{"query_echo":{"q":"","filters":{"analysis_type":["methodology"]},"warnings":[]},"total":{"value":240,"relation":"eq"},"page":7,"page_size":20,"self":"https://climateinsightsdirectory.org/api/v1/search?analysis_type=methodology&page=7","next":"https://climateinsightsdirectory.org/api/v1/search?analysis_type=methodology&page=8","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":"ucs-001170","url":"https://climateinsightsdirectory.org/document/ucs-001170","title":"Farmworkers at Risk","publisher":{"name":"Union of Concerned Scientists","organization":"union-of-concerned-scientists"},"document_type":"report","language":"en","date_published":"2019-12","source_url":"https://www.ucs.org/sites/default/files/2020-01/ucs-farmworkers-at-risk-appendices.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The document outlines the methodology and data used to analyze pesticide application rates and heat index exposure for farmworkers in the US. It details the use of USGS and NASS data to identify high-risk pesticides and calculate heat index days, providing specific state-level and county-level data for California, Florida, and Washington.","ai_generated":true,"unit":"ucs-001170#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The analysis identified the top 10 pesticides by weight for California, Florida, and Washington, focusing on labor-intensive crop categories such as \"Vegetables and fruit\" and \"Orchards and grapes\".","ai_generated":true,"unit":"ucs-001170#insight-1","date_modified":"2026-10-23"},{"text":"Several high-use pesticides in the studied states are associated with acute toxicity and long-term health effects, including carcinogenic, reproductive, and developmental toxicants or endocrine disruptors.","ai_generated":true,"unit":"ucs-001170#insight-2","date_modified":"2026-10-23"},{"text":"Specific pesticides used in the three target states carry severe EPA toxicity warnings; for example, Metam Potassium, Chloropicrin, Metam, Calcium Polysulfide, Chlorothalonil, Sulfuric Acid, and Allyl Isothiocyanate are all categorized with the signal word \"DANGER\".","ai_generated":true,"unit":"ucs-001170#insight-3","date_modified":"2026-10-23"},{"text":"Heat index calculations based on 1971-2000 data show significant variation in the number of days above 80°F across states, with Florida (194 days) and Texas (165 days) among the highest, while Maine (21 days) and Vermont (28 days) are among the lowest.","ai_generated":true,"unit":"ucs-001170#insight-4","date_modified":"2026-10-23"},{"text":"In California, the San Joaquin Valley counties of Merced (138 days), San Joaquin (133 days), and Stanislaus (131 days) exhibit high frequencies of days with a heat index above 80°F.","ai_generated":true,"unit":"ucs-001170#insight-5","date_modified":"2026-10-23"},{"text":"The methodology for heat index calculations is noted as a conservative estimate of recent conditions because substantial warming has occurred since the 1971-2000 average period.","ai_generated":true,"unit":"ucs-001170#insight-6","date_modified":"2026-10-23"}],"climate_tags":["extreme heat","pesticide","climate change"],"geography_discussed":["California","Florida","Washington"],"frame_perspective":[],"analysis_type":["methodology","data analysis"]},{"id":"sei-000712","url":"https://climateinsightsdirectory.org/document/sei-000712","title":"Estimating on-road vehicle fuel economy in Africa","publisher":{"name":"Stockholm Environment Institute","organization":"stockholm-environment-institute"},"document_type":"research paper","language":"en","date_published":"2019-03-26","source_url":"https://www.sei.org/publications/estimating-on-road-vehicle-fuel-economy-in-africa/","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","analysis_type"],"summary":{"text":"The paper develops a methodology to estimate fuel economy in sub-Saharan African cities, specifically Nairobi, by collecting data on both formal and informal vehicle fleets. Using general linear modelling (GLM) and artificial neural networks (ANN), the study found that fuel economy for bodabodas, tuktuks, passenger cars, and matatus in Nairobi is 2–3 times worse than in their countries of import. The GLM was identified as the more effective model for predicting fuel economy based on vehicle characteristics.","ai_generated":true,"unit":"sei-000712#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"Estimating fuel economy in sub-Saharan African (SSA) cities is hindered by official data that is often incomplete, inaccurate, inconsistent, and outdated, as well as the prevalence of unregistered, poorly maintained, and overloaded informal transport vehicles.","ai_generated":true,"unit":"sei-000712#insight-1","date_modified":"2026-10-23"},{"text":"In Nairobi, the fuel economy for specific vehicle types—bodabodas (4.6 ± 0.4 L/100 km), tuktuks (8.7 ± 4.6 L/100 km), passenger cars (22.8 ± 3.0 L/100 km), and matatus (33.1 ± 2.5 L/100 km)—was found to be 2–3 times worse than in the countries from which they were imported.","ai_generated":true,"unit":"sei-000712#insight-2","date_modified":"2026-10-23"},{"text":"The study compared two empirical models—general linear modelling (GLM) and artificial neural network (ANN)—to analyze the relationship between vehicle characteristics and fuel economy, concluding that GLM provided the better estimate.","ai_generated":true,"unit":"sei-000712#insight-3","date_modified":"2026-10-23"}],"climate_tags":["greenhouse gas emissions","air quality","urban transport"],"geography_discussed":[],"frame_perspective":[],"analysis_type":["methodology","empirical modelling"]},{"id":"ucs-000786","url":"https://climateinsightsdirectory.org/document/ucs-000786","title":"Inequitable Exposure to Air Pollution from Vehicles in California","publisher":{"name":"Union of Concerned Scientists","organization":"union-of-concerned-scientists"},"document_type":"report","language":"en","date_published":"2019-02","source_url":"https://www.ucs.org/sites/default/files/attach/2019/02/Inequitable-Exposure-Methods.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","analysis_type"],"summary":{"text":"The Union of Concerned Scientists utilized the InMAP (Intervention Model for Air Pollution) to estimate average annual concentrations of PM2.5 in California at a resolution of one square kilometer. By mapping these concentrations to census block groups and integrating data from the 2012–2016 American Community Survey, the study aimed to determine population-weighted exposure by demographic group. The authors justify the use of the reduced-complexity InMAP model over conventional chemical transport models (CTMs) by noting its superior performance on population-weighted metrics and its ability to provide the fine spatial resolution necessary to analyze exposure equity in racially or economically segregated urban areas.","ai_generated":true,"unit":"ucs-000786#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The study used the InMAP model to estimate average annual concentrations of PM2.5 in California with a maximum resolution of one square kilometer, incorporating tailpipe and refueling emissions from on-road vehicles based on the 2014 US EPA National Emissions Inventory.","ai_generated":true,"unit":"ucs-000786#insight-1","date_modified":"2026-10-23"},{"text":"To assess exposure by demographic group, PM2.5 concentrations were mapped to census block groups using area-weighted interpolation and combined with 2012–2016 American Community Survey data, using population-weighted annual average concentration as the primary metric.","ai_generated":true,"unit":"ucs-000786#insight-2","date_modified":"2026-10-23"},{"text":"The InMAP model is preferred for equity assessments because its fine spatial resolution allows for meaningful insights into exposure in urban areas with segregated populations, and it performs better on population-weighted metrics than area-based metrics compared to conventional CTMs.","ai_generated":true,"unit":"ucs-000786#insight-3","date_modified":"2026-10-23"},{"text":"The analysis assumed a no-effect threshold concentration of zero micrograms per cubic meter for health impacts, as no lower bound has been established for chronic PM2.5 exposure.","ai_generated":true,"unit":"ucs-000786#insight-4","date_modified":"2026-10-23"}],"climate_tags":["particulates","environmental justice"],"geography_discussed":[],"frame_perspective":[],"analysis_type":["methodology"]},{"id":"clipolin-001123","url":"https://climateinsightsdirectory.org/document/clipolin-001123","title":"The Global Landscape of Climate Finance 2019","publisher":{"name":"Climate Policy Initiative","organization":"climate-policy-initiative"},"document_type":"guide","language":"en","date_published":"2019","source_url":"https://www.climatepolicyinitiative.org/wp-content/uploads/2019/11/GLCF-2019-Methodology-Document.