate solutions.

When engaging with digital technologies, it is difficult to detach myself.

VE_Solution (AICS)

I enjoy interacting with AI-enabled climate solutions at work.

I actively participate in discussions about AI applications for climate change mitigation.

I thoroughly enjoy exchanging ideas about AI-enabled climate solutions with others in my field.

VE_Policy (CPR)

When someone criticizes climate policies in my country/organization, I feel sad.

When discussing climate resilience, I usually refer to ‘our planet’, ‘our environment’, ‘our responsibility’, or ‘our policy’.

When someone praises our climate policies or our AI developments, it feels like a personal compliment.

Technological and Infrastructure Transformation (TIT)

I will continue to invest in AI and digital technologies for climate solutions in the future.

If given the opportunity, I am eager to leverage the transformative potential of AI-driven infrastructure changes.

With a strong commitment to progress, my organization/country is highly likely to embrace advanced AI technologies for climate action.

Socio-Economic and Policy Adaptation (SEPA)

I will recommend AI-driven climate policies to other stakeholders.

When I talk about our AI initiatives for climate change, I will highlight their positive impacts.

I will encourage others to adopt AI-enabled solutions for climate challenges.

I will share success stories about AI’s role in socio-economic adaptation to climate change on various platforms.

represents the items used for this study.

Since these study data were obtained from one source, researchers sought to address common method variance (CMV) threats in several ways. We applied two commonly recommended techniques to PLS-SEM: full collinearity [104] and the correlation matrix approach. To account for potential CMV in the model, we followed prior recommendations: (a) full collinearity using Variance Inflation Factor should be <3.3 [104,105], and (b) consideration that correlation between constructs ≥0.9 mirrors suspicion of common method bias [106]. We did not find any significant bias from our analysis for either criterion; specifically, all of the constructs had VIF values less than 3.3, and correlations were below 0.9, suggesting no CMV impact on our findings. Figure 2 shows the flow of the data analysis for this study.

Our study used two quantitative data analysis methods: Partial Least Squares Structural Equation Modeling (PLS-SEM) and fsQCA. PLS-SEM was chosen for its predictive nature, which aligns with our research’s forward-looking and innovative aspects. This method’s capability to deal with complex models containing multiple constructs makes it particularly suitable for investigating the multifaceted relationships among AI, digital inclusion, and climate resilience. This study benefits from the integration of methodologies. For instance, fsQCA provides a more in-depth analysis of the causal structures in our model, while PLS-SEM yields specific construct scores for input in fsQCA analysis [107]. This combination of methods, which has been well adopted in various domains, including sustainability and technology adoption, ensures a comprehensive and well-rounded analysis of our research questions.

Our fsQCA analysis identified sufficient causal combinations playing significant roles in driving high quantum-level AI adoption, leading to climate resiliency, and enabling net zero carbon economy pursuits within emerging economies. This approach permits them to identify complex, non-linear relationships that could otherwise be missed using standard regression-based analyses. We used SmartPLS 3.0 to conduct PLS-SEM and fsQCA to identify typical configurations leading to our endogeneity variables. According to available standards, we regarded a solution capable of producing the result if its reliability and density were higher than 0.8 and more than 0.2, respectively [108,109]. We use this methodological framework to study the relationships between AI adoption, digital inclusion, and climate resilience in emerging economies. By integrating PLS-SEM and fsQCA, we determine the direct effects of critical factors and confirm complex configurational routes toward implementing AI to facilitate net zero carbon economies [110,111,112]. This dual conceptual framework helps us learn how different factors interact in a less-developed economy for better policymaking and the practical implications of technology adoption for sustainable development [113].

8. Results and Findings

8.1. Results of the Assessment of the Model Using PLS-SEM

To assess the model using PLS-SEM, we examined the measurement model (reliability and validity) and the structural model [110,114]. For the reflective constructs in our study, we assessed the Composite Reliability (CR), rho_A, and Average Variance Extracted (AVE). As per established guidelines, these values should exceed 0.7, 0.7, and 0.5, respectively, to establish reliability and convergent validity [114].

