{"query_echo":{"q":"","filters":{"analysis_type":["methodology report"]},"warnings":[]},"total":{"value":13,"relation":"eq"},"page":1,"page_size":20,"self":"https://climateinsightsdirectory.org/api/v1/search?analysis_type=methodology+report","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":"catf-001184","url":"https://climateinsightsdirectory.org/document/catf-001184","title":"Superhot Rock Methodology Report","publisher":{"name":"Clean Air Task Force","organization":"clean-air-task-force"},"document_type":"report","language":"en","date_published":"2025-05","source_url":"https://cdn.catf.us/wp-content/uploads/2025/06/25170717/shr-heat-mapping-methodology.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The report establishes a methodology for mapping superhot rock (SHR) geothermal resources globally, defining SHR as heat from subsurface formations exceeding 374°C. Using the LithoRef18 global reference model and a 'hydrothermal proxy' to account for non-steady-state heat flow, the study estimates the un-risked global resource. For depths of 4-8 km ('today's resource'), the estimated potential is 3,272 GWe (13,895 GWth). For depths of 8-12.5 km ('tomorrow's resource'), the potential increases to 59,930 GWe (245,581 GWth). The analysis identifies 125 countries with SHR potential, with significant resources in the US, China, Indonesia, and others. The report also analyzes flow rate uncertainties using a database of 33 petrothermal projects, determining a P50 flow rate of 55 l/s for its calculations.","ai_generated":true,"unit":"catf-001184#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The study estimates the un-risked global SHR resource at depths of 4-8 km (accessible with current technology) to be 3,272 GWe or 13,895 GWth.","ai_generated":true,"unit":"catf-001184#insight-1","date_modified":"2026-10-23"},{"text":"The un-risked global SHR resource at depths of 8-12.5 km (a medium-term 'stretch goal') is estimated at 59,930 GWe or 245,581 GWth.","ai_generated":true,"unit":"catf-001184#insight-2","date_modified":"2026-10-23"},{"text":"SHR potential was observed in 125 countries globally. At depths of 4-8 km, 46 countries have potential endowments greater than 50 GWe; at 8-12.5 km, this number increases to 86 countries.","ai_generated":true,"unit":"catf-001184#insight-3","date_modified":"2026-10-23"},{"text":"The methodology utilizes the LithoRef18 global reference model to calculate the depth to the steady-state 450°C conductive isotherm, applying a +5km correction to account for optimistic interpretations of convective and advective processes.","ai_generated":true,"unit":"catf-001184#insight-4","date_modified":"2026-10-23"},{"text":"To account for non-steady-state heat flow, the report uses a 'hydrothermal proxy' map created by calculating the difference between measured surface heat flow (from the IHFC database or Lucazeau 2019 model) and the LithoRef18 modelled heat flow, selecting areas with a residual anomaly greater than +40 mW/m².","ai_generated":true,"unit":"catf-001184#insight-5","date_modified":"2026-10-23"},{"text":"The 'Area Developable' method was used to estimate the prospective endowment, which assumes that only 1% of the mapped shallow heat is initially developable due to factors like surface/subsurface rights and infrastructure distance.","ai_generated":true,"unit":"catf-001184#insight-6","date_modified":"2026-10-23"},{"text":"Analysis of 33 petrothermal projects revealed that only 47% were commercially successful (excluding research projects), with a success rate of 46% specifically for hard-rock/crystalline basement projects.","ai_generated":true,"unit":"catf-001184#insight-7","date_modified":"2026-10-23"},{"text":"Probabilistic modelling of flow rates for petrothermal projects established a P50 flow rate of 55 l/s, with a P90 of 25 l/s and a P10 of 100 l/s.","ai_generated":true,"unit":"catf-001184#insight-8","date_modified":"2026-10-23"},{"text":"The report suggests that if approximately 25% of the global prospective SHR resource at the 4-12.5 km interval were developed, it could meet the entire predicted global electric demand for 2050.","ai_generated":true,"unit":"catf-001184#insight-9","date_modified":"2026-10-23"}],"climate_tags":["decarbonization","Levelized cost of energy","energy security"],"geography_discussed":["United States","Indonesia","Japan","Argentina","People's Republic of China","Bolivia","Mexico","Peru","Chile","Iceland","Russia","Mongolia","Sudan","Chad","Libya","Niger","Antarctica"],"frame_perspective":[],"analysis_type":["resource assessment","methodology report","techno-economic modelling"]},{"id":"iisd-000964","url":"https://climateinsightsdirectory.org/document/iisd-000964","title":"Developing and Piloting a Model for Estimating the Direct and Indirect Benefits of Nature-Based Solutions for Road Resilience in Indonesia","publisher":{"name":"International Institute for Sustainable Development","organization":"international-institute-for-sustainable-development"},"document_type":"report","language":"en","date_published":"2025-03-28","source_url":"https://www.iisd.org/publications/report/indonesia-nature-based-solutions-road-resilience","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The document introduces a mathematical model for quantifying the physical and economic impacts of road disruptions, such as flooding and landslides, and assessing the value of nature-based solutions in mitigating these risks. The model incorporates climate forecasts and spatially explicit assessments of ecosystem services to provide a comprehensive cost-benefit analysis of resilience investments. It has been piloted in two Indonesian locations and used to train technical staff at the Ministry of National Development Planning (BAPPENAS).","ai_generated":true,"unit":"iisd-000964#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"Nature-based