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The document details the operational framework for the 2019 Global Landscape of Climate Finance, defining climate finance as resources aimed at reducing emissions, enhancing greenhouse gas sinks, and increasing resilience to climate impacts. It establishes a standardized accounting methodology to avoid double counting and ensure data consistency across public and private sectors. The methodology prioritizes primary capital flows and financial commitments over disbursements, excluding secondary market transactions and investments that risk carbon lock-in. It provides a comprehensive sectoral breakdown for mitigation (e.g., renewable energy, energy efficiency, low-carbon transport) and adaptation (e.g., water management, coastal protection), and defines the geographic classification of countries into OECD and non-OECD regions.","ai_generated":true,"unit":"clipolin-001123#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The Climate Policy Initiative (CPI) defines climate finance as resources intended to reduce greenhouse gas emissions, enhance sinks, and increase the resilience of human and ecological systems to climate change impacts, aligning with the UNFCCC Standing Committee on Finance.","ai_generated":true,"unit":"clipolin-001123#insight-1","date_modified":"2026-10-23"},{"text":"To prevent double counting, the methodology only tracks new money targeting climate-specific outcomes, specifically excluding private R&D, manufacturing investments for low-GHG technologies, and policy-induced revenue support mechanisms like feed-in tariffs.","ai_generated":true,"unit":"clipolin-001123#insight-2","date_modified":"2026-10-23"},{"text":"The accounting scope focuses on financial commitments rather than disbursements, as consistent disbursement data is often unavailable across actors, although country-specific Landscapes for Germany, Indonesia, and Cote d’Ivoire do capture disbursement data.","ai_generated":true,"unit":"clipolin-001123#insight-3","date_modified":"2026-10-23"},{"text":"The methodology excludes investments that risk 'carbon lock-in,' such as financing for efficiency retrofits of coal-fired power plants, and omits risk management instruments like guarantees and insurance from total finance figures because their disbursements are contingent.","ai_generated":true,"unit":"clipolin-001123#insight-4","date_modified":"2026-10-23"},{"text":"Private climate finance is categorized into several streams, including large-scale renewable energy projects across 108 countries, small-scale solar (under 1MW), electric vehicle retail purchases and charging infrastructure, and non-energy projects in water, waste, and transport.","ai_generated":true,"unit":"clipolin-001123#insight-5","date_modified":"2026-10-23"},{"text":"Public climate finance is tracked through three primary channels: Development Finance Institutions (DFIs), governments and their agencies (including OECD-DAC reported bilateral finance), and national or multilateral climate funds.","ai_generated":true,"unit":"clipolin-001123#insight-6","date_modified":"2026-10-23"}],"climate_tags":["climate finance","climate change adaptation","renewable energy","energy efficiency","electric vehicle","afforestation","reforestation","carbon capture and storage","water management","coastal management","disaster risk reduction","climate bond"],"geography_discussed":["Germany","Indonesia","Ivory Coast","People's Republic of China","United States","Chile","France"],"frame_perspective":[],"analysis_type":["methodology"]},{"id":"napglob-000333","url":"https://climateinsightsdirectory.org/document/napglob-000333","title":"Facilitación del aprendizaje entre pares con los encargados de formular las políticas de adaptación: enfoques y observaciones de los Foros de Temas Específicos de la Red Global del PNAD","publisher":{"name":"NAP Global Network","organization":"nap-global-network"},"document_type":"guide","language":"es","date_published":"2019","source_url":"https://napglobalnetwork.org/wp-content/uploads/2019/05/napgn-es-2019-facilitating-peer-learning-with-adaptation-policy-makers.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The document outlines a framework for facilitating peer learning among adaptation policy-makers to move away from passive, presentation-heavy meetings toward interactive knowledge exchange. It details seven core planning principles—purpose, context, people, power, place, process, and monitoring—and provides specific methodologies for technical, relational, and reflective interaction, such as 'Knowledge Clinics' and 'Participatory Timelines'. These methods were tested across seven Thematic Issue Forums involving government representatives from 23 countries to accelerate the development and implementation of National Adaptation Plans.","ai_generated":true,"unit":"napglob-000333#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The NAP Global Network utilizes Thematic Issue Forums (TIFs) based on three interaction pillars: technical (expert input), relational (peer experience sharing), and reflective (analyzing national application).","ai_generated":true,"unit":"napglob-000333#insight-1","date_modified":"2026-10-23"},{"text":"Effective event planning for adaptation policy-makers requires addressing power dynamics to prevent sessions from being dominated by those in higher institutional hierarchies, suggesting the use of small groups, drawing, or theater to encourage participation from those with less power.","ai_generated":true,"unit":"napglob-000333#insight-2","date_modified":"2026-10-23"},{"text":"The guide recommends a comprehensive monitoring and evaluation (M&E) approach that goes beyond post-event surveys to include baseline setting before the event, rapid evaluations during the event, and follow-up interviews 2 to 3 months later to verify the implementation of planned actions.","ai_generated":true,"unit":"napglob-000333#insight-3","date_modified":"2026-10-23"},{"text":"Specific facilitation techniques are proposed to replace traditional PowerPoint presentations, including 'Knowledge Clinics' where a 'patient' presents a real problem to a group of 'doctors' for advice, and 'Knowledge Fairs' where participants exhibit projects via booths.","ai_generated":true,"unit":"napglob-000333#insight-4","date_modified":"2026-10-23"},{"text":"The 'cohort' approach, where teams of three representatives from each country attend complementary forums annually, helps build a community of trust and ongoing peer support for NAP processes.","ai_generated":true,"unit":"napglob-000333#insight-5","date_modified":"2026-10-23"},{"text":"Structured reflection, such as the use of manuals and self-assessments completed in small groups of compatriots, is used to translate forum learnings into concrete national actions.","ai_generated":true,"unit":"napglob-000333#insight-6","date_modified":"2026-10-23"}],"climate_tags":["national adaptation plan","climate change adaptation","climate policy","capacity building","climate finance"],"geography_discussed":["Madagascar","Vanuatu","Canada","Germany","United States","Austria"],"frame_perspective":[],"analysis_type":["guide","methodology"]},{"id":"ucs-000379","url":"https://climateinsightsdirectory.org/document/ucs-000379","title":"Climate Accountability Scorecard","publisher":{"name":"Union of Concerned Scientists","organization":"union-of-concerned-scientists"},"document_type":"report","language":"en","date_published":"2018-10","source_url":"https://www.ucsusa.org/sites/default/files/attach/2018/10/climate-accountability-scorecard-2018-appendix-methodology.