AI, energy crisis and climate change

Artificial Intelligence (AI) is driving societies, economies and geopolitics worldwide but at what cost to the environment and climate? 

There is a paradox of AI as both a catalyst for clean energy innovation and a growing threat to global energy supply, while worsening the climate crisis. 

From the massive power demands of data centres and AI factories to the potential of AI to optimise renewable energy systems, policymakers and innovators need to rethink how technology, particularly AI, aligns with the Sustainable Development Goals, especially Access to Affordable Clean Energy (SDG 7) and Climate Action (SDG 13).

Optimisation of public transport systems

In public transport, AI-powered systems use real-time data, predictive analytics and machine-learning algorithms to optimise bus and train schedules in response to passenger demand, traffic conditions, and operational constraints. 

By dynamically adjusting routes, departure times and fleet allocation, these systems reduce idle time, unnecessary mileage and congestion-related delays. 

The resulting efficiencies lower fuel and electricity consumption, reduce operational costs and extend the lifespan of vehicles and infrastructure. 

In doing so, AI-enabled scheduling not only improves service reliability and commuter satisfaction but also promotes energy-efficient, environmentally sustainable urban transport systems.

Access to clean energy in underserved regions

AI offers innovative solutions to expand clean energy access in remote and underserved areas by enabling decentralised energy systems, optimising microgrids and supporting off-grid renewable energy projects. AI-driven microgrids (localised energy grids that operate independently from the central grid) provide a sustainable solution for remote communities with limited access to conventional energy infrastructure.

AI algorithms optimise microgrid performance by balancing energy generation, storage, and consumption based on real-time data. By ensuring efficient energy distribution, AI-powered microgrids provide a reliable, affordable energy source for communities without access to traditional energy grids.

Smart energy consumption and demand response

Smart energy consumption and demand response are essential for balancing supply and demand in energy systems, especially as more renewable energy sources are integrated into the grid. AI supports these processes by enabling real-time monitoring, automated control and predictive analytics that help manage energy consumption more effectively. In demand response programmes, AI algorithms analyse data on electricity prices, grid load and consumer behaviour to encourage users to shift energy consumption to off-peak hours. AI also supports smart metering systems, which provide consumers with real-time information about their energy consumption patterns. Smart meters use AI to analyse usage data, identify energy-saving opportunities, and provide personalised recommendations to reduce energy use. In addition, AI-enabled home energy management systems allow households to monitor and control appliances remotely, optimising energy use based on factors such as time of day, weather conditions, and electricity rates. 

Way forward: Is AI a friend or foe?

It is instructive to acknowledge the complex interplay among AI, energy availability, and decarbonisation. While the AI revolution demands enormous energy resources and has a large carbon footprint, the technology can also help create more efficient, sustainable, and equitable energy systems globally. 

Indeed, in the context of global energy supply and climate change mitigation, AI is a double-edged sword. The technology is both a problem and a solution! 

Can AI truly become a partner in achieving the Sustainable Development Goals or will its energy demands and carbon footprint outweigh its promise?  

As discussed, AI technology is transforming industries by enhancing operational efficiency, improving decision-making and accelerating innovation across sectors such as finance, manufacturing, healthcare and logistics. 

Advanced algorithms enable automation, predictive analytics and real-time optimisation, helping organisations reduce waste, improve productivity and deliver more responsive services. However, the rapid expansion of AI systems is accompanied by a sharp rise in energy consumption, primarily driven by the computational intensity of training and running large models. 

The growing reliance on energy-hungry data centres is placing increasing pressure on electricity grids, particularly in regions with limited generation capacity or ageing infrastructure. If left unmanaged, this rising energy demand risks undermining climate commitments and slowing progress towards low-carbon development goals.

In some areas, the surge in data centre activity linked to AI deployment has already pushed up electricity prices for households and businesses. These cost increases can exacerbate energy poverty, undermine the competitiveness of small and medium-sized enterprises and trigger public resistance to further expansion of digital infrastructure.

As a result, the social and economic sustainability of AI adoption is becoming as critical as its technical performance. Addressing these challenges requires deliberate investment in energy-efficient AI models, renewable energy integration and smarter grid management.