solutions enhance road resilience by lowering the direct costs of infrastructure damage and reducing disruptions to road services.","ai_generated":true,"unit":"iisd-000964#insight-1","date_modified":"2026-10-23"},{"text":"A comprehensive analysis of nature-based solutions for road resilience highlights the importance of accounting for indirect and induced environmental, social, and economic benefits beyond direct financial gains.","ai_generated":true,"unit":"iisd-000964#insight-2","date_modified":"2026-10-23"},{"text":"The developed model utilizes climate forecasts and spatially explicit assessments of nearby ecosystem services to evaluate how floods and landslides affect road-related services.","ai_generated":true,"unit":"iisd-000964#insight-3","date_modified":"2026-10-23"}],"climate_tags":["nature-based solutions","ecosystem service","flood","landslide"],"geography_discussed":["Namibia"],"frame_perspective":[],"analysis_type":["methodology report","cost-benefit analysis"]},{"id":"iisd-001648","url":"https://climateinsightsdirectory.org/document/iisd-001648","title":"Developing and Piloting a Model for Estimating the Direct and Indirect Benefits of Nature-Based Solutions for Road Resilience in Indonesia","publisher":{"name":"International Institute for Sustainable Development","organization":"international-institute-for-sustainable-development"},"document_type":"report","language":"en","date_published":"2025-03","source_url":"https://www.iisd.org/system/files/2025-03/indonesia-nature-based-solutions-road-resilience.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The report introduces an Excel-based model designed to quantify the financial and economic viability of nature-based solutions (NBI), such as reforestation and mangrove restoration, to enhance road resilience against climate-induced hazards like floods and landslides in Indonesia. Moving beyond traditional CBAs, the model incorporates 'non-road benefits'—including socio-economic externalities like mental health, market access for crops and livestock, and air pollution—to provide a holistic view of investment value. The model was piloted in two distinct locations: Pekanbaru (an urban-rural connection) and West Kalimantan (a rural area). Results demonstrate that while conventional CBAs focusing only on road-related costs often show that costs outweigh benefits, the inclusion of non-road benefits makes these investments economically viable in all tested scenarios. For instance, in the Pekanbaru base case, the benefit-to-cost ratio (BCR) rose to 11.45 when non-road benefits were included.","ai_generated":true,"unit":"iisd-001648#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"Traditional cost-benefit analyses that exclude non-road benefits indicate that the costs of road resilience investments outweigh the benefits in both pilot locations.","ai_generated":true,"unit":"iisd-001648#insight-1","date_modified":"2026-10-23"},{"text":"Integrating non-road benefits into the analysis makes nature-based infrastructure investments economically viable across all five tested scenarios for both Pekanbaru and West Kalimantan.","ai_generated":true,"unit":"iisd-001648#insight-2","date_modified":"2026-10-23"},{"text":"In the Pekanbaru base case scenario, the model estimates a benefit-to-cost ratio (BCR) of 11.45 and an internal rate of return (IRR) of 66.6% when non-road benefits are included.","ai_generated":true,"unit":"iisd-001648#insight-3","date_modified":"2026-10-23"},{"text":"In the West Kalimantan base case scenario, the inclusion of non-road benefits results in a BCR of 6.05 and an IRR of 69.5%.","ai_generated":true,"unit":"iisd-001648#insight-4","date_modified":"2026-10-23"},{"text":"The relative importance of non-road benefits varies by location: Pekanbaru is most impacted by livestock revenue, access to essential services, and access to workplaces/schools, while West Kalimantan is most impacted by crop and fruit revenue and air pollution.","ai_generated":true,"unit":"iisd-001648#insight-5","date_modified":"2026-10-23"},{"text":"The model identifies 15 distinct non-road indicators for economic valuation, ranging from tangible costs like fuel and vehicle O&M to intangible impacts such as mental health and noise pollution.","ai_generated":true,"unit":"iisd-001648#insight-6","date_modified":"2026-10-23"}],"climate_tags":["nature-based solutions","climate change adaptation","ecosystem service","flood","landslide","greenhouse gas emissions","air pollution","reforestation","Mangrove restoration"],"geography_discussed":["Pekanbaru","West Kalimantan","Queensland"],"frame_perspective":[],"analysis_type":["methodology report","economic valuation","case study"]},{"id":"clibonds-001068","url":"https://climateinsightsdirectory.org/document/clibonds-001068","title":"Australian sustainable finance taxonomy methodology report","publisher":{"name":"Climate Bonds Initiative","organization":"climate-bonds-initiative"},"document_type":"report","language":"en","date_published":"2023-12","source_url":"https://www.climatebonds.net/files/documents/Transition-Methodologyfinal.