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The Union of Concerned Scientists (UCS) developed a methodology to assess eight major investor-owned oil, gas, and coal companies on their climate responsibility. The assessment uses 28 metrics across four broad areas: renouncing disinformation, planning for a carbon-free world, supporting effective climate policies, and disclosing climate risks. Companies are scored on a five-point scale from 'advanced' to 'egregious' based on publicly available data from July 1, 2016, to June 30, 2018.","ai_generated":true,"unit":"ucs-000379#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The study focuses on eight investor-owned fossil fuel companies whose products have contributed approximately 14 percent of global energy-related carbon dioxide and methane emissions. These include the five leading oil and gas companies (Chevron, ExxonMobil, BP, Royal Dutch Shell, and ConocoPhillips) and three leading US-based coal companies (Peabody Energy, CONSOL Energy, and Arch Coal).","ai_generated":true,"unit":"ucs-000379#insight-1","date_modified":"2026-10-23"},{"text":"The methodology evaluates companies across four primary areas of assessment: renouncing disinformation on climate science and policy (10 metrics), planning for a world free from carbon pollution (7 metrics), supporting fair and effective climate policies (7 metrics), and fully disclosing climate risks (4 metrics).","ai_generated":true,"unit":"ucs-000379#insight-2","date_modified":"2026-10-23"},{"text":"To assess indirect disinformation, the UCS identifies seven US industry groups and trade associations with documented roles in spreading climate disinformation and opposing US climate policy: the American Coalition for Clean Coal Electricity (ACCCE), American Legislative Exchange Council (ALEC), American Petroleum Institute (API), National Association of Manufacturers (NAM), National Mining Association (NMA), US Chamber of Commerce (US Chamber), and Western States Petroleum Association (WSPA).","ai_generated":true,"unit":"ucs-000379#insight-3","date_modified":"2026-10-23"},{"text":"The scoring system generally employs a five-point scale where 'Advanced' (+2) represents best practices, 'Good' (+1) meets emerging societal expectations, 'Fair' (0) is neutral, 'Poor' (-1) falls short of expectations, and 'Egregious' (-2) is acting very irresponsibly.","ai_generated":true,"unit":"ucs-000379#insight-4","date_modified":"2026-10-23"},{"text":"In the area of planning for a carbon-free world, the UCS established a contingent scoring requirement: companies must have a positive score in both the overall planning section and the 'company-wide commitments and targets' subsection to be scored on the execution of those plans. The document notes that no companies met this standard for the 2018 update.","ai_generated":true,"unit":"ucs-000379#insight-5","date_modified":"2026-10-23"}],"climate_tags":["fossil fuel industry","climate change disinformation","greenhouse gas emissions","carbon price","Paris Agreement","Net-zero emissions"],"geography_discussed":["United States"],"frame_perspective":[],"analysis_type":["methodology"]},{"id":"clibonds-000653","url":"https://climateinsightsdirectory.org/document/clibonds-000653","title":"绿色债券数据库评估方法","publisher":{"name":"Climate Bonds Initiative","organization":"climate-bonds-initiative"},"document_type":"guide","language":"zh","date_published":"2018-09","source_url":"https://www.climatebonds.net/files/drupal-files/files/Climate%20Bonds%20Initiative_Green%20Bond%20Database%20Methodology_Chinese%20version_final(1).pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","analysis_type"],"summary":{"text":"The Climate Bonds Initiative (CBI) employs a three-step screening process to identify and validate green bonds and loans for its database. This involves identifying self-labeled green instruments, verifying alignment with the Climate Bonds Taxonomy across eight sectors, and ensuring at least 95% of proceeds are allocated to eligible green assets. The methodology explicitly excludes fossil fuel energy and projects that extend the life of fossil fuel assets. Instruments are categorized as included, pending, or excluded, with a mechanism for reclassification based on new data or updated scientific criteria.","ai_generated":true,"unit":"clibonds-000653#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The process for classifying a green bond as eligible consists of three steps: identifying the labeled green bond, screening the assets or projects for alignment with the Climate Bonds Taxonomy, and judging whether the proceeds are used for eligible projects or assets.","ai_generated":true,"unit":"clibonds-000653#insight-1","date_modified":"2026-10-23"},{"text":"The Climate Bonds Taxonomy covers eight categories: energy, transport, buildings, water management, waste management and pollution control, nature-based assets (including land use, agriculture, and forestry), industrial and energy-intensive businesses, and information and communication technology (ICT).","ai_generated":true,"unit":"clibonds-000653#insight-2","date_modified":"2026-10-23"}],"climate_tags":["climate bond","renewable energy","energy efficiency","sustainable transport","green building","water management","waste management","nature-based solutions","Industrial decarbonization","climate finance","fossil-fuel divestment","Paris Agreement"],"geography_discussed":[],"frame_perspective":[],"analysis_type":["methodology","screening criteria"]},{"id":"clibonds-000666","url":"https://climateinsightsdirectory.org/document/clibonds-000666","title":"GREEN BOND SCREENING METHODOLOGY","publisher":{"name":"Climate Bonds Initiative","organization":"climate-bonds-initiative"},"document_type":"guide","language":"en","date_published":"2018-09","source_url":"https://www.climatebonds.net/files/drupal-files/files/Climate-Bonds-Initiative_GreenBondMethodology_092018.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The Climate Bonds Initiative (CBI) employs a three-step screening process to identify eligible green bonds and loans for its database: identifying green-labelled bonds, screening assets against the Climate Bonds Taxonomy, and evaluating the allocation of proceeds. To be included, a bond must allocate at least 95% of its proceeds to aligned green assets; any bond where more than 5% of proceeds are used for working capital, social projects, or non-aligned assets is excluded. The methodology explicitly excludes fossil fuel energy and any assets that extend the life of fossil fuel usage, such as 'clean coal'. The Climate Bonds Taxonomy covers eight categories: energy, transport, buildings, water management, waste management & pollution control, nature-based assets, industry & energy-intensive commercial, and information technology & communications (ICT).","ai_generated":true,"unit":"clibonds-000666#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The CBI uses a three-step process to determine if a green bond is eligible for its database: identifying green-themed labelled bonds, screening projects or assets for alignment with the Climate