{"attribution":{"source":"MIT AI Risk Repository, Domain Taxonomy of AI Risks v1 (MIT AI Risk Initiative)","license":"CC BY 4.0","license_url":"https://creativecommons.org/licenses/by/4.0/","citation":"Slattery, P., Saeri, A. K., Grundy, E. A. C., Graham, J., Noetel, M., Uuk, R., Dao, J., Pour, S., Casper, S., & Thompson, N. (2025). The AI Risk Repository: A comprehensive meta-review, database, and taxonomy of risks from artificial intelligence. arXiv:2408.12622."},"exported_at":"2026-09-11"}
{"rows":[{"ev_id":"03.06.00","quick_ref":"Cunha2023","paper_title":"Navigating the Landscape of AI Ethics and Responsibility","level":"Risk Category","risk_category":"Environmental and socioeconomic harms","risk_subcategory":null,"description":"\"At a time of increasing climate urgency,\nenergy consumption and the carbon footprint of AI applications are also matters of ethics\nand responsibility [68]. As with other energy-intensive technologies like proof-of-work\nblockchain, the call is to research more environmentally sustainable algorithms to offset\nthe increasing use scale.\"","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":6,"subdomain":"6.6"},{"ev_id":"05.15.00","quick_ref":"Hagendorff2024","paper_title":"Mapping the Ethics of Generative AI: A Comprehensive Scoping Review","level":"Risk Category","risk_category":"Sustainability","risk_subcategory":null,"description":"Generative models are known for their substantial energy requirements, necessitating significant amounts of electricity, cooling water, and hardware containing rare metals. The extraction and utilization of these resources frequently occur in unsustainable ways. Consequently, papers highlight the urgency of mitigating environmental costs for instance by adopting renewable energy sources and utilizing energy-efficient hardware in the operation and training of generative AI systems.","entity":"AI","intent":"Unintentional","timing":"Other","domain":6,"subdomain":"6.6"},{"ev_id":"10.09.00","quick_ref":"Paes2023","paper_title":"Social Impacts of Artificial Intelligence and Mitigation Recommendations: An Exploratory Study","level":"Risk Category","risk_category":"Environmental Impacts","risk_subcategory":null,"description":"\"The production process of these devices requires raw materials such as nickel, cobalt, and lithium in such high quantities that the Earth may soon no longer be able to sustain them in sufficient quantities.\"","entity":"Human","intent":"Unintentional","timing":"Post-deployment","domain":6,"subdomain":"6.6"},{"ev_id":"11.05.05","quick_ref":"Shelby2023","paper_title":"Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reduction","level":"Risk Sub-Category","risk_category":"Societal System Harms","risk_subcategory":"Environmental harms","description":"depletion or contamination of natural resources, and damage to built environments... that may occur throughout the lifecycle of digital technologies [170, 237] from “crale (mining) to usage (consumption) to grave (waste)”","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":6,"subdomain":"6.6"},{"ev_id":"13.01.06","quick_ref":"Solaiman2023","paper_title":"Evaluating the Social Impact of Generative AI Systems in Systems and Society","level":"Risk Sub-Category","risk_category":"Impacts: The Technical Base System","risk_subcategory":"Environmental Costs","description":"\"The computing power used in training, testing, and deploying generative AI systems, especially large scale systems, uses substantial energy resources and thereby contributes to the global climate crisis by emitting greenhouse gasses.\"","entity":"Human","intent":"Unintentional","timing":"Other","domain":6,"subdomain":"6.6"},{"ev_id":"13.02.05","quick_ref":"Solaiman2023","paper_title":"Evaluating the Social Impact of Generative AI Systems in Systems and Society","level":"Risk Sub-Category","risk_category":"Impacts: People and Society","risk_subcategory":"Ecosystem and Environment","description":"\"Impacts at a high-level, from the AI ecosystem to the Earth itself, are necessarily broad but can be broken down into components for evaluation.