{"query_echo":{"q":"","filters":{"analysis_type":["data-driven analysis"]},"warnings":[{"code":"facet_value_not_in_vocabulary","message":"Value 'data-driven analysis' for facet 'analysis_type' is not in the controlled vocabulary."}]},"total":{"value":4,"relation":"eq"},"page":1,"page_size":20,"self":"https://climateinsightsdirectory.org/api/v1/search?analysis_type=data-driven+analysis","stability":"beta","ai_marking":{"digital_source_type":{"uri":"http://cv.iptc.org/newscodes/digitalsourcetype/trainedAlgorithmicMedia","name":"Created using Generative AI"},"notice":"Summary and key insights are generated using artificial intelligence."},"results":[{"id":"ipie-000092","url":"https://climateinsightsdirectory.org/document/ipie-000092","title":"选举活动中的生成式智能：全球模式图谱 政策制定者摘要","publisher":{"name":"International Panel on the Information Environment","organization":"international-panel-on-the-information-environment"},"document_type":"executive summary","language":"vi","date_published":"2025-01","source_url":"https://cdn.prod.website-files.com/643ecb10be528d2c1da863cb/690a0cd40eb47fa7e1641fac_Final%20ZH%20Elections%20SFP.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","geography_discussed","analysis_type"],"summary":{"text":"The report finds that 80% of countries with competitive national elections in 2024 experienced GenAI-related events, with 69% of these events having a harmful impact. The majority of GenAI use focused on content production, and nearly half of the events had unknown sources.","ai_generated":true,"unit":"ipie-000092#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"GenAI was prevalent in 2024 elections, appearing in 80% of the countries that held competitive national elections. India and the United States recorded the highest number of events, with 30 each.","ai_generated":true,"unit":"ipie-000092#insight-1","date_modified":"2026-10-23"},{"text":"A significant portion of GenAI activity lacks clear attribution: 46% of events had unknown sources, while political candidates and parties were responsible for 25% and foreign actors for 20%. Among cases with unknown sources, 79% were suspected of political manipulation.","ai_generated":true,"unit":"ipie-000092#insight-2","date_modified":"2026-10-23"},{"text":"Most GenAI applications in elections were harmful, with 69% of events categorized as having a negative impact. Notably, 100% of GenAI use attributed to foreign actors served harmful purposes, whereas 38% of use by national parties and candidates was categorized as beneficial.","ai_generated":true,"unit":"ipie-000092#insight-3","date_modified":"2026-10-23"}],"climate_tags":[],"geography_discussed":["India","United States","Taiwan","Solomon Islands"],"frame_perspective":[],"analysis_type":["data-driven analysis","policy summary"]},{"id":"ipie-000087","url":"https://climateinsightsdirectory.org/document/ipie-000087","title":"Generative KI in Wahlkampagnen: Kartierung globaler Muster","publisher":{"name":"International Panel on the Information Environment","organization":"international-panel-on-the-information-environment"},"document_type":"executive summary","language":"de","date_published":"2025","source_url":"https://cdn.prod.website-files.com/643ecb10be528d2c1da863cb/690a0ca6fcede156eee4ac8e_Final%20DE%20Elections%20SFP.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The report analyzes 215 incidents of GenAI use in 2024 elections, finding that 80% of countries with competitive elections experienced such incidents. The majority of these cases involved content creation (90%) and were deemed harmful to the electoral process (69%). A significant portion of the sources remain unknown (46%), while political candidates (25%) and foreign actors (20%) are the primary identified sources. The document recommends transparency standards and disclosure requirements to protect electoral integrity.","ai_generated":true,"unit":"ipie-000087#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"Generative AI was utilized in 80% of the countries that held competitive national elections in 2024. The highest number of recorded incidents occurred in India and the United States, with 30 cases each.","ai_generated":true,"unit":"ipie-000087#insight-1","date_modified":"2026-10-23"},{"text":"The primary application of GenAI in elections is content creation, accounting for 90% of reported incidents. This includes the production of audio messages, images, videos, and social media posts.","ai_generated":true,"unit":"ipie-000087#insight-2","date_modified":"2026-10-23"},{"text":"Attributing the source of GenAI