The Energy Hunger Paradox of Artificial Intelligence: End of Clean Energy or Magic Wand for Sustainability?

Abstract

Artificial Intelligence (AI) plays a dual role in the clean energy transition, acting both as a major energy consumer and as a driver of sustainability. While AI enhances renewable energy forecasting, optimizes smart grids, and improves energy storage efficiency, the rapid growth of AI-driven data centers has significantly increased global electricity demand. AI-related energy consumption is projected to double by 2026 and triple by 2030, accounting for approximately 1.3% of global electricity use. This study adopts a multidisciplinary approach, synthesizing engineering, business, and policy insights to evaluate AI’s energy footprint and contributions to sustainability. The findings reveal that AI-driven optimization enhances smart grid efficiency and forecasting accuracy; however, infrastructure limitations, regulatory gaps, and economic constraints hinder AI’s alignment with sustainability goals. The results are systematically structured across five key themes: key findings, impact on energy consumption, risks and challenges, potential solutions, and policies and regulations. Supported by thematic tables and an original infographic, this study provides a comprehensive analysis of AI’s evolving role. By integrating AI with global sustainability policies, stakeholders can leverage its potential to accelerate the clean energy transition while minimizing the ecological footprint.

1. Introduction

The global electricity demand is increasing at an unprecedented pace due to rapid technological advancements, digitalization, and the widespread adoption of energy-intensive innovations such as Artificial Intelligence (AI) and cloud computing. Additionally, the transition to electric vehicles, urbanization, and the growing industrial needs of emerging economies are further driving electricity consumption. As global electricity demand continues to rise, the geographical distribution of consumption is also shifting significantly. Asia, particularly China, is becoming increasingly dominant in global energy consumption. Projections indicate that by 2025, Asia will account for half of the world’s electricity usage, with China alone consuming one-third of the global electricity supply. This growing demand is driven by rapid industrialization, urbanization, and the expansion of energy-intensive technologies, such as AI.

Figure 1 illustrates the evolution of global electricity demand by region from 1990 to 2025, showing how Asia’s share has steadily increased, while the relative consumption of the United States and OECD Europe has declined. The figure also highlights the significant increase in China’s electricity consumption, reflecting its position as a global economic powerhouse and leader in AI development.

AI is consuming an ever-growing share of global energy, with usage projected to reach staggering levels. In 2022, AI accounted for 2% of global energy consumption, which is comparable to the electricity usage of a small country. By 2026, the International Energy Agency (IEA) estimates that AI alone could consume as much electricity as Japan, the world’s fourth largest economy. Running AI models and servers demands two to three times the power of conventional applications. AI will significantly drive the projected 80% increase in U.S. data center energy demand by 2030. Today, popular systems like ChatGPT (https://openai.com/index/chatgpt/) process 200 million daily requests, consuming over half a million kilowatt-hours of electricity daily, which is equivalent to the energy needs of 17,000 U.S. homes [2]. These trends highlight the pressing challenge of balancing AI’s growth with sustainable energy practices.

It is undeniable that AI is revolutionizing industries with its ability to analyze data, learn patterns, and make intelligent decisions, driving advancements across sectors [3]. In healthcare, AI enhances diagnostics, personalizes medicine, and improves outcomes [4]. Finance leverages AI for trading, fraud detection, and customer service automation—manufacturing benefits from smart factories with AI-driven automation and predictive maintenance [5]. AI optimizes resource use in agriculture through precision farming and fosters innovations like smart cities to improve urban living. By driving efficiency and enabling innovation, AI transforms industries and creates new growth paradigms [6]. The energy transition encompasses a diverse range of sources beyond conventional fossil fuels, including solar, wind, hydro, geothermal, bioenergy, hydrogen, and emerging nuclear advancements. AI plays a crucial role in optimizing the efficiency and integration of these renewable sources into the energy grid and addressing challenges related to variability, storage, and system management [7]. AI is also increasingly utilized in energy extraction and transformation processes, particularly for optimizing unconventional energy sources and improving efficiency in complex energy environments. AI-driven models have been used to enhance fracturing fluid analysis in unconventional reservoirs, optimizing extraction processes and reducing energy waste [8]. In addition, AI-assisted monitoring of hydrate reservoir stability contributes to the improved management of energy extraction from deep-sea reserves [9]. These applications highlight AI’s potential not only as an energy consumer but also as a tool for improving energy efficiency and advancing sustainable resource utilization [10]. Integrating AI into energy-intensive industrial processes presents new opportunities to minimize energy losses and enhance system performance, thereby reinforcing its dual role in energy transition.

Conversely, AI’s rapid advancement has resulted in a surge in energy consumption, particularly in data centers, machine learning model training, and large-scale computational tasks. Training advanced models, such as deep learning networks, requires significant computational resources that demand substantial energy [11].

One of the primary drivers of AI’s escalating energy demand is the exponential growth in the size of machine learning models. Over the past few years, large language models (LLMs) have dramatically increased the number of parameters, requiring vast amounts of computational power for training and inference. This rapid expansion is directly correlated with increased energy consumption, as larger models require more powerful hardware, extensive cloud computing resources, and significant cooling infrastructure in data centers.

Figure 2 illustrates the evolution of AI model sizes from 2018 to 2022, highlighting an exponential growth trend. Notable models, such as OpenAI’s GPT series, Google’s T5, Microsoft’s Turing-NLG, and NVIDIA’s Megatron-LM, demonstrate the increasing scale of AI systems. This trend underscores the urgent need for more energy-efficient AI architectures and sustainable computational frameworks to mitigate the environmental impact of large-scale AI developments.

The proliferation of AI significantly increases energy consumption due to the computational demands of machine learning, cloud computing, and data centers [13]. Training deep learning models requires extensive Graphics Processing Unit (GPU) and Tensor Processing Unit (TPU) resources over long periods, while cloud computing and Internet of Things (IoT) devices further add to energy usage [14]. It is estimated that the global data center energy usage could double by 2026. In 2022, data centers consumed approximately 460 terawatt-hours (TWh) of electricity, and this figure is expected to rise to over 1000 TWh by 2026, effectively doubling consumption within this period [15]. In the United States, data centers accounted for about 4% of the nation’s electricity demand in 2022, consuming around 200 TWh. The IEA projects that by 2026, this consumption will increase to nearly 260 TWh, representing approximately 6% of the total U.S. electricity demand [16]. While these projections indicate a significant rise in energy consumption by data centers, the claim that they could account for up to 12% of the U.S. electricity consumption by 2026 is not supported by current data. However, recent reports suggest that, under specific scenarios, data centers could consume between 6.7% and 12% of U.S. electricity by 2028, highlighting the potential for substantial growth in energy demand over the next few years [17]. These projections are subject to various factors, including advancements in energy efficiency, adoption of renewable energy sources, and changes in data center technologies and operations.

AI’s rising energy demands pose environmental challenges, including increased greenhouse gas emissions and ecological strain, particularly in regions reliant on fossil fuels [18]. The environmental impact of AI is particularly evident in the carbon footprint of large language models (LLMs). Training these models requires vast computational power, which translates to substantial electricity consumption and CO₂ emissions. The carbon intensity of AI operations is also influenced by the energy efficiency of data centers (measured by Power Usage Effectiveness, PUE) and the carbon intensity of the power grid supplying them.

Table 1. Comparison of carbon emissions between models [12].

Model Name	Number of Parameters	Datacenter PUE	Carbon Intensity of Grid Used (gCO₂eq/kWh)	Power Consumption (MWh)	CO2eq Emissions (tons)
GPT-3	175B	1.1	429	1287	502
Gopher	280B	1.08	330	1066	352
OPT	175B	1.09	231	324	70
BLOOM	176B	1.2	57	433	25

presents a comparison of the energy consumption, CO₂ emissions, and PUE values for some of the largest AI models, including GPT-3, Gopher, OPT, and BLOOM. The table highlights the stark differences in carbon emissions depending on the energy efficiency of the data centers and the carbon intensity of the grid used. For instance, GPT-3 consumed 1287 MWh of electricity, resulting in 502 tons of CO₂ emissions, whereas BLOOM, which utilized a cleaner energy grid, produced only 25 tons of CO₂ despite its comparable size. These figures emphasize the importance of energy-efficient AI architectures and the use of low-carbon energy sources to reduce the environmental impact of AI-driven workloads.