\"","entity":"Human","intent":"Intentional","timing":"Post-deployment","domain":6,"subdomain":"6.6"},{"ev_id":"15.02.05","quick_ref":"Tan2022","paper_title":"The Risks of Machine Learning Systems","level":"Risk Sub-Category","risk_category":"Second-Order Risks","risk_subcategory":"Environmental","description":"The risk of harm to the natural environment posed by the ML system.","entity":"AI","intent":"Unintentional","timing":"Other","domain":6,"subdomain":"6.6"},{"ev_id":"16.06.01","quick_ref":"Weidinger2022","paper_title":"Taxonomy of Risks posed by Language Models","level":"Risk Sub-Category","risk_category":"Risk area 6: Environmental and Socioeconomic harms","risk_subcategory":"Environmental harms from operating LMs","description":"\"LMs (and AI more broadly) can have an environmental impact at different levels, including: (1) direct impacts from the energy used to train or operate the LM, (2) secondary impacts due to emissions from LM-based applications, (3) system-level impacts as LM-based applications influence human behaviour (e.g. increasing environmental awareness or consumption), and (4) resource impacts on precious metals and other materials required to build hardware on which the computations are run e.g. data centres, chips, or devices. Some evidence exists on (1), but (2) and (3) will likely be more significant","entity":"AI","intent":"Unintentional","timing":"Other","domain":6,"subdomain":"6.6"},{"ev_id":"17.06.01","quick_ref":"Weidinger2021","paper_title":"Ethical and social risks of harm from language models","level":"Risk Sub-Category","risk_category":"Automation, Access and Environmental Harms ","risk_subcategory":"Environmental harms from operation LMs ","description":"\"Large-scale machine learning models, including LMs, have the potential to create significant environmental costs via their energy demands, the associated carbon emissions for training and operating the models, and the demand for fresh water to cool the data centres where computations are run (Mytton, 2021; Patterson et al., 2021).\"","entity":"AI","intent":"Unintentional","timing":"Other","domain":6,"subdomain":"6.6"},{"ev_id":"18.06.02","quick_ref":"Weidinger2023","paper_title":"Sociotechnical Safety Evaluation of Generative AI Systems","level":"Risk Sub-Category","risk_category":"Socioeconomic and environmental harms ","risk_subcategory":"Environmental damage","description":"\"Creating negative environmental impacts though model development and deployment\"","entity":"AI","intent":"Unintentional","timing":"Other","domain":6,"subdomain":"6.6"},{"ev_id":"31.06.00","quick_ref":"EPIC2023","paper_title":"Generating Harms - Generative AI's impact and paths forwards","level":"Risk Category","risk_category":"Exacerbating Climate Change","risk_subcategory":null,"description":"\"the growing field of generative AI, which brings with it direct and severe impacts on our climate: generative AI comes with a high carbon footprint and similarly high resource price tag, which largely flies under the radar of public AI discourse. Training and running generative AI tools requires companies to use extreme amounts of energy and physical resources. Training one natural language processing model with normal tuning and experiments emits, on average, the same amount of carbon that seven people do over an entire year.121'","entity":"AI","intent":"Unintentional","timing":"Other","domain":6,"subdomain":"6.6"},{"ev_id":"32.04.00","quick_ref":"Stahl2024","paper_title":"The Ethics of ChatGPT – Exploring the Ethical Issues of an Emerging Technology","level":"Risk Category","risk_category":"Environmental impacts","risk_subcategory":null,"description":"Environmental harm, Sustainability","entity":"AI","intent":"Other","timing":"Other","domain":6,"subdomain":"6.6"},{"ev_id":"39.02.00","quick_ref":"Saghiri2022","paper_title":"A Survey of Artificial Intelligence Challenges: Analyzing the Definitions, Relationships, and Evolutions","level":"Risk Category","risk_category":"Energy Consumption","risk_subcategory":null,"description":"Some learning algorithms, including deep learning, utilize iterative learning processes [23]. This approach results in high energy consumption.","entity":"AI","intent":"Unintentional","timing":"Pre-deployment","domain":6,"subdomain":"6.6"},{"ev_id":"41.06.00","quick_ref":"Allianz2018","paper_title":"The Rise of Artificial Intelligence - Future Outlooks and Emerging Risks","level":"Risk Category","risk_category":"Environment ","risk_subcategory":null,"description":"\"AI is already helping to combat the impact of climate change with smart technology and sensors reducing emissions. However, it is also a key component in the development of nanobots, which could have dangerous environmental impacts by invisibly modifying substances at nanoscale.