{"query_echo":{"q":"","filters":{"analysis_type":["methodology"]},"warnings":[]},"total":{"value":240,"relation":"eq"},"page":11,"page_size":20,"self":"https://climateinsightsdirectory.org/api/v1/search?analysis_type=methodology&page=11","next":"https://climateinsightsdirectory.org/api/v1/search?analysis_type=methodology&page=12","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":"rmi-001100","url":"https://climateinsightsdirectory.org/document/rmi-001100","title":"SHIFT Calculator - Methodology","publisher":{"name":"RMI","organization":"rmi"},"document_type":"guide","language":"en","date_published":null,"source_url":"https://rmi.org/app/uploads/2021/10/rmi_shift_calculator_methodology.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The SHIFT Calculator estimates induced travel—the increase in vehicle miles traveled (VMT) following road capacity expansions—using empirical elasticities. It applies a 1.0 ratio for interstate highways and 0.75 for other freeways and arterials, predicting that a 1% increase in lane mileage leads to a corresponding increase in VMT over 5 to 10 years. The tool also calculates cumulative greenhouse gas emissions through 2050 based on two scenarios: a business-as-usual case and one aligned with the US Nationally Determined Commitment (NDC), which assumes 100% electrification of new passenger vehicles by 2035.","ai_generated":true,"unit":"rmi-001100#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The SHIFT Calculator estimates long-run induced VMT (steady state in 5 to 10 years) by applying specific elasticity ratios based on road type: a 1.0 ratio for interstate highways (Class 1) and a 0.75 ratio for other freeways, expressways, and principal arterial roads (Class 2 or 3).","ai_generated":true,"unit":"rmi-001100#insight-1","date_modified":"2026-10-23"},{"text":"Induced travel is driven by economic principles of supply and demand, where increasing roadway capacity reduces travel time and the 'price' of driving, which in turn increases the quantity of driving.","ai_generated":true,"unit":"rmi-001100#insight-2","date_modified":"2026-10-23"},{"text":"The calculator uses 2019 lane mileage and VMT data from the Federal Highway Administration (FHWA) and limits its geographic application to Class 1 roadways in US Metropolitan Statistical Areas (MSAs) or Class 2 or 3 roadways in urbanized counties.","ai_generated":true,"unit":"rmi-001100#insight-3","date_modified":"2026-10-23"},{"text":"To account for uncertainty and avoid false precision, induced VMT results are reported in confidence intervals of +/-20%.","ai_generated":true,"unit":"rmi-001100#insight-4","date_modified":"2026-10-23"},{"text":"Cumulative greenhouse gas (GHG) emissions are projected through 2050 under two scenarios: a business-as-usual (BAU) case and an NDC-aligned case. The NDC-aligned case assumes 100% electrification of new passenger vehicles by 2035 and rapid renewable power development.","ai_generated":true,"unit":"rmi-001100#insight-5","date_modified":"2026-10-23"},{"text":"The lifecycle emissions calculation includes direct emissions from gasoline and diesel combustion, as well as upstream emissions from fuel production and manufacturing emissions for light-duty vehicles.","ai_generated":true,"unit":"rmi-001100#insight-6","date_modified":"2026-10-23"}],"climate_tags":["greenhouse gas emissions","vehicle electrification","transport policy","Nationally Determined Contributions"],"geography_discussed":["Portland","Oregon"],"frame_perspective":[],"analysis_type":["methodology","impact assessment"]},{"id":"seforall-000202","url":"https://climateinsightsdirectory.org/document/seforall-000202","title":"Mini-Grid Emissions Tool Cover note","publisher":{"name":"Sustainable Energy for All","organization":"sustainable-energy-for-all"},"document_type":"guide","language":"en","date_published":null,"source_url":"https://www.seforall.org/system/files/2021-08/SEforALL_Carbon-emissions-methodology-note.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The Mini-Grid Emissions Tool, developed by Sustainable Energy for All (SEforALL), addresses the lack of standardized carbon tracking in the mini-grid sector. Unlike previous methods that rely on a single default emissions factor and total energy generated, this tool calculates emissions avoided based on actual energy consumed and the diverse range of previous energy sources used by customers. It incorporates specific data for different household energy access tiers, kerosene lamp types, and fossil fuel generator efficiencies to provide a more accurate estimate of climatic benefits.","ai_generated":true,"unit":"seforall-000202#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The tool was created because existing methodologies often overestimate mitigation impact by basing calculations on total energy