Bonds Taxonomy, and evaluating the allocation of proceeds.","ai_generated":true,"unit":"clibonds-000666#insight-1","date_modified":"2026-10-23"},{"text":"A bond is only eligible for the CBI Green Bond Database if it is expected to allocate at least 95% of its proceeds to aligned green assets.","ai_generated":true,"unit":"clibonds-000666#insight-2","date_modified":"2026-10-23"},{"text":"Bonds are excluded if more than 5% of proceeds are allocated to working capital, social assets/projects, or assets not aligned with the Climate Bonds Taxonomy, or if there is insufficient information to determine alignment.","ai_generated":true,"unit":"clibonds-000666#insight-3","date_modified":"2026-10-23"},{"text":"The Climate Bonds Taxonomy explicitly excludes fossil fuel energy and any assets or processes that extend the operational life of fossil fuel assets, specifically mentioning that 'clean coal' is excluded.","ai_generated":true,"unit":"clibonds-000666#insight-4","date_modified":"2026-10-23"},{"text":"The Climate Bonds Taxonomy is organized into eight distinct categories: Energy, Transport, Buildings, Water management, Waste management & pollution control, Nature-based assets (including land use, agriculture & forestry), Industry & energy-intensive commercial, and Information technology & communications (ICT).","ai_generated":true,"unit":"clibonds-000666#insight-5","date_modified":"2026-10-23"},{"text":"Bonds that cannot be immediately classified due to insufficient information are marked as 'Pending' and investigated until the end of the following quarter; if no alignment is found, they are moved to the Excluded Bonds list.","ai_generated":true,"unit":"clibonds-000666#insight-6","date_modified":"2026-10-23"}],"climate_tags":["climate bond","climate finance","renewable energy","offshore wind power","solar energy","geothermal energy","bioenergy","hydroelectricity","energy efficiency","electric vehicle","water management","waste management","circular economy","carbon capture and storage","sustainable agriculture","Paris Agreement"],"geography_discussed":["Turkey","New Zealand","United States","Canada"],"frame_perspective":[],"analysis_type":["methodology","screening criteria"]},{"id":"ucs-000637","url":"https://climateinsightsdirectory.org/document/ucs-000637","title":"The Science Connecting Extreme Weather to Climate Change","publisher":{"name":"Union of Concerned Scientists","organization":"union-of-concerned-scientists"},"document_type":"report","language":"en","date_published":"2018-06","source_url":"https://www.ucs.org/sites/default/files/attach/2018/06/extreme-weather-Appendix-B3.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","analysis_type"],"summary":{"text":"The document outlines the quantitative scoring system used to translate qualitative likelihood and confidence levels from the Climate Science Special Report (CSSR) and the IPCC Fifth Assessment Report (AR5) into a visual guide for extreme weather attribution in the continental United States.","ai_generated":true,"unit":"ucs-000637#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The methodology converts qualitative likelihood and confidence statements from the CSSR and AR5 into numerical values ranging from 0 to 99 to enable comparisons. A value of 0 indicates \"little or no evidence to support a claim,\" while 99 represents a likelihood of \"virtually certain.\"","ai_generated":true,"unit":"ucs-000637#insight-1","date_modified":"2026-10-23"},{"text":"Specific numerical values were assigned to qualitative terms: 'high confidence' was given a value of 83, 'medium confidence' 50, and 'low confidence' 17. For likelihoods, 'about as likely as not' was assigned 50, while terms 'likely' and higher used lower bounds of their ranges, and 'unlikely' and lower used upper bounds.","ai_generated":true,"unit":"ucs-000637#insight-2","date_modified":"2026-10-23"},{"text":"The resulting figure categorizes extreme weather events based on a threshold of 50. Events with values of 50 or higher are presented as having \"at minimum strong support for a connection,\" while those below 50 are characterized as having \"weak or growing support.\"","ai_generated":true,"unit":"ucs-000637#insight-3","date_modified":"2026-10-23"},{"text":"The analysis prioritizes statements from the Climate Science Special Report (CSSR) over the IPCC's Fifth Assessment Report (AR5) because the CSSR contains more recent research.","ai_generated":true,"unit":"ucs-000637#insight-4","date_modified":"2026-10-23"}],"climate_tags":["extreme weather event","extreme event attribution","climate change","wildfire","hurricane"],"geography_discussed":[],"frame_perspective":[],"analysis_type":["methodology"]},{"id":"ucs-001220","url":"https://climateinsightsdirectory.org/document/ucs-001220","title":"TECHNICAL BACKGROUNDER Underwater Rising Seas, Chronic Floods, and the Implications for US Coastal Real Estate","publisher":{"name":"Union of Concerned Scientists","organization":"union-of-concerned-scientists"},"document_type":"briefing","language":"en","date_published":"2018-06","source_url":"https://www.ucs.org/sites/default/files/attach/2018/06/underwater-analysis-technical-backgrounder.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The Union of Concerned Scientists used a combination of NOAA elevation data, tide gauge records, and Zillow's ZTRAX database to identify coastal properties at risk of chronic inundation. The analysis defines chronic inundation as areas flooded at least 26 times per year and evaluates three sea-level-rise scenarios: high (6.6 feet), intermediate (4.0 feet), and low (1.6 feet) by the end of the century. Key caveats include the assumption of constant population and demographics, the exclusion of local coastal defenses like sea walls, and the lack of consideration for future adaptation measures.","ai_generated":true,"unit":"ucs-001220#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The analysis defines the zone of chronic inundation as any area that experiences flooding at least 26 times per year, which averages to once every other week.","ai_generated":true,"unit":"ucs-001220#insight-1","date_modified":"2026-10-23"},{"text":"The study utilizes three sea-level-rise scenarios for the end of the century: a high scenario projecting a global average rise of 6.6 feet (2.0 m), an intermediate scenario projecting 4.0 feet (1.2 m), and a low scenario projecting 1.6 feet (0.5 m).","ai_generated":true,"unit":"ucs-001220#insight-2","date_modified":"2026-10-23"},{"text":"The low sea-level-rise scenario is used as a proxy for the Paris Agreement, as its projected warming aligns with the goal of keeping temperatures below 3.6°F (2°C) above preindustrial levels.","ai_generated":true,"unit":"ucs-001220#insight-3","date_modified":"2026-10-23"},{"text":"To identify at-risk properties, the researchers intersected chronic inundation zones with the Zillow Transaction and Assessment Database (ZTRAX), which provides data on residential and commercial properties.","ai_generated":true,"unit":"ucs-001220#insight-4","date_modified":"2026-10-23"},{"text":"The analysis may be conservative because it assumes that population, demographics, and the number of properties remain constant at present-day levels, ignoring future population growth.","ai_generated":true,"unit":"ucs-001220#insight-5","date_modified":"2026-10-23"},{"text":"The findings may overestimate flooding in some areas because the elevation data does not capture local coastal defenses such as sea walls or changes to the land like the installation of bulkheads.","ai_generated":true,"unit":"ucs-001220#insight-6","date_modified":"2026-10-23"},{"text":"The analysis does not account for future adaptation measures that communities might implement, such as restoring wetlands or building flood-control structures.","ai_generated":true,"unit":"ucs-001220#insight-7","date_modified":"2026-10-23"}],"climate_tags":["sea level rise","flood risk","Paris Agreement","climate change adaptation"],"geography_discussed":["Annapolis","Charleston","Broad Channel","San Francisco Bay Area"],"frame_perspective":[],"analysis_type":["methodology","risk assessment"]},{"id":"clibonds-000965","url":"https://climateinsightsdirectory.org/document/clibonds-000965","title":"GUIDANCE NOTE TO ISSUERS AND VERIFIERS: SUPPLEMENTARY NOTE TO THE WATER INFRASTRUCTURE CRITERIA","publisher":{"name":"Climate Bonds Initiative","organization":"climate-bonds-initiative"},"document_type":"guide","language":"en","date_published":"2018-04","source_url":"https://www.climatebonds.net/files/drupal-files/files/Climate%20Bonds-Water%20Infrastructure%20Criteria-Supplementary%20Guidance%20Note%20April%202018.