AI in Climate Transition: A Green Opportunity
The global economy faces pressing environmental crises, encompassing climate change, biodiversity loss, and pervasive pollution. Addressing these challenges necessitates rapid systemic changes across economies and a substantial increase in investments focused on climate and nature.
To achieve a net-zero transition, substantial investments in new low-carbon infrastructure, energy systems, and technologies are vital, particularly in emerging markets and developing economies (EMDEs). These regions, notably Africa, which holds approximately 60% of the world’s prime solar resources, received merely 2% of clean energy investments in 2023. Projections indicate that around $4 trillion in global climate-related investments will be required by 2030, with $2.4 trillion earmarked for EMDEs, excluding China.
This situation unfolds not simply as an opportunity for minor enhancements but as a chance for significant systemic transformation to create a new growth narrative. The push for green investment yields the potential to spur development and promote sustainable, inclusive, and resilient economic growth, potentially breaking the cycle of ‘secular stagnation.’
The transition to net-zero is more than a cost-driven mitigation strategy; it presents an exceptional opportunity for fostering innovation and engendering robust economic growth. Such transformations can enhance resource and energy efficiency, elevate human and social capital through improved health and productivity gains from reduced pollution, and stimulate economic growth driven by increased investment.
Artificial intelligence (AI) stands at the forefront of this transition. As a general-purpose technology, AI has the capacity to expedite profound system transformations by enhancing the efficiency and effectiveness with which innovation processes are scaled and capital is allocated. Its applicability across various sectors empowers AI to contribute significantly to the net-zero pathways of critical economic systems, allowing for a reimagining of interconnected sectors like power, transport, cities, and land use.
Despite its promise, the research on the interplay between AI and the low-carbon transition remains sparse. Though some studies have assessed AI’s conceptual impact on climate change, comprehensive analysis of its macro-level effects is lacking. Initiatives by industry leaders like Microsoft and PwC or Google and BCG aim to quantify AI’s potential emissions reduction capabilities, estimating reductions between 1.5-4% and 5-10% by 2030. However, the absence of peer review and full methodological disclosure raises questions about these findings’ reliability.
To delve deeper, the research identifies five critical areas where AI can effectively bolster the climate transition, classified along the lines of mitigation, adaptation, and resilience: transforming complex systems, innovating technology discovery and resource efficiency, encouraging behavioral changes, modeling climate systems and policy interventions, and managing adaptation and resilience.
By concentrating on the power, meat and dairy, and light road vehicle sectors—collectively responsible for nearly half of global emissions—this study adopts a bottom-up analytical approach, isolating AI’s specific impacts within these sectors. Unlike previous methodologies, this approach seeks to address the limitations of broader models by focusing on potential emissions reductions from low-carbon technologies that AI can facilitate.
Notably, the analysis anticipates that despite potential increases in emissions from AI applications—such as increased datacenter energy consumption—the net emissions reduction attributed to AI in the evaluated sectors would substantially outweigh these increases, underscoring the imperative of leveraging AI for climate transition.
It’s essential to note that this study refrains from investigating the full dynamic implications of AI applications on broader economic outcomes, including growth, investment, and job creation. The anticipated sectoral changes are likely interconnected, causing significant spillover effects and influencing macroeconomic outcomes alongside the broader AI-driven transformations across other sectors.
In closing, the need for active governance in shaping the applications and regulation of AI is highlighted, particularly in ensuring that its deployment in facilitating the low-carbon transition is equitable and sustainable.