generated rather than energy consumed, and by assuming all consumers previously used a single fuel source.","ai_generated":true,"unit":"seforall-000202#insight-1","date_modified":"2026-10-23"},{"text":"To improve accuracy, the tool accounts for three primary variables: the various energy sources used before the mini-grid was installed, the actual energy consumed by customers, and the specific generating sources of the mini-grid, including hybrid systems.","ai_generated":true,"unit":"seforall-000202#insight-2","date_modified":"2026-10-23"},{"text":"The methodology utilizes a tiered approach to household energy access to estimate consumption. Tier 1 households (initial access) are estimated to consume 4.86 kWh/month for lighting, phone charging, and radio. Upon upgrading to Tier 2 via a mini-grid, consumption is projected to reach a plateau of 10 kWh/month as they add appliances like TVs.","ai_generated":true,"unit":"seforall-000202#insight-3","date_modified":"2026-10-23"},{"text":"Baseline kerosene emissions are calculated using GOGLA weightings for lamp types (Pressurised, Hurricane, and Single Wick) and include black carbon as a significant short-lived pollutant.","ai_generated":true,"unit":"seforall-000202#insight-4","date_modified":"2026-10-23"},{"text":"The tool distinguishes between previous fossil fuel generators and those used as mini-grid backup. It assigns a default emissions factor of 1 kgCO2/kWh for smaller previously used diesel generators and 0.8 kgCO2/kWh for larger modern mini-grid backup generators (35–135kW).","ai_generated":true,"unit":"seforall-000202#insight-5","date_modified":"2026-10-23"},{"text":"For petrol generators, which are noted as more prevalent in rural Sub-Saharan Africa than small diesel units, the tool uses a default factor of 1.25164 kgCO2/kWh.","ai_generated":true,"unit":"seforall-000202#insight-6","date_modified":"2026-10-23"}],"climate_tags":["carbon dioxide emissions","energy access","photovoltaics"],"geography_discussed":["sub-Saharan Africa","East Africa","West Africa","South Asia","India"],"frame_perspective":[],"analysis_type":["methodology","technical guide"]},{"id":"sei-001473","url":"https://climateinsightsdirectory.org/document/sei-001473","title":"Klimatkalkylatorn: Metoddokument","publisher":{"name":"Stockholm Environment Institute","organization":"stockholm-environment-institute"},"document_type":"guide","language":"sv","date_published":null,"source_url":"https://www.sei.org/wp-content/uploads/2018/08/metoddokument-klimatkalkylatorn-juni-2018.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The document outlines the methodology for the Klimatkalkylatorn tool, which estimates a user's climate footprint across four consumption categories: housing, food, transport, and shopping, plus a fixed societal impact. It details the use of Swedish national statistics (SCB), the GTAP input-output model for indirect emissions, and specific emission factors for energy and transport. The tool compares individual results against a Swedish average of 10.7 tonnes CO2e, a global average of 6.4 tonnes CO2e, and a 2020 target of 7 tonnes CO2e per person.","ai_generated":true,"unit":"sei-001473#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"Housing emissions are estimated based on dwelling type, age, and floor area, using specific emission factors: district heating (0.0886 kg CO2e/kWh), electricity (0.0973 kg CO2e/kWh), fuel oil (0.288 kg CO2e/kWh), pellets (0.01872 kg CO2e/kWh), and corn-based biogas (0.0792 kg CO2e/kWh).","ai_generated":true,"unit":"sei-001473#insight-1","date_modified":"2026-10-23"},{"text":"Food impact is calculated using SCB expenditure data and the GTAP input-output model to account for indirect emissions and the split between domestic and imported products. To avoid penalizing users for the higher cost of organic food, the tool applies a 27% reduction in impact for 100% organic consumption, based on PRO price surveys.","ai_generated":true,"unit":"sei-001473#insight-2","date_modified":"2026-10-23"},{"text":"Transport emissions include both direct fuel emissions and indirect emissions from vehicle production. Due to a lack of detailed Swedish official statistics at the time of development, the tool used UK Defra data for personal vehicles and flights, applying a Radiative Forcing Index (RFI) factor of 1.9 for high-altitude flight impacts.","ai_generated":true,"unit":"sei-001473#insight-3","date_modified":"2026-10-23"},{"text":"The tool benchmarks individual results against a Swedish average of 10.7 tonnes CO2e per person (based on 2015 data from Naturvårdsverket), a global average of 6.4 tonnes CO2e per person (based on 2014 Climate Action Tracker data), and a WWF target of 7 tonnes CO2e per person for 2020.","ai_generated":true,"unit":"sei-001473#insight-4","date_modified":"2026-10-23"}],"climate_tags":["carbon footprint","energy transition","district heating","biofuel","food system","organic agriculture","Transportation emission","aviation"],"geography_discussed":["World","United Kingdom"],"frame_perspective":[],"analysis_type":["methodology"]},{"id":"sei-001889","url":"https://climateinsightsdirectory.org/document/sei-001889","title":"Methodology note: Water risk and global trade","publisher":{"name":"Stockholm Environment Institute","organization":"stockholm-environment-institute"},"document_type":"research paper","language":"en","date_published":null,"source_url":"https://www.sei.org/mediamanager/documents/Publications/Climate/SEI-WP-2016-07-TCI-Methodology-Indicator-8-note.