[
  {
    "id": "iea25",
    "category": "environment",
    "title": "Energy and AI",
    "publisher": "International Energy Agency",
    "year": 2025,
    "type": "Modelled estimate & scenarios",
    "scope": "Global data centres; 2024 baseline",
    "url": "https://www.iea.org/reports/energy-and-ai/energy-demand-from-ai",
    "extra": "https://build-up.ec.europa.eu/system/files/2025-04/PChtNmAZVk_11_04_2025_145800.pdf",
    "locator": "Energy demand from AI; executive summary; Box 2.1, pp. 56–57 (AI workload attribution); p. 246 (electricity-related carbon).",
    "finding": "Data centres used an estimated 415 TWh, about 1.5% of global electricity in 2024. Regional shares were 45% United States, 25% China and 15% Europe. Accelerated servers used 15% of all data-centre electricity in 2024, an imperfect proxy for AI. Electricity-related emissions from all data centres were around 180 Mt CO2, about 0.5% of global fuel-combustion CO2.",
    "limit": "Includes AI and other digital services. The remaining 15% is calculated by subtraction. Global shares do not measure local grid or water stress. The 2025 forecast has a newer 2026 update. No comprehensive application-level workload split exists: accelerated servers include non-AI work and exclude some AI. Electricity-related carbon excludes backup generation and hardware."
  },
  {
    "id": "iea26",
    "category": "environment",
    "title": "Key Questions on Energy and AI",
    "publisher": "International Energy Agency",
    "year": 2026,
    "type": "Modelled estimate & scenarios",
    "scope": "Global data centres; 2025 estimate and 2030 outlook",
    "url": "https://www.iea.org/reports/key-questions-on-energy-and-ai/executive-summary",
    "extra": "https://iea.blob.core.windows.net/assets/3179f7f8-01f6-4dd6-bffa-c9f7b73f1dc9/KeyQuestionsonEnergyandAI.pdf#page=26",
    "locator": "Executive summary; report pp. 25–26 (Figure 1.5)",
    "finding": "Estimated electricity demand is 485 TWh in 2025; the central 2030 projection is 950 TWh. AI-focused facilities reach about 465 TWh in 2030, roughly 49% of the projected total.",
    "limit": "Facility categories are not an audited census of AI-only workloads. Projections depend on adoption, efficiency, finance and grid constraints. Potential energy savings from AI applications are conditional, not automatic offsets."
  },
  {
    "id": "lbnl24",
    "category": "environment",
    "title": "2024 United States Data Center Energy Usage Report",
    "publisher": "Lawrence Berkeley National Laboratory",
    "year": 2024,
    "type": "Modelled estimate",
    "scope": "United States; 2023 data; all data centres",
    "url": "https://bies.lbl.gov/publications/2024-lbnl-data-center-energy-usage-report",
    "extra": "https://climate.mit.edu/sites/default/files/2025-02/lbnl-2024-united-states-data-center-energy-usage-report.pdf",
    "locator": "Report pp. 52, 55–57",
    "finding": "Estimated 176 TWh (4.4% of U.S. electricity), 66 billion litres of direct water consumption, nearly 800 billion litres of electricity-related water consumption, and 61 million tonnes CO₂e from the electricity mix in 2023.",
    "limit": "All data centres, not AI alone. Electricity-related water and emissions use regional grid factors, excluding company power purchase agreements. This is not a complete lifecycle inventory."
  },
  {
    "id": "water23",
    "category": "environment",
    "title": "Making AI Less ‘Thirsty’: Uncovering and Addressing the Secret Water Footprint of AI Models",
    "publisher": "Li, Yang, Islam & Ren",
    "year": 2023,
    "type": "Preprint / modelled estimate",
    "scope": "Model- and location-dependent water accounting",
    "url": "https://arxiv.org/abs/2304.03271",
    "locator": "Abstract and water-accounting methodology",
    "finding": "Separates water withdrawal from water consumption and examines how location and timing change an AI model’s water footprint.",
    "limit": "Modelled estimates depend on assumptions about hardware, electricity and cooling. They are not universal measurements of a chatbot request. The paper’s 2027 water-withdrawal projection is not an observed water-consumption total."
  },
  {
    "id": "google25",
    "category": "environment",
    "title": "Measuring the Environmental Impact of Delivering AI at Google Scale",
    "publisher": "Google researchers",
    "year": 2025,
    "type": "Company measurement / preprint",
    "scope": "Median Gemini Apps text prompt; May 2025",
    "url": "https://arxiv.org/html/2508.15734v1",
    "locator": "Sections 3.3–3.4; Table 2",
    "finding": "Reports 0.24 Wh of electricity, 0.26 mL of water consumption and 0.03 gCO₂e per median text prompt under its stated accounting boundaries.",
    "limit": "Provider-authored results, not an independent audit. The median is not the mean. Water covers data-centre cooling, not the water used to generate electricity. Carbon uses market-based electricity accounting plus selected embodied emissions. Training and other products are outside this prompt metric."
  },
  {
    "id": "bloom22",
    "category": "environment",
    "title": "Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Model",
    "publisher": "Luccioni, Viguier & Ligozat",
    "year": 2022,
    "type": "Lifecycle case study / preprint",
    "scope": "BLOOM final training run",
    "url": "https://arxiv.org/abs/2211.02001",
    "locator": "Abstract; emissions accounting",
    "finding": "Estimated 24.7 tonnes CO₂e for dynamic power alone, rising to 50.5 tonnes with the broader equipment and operational boundary.",
    "limit": "One model, training run and infrastructure setting. The broader total contains the narrower one; do not add them. These are not per-prompt or industry-wide values."
  },
  {
    "id": "ewaste24",
    "category": "environment",
    "title": "E-waste Challenges of Generative Artificial Intelligence",
    "publisher": "Wang et al. · Nature Computational Science",
    "year": 2024,
    "type": "Peer-reviewed scenario model",
    "scope": "Cumulative generative-AI e-waste, 2020–2030",
    "url": "https://www.nature.com/articles/s43588-024-00712-6",
    "locator": "Abstract; scenario analysis",
    "finding": "Projects 1.2–5.0 million tonnes of accumulated e-waste across development scenarios; circular strategies reduce modelled waste by 16–86%.",
    "limit": "Cumulative scenario range, not annual observed waste and not a confidence interval. Results depend on deployment and equipment replacement assumptions."
