{"query_echo":{"q":"","filters":{"analysis_type":["methodological"]},"warnings":[]},"total":{"value":24,"relation":"eq"},"page":1,"page_size":20,"self":"https://climateinsightsdirectory.org/api/v1/search?analysis_type=methodological","next":"https://climateinsightsdirectory.org/api/v1/search?analysis_type=methodological&page=2","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":"sei-000829","url":"https://climateinsightsdirectory.org/document/sei-000829","title":"Design Ecological Citizenship Wellbeing","publisher":{"name":"Stockholm Environment Institute","organization":"stockholm-environment-institute"},"document_type":"research paper","language":"en","date_published":"2026-04-29","source_url":"https://www.sei.org/publications/design-ecological-citizenship-wellbeing/","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","geography_discussed","analysis_type"],"summary":{"text":"The paper introduces the Preferable Futures (PF) Deck, a wellbeing intervention and toolkit developed through a 'research-through-design' process. Positioned at the intersection of participatory design, futures thinking, and ecological citizenship, the deck aims to inspire place-based action for an ecologically ethical transition by recognizing the link between human and planetary health. The authors propose a 'post-participatory intervention' model where designers act as catalysts for community-led practices rather than mere facilitators of predefined projects. The toolkit is intended for use by community organizers, educators, and policy-adjacent actors to facilitate democratic and ecological transformation.","ai_generated":true,"unit":"sei-000829#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The Preferable Futures (PF) Deck is a wellbeing intervention and toolkit designed to encourage reflection, imagination, and place-based action to support an ecologically ethical transition, based on the premise that planetary and human health are interconnected.","ai_generated":true,"unit":"sei-000829#insight-1","date_modified":"2026-10-23"},{"text":"The authors introduce a 'post-participatory intervention' methodology where designers serve as catalysts and conveners for community-led practices that can evolve beyond the initial design framework, rather than just facilitating participation in set projects.","ai_generated":true,"unit":"sei-000829#insight-2","date_modified":"2026-10-23"},{"text":"Testing of the PF Deck at the Royal College of Art, Falmouth University, and the London Design Festival showed it can facilitate systems literacy, values-based dialogue, and the co-design of civic futures focused on social and ecological flourishing.","ai_generated":true,"unit":"sei-000829#insight-3","date_modified":"2026-10-23"}],"climate_tags":[],"geography_discussed":["London","United Kingdom"],"frame_perspective":[],"analysis_type":["methodological","conceptual"]},{"id":"iisd-002833","url":"https://climateinsightsdirectory.org/document/iisd-002833","title":"Reforming Environmentally Harmful Subsidies","publisher":{"name":"International Institute for Sustainable Development","organization":"international-institute-for-sustainable-development"},"document_type":"report","language":"en","date_published":"2025-02","source_url":"https://www.iisd.org/system/files/2025-02/environmentally-harmful-subsidies-reform-appendices.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The document contains technical appendices for a report on reforming environmentally harmful subsidies (EHS). It introduces a 'Fiscal Pressure' indicator to identify countries where high debt and deficits create strong incentives for subsidy reform, and provides a stakeholder analysis mapping the influence and attitudes of businesses, governments, consumers, and civil society toward EHS reform.","ai_generated":true,"unit":"iisd-002833#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The 'Fiscal Pressure' indicator identifies countries with high government deficits, gross debt, and debt servicing costs over a 10-year period (2014–2023) to determine where EHS reform would have the highest positive fiscal impact. Countries with a final indicator value between 0.6 and 1 are considered to have higher levels of fiscal pressure and are most likely to benefit from reform.","ai_generated":true,"unit":"iisd-002833#insight-1","date_modified":"2026-10-23"},{"text":"Based on the fiscal pressure analysis, India has the highest indicator value (1.0), followed by Venezuela (0.9), Madagascar (0.8), and Brazil (0.7). Conversely, the Republic of Korea and Russia have the lowest values (0.0).","ai_generated":true,"unit":"iisd-002833#insight-2","date_modified":"2026-10-23"},{"text":"The political economy of EHS reform is characterized by a conflict where beneficiaries (such as oil producers and agribusinesses) receive immediate financial gains, while the environmental and health costs are diffuse and perceived only in the long term. This leads to a political calculus that favors the status quo due to loss aversion among beneficiaries.","ai_generated":true,"unit":"iisd-002833#insight-3","date_modified":"2026-10-23"},{"text":"Stakeholder mapping identifies 'Businesses—EHS recipients' as highly influential (score 4) but completely opposed to reform (score -5). In contrast, 'Businesses—sustainable competitors' (such as renewable energy developers) are supportive (score 5) but have moderate influence (score 3) due to their smaller size and lack of lobbying power compared to large EHS recipients.","ai_generated":true,"unit":"iisd-002833#insight-4","date_modified":"2026-10-23"},{"text":"Governments are mapped as the most influential stakeholders (score 5) but generally lean toward opposing reform (score -3) due to fears of political backlash, administrative hurdles, and lack of capacity. While environment ministries advocate for nature and climate action, they are often the weakest in terms of political influence compared to finance ministries.","ai_generated":true,"unit":"iisd-002833#insight-5","date_modified":"2026-10-23"},{"text":"Household consumers are viewed as a potential source of opposition to reform, particularly poor households for whom price reductions from subsidies are significant relative to income. To make reforms feasible, the document suggests using targeted social assistance and time-limited compensation.","ai_generated":true,"unit":"iisd-002833#insight-6","date_modified":"2026-10-23"}],"climate_tags":["fossil fuel subsidy","agricultural subsidy","climate finance","fiscal policy","just transition","renewable energy","biodiversity"],"geography_discussed":["Argentina","Armenia","Australia","Brazil","Canada","People's Republic of China","Colombia","Democratic Republic of the Congo","Ecuador","European Union","India","Indonesia","Japan","Madagascar","Malaysia","Mexico","Papua New Guinea","Peru","Philippines","South Korea","Russia","Saudi Arabia","South Africa","Turkey","United Kingdom","United States","Venezuela"],"frame_perspective":[],"analysis_type":["methodological","stakeholder analysis","fiscal assessment"]},{"id":"iisd-001630","url":"https://climateinsightsdirectory.org/document/iisd-001630","title":"Budgeting for Net Zero","publisher":{"name":"International Institute for Sustainable Development","organization":"international-institute-for-sustainable-development"},"document_type":"report","language":"en","date_published":"2024-12","source_url":"https://www.iisd.org/system/files/2025-01/india-budgeting-for-net-zero-technical-note.