Training large-scale AI models can result in significant carbon emissions, while data centers consume land, water, and rare earth metals, contributing to electronic waste [19]. The massive computer clusters powering AI, like ChatGPT, require four times more water than previously estimated, with 10–50 queries consuming about two liters of water primarily for cooling systems. This water usage, which relies on high-quality drinking water to avoid server damage, underscores the growing environmental impact of AI-driven data centers, prompting calls for improved efficiency and sustainability measures [20]. Addressing these issues involves renewable energy adoption, energy-efficient algorithms, and sustainable hardware practices. Aligning AI with sustainability principles is crucial for minimizing environmental impact and supporting global climate goals [21]. To further illustrate the scale of AI’s energy consumption and its environmental impact, Figure 3 presents key facts and figures regarding AI’s growing energy demand. The infographic highlights AI’s electricity and water usage, its carbon footprint, and the need for substantial clean energy investments to mitigate its environmental impact.

The global shift to renewable energy is vital for combating climate change and advancing sustainable development, aligning with Sustainable Development Goals (SDGs), specifically SDG 7 (Affordable and Clean Energy) and SDG 13 (Climate Action). Solar, wind, hydro, and geothermal energy have grown rapidly due to declining costs, technological advances, and supportive policies [22]. These sources offer solutions for decarbonizing power, enhancing energy security, and reducing fossil fuel reliance, with many nations committing to achieving net-zero emissions [23]. Achieving these goals requires the equitable, reliable, and sustainable integration of renewables into existing systems.

In line with the push for sustainable energy transitions, the global renewable energy capacity is projected to expand significantly over the next decade. Various energy sources—including solar PV, wind, hydropower, and bioenergy—are expected to be crucial in reducing carbon emissions and meeting Net-Zero Emission (NZE) targets. However, the pace of this expansion varies across different economic groups, with the G7 and G20 nations leading the investment in clean energy technologies.

Figure 4 presents the projected growth of renewable energy capacity from 2022 to 2030 under the IEA Net-Zero Emissions (NZE) scenario. The left panel illustrates the expected expansion of various renewable energy sources, while the right panel highlights the distribution of these investments among the G7, G20, and the rest of the world. These trends underscore the critical role of coordinated global action in scaling up renewable energy to meet the increasing electricity demands of AI and other emerging technologies.

Global investment in energy transition technologies, including energy efficiency, reached a record high of USD 1.3 trillion. However, annual investments must at least quadruple to remain on track to achieve the 1.5 °C Scenario in the International Renewable Energy Agency (IRENA)’s World Energy Transitions Outlook 2023 [25]. The IEA estimated that total energy investments would reach around USD 2.8 trillion, with more than USD 1.7 trillion allocated to clean energy technologies, including renewables, nuclear, grids, storage, low-emission fuels, efficiency improvements, and end-use renewables and electrification [26]. Projections indicated that global energy investment was set to exceed USD 3 trillion for the first time, with USD 2 trillion directed toward clean energy technologies and infrastructure. Investment in clean energy has accelerated since 2020, and spending on renewable power, grids, and storage is now higher than the total spending on oil, gas, and coal [27].

Despite this positive trajectory, disparities persist, particularly in emerging markets and developing economies. In 2024, these regions accounted for only about 15% of global clean energy spending, highlighting the need for increased investment to meet global climate and energy goals [28]. To align with international climate objectives, such as those outlined in the Paris Agreement, annual investments in renewable energy need to increase substantially. The IEA suggests that achieving net-zero emissions by 2050 will require a significant escalation in clean energy investments over the coming decades [27]. These figures underscore the importance of sustained and equitable investments in renewable energy infrastructure worldwide to facilitate a comprehensive and effective energy transition.

Renewables offer solutions for decarbonizing power, enhancing energy security, and reducing fossil fuel reliance, with many nations committing to net-zero emissions [29]. Achieving these goals requires the equitable, reliable, and sustainable integration of renewables into existing systems. Challenges remain, including the intermittency of solar and wind energy, which can disrupt grid stability [30]. Advanced forecasting and grid management are necessary to address supply-demand fluctuations [29]. Energy storage is another hurdle, as current technologies like lithium-ion batteries and pumped hydro face scalability and cost challenges. The limited availability of materials for batteries, such as lithium and cobalt, raises environmental and supply chain concerns [31]. Infrastructure upgrades, including enhanced transmission systems, are essential for scaling up decentralized energy production. AI adds complexity to the renewable energy transition by driving electricity demand. Data centers and high-performance computing, which are critical to AI operations, already consume significant amounts of energy. As AI adoption grows, its energy footprint could further strain renewable energy integration efforts [11].

While the role of AI in energy optimization has garnered significant attention, existing research primarily emphasizes its technical applications, such as improving grid management, enhancing energy forecasting, and optimizing renewable energy deployment. These studies highlight the potential of AI to contribute to the efficiency and sustainability of energy systems [32]. However, this focus often neglects AI’s dual role in energy, creating a critical research gap. Despite the evident environmental implications, there is a lack of comprehensive analysis of how AI’s energy footprint interacts with global sustainability efforts. Most studies focus on AI’s benefits while overlooking the carbon emissions and resource use associated with its energy demands. This omission creates an incomplete understanding of AI’s role in the energy ecosystem, hindering efforts to mitigate its environmental impact.

AI also has the potential to address the energy challenges it exacerbates. From developing energy-efficient algorithms to optimizing data center operations and integrating renewable energy into computational workflows, AI can provide innovative solutions to its growing energy demands [33]. However, the duality of AI, as both a contributor to and a solution for energy challenges, has not been sufficiently explored in the existing research [34]. Most studies treat AI as an external optimization tool rather than examining how its internal processes can become more sustainable. This creates a need for a balanced approach that recognizes AI’s potential to drive challenges and solutions in the energy field.

Addressing the complex relationship between AI and energy requires interdisciplinary collaboration; however, current research largely operates within siloed fields. Engineers and computer scientists focus on developing energy-efficient hardware and algorithms, while sustainability researchers emphasize renewable energy integration and environmental policies. At the same time, business and management studies often explore the economic implications of AI adoption, and communication researchers examine public perceptions and stakeholder engagement. However, these fields lack integration to comprehensively address the paradoxical and dual role of AI in energy systems.

The objectives of this study are to provide a comprehensive analysis of the energy demands associated with AI and examine their implications for the global transition to clean energy. By investigating AI’s dual role as both a challenge and a solution in global sustainability efforts, this study aims to highlight the complex interplay between technological advancements and environmental goals. Specifically, this research explores how AI’s growing energy footprint impacts renewable energy adoption while identifying opportunities for leveraging AI to enhance energy management and efficiency. This study also aims to integrate insights from the engineering, business, and communication disciplines to ensure a holistic approach to sustainably meet AI’s energy demands.

This study comprehensively analyzes AI’s energy dynamics, focusing on its dual role in the global energy transition and alignment with sustainability goals. Specifically, this research addresses several objectives. First, it analyzes AI’s energy demands and their implications for renewable energy systems by examining how AI’s growing energy consumption interacts with efforts to achieve SDG 7 and SDG 13. This analysis highlights the key areas of conflict and synergy to inform targeted strategies that optimize AI’s energy use while advancing renewable energy adoption. Second, this study investigates the barriers to aligning AI’s energy usage with sustainability objectives, addressing the technological and infrastructural challenges tied to SDG 7 and the policy and economic barriers to decarbonization under SDG 13. It examines the technical, economic, and policy challenges, including infrastructure and market constraints, that hinder the integration of AI with clean energy goals.

Furthermore, this research explores AI’s potential to address its energy challenges by assessing innovations such as energy-efficient algorithms, renewable energy integration, and advanced energy management techniques that reduce AI’s energy footprint. These developments contribute to SDG 7 by improving energy accessibility and SDG 13 by reducing greenhouse gas emissions. Additionally, this study emphasizes the development of interdisciplinary strategies for sustainable AI adoption, highlighting the need for collaboration among engineering, business, public policy, and communication disciplines. By integrating technical innovations, economic strategies, regulatory frameworks, and public engagement, these fields collectively address the challenges and opportunities of aligning AI with global sustainability goals and promoting equitable energy access and resilience in energy systems. Finally, this study evaluates the alignment of AI’s contributions with SDG targets by analyzing areas where AI advances global sustainability goals and conflicts may arise, and offers solutions to harmonize these roles. This research provides a nuanced understanding of AI’s energy interplay through these objectives, offering actionable insights for achieving a sustainable and equitable energy future.

