AI Solutions for Climate Resilience: Scaling Innovation and Efficiency

Abstract

The panel explored how artificial‑intelligence (AI) can be mobilised at scale to address both climate‑change mitigation and adaptation. Beginning with an overview of the Green Artificial Intelligence Learning Network (GRAIL) – a collaborative, not‑for‑profit effort that maps AI opportunities across sectors – the discussion moved through concrete research findings, sector‑specific use‑cases (power systems, food, built environment, materials, extreme‑weather response, data‑center sustainability), and emerging partnership models. Representatives from Google described operational decarbonisation, open‑source data products (Earth AI, Flood Hub, FireSat) and a new Climate‑Tech Centre in India. The Climb Collective outlined a startup‑driven grid‑modernisation programme for utilities in the Global South, while Open Climate Fix highlighted open‑source AI tools for grid balancing. McKinsey’s Ankur Puri explained how the consortium is quantifying economic and emissions impacts to focus scarce resources. Academics from UCL and the Alan Turing Institute illustrated university‑driven AI research and rapid‑deployment pilots, concluding with a shared appeal for radical, cross‑sector collaboration.

Detailed Summary

The moderator welcomed a globally‑sourced audience, noting the “triple challenge” of development, mitigation, and adaptation. He warned that the session would be fast‑paced—“a metaphor for the very little time we have to do something about climate change.” After apologising for the unconventional format (no traditional back‑and‑forth debate, rapid speaker turnover), he introduced the Green Artificial Intelligence Learning Network (GRAIL) and set the stage for a series of brief, self‑introduced talks.

2. GRAIL: Vision, Structure, and Early Outcomes

• Purpose: Create a collaborative ecosystem that links academia, industry, AI firms, philanthropic organisations, and governments to accelerate AI‑driven climate solutions.
• Funding & Governance: Grants, government programmes, venture capital, and corporate funds flow into a “deal‑flow pipeline” that feeds back into the network.
• Scale of First Summit (London, 2023): 200 participants, 115 organisations, 60 speakers covering AI for power, building materials, supply‑chain carbon markets, and more.
• Post‑Summit Deliverables:
  1. An online collaborative platform for co‑creation.
  2. Ongoing engagement with national governments.
  3. Taxonomy development for eight sectors (energy, built environment, materials, etc.) to surface “win‑win” economic‑value‑plus‑decarbonisation opportunities.

The moderator highlighted a Grantham Institute estimate that data‑centre emissions (0.5‑1.4 Gt CO₂e/yr) are far outweighed by the potential AI‑enabled emission reductions (3.5‑5.4 Gt CO₂e/yr).

3. Prof. David Sandalow – GRAIL Report Highlights

3.1 Report Scope & Accessibility

• 17‑chapter volume, freely downloadable, with a print edition on Amazon.
• Funded in part by the Japanese government (NITO, METI).

3.2 Core Takeaways (five‑point summary)

#	Takeaway	Details
1	AI can materially reduce GHG emissions – both incremental (efficiency gains) and transformational (new materials, novel processes).
2	AI’s own carbon footprint is tiny – estimated < 1 % of global emissions, consistent with Grantham and IEA figures.
3	Key barriers: data scarcity, shortage of trained AI‑climate personnel, and trust deficits.
4	Every climate‑related organisation should evaluate AI – establishing a dedicated AI team is becoming a norm rather than a novelty.
5	Action imperative: embed AI considerations into all mitigation strategies.

3.3 AI Capability Framework (Detect‑Predict‑Optimize‑Simulate)

• Detect: e.g., satellite‑based methane leak detection.
• Predict: weather forecasting for solar/wind output.
• Optimize: power‑flow management on transmission networks.
• Simulate: battery‑chemistry modelling, material‑science experiments.

3.4 Sector Deep‑dives

Sector	AI Applications & Observations
Power (28 % of global GHG)	Dynamic line rating, optimal power‑flow, real‑time operations; requires standardised data and skilled staff.
Food & Agriculture (≈ 30 % of emissions)	Soil‑sensor‑driven fertiliser management, virtual farms, AI‑enhanced crop‑type identification for smallholder fields.
Built Environment & Materials	AI‑accelerated materials discovery (e.g., battery chemistry), digital twins for building energy optimisation.
Extreme‑Weather Response	AI‑driven forecasting reduces cost by ~1 000× vs. traditional methods, enabling faster emergency response.
Data Centres	Recent report stresses the need for sustainable construction; AI can guide “smart‑siting” and energy‑source selection.