content is difficult, as the origin was unknown in 46% of cases. Among identified sources, political candidates and parties accounted for 25%, and foreign actors accounted for 20%.","ai_generated":true,"unit":"ipie-000087#insight-3","date_modified":"2026-10-23"},{"text":"The majority of GenAI applications in elections are detrimental, with 69% of recorded incidents classified as harmful. Notably, 100% of the GenAI use attributed to foreign actors served harmful purposes.","ai_generated":true,"unit":"ipie-000087#insight-4","date_modified":"2026-10-23"},{"text":"While largely harmful, GenAI has been used constructively by some political actors. In Japan, candidates used GenAI-powered chatbots to engage with voters, and 38% of reported uses by national political parties and candidates were classified as useful.","ai_generated":true,"unit":"ipie-000087#insight-5","date_modified":"2026-10-23"},{"text":"Specific examples of harmful GenAI use include a deepfake video in Bangladesh falsely announcing a candidate's withdrawal, a manipulated video in Taiwan targeting a presidential candidate, and cloned audio of US President Joe Biden in Namibia to support local parties.","ai_generated":true,"unit":"ipie-000087#insight-6","date_modified":"2026-10-23"}],"climate_tags":["climate change disinformation"],"geography_discussed":["India","United States","Romania","Bangladesh","Taiwan","Namibia","Ireland","Solomon Islands","Japan"],"frame_perspective":[],"analysis_type":["data-driven analysis","mapping of global patterns"]},{"id":"ipie-000091","url":"https://climateinsightsdirectory.org/document/ipie-000091","title":"Uzalishaji wa AI katika Kampeni za Uchaguzi: Kuchora Miundo ya Ulimwenguni","publisher":{"name":"International Panel on the Information Environment","organization":"international-panel-on-the-information-environment"},"document_type":"executive summary","language":"sw","date_published":"2025","source_url":"https://cdn.prod.website-files.com/643ecb10be528d2c1da863cb/690a0cc4817fd01ff7f2b539_Final%20SW%20Elections%20SFP.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The document analyzes the role of Generative AI in 2024 national elections across 50 countries. It finds that 80% of these countries experienced GenAI incidents, with 90% of those cases involving content creation such as audio, images, and video. A significant portion of these incidents (69%) were classified as harmful to electoral integrity, and nearly half (46%) originated from unknown sources. The report highlights specific examples of misuse in countries like Romania, Bangladesh, and Taiwan, while noting some beneficial uses in Japan. It concludes by recommending mandatory disclosure standards and increased transparency to protect democratic processes.","ai_generated":true,"unit":"ipie-000091#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"Generative AI was prevalent in the majority of competitive national elections in 2024, with 80% of the 50 analyzed countries recording GenAI incidents. The highest volume of incidents occurred in India and the United States, with 30 cases recorded in each.","ai_generated":true,"unit":"ipie-000091#insight-1","date_modified":"2026-10-23"},{"text":"The primary application of GenAI in these contexts is content generation, accounting for 90% of recorded incidents. This includes the creation of synthetic audio, images, videos, and social media posts used for both authentic and misleading communication.","ai_generated":true,"unit":"ipie-000091#insight-2","date_modified":"2026-10-23"},{"text":"A large proportion of GenAI content sources are anonymous, with 46% of cases having no identifiable source. Among identified sources, political candidates and parties accounted for 25%, while foreign actors accounted for 20%.","ai_generated":true,"unit":"ipie-000091#insight-3","date_modified":"2026-10-23"},{"text":"Most GenAI applications in elections were deemed harmful, with 69% of incidents described as playing a negative role in the electoral process. Notably, 100% of reported GenAI use by foreign actors was classified as having harmful objectives.","ai_generated":true,"unit":"ipie-000091#insight-4","date_modified":"2026-10-23"},{"text":"GenAI has been used to disrupt specific national elections, such as in Romania, where coordinated AI efforts contributed to the annulment of presidential election results. Other examples include deepfake videos in Bangladesh and Taiwan, and fake audio of US President Joe Biden in Namibia.","ai_generated":true,"unit":"ipie-000091#insight-5","date_modified":"2026-10-23"},{"text":"While