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The report outlines a methodology to distinguish between green, transition, and phase-down activities to mobilise private capital for Australia's net zero goals. It defines 'green' activities as consistent with Paris Agreement goals and 'transition' activities as those with a continued role in a net zero economy that lack low-carbon alternatives. The methodology recommends using internationally recognised 1.5°C aligned climate-science scenarios, specifically from the IEA and Climateworks, to objectively determine eligibility and avoid greenwashing and carbon lock-in.","ai_generated":true,"unit":"clibonds-001068#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The Taxonomy Technical Expert Group (TTEG) defines 'green' activities as those consistent with achieving net zero greenhouse gas emissions in accordance with the Paris Agreement temperature goal.","ai_generated":true,"unit":"clibonds-001068#insight-1","date_modified":"2026-10-23"},{"text":"The report distinguishes 'phase down' activities from 'transition' activities; phase down activities are those inconsistent with a net zero future, have available low-carbon alternatives, and have no pathway to decarbonise without being phased out, such as internal combustion engine passenger vehicles.","ai_generated":true,"unit":"clibonds-001068#insight-2","date_modified":"2026-10-23"},{"text":"To ensure objectivity and avoid subjectivity associated with 'risk' language, the TTEG recommends using internationally recognised, credible 1.5°C aligned climate science scenarios to identify eligible activities, specifically citing the IEA Net Zero Emissions (NZE) and Climateworks scenarios.","ai_generated":true,"unit":"clibonds-001068#insight-3","date_modified":"2026-10-23"},{"text":"The taxonomy will focus on six priority sectors: electricity generation and supply (energy), minerals, mining and metals, construction and the built environment, manufacturing/industry, transport, and agriculture.","ai_generated":true,"unit":"clibonds-001068#insight-4","date_modified":"2026-10-23"},{"text":"The methodology incorporates a 'sunset date' principle, asserting that activities cannot remain in the transition category indefinitely and must follow a predetermined pathway to become 1.5°C aligned.","ai_generated":true,"unit":"clibonds-001068#insight-5","date_modified":"2026-10-23"}],"climate_tags":["carbon neutrality","transition finance","Carbon lock-in","greenwashing","Paris Agreement"],"geography_discussed":["European Union","Singapore"],"frame_perspective":[],"analysis_type":["methodology report","policy framework"]},{"id":"cliinstca-000413","url":"https://climateinsightsdirectory.org/document/cliinstca-000413","title":"Evaluating the Macroeconomic Costs of Climate Change in Canada","publisher":{"name":"Canadian Climate Institute","organization":"canadian-climate-institute"},"document_type":"report","language":"en","date_published":"2022-03-19","source_url":"https://climateinstitute.ca/wp-content/uploads/2022/09/Evaluating-macroeconomic-costs-climate-change-canada.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The document details the technical approach for modeling the macroeconomic effects of climate change on Canada's GDP, employment, and welfare. Using the gTech model, researchers integrated 13 categories of climate impacts—ranging from agricultural productivity and forestry reductions to infrastructure damages and healthcare spending—across 14 climate scenarios (RCP4.5 and RCP8.5) and three asset growth scenarios. The methodology distinguishes between impacts modeled as productivity changes (e.g., labour and agriculture) and those modeled as direct capital expenditures (e.g., road and building repairs), with the latter scaled based on historical asset appreciation relative to GDP. The report identifies weather-related disasters as the largest determinant of overall macroeconomic impact and notes limitations regarding the deterministic nature of disaster modeling and the exclusion of migration and trade restrictions.","ai_generated":true,"unit":"cliinstca-000413#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The analysis utilized the gTech computable general equilibrium (CGE) model, which represents economic activity across all Canadian provinces, territories, and the United States, customized to run in 10-year increments from 2015 to 2095.","ai_generated":true,"unit":"cliinstca-000413#insight-1","date_modified":"2026-10-23"},{"text":"The modeling incorporated 14 different climate change scenarios based on two global emissions pathways (RCP4.5 and RCP8.5) and seven downscaled global climate models, resulting in 84 total simulated scenarios plus a reference case.","ai_generated":true,"unit":"cliinstca-000413#insight-2","date_modified":"2026-10-23"},{"text":"Weather-related disasters were identified as the primary driver of macroeconomic impacts, with estimated costs to 2015 infrastructure averaging $57 billion per year in the 2090s under the RCP8.5 scenario.","ai_generated":true,"unit":"cliinstca-000413#insight-3","date_modified":"2026-10-23"},{"text":"Climate impacts were categorized into two modeling approaches: productivity changes (where costs grow with the sector's size) and direct capital/operating expenditures (where costs are scaled by the growth of physical assets).","ai_generated":true,"unit":"cliinstca-000413#insight-4","date_modified":"2026-10-23"},{"text":"To account for the fact that residential buildings historically appreciate faster than the overall economy, the model adjusted climate damages to grow at approximately 1.2 times the rate of GDP.","ai_generated":true,"unit":"cliinstca-000413#insight-5","date_modified":"2026-10-23"},{"text":"The model accounts for potential economic benefits from climate change, such as increased agricultural productivity for certain crops and a potential increase in foreign tourism arrivals as high-latitude climates become more temperate.","ai_generated":true,"unit":"cliinstca-000413#insight-6","date_modified":"2026-10-23"},{"text":"The study focused on the direct economic impacts of premature deaths—specifically reduced labour force participation—and explicitly excluded non-monetary emotional costs to welfare.","ai_generated":true,"unit":"cliinstca-000413#insight-7","date_modified":"2026-10-23"},{"text":"A significant limitation of the current methodology is its deterministic approach to disasters, which uses expected annual costs rather than stochastic modeling to account for catastrophic extreme events.","ai_generated":true,"unit":"cliinstca-000413#insight-8","date_modified":"2026-10-23"},{"text":"The analysis did not