4. Google – Operational Decarbonisation (Uday Khemka)

4.1 Scope of Google’s Climate Footprint

• Data‑centre portfolio: focus on clean‑energy sourcing, low‑carbon siting, and carbon‑free electricity goals.
• Water & Resource Efficiency: AI‑driven leak detection for water infrastructure, optimisation of electricity distribution to reduce waste.

4.2 AI as an Efficiency Lever

• Internal Operations: AI optimises workload placement, cooling, and power‑usage‑effectiveness across Google’s global infrastructure.
• External Products & Data:
  • Earth AI: Open‑source satellite and weather datasets for climate research.
  • Flood Hub & FireSat: Real‑time flood and wildfire risk maps that can be licensed by insurers, real‑estate firms, and NGOs.

4.3 “Two Hockey‑Sticks” Narrative

1. Exponential AI growth – the accelerating capability curve.
2. Exponential GHG increase – the climate‑impact curve.

Google aims to intersect these curves via:

• Democratising data (open‑source Earth AI).
• Accelerating innovation (supporting AI‑driven climate‑tech startups).
• Scaling solutions (pilot programmes, e.g., low‑carbon steel, sustainable aviation fuel, green‑skill training in Tier‑2 Indian cities).

4.4 New Initiative – Google Climate‑Tech Centre (India)

• Partnership with India’s Principal Scientific Advisory Group.
• Five “first‑of‑its‑kind” pilots targeting low‑carbon steel, materials, built‑environment, sustainable aviation fuel, and green‑skill curricula.

5. Google Asia‑Pacific Perspective (Vrushali Goud)

• Extreme‑Event Exposure: APAC faces six‑times higher climate‑extreme risk than other regions.
• Energy Transition: AI to aid renewable integration, grid stability, and supply‑chain emissions reduction.
• Livelihoods & Agriculture:
  • Agricultural Landscape Understanding: AI delineates sub‑2‑ha field boundaries from satellite imagery; classifies crops; detects agronomic events (tillage, sowing, harvest).
  • Digital Public Goods: Data feed into India’s CRISHI DSS and state‑level platforms (e.g., Telangana’s ADEX).
  • Startup Ecosystem: Partnerships with Carbon Farm (France), Varaha (social‑enterprise), and Wadwani AI to translate data into actionable advisory services for farmers.

6. Energy‑Sector Interventions

6.1 Grid Modernisation – Nalin Agarwal (Climb Collective)

• Program Overview: Six‑year “Enterprise Support Organisation” (ESO) that mentors ~1,500 climate‑tech startups, with a focus on AI‑enabled power‑grid solutions for the Global South.
• Key Partnerships: UNESA (71 energy utilities, 750 GW clean‑power target), Delhi Climate Innovation Week (Google sponsor).
• Process: Issue problem statements → startups apply → pilots → scale‑up. 22 utilities have participated, yielding ~20 pilots; ~30 % conversion to large deployments.
• Future Platform (AI for Power Innovation):
  1. Open Innovation Programme (Electron Vibe).
  2. Knowledge Hub (peer‑sharing events at COPs, Climate Weeks).
  3. Solution Database (pre‑vetted AI tools for utilities).

6.2 Open‑Source Grid Tools – Dan Travers (Open Climate Fix)

• Mission: Bridge the gap between cutting‑edge AI research (e.g., DeepMind) and the risk‑averse electricity sector.
• Core Products:
  • Solar‑forecasting model (UK) – 20‑30 % accuracy improvement, now being piloted with Adani and the Rajasthan grid operator.
  • Open‑source code & data – freely available to accelerate adoption in other grids.
• Strategic Argument: Without AI‑driven scheduling, grids will rely on expensive backup (gas) generation, driving up costs and risking public backlash against the energy transition.