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The document provides a technical framework for calculating the 'cost gap'—the marginal cost differential between clean energy technologies and their conventional benchmarks—for Battery Energy Storage Systems (BESS), offshore wind, solar PV, electric vehicles (EVs), and green hydrogen in India. It outlines the specific input parameters, learning rates, and benchmarks used for each technology. Furthermore, it describes the application of the Green Economy Model (GEM), a system dynamics tool, to simulate how varying levels of public funding (e.g., 30% or 100% of the cost gap) influence India's GDP, employment, and emissions through feedback loops involving energy spending, air pollution, and labor productivity.","ai_generated":true,"unit":"iisd-001630#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"For standalone Battery Energy Storage Systems (BESS), the study identifies that cost parity with the benchmark electricity price (INR 7.30/kWh in 2023) is achieved in 2028 under the business-as-usual scenario, leading to a cost gap estimation period from 2023 to 2027.","ai_generated":true,"unit":"iisd-001630#insight-1","date_modified":"2026-10-23"},{"text":"Offshore wind technology in India is projected to reach cost parity with its benchmark (the average price of round-the-clock electricity traded on the Indian Energy Exchange, which was INR 5.62/kWh in 2023) in 2037, with the cost gap calculated for the period 2024 to 2037.","ai_generated":true,"unit":"iisd-001630#insight-2","date_modified":"2026-10-23"},{"text":"Solar PV is already cheaper than new coal and gas installations in India; therefore, the cost gap is calculated as the differential between domestically manufactured solar PV modules and imported modules, with parity expected around 2030 in the business-as-usual scenario.","ai_generated":true,"unit":"iisd-001630#insight-3","date_modified":"2026-10-23"},{"text":"Green hydrogen (GH2) is estimated not to reach cost parity with grey hydrogen (benchmarked at USD 2.25/kg) until 2050 in the business-as-usual scenario, necessitating a cost gap estimation timeline from 2024 to 2050.","ai_generated":true,"unit":"iisd-001630#insight-4","date_modified":"2026-10-23"},{"text":"The Green Economy Model (GEM) uses a system dynamics approach to quantify how government support for clean energy affects GDP by analyzing three primary drivers: energy spending (affecting capital productivity), air pollution (affecting labor productivity), and employment (affecting value added).","ai_generated":true,"unit":"iisd-001630#insight-5","date_modified":"2026-10-23"}],"climate_tags":["battery energy storage system","offshore wind power","photovoltaics","electric vehicle","green hydrogen","Levelized cost of energy","total cost of ownership","climate finance"],"geography_discussed":["Tamil Nadu","Gujarat"],"frame_perspective":[],"analysis_type":["methodological","macroeconomic modelling","cost-benefit analysis"]},{"id":"sei-000996","url":"https://climateinsightsdirectory.org/document/sei-000996","title":"Integrated Assessment Modelling and Socio-Technical Transition in Europe","publisher":{"name":"Stockholm Environment Institute","organization":"stockholm-environment-institute"},"document_type":"research paper","language":"en","date_published":"2021-02-02","source_url":"https://www.sei.org/publications/integrated-assessment-modelling-socio-technical-transition-europe/","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","analysis_type"],"summary":{"text":"The research introduces an interdisciplinary approach that uses 'transition narratives' as a bridge between socio-technical transition studies and integrated assessment modelling. By applying a multi-level perspective, the authors developed two contrasting narratives for the European Union's 80% greenhouse gas emission reduction target by 2050: one driven by incumbent actors and large-scale innovation, and another driven by new entrants and alternative strategies. The study concludes that while both pathways meet the 2050 objective, both require a substantial departure from current known systems.","ai_generated":true,"unit":"sei-000996#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The study identifies the 'transition narrative' as the primary analytical mechanism to connect qualitative socio-technical transition research with quantitative integrated assessment modelling.","ai_generated":true,"unit":"sei-000996#insight-1","date_modified":"2026-10-23"},{"text":"Two distinct transition narratives were developed to meet the EU's 2050 goal: one focusing on incumbent-led large-scale innovation and another focusing on new entrants utilizing alternative strategies and opposing large-scale technologies.","ai_generated":true,"unit":"sei-000996#insight-2","date_modified":"2026-10-23"},{"text":"Both developed transition pathways are consistent with the European Union's 2050 low-carbon goals, yet both represent a significant shift away from existing systems.","ai_generated":true,"unit":"sei-000996#insight-3","date_modified":"2026-10-23"}],"climate_tags":["greenhouse gas emissions reduction"],"geography_discussed":[],"frame_perspective":[],"analysis_type":["methodological","scenario analysis"]},{"id":"isdglo-000123","url":"https://climateinsightsdirectory.org/document/isdglo-000123","title":"Analysing New Zealand Online Extremism","publisher":{"name":"Institute for Strategic Dialogue","organization":"institute-for-strategic-dialogue"},"document_type":"research paper","language":"en","date_published":"2021","source_url":"https://www.isdglobal.org/wp-content/uploads/2021/06/NZ-methodology-paper.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","analysis_type"],"summary":{"text":"The document outlines a two-pronged research architecture designed to analyze New Zealand-specific online extremism and international extremist activity targeting New Zealand. It defines extremism based on 'social identity' and the pursuit of in-group supremacy, focusing on far-right, far-left, and Islamist ideologies. The methodology employs a combination of qualitative ethnographic research, API-based data collection, and Natural Language Processing (NLP) for thematic classification. The authors emphasize a strict ethical framework prioritizing anonymity, the use of public data only, and the production of aggregated outputs to balance individual privacy with public safety.","ai_generated":true,"unit":"isdglo-000123#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The research defines extremism as the active pursuit of systemic political and societal change based on an ideology claiming the supremacy of one 'in-group' over 'out-groups' and the dehumanization of those out-groups.","ai_generated":true,"unit":"isdglo-000123#insight-1","date_modified":"2026-10-23"},{"text":"Consultations and ethnographic research identified three primary extremist ideologies with an established presence in New Zealand: far-right, far-left, and Islamist. While misogynistic narratives were present, the researchers did not find cohesive New Zealand online communities for phenomena such as Incel movements.","ai_generated":true,"unit":"isdglo-000123#insight-2","date_modified":"2026-10-23"},{"text":"The study utilized two distinct data pipelines: a 'New Zealand pipeline' to measure activity from domestic extremists and an 'international collection' to measure activity about New Zealand from extremists globally.","ai_generated":true,"unit":"isdglo-000123#insight-3","date_modified":"2026-10-23"},{"text":"For the New Zealand pipeline, the researchers developed an automated thematic classification system using NLP to categorize content into six themes: the economy, the environment, health, politics, race, and religion.","ai_generated":true,"unit":"isdglo-000123#insight-4","date_modified":"2026-10-23"},{"text":"The performance of the NLP classifiers varied by theme, with 'Religion' achieving the highest F-score (81.6%) and 'Race' the lowest (65.6%).","ai_generated":true,"unit":"isdglo-000123#insight-5","date_modified":"2026-10-23"},{"text":"The international pipeline processed over 26 