\"","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":6,"subdomain":"6.6"},{"ev_id":"41.06.01","quick_ref":"Allianz2018","paper_title":"The Rise of Artificial Intelligence - Future Outlooks and Emerging Risks","level":"Risk Sub-Category","risk_category":"Environment ","risk_subcategory":"Accelerated development of nanotechnology produces uncontrolled production of toxic nanoparticles","description":"\"AI is a key component for the development of nanobots, which could have dangerous environmental implications by invisibly modifying substances at nanoscale. For example, nanobots could start chemical reactions that would create invisible nanoparticles that are toxic and potentially lethal.\"","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":6,"subdomain":"6.6"},{"ev_id":"44.01.00","quick_ref":"Coghlan2023 ","paper_title":"Harm to Nonhuman Animals from AI: a Systematic Account and Framework","level":"Risk Category","risk_category":"Intentional: socially condemned/illegal ","risk_subcategory":null,"description":"\"Many intentional harms, including confinement, husbandry procedures like tail-docking, and slaughter, are legal or socially accepted, while others such as wildlife trafficking and violence against companion animals are generally socially condemned and often illegal. AI can be designed or adopted by humans who harm animals to pursue their goals more effectively. We therefore distinguish AI-facilitated intentional harms that are currently socially accepted and generally legal, from uses and abuses of AI that cause harms that are not socially accepted and are often illegal.\"","entity":"Human","intent":"Intentional","timing":"Post-deployment","domain":6,"subdomain":"6.6"},{"ev_id":"44.01.01","quick_ref":"Coghlan2023 ","paper_title":"Harm to Nonhuman Animals from AI: a Systematic Account and Framework","level":"Risk Sub-Category","risk_category":"Intentional: socially condemned/illegal ","risk_subcategory":"AI intentionally designed and used to harm animals in ways that contradict social values or are illegal","description":null,"entity":"Human","intent":"Intentional","timing":"Post-deployment","domain":6,"subdomain":"6.6"},{"ev_id":"44.01.02","quick_ref":"Coghlan2023 ","paper_title":"Harm to Nonhuman Animals from AI: a Systematic Account and Framework","level":"Risk Sub-Category","risk_category":"Intentional: socially condemned/illegal ","risk_subcategory":"AI designed to benefit animals, humans, or ecosystems is intentionally abused to harm animals in ways that contradict social values or are illegal","description":null,"entity":"Human","intent":"Intentional","timing":"Post-deployment","domain":6,"subdomain":"6.6"},{"ev_id":"44.02.00","quick_ref":"Coghlan2023 ","paper_title":"Harm to Nonhuman Animals from AI: a Systematic Account and Framework","level":"Risk Category","risk_category":"Intentional: socially accepted/legal ","risk_subcategory":null,"description":"\"AI designed to impact animals in harmful ways that reflect and amplify existing social values or are legal\"","entity":"Human","intent":"Intentional","timing":"Post-deployment","domain":6,"subdomain":"6.6"},{"ev_id":"44.03.00","quick_ref":"Coghlan2023 ","paper_title":"Harm to Nonhuman Animals from AI: a Systematic Account and Framework","level":"Risk Category","risk_category":"Unintentional: direct ","risk_subcategory":null,"description":"\"AI designed to benefit animals, humans, or ecosystems has unintended harmful impact on animals\"","entity":"Other","intent":"Unintentional","timing":"Other","domain":6,"subdomain":"6.6"},{"ev_id":"44.03.01","quick_ref":"Coghlan2023 ","paper_title":"Harm to Nonhuman Animals from AI: a Systematic Account and Framework","level":"Risk Sub-Category","risk_category":"Unintentional: direct ","risk_subcategory":"AI is designed in a way that shows ignorant, reckless, or prejudiced lack of consideration for its impact on animals ","description":null,"entity":"Human","intent":"Unintentional","timing":"Pre-deployment","domain":6,"subdomain":"6.6"},{"ev_id":"44.03.02","quick_ref":"Coghlan2023 ","paper_title":"Harm to Nonhuman Animals from