{"query_echo":{"q":"","filters":{"analysis_type":["methodological"]},"warnings":[]},"total":{"value":24,"relation":"eq"},"page":2,"page_size":20,"self":"https://climateinsightsdirectory.org/api/v1/search?analysis_type=methodological&page=2","stability":"beta","ai_marking":{"digital_source_type":{"uri":"http://cv.iptc.org/newscodes/digitalsourcetype/trainedAlgorithmicMedia","name":"Created using Generative AI"},"notice":"Summary and key insights are generated using artificial intelligence."},"results":[{"id":"wri-000056","url":"https://climateinsightsdirectory.org/document/wri-000056","title":"Calculating Indicators From Global Geospatial Data Sets for Benchmarking and Tracking Change in the Urban Environment","publisher":{"name":"World Resources Institute","organization":"world-resources-institute"},"document_type":"briefing","language":"en","date_published":null,"source_url":"https://www.wri.org/research/calculating-indicators-global-geospatial-datasets-urban-environment","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","analysis_type"],"summary":{"text":"The document outlines a methodology for calculating urban indicators using open-source geospatial data and zonal statistics, enabling cities to benchmark their performance against global goals like the Sustainable Development Goals.","ai_generated":true,"unit":"wri-000056#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The methodology utilizes geospatial analysis and zonal statistics applied to published geospatial datasets and city boundaries to create comparable indicators for any urban area on Earth, primarily relying on open-source data to ensure low-cost replication and scaling.","ai_generated":true,"unit":"wri-000056#insight-1","date_modified":"2026-10-23"},{"text":"The indicators are designed to help urban policymakers and civil society perform internal city assessments, compare themselves with other cities, and measure progress against self-defined metrics or global benchmarks, specifically the Sustainable Development Goals.","ai_generated":true,"unit":"wri-000056#insight-2","date_modified":"2026-10-23"},{"text":"The indicators cover several urban environment themes, including biodiversity, flooding, heat, air quality, access to urban amenities, land protection and restoration, and climate change mitigation.","ai_generated":true,"unit":"wri-000056#insight-3","date_modified":"2026-10-23"}],"climate_tags":["climate change mitigation","air quality","biodiversity","flood","urban heat island","Sustainable Development Goals"],"geography_discussed":[],"frame_perspective":[],"analysis_type":["methodological"]},{"id":"wri-000466","url":"https://climateinsightsdirectory.org/document/wri-000466","title":"Spatial Database of Planted Trees (SDPT Version 2.0)","publisher":{"name":"World Resources Institute","organization":"world-resources-institute"},"document_type":"research paper","language":"en","date_published":null,"source_url":"https://www.wri.org/research/spatial-database-planted-trees-sdpt-version-2","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The Spatial Database of Planted Trees (SDPT) version 2.0 provides a global spatial differentiation between planted forests, tree crops, and natural forests. In 2020, the database identified approximately 264 million hectares of planted forests (6.5% of total tree cover) and 65 million hectares of tree crops. The update includes new carbon removal factors ranging from 0.43 to 15 t C/ha/yr for planted forests and 0.43 to 11 t C/ha/yr for tree crops, and incorporates additional data for Brazil, Cambodia, Cote d’Ivoire, and Ghana in version 2.1.","ai_generated":true,"unit":"wri-000466#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"In 2020, there were approximately 264 million hectares of planted forests globally, representing 6.5 percent of total tree cover, alongside 65 million hectares of tree crops.","ai_generated":true,"unit":"wri-000466#insight-1","date_modified":"2026-10-23"},{"text":"The SDPT v2.0 captures 90 percent of the total global planted forest area for 2020 as reported by the FAO.","ai_generated":true,"unit":"wri-000466#insight-2","date_modified":"2026-10-23"},{"text":"Updated carbon removal factors for planted forests are estimated between 0.43 and 15 metric tonnes of aboveground carbon per hectare per year, while tree crops range from 0.43 to 11 metric tonnes per hectare per year.","ai_generated":true,"unit":"wri-000466#insight-3","date_modified":"2026-10-23"}],"climate_tags":["carbon sequestration","illegal logging"],"geography_discussed":["Brazil","Cambodia","Ivory Coast","Ghana"],"frame_perspective":[],"analysis_type":["methodological","quantitative"]},{"id":"wri-000742","url":"https://climateinsightsdirectory.org/document/wri-000742","title":"Technical Note for a Dataset Modeling the Societal Health and Climate Benefits Associated With Transitioning the US School Bus Fleet From Diesel to Electric","publisher":{"name":"World Resources Institute","organization":"world-resources-institute"},"document_type":"research paper","language":"en","date_published":null,"source_url":"https://www.wri.org/research/technical-note-dataset-modeling-societal-health-and-climate-benefits-electric-school-buses","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","analysis_type"],"summary":{"text":"The document outlines a research effort to model the health and climate impacts of replacing aging diesel school buses with new diesel or electric alternatives across the contiguous United States. It details a dataset that provides monetized unit and total impacts for each county, focusing on the reduction of harmful pollutants and greenhouse gas emissions.","ai_generated":true,"unit":"wri-000742#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"Approximately 90 percent of the roughly 500,000 school buses in the United States utilize diesel, which emits particulate matter, nitrogen oxides, and volatile organic compounds that can cause cognitive and respiratory impairment.","ai_generated":true,"unit":"wri-000742#insight-1","date_modified":"2026-10-23"},{"text":"Electric school buses are identified as having the lowest greenhouse gas emissions of any school bus category and produce zero tailpipe emissions, even when the electricity generation process is considered.","ai_generated":true,"unit":"wri-000742#insight-2","date_modified":"2026-10-23"}],"climate_tags":["greenhouse gas emissions","air pollution","public health","vehicle electrification"],"geography_discussed":[],"frame_perspective":[],"analysis_type":["methodological","quantitative"]},{"id":"wri-001092","url":"https://climateinsightsdirectory.org/document/wri-001092","title":"Estimating Future Local Climate Hazard Probabilities","publisher":{"name":"World Resources Institute","organization":"world-resources-institute"},"document_type":"research paper","language":"en","date_published":null,"source_url":"https://www.wri.org/research/estimating-future-local-climate-hazard-probabilities","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","analysis_type"],"summary":{"text":"The document outlines a method for generating probability models of complex climate hazard indicators by selecting and calibrating downscaled global climate models. These local estimates are intended to support long-term planning for government, the private sector, and civil society across various domains including agriculture and disaster mitigation.","ai_generated":true,"unit":"wri-001092#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The described method involves the selection and calibration of downscaled global climate models to create probability models for complex climate hazard indicators.","ai_generated":true,"unit":"wri-001092#insight-1","date_modified":"2026-10-23"},{"text":"Local estimates of future climate event frequencies are identified as beneficial for long-term planning and decision-making within civil society, the private sector, and government.","ai_generated":true,"unit":"wri-001092#insight-2","date_modified":"2026-10-23"},{"text":"The resulting probability distributions are designed to inform decision-making in specific fields, including risk management, agriculture, infrastructure planning, public health, and climate adaptation.","ai_generated":true,"unit":"wri-001092#insight-3","date_modified":"2026-10-23"}],"climate_tags":["climate change adaptation","public health","agriculture","risk management"],"geography_discussed":[],"frame_perspective":[],"analysis_type":["methodological"]}]}