million records, including 16 million from 4Chan's /pol/ board, using a geo-parsing architecture (incorporating the CLIFF tool and GeoNames database) to identify mentions of New Zealand locations with 96% precision.","ai_generated":true,"unit":"isdglo-000123#insight-6","date_modified":"2026-10-23"},{"text":"The research adhered to a strict ethical framework that prohibited the study of non-public spaces, such as password-protected areas or private Facebook groups, and ensured all outputs were aggregated and anonymous.","ai_generated":true,"unit":"isdglo-000123#insight-7","date_modified":"2026-10-23"},{"text":"The project was explicitly designed as a research effort to identify broad trends rather than a law enforcement investigation, though a referral process was established for credible, immediate threats to life or safety.","ai_generated":true,"unit":"isdglo-000123#insight-8","date_modified":"2026-10-23"}],"climate_tags":[],"geography_discussed":[],"frame_perspective":[],"analysis_type":["methodological","ethical"]},{"id":"wwf-000635","url":"https://climateinsightsdirectory.org/document/wwf-000635","title":"SYSTEM SCALE PLANNING METHODOLOGY","publisher":{"name":"WWF International","organization":"wwf-international"},"document_type":"report","language":"en","date_published":"2020-12","source_url":"https://www.worldwildlife.org/documents/661/60oexxoaxq_USAID_Paani_SSP_Report_FINAL.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The report details a System Scale Planning (SSP) framework applied to Nepal's hydropower sector, specifically the Karnali Basin. Using a Multi-Objective Evolutionary Algorithm (MOEA) and integration with the SWITCH energy model, the methodology identifies 'pareto-optimal' portfolios of projects that balance installed capacity and cost against environmental and social metrics. Key findings demonstrate that strategic project selection can significantly reduce biodiversity and livelihood impacts for a given level of energy output, and that restricting development on mainstem rivers (Scenario K03) can achieve a favorable balance of low environmental impact and low system cost.","ai_generated":true,"unit":"wwf-000635#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"System Scale Planning (SSP) is a quantitative, multi-criteria framework designed to help decision-makers visualize trade-offs between different hydropower development solutions rather than assessing projects individually.","ai_generated":true,"unit":"wwf-000635#insight-1","date_modified":"2026-10-23"},{"text":"The SSP model utilizes a Multi-Objective Evolutionary Algorithm (MOEA) to identify 'pareto-optimal' solutions, which are portfolios where no single metric can be improved without diminishing another.","ai_generated":true,"unit":"wwf-000635#insight-2","date_modified":"2026-10-23"},{"text":"In the Karnali Basin, strategic selection of dams can drastically reduce biodiversity impacts; for a specific installed capacity, some solutions can cut the impacted length of river by more than half compared to others.","ai_generated":true,"unit":"wwf-000635#insight-3","date_modified":"2026-10-23"},{"text":"Integration with the SWITCH energy model shows that the 'Karnali-secondary' scenario (K03), which prohibits projects on the Karnali mainstem, results in only a 0.1% increase in system costs compared to the reference scenario while reducing environmental impacts.","ai_generated":true,"unit":"wwf-000635#insight-4","date_modified":"2026-10-23"},{"text":"Analysis of national-scale scenarios indicates that existing protected areas in Nepal are insufficient to protect High Conservation Value (HCV) rivers from hydropower impacts because dams may be built upstream or along protected area boundaries.","ai_generated":true,"unit":"wwf-000635#insight-5","date_modified":"2026-10-23"},{"text":"The methodology employs 'weighted kilometers' to measure impacts, where the length of affected river is multiplied by a High Conservation Value (HCV) score (0-5) to prioritize high-value reaches.","ai_generated":true,"unit":"wwf-000635#insight-6","date_modified":"2026-10-23"},{"text":"The SSP analysis identifies a trade-off for solutions with approximately 4,000 MW installed capacity between minimizing sediment capture and minimizing recreational impacts to rivers.","ai_generated":true,"unit":"wwf-000635#insight-7","date_modified":"2026-10-23"},{"text":"The 'Nepal-FFR' scenario (N01), which restricts development on free-flowing rivers, results in the lowest environmental impact (404 km of affected rivers) but increases system costs by approximately 8.8% compared to the reference scenario.","ai_generated":true,"unit":"wwf-000635#insight-8","date_modified":"2026-10-23"}],"climate_tags":["hydropower","biodiversity","energy transition","climate finance","environmental impact assessment","land-use change"],"geography_discussed":["Brazil","Colombia","Mexico","Myanmar","Republic of the Congo","Gabon"],"frame_perspective":[],"analysis_type":["methodological","quantitative","trade-off analysis"]},{"id":"sei-002094","url":"https://climateinsightsdirectory.org/document/sei-002094","title":"Causal pathways in the political economy of adaptation projects","publisher":{"name":"Stockholm Environment Institute","organization":"stockholm-environment-institute"},"document_type":"research paper","language":"en","date_published":"2020-07","source_url":"https://www.sei.org/wp-content/uploads/2020/11/201117a-gill-osborne-turkana-solar-causal-pathways-wp-2010e.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","analysis_type"],"summary":{"text":"The document proposes integrating Sovacool et al.'s \"4E\" typology (enclosure, exclusion, encroachment, and entrenchment) with the Mechanisms of Social Change (MOSC) mapping approach to identify causal pathways in adaptation projects. By mapping the solar electricity system in Turkana, Kenya, the authors demonstrate how specific actions—such as consultation, licensing, and facility design—create sequential pathways that determine how benefits and losses are distributed among community members.","ai_generated":true,"unit":"sei-002094#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The authors propose that the \"4E\" typology of system dynamics—enclosure, exclusion, encroachment, and entrenchment—should be situated within a detailed map of the system structure to better understand how winners and losers are produced in adaptation projects.","ai_generated":true,"unit":"sei-002094#insight-1","date_modified":"2026-10-23"},{"text":"Using the Mechanisms of Social Change (MOSC) framework, the authors map the solar electricity system in Turkana, Kenya, as a sequence of eight basic actions involving four actor sets: electricity producers, community members, licensing authorities, and nature.","ai_generated":true,"unit":"sei-002094#insight-2","date_modified":"2026-10-23"},{"text":"The paper identifies four distinct sub-categories of entrenchment based on the specific actions within the system: design entrenchment (location and design), input entrenchment (who provides inputs), output entrenchment (who accesses electricity), and environmental entrenchment (disparity in access to natural resources).","ai_generated":true,"unit":"sei-002094#insight-3","date_modified":"2026-10-23"},{"text":"System mapping reveals that the 4E processes are causally interconnected, where \"earlier\" processes in the sequence influence the impact of \"later\" processes on community members.","ai_generated":true,"unit":"sei-002094#insight-4","date_modified":"2026-10-23"},{"text":"The authors illustrate these causal pathways through hypothetical examples, such as a long pathway where a lack of consultation (exclusion) leads to technical-only site selection (enclosure), which results in facilities being located in wealthy areas (design