Backcasting 2035 AI-Induced Emission Reduction Projections (with Claude Code)

In June 2025, a team of researchers from the London School of Economics (Grantham Research Institute) and Systemiq published a novel, peer-reviewed study in Nature. The article “Green and Intelligent: the role of AI in the climate transition” estimates the potential for GHG emission reductions in the power, meat & diary, and light vehicle sectors (collectively contributing around 50% of global emissions). Their finding is that through efficiency gains, adoption of greener product alternatives, or a combination of both, AI-induced emission reductions across these three sectors could reach between 3.2 – 5.2 GtCO2e in 2035, against the business-as-usual scenario.

Inspired by learning from a Kith Climate workshop this summer, I partnered with Claude Code to perform a backcasting analysis, using the Nature study as an anchor. For each sector, I asked Claude to identify the most significant market actors (in China, the United States and the European Union) required to integrate AI in order to realize the study’s 2035 projections.

For example, in the power sector Claude identified 27 grid operators whose AI integration would be critical to reaching the efficiency gains projected in the study. Similarly, in the Meat & Diary sector, Claude found the key 14 alternative-protein market actors whose AI-integration could compel consumers to adopt different consumption patterns, thereby reducing sector emissions. Lastly, 8 light vehicle firms were assessed on their current state of AI integration. This analysis, and the study, does not account for any spillover effects from these efficiency and adoption gains (more on that in the Methodology).

This backcasting analysis revealed a troubling trend. Despite the ubiquity of AI promotion on earnings calls and corporate marketing, few market actors analyzed had integrated AI to their business models, based upon the publicly available information Claude could analyze. Each market actor was ranked by 1 (AI deployed & quantified); 2 (real but partial or adjacent); or 3 (no evidence found, or deprioritized). Across the three sectors, only 6 of the 49 total market actors analyzed ranked 1, meaning AI deployed & quantified. Jump to the full sector analysis by clicking below:

Jump to Power – Jump to Alternative Proteins – Jump to Light Vehicles

In September 2026, concrete case studies demonstrating how AI of any kind can reduce emissions at scale remain scarce. The Nature study anchoring this backcasting analysis is one of only a few efforts to model what future AI-induced emission reductions may look like. The value of this analysis is to demonstrate the current state of AI integration among market actors critical to realizing 2035 emission reductions, and illuminating opportunities for stakeholder pressure to get there faster. Could shareholder resolutions pressure these companies to hasten AI adoption for efficiency, much like they have around climate change? Could activists engage market actors (as they have so successfully around data center development) to accelerate consumer adoption of alternative proteins? Please use this analysis to stimulate that conversation, and contact me here with your feedback.

Power

In the power sector below, only one of the 27 grid operators analyzed

Alternative Proteins

In the alt-proteins sector below,

Light Vehicles

In the light vehicle sector below,

Contact Jeremy Tamanini to continue the conversation on this topic, as well as reading these related recent insights from the practice:

Applied AI for Sustainability: Opportunities for Integration (link here)

AI Everywhere: Tangible Applications for Sustainability Teams (link here)

The AI Elephant in the Room (link here)

AI x Sustainability in Trump 2.0 (link here)

Remarks to the National Sustainability Society (link here)

AI in Building & Construction: Tangible Applications for Sustainability Teams (link here)

How to Work with Satellite-Based Sustainability Data (link here)