consider the potential for interprovincial or international migration, which the authors suggest would likely mute national macroeconomic impacts while increasing regional effects.","ai_generated":true,"unit":"cliinstca-000413#insight-9","date_modified":"2026-10-23"}],"climate_tags":["agricultural productivity","forestry","tourism industry","coastal flood","electric energy consumption","climate finance"],"geography_discussed":["United States"],"frame_perspective":[],"analysis_type":["methodology report","macroeconomic modeling","sensitivity analysis"]},{"id":"sei-002757","url":"https://climateinsightsdirectory.org/document/sei-002757","title":"Serious Game System – MONITOR ECOTECH","publisher":{"name":"Stockholm Environment Institute","organization":"stockholm-environment-institute"},"document_type":"report","language":"en","date_published":"2020-02-29","source_url":"https://www.sei.org/wp-content/uploads/2022/03/serious-game-system-compressed.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The document details the creation of MONITOR ECOTECH, a digital 'alpha' version of a serious game system (SGS) developed by Uppsala University as part of the BONUS RETURN project. The system aims to provide a participatory learning space for stakeholders to test the feasibility and vulnerability of various eco-technologies used to reduce nutrient and pollution emissions in the Baltic Sea Region. Built using the Unity 3D engine, the game uses socio-ecological data from environmental modelling and stakeholder consultations to simulate the impact of different technology combinations on nutrient flows. It differs from its board game predecessor, SELECT ECOTECH, by offering higher data granularity, real-time tracking of emissions, and the ability to simulate longer timeframes and more complex scenarios, specifically targeting 'wicked' problems where traditional environmental monitoring and assessment are insufficient.","ai_generated":true,"unit":"sei-002757#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"MONITOR ECOTECH is designed to address the limitations of traditional environmental monitoring and assessment (EMA), which the authors argue is too 'techno-centric' and fails to account for qualitative local knowledge or 'wicked' situations where values are disputed and decisions are urgent.","ai_generated":true,"unit":"sei-002757#insight-1","date_modified":"2026-10-23"},{"text":"The digital system provides several advantages over the previous board game version, including the ability to track nutrient emissions with finer granularity via bar charts and flow data, the capacity to test a larger number of eco-technology constellations, and the ability to simulate longer game sessions to monitor performance over time.","ai_generated":true,"unit":"sei-002757#insight-2","date_modified":"2026-10-23"},{"text":"The game's data is derived from two primary sources: the BONUS RETURN Environmental Modelling work package (WP4), which provided baseline emissions for agriculture, forestry, and urban land use in three catchments, and stakeholder/expert consultations for costs and reduction potentials of technologies that could not be modelled via the Soil and Water Assessment Tool (SWAT).","ai_generated":true,"unit":"sei-002757#insight-3","date_modified":"2026-10-23"},{"text":"The system uses a representational rather than a photorealistic approach to data and graphics, prioritizing 'believability' and systemic clarity over absolute data fidelity to avoid cognitive overload for the players.","ai_generated":true,"unit":"sei-002757#insight-4","date_modified":"2026-10-23"},{"text":"The game architecture utilizes an abstract hexagonal representation of land use areas (agriculture, forestry, and urban) surrounding a center hexagon representing the Baltic Sea, allowing players to monitor the causal relationships between their technology choices and emissions entering the sea.","ai_generated":true,"unit":"sei-002757#insight-5","date_modified":"2026-10-23"}],"climate_tags":["water management","bioenergy"],"geography_discussed":["Finland","Sweden","Poland","Vantaanjoki river basin","Słupia river basin","Fyrisån river basin"],"frame_perspective":[],"analysis_type":["system description","methodology report"]},{"id":"ucs-000209","url":"https://climateinsightsdirectory.org/document/ucs-000209","title":"A Survey of Federal Scientists","publisher":{"name":"Union of Concerned Scientists","organization":"union-of-concerned-scientists"},"document_type":"report","language":"en","date_published":"2018-05-04","source_url":"https://www.ucs.org/sites/default/files/2019-09/2018-survey-of-federal-scientists-methodology-report.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","analysis_type"],"summary":{"text":"The 2018 Survey of Federal Scientists targeted 63,248 contacts across 16 US federal agencies to investigate scientific integrity and the impact of political interference on science-based decision-making. Administered by the Center for Survey Statistics and Methodology at Iowa State University for the Union of Concerned Scientists, the survey utilized a multimode approach including online web surveys, PDF forms, and phone interviews. The final data set comprised 4,211 responses, representing an overall response rate of 6.67%. The survey instrument specifically questioned respondents on workforce reductions, the omission of politically contentious terms—including 'climate change'—and the effectiveness of agency scientific integrity and whistleblower policies.","ai_generated":true,"unit":"ucs-000209#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The final sample for the 2018 Survey of Federal Scientists included 63,248 contacts across 16 primary federal agencies.","ai_generated":true,"unit":"ucs-000209#insight-1","date_modified":"2026-10-23"},{"text":"The overall response rate for the survey was 6.67%, with 4,211 surveys