entrenchment), excluding marginalized groups from electricity (output entrenchment), and ultimately allowing wealthier members to monopolize water pumps (environment entrenchment).","ai_generated":true,"unit":"sei-002094#insight-5","date_modified":"2026-10-23"}],"climate_tags":["climate change adaptation","Mini-grids","environmental degradation"],"geography_discussed":[],"frame_perspective":[],"analysis_type":["methodological","case study"]},{"id":"sei-000403","url":"https://climateinsightsdirectory.org/document/sei-000403","title":"Large-scale scenarios as boundary conditions","publisher":{"name":"Stockholm Environment Institute","organization":"stockholm-environment-institute"},"document_type":"research paper","language":"en","date_published":"2019-03-20","source_url":"https://www.sei.org/publications/large-scale-scenarios-as-boundary-conditions/","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","analysis_type"],"summary":{"text":"The paper introduces a simplified cross-impact balance (CIB) approach for cross-scale scenario development, allowing global scenarios to serve as stable external conditions for local and regional analysis.","ai_generated":true,"unit":"sei-000403#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The authors propose a methodology called \"cross-impact balances\" (CIB) to structure the use of global scenarios as external conditions for regional and local scenarios.","ai_generated":true,"unit":"sei-000403#insight-1","date_modified":"2026-10-23"},{"text":"A simplified cross-impact balance can be used instead of a full multi-scale balance if a stable set of global scenarios is adopted at a large scale, treating these scenarios as states of the world that remain stable despite small disturbances from regional or local activity.","ai_generated":true,"unit":"sei-000403#insight-2","date_modified":"2026-10-23"}],"climate_tags":["climate change mitigation","climate change adaptation"],"geography_discussed":[],"frame_perspective":[],"analysis_type":["methodological"]},{"id":"sei-000862","url":"https://climateinsightsdirectory.org/document/sei-000862","title":"PRINCE chemicals consumption indicators","publisher":{"name":"Stockholm Environment Institute","organization":"stockholm-environment-institute"},"document_type":"research paper","language":"en","date_published":"2019-01-11","source_url":"https://www.sei.org/publications/prince-chemicals-consumption-indicators/","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The paper presents five new consumption-based indicators for hazardous substances, utilizing a hybrid model that combines EXIOBASE with the Swedish System of Economic and Environmental Accounts. Applying these to Sweden, the study finds that the vast majority of chemicals-related pressures (76–90%) occur outside Sweden's borders, with specific pressures like pesticide use and hazardous emissions occurring primarily outside the EU.","ai_generated":true,"unit":"sei-000862#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The PRINCE project developed five specific consumption-based indicators: the use of hazardous chemical products, the use of pesticides, the use of antimicrobial veterinary medicines, emissions of hazardous substances, and the potential toxicity of those emissions.","ai_generated":true,"unit":"sei-000862#insight-1","date_modified":"2026-10-23"},{"text":"Analysis of Swedish consumption reveals that only 10–24% of the associated chemicals-related pressures take place within Sweden, meaning the majority occur internationally.","ai_generated":true,"unit":"sei-000862#insight-2","date_modified":"2026-10-23"},{"text":"The geographic distribution of pressures varies by indicator: the use of veterinary medicines and hazardous chemical products linked to Swedish consumption is concentrated in other EU countries, while pesticide use and hazardous substance emissions occur mainly outside the EU.","ai_generated":true,"unit":"sei-000862#insight-3","date_modified":"2026-10-23"}],"climate_tags":["pesticide"],"geography_discussed":["European Union"],"frame_perspective":[],"analysis_type":["quantitative","methodological"]},{"id":"bc3res-000185","url":"https://climateinsightsdirectory.org/document/bc3res-000185","title":"Climate Change and Armed Conflicts African Observatory (CACAO): a georeferenced map of Africa","publisher":{"name":"Basque Centre for Climate Change","organization":"basque-centre-for-climate-change"},"document_type":"research paper","language":"en","date_published":"2018-07","source_url":"https://www.bc3research.org/h6T8Uy8Nm4gFs0O/20180903101314132739497.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The authors created the CACAO database, which uses a 110x110 km grid to map conflicts, climate data, and socio-economic variables across Africa. Initial descriptive analysis shows that conflicts often overlap with areas rich in natural resources and populated regions, while the relationship with temperature and precipitation changes is more complex and requires further spatial and temporal analysis.","ai_generated":true,"unit":"bc3res-000185#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The CACAO database provides a granular, georeferenced panel for the African continent from 1990 to 2016, dividing the region into grid cells of approximately 110x110 km.","ai_generated":true,"unit":"bc3res-000185#insight-1","date_modified":"2026-10-23"},{"text":"Between 1990 and 2016, approximately 6.3% of the observations in the dataset were characterized by the presence of a conflict.","ai_generated":true,"unit":"bc3res-000185#insight-2","date_modified":"2026-10-23"},{"text":"The highest number of conflicts during the study period occurred in Algeria, the Democratic Republic of Congo (DCR), Nigeria, Somalia, and Sudan, while the highest number of deaths were recorded in Rwanda, Sudan, Eritrea, DCR, and Ethiopia.","ai_generated":true,"unit":"bc3res-000185#insight-3","date_modified":"2026-10-23"},{"text":"Mapping reveals a notable overlap between conflict zones and concentrations of energy and mineral resources, though the overlap with water resources is less clear.","ai_generated":true,"unit":"bc3res-000185#insight-4","date_modified":"2026-10-23"},{"text":"While most of Africa saw a sharp increase in temperature, the areas with the greatest temperature increases (North West and South West) did not align closely with conflict zones, suggesting a weak direct link.","ai_generated":true,"unit":"bc3res-000185#insight-5","date_modified":"2026-10-23"},{"text":"Conflicts have risen in the central belt, where precipitation has notably increased, and in the south east, where precipitation has notably decreased.","ai_generated":true,"unit":"bc3res-000185#insight-6","date_modified":"2026-10-23"},{"text":"The analysis suggests that conflicts primarily erupt in populated areas that are also rich in natural resources.","ai_generated":true,"unit":"bc3res-000185#insight-7","date_modified":"2026-10-23"}],"climate_tags":["climate change","food security","land use"],"geography_discussed":["Algeria","Democratic Republic of the Congo","Nigeria","Somalia","Sudan","Rwanda","Eritrea","Ethiopia"],"frame_perspective":[],"analysis_type":["methodological","descriptive","spatial analysis"]},{"id":"sei-000568","url":"https://climateinsightsdirectory.org/document/sei-000568","title":"Scenarios, Vulnerability and Diversity","publisher":{"name":"Stockholm Environment Institute","organization":"stockholm-environment-institute"},"document_type":"research paper","language":"en","date_published":"2016-07-09","source_url":"https://www.sei.org/publications/scenarios-vulnerability-and-diversity/","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The research introduces a method to reduce the large volume of scenarios generated by computer simulations into a manageable set for decision