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It introduces the notion of algorithmically facilitated emissions to initiate a shift from a logic of \u2018climate collapse by design\u2019 to a logic of \u2018emissions reduction by design\u2019. Reducing consumption-based emissions from high-income households and countries is critical for avoiding runaway climate change, and it necessitates redesigning the digital infrastructures that connect production and consumption. Big tech's high-reach artificial intelligence platforms hold a central infrastructural position in many markets and societies. They discursively turn issues into commodities, thereby incentivising unsustainable mass consumption and high-carbon lifestyles. A main argument advanced in the article is that algorithmic and environmental harms are inextricably linked, but neither research nor policy has the terminology to discuss and address this. While the direct negative effects of artificial intelligence development, foundation model training, data centres, and digital devices on the environment are receiving increasing attention in academia and society, the downstream harms resulting from the environmentally unsustainable values underpinning decisions made by artificial intelligence-infused general-purpose platforms remain largely unnoticed. The commentary proposes that the development and impact assessment of big tech platforms ought to be brought in line with a default logic of \u2018emissions reduction by design\u2019. For this purpose, we introduce the concept of algorithmically facilitated emissions, defined as downstream emissions that are made possible, more likely, or more intense because people or organisations act in response to or anticipate algorithmic decisions that prioritise high-carbon practices and lifestyles.<\/jats:p>","DOI":"10.1177\/20539517251365226","type":"journal-article","created":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T06:16:10Z","timestamp":1754547370000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":3,"title":["Unsustainable artificial intelligence and algorithmically facilitated emissions: The case for emissions-reduction-by-design"],"prefix":"10.1177","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8293-8208","authenticated-orcid":false,"given":"Jutta","family":"Haider","sequence":"first","affiliation":[{"name":"Swedish School of Library and Information Science, University of Bor\u00e5s, Boras, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4777-3134","authenticated-orcid":false,"given":"Malte","family":"R\u00f6dl","sequence":"additional","affiliation":[{"name":"Division of Environmental Communication, Department of Urban and Rural Development, Swedish University of Agricultural Sciences, Uppsala, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6735-3893","authenticated-orcid":false,"given":"James","family":"White","sequence":"additional","affiliation":[{"name":"Department for Technology and Society, Lund University, Lund, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2025,8,6]]},"reference":[{"key":"e_1_3_3_2_1","doi-asserted-by":"crossref","unstructured":"Axelsson Katarina Gong J Dugast C et al. 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