  },
  {
    "id": "ilo25",
    "category": "economy",
    "title": "Generative AI and Jobs: A Refined Global Index of Occupational Exposure",
    "publisher": "International Labour Organization",
    "year": 2025,
    "type": "Task-based exposure model",
    "scope": "Global employment; 2025 index",
    "url": "https://www.ilo.org/publications/generative-ai-and-jobs-refined-global-index-occupational-exposure",
    "locator": "Abstract; global estimates",
    "finding": "About 25% of employment is in occupations with some GenAI exposure; 3.3% is in the highest category. Exposure is 34% in high-income and 11% in low-income countries.",
    "limit": "Technical exposure is not actual adoption, a redundancy forecast or measured job loss. The highest category is included within total exposure. Human input remains necessary for many tasks."
  },
  {
    "id": "wef25",
    "category": "economy",
    "title": "The Future of Jobs Report 2025",
    "publisher": "World Economic Forum",
    "year": 2025,
    "type": "Employer-survey forecast",
    "scope": "Global formal employment; 2025–2030",
    "url": "https://www.weforum.org/publications/the-future-of-jobs-report-2025/in-full/2-jobs-outlook/",
    "locator": "Section 2.1; Figure 2.1",
    "finding": "Projects 170 million roles created and 92 million roles lost, a net increase of 78 million, across technological, demographic, economic and green-transition trends.",
    "limit": "These are expectations extrapolated from an employer survey, not jobs already created and not an AI-only forecast. A net gain does not guarantee a smooth transition for affected workers."
  },
  {
    "id": "canaries26",
    "category": "economy",
    "title": "Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of Artificial Intelligence",
    "publisher": "Stanford Digital Economy Lab",
    "year": 2026,
    "type": "Observational working paper",
    "scope": "U.S. ADP payroll sample through June 2026",
    "url": "https://digitaleconomy.stanford.edu/publication/canaries-in-the-coal-mine-six-facts-about-the-recent-employment-effects-of-artificial-intelligence/",
    "locator": "Revision of 12 August 2026; six findings",
    "finding": "Employment of 22–25-year-olds in exposed occupations is 19% below the path implied by less-exposed peers; adjustment is mainly through weaker hiring. The sample does not show widespread economy-wide job loss.",
    "limit": "Descriptive association, not an established causal effect of AI. Patterns weaken with education controls and differ from national benchmarks. This replaces earlier 2025 versions for the site’s headline."
  },
  {
    "id": "qje25",
    "category": "economy",
    "title": "Generative AI at Work",
    "publisher": "Brynjolfsson, Li & Raymond · Quarterly Journal of Economics",
    "year": 2025,
    "type": "Peer-reviewed field study",
    "scope": "5,172 customer-support agents at one firm",
    "url": "https://academic.oup.com/qje/article/140/2/889/7990658",
    "locator": "Abstract and Section IV",
    "finding": "Access to an AI assistant increased issues resolved per hour by 15% on average, with different effects across worker experience levels.",
    "limit": "Staggered rollout at one firm, not a universal productivity gain. Published in 2025 using earlier workplace data. Output per hour is different from wages, employment or economy-wide growth."
  },
  {
    "id": "metr25",
    "category": "economy",
    "title": "Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity",
    "publisher": "METR",
    "year": 2025,
    "type": "Randomized trial / research report",
    "scope": "16 developers; 246 tasks; early-2025 tools",
    "url": "https://metr.org/blog/2025-07-10-early-2025-ai-experienced-os-dev-study/",
    "locator": "Main results; study paper",
    "finding": "Developers took 19% longer when AI was allowed. The reported interval for additional time was 2–39%.",
    "limit": "Experienced maintainers in familiar, mature repositories. This snapshot does not establish the effect of current coding agents. The 2026 follow-up raises selection and measurement problems."
  },
  {
    "id": "metr26",
    "category": "economy",
    "title": "We Are Changing Our Developer Productivity Experiment Design",
    "publisher": "METR",
    "year": 2026,
    "type": "Research update",
    "scope": "Late-2025 follow-up; published February 2026",
    "url": "https://metr.org/blog/2026-02-24-uplift-update/",
    "locator": "Opening findings; selection effects",
    "finding": "The follow-up suggests improved speed, but the researchers regard its estimated effect as unreliable because of participant selection and time measurement issues.",
    "limit": "The authors do not present a clean, current speedup estimate. The update prevents treating the early-2025 slowdown as a timeless result."
  },
  {
    "id": "oecd26",
    "category": "economy",
    "title": "Venture Capital Investments in Artificial Intelligence through 2025",
    "publisher": "OECD",
    "year": 2026,
    "type": "Investment database analysis",
    "scope": "Global AI venture capital; Preqin data",
    "url": "https://www.oecd.org/en/publications/venture-capital-investments-in-artificial-intelligence-through-2025_a13752f5-en/full-report.html",
    "locator": "Key messages; Figures 2–3",
    "finding": "AI firms received $258.7 billion in venture capital in 2025, about 61% of $427.1 billion total VC. AI VC was $257.3 billion in 2021 and $123.6 billion in 2023.",
    "limit": "Database coverage and AI classification shape totals. Dollar values are as reported, not inflation-adjusted here. Investment is not revenue, profit, jobs or proof of social benefit."
  },
  {
    "id": "index26",
    "category": "economy",
    "title": "AI Index Report 2026 · Economy",
    "publisher": "Stanford HAI",
    "year": 2026,
    "type": "Research synthesis / investment database",
    "scope": "Quid data; newly funded AI firms in 2025",
    "url": "https://hai.stanford.edu/ai-index/2026-ai-index-report/economy",
    "extra": "https://hai.stanford.edu/assets/files/ai_index_report_2026_chapter_4_economy.pdf#page=10",
    "locator": "Chapter 4, Figures 4.2.4 and 4.2.9; pp. 180, 182",
    "finding": "Counts 3,499 newly funded AI companies worldwide in 2025, including 1,953 in the U.S., 172 in the U.K. and 161 in China.",
    "limit": "Counts funded companies, not newly incorporated firms or individual entrepreneurs. Quid’s private-financing methodology differs from the OECD’s Preqin VC series. The two datasets should not be merged."
  },
  {
    "id": "kenya26",
    "category": "economy",
    "title": "The Uneven Impact of Generative Artificial Intelligence on Entrepreneurial Performance: Evidence from a Field Experiment in Kenya",
    "publisher": "Otis et al. · Management Science / Berkeley Haas",
    "year": 2026,
    "type": "Peer-reviewed randomized field experiment",
    "scope": "Small businesses in Kenya",
    "url": "https://haas.berkeley.edu/ibsi/research/the-uneven-impact-of-gen-ai/",
    "extra": "https://doi.org/10.1287/mnsc.2024.06909",
    "locator": "Study results; journal publication link",
    "finding": "Access to a GPT-4 business assistant did not yield a statistically established average revenue/profit benefit. Effects were less favourable for initially low-performing entrepreneurs.",
    "limit": "Existing businesses, not a count of AI-enabled new founders. An insignificant average is not proof of zero effect; subgroup findings do not predict every firm’s outcome."