AI: a Systematic Account and Framework","level":"Risk Sub-Category","risk_category":"Unintentional: direct ","risk_subcategory":"AI harms animals due to mistake or misadventure in the way the AI operates in practice ","description":null,"entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":6,"subdomain":"6.6"},{"ev_id":"44.04.00","quick_ref":"Coghlan2023 ","paper_title":"Harm to Nonhuman Animals from AI: a Systematic Account and Framework","level":"Risk Category","risk_category":"Unintentional: indirect ","risk_subcategory":null,"description":"\"AI impacts human or ecological systems in ways that ultimately harm animals\"","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":6,"subdomain":"6.6"},{"ev_id":"44.04.01","quick_ref":"Coghlan2023 ","paper_title":"Harm to Nonhuman Animals from AI: a Systematic Account and Framework","level":"Risk Sub-Category","risk_category":"Unintentional: indirect ","risk_subcategory":"Indirect Material Harms ","description":"\"AI proliferation causes harm to the environment through energy use and e-waste thereby destroying animal habitat\"","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":6,"subdomain":"6.6"},{"ev_id":"44.04.02","quick_ref":"Coghlan2023 ","paper_title":"Harm to Nonhuman Animals from AI: a Systematic Account and Framework","level":"Risk Sub-Category","risk_category":"Unintentional: indirect ","risk_subcategory":"Harms from Estrangement ","description":"\"Replacement by AI of human observation and interaction leads to neglect of certain interests\"","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":6,"subdomain":"6.6"},{"ev_id":"44.04.03","quick_ref":"Coghlan2023 ","paper_title":"Harm to Nonhuman Animals from AI: a Systematic Account and Framework","level":"Risk Sub-Category","risk_category":"Unintentional: indirect ","risk_subcategory":"Epistemic Harms ","description":"\"Algorithmic recommender systems reinforce and amplify anthropocentric bias or desire of some people for animal cruelty as entertainment — leading to greater harm to animals through reinforcement of meat eating from factory farms, cruel uses of animals for entertainment, etc\"","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":6,"subdomain":"6.6"},{"ev_id":"44.05.00","quick_ref":"Coghlan2023 ","paper_title":"Harm to Nonhuman Animals from AI: a Systematic Account and Framework","level":"Risk Category","risk_category":"Foregone benefits ","risk_subcategory":null,"description":"\"AI is disused (not developed or deployed) in directions that would benefit animals (and instead developments that harm or do no benefit to animals are invested in)\"","entity":"Human","intent":"Other","timing":"Other","domain":6,"subdomain":"6.6"},{"ev_id":"47.04.05","quick_ref":"G'sell2024","paper_title":"Regulating under Uncertainty: Governance Options for Generative AI","level":"Risk Sub-Category","risk_category":"Environmental, economical, and societal challenges ","risk_subcategory":"Environmental cost (energy consumption) ","description":"\"Training large AI models requires a substantial amount of computing power to handle vast datasets, which translates into high energy consumption.\"","entity":"Other","intent":"Unintentional","timing":"Pre-deployment","domain":6,"subdomain":"6.6"},{"ev_id":"47.04.06","quick_ref":"G'sell2024","paper_title":"Regulating under Uncertainty: Governance Options for Generative AI","level":"Risk Sub-Category","risk_category":"Environmental, economical, and societal challenges ","risk_subcategory":"Environmental cost (water consumption) ","description":"\"Data centers use water for cooling to prevent servers from overheating. The water consumption associated with AI training and inference processes can be substantial, impacting local water resources.\"","entity":"Other","intent":"Unintentional","timing":"Pre-deployment","domain":6,"subdomain":"6.6"},{"ev_id":"48.05.00","quick_ref":"NIST2024","paper_title":"Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile","level":"Risk Category","risk_category":"Environmental Impacts ","risk_subcategory":null,"description":"\"Impacts due to high compute resource utilization in training or operating GAI models, and related outcomes that may adversely impact ecosystems.