included in the final data set from 63,126 eligible contacts.","ai_generated":true,"unit":"ucs-000209#insight-2","date_modified":"2026-10-23"},{"text":"The final data set consisted of 3,783 complete surveys and 428 partially complete surveys, the vast majority of which (4,178) were completed online.","ai_generated":true,"unit":"ucs-000209#insight-3","date_modified":"2026-10-23"},{"text":"Response rates varied significantly by agency, with the highest rate at the US Geological Survey (19.12%) and the lowest at the US Census Bureau (2.17%).","ai_generated":true,"unit":"ucs-000209#insight-4","date_modified":"2026-10-23"},{"text":"The survey instrument included specific questions asking scientists if they had been told to avoid work on or omit the phrase 'climate change' from their work.","ai_generated":true,"unit":"ucs-000209#insight-5","date_modified":"2026-10-23"},{"text":"The survey investigated barriers to science-based decisions, including the influence of political appointees, the White House, Congress, and industry stakeholders.","ai_generated":true,"unit":"ucs-000209#insight-6","date_modified":"2026-10-23"},{"text":"The methodology employed a multimode approach to maximize response, offering online web surveys, downloadable PDFs, and confidential phone interviews.","ai_generated":true,"unit":"ucs-000209#insight-7","date_modified":"2026-10-23"},{"text":"To ensure anonymity, the survey used an 'Anonymizing Responses' feature that removed email addresses, IP addresses, and location data upon completion.","ai_generated":true,"unit":"ucs-000209#insight-8","date_modified":"2026-10-23"},{"text":"The survey targeted agencies based on their science-based missions, implementation of science-based policies, or history of scientific integrity concerns.","ai_generated":true,"unit":"ucs-000209#insight-9","date_modified":"2026-10-23"}],"climate_tags":["climate change","environmental impact assessment"],"geography_discussed":[],"frame_perspective":[],"analysis_type":["methodology report","response rate analysis"]},{"id":"cdkn-000913","url":"https://climateinsightsdirectory.org/document/cdkn-000913","title":"Champion CSA Screening Methodology","publisher":{"name":"Climate and Development Knowledge Network","organization":"climate-and-development-knowledge-network"},"document_type":"report","language":"en","date_published":"2016-12","source_url":"https://cdkn.org/sites/default/files/files/Champion-CSA-Screening-Methodology.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The document details a screening process to identify champion Climate-Smart Agriculture (CSA) practices in Nepal, utilizing a combination of on-farm trials, household surveys, and the Analytic Hierarchy Process (AHP). The methodology evaluates technologies based on three pillars—food security, adaptation, and mitigation—alongside Gender Equality and Social Inclusion (GESI). The process resulted in a final list of 17 champion CSAs, with specific allocations for the high-hill, mid-hill, and Terai regions. Findings indicate that farmers prioritize food security above all other criteria, and that packaging multiple technologies is more effective for building resilience than standalone practices.","ai_generated":true,"unit":"cdkn-000913#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The selection process identified 17 champion CSAs or packages for Nepal: 9 for the high-hill region, 12 for the mid-hill region, and 10 for the Terai region, with six technologies serving as champions across all three zones.","ai_generated":true,"unit":"cdkn-000913#insight-1","date_modified":"2026-10-23"},{"text":"Farmers across all study sites and genders consistently ranked 'food security' as the most important priority when selecting CSA options, while 'gender equity and social inclusion (GESI)' was generally ranked as the least important.","ai_generated":true,"unit":"cdkn-000913#insight-2","date_modified":"2026-10-23"},{"text":"The report argues that 'CSA Packages'—combining multiple related technologies—are more effective at addressing complex climate challenges and increasing household resilience than individual standalone technologies.","ai_generated":true,"unit":"cdkn-000913#insight-3","date_modified":"2026-10-23"},{"text":"In the Terai region, solar-based irrigation was overwhelmingly prioritized by both men and women due to its ability to increase food production and cropping intensity in water-stressed areas.","ai_generated":true,"unit":"cdkn-000913#insight-4","date_modified":"2026-10-23"},{"text":"Gender-based differences in CSA prioritization were observed in Lamjung, where women prioritized home gardens and new seeds for family nutrition, while men prioritized plastic houses and agroforestry for commercial income.","ai_generated":true,"unit":"cdkn-000913#insight-5","date_modified":"2026-10-23"},{"text":"The methodology employs a rigorous four-step process: (I) selection of potential CSAs, (II) documentation of evidence via on-farm trials and surveys, (III) consolidation of results through expert judgment, and (IV) validation with stakeholders and local communities.","ai_generated":true,"unit":"cdkn-000913#insight-6","date_modified":"2026-10-23"},{"text":"Certain technologies were selected as champions based on stakeholder recommendations and international experience despite a lack of local field-level evidence, specifically ICT-based agro-advisory and Weather-Index based Insurance.","ai_generated":true,"unit":"cdkn-000913#insight-7","date_modified":"2026-10-23"}],"climate_tags":["Climate-smart agriculture","food security","climate change adaptation","climate change mitigation","agroforestry"],"geography_discussed":["Kaski District","Lamjung District","Nawalparasi District"],"frame_perspective":[],"analysis_type":["methodology report","multi-criteria analysis","participatory action research"]},{"id":"sei-001420","url":"https://climateinsightsdirectory.org/document/sei-001420","title":"Introducing the Transnational Climate Impacts Index: Indicators of country-level exposure – methodology report","publisher":{"name":"Stockholm Environment Institute","organization":"stockholm-environment-institute"},"document_type":"research paper","language":"en","date_published":"2016-06","source_url":"https://www.sei.org/wp-content/uploads/2016/06/introducing-the-transnational-climate-impacts-index-indicators-of-country-level-exposure-methodology-report.