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The guidance note outlines the processes for evaluating the climate impact of water infrastructure bonds. It details a Mitigation Assessment to quantify greenhouse gas (GHG) emissions reductions against baselines and an Adaptation & Resilience Assessment based on a binary scorecard. The latter evaluates an issuer's Vulnerability Assessment and Adaptation Plan across four key areas: water allocation, governance, diagnostic technical assessments (including hydrologic modeling), and the robustness of adaptation strategies. The document emphasizes the use of science-based methodologies, such as the WRI/WBCSD GHG Protocol and IPCC climate projections, and provides extensive examples from the San Francisco Public Utilities Commission to illustrate compliance.","ai_generated":true,"unit":"clibonds-000965#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The Mitigation Assessment requires issuers to estimate the GHG mitigation impact over the project's operational lifetime, defined as decreased emissions or increased sequestration relative to a 'business as usual' baseline.","ai_generated":true,"unit":"clibonds-000965#insight-1","date_modified":"2026-10-23"},{"text":"For mitigation, the document strongly recommends using methodologies compliant with the World Resources Institute and World Business Council on Sustainable Development’s Greenhouse Gas Protocol.","ai_generated":true,"unit":"clibonds-000965#insight-2","date_modified":"2026-10-23"},{"text":"The Adaptation and Resilience Assessment uses a scorecard methodology consisting of binary questions to evaluate the issuer's Vulnerability Assessment and Adaptation Plan.","ai_generated":true,"unit":"clibonds-000965#insight-3","date_modified":"2026-10-23"},{"text":"The Adaptation scorecard is divided into four parts: Allocation (water sharing within a basin), Governance (formal sharing and regulation), Diagnostic Assessment (hydrologic system changes), and the Adaptation Plan itself.","ai_generated":true,"unit":"clibonds-000965#insight-4","date_modified":"2026-10-23"},{"text":"A high-scoring Diagnostic Assessment requires the use of a credible hydrologic model at least at the sub-basin scale to simulate the impact of projects under various climate scenarios.","ai_generated":true,"unit":"clibonds-000965#insight-5","date_modified":"2026-10-23"},{"text":"Robust diagnostic models should incorporate multi-modal ensemble climate projections, such as those from the IPCC, to assess risks over the investment horizon, which may exceed 50 years.","ai_generated":true,"unit":"clibonds-000965#insight-6","date_modified":"2026-10-23"},{"text":"The guidance specifies that an Adaptation Plan is mandatory if the Vulnerability Assessment shows that climate change or sea level rise has already impacted, or will impact, operations and environmental targets.","ai_generated":true,"unit":"clibonds-000965#insight-7","date_modified":"2026-10-23"},{"text":"Effective water governance for climate resilience includes having a formal process for re-evaluating decadal trends in precipitation and flow to update 'normal' baseline conditions.","ai_generated":true,"unit":"clibonds-000965#insight-8","date_modified":"2026-10-23"}],"climate_tags":["water distribution system","carbon accounting","climate change adaptation","climate resilience","vulnerability assessment","water-management","sea level rise","climate bond"],"geography_discussed":["San Francisco","California","United States","Australia","United Kingdom","Canada","Danube"],"frame_perspective":[],"analysis_type":["guidance","methodology"]},{"id":"iisd-002507","url":"https://climateinsightsdirectory.org/document/iisd-002507","title":"Outil de planification pour l’adaptation, les moyens de subsistance et les écosystèmes","publisher":{"name":"International Institute for Sustainable Development","organization":"international-institute-for-sustainable-development"},"document_type":"guide","language":"fr","date_published":"2018","source_url":"https://www.iisd.org/system/files/publications/alive-tool-manual-french.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The ALivE tool guides users through a three-phase process: collecting data on livelihoods and ecosystems, analyzing risks and prioritizing EbA options, and integrating these options into policy and planning. It emphasizes a participatory approach, gender sensitivity, and adaptive management to ensure sustainable climate resilience.","ai_generated":true,"unit":"iisd-002507#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The ALivE tool is structured into three modules and five progressive steps: understanding the context, analyzing risks for ecosystems and livelihoods, identifying and prioritizing EbA options, designing project activities, and identifying monitoring and evaluation (M&E) elements.","ai_generated":true,"unit":"iisd-002507#insight-1","date_modified":"2026-10-23"},{"text":"Ecosystem-based adaptation (EbA) is defined as the use of biodiversity and ecosystem services to help people adapt to the adverse effects of climate change, involving sustainable management, conservation, and restoration of ecosystems.","ai_generated":true,"unit":"iisd-002507#insight-2","date_modified":"2026-10-23"},{"text":"Effective EbA requires the integration of climate information, a participatory community-led approach, gender considerations, adaptive management principles, and multi-level governance.","ai_generated":true,"unit":"iisd-002507#insight-3","date_modified":"2026-10-23"},{"text":"The tool employs a multi-criteria analysis to prioritize EbA options based on five effectiveness criteria: risk reduction potential, adaptive capacity improvement, benefits for vulnerable groups/gender equality, sustainable use of biodiversity, and ecosystem resilience.","ai_generated":true,"unit":"iisd-002507#insight-4","date_modified":"2026-10-23"},{"text":"EbA options are further filtered by feasibility using at least four criteria from a default list, which includes affordable cost, technical feasibility, political feasibility, maintenance cost, monitorability, flexibility, number of beneficiaries, and cultural appropriateness.","ai_generated":true,"unit":"iisd-002507#insight-5","date_modified":"2026-10-23"},{"text":"The manual identifies specific EbA options across different ecosystems, such as mangrove restoration for coastal protection, agroforestry to reduce crop damage from extreme temperatures, and sustainable grassland management for pastoralist resilience.","ai_generated":true,"unit":"iisd-002507#insight-6","date_modified":"2026-10-23"},{"text":"To ensure long-term sustainability and scaling, EbA must be integrated into existing policies and planning processes, such as National Adaptation Plans (NAPs), which provide strategic opportunities for funding and institutionalization.","ai_generated":true,"unit":"iisd-002507#insight-7","date_modified":"2026-10-23"},{"text":"The tool advocates for adaptive management, an iterative process of planning, implementing, monitoring, and adjusting actions based on observed impacts to reduce uncertainty associated with climate variability.","ai_generated":true,"unit":"iisd-002507#insight-8","date_modified":"2026-10-23"}],"climate_tags":["ecosystem-based adaptation","climate resilience","biodiversity","ecosystem service","Climate risk assessment","national adaptation plan","Community based adaptation","nature-based solutions","Mangrove restoration","agroforestry","climate finance"],"geography_discussed":["People's Republic of China","Canada"],"frame_perspective":[],"analysis_type":["guide","methodology","tool manual"]},{"id":"iisd-002508","url":"https://climateinsightsdirectory.org/document/iisd-002508","title":"Adaptation, Livelihoods and Ecosystems Planning Tool: User Manual","publisher":{"name":"International Institute for Sustainable Development","organization":"international-institute-for-sustainable-development"},"document_type":"guide","language":"en","date_published":"2018","source_url":"https://www.iisd.org/system/files/publications/alive-tool-manual-full.