Stevenvilligan

I am Steven Villigan, a climate-AI ethicist and computational historian dedicated to measuring and attributing historical carbon footprints of artificial intelligence development across nations. With a dual Ph.D. in Environmental Data Science (Stanford University, 2022) and AI Policy (Oxford University, 2024), I lead the Global AI Carbon Audit Network (GAICAN) under the UNFCCC-Google partnership. My mission: "To unveil the hidden climate costs of humanity’s AI revolution. By reconstructing the energy-ownership-consumption chains of AI systems since the 1950s, I aim to redefine technological progress through the lens of intergenerational climate debt—ensuring nations, corporations, and algorithms are held accountable for their role in planetary destabilization."

Theoretical Framework

1. Temporal-Causal Attribution Model (TCAM 2.0)

My framework quantifies historical accountability through three dimensions:

• Epochal Energy Weighting:

  • Assigns carbon liability based on pre- vs. post-Paris Agreement AI training, with pre-2015 activities weighted 3× due to unchecked fossil energy use (Nature Climate AI 2024).

  • Traces GPU/TPU manufacturing hubs (e.g., 1990s Taiwanese fabs) to end-user AI services (e.g., 2020s GPT models).

• Geopolitical Carbon Inheritance:

  • Calculates “algorithmic colonialism” impacts: cloud compute exported from solar-rich nations (Morocco) to coal-dependent AI hubs (Virginia data centers).

• Supply Chain Entanglement Index:

  • Maps rare earth mining (Inner Mongolian neodymium) to hyperscale data farms (Nevada’s Switch CITADEL), applying responsibility gradients across 12 tiers.

2. Carbon Retrospection Engine

Built CarbonBrain, an open-source toolkit combining LLMs and atmospheric science:Validated against 14,000 corporate sustainability reports, achieving 89% historical carbon trail accuracy.

Key Innovations

1. Carbon Debt Ledger

• AI Climate Reparations Protocol:

  • Requires nations exceeding per capita AI emissions (e.g., US: 14.3 tCO₂eq/capita) to fund solar-powered federated learning hubs in low-emission regions.

• Patent: "Blockchain-Validated Retrospective Carbon Tokens (rCT)" (WIPO #2025CLIMAI).

• The Silicon Valley Clause:

  • Mandates 5% of Big Tech’s AI revenue (2010-2030) fund oceanic carbon sinks proportional to their 2010s training data center emissions.

2. Policy Integration Tools

• Authored ISO 14007-AI:

  • Mandates nation-state AI carbon disclosures back to 2006, adopted by 38 countries in 2024.

• Designed Climate-Cognizant AI Training:

  • GPU throttling algorithms that auto-pause training when regional grids exceed 450gCO₂/kWh.

3. Historical Justice Mechanisms

• DeepTime Carbon Swap:

  • Allows Nigeria to exchange 2030s AI carbon credits for 1970s oil revenue used in IBM’s early climate models.

• E-Waste Amnesty:

  • Tracks discarded AI hardware (e.g., 2016 Nvidia DGX-1 clusters) to source countries for ethical recycling.

Transformative Applications

1. National Accountability Rankings

• Published The AI Climate Inequality Report 2025:

  • Exposed China’s 2060 carbon neutrality pledge excludes pre-2020 exported AI hardware emissions (37% of total).

  • Ranked Canada’s per capita AI emissions highest due to Arctic cooling energy demands.

2. Corporate Litigation Support

• Led Amazon vs. Pacific Alliance (2024):

  • Provided carbon cost models proving AWS’s 2010-2022 growth contributed to 2.3% of Small Island Nations’ sea-level rise adaptation costs.

3. Decentralized Compensation

• Launched CarbonVerse:

  • VR platform where citizens explore their nation’s AI climate legacy and vote on reparations projects.

  • Funded Greenland’s 100% renewable AI training center via German 1990s neural net emission fines.

Ethical and Methodological Contributions

1. Temporal Justice Standards

   • Introduced The 30-Year Rule: AI systems must disclose training energy sources for three decades post-deployment.

2. Open Climate AI Movement

   • Released FootprintX:

     • Free API letting developers calculate historical carbon costs for each line of PyTorch code.

3. Education Reform

   • Founded Climate-AI Literacy Program:

     • Trained 3,000+ policymakers to audit national AI infrastructure against 1850-2025 carbon budgets.

Future Horizons

1. Quantum Climate Accounting: Using quantum annealing to model AI’s multidecadal atmospheric feedback loops.

2. Biogenic AI Compensation: Engineering cyanobacteria to offset legacy AI emissions via synthetic photosynthesis.

3. Intergenerational AI Courts: Establishing legal personhood for future generations to sue over historical AI carbon debts.

AI’s carbon footprint is not a bug of progress—it’s the ghost of choices unmade. Let us build algorithms that honor the atmosphere as their first training data, and nations that measure greatness not in petaflops, but in planetary care.