The growing role of AI in technological innovation is accompanied by escalating energy demands, raising critical questions about its implications for sustainability. While AI offers transformative benefits, such as optimizing renewable energy systems and enhancing grid stability, its computational processes require vast amounts of electricity, contributing to a growing carbon footprint and straining global energy resources. This study explores the significance of AI’s energy demands and their implications for sustainability efforts, particularly in the context of SDG 7 and SDG 13, through the first research question: How significant are AI’s energy demands, and what are their implications for sustainability efforts?

Integrating AI with renewable energy goals presents considerable challenges. Infrastructure limitations, reliance on non-renewable energy sources, high computational energy demands, and scalability constraints underscore the need for comprehensive strategies to overcome these barriers. This context motivates the second research question: What challenges hinder the integration of AI with renewable energy goals?

Despite its energy demands, AI has the potential to play a pivotal role in advancing renewable energy adoption. By improving grid stability, optimizing energy storage systems, forecasting renewable energy generation, and supporting predictive maintenance, AI can address many challenges faced by renewable energy systems. To explore this potential, the third research question is: How can AI overcome its energy challenges and support renewable energy adoption?

Aligning AI development with global sustainability goals requires interdisciplinary strategies that bridge technological innovation, policy frameworks, and business practices. Collaboration among engineering, public policy, and business disciplines is essential for developing energy-efficient AI technologies, improving renewable energy integration, and creating equitable and scalable solutions. This leads to the fourth research question: What interdisciplinary strategies are necessary to align AI’s energy use with global sustainability goals?

These research questions provide a framework for understanding AI’s dual role as an energy consumer and a critical enabler of sustainable energy transitions. This study’s objectives are closely linked to its research questions, which will be addressed in the findings. The objectives are to analyze AI’s energy demands, identify barriers to aligning AI with renewable energy goals, explore AI’s potential to address its energy challenges, and develop interdisciplinary strategies for its sustainable adoption.

This study employs a comprehensive and interdisciplinary methodology, synthesizing insights from engineering, business, and public policy to provide a nuanced understanding of AI’s dual role as an energy consumer and enabler of sustainability. A conceptual analysis framework underpins the research design, integrating evaluations of peer-reviewed literature, industry reports, and policy documents to align with SDG 7 and SDG 13. Data were sourced from authoritative databases like Scopus and Web of Science, as well as reports from leading industry players and international organizations. Employing thematic and comparative analyses, this study maps AI’s energy demands, its role in renewable energy optimization, and its alignment with sustainability objectives while addressing ethical considerations by relying on credible and inclusive sources.

The practical implications of this study are significant for all stakeholders. Policymakers can leverage these findings to shape regulations that promote energy-efficient AI technologies and their sustainable integration into renewable systems. Technologists and business leaders are provided with actionable strategies to enhance AI’s energy efficiency, align AI applications with global climate goals, and navigate potential synergies and conflicts between AI and renewable energy capacities. Ultimately, this research offers a roadmap for harnessing AI’s transformative potential while addressing its energy and environmental challenges, thereby ensuring a balanced and sustainable energy transition.

2. Materials and Methods

This study employs a comprehensive and interdisciplinary methodology to align with the objectives outlined in the Introduction section. By synthesizing insights from engineering, business, and public policy, this methodology provides a nuanced understanding of AI’s dual role in energy systems, addressing its challenges and opportunities for advancing global sustainability goals.

This research utilizes a conceptual analysis framework that integrates critical evaluations of the existing literature, industry reports, and policy documents. This interdisciplinary approach ensures that the study transcends traditional boundaries, offering a holistic perspective on AI’s impact on energy consumption and sustainability. The design explicitly ties research inquiries to SDG 7 (Affordable and Clean Energy) and SDG 13 (Climate Action), ensuring their relevance to global objectives.

2.1. Literature Retrieval and Search Methodology

To ensure a comprehensive and systematic review, data were collected from the Scopus and Web of Science (WoS) databases, focusing on peer-reviewed articles that examined AI, renewable energy systems and sustainability. The literature search was structured as follows.

• Keyword Selection: Relevant search terms were identified based on prior studies and emerging research trends in AI and energy sustainability. These included “Artificial Intelligence and energy efficiency”, “AI in renewable energy forecasting”, “AI-driven grid stability”, “AI carbon footprint”, and “sustainable AI models.
• Boolean Operator Usage: To ensure precise results, search queries were refined using Boolean logic (e.g., “Artificial Intelligence” AND “Renewable Energy” AND “Sustainability”).
• Time Range: Literature was restricted to the past five years (2019–2024) to focus on the latest advancements while incorporating foundational studies.
• Inclusion and Exclusion Criteria: Articles were filtered based on relevance, citation impact, and methodological rigor, excluding non-peer-reviewed sources and conference papers lacking empirical validation.

In addition to the academic literature, industry reports from organizations such as the IEA, IRENA, and WEF were reviewed to provide practical insights into technological advancements and energy consumption trends. Policy documents were analyzed to ensure their alignment with global sustainability frameworks.

This study considers the broader energy transition landscape, incorporating various energy types, such as traditional fossil fuels, renewables (solar, wind, hydro, geothermal, and bioenergy), and emerging solutions like hydrogen and advanced nuclear technologies. AI’s applications across these energy sources were analyzed through the literature retrieval and comparative analysis process, ensuring a comprehensive understanding of its role in the evolving energy ecosystem.

Figure 5 illustrates the structured search methodology employed in this study to streamline and visualize the literature search process.

2.2. Analytical Framework

A dual-method analytical strategy was employed:

Thematic Analysis: Recurring themes in the literature were identified and categorized to map AI’s energy demands, contributions to renewable energy systems, and sustainability challenges. The analysis emphasized key intersections, such as AI-driven energy optimization, grid stability, and the environmental impact of AI infrastructure.

Comparative Analysis: This component examined two dimensions:

• AI Energy Consumption vs. Renewable Energy Capacities: A detailed comparison illuminated potential synergies and conflicts, quantifying AI’s energy requirements relative to renewable energy system capabilities.
• Alignment with SDGs: AI applications were evaluated against specific sustainability targets, particularly SDG 7, SDG 9 (Industry, Innovation, and Infrastructure), and SDG 13. This provided a structured framework for assessing AI’s transformative potential in achieving global sustainability objectives.

The retrieved literature was analyzed and categorized into key themes to systematically evaluate AI’s role in the clean energy transition. The findings are presented in structured tables under seven subsections, each focusing on a distinct thematic area: Key Findings, Impact on Energy Consumption, Risks and Challenges, Potential Solutions, and Policy and Regulation. These structured tables clearly synthesize the reviewed literature, ensuring a comprehensive and accessible overview of AI’s contributions and challenges in the energy sector.

While the study primarily relied on secondary data, ethical considerations were observed by ensuring the use of credible, traceable and peer-reviewed sources. The analysis avoids biases by integrating diverse perspectives across disciplines and geographies, ensuring inclusivity in addressing global sustainability challenges.

By employing this rigorous and interdisciplinary methodology, this study provides actionable insights for policymakers, technologists, and business leaders, contributing to a deeper understanding of AI’s role in the clean energy transition and in global sustainability efforts.

3. Findings

3.1. AI as an Energy Consumer

This growing energy demand is primarily driven by the rapid expansion of data centers, which serve as the backbone of the AI infrastructure. Data centers house vast networks of servers that process and store massive amounts of data, requiring continuous power for both the computational workloads and cooling systems. As AI applications become more sophisticated and widespread, the computational intensity of model training and inference increases, leading to a sharp increase in data center energy consumption. This trend highlights the urgent need to assess the sustainability of AI’s energy footprint and explore strategies for improving energy efficiency. Figure 6 presents the increasing power demand of data centers between 2015 and 2023, along with the estimated total data center workload demand (right axis). The data indicate a steady rise in energy consumption, with significant acceleration after 2020. This trend reflects the growing computational requirements of AI-driven applications, cloud computing, and large-scale machine learning models. By 2023, data center energy demand is estimated to exceed 400 TWh, reinforcing concerns about the sustainability of AI’s rapid expansion.

AI systems are increasingly recognized as significant energy consumers, with model training, inference, and data storage being the primary contributors to their substantial energy demands. AI systems are increasingly recognized as significant energy consumers, with model training, inference, and data storage being primary contributors to their substantial energy demands. Training large-scale machine learning models, particularly deep learning frameworks, necessitates iterative computations over massive datasets that are performed on high-performance GPUs or TPUs. These processes require substantial computational resources and consume large amounts of electricity.

