{"query_echo":{"q":"","filters":{"analysis_type":["methodology guide"]},"warnings":[{"code":"facet_value_not_in_vocabulary","message":"Value 'methodology guide' 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=methodology+guide","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-000044","url":"https://climateinsightsdirectory.org/document/wri-000044","title":"Transport Emissions & Social Cost Assessment: Methodology Guide","publisher":{"name":"World Resources Institute","organization":"world-resources-institute"},"document_type":"guide","language":"en","date_published":null,"source_url":"https://www.wri.org/research/transport-emissions-social-cost-assessment-methodology-guide","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The Transport Emissions & Social Cost Assessment (TESCA) guide establishes a macro-level framework to quantify emissions and monetize social costs for 18 transport modes. It targets developing regions where statistical systems are weak, providing a process that includes data quality analysis, emissions inventory estimation, and the calculation of eco-efficiency indicators. The methodology was tested in Chengdu, China, and aims to support more cost-efficient policy-making by highlighting the uncertainties inherent in social cost and health cost data.","ai_generated":true,"unit":"wri-000044#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The methodology and its accompanying tool are designed to estimate inventories for three greenhouse gases (CO2, CH4, and N2O) and six air pollutants (NOX, SOX, PM2.5, PM10, CO, and HC) across 18 different transport modes at city or national levels.","ai_generated":true,"unit":"wri-000044#insight-1","date_modified":"2026-10-23"},{"text":"The transport emissions impact evaluation process consists of six main components: defining the study scope, performing data quality analysis, estimating emissions inventories, evaluating social costs, calculating indicative results (such as eco-efficiency indicators), and conducting a results review with local justification.","ai_generated":true,"unit":"wri-000044#insight-2","date_modified":"2026-10-23"},{"text":"The assessment tool (version 1.0), first designed in 2014, was successfully tested in Chengdu, the capital of Sichuan province in southwest China.","ai_generated":true,"unit":"wri-000044#insight-3","date_modified":"2026-10-23"},{"text":"Evaluations of social costs, particularly health costs, are subject to higher levels of uncertainty than emissions inventory estimates due to the uneven availability and reliability of data.","ai_generated":true,"unit":"wri-000044#insight-4","date_modified":"2026-10-23"}],"climate_tags":["greenhouse gas emissions","Transportation emission","climate finance","urban transport"],"geography_discussed":["Chengdu","Sichuan","People's Republic of China"],"frame_perspective":[],"analysis_type":["methodology guide"]},{"id":"wri-000155","url":"https://climateinsightsdirectory.org/document/wri-000155","title":"Understanding Future Projections","publisher":{"name":"World Resources Institute","organization":"world-resources-institute"},"document_type":"guide","language":"en","date_published":null,"source_url":"https://www.wri.org/aqueduct/help-center/understanding-future-projections","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","analysis_type"],"summary":{"text":"The document details the technical framework of Aqueduct's future projections, which utilize the HYPFLOWSCI6 dataset and five General Circulation Models (GCMs) to estimate water supply, use, stress, depletion, and variability. It describes three socioeconomic and climate scenarios (SSP1-RCP2.6, SSP3-RCP7.0, and SSP5-RCP8.5) with temperature projections ranging from 1.3°C to 5.7°C by 2100. The guide emphasizes that these scenarios represent different development pathways rather than simple best- or worst-case outcomes, noting that higher economic activity in optimistic scenarios can actually increase water stress. Technical processing includes spatial aggregation at the HydroBASINS Level 6 sub-basin level and temporal aggregation using 10-year trailing moving averages for the years 2030, 2050, and 2080. Limitations are noted regarding irrigation data (projected only to 2050) and livestock water demand (held constant after 2014).","ai_generated":true,"unit":"wri-000155#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"Aqueduct's projections are based on the HYPFLOWSCI6 dataset and utilize five General Circulation Models (GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0, and UKESM1-0-LL) to account for climate modeling uncertainties, displaying the median of these models as the central estimate.","ai_generated":true,"unit":"wri-000155#insight-1","date_modified":"2026-10-23"},{"text":"The tool employs three CMIP6 scenarios with varying temperature projections by 2100: the optimistic SSP1-RCP2.6 (1.3°C to 2.4°C), the business-as-usual SSP3-RCP7.0 (2.8°C to 4.6°C), and the pessimistic SSP5-RCP8.5 (3.3°C to 