AI’s role global collaboration on intelligent decarbonisation
This article is authored by Amit Bhave and others from ORF.
Artificial Intelligence (AI) combined with cyber-physical systems (CPS) can play a vital role in eliminating greenhouse gas emissions across sectors. The transition from fossil fuels to renewables is achieved through electrification, introducing complexity in systems deployment, integration, and efficient orchestration of electrified economic systems. AI-driven CPS are uniquely suited to manage this complexity, potentially accelerating decarbonisation efforts. This policy brief advocates for the mainstreaming of AI-driven CPS for climate change risk mitigation. To effectively realise the Intelligent Decarbonation (IDC) potential, AI-driven CPS must be elevated to a global level of collaboration and coordination, fostering clear IDC principles and guidelines, capacity building and technology transfer. The importance of IDC governance is emphasised to avoid unwanted path dependency and to avert a technology-centric approach, which has proven to yield limited results. A shift from trustworthy to sustainable AI is necessary to eliminate AI’s own carbon footprint.
AI has the potential to contribute to the UN Sustainable Development Goals (SDGs). The International Telecommunication Union (ITU) has identified 281 AI projects by 40 different UN entities addressing various urgent issues, including climate action (SDG 13), which is the focus of this brief. Outside the UN context, an increasing number of promising studies also highlight the use of AI in achieving carbon-neutral transformation goals. Specifically, the Cambridge Centre for Advanced Research and Education in Singapore (CARES) analysed projects across sectors, demonstrating that cyber-physical systems in combination with AI offer a substantially higher potential for emissions reduction and lower costs of reducing CO2 compared to conventional approaches.
As detailed in this brief, the main reason for such abatement potential relates to the capability and high efficiency potential of AI-enabled CPS that are capable of integrating, operating, and managing increasingly more complex and fully electrified economic systems. Based on these promising developments, this brief advocates for the mainstreaming of AI-driven CPS, or what will be referred to here as IDC, as a vital technology for climate crisis risk mitigation and adaptation efforts. To understand the full potential of IDC, the brief outlines its key components, with a focus on AI-enhanced CPS and their applications across multiple sectors.
While recognising IDC’s potential, the limitations of relying solely on climate technology as a remedy for climate change must be acknowledged. An overreliance on technology as the primary solution has demonstrated limited success, as evidenced by repeated warnings from the UN and IPCC. IDC faces its own limitations, such as lack of data, absence of CPS and AI solutions, and unintended reverse effects of optimisations. Furthermore, IDC is still in its early stages, making any exploration of its long-term impacts merely speculative. AI research also scarcely focuses on the climate crisis. However, the issue of technology solutionism is not just about engineering bottlenecks; technology itself can be part of the problem.
As certain technologies from previous industrial revolutions contribute to today’s climate crisis, AI might become part of a future crisis. This concern is not solely about AI’s own immense energy and water consumption, which yet must be addressed by the G20. Rather, it is more about the longer-term risks attributed to widespread AI adaptation, which are not inherent to the technology, but rather how it is used and regulated. Like climate action, AI presents a critical juncture, giving policymakers the opportunity to steer AI towards benefiting people and the planet. To effectively address opportunities and risks, this policy brief proposes a G20 Intelligent Decarbonisation Action Initiative (IDC AI) to foster collaboration and offer clear principles and guidelines for integrating sustainable AI into climate action.
The paper can be accessed by clicking here.
This article is authored by Amit Bhave and others from ORF.
