{"query_echo":{"q":"","filters":{"analysis_type":["methodological summary"]},"warnings":[{"code":"facet_value_not_in_vocabulary","message":"Value 'methodological summary' for facet 'analysis_type' is not in the controlled vocabulary."}]},"total":{"value":3,"relation":"eq"},"page":1,"page_size":20,"self":"https://climateinsightsdirectory.org/api/v1/search?analysis_type=methodological+summary","stability":"beta","ai_marking":{"digital_source_type":{"uri":"http://cv.iptc.org/newscodes/digitalsourcetype/trainedAlgorithmicMedia","name":"Created using Generative AI"},"notice":"Summary and key insights are generated using artificial intelligence."},"results":[{"id":"sei-001863","url":"https://climateinsightsdirectory.org/document/sei-001863","title":"Consumption Compass 2.0: a summary of methods and data used","publisher":{"name":"Stockholm Environment Institute","organization":"stockholm-environment-institute"},"document_type":"research paper","language":"en","date_published":"2025-04","source_url":"https://www.sei.org/wp-content/uploads/2025/05/sei-working-paper-axelsson-methods-consumption-compass-may-2025.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","analysis_type"],"summary":{"text":"The Consumption Compass 2.0 uses a hybrid scaling approach to downscale national consumption-based emissions data to Demographic Statistical Areas (DeSO) in Sweden. It integrates diverse datasets, including Mosaic lifestyle profiles, household budget surveys, and energy consumption data, to provide local-level insights. The tool also includes a scenario function for municipalities to model the impact of consumption shifts in transport, energy, food, and goods through 2050, accounting for rebound emissions.","ai_generated":true,"unit":"sei-001863#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The Consumption Compass 2.0 employs a hybrid scaling approach to estimate emissions at the local level, combining bottom-up data collection with top-down validation to ensure local sums align with national totals. This process utilizes Demographic Statistical Areas (DeSO), which divide Sweden into 5,984 geographical areas.","ai_generated":true,"unit":"sei-001863#insight-1","date_modified":"2026-10-23"}],"climate_tags":["carbon accounting","carbon footprint","sustainable consumption","climate change mitigation"],"geography_discussed":[],"frame_perspective":[],"analysis_type":["methodological summary","data analysis"]},{"id":"sei-001472","url":"https://climateinsightsdirectory.org/document/sei-001472","title":"Consumption Compass: a summary of the methods used","publisher":{"name":"Stockholm Environment Institute","organization":"stockholm-environment-institute"},"document_type":"report","language":"en","date_published":"2022-04","source_url":"https://www.sei.org/wp-content/uploads/2022/04/method-summary.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","analysis_type"],"summary":{"text":"The Consumption Compass is an Excel-based tool launched in April 2022 that provides CO2e estimates for all Swedish municipalities and postcodes. It employs a combination of bottom-up and top-down methodologies to downscale national consumption-based emissions data. The bottom-up approach is used exclusively for personal vehicle emissions, while the top-down approach utilizes four different approximations: MOSAIC lifestyle profile data, household expenditure surveys, other specific datasets (such as energy consumption and air travel), and per capita disposable income as a proxy. The tool integrates data from Statistics Sweden (SCB), InsightOne, and the Klimatkalkylatorn database to help local authorities prioritize climate actions.","ai_generated":true,"unit":"sei-001472#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The Consumption Compass provides consumption-based emissions estimates (CO2e) at both the municipal and postcode level for every municipality in Sweden.","ai_generated":true,"unit":"sei-001472#insight-1","date_modified":"2026-10-23"},{"text":"A bottom-up approach, which measures actual consumption to estimate greenhouse gas footprints, is used only for personal vehicle emissions, accounting for 14% of total emissions.","ai_generated":true,"unit":"sei-001472#insight-2","date_modified":"2026-10-23"},{"text":"The largest portion of emissions (48%) is downscaled using MOSAIC profile data, which utilizes 44 consumer lifestyle profiles to distribute emissions per postcode across 60 COICOP categories.","ai_generated":true,"unit":"sei-001472#insight-3","date_modified":"2026-10-23"},{"text":"Food-related emissions are estimated by combining MOSAIC expenditure data with diet data from the Klimatkalkylatorn database, which contains over 800,000 user profiles as of April 2022.","ai_generated":true,"unit":"sei-001472#insight-4","date_modified":"2026-10-23"},{"text":"Eight percent of total emissions are downscaled using a 2009 household expenditure survey from Statistics Sweden (SCB), distributed to municipalities via the Swedish Association of Local Authorities and Regions (SKR) classification system.","ai_generated":true,"unit":"sei-001472#insight-5","date_modified":"2026-10-23"},{"text":"Specific datasets are used for 22% of emissions, including municipal kWh data for electricity and direct heating, Boverket energy declarations for district heating, and Swedish Environmental Protection Agency data for air travel.","ai_generated":true,"unit":"sei-001472#insight-6","date_modified":"2026-10-23"},{"text":"For 29 COICOP categories where detailed data is unavailable, per capita disposable income is used as a proxy to downscale emissions to the postcode level, representing 8% of total emissions.","ai_generated":true,"unit":"sei-001472#insight-7","date_modified":"2026-10-23"}],"climate_tags":["greenhouse gas emissions","district heating"],"geography_discussed":[],"frame_perspective":[],"analysis_type":["methodological summary"]},{"id":"wri-000420","url":"https://climateinsightsdirectory.org/document/wri-000420","title":"A Green Economy Model for India: Technical Summary of Methods and Data Used","publisher":{"name":"World Resources Institute","organization":"world-resources-institute"},"document_type":"briefing","language":"en","date_published":null,"source_url":"https://www.wri.org/research/green-economy-model-india-technical-summary-methods-and-data-used","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The document introduces a Green Economy Model (GEM) for India, developed by the World Resources Institute India and KnowlEdge Srl. Based on a 2015 model by Andrea M. Bassi, this system dynamics tool is customized to India's national context, incorporating primary and allied sectors that influence climate change to help inform low-carbon development pathways.","ai_generated":true,"unit":"wri-000420#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The green economy model for India is a system dynamics model customized for the national context through its structure and input data, incorporating primary and allied sectors that affect climate change.","ai_generated":true,"unit":"wri-000420#insight-1","date_modified":"2026-10-23"},{"text":"The model was jointly developed by World Resources Institute India and KnowlEdge Srl (Switzerland), utilizing the Green Economy Model (GEM) published by Andrea M. Bassi in 2015.","ai_generated":true,"unit":"wri-000420#insight-2","date_modified":"2026-10-23"},{"text":"The primary objective of the model is to provide tools to assist in making informed policy decisions to transition India toward a low-carbon development pathway.","ai_generated":true,"unit":"wri-000420#insight-3","date_modified":"2026-10-23"}],"climate_tags":["Low carbon development","green economy"],"geography_discussed":["Switzerland"],"frame_perspective":[],"analysis_type":["methodological summary"]}]}