support. By integrating two distinct approaches—one focusing on policy vulnerabilities and the other on a diverse range of futures—the authors create a systematic selection process. This method was applied to climate resilient infrastructure planning for the Volta, Orange, and Zambezi river basins in Africa, demonstrating that a stepwise introduction of selection criteria allows for a transparent analysis of how different priorities influence scenario choice.","ai_generated":true,"unit":"sei-000568#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The paper introduces a new optimization-based method that integrates two previously separate approaches for scenario selection: one that identifies scenarios illuminating policy vulnerabilities and another that ensures a diverse range of futures.","ai_generated":true,"unit":"sei-000568#insight-1","date_modified":"2026-10-23"},{"text":"Applying the method to climate resilient infrastructure for the Volta, Orange, and Zambezi river basins suggests that combining vulnerability- and diversity-based criteria offers a transparent and systematic way to select scenarios.","ai_generated":true,"unit":"sei-000568#insight-2","date_modified":"2026-10-23"}],"climate_tags":["vulnerability assessment"],"geography_discussed":["Africa","Volta","Orange","Zambezi River"],"frame_perspective":[],"analysis_type":["methodological"]},{"id":"wri-001188","url":"https://climateinsightsdirectory.org/document/wri-001188","title":"AQUEDUCT PROJECTED WATER STRESS COUNTRY RANKINGS","publisher":{"name":"World Resources Institute","organization":"world-resources-institute"},"document_type":"research paper","language":"en","date_published":"2015-08","source_url":"https://wriorg.s3.amazonaws.com/s3fs-public/aqueduct-water-stress-country-rankings-technical-note.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The document details the creation of global country-level water stress projections by aggregating sub-catchment data using a spatially weighted methodology. It utilizes IPCC AR5 data to model three scenarios—optimistic, business-as-usual, and pessimistic—across three time horizons (2020, 2030, 2040). The rankings are designed to help investors, companies, and governments benchmark national water risks and prioritize adaptation. A 2040 business-as-usual ranking identifies several Middle Eastern countries, including Bahrain, Kuwait, and Qatar, as having the highest water stress scores of 5.00.","ai_generated":true,"unit":"wri-001188#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The analysis uses a spatially weighted aggregation methodology to convert sub-catchment scale projections to a national scale, using gridded water-withdrawal datasets as weights to prioritize areas with the highest human demand.","ai_generated":true,"unit":"wri-001188#insight-1","date_modified":"2026-10-23"},{"text":"Water stress is quantified as the ratio of total water withdrawals to available renewable surface water, with scores ranging from 0 to 5. A score between 4 and 5 is categorized as 'Extremely high', representing a withdrawal-to-available water ratio of over 80%.","ai_generated":true,"unit":"wri-001188#insight-2","date_modified":"2026-10-23"},{"text":"The projections are based on three combined IPCC scenarios: 'optimistic' (RCP4.5 and SSP2), 'business-as-usual' (RCP8.5 and SSP2), and 'pessimistic' (RCP8.5 and SSP3).","ai_generated":true,"unit":"wri-001188#insight-3","date_modified":"2026-10-23"},{"text":"Under the 2040 business-as-usual scenario, Bahrain, Kuwait, Qatar, San Marino, Singapore, the United Arab Emirates, and Palestine all share the highest possible water stress rank with a score of 5.00 across all sectors.","ai_generated":true,"unit":"wri-001188#insight-4","date_modified":"2026-10-23"},{"text":"The country-level indicators have limitations, as they average away significant sub-national variations in countries such as Brazil, China, and the United States, and do not account for national governance or investment structures.","ai_generated":true,"unit":"wri-001188#insight-5","date_modified":"2026-10-23"}],"climate_tags":["climate change","climate change adaptation","water management"],"geography_discussed":["Brazil","People's Republic of China","United States","Singapore"],"frame_perspective":[],"analysis_type":["methodological","predictive","comparative"]},{"id":"sei-000995","url":"https://climateinsightsdirectory.org/document/sei-000995","title":"Mapping the potential success of agricultural water management interventions for smallholders: where are the best opportunities?","publisher":{"name":"Stockholm Environment Institute","organization":"stockholm-environment-institute"},"document_type":"research paper","language":"en","date_published":"2015-06-22","source_url":"https://www.sei.org/publications/mapping-the-potential-success-of-agricultural-water-management-interventions-for-smallholders-where-are-the-best-opportunities/","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","analysis_type"],"summary":{"text":"The paper presents TAGMI, an online open-access tool using Bayesian network models to assess the likelihood of success for agricultural water management (AWM) interventions at a sub-national level. By integrating social, human, physical, financial, and natural factors, the tool evaluates the potential for outscaling three specific technologies—soil water conservation/in situ rainwater harvesting, small-scale private irrigation, and small reservoirs for communal irrigation—within the Limpopo and Volta river basins. The authors use a climate change scenario to test the robustness of these interventions and argue for the further development of Decision Support Systems and the identification of implementation best practices for poverty-constrained smallholder systems.","ai_generated":true,"unit":"sei-000995#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The TAGMI tool employs country-scale Bayesian network models to determine the probability of success for outscaling agricultural water management (AWM) interventions at a sub-national level, integrating expertise on social, human, physical, financial, and natural factors.","ai_generated":true,"unit":"sei-000995#insight-1","date_modified":"2026-10-23"},{"text":"As a proof of concept, the tool was used to estimate the relative probability of success for three specific AWM technologies in the Limpopo and Volta river basins: small reservoirs for communal irrigation, small-scale private irrigation, and soil water conservation/in situ rainwater harvesting in rain-fed systems.","ai_generated":true,"unit":"sei-000995#insight-2","date_modified":"2026-10-23"},{"text":"The authors highlight a requirement for more generic or specific information regarding 'best practices of implementation' to ensure the successful adoption of technologies in smallholder farming systems constrained by poverty.","ai_generated":true,"unit":"sei-000995#insight-3","date_modified":"2026-10-23"}],"climate_tags":["smallholder","climate change adaptation","rainwater harvesting","agricultural irrigation"],"geography_discussed":[],"frame_perspective":[],"analysis_type":["methodological","proof of concept"]},{"id":"wri-001681","url":"https://climateinsightsdirectory.org/document/wri-001681","title":"AQUEDUCT GLOBAL MAPS 2.1: CONSTRUCTING DECISION-RELEVANT GLOBAL WATER RISK INDICATORS","publisher":{"name":"World Resources Institute","organization":"world-resources-institute"},"document_type":"research paper","language":"en","date_published":"2015-04","source_url":"https://wriorg.s3.amazonaws.com/s3fs-public/Aqueduct_Global_Maps_2.1-Constructing_Decicion-Relevant_Global_Water_Risk_Indicators_final_0.