  },
  {
    "id": "itu25",
    "category": "society",
    "title": "Measuring Digital Development: Facts and Figures 2025",
    "publisher": "International Telecommunication Union",
    "year": 2025,
    "type": "Statistical estimates",
    "scope": "Global internet use; 2025",
    "url": "https://www.itu.int/pub/D-IND-ICT_MDD-2025-3/",
    "extra": "https://www.itu.int/en/mediacentre/Pages/PR-2025-11-17-Facts-and-Figures.aspx",
    "locator": "Internet-use estimates",
    "finding": "About 74% of the world uses the internet; 2.2 billion people remain offline.",
    "limit": "Measures internet use, not AI adoption, meaningful connectivity or AI literacy. Internet access is only one condition for access to online AI services."
  },
  {
    "id": "nigeria25",
    "category": "society",
    "title": "From Chalkboards to Chatbots: Evaluating the Impact of Generative AI on Learning Outcomes in Nigeria",
    "publisher": "World Bank",
    "year": 2025,
    "type": "Randomized trial / working paper",
    "scope": "Six-week secondary-school programme; Edo State",
    "url": "https://documents.worldbank.org/en/publication/documents-reports/documentdetail/099548105192529324",
    "extra": "https://documents1.worldbank.org/curated/en/099548105192529324/pdf/IDU-c09f40d8-9ff8-42dc-b315-591157499be7.pdf",
    "locator": "Abstract; treatment design",
    "finding": "A teacher-supported programme using a GPT-4-powered tutor improved a combined English, AI-knowledge and digital-skills assessment by 0.31 standard deviations.",
    "limit": "The effect is for the full programme, not isolated AI access. A standard deviation is not a percentage-point gain. Long-term persistence and transfer to other settings remain uncertain."
  },
  {
    "id": "learning25",
    "category": "society",
    "title": "Generative AI without Guardrails Can Harm Learning: Evidence from High School Mathematics",
    "publisher": "Bastani et al. · PNAS",
    "year": 2025,
    "type": "Peer-reviewed randomized trial",
    "scope": "Nearly 1,000 high-school students; Türkiye",
    "url": "https://hamsabastani.github.io/education_llm.pdf",
    "extra": "https://doi.org/10.1073/pnas.2422633122",
    "locator": "Abstract; main results",
    "finding": "The standard AI interface improved assisted practice grades by 48%, but subsequent unaided exam grades were 17% lower than the control. Teacher-designed tutor safeguards largely mitigated that harm.",
    "limit": "One school, subject and tool design. The guarded tutor did not establish a positive unaided exam effect. Practice performance and independent learning are different outcomes."
  },
  {
    "id": "masai26",
    "category": "society",
    "title": "MASAI: AI-supported Mammography Screening",
    "publisher": "Lund University · The Lancet",
    "year": 2026,
    "type": "Peer-reviewed randomized trial",
    "scope": "About 106,000 women; Sweden; human radiologists + AI",
    "url": "https://www.lunduniversity.lu.se/article/ai-support-breast-cancer-screening-fewer-missed-cancer-cases",
    "extra": "https://doi.org/10.1016/S0140-6736(25)02464-X",
    "locator": "Final trial summary; prior screening results",
    "finding": "The programme reported 29% more screening-detected cancers and 44% less screen-reading workload than standard double reading.",
    "limit": "Specific clinical workflow with human radiologists, not a general chatbot. These outcomes do not establish a mortality reduction or guarantee transfer to other health systems."
  },
  {
    "id": "bias24",
    "category": "society",
    "title": "AI Generates Covertly Racist Decisions about People Based on Their Dialect",
    "publisher": "Hofmann et al. · Nature",
    "year": 2024,
    "type": "Peer-reviewed model evaluation",
    "scope": "Tested language models; African American English",
    "url": "https://www.nature.com/articles/s41586-024-07856-5",
    "locator": "Abstract; employment and justice experiments",
    "finding": "Tested models produced discriminatory judgments from dialect cues, including less-prestigious job assignments for speakers of African American English.",
    "limit": "Controlled model outputs, not a population estimate of actual hiring or court outcomes. Findings apply to the tested models and tasks."
  },
  {
    "id": "belief24",
    "category": "society",
    "title": "Durably Reducing Conspiracy Beliefs through Dialogues with AI",
    "publisher": "Costello, Pennycook & Rand · Science / MIT Sloan",
    "year": 2024,
    "type": "Peer-reviewed experiment",
    "scope": "Personalised, evidence-based AI conversations",
    "url": "https://mitsloan.mit.edu/press/can-ai-talk-us-out-conspiracy-theories",
    "extra": "https://doi.org/10.1126/science.adq1814",
    "locator": "Study design and main findings",
    "finding": "Participants’ belief in their selected conspiracy theory fell by about 20% on average after the intervention; effects persisted at two months.",
    "limit": "Belief ratings in a controlled intervention, not a 20% fall in online misinformation. The same result does not make all AI-generated statements reliable."
  },
  {
    "id": "nist24",
    "category": "society",
    "title": "AI Risk Management Framework: Generative Artificial Intelligence Profile",
    "publisher": "National Institute of Standards and Technology",
    "year": 2024,
    "type": "Risk-management guidance",
    "scope": "Generative AI across the system lifecycle",
    "url": "https://www.nist.gov/publications/artificial-intelligence-risk-management-framework-generative-artificial-intelligence",
    "extra": "https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.600-1.pdf",
    "locator": "Risks and suggested actions",
    "finding": "Addresses confabulation, privacy, harmful bias, information integrity and other risks, with actions for evaluation, governance and monitoring.",
    "limit": "Guidance, not a measured incidence rate or a guarantee of safety. The practical actions on this site are editorial applications of the cited evidence."