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The ALivE tool is a decision-support system that guides users through a three-phase process to build climate resilience. Phase 1 involves gathering data via secondary research and participatory methods. Phase 2 uses the tool to analyse risks, identify and prioritise EbA options based on effectiveness and feasibility, and design project activities. Phase 3 focuses on integrating these options into broader policies and planning processes to ensure sustainability and scale.","ai_generated":true,"unit":"iisd-002508#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The ALivE tool is structured into three modules and five steps: Module A focuses on understanding the context and analysing risks to ecosystems and livelihoods; Module B identifies and prioritises EbA options and designs project activities; and Module C establishes a monitoring and evaluation (M&E) framework.","ai_generated":true,"unit":"iisd-002508#insight-1","date_modified":"2026-10-23"},{"text":"Ecosystem-based Adaptation (EbA) is defined as the use of biodiversity and ecosystem services to help people adapt to climate change, involving the restoration, conservation, and sustainable management of ecosystems to support both human livelihoods and ecosystem health.","ai_generated":true,"unit":"iisd-002508#insight-2","date_modified":"2026-10-23"},{"text":"Effective EbA must be gender-sensitive, recognising that men and women experience climate impacts differently and may have different capacities to adapt, which requires responsive planning regarding decision-making and resource access.","ai_generated":true,"unit":"iisd-002508#insight-3","date_modified":"2026-10-23"},{"text":"The tool employs a multi-criteria analysis to prioritise EbA options based on five effectiveness criteria: risk reduction potential, improvement of adaptive capacity, benefits for vulnerable groups/gender equality, sustainable use of biodiversity, and the building of ecosystem resilience.","ai_generated":true,"unit":"iisd-002508#insight-4","date_modified":"2026-10-23"},{"text":"Feasibility of prioritised EbA options is assessed using at least four criteria selected from a default list including affordability, technical feasibility, political feasibility, maintenance costs, monitorability, flexibility, number of beneficiaries, and cultural appropriateness.","ai_generated":true,"unit":"iisd-002508#insight-5","date_modified":"2026-10-23"},{"text":"The manual emphasises adaptive management as an iterative process of 'learning by doing,' where adaptation actions are followed by long-term monitoring to refine strategies based on new information and uncertainties.","ai_generated":true,"unit":"iisd-002508#insight-6","date_modified":"2026-10-23"},{"text":"To achieve scale and sustainability, EbA should be integrated into existing policies and planning processes, such as National Adaptation Plans (NAPs), which provide strategic opportunities for institutionalisation and financing.","ai_generated":true,"unit":"iisd-002508#insight-7","date_modified":"2026-10-23"}],"climate_tags":["ecosystem-based adaptation","climate change adaptation","ecosystem service","Climate risk assessment","adaptive capacity","vulnerability assessment","national adaptation plan","biodiversity conservation","climate finance"],"geography_discussed":["People's Republic of China","Canada","United States"],"frame_perspective":[],"analysis_type":["guide","methodology"]},{"id":"iisd-002509","url":"https://climateinsightsdirectory.org/document/iisd-002509","title":"cg's\"ng, hLljsf]kfh{g / kfl/l:yltsLo k|0ffnL of]hgf ;fdfu|L M k|of]ustf{sf nflu lgb]{zLsf","publisher":{"name":"International Institute for Sustainable Development","organization":"international-institute-for-sustainable-development"},"document_type":"guide","language":"da","date_published":"2018","source_url":"https://www.iisd.org/system/files/publications/alive-tool-manual-nepali.pdf","generator":"Climate Insights Directory","generated_fields":["summary","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The guide introduces the ALivE tool, which uses a step-by-step process to analyze the relationship between ecosystems, livelihoods, and climate change. It defines Ecosystem-based Adaptation (EbA) as the use of biodiversity and ecosystem services as part of an adaptation strategy to help people adapt to the adverse effects of climate change. The tool guides users through five modules: understanding the context, analyzing vulnerabilities, identifying EbA options, planning implementation, and establishing monitoring and evaluation (M&E) frameworks. It emphasizes participatory approaches, gender sensitivity, and the integration of EbA into national policies and National Adaptation Plans (NAPs).","ai_generated":true,"unit":"iisd-002509#summary","date_modified":"2026-10-23"},"key_insights":[],"climate_tags":["ecosystem-based adaptation","climate change adaptation","ecosystem service","biodiversity","national adaptation plan","community resilience","nature-based solutions","climate finance","vulnerability assessment"],"geography_discussed":["Canada","Costa Rica","Switzerland","United Kingdom","United States"],"frame_perspective":[],"analysis_type":["guide","methodology","framework"]},{"id":"rmi-000974","url":"https://climateinsightsdirectory.org/document/rmi-000974","title":"SUNSHINE FOR MINES: A SECOND LIFE FOR LEGACY MINING SITES","publisher":{"name":"RMI","organization":"rmi"},"document_type":"briefing","language":"en","date_published":"2017-11","source_url":"https://rmi.org/app/uploads/2017/11/RMI_SecondLifeLegacyMiningSites.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"RMI's Sunshine for Mines program proposes a systematic approach to repurposing legacy mine sites, which are often liabilities due to contamination and maintenance costs, into productive energy assets. The methodology involves a multi-stage screening process covering environmental and regulatory constraints, technical feasibility (resource screening and reserve quantification), and financial viability via Net Present Value (NPV) calculations. The brief highlights that while mining companies often face high hurdle rates, the economic case is strengthened when compared to the 'business-as-usual' cost of maintaining closed sites. A practical application of this model with BHP in North America identified a subset of sites with a collective potential exceeding 0.5 GW, primarily for solar PV.","ai_generated":true,"unit":"rmi-000974#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"Legacy mine sites often represent financial liabilities for companies due to the high costs of long-term care, maintenance, and regulatory compliance, with medium-sized closed sites potentially requiring 5–10 staff members and costing several million