\"","entity":"Other","intent":"Unintentional","timing":"Pre-deployment","domain":6,"subdomain":"6.6"},{"ev_id":"49.03.04","quick_ref":"Bengio2024","paper_title":"International Scientific Report on the Safety of Advanced AI","level":"Risk Sub-Category","risk_category":"Systemic Risks ","risk_subcategory":"Risks to the environment","description":"\"Growing compute use in general- purpose AI development and deployment has rapidly increased energy usage associated with general- purpose AI. This trend might continue, potentially leading to strongly increasing CO2 emissions.\"","entity":"Human","intent":"Unintentional","timing":"Other","domain":6,"subdomain":"6.6"},{"ev_id":"54.01.02","quick_ref":"Leech2024 ","paper_title":"Ten Hard Problems in Artificial Intelligence We Must Get Right","level":"Risk Sub-Category","risk_category":"Negative impacts of AI use ","risk_subcategory":"Environmental cost ","description":"\"Large-scale DL systems can produce signicant carbon emissions as a result of the computational demands of training runs and inference [539]\"","entity":"AI","intent":"Unintentional","timing":"Pre-deployment","domain":6,"subdomain":"6.6"},{"ev_id":"56.03.00","quick_ref":"GOS2023","paper_title":"Future Risks of Frontier AI ","level":"Risk Category","risk_category":"Environmental impacts ","risk_subcategory":null,"description":"\"Increasing use of AI systems, and their growing energy needs, could also have environmental impacts. All of these could become more acute as AI becomes more capable.\"","entity":"Human","intent":"Unintentional","timing":"Post-deployment","domain":6,"subdomain":"6.6"},{"ev_id":"58.09.00","quick_ref":"Abercrombie2024","paper_title":"A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms","level":"Risk Category","risk_category":"Environmental ","risk_subcategory":null,"description":"\"Environmental - Damage to the environment directly or indirectly caused by a technology system or set of systems.\"","entity":"AI","intent":"Other","timing":"Post-deployment","domain":6,"subdomain":"6.6"},{"ev_id":"58.09.01","quick_ref":"Abercrombie2024","paper_title":"A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms","level":"Risk Sub-Category","risk_category":"Environmental ","risk_subcategory":"Biodiversity loss ","description":"\"Biodiversity loss - Over-expansion of technology infrastructure, or inadequate alignment of technology with sustainable practices, leading to deforestation, habitat destruction, and fragmentation and loss of biodiversity.\"","entity":"Other","intent":"Unintentional","timing":"Other","domain":6,"subdomain":"6.6"},{"ev_id":"58.09.02","quick_ref":"Abercrombie2024","paper_title":"A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms","level":"Risk Sub-Category","risk_category":"Environmental ","risk_subcategory":"Carbon emissions ","description":"\"Carbon emissions - Release of carbon dioxide, nitric oxide and other gases, increasing carbon emissions, exacerbating climate change, and negatively impacting local communities.\"","entity":"Other","intent":"Other","timing":"Other","domain":6,"subdomain":"6.6"},{"ev_id":"58.09.03","quick_ref":"Abercrombie2024","paper_title":"A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms","level":"Risk Sub-Category","risk_category":"Environmental ","risk_subcategory":"Electronic waste ","description":"\"Electronic waste - Electrical or electronic equipment that is waste, including all components, sub-assemblies and consumables that are part of the equipment at the time the equipment becomes waste\"","entity":"Other","intent":"Other","timing":"Other","domain":6,"subdomain":"6.6"},{"ev_id":"58.09.04","quick_ref":"Abercrombie2024","paper_title":"A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms","level":"Risk Sub-Category","risk_category":"Environmental ","risk_subcategory":"Excessive energy consumption ","description":"\"Excessive energy consumption - Excessive energy use, leading to energy bottlenecks and shortages for communities, organisations, and businesses.\"","entity":"Human","intent":"Unintentional","timing":"Other","domain":6,"subdomain":"6.6"},{"ev_id":"58.09.05","quick_ref":"Abercrombie2024","paper_title":"A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms","level":"Risk Sub-Category","risk_category":"Environmental ","risk_subcategory":"Excessive landfill ","description":"\"Excessive landfill - Excessive disposal of electrical or electronic equipment leading to ecological/biodiversity damage, and disrupting the livelihoods and eroding the rights of local communities.