  },
  {
    "id": "gcorp26",
    "category": "environment",
    "title": "Google Environmental Report 2026",
    "publisher": "Google",
    "year": 2026,
    "type": "Company environmental inventory",
    "scope": "Calendar 2025; operations and value chain",
    "url": "https://sustainability.google/reports/google-2026-environmental-report/",
    "locator": "PDF pp. 5, 27–28, 69–70 and 90; GHG emissions table",
    "finding": "Reports 18,847,100 tCO₂e GHG Protocol-aligned emissions in 2025, versus 15,937,300 in 2024, using market-based Scope 2. Annual global electricity purchases matched 100% of consumption. Operational emissions fell about 2%.",
    "limit": "Not zero emissions. Its separate 14,473,100 tCO₂e ambition-based total excludes some Scope 3 categories and applies additional market instruments. This is company-wide, not AI-only. Selected inventory metrics receive limited assurance; this is not a blanket endorsement of all report claims.",
    "checked": "2026-09-10",
    "extra": "https://sustainability.google/files/1699a2e19277e478b38a659f00cb38398a836e8c/download-1699a2e1.pdf#page=90"
  },
  {
    "id": "gflex25",
    "category": "environment",
    "title": "How we’re making data centers more flexible to benefit power grids",
    "publisher": "Google · Michael Terrell",
    "year": 2025,
    "type": "Company implementation announcement",
    "scope": "Utility agreements announced 4 August 2025",
    "url": "https://blog.google/innovation-and-ai/infrastructure-and-cloud/global-network/how-were-making-data-centers-more-flexible-to-benefit-power-grids/",
    "locator": "Delivering flexibility to strengthen grids; Evolving for future energy needs",
    "finding": "Google describes non-urgent workload shifting and agreements with Indiana Michigan Power and TVA targeting machine-learning demand response.",
    "limit": "Early deployment at selected locations. An agreement is not a measured emissions reduction. Reliability and service deadlines limit how much work can shift.",
    "checked": "2026-09-10"
  },
  {
    "id": "mcorp26",
    "category": "environment",
    "title": "Responsibly building the AI future",
    "publisher": "Microsoft · Brad Smith & Melanie Nakagawa",
    "year": 2026,
    "type": "Company sustainability disclosure",
    "scope": "FY2025; published 9 July 2026",
    "url": "https://blogs.microsoft.com/on-the-issues/2026/07/09/responsibly-building-the-ai-future/",
    "locator": "Progress amid growth; footnotes 1–3",
    "finding": "Reports 100% annual global electricity matching with renewable energy and a 25% year-on-year increase in total Scope 1, 2 and 3 emissions. Infrastructure growth and pausing non-additional unbundled certificates contributed.",
    "limit": "Annual matching does not mean zero-hourly grid emissions or a carbon-negative company. Accounting choices affect year-on-year comparisons. Modelled avoided emissions are separate from its inventory.",
    "checked": "2026-09-10",
    "extra": "https://www.microsoft.com/en-us/corporate-responsibility/topics/sustainability/report/"
  },
  {
    "id": "mcool24",
    "category": "environment",
    "title": "Sustainable by design: Next-generation datacenters consume zero water for cooling",
    "publisher": "Microsoft · Steve Solomon",
    "year": 2024,
    "type": "Engineering design / projected saving",
    "scope": "New designs announced December 2024",
    "url": "https://www.microsoft.com/en-us/microsoft-cloud/blog/2024/12/09/sustainable-by-design-next-generation-datacenters-consume-zero-water-for-cooling/",
    "locator": "Zero-water evaporation; Pilot projects and implementation; footnote",
    "finding": "Closed-loop chip cooling avoids evaporative water consumption. Microsoft projects over 125 million litres avoided per facility annually relative to its FY2024 global average withdrawal WUE of 0.30 L/kWh.",
    "limit": "A projected comparison, not an observed fleet-wide saving. Initial fill and other water uses remain. The company describes a small energy increase versus evaporative designs; the announcement schedules pilots in 2026 and new sites from late 2027.",
    "checked": "2026-09-10"
  },
  {
    "id": "mfair25",
    "category": "environment",
    "title": "Inside the world’s most powerful AI datacenter",
    "publisher": "Microsoft · Scott Guthrie",
    "year": 2025,
    "type": "Company facility case study",
    "scope": "Fairwater, Wisconsin; 18 September 2025 announcement",
    "url": "https://blogs.microsoft.com/blog/2025/09/18/inside-the-worlds-most-powerful-ai-datacenter/",
    "locator": "Cooling section",
    "finding": "Describes a closed-loop cooling system serving over 90% of capacity at the featured site. Water circulates without evaporation losses; the remainder uses air cooling and water on hot days.",
    "limit": "Facility design disclosure, not an independently measured annual saving or the configuration of Microsoft’s entire fleet. The initial water fill and remaining systems prevent interpreting this as no water footprint.",
    "checked": "2026-09-10"
  },
  {
    "id": "aws26",
    "category": "environment",
    "title": "Five things to know from Amazon’s 2025 Sustainability Report",
    "publisher": "Amazon · Tyler Greenawalt",
    "year": 2026,
    "type": "Company sustainability disclosure",
    "scope": "Calendar 2025; published in 2026",
    "url": "https://www.aboutamazon.com/news/sustainability/amazon-sustainability-report-2025",
    "locator": "Carbon progress; section 3 on data centres",
    "finding": "Reports reclaimed-water cooling at 26 data centres, preserving nearly 850 million litres of potable water. Annual electricity was matched with renewables for a third consecutive year. Absolute company emissions increased in 2025.",
    "limit": "Company-reported potable-water substitution is not a reduction of the same volume in total water consumption. Amazon-wide carbon includes retail and logistics as well as AWS, so it is not an AI inventory or a like-for-like company ranking.",
    "checked": "2026-09-10"
  },
  {
    "id": "mitpower22",
    "category": "environment",
    "title": "Great Power, Great Responsibility: Recommendations for Reducing Energy for Training Language Models",
    "publisher": "McDonald et al. · MIT Lincoln Laboratory and collaborators",
    "year": 2022,
    "type": "Peer-reviewed systems experiment",
    "scope": "Language-model training and inference on tested platforms",
    "url": "https://arxiv.org/abs/2205.09646",
    "locator": "Abstract; power-capping experiments; Findings of NAACL 2022",
    "finding": "A transformer training example used about 15% less energy with GPU power capping, with a marginal increase in computation time.",
    "limit": "A result for tested configurations, not a guaranteed saving for current chips or an entire data centre. Measure energy to complete the job, quality and runtime together.",
    "checked": "2026-09-10",
    "extra": "https://doi.org/10.18653/v1/2022.findings-naacl.151"
  },
  {
    "id": "tinychat24",
    "category": "environment",
    "title": "TinyChat 2.0: Accelerating Edge AI with Efficient LLM and VLM Deployment",
    "publisher": "MIT Han Lab · Yang et al.",
    "year": 2024,
    "type": "Research implementation / benchmarks",
    "scope": "Edge LLM and VLM inference; 12 December 2024",
    "url": "https://hanlab.mit.edu/blog/tinychat20",
    "locator": "TL;DR; Conclusion; benchmark configurations",
    "finding": "Reports 4× memory savings and 3–4× decoding speedups from the AWQ/TinyChat stack, with further prefilling improvements in version 2.0.",