Beyond AI itself, the infrastructure supporting its operations—particularly data centers—has become a major driver of global electricity demand. The rapid expansion of cloud computing, AI-driven applications, and digital services has led to a significant increase in power consumption by data centers worldwide. This trend is expected to continue, as projections indicate that data centers will add 223 TWh of electricity demand by 2030, making them one of the fastest growing energy consumers globally.

Figure 7 illustrates the projected growth in global electricity demand from 2023 to 2030, highlighting the increasing energy requirements of data centers compared to other sectors.

However, data centers are not the only factor driving the increased electricity demand. AI applications and cryptocurrencies are also significant contributors to this growth. The energy demands of AI-driven workloads, including deep learning models and large-scale inference tasks, continue to escalate, placing additional strain on the global electricity supply.

Figure 8 presents the projected global electricity demand from data centers, AI, and cryptocurrencies between 2019 and 2026. The data illustrate three scenarios: a low case, where efficiency improvements mitigate energy growth; a base case, where demand follows current trends; and a high case, where rapid AI adoption and computational demands accelerate power consumption dramatically. These projections highlight the urgent need for sustainable energy strategies to accommodate AI’s rapid expansion.

Training large-scale machine learning models, particularly deep learning frameworks, necessitates iterative computations over massive datasets performed on high-performance GPUs or TPUs. These processes require substantial computational resources and consume vast amounts of electricity [38]. Training state-of-the-art natural language processing models like Generative Pre-trained Transformer—GPT-3 can consume over 1200 megawatt-hours of electricity, which is equivalent to the annual energy usage of hundreds of households [39]. Similarly, inference tasks, which involve deploying trained models to generate predictions or make real-time decisions, increase energy consumption due to the continuous demand for computational power. Additionally, the storage and processing of vast amounts of data necessary for AI operations contribute significantly to energy use, with data centers operating 24/7 to ensure uninterrupted services [40].

Data centers are the backbone of AI infrastructure and are pivotal in meeting computational and storage demands. These centers host thousands of servers that consume electricity to process tasks and cooling systems required to maintain optimal operating temperatures. The proliferation of cloud computing platforms has amplified energy consumption as the demand for scalable and remote AI services increases [41]. Although cloud computing enables seamless access to vast computational resources, its energy footprint has become an increasing concern. Studies indicate that data centers accounted for approximately 1–1.5% of global electricity consumption in 2023—a figure projected to rise with the expanding adoption of AI technologies [42]. In response to these growing demands, the trend toward distributed and edge computing has partially mitigated the energy pressures at centralized facilities. By decentralizing data processing and storage, edge computing aims to reduce latency and optimize energy efficiency. However, this approach introduces new challenges in managing energy use across a dispersed infrastructure, particularly in balancing energy loads and maintaining operational efficiencies.

The rapid growth of AI technologies has led to a corresponding increase in their carbon footprint, posing significant challenges to global sustainability efforts. Many data centers still rely on non-renewable energy sources, leading to considerable greenhouse gas emissions [43]. The short lifecycle of hardware components exacerbates their environmental impact, contributing to electronic waste and the depletion of critical resources. As AI models grow larger and more complex, their energy requirements scale exponentially, raising concerns about their alignment with global climate goals; without proactive measures to curb the environmental costs of AI, its carbon footprint risks offsetting the societal and technological benefits it offers in various fields.

Training advanced models, such as deep neural networks with millions or billions of parameters, is particularly energy intensive. These models require intensive computations across processors, consuming large amounts of electricity [44]. Data centers, which house the servers operating continuously for training and inference tasks, further amplify these energy demands [45]. As AI applications expand across sectors, the associated energy consumption and environmental impacts are expected to rise, heightening concerns about the compatibility of AI with global climate goals [46]. AI’s energy use contributes to a growing carbon footprint, particularly when powered by fossil fuels. Training state-of-the-art models can generate substantial carbon emissions, the scale of which depends on the energy mix used for training and the complexity of the model [47]. The widespread adoption of AI has further magnified concerns regarding its environmental footprint. While AI offers transformative benefits—such as enhanced diagnostic accuracy and improved logistics—its energy-intensive nature challenges global sustainability goals, including those outlined in the Paris Agreement and SDG 13. These challenges highlight the urgent need for innovations in energy management and efficiency.

Despite these challenges, the potential of AI to contribute positively to sustainability efforts should not be overlooked. AI systems can optimize energy usage through machine learning algorithms that enhance grid stability, predict renewable energy generation, and improve energy efficiency. This dual role—where AI acts as both an energy-intensive consumer and a critical enabler of sustainable energy transitions—underscores the necessity of a balanced approach to its development and deployment. Further research on energy-efficient AI architectures and renewable energy integration is essential to align AI with broader climate goals.

As AI continues to evolve, addressing its energy consumption is becoming increasingly critical. The IEA forecasts that electricity consumption from data centers, AI, and the cryptocurrency sector could double by 2026, underscoring the pressing need for sustainable solutions [2,15,48]. Moreover, a report supported by the U.S. Department of Energy projects that the power demand for U.S. data centers could nearly triple by 2028, potentially consuming up to 12% of the nation’s electricity [49]. These projections highlight the significant impact that AI could have on global energy resources if current trends continue. Considering these challenges, it is imperative to explore strategies that mitigate AI’s energy consumption while harnessing its potential to optimize energy systems. To provide a structured overview of the key insights discussed,

Table 2. AI as an energy consumer.

Key Findings	Impact on Energy Consumption	Risks and Challenges	Potential Solutions	Policy and Regulation
AI systems require continuous computational power for model training, inference, and data storage. The increasing adoption of AI applications is driving higher energy demand globally. AI data centers operate around the clock as the backbone of AI infrastructure.	AI-driven workloads require high-performance computing, leading to constant energy consumption. The rise of cloud computing and large-scale AI models is accelerating power demands. Cooling systems in data centers significantly add to AI’s overall energy footprint.	AI’s dependence on non-renewable energy sources contributes to environmental sustainability concerns. The short lifecycle of AI hardware leads to increased electronic waste and resource depletion. Energy demand from AI is growing faster than improvements in energy efficiency.	AI hardware and algorithms can be optimized to reduce unnecessary energy use. AI-powered energy management can improve efficiency and minimize waste. The integration of renewable energy into AI infrastructure can help mitigate environmental impact.	Governments can introduce energy efficiency standards for AI operations. AI industry players should be encouraged to transition to sustainable energy sources. Regulations should align AI growth with long-term environmental sustainability goals.

summarizes the findings of the section.

3.2. AI as an Energy Optimizer

Despite its significant energy demand, AI has emerged as a transformative force for optimizing renewable energy systems and management. Machine learning algorithms enhance grid stability, predict renewable energy generation and improve energy efficiency. This dual role underscores AI’s critical place in sustainable energy transitions, emphasizing the need for further exploration of its global implications [50]. AI-powered tools analyze vast amounts of data in real time to improve the efficiency and reliability of energy grids [51]. Smart grid technologies use AI to monitor energy demand, predict consumption patterns, and dynamically allocate resources to minimize waste. Predictive analytics enabled by machine learning algorithms can forecast equipment failures, optimize maintenance schedules, and ensure an uninterrupted energy supply [52]. These capabilities reduce operational costs and enhance the resilience of energy systems against disruptions, such as natural disasters and cyberattacks.

The potential impact of AI on grid optimization is significant. AI can enhance the efficiency, reliability, and resilience of power grids by rapidly processing vast amounts of data to assist in decision-making and identify patterns. AI-accelerated power grid models can improve capacity and transmission studies, aiding better grid management [53]. The integration of AI into power grids is gaining momentum globally. Recent studies indicate that new patents for AI applications in power grids have grown sixfold in recent years, with the United States and China leading smart grid development [54]. Furthermore, AI’s ability to forecast renewable energy production can assist grid operators in managing the variability of renewable sources, thereby facilitating their integration into the energy mix. AI’s potential to make the electric grid smarter and more resilient is being actively explored. Researchers are developing AI-driven methods to extract insights from vast quantities of grid data to ensure greater reliability and efficiency [53]. Additionally, initiatives have been launched to assess AI’s energy opportunities and challenges, accelerate the deployment of clean energy, and manage the growing energy demand for AI technologies. These developments underscore AI’s transformative potential in modernizing power grids, promoting sustainability, and meeting escalating global energy demands [55].