\"","entity":"Human","intent":"Unintentional","timing":"Other","domain":6,"subdomain":"6.6"},{"ev_id":"58.09.06","quick_ref":"Abercrombie2024","paper_title":"A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms","level":"Risk Sub-Category","risk_category":"Environmental ","risk_subcategory":"Excessive water consumption ","description":"\"Excessive water consumption - Excessive use of water to cool data centres and for other purposes, leading to water restrictions or shortages for local communities or businesses.\"","entity":"Human","intent":"Unintentional","timing":"Other","domain":6,"subdomain":"6.6"},{"ev_id":"58.09.07","quick_ref":"Abercrombie2024","paper_title":"A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms","level":"Risk Sub-Category","risk_category":"Environmental ","risk_subcategory":"Natural resource depletion","description":"\"Natural resource depletion - Extraction of minerals, metals, rare earths, and fossil fuels that deplete natural resources and increase carbon emissions.\"","entity":"Human","intent":"Other","timing":"Other","domain":6,"subdomain":"6.6"},{"ev_id":"58.09.08","quick_ref":"Abercrombie2024","paper_title":"A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms","level":"Risk Sub-Category","risk_category":"Environmental ","risk_subcategory":"Pollution ","description":"\"Pollution - Actual or potential pollution to the air, ground, noise, or water caused by a technology system.\"","entity":"AI","intent":"Other","timing":"Post-deployment","domain":6,"subdomain":"6.6"},{"ev_id":"60.03.04","quick_ref":"Bengio2025","paper_title":"International AI Safety Report 2025","level":"Risk Sub-Category","risk_category":"Systemic risks ","risk_subcategory":"Risks to the environment","description":"\"General- purpose AI is a moderate but rapidly growing contributor to global environmental impacts through energy use and greenhouse gas (GHG) emissions. Current estimates indicate that data centres and data transmission account for an estimated 1% of global energy- related GHG emissions, with AI consuming 10–28% of data centre energy capacity. AI energy demand is expected to grow substantially by 2026, with some estimates projecting a doubling or more, driven primarily by general-purpose AI systems such as language models.\"","entity":"AI","intent":"Unintentional","timing":"Other","domain":6,"subdomain":"6.6"},{"ev_id":"61.01.05","quick_ref":"Uuk2025","paper_title":"A Taxonomy of Systemic Risks from General-Purpose AI ","level":"Risk Sub-Category","risk_category":"Types of systemic risks from general-purpose AI","risk_subcategory":"Environment ","description":"\"The impact of AI on the environment, including risks related to climate change and pollution.\"","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":6,"subdomain":"6.6"},{"ev_id":"61.02.23","quick_ref":"Uuk2025","paper_title":"A Taxonomy of Systemic Risks from General-Purpose AI ","level":"Risk Sub-Category","risk_category":"Sources of systemic risks from general-purpose AI ","risk_subcategory":"Energy-intensive processes","description":"\"AI data collection, storage, and model training are energy-intensive, contributing to environmental risks.\"","entity":"Other","intent":"Unintentional","timing":"Pre-deployment","domain":6,"subdomain":"6.6"},{"ev_id":"62.11.00","quick_ref":"Gipiškis2024","paper_title":"Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems","level":"Risk Category","risk_category":"Negative Externality Domains (Manufacturing of AI Hardware) ","risk_subcategory":null,"description":null,"entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":6,"subdomain":"6.6"},{"ev_id":"62.11.01","quick_ref":"Gipiškis2024","paper_title":"Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems","level":"Risk Sub-Category","risk_category":"Negative Externality Domains (Manufacturing of AI Hardware) ","risk_subcategory":"Environmental harms from exploitation of natural resources","description":null,"entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":6,"subdomain":"6.6"},{"ev_id":"62.12.00","quick_ref":"Gipiškis2024","paper_title":"Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems","level":"Risk Category","risk_category":"Negative Externality Domains (Running AI