    "limit": "Memory and latency benchmarks are not carbon measurements. Gains depend on model, device, kernels and workload; do not convert a speedup directly into an equal energy reduction.",
    "checked": "2026-09-10",
    "extra": "https://hanlab.mit.edu/blog/tinychat"
  },
  {
    "id": "processing24",
    "category": "environment",
    "title": "Power Hungry Processing: Watts Driving the Cost of AI Deployment?",
    "publisher": "Luccioni, Jernite & Strubell · FAccT",
    "year": 2024,
    "type": "Peer-reviewed benchmark study",
    "scope": "Task-specific and general-purpose models; preprint 2023, FAccT 2024",
    "url": "https://arxiv.org/abs/2311.16863",
    "locator": "Sections 4.2 and 5; model and task comparisons",
    "finding": "For tested classification and extractive-question-answering tasks, task-specific systems required less inference energy than general-purpose generative models. Output length and architecture also affected emissions.",
    "limit": "Selected models and benchmarks, not a universal energy ranking. Accuracy differs across models. A proposed deployment must meet its own quality requirements before choosing a smaller system.",
    "checked": "2026-09-10"
  },
  {
    "id": "princeton24",
    "category": "environment",
    "title": "System-level impacts of voluntary carbon-free electricity procurement strategies",
    "publisher": "Xu et al. · Princeton / Joule",
    "year": 2024,
    "type": "Peer-reviewed electricity-system model",
    "scope": "U.S. electricity procurement scenarios",
    "url": "https://acee.princeton.edu/acee-news/buyer-beware-most-clean-power-purchasing-strategies-do-little-to-cut-emissions/",
    "locator": "22 January 2024 university account of 11 January Joule paper; Time for a better alternative",
    "finding": "Hourly matching consistently reduced system-wide emissions in the modelled scenarios; annual matching often added little long-term reduction. More demanding matching supports storage and firm clean supply.",
    "limit": "A model with region, cost and policy assumptions, not proof that every annual renewable contract has no benefit. Hourly matching can cost more. Research was supported by Google and the Princeton Zero-carbon Technology Consortium.",
    "checked": "2026-09-10"
  },
  {
    "id": "sbti26",
    "category": "environment",
    "title": "Corporate Net-Zero Standard Version 2.0",
    "publisher": "Science Based Targets initiative",
    "year": 2026,
    "type": "Climate target-setting standard",
    "scope": "Published June 2026; transition arrangements apply",
    "url": "https://files.sciencebasedtargets.org/production/files/Corporate-Net-Zero-Standard-version-2.pdf",
    "locator": "Chapters 1–5; net-zero definition; accompanying transition guidance",
    "finding": "Defines net zero through deep Scope 1–3 reductions and durable removal and storage for residual emissions. Sets governance, implementation and reporting expectations; electricity accounting covers location and time.",
    "limit": "A voluntary standard, not proof that a company has achieved its target. V1.3.1 remains available for submissions during the transition, with V2.0 required for new submissions from 1 February 2028. Do not present future provisions as already universal.",
    "checked": "2026-09-10",
    "extra": "https://sciencebasedtargets.org/blog/introducing-the-sbti-corporate-net-zero-standard-version-2-0"
  },
  {
    "id": "neutral24",
    "category": "environment",
    "title": "Net-Zero Jargon Buster: A guide to common terms",
    "publisher": "Science Based Targets initiative",
    "year": 2024,
    "type": "Terminology guidance",
    "scope": "Carbon-neutral versus net-zero claims",
    "url": "https://sciencebasedtargets.org/blog/net-zero-jargon-buster-a-guide-to-common-terms",
    "locator": "Carbon neutral; Net-zero; Beyond value chain mitigation",
    "finding": "Explains that carbon-neutral claims can rely on offsets and need not demonstrate direct reductions or cover all greenhouse gases. Climate finance outside a value chain should complement internal reductions.",
    "limit": "Terminology and historical V1.2 guidance; use the 2026 standard for current technical provisions. Scope and reporting year must accompany any neutrality claim.",
    "checked": "2026-09-10"
  },
  {
    "id": "oecdwork24",
    "category": "economy",
    "title": "Using AI in the workplace: Opportunities, risks and policy responses",
    "publisher": "OECD",
    "year": 2024,
    "type": "Employer/worker survey synthesis and policy guidance",
    "scope": "5,334 workers and 2,053 firms; finance and manufacturing in seven countries",
    "url": "https://www.oecd.org/content/dam/oecd/en/publications/reports/2024/03/using-ai-in-the-workplace_02d6890a/73d417f9-en.pdf",
    "locator": "pp. 3–4 and 12–13; Box 1 and Figure 7",
    "finding": "Training and worker consultation are associated with better reported worker outcomes. Recommends skills support, social dialogue, transition assistance, privacy and human oversight.",
    "limit": "Survey associations are not randomized evidence that consultation causes improvements. Earlier broad-AI adoption differs from current GenAI. Historic legal descriptions in this report are not used as current law.",
    "checked": "2026-09-10"
  },
  {
    "id": "sme25",
    "category": "economy",
    "title": "Generative AI and the SME Workforce: New Survey Evidence",
    "publisher": "OECD",
    "year": 2025,
    "type": "Representative business survey",
    "scope": "Over 5,000 SMEs in seven countries; fieldwork late 2024",
    "url": "https://www.oecd.org/en/publications/generative-ai-and-the-sme-workforce_2d08b99d-en/full-report.html",
    "locator": "Executive summary; Figures 2.7 and 3.6",
    "finding": "31% of surveyed SMEs used GenAI. Among users, 65% reported better employee performance, 35% an ability to scale up and 26% increased revenue.",
    "limit": "Owner/manager reports, not causal estimates or measured profit growth. The sample covers Austria, Canada, Germany, Ireland, Japan, Korea and the UK; percentages do not describe all businesses worldwide.",
    "checked": "2026-09-10",
    "extra": "https://www.oecd.org/content/dam/oecd/en/publications/reports/2025/11/generative-ai-and-the-sme-workforce_83bafdfb/2d08b99d-en.pdf"
  },
  {
    "id": "workbank25",
    "category": "economy",
    "title": "Future of Work with AI Agents: Auditing Automation and Augmentation Potential across the U.S. Workforce",
    "publisher": "Shao et al. · Stanford",
    "year": 2025,
    "type": "Worker survey and expert capability assessment / preprint",
    "scope": "1,500 workers; 844 tasks; 104 occupations",
    "url": "https://arxiv.org/abs/2506.06576",
    "locator": "Abstract; Sections 2–3; WORKBank",
    "finding": "Worker preferences and expert assessments of feasible automation often diverge. Workers commonly seek help with repetitive tasks while retaining agency; preferred human involvement varies by occupation.",
    "limit": "Elicited preferences and expert ratings, not observed outcomes from deployment. It is not a referendum of all U.S. workers or a forecast of employment.",
    "checked": "2026-09-10",
    "extra": "https://hai.stanford.edu/news/what-workers-really-want-from-artificial-intelligence"
  },
  {
    "id": "hbs23",
    "category": "economy",
    "title": "Humans vs. Machines: Untangling the Tasks AI Can (and Can’t) Handle",
    "publisher": "Harvard Business School · Rachel Layne / McFowland et al.",