predominantly harmful, GenAI also had constructive applications; 38% of reported use by national political parties and candidates was classified as beneficial. For example, candidates in Japan used GenAI chatbots to engage voters and answer basic questions.","ai_generated":true,"unit":"ipie-000091#insight-6","date_modified":"2026-10-23"}],"climate_tags":["climate change disinformation"],"geography_discussed":["India","United States","Romania","Bangladesh","Taiwan","Namibia","Solomon Islands","Japan","Switzerland"],"frame_perspective":[],"analysis_type":["data-driven analysis","global assessment","policy recommendation"]},{"id":"sei-001279","url":"https://climateinsightsdirectory.org/document/sei-001279","title":"The Sustainable Development Goals viewed through a climate lens","publisher":{"name":"Stockholm Environment Institute","organization":"stockholm-environment-institute"},"document_type":"policy brief","language":"en","date_published":"2018-12","source_url":"https://www.sei.org/wp-content/uploads/2018/12/climate-action-sdgs-pb-corrected.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","analysis_type"],"summary":{"text":"The document argues that while there is significant global potential for policy coherence between the Paris Agreement and the 2030 Agenda, this must be translated into national-level implementation. Using the NDC-SDG Connections tool, the authors analyzed over 7,000 climate activities, finding strong synergies in energy, agriculture, and water. However, it warns that trade-offs—such as bioenergy competing with food security for land—are rarely documented in policy papers and must be proactively managed to avoid maladaptation.","ai_generated":true,"unit":"sei-001279#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"Analysis of 164 NDCs and over 7,000 climate activities indicates a high potential for policy coherence between the 2030 Agenda for Sustainable Development and the Paris Climate Agreement at a global level, though translating this to national implementation remains a challenge.","ai_generated":true,"unit":"sei-001279#insight-1","date_modified":"2026-10-23"},{"text":"Energy-related NDC activities show strong synergies with SDGs 4, 5, 8, 11, 12, and 13, often linking energy efficiency to education or requiring international partnerships and financial support (SDG 17). However, they show almost no links to SDGs 1, 6, and 15, suggesting potential trade-offs where bioenergy may threaten biodiversity or compete for water and soil.","ai_generated":true,"unit":"sei-001279#insight-2","date_modified":"2026-10-23"},{"text":"Agricultural NDC activities are primarily linked to SDG 2 and have strong connections to SDG 6 (via irrigation and water management) and SDG 15 (via soil management and agroforestry). There is a notable lack of connection to energy (SDG 7), with less than 1.5% of agriculture activities relating to it, which may mask conflicts over arable land for bioenergy or solar power.","ai_generated":true,"unit":"sei-001279#insight-3","date_modified":"2026-10-23"},{"text":"Water-themed NDC activities primarily support SDG 6 but also link to SDG 3 (health and well-being), SDG 12 (waste-water treatment), and SDG 13. The authors highlight that rising demands for water could lead to sectoral competition and maladaptive practices if trade-offs are not managed.","ai_generated":true,"unit":"sei-001279#insight-4","date_modified":"2026-10-23"},{"text":"Social issues, including health (SDG 3), education (SDG 4), gender equality (SDG 5), and inequality (SDG 10), are underrepresented in current NDCs despite the high confidence that climate change will adversely impact these areas.","ai_generated":true,"unit":"sei-001279#insight-5","date_modified":"2026-10-23"},{"text":"Trade-offs are rarely mentioned in international policy documents and are harder to identify than synergies, but they must be anticipated early to avoid undermining policy implementation. The authors suggest integrating a chapter on challenges in NDCs and considering trade-offs in the UNFCCC global stocktake and Green Climate Fund financing processes.","ai_generated":true,"unit":"sei-001279#insight-6","date_modified":"2026-10-23"}],"climate_tags":["Paris Agreement","Nationally Determined Contributions","Sustainable Development Goals","energy efficiency","bioenergy","sustainable agriculture","water management","climate change adaptation","climate change mitigation","climate finance","Green Climate Fund"],"geography_discussed":[],"frame_perspective":[],"analysis_type":["data-driven analysis","policy assessment"]}]}