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The document details a methodology for assessing 'embedded' water risk in global trade by combining the IOTA hybrid MRIO model with water stress data from the World Resources Institute's Aqueduct database. It explains how production of agricultural commodities is linked to final consumers and how country-level water risk is calculated using two distinct indicators: aggregate national water stress and the proportion of land area under stress. The methodology is illustrated through examples of maize and rice consumption, showing that regions like south and central Asia face high consumption-based water risk. Future improvements aim to increase spatial resolution to the sub-country level and expand the range of risk indicators to include groundwater scarcity and flood likelihood.","ai_generated":true,"unit":"sei-001889#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The methodology utilizes the IOTA (Input-Output Trade Analysis) model, a hybridized physical-financial multi-regional input-output (MRIO) model, to allocate the production of raw agricultural commodities to their final point of consumption.","ai_generated":true,"unit":"sei-001889#insight-1","date_modified":"2026-10-23"},{"text":"Country-level water risk is determined using two different indicators derived from the WRI Aqueduct database: one based on total water withdrawal divided by total blue water availability at the aggregate national level, and another based on the proportion of a country's land area with a Baseline Water Stress (BWS) score of 3 or higher (including 'arid' areas).","ai_generated":true,"unit":"sei-001889#insight-2","date_modified":"2026-10-23"},{"text":"The two water risk indicators can produce contrasting results; for example, Egypt is classified as low risk by the aggregate national indicator but high risk by the land area indicator because 97% of its water withdrawal occurs in sources classified as 'Low' which only cover 19% of its land area.","ai_generated":true,"unit":"sei-001889#insight-3","date_modified":"2026-10-23"},{"text":"Analysis of maize consumption indicates that south and central Asia, Mexico, and southern Africa have a high reliance on maize produced in countries with high water scarcity, while South American consumption generally shows low levels of risk.","ai_generated":true,"unit":"sei-001889#insight-4","date_modified":"2026-10-23"},{"text":"Analysis of rice consumption shows that central and south Asia maintain the highest level of consumption-based water risk, with increased risk also noted in North America, Europe, and northern and eastern Africa compared to maize.","ai_generated":true,"unit":"sei-001889#insight-5","date_modified":"2026-10-23"},{"text":"The authors identify several areas for future methodological improvement, including correlating commodity production at a sub-country level with basin-level scarcity, incorporating sub-national trade information via the SEI-PCS model, and expanding risk indicators to include groundwater scarcity, flood likelihood, and drought severity.","ai_generated":true,"unit":"sei-001889#insight-6","date_modified":"2026-10-23"}],"climate_tags":["water scarcity","agricultural production","climate resilience","water footprint","international trade"],"geography_discussed":["Egypt","Brazil","Sweden","Asia","Mexico","Africa","Europe","South America","North America"],"frame_perspective":[],"analysis_type":["methodology","pilot study"]},{"id":"ucs-000484","url":"https://climateinsightsdirectory.org/document/ucs-000484","title":"Methodology and References","publisher":{"name":"Union of Concerned Scientists","organization":"union-of-concerned-scientists"},"document_type":"fact sheet","language":"en","date_published":null,"source_url":"https://www.ucs.org/sites/default/files/attach/2017/08/cv-factsheet-fuel-efficiency-states-methodology-references_1.