dollars annually to maintain.","ai_generated":true,"unit":"rmi-000974#insight-1","date_modified":"2026-10-23"},{"text":"RMI has developed a screening methodology to prioritize legacy sites for renewable development, which includes an environmental, permitting, and regulatory scorecard to ensure development is favorable before technical assessments begin.","ai_generated":true,"unit":"rmi-000974#insight-2","date_modified":"2026-10-23"},{"text":"Technical feasibility is determined through resource screening—including site characterization and levelized cost of electricity (LCOE) calculations—and reserve quantification, which uses 30 percent design estimates for solar and 10-year numerical simulations for wind.","ai_generated":true,"unit":"rmi-000974#insight-3","date_modified":"2026-10-23"},{"text":"The economic viability of a project is assessed using Net Present Value (NPV), which considers capital and operating costs against revenue from electricity sales, while comparing the internal rate of return (IRR) against the company's hurdle rate.","ai_generated":true,"unit":"rmi-000974#insight-4","date_modified":"2026-10-23"},{"text":"Interconnection costs are a critical factor in the economic case for development, with estimated costs ranging between $300,000 and $500,000 per mile for sites located far from the nearest grid substation.","ai_generated":true,"unit":"rmi-000974#insight-5","date_modified":"2026-10-23"},{"text":"Beyond solar and wind, the methodology screens for alternative generation such as geothermal and biomass, as well as storage technologies including pumped hydro, Li-Ion batteries, and flywheels.","ai_generated":true,"unit":"rmi-000974#insight-6","date_modified":"2026-10-23"},{"text":"An application of this methodology to BHP's North American portfolio identified a subset of legacy sites with a collective renewable energy potential of over 0.5 GW, with solar PV being the primary opportunity for most sites.","ai_generated":true,"unit":"rmi-000974#insight-7","date_modified":"2026-10-23"}],"climate_tags":["photovoltaics","wind power","energy storage","geothermal energy","biomass","greenhouse gas emissions","land use","wide area synchronous grid"],"geography_discussed":["North America","California"],"frame_perspective":[],"analysis_type":["methodology","case study"]},{"id":"iisd-000826","url":"https://climateinsightsdirectory.org/document/iisd-000826","title":"Practice Guide to Auditing the United Nations Sustainable Development Goals: Gender equality","publisher":{"name":"International Institute for Sustainable Development","organization":"international-institute-for-sustainable-development"},"document_type":"guide","language":"en","date_published":"2017-09-11","source_url":"https://www.iisd.org/publications/guide/practice-guide-auditing-united-nations-sustainable-development-goals-gender","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The document introduces a practice guide developed by the Canadian Audit and Accountability Foundation, in collaboration with IISD and Women Deliver, to assist auditors in conducting performance audits related to gender equality and the United Nations Sustainable Development Goals.","ai_generated":true,"unit":"iisd-000826#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The guide represents the first methodology from the Canadian Audit and Accountability Foundation specifically focused on the United Nations Sustainable Development Goals.","ai_generated":true,"unit":"iisd-000826#insight-1","date_modified":"2026-10-23"},{"text":"The primary objective of the guide is to support auditors in the planning of performance audits that address gender equality as part of the UN 2030 Agenda for Sustainable Development.","ai_generated":true,"unit":"iisd-000826#insight-2","date_modified":"2026-10-23"}],"climate_tags":["gender equality","Sustainable Development Goals"],"geography_discussed":["Canada"],"frame_perspective":[],"analysis_type":["methodology"]},{"id":"client-000750","url":"https://climateinsightsdirectory.org/document/client-000750","title":"Brussels measurement campaign methodology","publisher":{"name":"ClientEarth","organization":"clientearth"},"document_type":"briefing","language":"en","date_published":"2017-06","source_url":"https://www.clientearth.org/media/s1am2jco/methodology-brief-for-brussels-air-quality-monitoring-2017-ce-en.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","analysis_type"],"summary":{"text":"The document describes the use of Palmes-type NO2 diffusion tubes to monitor air quality across several Brussels locations from 19 February to 20 March 2017. It details the placement of tubes according to Directive 2008/50/EC, the laboratory analysis by Gradko International Limited, and a validation process at the Avenue de la Couronne station which revealed a 24% under-estimation of concentrations by the diffusion tubes. ClientEarth notes it adopted a conservative approach by using the lowest recorded concentrations for locations with multiple samples and publishing raw data without applying the bias correction factor.","ai_generated":true,"unit":"client-000750#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The measurement campaign utilized Palmes-type nitrogen dioxide (NO2) diffusion tubes, which the European Environment Agency categorizes as an \"indicative\" monitoring technique with a margin of uncertainty of ± 25%.","ai_generated":true,"unit":"client-000750#insight-1","date_modified":"2026-10-23"},{"text":"Monitoring took place over four weeks between 19 February and 20 March 2017, with sampling points located at Rue Belliard, Rue de la Loi, Arts-Loi, Avenue des Arts, the Esplanade of the European Parliament, and Avenue de la Couronne.","ai_generated":true,"unit":"client-000750#insight-2","date_modified":"2026-10-23"},{"text":"Sampling points were sited according to Directive 2008/50/EC, specifically placed 2.5 m above ground, within 10 m of the kerbside, and at least 25 m from the edge of major junctions.","ai_generated":true,"unit":"client-000750#insight-3","date_modified":"2026-10-23"},{"text":"Validation at the Avenue de la Couronne official monitoring station showed that the diffusion tubes under-estimated actual NO2 concentrations by 24%, as the tubes recorded between 41.6 and 43.1 ±g/m3 while the official station recorded 52.5 ±g/m3.","ai_generated":true,"unit":"client-000750#insight-4","date_modified":"2026-10-23"}],"climate_tags":[],"geography_discussed":[],"frame_perspective":[],"analysis_type":["methodology","validation"]},{"id":"cdkn-000245","url":"https://climateinsightsdirectory.org/document/cdkn-000245","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":"2017-05-31","source_url":"https://cdkn.org/resource/report-champion-csa-screening-methodology","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"This report details the methodology for selecting 'champion' Climate-Smart Agriculture (CSA) options in Nepal, focusing on a screening process that combines expert judgment with farmer feedback to identify 17 champion CSAs or packages across different agro-ecological regions.","ai_generated":true,"unit":"cdkn-000245#summary","date_modified":"2026-08-17"},"key_insights":[{"text":"The selection of champion CSAs follows a four-step process: selecting potential CSAs for evaluation, documenting evidence, consolidating results, and validating and finalizing the champion CSAs. Evaluation relies on a 'CSA screening process' involving experts who assess CSA qualification and farmers who assess feasibility and suitability for local climatic vulnerability. Evidence is gathered via on-farm experimentation, piloting data, stakeholder opinions, key informant and household surveys, and the analytical hierarchy process (AHP) to determine farmer preferences.","ai_generated":true,"unit":"cdkn-000245#insight-1","date_modified":"2026-08-17"},{"text":"The