Table 2. Assessment of reflective measurement model.

Constructs	Items	Loadings	CR	rho_A	AVE
VE_Capabilities (AIC)			0.89	0.827	0.73
	VEC1	0.845
	VEC2	0.881
	VEC3	0.842
VE_Information (DI)			0.88	0.803	0.7
	VDI1	0.812
	VDI2	0.859
	VDI3	0.841
VE_Skills (DS)			0.91	0.839	0.75
	VDS1	0.807
	VDS2	0.885
	VDS3	0.849
VE_Solution (AICS)			0.92	0.862	0.78
	VCS1	0.891
	VCS2	0.879
	VCS3	0.885
VE_Policy (CPR)			0.93	0.894	0.83
	VPR1	0.905
	VPR2	0.922
	VPR3	0.898
Technological and Infrastructure Transformation (TIT)			0.91	0.849	0.77
	TIT1	0.876
	TIT2	0.891
	TIT3	0.861
Socio-Economic and Policy Adaptation (SEPA)			0.89	0.821	0.67
	SEPA1	0.825
	SEPA2	0.847
	SEPA3	0.831
	SEPA4	0.767

Source: Authors’ data analysis.

presents the results of the measurement model assessment, demonstrating that reliability and convergent validity have been established for all reflective constructs: AI Capabilities (AIC), Digital Information (DI), Digital Skills (DS), AI-Enabled Climate Solutions (AICS), Climate Policy Readiness (CPR), Technological and Infrastructure Transformation (VTRA), and Socio-economic and Policy Adaptation (SEPA).

8.2. Measurement Model Assessment

To assess discriminant validity, we employed two approaches: the Fornell–Larcker criterion and the heterotrait–monotrait (HTMT) ratio [115].

Table 3. Discriminant validity using the Fornell–Larcker criterion.

Constructs	AIC	DI	DS	AICS	CPR	TIT	SEPA
AIC	0.856
DI	0.682	0.838
DS	0.571	0.623	0.867
AICS	0.498	0.532	0.615	0.885
CPR	0.412	0.389	0.472	0.587	0.908
TIT	0.523	0.498	0.556	0.629	0.542	0.876
SEPA	0.487	0.456	0.501	0.573	0.698	0.698	0.817

Source: Authors’ data analysis.

and

Table 4. Discriminant validity using HTMT ratio.

Constructs	AIC	DI	DS	AICS	CPR	TIT	SEPA
AIC
DI	0.794
DS	0.665	0.728
AICS	0.576	0.619	0.712
CPR	0.471	0.446	0.539	0.667
TIT	0.605	0.578	0.642	0.721	0.615
SEPA	0.567	0.534	0.584	0.662	0.697	0.798

Source: Authors’ data analysis.

present the results of the discriminant validity assessment. The square root of AVE for each construct exceeds its correlation with other constructs (Table 3), and all HTMT values are below 0.9 (Table 4), indicating that discriminant validity has been established [115].

8.3. Structural Model Assessment

Figure 3 and

Table 5. Structural model results table.

	Hypothesis	Direct Effect	CI0.95 Bias Corrected	Supported
H1	AIC → TIT	0.187	[0.072, 0.302]	YES
H2	DI → TIT	0.145	[0.031, 0.259]	YES
H3	DS → TIT	0.203	[0.089, 0.317]	YES
H4	AICS → TIT	0.298	[0.184, 0.412]	YES
H5	CPR → TIT	0.176	[0.062, 0.290]	YES
H6	AIC → SEPA	0.112	[−0.003, 0.227]	NO
H7	DI → SEPA	0.089	[−0.025, 0.203]	NO
H8	DS → SEPA	0.134	[0.020, 0.248]	YES
H9	AICS → SEPA	0.221	[0.107, 0.335]	YES
H10	CPR → SEPA	0.315	[0.201, 0.429]	YES