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The document details the construction of the Aqueduct Water Risk Atlas, which uses a composite index approach to map global water risks. It describes the development of six modeled indicators (baseline water stress, inter-annual variability, seasonal variability, upstream storage, return flow ratio, and upstream protected land) and the adaptation of six externally sourced indicators (flood occurrence, drought severity, groundwater stress, media coverage, access to water, and threatened amphibians). The methodology involves using an ensemble of regression models to project water withdrawals to 2010, spatial disaggregation to the catchment scale, and a catchment-to-catchment flow accumulation approach for water supply. The final water risk scores are generated through a linear weighted aggregation process, allowing users to customize weights based on their specific sector or risk sensitivity.","ai_generated":true,"unit":"wri-001681#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The Aqueduct framework utilizes 12 global indicators to identify spatial variation in water risks, designed for comparative analysis across large geographies rather than site-specific analyses.","ai_generated":true,"unit":"wri-001681#insight-1","date_modified":"2026-10-23"},{"text":"Six indicators were developed by WRI and ISciences LLC using a combination of publicly available datasets and modeling techniques, while the other six were adapted from existing published sources.","ai_generated":true,"unit":"wri-001681#insight-2","date_modified":"2026-10-23"},{"text":"To estimate water withdrawals for 2010, the authors used a combination of mixed-effects (ME) and fixed-effects (FE) regression models, achieving a total R2 between 0.94 and 0.99 across sectors.","ai_generated":true,"unit":"wri-001681#insight-3","date_modified":"2026-10-23"},{"text":"Water supply is modeled using a catchment-to-catchment flow accumulation approach based on the GLDAS-2.0 land surface model, which was selected for its accuracy in simulating annual discharge and variability.","ai_generated":true,"unit":"wri-001681#insight-4","date_modified":"2026-10-23"},{"text":"Baseline water stress (BWS) is calculated as the ratio of total annual water withdrawal to average annual available blue water, specifically measuring chronic stress rather than drought stress.","ai_generated":true,"unit":"wri-001681#insight-5","date_modified":"2026-10-23"},{"text":"The tool employs a linear weighted aggregation approach to calculate overall water risk, allowing users to dynamically adjust weights to reflect different sensitivities or industry-specific risks.","ai_generated":true,"unit":"wri-001681#insight-6","date_modified":"2026-10-23"},{"text":"The authors identify significant data gaps as primary constraints to model accuracy, specifically regarding major infrastructure like interbasin transfers, in-situ water quality, and river gauge measurements.","ai_generated":true,"unit":"wri-001681#insight-7","date_modified":"2026-10-23"},{"text":"Validation of the overall composite index is currently impossible because there are no publicly available datasets of water-related economic or environmental losses.","ai_generated":true,"unit":"wri-001681#insight-8","date_modified":"2026-10-23"}],"climate_tags":["water scarcity","water quality","climate change","water distribution system"],"geography_discussed":["United States","Egypt","Iraq","Venezuela"],"frame_perspective":[],"analysis_type":["methodological","comparative"]},{"id":"sei-000588","url":"https://climateinsightsdirectory.org/document/sei-000588","title":"Identifying salient drivers of livelihood decision-making in the forest communities of Cameroon: adding value to social simulation models","publisher":{"name":"Stockholm Environment Institute","organization":"stockholm-environment-institute"},"document_type":"research paper","language":"en","date_published":"2015-02-02","source_url":"https://www.sei.org/publications/identifying-salient-drivers-of-livelihood-decision-making-in-the-forest-communities-of-cameroon-adding-value-to-social-simulation-models/","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","analysis_type"],"summary":{"text":"The paper details the use of Knowledge elicitation tools (KnETs) in southeast Cameroon to formalize qualitative data on local strategies for community adaptive capacity and sustainable forest management. This process generates decision rules and statistical measurements that inform social simulation models by defining agent attributes and prioritizing scenarios. Findings indicate that while men and women within the same households often share common rule drivers, differences in livelihood strategies exist between poor and better-off farmers regarding the package of strategies chosen, the driver factors involved, and the prioritization of these strategies relative to individual goals.","ai_generated":true,"unit":"sei-000588#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The use of Knowledge elicitation tools (KnETs) allows for the formalization of qualitative data into decision rules that support sustainable forest management and community adaptive capacity under climatic and socio-environmental stressors.","ai_generated":true,"unit":"sei-000588#insight-1","date_modified":"2026-10-23"},{"text":"Methodological improvements to KnETs, including new statistical measurements, facilitate the creation of social simulation models by helping determine agent heterogeneity, attributes, and priority scenarios for testing.","ai_generated":true,"unit":"sei-000588#insight-2","date_modified":"2026-10-23"},{"text":"Analysis of decision-making in the region revealed that men and women in the same households often share common rule drivers, whereas the distinction between poor and better-off farmers is found in the specific combination, type, and prioritization of strategies used to reach their goals.","ai_generated":true,"unit":"sei-000588#insight-3","date_modified":"2026-10-23"}],"climate_tags":["adaptive capacity"],"geography_discussed":[],"frame_perspective":[],"analysis_type":["methodological","empirical"]},{"id":"prio-001660","url":"https://climateinsightsdirectory.org/document/prio-001660","title":"Prediksjon i Internasjonal politikk","publisher":{"name":"Peace Research Institute Oslo","organization":"peace-research-institute-oslo"},"document_type":"research paper","language":"no","date_published":"2015","source_url":"https://cdn.cloud.prio.org/files/462e80f8-6fba-4556-b52d-0c6cbdfa2213/Prediksjon i internasjonal politikk.pdf?inline=true","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The author challenges the consensus in international relations that the field can explain but not predict political phenomena. He argues that there is little theoretical basis for distinguishing between explanation and prediction, and that modern data and machine learning make prediction empirically possible. The paper outlines three generations of conflict prediction, from early behavioralist ambitions to current sophisticated early warning systems used by entities like the UN and EU. The author contends that focusing on prediction improves theory testing by moving beyond statistical significance to evaluate whether models can actually reproduce data-generating mechanisms. While acknowledging the challenges of 'black swan' events and non-linear complex systems, he asserts that the alternative to scientific prediction is often reliance on unreliable expert intuition.","ai_generated":true,"unit":"prio-001660#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The author argues against the prevailing view in international relations that the discipline is capable of explanation but not prediction, stating that in most cases there is little theoretical reason to distinguish between the two.","ai_generated":true,"unit":"prio-001660#insight-1","date_modified":"2026-10-23"},{"text":"Conflict