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The report develops the Transnational Climate Impacts (TCI) Index to measure how countries are exposed to climate risks originating outside their borders. Using a framework of four risk pathways—biophysical, finance, people, and trade—alongside a global context indicator, the authors identify nine indicators of exposure. The resulting index reveals that many high-income, small European nations are highly exposed to transnational risks, contrasting with traditional vulnerability indices that primarily highlight low-income nations. The authors present this as a tool to raise awareness for national adaptation planning, though they note it is a preliminary instrument not yet intended for direct decision-making.","ai_generated":true,"unit":"sei-001420#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The authors define transnational climate impacts as those that \"occur in one place as a consequence of climate impacts somewhere else,\" distinguishing them from direct impacts that occur within a country's own borders.","ai_generated":true,"unit":"sei-001420#insight-1","date_modified":"2026-10-23"},{"text":"The framework identifies four primary pathways for the transmission of transnational climate risks: biophysical (e.g., transboundary river basins), finance (e.g., foreign direct investment and remittances), people (e.g., asylum and migration), and trade (e.g., supply chain disruptions and food import dependency).","ai_generated":true,"unit":"sei-001420#insight-2","date_modified":"2026-10-23"},{"text":"The TCI Index utilizes nine indicators: transboundary water dependency, bilateral climate-weighted foreign direct investment, remittance flows, openness to asylum, migration from climate vulnerable countries, trade openness, cereal import dependency ratio, embedded water risk, and the KOF Globalization Index.","ai_generated":true,"unit":"sei-001420#insight-3","date_modified":"2026-10-23"},{"text":"Analysis of the TCI Index shows that small industrialized nations in Europe—including the Netherlands, Belgium, Luxembourg, Portugal, Montenegro, Malta, Austria, and Lithuania—exhibit high exposure to transnational impacts, despite often ranking low on direct vulnerability indices like ND-GAIN.","ai_generated":true,"unit":"sei-001420#insight-4","date_modified":"2026-10-23"},{"text":"The four highest-scoring countries on the TCI Index are located in the Middle East and North Africa (MENA) region: Jordan, Lebanon, Kuwait, and the United Arab Emirates.","ai_generated":true,"unit":"sei-001420#insight-5","date_modified":"2026-10-23"},{"text":"The TCI Index results demonstrate significantly less correlation with national wealth or human development than traditional climate vulnerability indices, suggesting that transnational risk is driven by complex, country-specific interdependencies rather than just poverty.","ai_generated":true,"unit":"sei-001420#insight-6","date_modified":"2026-10-23"},{"text":"The 'embedded water risk' indicator, developed using the IOTA model, reveals that countries like Afghanistan, China, India, Turkey, Nepal, Pakistan, and Central Asian states are highly dependent on commodities grown in water-stressed areas.","ai_generated":true,"unit":"sei-001420#insight-7","date_modified":"2026-10-23"},{"text":"The authors caution that the TCI Index is a \"crude tool\" intended to provoke discussion and raise awareness rather than to serve as a definitive basis for immediate policy decision-making.","ai_generated":true,"unit":"sei-001420#insight-8","date_modified":"2026-10-23"}],"climate_tags":["climate change adaptation","Climate risk assessment","food security","climate migration","climate change vulnerability"],"geography_discussed":["Europe","sub-Saharan Africa","Middle East and North Africa","Sweden"],"frame_perspective":[],"analysis_type":["methodology report","quantitative analysis","index development"]},{"id":"wwf-000471","url":"https://climateinsightsdirectory.org/document/wwf-000471","title":"Reviving the Ocean Economy: the case for action - 2015","publisher":{"name":"WWF International","organization":"wwf-international"},"document_type":"report","language":"en","date_published":"2015-04","source_url":"https://www.worldwildlife.org/documents/876/44wvgtrg2z_BCG_methodology___Reviving_the_Ocean_Economy___WWF_report_20_4_15.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The document details the methodology used to value the global ocean economy, estimating a total asset value of 24 trillion USD and an annual economic contribution of approximately 2.5 trillion USD. It employs market-based and value-based (Net Present Value) approaches across seven asset categories and five annual activity categories, while explicitly excluding oil and gas production to avoid inflating the 'blue economy' value.","ai_generated":true,"unit":"wwf-000471#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The total asset value of the ocean is estimated at 24 trillion USD, aggregated across seven primary value-generating categories: carbon absorption (7.8 Tn USD), productive coastline (5.2 Tn USD), shipping lanes (4.3 Tn USD), carbon absorption (listed twice in table, but distinct from others), marine fisheries (2.9 Tn USD), seagrass (2.1 Tn USD), coral reefs (1.0 Tn USD), and mangroves (0.9 Tn