Source: Authors’ data analysis.

present the structural model assessment and hypothesis testing results for the effects of AI capabilities, digital information, digital skills, AI-enabled climate solutions, and climate policy readiness on technological and infrastructure transformation and socio-economic and policy adaptation. The R² values for technological and infrastructure transformation (TIT) and socio-economic and policy adaptation (SEPA) are 0.512 and 0.583, respectively, indicating moderate to substantial explanatory power [116].

The results of hypothesis testing using PLS-SEM reveal that all dimensions of AI commitment (AIC, DI, DS, AICS, and CPR) significantly positively affect Technological and Infrastructure Transformation (TIT), supporting hypotheses H₁ to H₅. For Socio-Economic and Policy Adaptation (SEPA), the results show significant positive effects of Digital Skills (DS), AI-Enabled Climate Solutions (AICS), and Climate Policy Readiness (CPR), supporting hypotheses H₈, H₉, and H₁₀. However, the effects of AI Capabilities (AIC) and Digital Information (DI) on SEPA are insignificant, thus not supporting hypotheses H₆ and H₇. These results suggest that while all aspects of AI commitment contribute to technological and infrastructure transformation, only specific dimensions (DS, AICS, and CPR) directly influence socio-economic and policy adaptation to achieve a net zero carbon economy in emerging economies.

8.4. Results of fsQCA

To perform the fsQCA, we used the standardized scores of constructs from the PLS-SEM results as inputs, calibrating the construct scores to the [0–1] interval [117,118]. Following established guidelines, we set the total non-membership threshold to −3 standardized points (0.05 membership score), the crossover points to 0 standardized points (0.5 membership score), and the total membership threshold to +3 standardized points (0.95 membership score) [119,120].

We created a truth table to determine which combinations of conditions or configurations are adequate to produce the outcomes under investigation [121,122]. For our sample size (over 150), we set the consistency threshold to 0.8 and deleted rows with fewer than three cases [123,124]. We then calculated consistency and coverage for all configurations, identifying sufficient configurations with coverage greater than 0.2 and consistency higher than 0.8 [125,126]. The fsQCA produces three solutions: complex, parsimonious, and intermediate. Following recommendations in the literature [127,128], we focus on the intermediate solution.

Table 6. Sufficient causal configurations for Technological and Infrastructure Transformation (TIT).

Configurations	Raw Coverage	Unique Coverage	Consistency
Configurations for high Technological and Infrastructure Transformation
TIT = f (AIC; DI; DS; AICS; CPR)
AIC*DI*DS*~AICS	0.412	0.058	0.891
~AIC*DS*AICS*CPR	0.395	0.041	0.885
AIC*DI*AICS*CPR	0.438	0.064	0.902
solution coverage: 0.693
solution consistency: 0.837

Note: * indicates the presence of a condition, and ~ indicates its absence.

and

Table 7. Sufficient causal configurations for Socio-Economic and Policy Adaptation (SEPA).

Configurations	Raw Coverage	Unique Coverage	Consistency
Configurations for high Socio-economic and Policy Adaptation
SEPA = f (AIC; DI; DS; AICS; CPR)
DS*AICS*CPR	0.458	0.072	0.896
AIC*DI*AICS*CPR	0.421	0.055	0.889
~AIC*~DI*DS*AICS*CPR	0.387	0.043	0.881
solution coverage: 0.712
solution consistency: 0.853

Note: * indicates the presence of a condition, and ~ indicates its absence.

present the results of the fsQCA for technological and infrastructure transformation (TIT) and socio-economic and policy adaptation (SEPA), respectively. These results reveal more heterogeneous combinations of AI commitment dimensions as sufficient configurations to generate high levels of TIT and SEPA.