5.7°C).","ai_generated":true,"unit":"wri-000155#insight-2","date_modified":"2026-10-23"},{"text":"Scenarios should be interpreted as policy and development pathways; for instance, the optimistic scenario may result in higher water stress than the pessimistic one because it assumes increased economic activity and development leading to higher water demand.","ai_generated":true,"unit":"wri-000155#insight-3","date_modified":"2026-10-23"},{"text":"Data is spatially aggregated at the HydroBASINS Level 6 sub-basin level and temporally aggregated using a 10-year trailing moving average and Theil-Sen regression for 30-year trends centered on 2030, 2050, and 2080.","ai_generated":true,"unit":"wri-000155#insight-4","date_modified":"2026-10-23"},{"text":"The projections have specific data limitations: crop extent data for irrigation is only projected to 2050, and livestock water demand data has remained constant since 2014, which may lead to underestimations of future demand.","ai_generated":true,"unit":"wri-000155#insight-5","date_modified":"2026-10-23"}],"climate_tags":["water-supply management","climate change","climate modeling","agricultural irrigation"],"geography_discussed":[],"frame_perspective":[],"analysis_type":["methodology guide"]},{"id":"wri-000553","url":"https://climateinsightsdirectory.org/document/wri-000553","title":"Sustainability Dashboard Methodology","publisher":{"name":"World Resources Institute","organization":"world-resources-institute"},"document_type":"guide","language":"en","date_published":null,"source_url":"https://www.wri.org/sustainability-wri/dashboard/methodology","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The World Resources Institute (WRI) employs a comprehensive greenhouse gas (GHG) accounting methodology covering Scope 1, 2, and 3 emissions. The inventory uses an operational control consolidation approach and has established 2019 as its base year for 2030 reduction targets. Scope 1 includes direct emissions from diesel generators and office refrigerators. Scope 2 utilizes both location-based and market-based methods for purchased electricity, incorporating renewable energy certificates for US operations. Scope 3 is the most extensive, covering purchased goods and services (Category 1) via a spend-based USEEIO model, fuel- and energy-related activities (Category 3), waste (Category 5), business travel (Category 6), employee commuting (Category 7), and endowment investments (Category 15). The methodology emphasizes the use of regional emission factors and annual staff surveys to estimate impacts across WRI's global network.","ai_generated":true,"unit":"wri-000553#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"WRI uses an 'Operational control' consolidation approach to define its organizational boundary, including all material offices within its global network. The organization updated its base year to 2019 to better reflect its international offices and to set new reduction targets for 2030.","ai_generated":true,"unit":"wri-000553#insight-1","date_modified":"2026-10-23"},{"text":"Scope 2 emissions are calculated using two methods: a location-based method using grid average emission factors and a market-based method that accounts for contractual purchases, such as Green-e energy-certified wind power for US operations.","ai_generated":true,"unit":"wri-000553#insight-2","date_modified":"2026-10-23"},{"text":"Scope 3, Category 1 (Purchased Goods and Services) represents the majority of WRI's total emissions and is estimated using a spend-based approach via the US EPA's Environmentally Extended Input-Output (USEEIO) model.","ai_generated":true,"unit":"wri-000553#insight-3","date_modified":"2026-10-23"},{"text":"For business travel (Scope 3, Category 6), WRI applies a 1.9x multiplier for radiative forcing to air travel emissions to capture the increased warming impact in the upper atmosphere, which approximately doubled the magnitude of these emissions.","ai_generated":true,"unit":"wri-000553#insight-4","date_modified":"2026-10-23"},{"text":"Employee commuting (Scope 3, Category 7) is calculated using a pattern-based survey format introduced in 2016 to capture multi-modal trips, applying a scaling factor (ranging from 0.61 to 0.92) to account for non-respondents.","ai_generated":true,"unit":"wri-000553#insight-5","date_modified":"2026-10-23"},{"text":"WRI accounts for emissions from its US-based endowment portfolio (Scope 3, Category 15) using a combination of GHG Protocol guidance and PCAF methodology, applying a materiality threshold of 80% to the activity data.","ai_generated":true,"unit":"wri-000553#insight-6","date_modified":"2026-10-23"}],"climate_tags":["greenhouse gas inventory","GHG Protocol","Scope 2","Scope 3","carbon accounting","waste management"],"geography_discussed":["United States","People's Republic of China","India","Indonesia","Brazil","Mexico","Netherlands","United Kingdom","Ethiopia","Kenya","Democratic Republic of the Congo"],"frame_perspective":[],"analysis_type":["methodology guide"]}]}