7. Consulting & Quantitative Impact – Ankur Puri (McKinsey)

• Role: Leads QuantumBlack’s AI practice in India; part of a global team supporting GRAIL.
• Framework: Four strategic challenges – Operational Improvement, Strategic Intelligence & Foresight, Transformation & Innovation, Autonomous Operations.
• Knowledge‑Base Construction: Mapping sector‑specific AI use‑cases (e.g., network planning, asset management, field‑force execution) to stakeholder needs.
• Quantification Work: Ongoing effort to attach cost‑benefit and emissions‑impact metrics to each AI application, allowing resources to be focused on the highest‑return opportunities.

8. Academic Leadership – Rob Thompson (University College London)

• UCL Grand Challenges: Cross‑faculty, self‑funded research programmes tackling complex societal problems, including climate.
• AI Integration: AI is embedded as an “enabling layer” across disciplines; notable outputs include:
  1. Campus‑wide energy‑demand forecasting from sensor data.
  2. Carbon Re spin‑out – deep‑reinforcement‑learning optimisation of cement‑process emissions.
  3. Partnership with PGM Real Estate – AI‑enabled sustainability in built‑environment assets.
  4. Open‑source sea‑ice classification tool for Inuit communities.
• Call‑to‑Action: Researchers should move from “knowledge on a shelf” to deployed, impact‑oriented solutions, leveraging GRAIL’s network.

9. Alan Turing Institute – Adam Sobey

• Institute Missions: Environment (forecasting & climate), Sustainability, Defense & Security, Health, Foundational Research.
• Demonstrated Impact:
  • 18 % emissions reduction in shipping through AI‑driven route optimisation.
  • 42 % reduction in building HVAC emissions via AI‑based controls.
  • An underground urban farm powered entirely by renewable energy, showcasing AI‑guided vertical agriculture.
• Strategic Outlook: Emphasises radical collaboration – the Institute works with Lloyd’s Register Foundation to channel AI expertise into the Global South and to scale proven pilots globally.

10. Closing Remarks

The moderator thanked all participants, highlighted the shared theme of radical, action‑oriented collaboration, and invited the audience to applaud the speakers. A brief transition announced the awarding of “momento” items to Indian attendees and closed the session.

Key Takeaways

• AI’s net climate impact is overwhelmingly positive – its own emissions are < 1 % of global GHGs, while potential AI‑enabled reductions could exceed 5 Gt CO₂e yr⁻¹.
• Two “hockey‑sticks” must intersect: exponential AI capability growth with the exponential rise in GHG emissions; coordinated effort can bend the climate curve.
• Sector‑specific AI win‑wins:
  • Power: dynamic line rating, optimal power‑flow, AI‑driven grid forecasting to avoid costly backup generation.
  • Food & Agriculture: AI‑enhanced field‑boundary mapping, crop‑type classification, and event detection for smallholder farms.
  • Built Environment & Materials: AI‑accelerated material discovery and digital‑twin optimisation for buildings and cement plants.
  • Extreme‑Weather: AI‑based forecasts cut response costs by ~1 000×, improving resilience.
• Collaborative ecosystem (GRAIL) is the linchpin – brings together academia, industry, NGOs, and governments, feeding ideas into grant, venture‑capital, and policy pipelines.
• Data‑centre emissions are a small fraction of AI’s climate benefit, yet Google is actively reducing them through clean‑energy sourcing, smart siting, and AI‑driven operational efficiency.
• Open‑source tools accelerate global adoption – Open Climate Fix’s solar‑forecast model and the Climb Collective’s vetted solution database lower entry barriers for utilities worldwide.
• Quantifying economic and emissions impact (McKinsey’s effort) is essential to prioritise scarce resources on the highest‑return AI applications.
• Academic‑driven Grand Challenges translate theory into practice, exemplified by UCL’s campus‑wide AI energy‑optimisation and open‑source climate‑science tools.
• Radical, cross‑sector collaboration is the only viable path forward, as repeatedly urged by all speakers.

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Prepared from the verbatim transcript of the “AI Solutions for Climate Resilience: Scaling Innovation and Efficiency” panel at the AI Summit, Delhi.

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