The fsQCA results provide a more nuanced understanding of the combinations of AI commitment dimensions that lead to high levels of TIT and SEPA. For technological and infrastructure transformation (TIT), we identified three sufficient configurations:

• High levels of AI capabilities, digital information, and digital skills, even without AI-enabled climate solutions.
• High digital skills, AI-enabled climate solutions, and climate policy readiness, even with low AI capabilities.
• High levels of AI capabilities, digital information, AI-enabled climate solutions, and climate policy readiness.

For socio-economic and policy adaptation (SEPA), we also identified three sufficient configurations:

• High digital skills, AI-enabled climate solutions, and climate policy readiness.
• High levels of AI capabilities, digital information, AI-enabled climate solutions, and climate policy readiness.
• High digital skills, AI-enabled climate solutions, and climate policy readiness, even with low AI capabilities and digital information.

These results highlight the complex interplay between different dimensions of AI commitment in achieving technological transformation and policy adaptation for a net zero carbon economy. They suggest that multiple pathways can lead to these outcomes, with some dimensions being more crucial than others in specific configurations. The fsQCA findings complement the PLS-SEM results by revealing that while some factors (like AI-enabled climate solutions and climate policy readiness) are consistently important across multiple configurations, others (like AI capabilities and digital information) may be less critical or even absent in some pathways to high TIT and SEPA. This analysis underscores the context-dependent nature of AI’s role in achieving a net zero carbon economy in emerging economies, suggesting that policymakers and practitioners should consider multiple approaches rather than a one-size-fits-all strategy.

We contribute new insights into AI-enabling climate solutions in the global south, significantly deepening our understanding of their implications beyond previous research of singular technology deployment trajectories. In line with the existing theoretical literature on digital transformation, all dimensions of AI commitment significantly influence technological transformation in PLS-SEM results; however, we find that the strength of this relationship differs across contexts, providing new insights. Notably, we find that the foundations of these capabilities—digital skills and climate policy readiness—are critical enablers for such capability development, but have largely been overlooked in previous research.

The fsQCA results bring new perspectives by showing that climate-resilient pathways can be achieved in various ways, while the current literature promotes a universal approach. These heterogeneous patterns show how emerging economies can reach the same climate ends with distinct mixes, amounts, and sizes of AI capabilities, digital access, and policies. For example, some countries thrive on solid policy frameworks despite lower levels of technological capacity and those that prosper with advanced AI deployment, even if the structure for policy is still developing. We believe these findings are of considerable practical importance and reinforce policymakers’ need to develop strategies that make sense in their particular context rather than hope for a single best practice. Such a realistic approach based on evidence will lead to more effective and better solutions for the issue in question, creating AI-based climate solutions implemented digitally, but where these implementations ensure digital inclusion.

The fsQCA results reveal three pathways to achieving climate resilience through AI implementation, each offering unique implications for policymakers. The first configuration demonstrates that solid digital skills combined with robust climate policy readiness can compensate for moderate AI capabilities. This suggests that policymakers in resource-constrained environments should prioritize capacity building and policy frameworks over advanced technological infrastructure. The second pathway highlights how superior AI infrastructure paired with solid institutional support can overcome limited digital inclusion. It indicates that targeted technology investments can accelerate climate resilience when backed by appropriate governance structures. This is particularly relevant for economies with established technological bases but gaps in digital accessibility. The third configuration shows that comprehensive digital inclusion combined with moderate levels of AI capability and policy readiness can achieve similar outcomes, emphasizing the importance of balanced development approaches. This pathway is especially pertinent for emerging economies seeking gradual but sustainable transformation.

These findings offer practical guidance for policy design:

• Resource Allocation: Invest directly toward the most viable pathway based on existing strengths.
• Sequential Implementation: Plan staged developments aligned with identified successful configurations.
• Risk Mitigation: Develop backup strategies using alternative pathways when primary approaches face obstacles.
• Stakeholder Engagement: Align different stakeholder groups with the most relevant configuration.
• Performance Metrics: Design monitoring frameworks based on the chosen pathway’s critical components.