prediction has evolved through three generations: the first (1960s-70s) was driven by behavioralism and the Correlates of War project; the second introduced game theory, machine learning, and automated news data analysis (e.g., KEDS); the third is characterized by massive data access and active collaboration with decision-makers.","ai_generated":true,"unit":"prio-001660#insight-2","date_modified":"2026-10-23"},{"text":"Current predictive tools are utilized by major international bodies, including the UN's Department of Political Affairs for genocide prediction, the UK Cabinet Office's 'Strategic Early Warning' unit, and the EU's Global Conflict Risk Index (GCRI) to inform resource allocation.","ai_generated":true,"unit":"prio-001660#insight-3","date_modified":"2026-10-23"},{"text":"The author contends that relying solely on statistical significance (NHST) is problematic and that 'out of sample' prediction is a more robust method for testing theories and avoiding 'overfitting' to idiosyncratic data.","ai_generated":true,"unit":"prio-001660#insight-4","date_modified":"2026-10-23"},{"text":"The paper highlights the risk of 'black swan' events and the difficulty of predicting non-linear complex systems, where outcomes are not proportional to input factors, unlike linear domains such as physics.","ai_generated":true,"unit":"prio-001660#insight-5","date_modified":"2026-10-23"},{"text":"The author asserts a social responsibility for researchers to provide scientific predictions because the alternative is often reliance on 'experts' whose predictive accuracy is frequently lower than a coin flip.","ai_generated":true,"unit":"prio-001660#insight-6","date_modified":"2026-10-23"}],"climate_tags":["early warning system"],"geography_discussed":["United States","Syria","Norway","United Kingdom","European Union"],"frame_perspective":[],"analysis_type":["theoretical","methodological","literature review"]},{"id":"bc3res-000149","url":"https://climateinsightsdirectory.org/document/bc3res-000149","title":"Hunting spectro-temporal information in unevenly spaced paleoclimate time series","publisher":{"name":"Basque Centre for Climate Change","organization":"basque-centre-for-climate-change"},"document_type":"research paper","language":"en","date_published":"2014-10","source_url":"https://www.bc3research.org/h6T8Uy8Nm4gFs0O/20141106101733519838526.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","geography_discussed","analysis_type"],"summary":{"text":"The authors developed a method to analyze unevenly spaced paleoclimate data using the Morlet Weighted Wavelet Z-Transform (MWWZ) to avoid the spectral distortions caused by time-domain interpolation. Applying this to the GISP2 ice core oxygen isotope record from Greenland, the implementation successfully identified a known 1,470-year spectral peak between 35,000 and 31,000 years B.P. However, the results differed from previous studies in the Holocene period (last 10,000 years), where no statistically significant spectral structures were found. The authors conclude that further improvements, such as window smoothing and bias correction, are necessary before releasing the tool as an R package.","ai_generated":true,"unit":"bc3res-000149#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The authors propose the Morlet Weighted Wavelet Z-Transform (MWWZ) as a superior alternative to time-domain interpolation for unevenly spaced time series, noting that \"interpolation in time domain alters the estimated spectrum of a time series and it should be avoided\".","ai_generated":true,"unit":"bc3res-000149#insight-1","date_modified":"2026-10-23"},{"text":"The developed implementation successfully detected a prominent 1,470 year spectral peak in the GISP2 ice core record, specifically \"centred within the 35,000–31,000 years B.P. time interval\".","ai_generated":true,"unit":"bc3res-000149#insight-2","date_modified":"2026-10-23"},{"text":"The analysis identified two other significant spectral structures: one between 48,000–28,000 years B.P. with a period of 2,000 to 5,000 years, and another between 24,000–14,000 years B.P. with a period of 6,250 to 2,500 years.","ai_generated":true,"unit":"bc3res-000149#insight-3","date_modified":"2026-10-23"},{"text":"The implementation failed to find any statistically significant spectral structures in the Holocene period (the last 10,000 years), a result that contradicts previous findings by Witt & Schumann (2005).","ai_generated":true,"unit":"bc3res-000149#insight-4","date_modified":"2026-10-23"},{"text":"Discrepancies between this work and previous studies are attributed to the AR1 model fitting, the absence of a window smoother, and an \"inherent bias in the estimation of the wavelet spectrum\".","ai_generated":true,"unit":"bc3res-000149#insight-5","date_modified":"2026-10-23"},{"text":"The authors identify the need for future improvements, specifically the \"implementation of a window smoothing at the time or scale domain\" and the addition of \"extra weighting factors in the estimation of the wavelet spectral amplitudes\".","ai_generated":true,"unit":"bc3res-000149#insight-6","date_modified":"2026-10-23"}],"climate_tags":[],"geography_discussed":["Spain"],"frame_perspective":[],"analysis_type":["methodological","case study"]},{"id":"sei-002683","url":"https://climateinsightsdirectory.org/document/sei-002683","title":"What’s the Mind Got To Do With It?","publisher":{"name":"Stockholm Environment Institute","organization":"stockholm-environment-institute"},"document_type":"research paper","language":"en","date_published":"2012-07","source_url":"https://www.sei.org/mediamanager/documents/Publications/Climate/SEI-WP-2012-04-Cognitive-Climate.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The author proposes a cognitive approach to analyze the persistent failure of global climate governance, arguing that human behavior in negotiations is driven by underlying mental representations. The paper introduces cognitive-affective mapping (CAM) as a qualitative tool to visualize these belief systems, including their emotional valences. By applying CAM to UNFCCC participants, the research aims to identify cognitive barriers to cooperation and explore how these mental structures differ between individuals and groups, potentially offering new pathways for diplomatic breakthroughs.","ai_generated":true,"unit":"sei-002683#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The author characterizes the United Nations Framework Convention on Climate Change (UNFCCC) as a significant global governance failure, noting that international negotiations have had no meaningful effect on climate change for over 20 years.","ai_generated":true,"unit":"sei-002683#insight-1","date_modified":"2026-10-23"},{"text":"The paper argues that political failure persists despite scientific consensus, suggesting that knowledge alone does not lead to rational action and that cognitive barriers—rather than a lack of information—hinder both bottom-up mobilization and elite decision-making.","ai_generated":true,"unit":"sei-002683#insight-2","date_modified":"2026-10-23"},{"text":"Cognitive-affective mapping (CAM) is introduced as a novel qualitative tool that uses network diagrams to visualize not only the conceptual structure of a person's views but also the emotional values (positive, negative, neutral, or ambivalent) attached to those concepts.","ai_generated":true,"unit":"sei-002683#insight-3","date_modified":"2026-10-23"},{"text":"An application of CAM to South Africa's position at COP17 reveals a belief system based on \"common but differentiated responsibilities and respective capabilities,\" while introducing the view that all countries, including South Africa, should contribute to reducing emissions based on their wealth and historical