AI is revolutionizing the renewable energy sector by addressing the challenges of forecasting, storage, and distribution. Accurate weather forecasting powered by AI enables better prediction of renewable energy generation from inherently variable sources like wind and solar. AI models have improved wind energy forecasting by 25–30%, enabling grid operators to plan allocations more effectively and reduce their reliance on fossil fuel-based backup systems [56]. AI algorithms also optimize energy storage systems by predicting demand surges and strategically charging or discharging batteries to balance the supply and demand [57].

In the distribution phase, AI-driven solutions enhance grid stability by dynamically routing energy to areas of greatest need, minimizing transmission losses, and improving efficiency. Integrating AI into energy systems has demonstrated significant potential for enhancing efficiency and reducing distribution losses. In pilot projects across Europe and North America, AI-driven strategies have achieved up to a 15% increase in energy efficiency by optimizing the control of energy storage systems [58]. These advancements are part of a broader trend toward AI adoption in the energy sector. The global AI in the energy market is projected to grow at a compound annual growth rate (CAGR) of 17.2%, reaching approximately USD 14.0 billion by 2029 [59]. Furthermore, AI’s role in energy optimization extends beyond distribution losses. AI-enabled demand-response strategies have been identified as practical solutions for increasing the sustainability of energy systems while reducing the associated costs [60]. These developments underscore AI’s transformative potential in modernizing power grids, promoting sustainability, and meeting escalating global energy demands.

AI enhances energy management by enabling real-time adjustments to supply and demand. Renewable energy grids often face challenges due to fluctuating consumption and unpredictable weather conditions [61]. AI algorithms can predict energy consumption patterns, allowing grids to dynamically adjust their supply and improve reliability [62]. AI is critical for accelerating the energy transition by optimizing the integration of renewable energy sources into the grid. By enabling real-time adjustments to supply and demand, AI algorithms can predict energy consumption patterns, improve grid stability, and address the challenges posed by fluctuating consumption and variable renewable sources like wind and solar. AI-driven technologies, such as digital twins and predictive maintenance, enhance efficiency and reliability, reduce downtime and operational costs, and support a more sustainable and competitive global energy sector. AI-supported predictive maintenance has also transformed renewable energy systems by identifying potential equipment problems using sensor data. This proactive approach prevents system failures, reduces energy waste, and extends the lifespan of the infrastructure [63]. AI-based predictive maintenance in wind farms has extended turbine lifespans by an average of five years, while reducing maintenance costs by 30% [64].

AI further optimizes energy storage systems by managing battery health, predicting storage needs, and optimizing charge-discharge cycles. This ensures the efficient storage of excess renewable energy during peak demand periods, maximizing value and reducing inefficiencies [65]. These applications underscore AI’s pivotal role in facilitating the global transition to clean energy systems. By enhancing grid efficiency, improving renewable energy forecasting, and supporting energy storage and predictive maintenance, AI provides a comprehensive suite of tools for overcoming the technical and operational barriers. This capacity highlights AI’s importance in accelerating the adoption of sustainable energy solutions while addressing global climate goals. To present a concise summary of the key insights explored,

Table 3. AI as an energy optimizer.

Key Findings	Impact on Energy Consumption	Risks and Challenges	Potential Solutions	Policy and Regulation
AI enhances energy efficiency by optimizing power grids, predicting energy demand, and reducing energy waste. Machine learning algorithms improve the stability and reliability of renewable energy systems. AI-driven predictive maintenance helps extend the lifespan of energy infrastructure and reduces operational costs.	AI improves energy distribution by dynamically balancing supply and demand in real time. Smart grid technologies powered by AI reduce transmission losses and improve overall efficiency. AI-driven forecasting minimizes reliance on fossil fuel backup systems in renewable energy grids.	AI models require significant computational power, which may offset energy efficiency gains. The integration of AI into existing energy infrastructure can be costly and complex. AI-powered systems depend on high-quality data, which can be limited or inconsistent in certain regions.	AI can be used to optimize renewable energy storage and improve battery management. Developing decentralized AI systems can enhance energy efficiency across distributed power networks. AI-powered smart grids can dynamically adapt to changing energy demands and improve sustainability.	Governments can support AI-driven energy solutions through incentives and funding for smart grid research. Regulatory frameworks should ensure AI applications in energy align with sustainability goals. Policies should encourage collaboration between AI developers, energy providers, and policymakers to maximize efficiency gains.

outlines the findings of the AI as an Energy Optimizer section, highlighting its role in enhancing energy efficiency and sustainability.

3.3. Key Challenges of Renewable Energy Transition and Potential Contributions

A major challenge in the renewable energy transition is the intermittency of sources like wind and solar, which depend on weather conditions and vary daily and seasonally [66]. Solar generation declines at night or on cloudy days, while wind energy depends on wind strength [67]. This variability requires backup systems, grid management, and storage solutions to ensure a stable energy supply, especially in regions with high renewable energy penetration. The successful integration of renewable energy also hinges on modernizing aging grid infrastructures initially designed for centralized power generation. A study by the U.S. Department of Energy (DOE) emphasizes that grid modernization requires integrating digital technologies, such as smart meters, sensors, and advanced communication networks, to accommodate bi-directional energy flows and distributed generation [68]. Failure to upgrade grids risks curtailing the potential of renewable energy and could lead to operational inefficiencies and power outages. Digital twins, which create virtual models of physical grids, are emerging as tools for simulating grid behavior under renewable-heavy scenarios, thereby aiding proactive planning and stability management.

Energy storage is another critical challenge, as the limited storage capacity restricts the use of the excess energy generated during high-production periods [69]. Current technologies, such as lithium-ion batteries, face constraints in terms of energy density, efficiency, and cost [70]. In addition to lithium-ion batteries, emerging technologies such as flow batteries, solid-state batteries, and green hydrogen are reshaping the energy storage landscape. Scaling large-scale storage solutions, including pumped hydro and next-generation batteries, is essential to balance the supply and demand as renewables expand [71]. Scalability is also a challenge, given the growing global energy demand and the underdeveloped infrastructure in many areas [72]. Expanding infrastructure, such as transmission lines and substations, requires significant investment, particularly in regions where renewable resources are distant from population centers. Energy storage is critical for ensuring flexibility, stability, and reliability in the electricity system, where renewable energy is expected to account for 69% of the total capacity by 2030 and 80% by 2050. The European Commission’s 2023 recommendations emphasize the need to expand storage capacity, address deployment barriers, and foster research on advanced technologies to support the growing demands of renewable integration and system flexibility [73].

One of the critical bottlenecks in the renewable energy transition is the insufficient and uneven distribution of investments required for infrastructure development. According to the International Renewable Energy Agency (IRENA), achieving a global energy system transformation to meet the Paris Agreement targets requires cumulative investments of USD 131 trillion by 2050 [74]. This challenge is more pronounced in developing nations, which receive only about 15% of global clean energy investments despite representing over 60% of the global population [75]. This disparity necessitates innovative financing mechanisms, international cooperation, and enhanced policy frameworks to mobilize capital and support energy equity. Coordinated efforts between governments, businesses, and utilities are necessary to overcome these barriers. AI offers solutions for improving renewable energy reliability. AI algorithms optimize energy generation and distribution by analyzing real-time data, thereby enabling dynamic adjustments to match supply and demand [76]. This reduces waste and reliance on fossil fuel backups, enhancing grid flexibility. AI’s potential extends beyond grid optimization to foster decarbonization strategies. AI predictive analytics can accurately forecast energy demand, reduce energy waste, and improve demand-response strategies. Furthermore, machine learning algorithms are used to optimize energy mix strategies, ensuring that renewable sources are maximized while minimizing reliance on fossil fuels [77]. AI also improves efficiency through predictive maintenance for technologies like wind turbines and solar panels and optimizes energy flow across the grid to minimize losses. Additionally, AI supports decentralized energy sources, such as rooftop solar panels, by creating smarter, more responsive grids that handle distributed generation complexities.

The transition to renewable energy also poses socio-economic challenges, such as job displacement in fossil fuel-dependent regions. The International Labour Organization (ILO) estimates that while renewable energy could create approximately 24 million new jobs globally by 2030, around 6 million jobs in fossil fuel sectors could be lost [78]. Ensuring a just transition requires reskilling programs, labor market policies, and social safety nets to mitigate inequalities and foster inclusive growth. To capture the key challenges discussed,

Table 4. Challenges of the renewable energy transition.