Ultimately, aligning AI development with affordability, environmental responsibility, and broad societal buy-in is essential to ensure that its benefits are inclusive and sustainable over the long term.

Globally, efforts are underway to address the complex nexus between AI, energy and decarbonisation. Strategies to reduce AI’s carbon footprint are focusing on using renewable energy in data centres, developing more efficient algorithms, and optimising models to reduce their size and energy requirements.

This is complemented by strategies for using AI to improve energy generation and demand management, while also driving decarbonisation. The UK government’s terms of reference for the independent Review of AI Deployment in Electricity Networks, published on 16 December 2025, outline a comprehensive assessment to address the growing complexities. 

As electricity systems face challenges in forecasting, optimisation, flexibility, and decarbonisation, AI technologies are seen as key to transforming planning, operations, and management, potentially enabling innovative approaches such as autonomous grids. 

The UK review aims to map current and emerging AI applications with high-impact potential for the UK’s energy system, identify technical, regulatory, and data-related barriers and enablers, evaluate benefits for affordability, security, flexibility, and decarbonisation alongside associated risks, and deliver actionable recommendations for safe and rapid deployment. 

This initiative supports broader objectives, such as Clean Power 2030 and the ambition to make Britain a clean energy superpower and is expected to deliver a final report by summer 2026.

The WEF report titled “From Paradox to Progress: A Net-Positive AI Energy Framework,” published on 11 December 2025, attempts to address this challenge presented by AI to the climate agenda and the efforts to resolve the global energy crisis.

The report correctly observes that without proactive measures, AI might inadvertently exacerbate system vulnerabilities and environmental risks, as evidenced by projections of soaring energy consumption. “By 2035, global data centre electricity use could exceed 1,200 terawatt-hours (TWh), up from 420 TWh in 2024.” It then proposes that achieving a net-positive AI energy outcome requires deliberate alignment among stakeholders to ensure that AI’s benefits in resource savings outweigh its overall use, fostering resilience and competitiveness. 

The WEF framework for net-positive AI revolves around three action drivers—designing for efficiency, deploying for impact, and shaping demand wisely—complemented by enablers like consumer education, ecosystem collaboration, and transparent accountability. 

This report draws from over 130 global use cases demonstrating tangible advantages, such as reduced costs, improved grid stability, and lower CO emissions. 

Challenges like transformer shortages and infrastructure constraints underscore the urgency of responsible scaling to avoid widening digital divides. 

However, AI offers substantial opportunities, including optimising data centre cooling and enhancing Heating, Ventilation, and Air Conditioning (HVAC) systems, grids, and industrial operations. “Net-positive AI energy means ensuring that the energy and resource savings enabled by AI outweigh its life cycle consumption – turning responsible scaling into a source of competitiveness and resilience.”

Several companies and tech giants are proposing to establish AI data centres in space to address the escalating energy demands of terrestrial facilities, leveraging near-constant solar power in orbit for up to eight times greater efficiency and natural radiative cooling in a vacuum to eliminate water-intensive systems.

Google’s Project Suncatcher envisions constellations of solar-powered satellites equipped with TPUs and inter-satellite laser links, with prototype launches planned for 2027 to demonstrate scalable machine-learning compute. 

Startup Starcloud, backed by Nvidia, launched an AI-equipped satellite with H100 GPUs into orbit in late 2025 and aims to build gigawatt-scale orbital facilities featuring massive 4-kilometre solar arrays. 

SpaceX and Blue Origin are also advancing similar initiatives, potentially integrating compute into upgraded Starlink satellites or new orbital platforms. “In space, you get almost unlimited, low-cost renewable energy,” highlighting the potential for dramatic reductions in costs and carbon emissions compared to Earth-based operations. 

Yes, in addressing the climate change agenda and achieving an adequate global energy supply, AI is both a friend and a foe. 

However, the creative and intentional deployment of AI, such as the WEF’s Net-Positive AI Energy Framework and revolutionary innovations, such as building AI data centres in space, will make AI more of a friend than a foe.

Continued from last week, this is the second of a two-part excerpt from the book ‘Deploying Artificial Intelligence to Achieve the UN Sustainable Development Goals: Enablers, Drivers and Strategic Framework’.

Professor Arthur G.O. Mutambara is director and professor of the IFK at the University of Johannesburg (UJ)