    "year": 2023,
    "type": "Field experiment and researcher interpretation",
    "scope": "BCG consultants; GPT-4; 2023",
    "url": "https://www.library.hbs.edu/working-knowledge/humans-vs-machines-untangling-the-tasks-ai-can-and-cant-handle",
    "locator": "Research summary and Edward McFowland III interview, 22 November 2023",
    "finding": "AI helped on some consulting tasks, but on a task beyond its capabilities users were 19 percentage points less likely to reach the correct answer. McFowland recommends methodical tests in the organization’s own context.",
    "limit": "A 2023 task-specific result, not the error rate of every AI product. Researcher recommendations are interpretation, distinct from experimentally measured effects.",
    "checked": "2026-09-10",
    "extra": "https://aiinstitute.hbs.edu/navigating-the-jagged-technological-frontier/"
  },
  {
    "id": "ada25",
    "category": "society",
    "title": "How do people feel about AI? Wave two of a UK attitudes survey",
    "publisher": "Ada Lovelace Institute & Alan Turing Institute",
    "year": 2025,
    "type": "Nationally representative public survey",
    "scope": "3,513 UK residents; report 25 March 2025",
    "url": "https://www.adalovelaceinstitute.org/press-release/ada-turing-survey-2025/",
    "locator": "Governance and representation findings; linked full survey",
    "finding": "72% said laws and regulation would increase their comfort with AI. Half felt their views and values were not represented in current AI decisions.",
    "limit": "Public preferences, not evidence that any particular regulation will work or a global opinion estimate. Question wording and country context matter.",
    "checked": "2026-09-10",
    "extra": "https://attitudestoai.uk/assets/documents/How-do-people-feel-about-AI-2025-Ada-Lovelace-Institute.pdf"
  },
  {
    "id": "melbourne25",
    "category": "society",
    "title": "Trust, attitudes and use of artificial intelligence: A global study 2025",
    "publisher": "University of Melbourne & KPMG · Gillespie, Lockey and colleagues",
    "year": 2025,
    "type": "Cross-country public survey",
    "scope": "48,340 adults in 47 countries; November 2024–January 2025",
    "url": "https://mbs.edu/-/media/PDF/Research/Trust_in_AI_Report.pdf?rev=0ee82285b2b0439bba524dbddc58214a",
    "locator": "Key findings; pp. 12–17 methods; workplace governance section",
    "finding": "66% reported intentionally using AI with some regularity; 46% were willing to trust AI. 47% of employees reported AI training and 40% workplace GenAI policy or guidance.",
    "limit": "Self-reports, not observed skill or audited workplace conduct. Samples targeted age, gender and region with documented exceptions; emerging-economy samples overrepresent university education. Cross-country results are not a census of humanity.",
    "checked": "2026-09-10",
    "extra": "https://fbe.unimelb.edu.au/newsroom/media-release-global-study-reveals-trust-of-ai-remains-a-critical-challenge-reflecting-tension-between-benefits-and-risks"
  },
  {
    "id": "chien25",
    "category": "environment",
    "title": "Can generative AI get the power it demands without reversing grid decarbonization?",
    "publisher": "Andrew A. Chien · Stanford Energy Seminar",
    "year": 2025,
    "type": "Expert research perspective",
    "scope": "22 January 2025 seminar; University of Chicago professor, then visiting Stanford",
    "url": "https://energy.stanford.edu/events/lecturepresentationtalk/stanford-energy-seminar-andrew-chien-can-generative-ai-get-power-it",
    "locator": "Talk abstract and speaker biography",
    "finding": "Argues for cooperation between computing loads and electricity grids: flexible workloads can respond to variable supply and help integrate clean energy.",
    "limit": "A research perspective, not a measured fleet-wide savings percentage. The site does not reuse the abstract’s older demand projections or imply that Chien is a permanent Stanford professor.",
    "checked": "2026-09-10"
  },
  {
    "id": "strubell25",
    "category": "environment",
    "title": "Confronting the AI/energy conundrum",
    "publisher": "MIT Energy Initiative · Emma Strubell and symposium participants",
    "year": 2025,
    "type": "Expert symposium perspective",
    "scope": "July 2025 report of May symposium",
    "url": "https://news.mit.edu/2025/confronting-ai-energy-conundrum-0702",
    "locator": "Securing growth with sustainability",
    "finding": "Carnegie Mellon professor Emma Strubell cautions that efficiency can stimulate increased use, so improved per-task efficiency need not lower aggregate resource demand.",
    "limit": "Expert interpretation of rebound risk, not a universal numerical prediction. Older third-party demand and inference-share estimates elsewhere in the article are not adopted here.",
    "checked": "2026-09-10"
  },
  {
    "id": "ainow25",
    "category": "environment",
    "title": "AI Now 2025 Landscape Report: A roadmap for action",
    "publisher": "AI Now Institute",
    "year": 2025,
    "type": "Policy research / advocacy recommendations",
    "scope": "Data-centre oversight; June 2025",
    "url": "https://ainowinstitute.org/wp-content/uploads/2025/06/FINAL-20250603_AINowLandscapeReport_Full.pdf",
    "locator": "Chapter 4; printed pp. 83–84 (PDF pp. 82–83)",
    "finding": "Proposes public energy and water reporting, hearings, site assessments and protections for communities affected by infrastructure development.",
    "limit": "Normative policy recommendations, not survey findings or proof of their effectiveness. The report also advocates restricting expansion; this site uses its disclosure and accountability proposals without presenting them as a consensus.",
    "checked": "2026-09-10"
  },
  {
    "id": "ieaafford26",
    "category": "environment",
    "title": "Data-centre electricity use surged in 2025: options for affordability and integration",
    "publisher": "International Energy Agency",
    "year": 2026,
    "type": "Institutional energy analysis",
    "scope": "2026 update on data-centre growth and policy options",
    "url": "https://www.iea.org/news/data-centre-electricity-use-surged-in-2025-even-with-tightening-bottlenecks-driving-a-scramble-for-solutions",
    "locator": "Final two paragraphs; Key Questions on Energy and AI",
    "finding": "Explains that concentrated new loads can require generation and grid investment, while policy design and flexible operation can manage affordability pressures.",
    "limit": "Does not establish that every data centre raises household bills or that all cost-allocation approaches are equally effective.",
    "checked": "2026-09-10"
  },
  {
    "id": "eia26",
    "category": "environment",
    "title": "Monthly Energy Review, Table 7.6: Electricity End Use",
    "publisher": "U.S. Energy Information Administration",
    "year": 2026,
    "type": "National statistics",
    "scope": "United States, 2023; August 2026 table edition.",
    "url": "https://www.eia.gov/totalenergy/data/monthly/pdf/sec7_19.pdf",
    "locator": "Table 7.6, 2023 Total row and footnotes a–d, g–h.",
    "finding": "2023 electricity retail sales: residential 1,450,025 million kWh; industrial 1,009,256 million kWh. Converted here to 1,450 and 1,009 TWh.",
    "limit": "Retail sales exclude self-generated electricity used onsite. The LBNL data-centre estimate uses a different method. These are scale comparisons, not components to sum. Industrial includes agriculture and irrigation."