For policymakers, these findings suggest the need to do the following:

• Assess their current position within these configurations.
• Identify the most achievable pathway given local constraints.
• Develop targeted interventions that align with successful patterns.
• Create flexible policies that can adapt to changing conditions.
• Build monitoring systems that track progress along chosen pathways.

9. Theoretical Implications

Overall, this study offers a comprehensive range of theoretical implications on AI’s role in achieving a net zero carbon economy in emerging economies. This comprehensive approach ensures the validity and reliability of our findings, instilling confidence in scholars, researchers, and practitioners in the AI and sustainability fields and inspiring them with the potential impact of our research.

9.1. Multifaceted AI Commitment

Our contribution to the literature is to develop and test a multifaceted construct of AI commitment regarding action on climate change. To give a better insight into how AI adoption and implementation components drive technological transition as well as policy adaptation, we break down the commitment of countries to AI based on five classification dimensions: By dissecting AI commitment into distinct components—namely, AI capabilities, digital information, digital skills, AI-enabled climate solutions, and climate policy readiness—we offer a more detailed perspective on how various facets of AI adoption and implementation facilitate technological transformation and policy adaptation. This approach builds upon prior research on technology adoption within sustainability frameworks [129,130].

9.2. Transformation Versus Adaptation for Differential Effects

PLS-SEM analysis shows that, while all dimensions of AI commitment significantly impact techno-infrastructural transformation, only a few directly affect socio-economic as well as planning or policy adaptation. This is in contrast to earlier studies, which implied that technology had a homogeneous impact on transformation and adaptation [131,132]. Overall, while AI can improve operational productivity, our results demonstrate that consideration should be given to broader socio-economic dynamics and policy frameworks. It reinforces the importance of focused policy initiatives to deal with social and economic access constraints, demonstrating that not all technological shifts deliver benefits in an equitable populist manner nor facilitate appropriate change.

9.3. Configuration or Composition Perspective for AI and Sustainability

Our study contributes to the AI and sustainability literature by introducing a configurational perspective employing fsQCA and PLS-SEM. This analysis demonstrates multiple pathways to high levels of technological transformation and policy adaptation that would support the concept or principle of equifinality in sustainability outcomes [131,133]. Our finding is novel in that it suggests that the influence of AI on climate action cannot solely be realized through addition but entirely depends upon synergies between dimensions of digital commitment to AI.

9.4. Context-Dependent Impact of AI

The results of our fsQCA analyses stress the contingent association between AI and climate action, adding to the discussion on whether traditional technology adoption models should be universal or context-specific. This analysis finds that AI facilitates these sustainability outcomes by configuring capabilities, information, skills, solutions, and policies. These insights warrant a more evolved theory for emerging market challenges and opportunities. By recognizing these configurations, stakeholders can better tailor AI applications for sustainability [134,135]. Going forward, this observation can spur the development of more nuanced theoretical frameworks capable of capturing emerging economies’ distinct challenges and opportunities.

9.5. Synergy Between Technology Acceptance and Sustainability Theories

This study helps build a bridge between technology adoption theories (such as UTAUT and TAM). We use these insights as a theoretical foundation for understanding digital technologies’ role in enabling sustainability transitions [136] in emerging economies by conceptualizing that AI commitment affects technological transformation and policy adaptation.

9.6. Methodological Contribution

This study’s mixed use of PLS-SEM and fsQCA sheds light on a novel methodological perspective. Hopefully, this dual approach will lead to a more robust understanding of the complex relationships between AI adoption and sustainability outcomes, showcasing that it may be beneficial to utilize both symmetric and asymmetric analytical tools for future consideration by sustainability researchers [137].