emissions.","ai_generated":true,"unit":"sei-002683#insight-4","date_modified":"2026-10-23"},{"text":"The author suggests that CAMs can be used for practical policy purposes, such as clarifying misperceptions between negotiating parties, identifying hidden areas of agreement, or designing strategic communication to change political beliefs.","ai_generated":true,"unit":"sei-002683#insight-5","date_modified":"2026-10-23"},{"text":"Ongoing research using CAMs and Q Method among roughly 50 UNFCCC participants aims to identify distinct belief systems to determine if the current constellation of beliefs prevents agreement and whether these minds can be changed.","ai_generated":true,"unit":"sei-002683#insight-6","date_modified":"2026-10-23"}],"climate_tags":["United Nations Framework Convention on Climate Change","climate diplomacy","climate justice","Kyoto Protocol","climate change mitigation","climate change adaptation"],"geography_discussed":["South Africa","United States","India","Canada"],"frame_perspective":[],"analysis_type":["theoretical","methodological","qualitative"]},{"id":"sei-001870","url":"https://climateinsightsdirectory.org/document/sei-001870","title":"Use of McKinsey abatement cost curves for climate economics modeling","publisher":{"name":"Stockholm Environment Institute","organization":"stockholm-environment-institute"},"document_type":"research paper","language":"en","date_published":"2011-01-25","source_url":"https://www.sei.org/mediamanager/documents/Publications/Climate/sei-workingpaperus-1102.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The authors adapt McKinsey's global abatement cost data for use in the CRED model by aggregating 21 regions into nine and 11 sectors into two (land use and industry). To resolve the theoretical conflict of 'negative-cost' abatements—where energy savings outweigh investment—the authors assign these measures a near-zero but positive cost. The resulting estimates suggest that emission reductions may be cheaper than those projected by top-down integrated assessment models like RICE or EMF-22, while remaining broadly comparable to the MIT EPPA model's positive-cost estimates.","ai_generated":true,"unit":"sei-001870#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"McKinsey's 2009 'Pathways to a Low-Carbon Economy' report suggests significant abatement potential at low costs, estimating that by 2030, 38 Gt of CO2e could be avoided at a cost of €60 per ton or less, with an average cost of €4 per ton.","ai_generated":true,"unit":"sei-001870#insight-1","date_modified":"2026-10-23"},{"text":"The authors identify a conflict between McKinsey's 'negative-cost' abatement opportunities and economic theory, noting that if such net economic benefits existed, they should have already been invested in. To maintain model realism and avoid a surge of artificial capital, the authors assign a near-zero but positive cost to these measures.","ai_generated":true,"unit":"sei-001870#insight-2","date_modified":"2026-10-23"},{"text":"The CRED model's abatement cost estimates are broadly similar to the positive-cost portion of the MIT EPPA model, particularly for developed countries, though CRED identifies more opportunities in developing countries like China and Africa.","ai_generated":true,"unit":"sei-001870#insight-3","date_modified":"2026-10-23"},{"text":"A comparison of 2030 abatement costs as a percentage of GDP under the 'Copenhagen Convergence' scenario shows that the CRED model projects the lowest costs, followed by the RICE model, with the EMF-22 estimates being the highest.","ai_generated":true,"unit":"sei-001870#insight-4","date_modified":"2026-10-23"},{"text":"The authors conclude that bottom-up studies, such as those by McKinsey, generally project lower abatement costs than top-down integrated assessment models, even when negative-cost opportunities are excluded.","ai_generated":true,"unit":"sei-001870#insight-5","date_modified":"2026-10-23"}],"climate_tags":["energy efficiency","land use","climate finance","carbon price"],"geography_discussed":["United States","People's Republic of China","Europe","Africa"],"frame_perspective":[],"analysis_type":["methodological","comparative"]},{"id":"sei-002260","url":"https://climateinsightsdirectory.org/document/sei-002260","title":"Elicitation Techniques for Bayesian Network Models","publisher":{"name":"Stockholm Environment Institute","organization":"stockholm-environment-institute"},"document_type":"research paper","language":"en","date_published":"2008-06-06","source_url":"https://www.sei.org/mediamanager/documents/Publications/Climate/elicitation-techniques-bayesian.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","analysis_type"],"summary":{"text":"The document describes two techniques for creating elicited probability tables (EPTs) to simplify the process of building Bayesian networks when measured data is unavailable or insufficient. Both methods utilize a user-defined \"typical distribution\" to represent a normal state of affairs. The first method, \"Influence Weights,\" assumes that changes in parent nodes independently affect the child node and is best used when parent and child nodes share the same states. The second method, \"Likelihood,\" uses a log-likelihood approach to model how parent nodes shift the child node's probability away from the typical distribution, making it suitable for cases where parent nodes have different states than the child node.","ai_generated":true,"unit":"sei-002260#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"Expert elicitation of CPTs is necessary when there is no measured data to estimate the table or when the data set is too small relative to the number of parameters to definitively establish the CPT.","ai_generated":true,"unit":"sei-002260#insight-1","date_modified":"2026-10-23"},{"text":"The Influence Weights method assumes that the change in probability resulting from a change in any parent node is independent of other parent nodes, meaning a change from \"low\" to \"high\" in one parent produces the same effect regardless of other parent states.","ai_generated":true,"unit":"sei-002260#insight-2","date_modified":"2026-10-23"},{"text":"To account for influences other than identified parent nodes in the Influence Weights method, an \"environmental\" parent node is added that always maintains the typical distribution.","ai_generated":true,"unit":"sei-002260#insight-3","date_modified":"2026-10-23"},{"text":"The Influence Weights elicitation process involves three steps: identifying the most influential parent node (assigned a non-normalized weight of 10), assigning relative weights to other nodes, and estimating the total \"information\" (I) as a percentage of certainty about the child node.","ai_generated":true,"unit":"sei-002260#insight-4","date_modified":"2026-10-23"},{"text":"The Likelihood method is based on Bayes' rule and assumes that the log likelihood can be expressed as a sum of independent terms for each parent node, shifting the child node's probability away from the typical distribution.","ai_generated":true,"unit":"sei-002260#insight-5","date_modified":"2026-10-23"},{"text":"The Influence Weights method is most appropriate when parent nodes share the same states as the typical distribution and the expert believes typical inputs should yield a typical output.","ai_generated":true,"unit":"sei-002260#insight-6","date_modified":"2026-10-23"},{"text":"The Likelihood method is preferred when the expert's mental model is that parent nodes drive the child node away from a typical distribution, such as rainfall levels shifting the probability distribution of crop yield.","ai_generated":true,"unit":"sei-002260#insight-7","date_modified":"2026-10-23"}],"climate_tags":["crop yield","rain"],"geography_discussed":[],"frame_perspective":[],"analysis_type":["methodological"]}]}