Key Findings	Impact on Energy Consumption	Risks and Challenges	Potential Solutions	Policy and Regulation
Renewable energy sources like wind and solar are intermittent and depend on environmental conditions. The existing energy infrastructure was built for centralized fossil fuel-based systems, making integration of renewables challenging. Energy storage capacity is insufficient to balance fluctuations in renewable energy production.	The variability of renewable energy sources creates instability in power grids, requiring backup systems. Inefficiencies in energy storage and distribution lead to energy losses. Expanding renewable energy requires significant investment in new infrastructure and smart grid technologies.	Without sufficient storage solutions, excess renewable energy often goes unused. Upgrading transmission networks to handle decentralized renewable energy sources is costly and time-consuming. Developing countries face barriers in adopting renewable energy due to infrastructure and financial constraints.	Advanced battery technologies and AI-powered storage management can help balance energy supply and demand. Smart grids and AI-driven forecasting can improve the reliability of renewable energy integration. Investment in new energy infrastructure and policies supporting renewable deployment can accelerate the transition.	Governments must establish incentives for energy storage solutions and smart grid development. Regulatory frameworks should ensure grid modernization aligns with renewable energy goals. International cooperation is necessary to bridge gaps in renewable energy adoption across different regions.

provides a summary of the obstacles and considerations in the Renewable Energy Transition process, along with potential solutions for a sustainable shift.

3.4. Barriers to Aligning AI with Renewable Energy Goals

AI’s rapid growth and high energy demands challenge its alignment with renewable energy goals. Large-scale AI models require significant computational power for training and inference, leading to substantial energy consumption [79]. The training of large-scale AI models generates significant carbon emissions, complicating their alignment with renewable energy objectives. Recent advances, such as distributed training systems and energy-efficient algorithms, have aimed to reduce this footprint; however, their widespread adoption remains limited. Incorporating renewable energy into AI data centers is critical; however, as of 2023, only 20% of global data centers source their energy from renewables [80]. This demand risks exceeding the capacities of intermittent renewable sources like wind and solar, which depend on weather and time of day [81]. Additionally, renewable energy infrastructure faces scalability issues with regional limitations and insufficient storage solutions to support large-scale AI operations [82]. AI systems are geographically concentrated in regions with insufficient renewable energy capacity. For instance, the United States and China collectively account for over 50% of AI-related energy use; however, their renewable energy adoption varies significantly [83]. Addressing scalability requires decentralized AI models and enhanced storage solutions, such as vanadium flow batteries and hydrogen storage, to mitigate the mismatches between energy production and consumption. Investments in grid connectivity, energy storage, and diversified energy sources are essential to address these challenges. Economic barriers also hinder AI’s integration with renewable energy systems. High upfront costs for AI technologies, including hardware, sensors, and advanced algorithms, limit their adoption, especially in developing countries [84].

While high upfront costs hinder adoption, renewable energy-powered AI presents substantial economic opportunities. This includes cost savings from improved energy efficiency, optimized grid management, and reduced carbon emissions. Scaling green AI solutions has the potential to significantly boost the global economy. AI could add USD 13 trillion to the global economy by 2030 [85]. Additionally, research suggests that AI applications in sectors like agriculture, energy, and transport could contribute up to USD 5.2 trillion to the global economy by 2030 [86]. While specific figures for green AI solutions vary, these projections underscore the substantial economic value that AI, particularly when aligned with sustainability goals, can unlock by 2030. Encouraging private investment through green bonds and public-private partnerships can accelerate this transition.

While AI can reduce long-term operational costs, substantial financial support from the public and private sectors is necessary to make these investments viable [87]. Policy gaps further complicate AI’s alignment with renewable energy. The absence of comprehensive regulatory standards creates uncertainty among stakeholders [88]. Although policy gaps hinder the integration of AI with renewable energy, promising frameworks are emerging. The European Union’s AI Act emphasizes energy efficiency and sustainability in AI systems, providing incentives for renewable energy-powered data centers [89]. These policies demonstrate how governments can promote alignment through targeted regulations and financial incentives. Effective integration requires coordinated policies across the energy, technology, and environmental sectors to ensure sustainability, efficiency, and equity. Governments must develop frameworks to promote energy-efficient AI use, ensure data privacy, and encourage cross-sector collaboration. While high computational energy demands and scalability limitations hinder the balance between AI’s energy needs and clean energy availability, coordination between governments, businesses, and the research community is required to develop sustainable, scalable, and economically viable AI solutions.

Aligning AI with renewable energy goals also raises ethical and social challenges. Ensuring equitable access to energy-efficient AI technologies is crucial, particularly in developing countries that face energy shortages and digital divides. Ensuring equitable access to energy-efficient AI technologies is crucial, especially in developing countries facing energy shortages and digital divides. High-income nations are well-positioned to leverage AI for productivity gains, while developing countries may encounter obstacles due to limited digital infrastructure. This disparity could transform a temporary buffer against AI-driven changes into a long-term barrier to economic prosperity [90]. Global partnerships and proactive strategies to support developing nations, including access to digital infrastructure, upskilling, and social dialogue, are necessary prerequisites for closing the technological gap and ensuring that the AI revolution does not leave significant portions of the world’s population behind.

Significant infrastructure and human capital investments are essential for developing countries to fully benefit from AI. Expanding access to electricity and reliable Internet is vital, as is providing education and training in basic digital literacy and advanced AI skills. Building robust local technology ecosystems and fostering partnerships with international technology firms are key to ensuring that these nations thrive in an AI-driven world [91]. The United Nations Development Programme (UNDP) emphasizes the importance of strengthening local AI ecosystems through initiatives like the AI Hub for Sustainable Development, which focuses on enhancing data quality, talent development, and accessible computing power. Additionally, the World Bank highlights that, with the exceptions of China and India, emerging markets have received a modest share of global investment in advanced technologies, underscoring the need for increased infrastructure investment in these regions [92]. By addressing these areas, developing countries can better position themselves to leverage AI for sustainable development and for economic growth. To highlight the key obstacles in integrating AI with renewable energy targets,

Table 5. Barriers to aligning AI with renewable energy goals.

Key Findings	Impact on Energy Consumption	Risks and Challenges	Potential Solutions	Policy and Regulation
AI’s energy consumption is increasing faster than the adoption of renewable energy sources. Many AI data centers still rely on fossil fuels, limiting their contribution to sustainability. The geographic concentration of AI infrastructure in regions with high energy demands creates sustainability challenges.	Large-scale AI models require substantial computational power, contributing to high energy demand. AI-driven workloads risk exceeding the availability of clean energy sources. Renewable energy’s current scalability is insufficient to fully power AI operations.	Only a small percentage of AI infrastructure currently runs on renewable energy. Energy storage and grid limitations prevent AI from fully utilizing intermittent renewable sources. High costs and lack of incentives slow the transition to renewable-powered AI systems.	AI infrastructure should integrate energy-efficient hardware and optimize computational workloads. Decentralized AI processing and distributed computing can reduce energy strain on central data centers. Improved energy storage and smart grid integration can help balance renewable energy supply with AI demand.	Governments should enforce sustainability standards for AI energy consumption. Incentives should encourage AI companies to invest in renewable energy infrastructure. International regulations can ensure AI’s growth aligns with climate goals and energy transition policies.

summarizes the barriers and potential strategies for achieving sustainable alignment.

3.5. AI as a Solution to Its Energy Challenges

Developing energy-efficient hardware accelerates AI’s potential to mitigate its own energy challenges. Custom AI chips, such as Google’s TPUs and NVIDIA’s Ampere GPUs, are optimized for specific tasks, enabling higher computational efficiency than that of traditional CPUs. These hardware advancements and software-level optimizations represent a significant step toward aligning AI with sustainability goals. Techniques like pruning, quantization, and knowledge distillation optimize models by lowering the computational load without compromising accuracy. For instance, pruning removes unnecessary neural network weights, and quantization reduces numerical precision, making AI systems more compatible with renewable energy resources. Meta-learning offers another approach to improving energy efficiency. By “learning to learn”, AI systems can autonomously optimize their operations, reducing computational requirements [93].