USD).","ai_generated":true,"unit":"wwf-000471#insight-1","date_modified":"2026-10-23"},{"text":"The annual economic value of ocean-related activities is estimated between 2.4 trillion USD (bottom-up analysis) and 2.6 trillion USD (top-down analysis). If treated as a standalone economy, the ocean economy would rank as the 7th largest global economy.","ai_generated":true,"unit":"wwf-000471#insight-2","date_modified":"2026-10-23"},{"text":"Approximately 70% of the annual value of the ocean economy is either at risk or dependent on healthy oceans. This includes direct output of the ocean, services enabled by the ocean, and adjacent benefits, while trade and transportation are not considered dependent on ocean health.","ai_generated":true,"unit":"wwf-000471#insight-3","date_modified":"2026-10-23"},{"text":"The valuation methodology utilizes two primary approaches: Market Based, which looks at the quantity of a resource priced at current market value, and Value Based, which uses Net Present Value (NPV) of future annual value generation. For NPV calculations, a global risk-free rate of 4.1% and a global risk premium of 7.1% were applied.","ai_generated":true,"unit":"wwf-000471#insight-4","date_modified":"2026-10-23"},{"text":"To ensure the valuation reflects the 'true' value of the ocean, all oil and gas production revenue and expenditures were excluded from the analysis, as these activities would remain operational regardless of the ocean's presence.","ai_generated":true,"unit":"wwf-000471#insight-5","date_modified":"2026-10-23"}],"climate_tags":["blue economy","carbon sequestration","coral reef restoration","ocean acidification"],"geography_discussed":["United States","European Union","People's Republic of China","Canada","Australia","South Africa","Mozambique"],"frame_perspective":[],"analysis_type":["economic valuation","methodology report"]},{"id":"ucs-000677","url":"https://climateinsightsdirectory.org/document/ucs-000677","title":"Driving the Fox from the Henhouse Improving Oversight of Food Safety at the FDA and USDA","publisher":{"name":"Union of Concerned Scientists","organization":"union-of-concerned-scientists"},"document_type":"report","language":"en","date_published":"2010-07-29","source_url":"https://www.ucs.org/sites/default/files/2019-09/food-safety-survey-methodology.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The report details the implementation of a survey conducted by the Union of Concerned Scientists (UCS) and Iowa State University's Center for Survey Statistics and Methodology (CSSM) to examine the role of science and government oversight in food safety. It outlines the sampling process for 8,122 employees across the FDA, USDA Food Safety and Inspection Service (FSIS), and USDA Agriculture Research Service (ARS), the data collection procedures used between March and May 2010, and the resulting response rate of 21.62%. The document also includes the full survey instrument, which investigates political interference in science, job satisfaction, and the influence of business interests on public health decisions.","ai_generated":true,"unit":"ucs-000677#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The final eligible sample for the survey consisted of 7,911 individuals, with a total of 1,710 responses received, resulting in an overall response rate of 21.62%.","ai_generated":true,"unit":"ucs-000677#insight-1","date_modified":"2026-10-23"},{"text":"Response rates varied by agency, with the Agricultural Research Service (ARS) showing the highest participation at 33.73%, followed by the Food Safety and Inspection Service (FSIS) at 22.30% and the Food and Drug Administration (FDA) at 19.27%.","ai_generated":true,"unit":"ucs-000677#insight-2","date_modified":"2026-10-23"},{"text":"The survey sample was composed of 4,995 FSIS employees, 2,874 FDA employees, and 253 ARS employees, totaling 8,122 records.","ai_generated":true,"unit":"ucs-000677#insight-3","date_modified":"2026-10-23"},{"text":"Data collection was hindered by inconsistent agency support; while the FSIS eventually confirmed the survey was legitimate, some FDA directors initially labeled it an \"unsanctioned survey,\" which led some employees to believe they should not participate.","ai_generated":true,"unit":"ucs-000677#insight-4","date_modified":"2026-10-23"},{"text":"The survey instrument was designed to measure several specific areas of scientific integrity, including requests to \"inappropriately exclude or alter technical information,\" the use of \"incomplete, inaccurate or misleading information,\" and the influence of \"business interests\" on public health.","ai_generated":true,"unit":"ucs-000677#insight-5","date_modified":"2026-10-23"}],"climate_tags":["public health"],"geography_discussed":["Washington, D.C.","Iowa"],"frame_perspective":[],"analysis_type":["methodology report","survey design"]},{"id":"rmi-000794","url":"https://climateinsightsdirectory.org/document/rmi-000794","title":"Reinventing Fire Transportation Sector Methodology","publisher":{"name":"RMI","organization":"rmi"},"document_type":"report","language":"en","date_published":null,"source_url":"https://rmi.org/app/uploads/2017/05/reinventingfire_transportationsectormethodology.