Hardware) ","risk_subcategory":null,"description":null,"entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":6,"subdomain":"6.6"},{"ev_id":"62.12.01","quick_ref":"Gipiškis2024","paper_title":"Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems","level":"Risk Sub-Category","risk_category":"Negative Externality Domains (Running AI Hardware) ","risk_subcategory":"Environmental harms from energy usage","description":null,"entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":6,"subdomain":"6.6"},{"ev_id":"62.39.00","quick_ref":"Gipiškis2024","paper_title":"Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems","level":"Risk Category","risk_category":"Impacts of AI (Environment) ","risk_subcategory":null,"description":"- ","entity":null,"intent":null,"timing":null,"domain":6,"subdomain":"6.6"},{"ev_id":"62.39.01","quick_ref":"Gipiškis2024","paper_title":"Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems","level":"Risk Sub-Category","risk_category":"Impacts of AI (Environment) ","risk_subcategory":"High energy consumption of large models","description":"\"Training and deploying large models require substantial energy expenditure. The trend toward developing larger models exacerbates this issue. This can lead to excessive energy usage and have a negative environmental impact.\"","entity":"Other","intent":"Other","timing":"Other","domain":6,"subdomain":"6.6"},{"ev_id":"65.23.06","quick_ref":"IBM2025","paper_title":"AI Risk Atlas ","level":"Risk Sub-Category","risk_category":"Non-technical risks (Societal impact)","risk_subcategory":"Impact on the environment ","description":"\"AI, and large generative models in particular, might produce increased carbon emissions and increase water usage for their training and operation.\"","entity":"AI","intent":"Other","timing":"Post-deployment","domain":6,"subdomain":"6.6"},{"ev_id":"66.11.04","quick_ref":"Li2025","paper_title":"A Closer Look at the Existing Risks of Generative AI: Mapping the Who, What, and How of Real-World Incidents","level":"Risk Sub-Category","risk_category":"Physical","risk_subcategory":"Property damage","description":"\"Action(s) that lead directly or indirectly to the damage or destruction of tangible property eg. buildings, possessions, vehicles, robots\"","entity":"Other","intent":"Other","timing":"Other","domain":6,"subdomain":"6.6"},{"ev_id":"66.12.01","quick_ref":"Li2025","paper_title":"A Closer Look at the Existing Risks of Generative AI: Mapping the Who, What, and How of Real-World Incidents","level":"Risk Sub-Category","risk_category":"Environment","risk_subcategory":"Pollution","description":"\"Actual or potential pollution to the air, ground, noise, or water caused by a technology system\"","entity":"AI","intent":"Other","timing":"Other","domain":6,"subdomain":"6.6"},{"ev_id":"66.12.02","quick_ref":"Li2025","paper_title":"A Closer Look at the Existing Risks of Generative AI: Mapping the Who, What, and How of Real-World Incidents","level":"Risk Sub-Category","risk_category":"Environment","risk_subcategory":"Excessive energy consumption","description":"\"Excessive energy use resulting in energy bottlenecks and shortages for communities, organisations and businesses\"","entity":"Other","intent":"Unintentional","timing":"Other","domain":6,"subdomain":"6.6"},{"ev_id":"68.05.00","quick_ref":"Chin2025","paper_title":"Dimensional Characterization and Pathway Modeling for Catastrophic AI Risks","level":"Risk Category","risk_category":"Environmental risk","risk_subcategory":null,"description":"\"AI models are often trained using large amounts of computation. This process is very energy intensive, potentially leading to significant greenhouse emissions depending on the energy sources [132]. Experts believe drastically increasing carbon emissions could accelerate climate change, which may constitute a catastrophic risk [133].\"","entity":"Other","intent":"Unintentional","timing":"Pre-deployment","domain":6,"subdomain":"6.6"},{"ev_id":"71.03.01","quick_ref":"Tang2025","paper_title":"Risks of AI Scientists: Prioritizing Safeguarding Over Autonomy","level":"Risk Sub-Category","risk_category":"Environment","risk_subcategory":"Nature ","description":"\"Short-term or long-term Negative effects on the natural environment\"","entity":"Other","intent":"Other","timing":"Other","domain":6,"subdomain":"6.6"}]}