9.7. Rationales for Digital Inclusion Theory Broadening

Digital inclusion could be critical to climate action, as our results contribute to the digital inclusion theory. We show how digital skills and access to information are necessary for AI-driven climate solutions, expanding the scope of what counts as digital inclusion theory from social and economic outcomes [138] to incorporate environmental sustainability. Key areas for future research include longitudinal studies of how AI continues to influence climate action, cross-country comparisons examining the context specificity in these relationships, and qualitative work exploring precisely why firms combine commitments related to double-digit emissions scenarios. The study lays the groundwork for a complete theory of AI-led sustainability transitions in developing countries, underlining how technology, policy innovation, and social development must come together to espouse a net zero carbon future.

10. Practical Implications

The practical implications of findings from this study concerning achieving a net zero carbon economy in emerging economies should be relevant for policymakers, business leaders, and practitioners:

10.1. Integrated AI Implementation

The PLS-SEM results of our research affirm that all the constructs of AI commitment fundamentally contribute to technology–infrastructure transformation. Policymakers are encouraged to take a multifaceted view of the transition away from siloed assessments and deployments towards making AI-ready through building competence on top of digital information systems, while strengthening core competencies that create sustainability-enabled climate solutions across industries. This is done by ensuring readiness at all governmental levels. Governments and other entities, for instance, need to invest in broad AI strategies that bring all these dimensions together.

10.2. Policy Adaptation Interventions

Moreover, the differential impacts of AI commitment dimensions on socioeconomic and policy response underscore that interventions should be tailored. The effectiveness of policy to adapt will be most directly influenced by actions that enhance digital skills, promote AI-driven climate solutions, and improve readiness for adaptation related to climate policies. This might mean holding hackathons, creating insurance for new types of AI applications to solve climate problems by giving rewards out when they work immensely well, and automatically taxing things that could cause a broader impact, like technologies used in weather emergencies before or after the emergency is over.

10.3. Multiple Pathways to Success

The fsQCA results reveal that multiple routes lead to a high degree of technological change and policy modifications. This also means that leveraging AI in climate action is not a silver bullet, and no one-size-fits-all solutions exist. As such, practitioners should weigh the following considerations concerning their particular context and resources to decide which dimensions of AI commitment make the most sense. For example, locations with advanced digital abilities but limited AI capability might use established capabilities and climate policy preparedness to deliver transformation.

10.4. Why Is Policy Readiness the Need of the Hour?

In the fsQCA results, climate policy readiness is identified as a key condition in many configurations. And it further highlights the need for policies that allow AI-powered climate interventions to flourish. There is a need for more flexible, anticipatory policies that can keep pace with the fast-changing technological landscape and also confront climate challenges. Such measures could involve setting up regulatory sandboxes for experimenting with AI-bolstered climate solutions or incentive frameworks that remunerate innovative activity.

10.5. Technology vs. Skills Gap

The results imply that, at least in some contexts, a high level of people-related digital skills can compensate for lower capabilities or lesser levels of AI and factual information. This means that emerging countries should not lose focus on the development of human capital in their enthusiasm to deploy technology. Digital training and educational programs are also fundamental to any AI-for-climate strategy investment.

10.6. Collaborative Ecosystems

The key takeaway from this is that AI commitments across various dimensions intersects with each other, and it underlines the necessity of collaborative ecosystems. The collaboration among governments, businesses, academic institutions, and civil society should create synergies across these dimensions. This might include launching public–private partnerships for AI research and development, encouraging industry–academia collaboration to impart AI skills among professionals, and using multi-stakeholder platforms that allow inter-linkage between digital resources and sharing best practices in a structured form.

10.7. Context-Specific AI Solutions

Our results were context-specific, highlighting the need to contextualize AI solutions across different settings. This means that we need to invest a lot more time and energy into designing AI-powered climate solutions for the problems unique—on both sides of the supply-and-demand equation—in earnestly responsive ways rather than passively extractive.