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The Reinventing Fire transportation analysis evaluates efficiency pathways for U.S. autos, heavy trucks, and airplanes through 2050. It distinguishes between 'Improved Design' (technological upgrades) and 'Improved Use' (reducing vehicle-miles traveled), while incorporating a consumer adoption model for high-efficiency vehicles and a supply-cost model for inedible biofuels. The methodology integrates these findings into an economy-wide model to calculate net present value savings, estimating a total net saving of $3,757 billion in 2009 dollars.","ai_generated":true,"unit":"rmi-000794#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The analysis identifies three vehicle efficiency tiers for cars and light trucks: a baseline derived from EIA data, an 'Evolutionary' tier with 50% better fuel economy than the baseline, and a 'Revolutionary' tier consisting of superefficient battery-electric and fuel cell vehicles.","ai_generated":true,"unit":"rmi-000794#insight-1","date_modified":"2026-10-23"},{"text":"To drive the adoption of 'Revolutionary' vehicles, the model proposes a size- and revenue-neutral feebate to be phased out 10–15 years after introduction, helping to reduce costs across learning curves for carbon fiber and batteries/fuel cells.","ai_generated":true,"unit":"rmi-000794#insight-2","date_modified":"2026-10-23"},{"text":"The methodology estimates that a combination of design and use improvements for heavy trucks could result in a 67% cumulative reduction in fuel consumption by 2050, including a 30% technology-based efficiency gain beyond EIA baseline improvements.","ai_generated":true,"unit":"rmi-000794#insight-3","date_modified":"2026-10-23"},{"text":"For aviation, the analysis models a transition from standard tube-and-wing configurations to advanced designs such as blended wing body (BWB) and strut-braced wing (SBW), with some advanced replacements potentially offering a 70% reduction in fuel burn per seat.","ai_generated":true,"unit":"rmi-000794#insight-4","date_modified":"2026-10-23"},{"text":"The biofuels model focuses exclusively on inedible feedstocks—agricultural residue, mill residue, dedicated energy crops, municipal solid waste, and forestry residue—to avoid adverse effects on world food markets.","ai_generated":true,"unit":"rmi-000794#insight-5","date_modified":"2026-10-23"},{"text":"The total estimated net savings for the transportation sector under the Reinventing Fire scenario is $3,757 billion (2009$ NPV), with the largest savings coming from autos ($2,560 billion) and trucks ($851 billion).","ai_generated":true,"unit":"rmi-000794#insight-6","date_modified":"2026-10-23"}],"climate_tags":["vulnerability assessment","electric vehicle","fuel cell vehicle","biofuel","carbon fibers"],"geography_discussed":["World"],"frame_perspective":[],"analysis_type":["techno-economic analysis","methodology report","forecasting"]},{"id":"ucs-001187","url":"https://climateinsightsdirectory.org/document/ucs-001187","title":"Fries, Face Wash, Forests: Scoring America's Top Brands on their Palm Oil Commitments","publisher":{"name":"Union of Concerned Scientists","organization":"union-of-concerned-scientists"},"document_type":"report","language":"en","date_published":null,"source_url":"https://www.ucs.org/sites/default/files/attach/2015/03/ucs-palm-oil-methodology-2015_0.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","analysis_type"],"summary":{"text":"The Union of Concerned Scientists evaluated 40 companies in the packaged food, personal care, fast food, and store brands sectors based on their commitments to deforestation-free and peat-free palm oil, traceability, transparency, and current sourcing. Scoring was based on public documents available as of January 16, 2015, and included a weighted analysis of current sourcing. Results showed significant variance, with Nestle scoring the highest (90.5) and several companies, including Clorox and various store brands, scoring zero.","ai_generated":true,"unit":"ucs-001187#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The evaluation covered 40 companies divided equally into four sectors: packaged food, personal care, fast food, and store brands. The store brands sector was a new addition for the 2015 scorecard, consisting of the top 10 US-based companies from food, drug, and discount retail store industries, excluding Sysco.","ai_generated":true,"unit":"ucs-001187#insight-1","date_modified":"2026-10-23"},{"text":"Companies were scored on a 100-point scale across five criteria: Deforestation-Free (20 points), Peat-Free (20 points), Traceability (20 points), Transparency (20 points), and Current Sourcing (20 points). Full points for deforestation-free commitments required protecting all forests, including \"secondary, disturbed, or high carbon stock forests.\"","ai_generated":true,"unit":"ucs-001187#insight-2","date_modified":"2026-10-23"},{"text":"The 'Current Sourcing' score used a weighted equation based on the type of palm oil purchased. Deforestation- and peat-free palm oil received full weight, while physically sourced CSPO or sustainable palm oil was weighted at 0.5, and palm fruit oil sourced through GreenPalm (PFOGP) was weighted at 0.25.","ai_generated":true,"unit":"ucs-001187#insight-3","date_modified":"2026-10-23"},{"text":"The Union of Concerned Scientists categorized companies based on their total scores: 'Strong Commitment' (66 or higher), 'Some Commitment' (36 to 65), 'Little Commitment' (1 to 35), and 'No Commitment' (0).","ai_generated":true,"unit":"ucs-001187#insight-4","date_modified":"2026-10-23"},{"text":"In the packaged food sector, Nestle achieved the highest total score of 90.5, followed by Danone (89.2) and Kellogg Co. (88.5). In contrast, Kraft scored 10.","ai_generated":true,"unit":"ucs-001187#insight-5","date_modified":"2026-10-23"},{"text":"In the personal care sector, Colgate-Palmolive scored the highest at 88, while Clorox received a total score of 0.","ai_generated":true,"unit":"ucs-001187#insight-6","date_modified":"2026-10-23"},{"text":"The fast food and store brands sectors showed lower overall commitment levels. In fast food, Dunkin’ Brands scored 70, while Yum! Brands scored 0. In store brands, Safeway scored 72.1, while Target, Costco, Dollar General, and Dollar Tree all scored 0.","ai_generated":true,"unit":"ucs-001187#insight-7","date_modified":"2026-10-23"}],"climate_tags":["deforestation","peatland","carbon footprint"],"geography_discussed":[],"frame_perspective":[],"analysis_type":["corporate scorecard","methodology report"]}]}