Reinforcement learning (RL) is emerging as a key approach for improving AI energy efficiency. RL algorithms autonomously optimize energy usage by learning from real-time feedback. In 2016, DeepMind applied reinforcement learning to optimize cooling systems in Google’s data centers, achieving a 40% reduction in cooling energy consumption without compromising the performance [94]. Extending these applications to AI training and inference processes can significantly reduce the overall energy use, thereby making AI systems more sustainable. Zero-shot learning (ZSL), which enables AI models to perform tasks without explicit retraining for each new task, reduces the need for resource-intensive retraining processes. Meta-learning can adjust model architectures and hyperparameters based on prior experiences, enabling efficient processes and minimizing energy consumption. AI also optimizes renewable energy systems by dynamically managing resources to balance the supply and demand [95]. AI-driven optimization plays a critical role in enhancing the efficiency of energy storage systems. Machine learning algorithms analyze real-time data to predict energy storage needs, and maximize the utilization of systems like lithium-ion batteries and emerging technologies like solid-state and flow batteries. AI predicts energy availability by analyzing weather and consumption data, adjusting the supply in real time, and effectively integrating storage solutions [96]. This reduces the reliance on non-renewable backup power and enhances the stability of renewable grids, supporting a transition to sustainable energy systems. AI models and renewable energy systems can align with sustainability goals by reducing computational energy requirements and optimizing resource management. These innovations can contribute to the global transition to renewable energy by reducing environmental impacts and integration challenges.

As AI-driven data centers continue to expand, major technology companies are making substantial investments in renewable energy to offset their growing energy demands. Leading corporations, such as Amazon, Meta, Microsoft, and Google, have committed to sourcing clean energy through large-scale solar and wind projects to power their operations sustainably. These investments aim to mitigate the carbon footprint of AI infrastructure while ensuring energy security for their expanding digital services.

Figure 9 illustrates the renewable energy capacities (solar and wind) procured by major technology companies. Amazon leads the sector with the highest renewable energy capacity, followed by Meta, Microsoft, and Google. These initiatives demonstrate the potential of AI-driven industries to transition toward sustainable energy models while maintaining their computational growth.

Table 6. AI as a solution to its energy challenges.

Key Findings	Impact on Energy Consumption	Risks and Challenges	Potential Solutions	Policy and Regulation
AI can optimize energy consumption by improving power grid and storage efficiency. AI-powered systems can dynamically adjust energy demand and reduce data center waste. Advanced AI models help forecast energy needs and optimize renewable energy distribution.	AI-driven cooling systems in data centers can significantly reduce energy use. Predictive maintenance enabled by AI minimizes energy loss in renewable energy infrastructure. AI can accelerate the adoption of decentralized energy grids, reducing reliance on fossil fuels.	AI’s computational processes still require large amounts of power, limiting net energy savings. High upfront costs and technical expertise are barriers to implementing AI-driven energy solutions. The effectiveness of AI solutions depends on access to high-quality real-time data.	AI can be integrated with smart grids to optimize energy efficiency and distribution. Machine learning models can improve energy storage management and reduce reliance on backup fossil fuel systems. Developing low-power AI chips and energy-efficient algorithms can help reduce AI’s own energy demands.	Governments should support AI-driven energy efficiency programs through incentives and funding. Regulations should promote transparency in AI’s energy consumption and efficiency improvements. Policies should encourage collaboration between AI developers, energy providers, and regulators to maximize sustainability benefits.

summarizes the key strategies, innovations, and policy considerations that contribute to improving AI’s energy efficiency and sustainability to illustrate how it can address its own energy challenges.

3.6. Sustainable Development Goals Alignment

AI significantly contributes to SDG 7 by optimizing renewable energy systems and ensuring affordable, reliable, and sustainable energy access [98]. AI enhances energy management by forecasting renewable energy production, addressing intermittency issues, and enabling stable energy integration [99]. Smart grids powered by AI improve energy efficiency and resilience, especially in underserved regions, by supporting decentralized renewable energy systems and reducing infrastructure costs [100]. These advancements help to scale renewable energy solutions, aligning with the goals of SDG 7. However, AI’s energy-intensive nature poses challenges. Training machine learning models and powering data centers require significant amounts of electricity, which is often sourced from fossil fuels. This reliance could undermine SDG 7 if renewables do not power AI energy consumption. Addressing this issue is critical to ensuring that AI supports clean energy goals while reducing its carbon footprint. AI also plays a vital role in achieving SDG 13 by enhancing energy efficiency, and reducing greenhouse gas emissions. AI optimizes renewable energy systems, industrial processes, and building energy management, thereby minimizing waste and emissions. It also improves carbon capture technologies, further supporting climate change mitigation [101]. AI significantly contributes to climate change mitigation by enhancing carbon capture and storage (CCS) systems. AI-driven models improve the efficiency and accuracy of carbon dioxide (CO₂) sequestration in geological formations, accelerating simulations from months to days [102]. Additionally, AI techniques optimize the estimation of carbon storage capacities, enabling more effective and safer CO₂ injection processes. These advancements facilitate the scaling of CCS technologies, making them more viable solutions for reducing atmospheric CO₂ levels [103]. However, AI’s energy demands could exacerbate emissions if it relies on non-renewable sources. Sustainable AI infrastructure powered by clean energy must align with SDG 13.

For SDG 9, AI fosters innovation in renewable energy by improving the efficiency of technologies like solar panels and wind turbines through predictive maintenance and performance optimization [10]. AI significantly contributes to developing resilient infrastructure under SDG 9 by enhancing the design and management of microgrids. AI-driven microgrids improve energy resilience and equity in regional communities by optimizing energy distribution, ensuring equitable pricing, and enhancing grid stability [104]. Additionally, microgrid AI applications support energy management systems, fault detection, generation sizing, and load forecasting, which are crucial for efficiently operating these decentralized energy systems [105]. By integrating AI technologies, microgrids can better manage renewable energy sources, respond to energy demands, and maintain a reliable power supply, fostering sustainable industrialization and innovation, as envisioned in SDG 9. To highlight AI’s role in supporting global sustainability efforts,

Table 7. Sustainable development goals alignment.

Key Findings	Impact on Energy Consumption	Risks and Challenges	Potential Solutions	Policy and Regulation
AI can support SDG 7 (Affordable and Clean Energy) by improving energy efficiency and integrating renewables. AI-driven solutions align with SDG 13 (Climate Action) by optimizing carbon capture and reducing emissions. AI contributes to SDG 9 (Industry, Innovation, and Infrastructure) by enhancing smart grid technologies and energy resilience.	AI helps maximize the efficiency of renewable energy sources, reducing reliance on fossil fuels. AI-powered energy forecasting and grid management minimize energy waste. AI-driven smart infrastructure can improve energy accessibility in underserved regions.	AI’s growing energy footprint could undermine SDG 7 and SDG 13 without clean energy. The gap in AI adoption between developed and developing countries may widen global energy inequalities. AI’s reliance on high-performance computing could create conflicts with energy efficiency goals.	AI should be integrated into clean energy strategies to enhance renewable energy adoption. Policies should promote AI-driven innovations in grid management, energy efficiency, and emission reduction. Investments in AI-driven carbon capture technologies can accelerate climate action efforts.	Governments should establish regulatory frameworks to ensure AI’s alignment with sustainability goals. Incentives should support AI applications that enhance renewable energy integration and efficiency. International collaboration is needed to ensure equitable access to AI-driven clean energy solutions.

summarizes its alignment with the Sustainable Development Goals (SDGs) and its potential contributions to achieving these targets.

3.7. Interdisciplinary Strategies for Sustainable AI

AI is critical for advancing SDG 7 and SDG 13 by optimizing renewable energy systems, reducing greenhouse gas emissions, and improving energy efficiency. However, AI’s growing energy demands may strain renewable capacities due to intermittency and storage challenges. Addressing these conflicts requires collaboration among policymakers, businesses, and researchers to develop energy-efficient AI technologies and hybrid energy systems that balance innovation and sustainability.

One key challenge in aligning AI with sustainability goals is the development of energy-efficient AI models. The computational power needed for training and deploying AI-intensive learning models is growing rapidly, raising environmental concerns. Engineers are working on algorithms and hardware, such as energy-efficient processors and AI chips, to reduce energy consumption while maintaining performance [106].