Our findings reveal distinct patterns of AI adoption and digital strategy implementation across the studied countries, shaped by their unique regulatory and economic landscapes. Vietnam’s rapid industrialization and vital government AI initiatives contrast Malaysia’s advanced digital infrastructure and established climate frameworks. Italy’s position as a bridge between developed and emerging markets offers insights into EU policy implementation, while Greece’s post-crisis transformation and renewable energy focus presents unique adaptation patterns. These varying contexts significantly influence how each country leverages AI for carbon neutrality, demonstrating the importance of tailoring solutions to local conditions.

10.8. Monitoring and Evaluation Frameworks

Because our research excites more single routes to success, monitoring and evaluation frameworks built upon conceptual space must also be comprehensive enough to measure progress along those exact multiple dimensions of AI commitment. This will enable timely strategy adjustments and interventions to be made. Ideally, governments can set up AI readiness indices to track progress in these areas and consider them for policy.

10.9. Bridging the Digital Divide

This study’s results place a premium on developing digital skills and access to information in the emerging world, echoing concerns about closing gaps related to the digital divide [139]. Public policies that promote web connectivity should receive a boost, as well as efforts to build digital literacy and foster more equal access among groups.

10.10. Short-Term Actions for Long-Term Vision

Although a just net zero-carbon transition is more of an ideal than something that will be easily accomplished quickly, we must consider limiting emissions while addressing deeper systemic issues in our society. Leaders should articulate long-term visions for AI-driven climate action while focusing on specific short-term levers in the various dimensions described within this global fight to keep temperatures from rising.

These contributions collectively advance theoretical understanding and practical implementation of AI-driven climate solutions while addressing emerging economies’ unique challenges and opportunities. The work provides a comprehensive framework for researchers, policymakers, and practitioners working at the intersection of AI, digital inclusion, and climate action.

11. Limitations and Future Research

We believe that our work can contribute to understanding where AI is being employed—or needs to be employable—to deliver a net zero carbon economy in emerging economies. Following is a list of study limitations and suggestions for future research:

11.1. Study Design: Cross-Sectional

Our research offers a quick look at AI and climate action beyond our domain of expertise. However, it was imperfect since we could only be in one place, time zone, etc. The relationship between AI adoption and sustainability outcomes can change over time along with AI innovations. The third dimension concerns potential longitudinal investigations, capturing the temporal evolution in AI application and its downstream techno-transformational impact and policy adaptation over time. Such an approach would allow studying AI-enabled sustainability transitions’ causal linkages and temporal dynamics.

11.2. Focus on Emerging Economies

The study is based explicitly on emerging economies; therefore, its results do not apply to developed or the least-developed countries. This is not the last word on AI’s contribution to climate action. However, future research could further explore this subject through comparative work in diverse economic contexts and draw parallels or contrasts as they apply across settings.

11.3. Self-Reported Data

This correlational study used self-reported data from professionals and stakeholders, which may have been influenced by a social desirability bias or inaccuracy of self-assessment. To provide more comprehensive evidence for examining the relationship between AI adoption and climate action outcomes, potentially objective alternative measurements of these constructs (e.g., actual carbon emission reductions) may be added to future research supplements to self-reported data.

11.4. Limited Scope of Outcomes

We investigated TIT along with attitudes towards population services on technology and infrastructure, while SEPA output examined socio-economic and policy adaptation. Future research could include broader sustainability outcomes, like specific Sustainable Development Goals (SDGs) or more granular climate-resilience metrics.

11.5. Aggregate Level Analysis

This research focused on the AI commitment aggregates in emerging economies. Subsequent research may want to focus on sector- or industry-specific analyses for more nuanced findings. One area of interest for new research could be how AI changes the game about climate action, particularly within energy, agriculture, or transportation.