methodology resulted in a final list of 17 champion CSAs or CSA packages for Nepal. These are distributed across agro-ecological regions: nine for the high-hill region, 12 for the mid-hill region, and 10 for the Terai region, with six CSAs identified as champions for all three regions.","ai_generated":true,"unit":"cdkn-000245#insight-2","date_modified":"2026-08-17"},{"text":"Analysis of farmer preferences indicates that food security is given overwhelmingly high importance regardless of gender or location, whereas gender equity and social inclusion (GESI) dimensions are given low importance when selecting CSA options.","ai_generated":true,"unit":"cdkn-000245#insight-3","date_modified":"2026-08-17"}],"climate_tags":["climate-smart agriculture","food security","agro-ecological regions"],"geography_discussed":["Madhesh"],"frame_perspective":[],"analysis_type":["Methodology","Evaluation"]},{"id":"clipolin-001121","url":"https://climateinsightsdirectory.org/document/clipolin-001121","title":"The Global Landscape of Climate Finance 2017: Methodology","publisher":{"name":"Climate Policy Initiative","organization":"climate-policy-initiative"},"document_type":"report","language":"en","date_published":"2017","source_url":"https://www.climatepolicyinitiative.org/wp-content/uploads/2017/10/GLCF-2017-Methodology-Document.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","analysis_type"],"summary":{"text":"The document details the methodology for the Global Landscape of Climate Finance 2017, defining climate finance as capital flows for low-carbon and climate-resilient development. It specifies the inclusion of mitigation, adaptation, and dual-benefit finance while excluding fossil fuel-based lower-carbon generation and private R&D. The accounting scope focuses on annual financial commitments, covering grants, loans, equity, and balance sheet financing, while excluding risk management instruments and secondary market transactions to avoid double counting. It further defines the classification of public and private finance, the data sources used (such as BNEF and OECD), and the sectoral breakdowns for mitigation and adaptation activities.","ai_generated":true,"unit":"clipolin-001121#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The methodology defines climate finance as capital flows directed toward low-carbon and climate-resilient development interventions that provide direct or indirect greenhouse gas mitigation or adaptation benefits, including support for policy implementation and capacity building.","ai_generated":true,"unit":"clipolin-001121#insight-1","date_modified":"2026-10-23"},{"text":"Mitigation finance is defined as resources for reducing or avoiding greenhouse gas emissions or enhancing GHG sinks, but specifically excludes private R&D in technology, manufacturing of green technologies, and fossil fuel-based lower-carbon generation to prevent double counting and carbon lock-in.","ai_generated":true,"unit":"clipolin-001121#insight-2","date_modified":"2026-10-23"},{"text":"Adaptation finance is defined as resources aimed at reducing the vulnerability of human or natural systems to climate change impacts by increasing resilience and adaptive capacity.","ai_generated":true,"unit":"clipolin-001121#insight-3","date_modified":"2026-10-23"},{"text":"The accounting scope tracks annual financial commitments rather than disbursements, including grants, low-cost and market term loans, project-level equity, and balance sheet financing, while excluding risk management instruments and secondary market transactions.","ai_generated":true,"unit":"clipolin-001121#insight-4","date_modified":"2026-10-23"},{"text":"Private climate finance is analyzed through large-scale renewable energy projects (over 246 GW capacity across 81 countries), small-scale renewable energy, and solar heating systems, treating state-owned enterprises as private entities due to data limitations.","ai_generated":true,"unit":"clipolin-001121#insight-5","date_modified":"2026-10-23"},{"text":"Public climate finance includes commitments from Development Finance Institutions (DFIs), bilateral climate-related development finance reported to the OECD-DAC, and national and multilateral Climate Funds.","ai_generated":true,"unit":"clipolin-001121#insight-6","date_modified":"2026-10-23"}],"climate_tags":["climate finance","climate change adaptation","renewable energy","energy efficiency","sustainable transport","afforestation","reforestation","carbon capture and storage","disaster risk reduction","coastal management","public finance","private investment"],"geography_discussed":[],"frame_perspective":[],"analysis_type":["methodology"]},{"id":"ucs-000344","url":"https://climateinsightsdirectory.org/document/ucs-000344","title":"UCS State Green Banks Analysis","publisher":{"name":"Union of Concerned Scientists","organization":"union-of-concerned-scientists"},"document_type":"research paper","language":"en","date_published":"2016-12","source_url":"https://www.ucsusa.org/sites/default/files/attach/2016/12/clean-energy-finance-methodology.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The document details a spreadsheet model designed to quantify the effects of hypothetical green banks in Maine, New Hampshire, and Vermont from 2017 to 2031. It outlines the financial assumptions, including initial capitalization based on New York's per-capita rates, a 5% borrower interest rate, and a 5:1 private-sector leverage ratio. The methodology explains how funds are allocated across energy efficiency, solar photovoltaics, and community wind, and how these investments are translated into installed capacity, electricity savings, and avoided carbon emissions using data from NREL, ACEEE, and the EPA.","ai_generated":true,"unit":"ucs-000344#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The model assumes initial capitalization for hypothetical green banks based on the New York Green Bank's per-capita rate of $10.65, resulting in $14 million for Maine, $14 million for New Hampshire, and $7 million for Vermont.","ai_generated":true,"unit":"ucs-000344#insight-1","date_modified":"2026-10-23"},{"text":"Financial assumptions for the green bank loans include a 5 percent interest rate for borrowers and a leverage ratio where every one green bank dollar attracts five private-sector dollars.","ai_generated":true,"unit":"ucs-000344#insight-2","date_modified":"2026-10-23"},{"text":"Loan terms are differentiated by project type, with a seven-year term for energy efficiency projects and a 10-year term for renewable energy projects.","ai_generated":true,"unit":"ucs-000344#insight-3","date_modified":"2026-10-23"},{"text":"Fund allocation varies by state: Vermont's model assumes 40% for energy efficiency, 50% for solar photovoltaics, and 10% for community wind; Maine and New Hampshire assume 50% for energy efficiency, 40% for solar, and 10% for community wind.","ai_generated":true,"unit":"ucs-000344#insight-4","date_modified":"2026-10-23"},{"text":"The analysis period spans 15 years, from 2017 to 2031, and incorporates the December 2015 five-year extension of federal tax credits for wind and solar.","ai_generated":true,"unit":"ucs-000344#insight-5","date_modified":"2026-10-23"},{"text":"To calculate avoided carbon emissions, the model uses EPA Clean Power Plan average emissions rates of 873 lbs/MWh for Maine and 1,119 lbs/MWh for New Hampshire, while using a 2014 regional rate of 1,107 lbs/MWh for Vermont.","ai_generated":true,"unit":"ucs-000344#insight-6","date_modified":"2026-10-23"}],"climate_tags":["Green bank","transition finance","energy efficiency","photovoltaics","offshore wind power","greenhouse gas emissions reduction"],"geography_discussed":["New York","Connecticut","Michigan","Pennsylvania","Virginia"],"frame_perspective":[],"analysis_type":["quantitative modeling","methodology"]}]}