Beyond traditional AI chips, new hardware solutions, such as neuromorphic computing and photonic processors, are redefining energy efficiency. Neuromorphic chips, inspired by the human brain, utilize spiking neural networks (SNNs) to process information with an exceptional energy efficiency. By emulating the brain’s event-driven communication, these chips perform computations only when necessary, significantly reducing power consumption compared to traditional processors. For instance, research has demonstrated that neuromorphic hardware can process complex neural networks while consuming four to sixteen times less energy than conventional systems [107].

Software optimizations, like pruning models and using efficient algorithms, also help reduce energy use. Additionally, AI can optimize power consumption by adjusting workloads based on energy availability [108]. Integrating renewable energy into power grids is another challenge that requires technological innovations. AI can predict fluctuations in renewable energy generation and consumption, improving demand-supply balancing and enabling better energy distribution [109]. AI-powered smart grids and energy management systems can optimize real-time energy distribution, reducing waste [110]. AI also aids in improving energy storage systems, which are crucial for balancing the renewable energy supply. From a business perspective, companies must consider AI’s environmental impact and implement strategies that align with sustainability. AI can optimize energy use within businesses, for example, by enhancing building management systems to reduce consumption [111,112]. Integrating renewable energy into AI infrastructure, such as powering data centers with clean energy, can significantly reduce carbon footprints.

AI can advance circular economy practices within renewable energy systems by enhancing recycling and minimizing waste. AI algorithms optimize the recovery of rare earth materials from used batteries, which is critical for producing new energy storage systems. Companies can also apply circular economy principles to minimize waste in AI systems. AI can also drive economic benefits in renewable energy by optimizing systems and reducing the costs. Predictive analytics help businesses identify cost-effective energy sources, improve asset management, and reduce operational inefficiencies, leading to energy savings and increased profitability [113]. AI in renewable energy offers significant economic benefits, including reduced operational costs, improved energy efficiency, and enhanced infrastructure reliability, emphasizing the need to prioritize AI innovation in renewable strategies [114].

Public education on AI’s role in sustainability and advocacy for transparency in its energy consumption are critical to maximizing its potential. Addressing ethical and equity challenges is essential, as energy-efficient AI technologies are often concentrated in high-income regions, limiting access to developing countries [115]. International partnerships, funding mechanisms, and transparent reporting on AI’s environmental impact can promote equitable benefits, drive energy efficiency, and ensure alignment with global sustainability goals. To emphasize the multifaceted approach needed for sustainable AI,

Table 8. Interdisciplinary strategies for sustainable AI.

Key Findings	Impact on Energy Consumption	Risks and Challenges	Potential Solutions	Policy and Regulation
AI’s role in energy sustainability requires collaboration between technology, business, and policy sectors. Advancements in AI hardware and algorithms can reduce its environmental impact. AI can enhance circular economy practices by improving energy efficiency and waste reduction.	AI-driven optimizations in smart grids and energy management can lower power demand. Energy-efficient AI models and specialized hardware can reduce overall electricity consumption. AI applications in predictive maintenance extend the lifespan of renewable energy infrastructure, reducing resource use.	The lack of standardized sustainability practices in AI development hinders large-scale adoption. High implementation costs and technological gaps slow the integration of AI in energy systems. Ethical concerns around energy equity and digital infrastructure access create disparities in AI adoption.	Collaboration between AI researchers, energy experts, and policymakers is essential for sustainability-focused innovation. AI-driven energy monitoring and demand-response systems can optimize power distribution and reduce waste. Investment in low-power AI architectures and edge computing can decentralize energy consumption.	Policies should encourage interdisciplinary research to align AI with energy efficiency and sustainability. International cooperation is necessary to ensure equitable AI-driven energy transitions. Regulations should incentivize AI companies to adopt energy-efficient computing solutions and sustainable business practices.

outlines key interdisciplinary strategies that integrate technological, policy, and economic perspectives to enhance AI’s energy efficiency and environmental impact.

Figure 10 visually maps the dual role of AI in energy sustainability, highlighting its increasing energy demand and contribution to clean energy solutions. On the left side, the diagram illustrates AI’s substantial energy consumption, particularly in data centers and model training, which is expected to double by 2026 and potentially triple by 2030. These trends contribute to a growing carbon footprint, underscoring the need for regulatory interventions and investments in energy-efficient AI systems. The right side of the diagram presents AI’s contributions to renewable energy, including its role in optimizing smart grids, improving energy forecasting, and accelerating decarbonization. AI-driven solutions have demonstrated the potential to enhance grid efficiency and reduce dependence on fossil fuels by enabling better energy distribution and demand management. Additionally, advancements in energy-efficient AI models are essential for lowering computational loads and infrastructure energy use, thereby aligning AI development with sustainability goals, such as SDG 7 and SDG 13.

This study’s findings reaffirm the complexities of AI’s role in the clean energy transition. As outlined in the research objectives, AI’s growing energy footprint presents both challenges and opportunities for sustainability. Analyzing AI’s energy demands highlights the urgent need to optimize its energy consumption while maximizing its potential in renewable energy applications. Critical barriers—such as infrastructure limitations, regulatory gaps, and economic constraints—impede AI’s alignment with sustainability goals. By systematically examining these challenges alongside potential solutions, a comprehensive perspective on AI’s evolving impact on the global energy landscape is essential to better understand how AI’s integration into energy systems can be optimized to support SDG 7 and SDG 13.

4. Conclusions

This study comprehensively analyzes AI’s dual role as a significant energy consumer and a transformative enabler in the global clean energy transition. These findings underscore the urgency of addressing AI’s rapidly growing energy demands, which are contributing to substantial electricity consumption and carbon emissions. Data centers, accounting for 1–1.3% of global electricity use in 2023 and projected to double by 2026, highlight the need for energy-efficient AI models, renewable energy integration, and innovations in advanced hardware to align AI with sustainability goals, such as SDG 7 and SDG 13.

Simultaneously, AI demonstrates immense potential for optimizing renewable energy systems, mitigating grid inefficiencies and enhancing energy forecasting. Real-world applications, such as optimizing wind farms and managing rural microgrids, illustrate AI’s capacity to overcome renewable energy challenges like intermittency and inefficiency. However, realizing this potential requires an interdisciplinary approach that integrates technological innovation, sustainable business strategies, and public engagement.

This study systematically addressed four key research questions guiding the investigation. First, regarding AI’s energy demands and their implications for sustainability efforts, the findings demonstrated that AI-driven data centers and computational processes significantly increased global electricity consumption, with projections indicating a potential doubling of AI-related energy demand by 2026. These insights highlight the urgent need for energy-efficient AI models and policy interventions to mitigate environmental impact.

Second, while exploring the challenges hindering AI’s integration with renewable energy goals, this study identified scalability issues, economic constraints, and infrastructural barriers as critical obstacles. AI’s reliance on intermittent renewable sources, along with storage and grid limitations, complicates its sustainability alignment. Addressing these barriers requires investment in decentralized AI processing, improved energy storage solutions and supportive regulatory frameworks.

Third, in assessing how AI can overcome energy challenges and support renewable energy adoption, the findings revealed AI’s potential to optimize energy consumption through predictive maintenance, smart grid integration, and enhanced renewable forecasting. AI-driven solutions have been shown to improve energy efficiency by up to 40% in smart grid applications and reduce the reliance on fossil fuel-based backups. These technological advancements demonstrate AI’s capacity to facilitate a cleaner energy transition.

Finally, regarding the interdisciplinary strategies necessary to align AI’s energy use with global sustainability goals, this study emphasizes the importance of collaboration among engineering, business, and policy disciplines. These findings underscore the need for sustainable AI governance, investment in energy-efficient hardware, and market incentives for AI-powered, clean energy solutions.

This study emphasizes the need for robust policy frameworks that promote energy-efficient AI deployment, incentivize clean energy investment, and address scalability and equity challenges. Collaboration among governments, technologists, and businesses is essential to establish standards and encourage international cooperation. For technologists, developing energy-efficient algorithms and AI-driven energy management systems is critical. Businesses should prioritize green branding and invest in renewable energy solutions, and educators and communicators must engage the public to build awareness of AI’s dual role.

By synthesizing these insights, this study reinforces the necessity of integrating AI innovations with renewable energy policies to ensure a balanced, sustainable, and scalable transition. Future research should focus on empirical case studies quantifying AI’s direct contributions to global sustainability targets while developing novel frameworks to enhance interdisciplinary collaboration in AI-driven energy solutions. By addressing these challenges and harnessing AI’s opportunities, stakeholders can align AI development with global sustainability goals, creating a pathway for a sustainable and equitable energy future.