  },
  {
    "id": "usgs25",
    "category": "environment",
    "title": "Water use across the conterminous United States, water years 2010–20",
    "publisher": "U.S. Geological Survey",
    "year": 2025,
    "type": "Modelled national water assessment",
    "scope": "Contiguous 48 states; average over water years 2010–2020.",
    "url": "https://pubs.usgs.gov/publication/pp1894D/full",
    "extra": "https://pubs.usgs.gov/pp/1894/d/pp1894D.pdf",
    "locator": "Key Points; tables 1–3; Water-Use Concepts.",
    "finding": "Daily consumption in million US gallons: crop irrigation 75,698; public supply 4,219; freshwater thermoelectric power 2,904. Withdrawals: 105,497; 35,440; 82,656, respectively.",
    "limit": "An older multi-year average, not a 2023 census. Public supply serves multiple end uses; power generation overlaps data centres’ indirect footprint. Annual equivalents use 3.785411784 litres per gallon and 365 days."
  },
  {
    "id": "eeatextile",
    "category": "environment",
    "title": "Water use for EU’s textiles consumption",
    "publisher": "European Environment Agency",
    "year": 2026,
    "type": "Living indicator · accessed September 2026",
    "scope": "EU27 household textile purchases in 2022; global upstream production.",
    "url": "https://www.eea.europa.eu/en/circularity/sectoral-modules/textiles/water-use-for-eus-textiles-consumption",
    "locator": "Opening finding, Definition and Methodology; underlying ETC/CE 2025/6 research.",
    "finding": "Estimates about 6,000 million cubic metres of blue-water use, equivalent to 6,000 billion litres. About 85% occurs outside Europe.",
    "limit": "Includes surface and groundwater consumption across supply chains, not rainwater. Geography, year and boundary differ from US onsite data-centre cooling. No like-for-like efficiency ratio is inferred; methods differ from older EEA studies."
  },
  {
    "id": "loreal24",
    "category": "environment",
    "title": "2024 Universal Registration Document: Water policies",
    "publisher": "L’Oréal",
    "year": 2025,
    "type": "Company disclosure",
    "scope": "L’Oréal value chain and operated-site water policies; 2024 reporting year.",
    "url": "https://www.loreal-finance.com/eng/2024-universal-registration-document/en/article/222/",
    "locator": "Section 4.4.2, Water policies and Waterloop.",
    "finding": "Identifies water impacts in raw-material farming, manufacturing and consumer rinsing. Waterloop reuses treated water for industrial processes, while product ingredients and human consumption still require mains water.",
    "limit": "One company’s disclosure, not a water inventory for the entire cosmetics industry or an independently verified sector-wide saving."
  },
  {
    "id": "unenergy25",
    "category": "environment",
    "title": "Energy Statistics Pocketbook 2025",
    "publisher": "United Nations Statistics Division",
    "year": 2025,
    "type": "Official statistics",
    "scope": "World final electricity consumption, 2022; energy balance in exajoules.",
    "url": "https://unstats.un.org/unsd/energystats/pubs/documents/2025pb-web.pdf",
    "locator": "Printed pp. 46–47: World energy balance. Electricity column; final energy consumption and sector rows.",
    "finding": "88.4 EJ total: industry 37.4, households 24.5, commerce/public services 16.0, other energy use 8.8, transport 1.7. Shares are calculated from these rounded values; 1 EJ = 277.7778 TWh.",
    "limit": "2022, not 2024. Final use excludes energy industries’ own use and losses. Data centres are embedded in sectors, not an additional slice. Other includes agriculture and unspecified uses; sector classifications differ from end-use applications.",
    "extra": ""
  },
  {
    "id": "lighting26",
    "category": "environment",
    "title": "The next wave of LED lighting: Smarter, circular and more efficient",
    "publisher": "International Energy Agency",
    "year": 2026,
    "type": "Intergovernmental analysis",
    "scope": "Global electricity for lighting in buildings and outdoors, 2024.",
    "url": "https://www.iea.org/commentaries/the-next-wave-of-led-lighting-smarter-circular-and-more-efficient",
    "locator": "Opening section, published 6 March 2026.",
    "finding": "About 2,200 TWh, around 8% of global electricity demand, went to lighting in buildings and outdoor applications in 2024.",
    "limit": "Excludes industrial and agricultural lighting. Lighting is an end use inside economic sectors, not a separate sector to add to a sectoral electricity chart.",
    "extra": ""
  },
  {
    "id": "sony24",
    "category": "environment",
    "title": "PlayStation energy efficiency measurements",
    "publisher": "Sony Interactive Entertainment",
    "year": 2024,
    "type": "Manufacturer measurements",
    "scope": "PS5 CFI-2016 disc model; HD mode, tested 26 January 2024.",
    "url": "https://www.playstation.com/en-gb/legal/ecodesign/",
    "locator": "CFI-2016 (not Digital Edition), HD power figures, tested 26/01/2024.",
    "finding": "Three-game average 216.8 W; streaming media 56.3 W. One hour at these measured rates equals 216.8 and 56.3 Wh of console electricity.",
    "limit": "One console model and test setup; excludes television, router, networks and data centres. Not all gaming, cloud gaming or streaming devices.",
    "extra": ""
  },
  {
    "id": "ofcom22",
    "category": "environment",
    "title": "Carbon emissions of streaming and digital terrestrial television",
    "publisher": "Ofcom · commissioned research by Carnstone",
    "year": 2022,
    "type": "Commissioned modelling study",
    "scope": "UK digital terrestrial television and broadcaster/subscription streaming, 2021 scenario.",
    "url": "https://www.ofcom.org.uk/siteassets/resources/documents/research-and-data/technology-research/2022/carbon-emissions-of-streaming-and-digital-terrestrial-television-3.pdf?v=322186",
    "locator": "Appendix B, printed p. 23: energy consumption including TV per device hour, 2021.",
    "finding": "112.7 Wh per device hour for OTT streaming and 76.2 Wh for digital terrestrial television, including viewing-device electricity in the system estimate.",