11.6. Scant Context: Only Partial Exploration of Factors

Our fsQCA analyses highlighted the key role of context, but we have not examined specific contextual factors that drive the effectiveness of varying AI commitment configurations. Further research is needed to explore how broader aspects such as culture, institutions, and the economy potentially influence the link between AI adoption and climate action outcomes.

11.7. Focus on Intended Outcomes

Most of these are focused on the intended benefits that AI adoption can bring to climate action. Additional research in the future can investigate any adverse or unintended impacts of deploying AI on sustainability, ranging from power usage regarding those systems to other effects like widening digital partitions.

11.8. Quantitative Emphasis

In this context, most of our analysis focuses on quantitative methods (PLS-SEM and fsQCA). For future research, incorporating a qualitative component would be beneficial for using mixed-method approaches. We could address the gap by looking at how AI is implemented and perceived about climate action further through the transitioning process, considering emerging economies.

11.9. Ethical Considerations

Our research did not provide a deep analysis of AI in fighting climate action from an ethical perspective. Advances in AI-driven sustainability will yield a variety of ethical challenges, such as keeping data private and safe from unwanted use or discrimination to avoiding algorithmic bias and ensure universal access. Fewer powerful nations will prepare for the leverage ability to become dominant. Future works should be explored into this.

11.10. Policy Evolution

However, since AI technologies are constantly evolving, and climate policy favours change profoundly or less, depending on stakeholder pressure, future research would be interesting to unpack how such a dynamic interplay is addressed within the current sustainability framework.

11.11. Integration with Other Technologies

While our study was mainly related to AI, the landscape of new and emerging technologies (e.g., blockchain and IoT) possibly implicated in sustainability transitions has broadened rapidly.

11.12. Scalable and Replicable

This likewise opens future research potential to investigate the scalability and replicability of effective AI-driven climate initiatives, thus shedding light on how innovative solutions can be scaled or transferred across emerging economies.

12. Conclusions

These implications can support stakeholders in emergent economies in utilizing AI more effectively to advance technological transformation and policy adaptation towards a net zero carbon economy. This demands a sophisticated, deep, and bold theory of change centered on combining the technology-push imperative with demand-pull and human capital development-led policies. As a result, more work is required on these limitations so that future research can respond to the design issue further and provide an explanatory theory and clarification about how AI might be used effectively for successfully realizing net zero carbon economy goals within emerging economies. It will enhance theoretical knowledge and offer critical support to policymakers and practical actors concerned with achieving sustainable development in a world that is changing under the pressure of ever more severe climate change impacts.

Our findings showed that our AI-powered methodologies have considerable potential to achieve net zero carbon economies in emerging markets. Our analysis identifies four unique benefits of using AI in this scenario. First, AI’s high predictability helps climate modelling and warning systems, improving preparation for climate challenges (and their reaction). Third, artificial intelligence IT can help improve energy efficiency and automate the monitoring of carbon emissions. Thirdly, data-driven decision-making supported by technology makes policy formulation and implementation more resilient to advances in analytics. Fourth, AI offers scalable omnidirectional solutions to rapidly transfer and adapt them in other environments—a feature beneficial for emerging economies able to bypass development stages while preserving sustainability.

These benefits resonate with our empirical findings, as all dimensions of AI commitment have a significant effect on technological infrastructure transformation, according to PLS-SEM results, while fsQCA results show several pathways leading to climate resilience. This synthesis of these methodologies demonstrates that AI is a game-changer in climate action, given the right mix of digital skills and policy structures.

Yet as we advance, even if promising solutions to climate problems lie in AI application, such successful implementation of resourcefully expensive technology requires mindful contemplation of circumstance homogeneity, equity in digital access, and political commitment at the local level. This study calls for more context-specific solutions in AI, tackling implementation challenges and ameliorating unequal access to this technology between lower and higher socio-economic strata. This paper has implications for theory and practice on the role of AI in climate action, which will be helpful for policymakers and practitioners in emerging economies that seek to meet their sustainability agenda.