    "limit": "Modelled UK viewing mix in 2021, not a current global streaming average. Device hours differ from viewer hours. Covers networks and equipment as well as data centres, so it cannot be compared directly with a server-only AI prompt.",
    "extra": ""
  },
  {
    "id": "linkedin26",
    "category": "economy",
    "title": "Building a Future of Work That Works",
    "publisher": "LinkedIn Economic Graph",
    "year": 2026,
    "type": "Company labour-market analysis",
    "scope": "LinkedIn global employment and AI-role analysis, 2023–2025; report January 2026.",
    "url": "https://economicgraph.linkedin.com/research/labor-market-report-2026",
    "locator": "PDF pp. 10–13: AI roles, data-centre employment, and firm employment by category.",
    "finding": "LinkedIn reports at least 1.3 million new AI-related job opportunities in 2023–2025. In its sample of 100+ manually classified firms, employment grew 92% at foundation-model companies, 25% at AI hardware companies, 20% in computer vision/robotics, 18% in AI tech/platforms, 6% in vertical applications and 8% in Big Tech over a year.",
    "limit": "Platform-based coverage and company classifications, not an audited census or causal estimate of global net job creation. The report’s occupation chart is not used for exact role-by-role counts. Roles may predate the AI boom.",
    "extra": "https://economicgraph.linkedin.com/content/dam/me/economicgraph/en-us/PDF/linkedIn-labor-market-report-building-a-future-of-work-that-works-jan-2026.pdf"
  },
  {
    "id": "coding25",
    "category": "economy",
    "title": "The Effects of Generative AI on High-Skilled Work: Evidence from Three Field Experiments with Software Developers",
    "publisher": "Cui, Demirer, Jaffe, Musolff, Peng & Salz · Microsoft Research",
    "year": 2025,
    "type": "Randomized field experiments",
    "scope": "4,867 developers at Microsoft, Accenture and an anonymous Fortune 100 company.",
    "url": "https://www.microsoft.com/en-us/research/publication/the-effects-of-generative-ai-on-high-skilled-work-evidence-from-three-field-experiments-with-software-developers/",
    "locator": "June 2025 abstract and linked preprint; pooled estimate.",
    "finding": "Access to an AI coding assistant increased completed tasks by an estimated 26.08% (standard error 10.3 percentage points) in the pooled analysis.",
    "limit": "Each trial was noisy. Code-completion tools, tasks and adoption differed from the METR study. This is not a current benchmark for every coding agent, quality metric, or macroeconomic growth estimate.",
    "extra": ""
  },
  {
    "id": "wharton25",
    "category": "economy",
    "title": "The Projected Impact of Generative AI on Future Productivity Growth",
    "publisher": "Penn Wharton Budget Model · University of Pennsylvania",
    "year": 2025,
    "type": "Economic projection",
    "scope": "United States; model published 8 September 2025.",
    "url": "https://budgetmodel.wharton.upenn.edu/p/2025-09-08-the-projected-impact-of-generative-ai-on-future-productivity-growth/",
    "locator": "Key Points and GDP/productivity projections.",
    "finding": "Projects GDP and productivity levels 1.5% higher by 2035, nearly 3% by 2055, and 3.7% by 2075 relative to a baseline without the modelled GenAI contribution.",
    "limit": "These are future GDP-level differences, not annual growth rates or gains already realized. Depends on adoption, task exposure and cost-saving assumptions informed by limited early evidence.",
    "extra": ""
  },
  {
    "id": "flood24",
    "category": "society",
    "title": "Global prediction of extreme floods in ungauged watersheds",
    "publisher": "Nearing et al. · Google Research and collaborators · Nature",
    "year": 2024,
    "type": "Peer-reviewed modelling study",
    "scope": "Global riverine flood prediction, including watersheds without local stream gauges.",
    "url": "https://doi.org/10.1038/s41586-024-07145-1",
    "locator": "Abstract and Conclusion: comparison with GloFAS nowcasts.",
    "finding": "AI forecasts at up to five days’ lead time achieved reliability similar to or better than a leading global model’s same-day estimates; forecasts were made publicly available.",
    "limit": "Forecast skill is not a measured count of lives saved or losses avoided. River flooding differs from all types of flooding; effective warning delivery also matters.",
    "extra": ""
  },
  {
    "id": "alphafold23",
    "category": "society",
    "title": "AlphaFold uses open data and AI to discover the 3D protein universe",
    "publisher": "EMBL European Bioinformatics Institute",
    "year": 2023,
    "type": "Research infrastructure case study",
    "scope": "AlphaFold Protein Structure Database, 2022 expansion reported February 2023.",
    "url": "https://www.ebi.ac.uk/about/news/perspectives/alphafold-using-open-data-and-ai-to-discover-the-3d-protein-universe/",
    "locator": "“Exceeding expectations: 200 million protein structure predictions.”",
    "finding": "The open database expanded to over 200 million predicted protein structures, helping researchers generate hypotheses about biological function and disease.",
    "limit": "Predicted structures are not experimental validation, approved medicines or demonstrated patient outcomes. Experimental follow-up remains necessary.",
    "extra": "https://alphafold.ebi.ac.uk/"
  },
  {
    "id": "steel25",
    "category": "environment",
    "title": "Demand and Supply Measures for the Steel and Cement Transition",
    "publisher": "International Energy Agency",
    "year": 2025,
    "type": "Intergovernmental sector analysis",
    "scope": "Steel and cement; global energy- and process-related emissions.",
    "url": "https://www.iea.org/reports/demand-and-supply-measures-for-the-steel-and-cement-transition/executive-summary",
    "locator": "Executive summary, opening section.",
    "finding": "Steel and cement account for 14% of global energy- and process-related emissions on a direct basis.",
    "limit": "Industrial direct emissions exclude purchased electricity. This denominator includes process emissions, unlike the fuel-combustion denominator in the data-centre comparison. This is contextual scale, not a like-for-like lifecycle ranking.",
    "extra": ""
  }
]