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The document details the technical approach for estimating the economic and environmental impacts of fuel efficiency standards. It specifies the use of updated census data, regional energy projections, and specific emissions derivation methods for cities and states.","ai_generated":true,"unit":"ucs-000484#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"Calculations for fuel savings (both monetary and volume) and emissions reductions are based on a 2010 methodology from the Union of Concerned Scientists (UCS) and Natural Resources Defense Council (NRDC), updated with 2017 US Census Bureau data and Energy Information Administration (EIA) projections for fuel prices, energy consumption, and vehicle sales.","ai_generated":true,"unit":"ucs-000484#insight-1","date_modified":"2026-10-23"},{"text":"Jobs data estimates are derived from a 5 percent scenario from a 2011 Ceres report, selected because it aligns with GDP and total job growth projections estimated in a 2016 UCS report.","ai_generated":true,"unit":"ucs-000484#insight-2","date_modified":"2026-10-23"},{"text":"City-specific emissions are estimated by deriving per capita emissions for eGRID subregions using countywide census data, then applying those to cities within the subregions based on metropolitan statistical area (MSA) census data, with specific exceptions for New York City, Jackson (MS), Vermont, and New Hampshire.","ai_generated":true,"unit":"ucs-000484#insight-3","date_modified":"2026-10-23"}],"climate_tags":["greenhouse gas emissions","employment"],"geography_discussed":["New York City","Jackson","Vermont","New Hampshire"],"frame_perspective":[],"analysis_type":["methodology"]},{"id":"ucs-000586","url":"https://climateinsightsdirectory.org/document/ucs-000586","title":"State of Charge: Technical Appendix","publisher":{"name":"Union of Concerned Scientists","organization":"union-of-concerned-scientists"},"document_type":"report","language":"en","date_published":null,"source_url":"https://www.ucs.org/sites/default/files/2019-09/electric-car-global-warming-emissions-appendix.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The document provides the technical basis for estimating EV charging emissions and costs in the US. It explains the use of average emissions intensity based on EPA eGRID data, adjusted for transmission losses and upstream feedstock emissions. It also describes a modified Oak Ridge Competitive Electricity Dispatch (ORCED) model used for hourly emissions estimates. Additionally, it outlines the process for calculating residential charging costs across 50 US cities, accounting for tiered rates, time-of-use (TOU) plans, and specific utility data.","ai_generated":true,"unit":"ucs-000586#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The analysis uses average emissions intensity rather than marginal emissions to provide consumers with an estimate of typical global warming emissions on the current grid, treating electricity as a \"shared resource, or pool of electrons, available to all electricity consumers.\"","ai_generated":true,"unit":"ucs-000586#insight-1","date_modified":"2026-10-23"},{"text":"Regional emissions estimates are based on the EPA's eGRID 2012 v1.0 database (using 2009 data) for 26 subregions, which are adjusted to include transmission and distribution losses and upstream emissions from mining and transporting fuel.","ai_generated":true,"unit":"ucs-000586#insight-2","date_modified":"2026-10-23"},{"text":"Upstream feedstock emissions rates were calculated using the GREET life-cycle emissions model developed by Argonne National Laboratory, with natural gas rates used as a conservative estimate for \"generic fossil\" fuel types.","ai_generated":true,"unit":"ucs-000586#insight-3","date_modified":"2026-10-23"},{"text":"To estimate hourly emissions intensity for 13 regions, the authors used a modified Oak Ridge Competitive Electricity Dispatch (ORCED) model, which estimates the mix of power plants generating electricity based on hourly demand levels.","ai_generated":true,"unit":"ucs-000586#insight-4","date_modified":"2026-10-23"},{"text":"Hourly emissions results indicate that most regions have slightly higher average emissions intensities during non-peak hours, though this varies; for example, in California, evening intensities can be higher because hydro dams are controlled to generate more power during daytime peak demand.","ai_generated":true,"unit":"ucs-000586#insight-5","date_modified":"2026-10-23"},{"text":"Residential charging cost calculations for 50 US cities assume a daily driving distance of 30 miles and a plug-to-wheel efficiency of 0.34 kWh/mile, based on the first-generation Nissan LEAF.","ai_generated":true,"unit":"ucs-000586#insight-6","date_modified":"2026-10-23"},{"text":"For time-of-use (TOU) rate plans, the analysis assumes that 70% of charging is performed during off-peak hours and the remaining 30% occurs at random times throughout the day.","ai_generated":true,"unit":"ucs-000586#insight-7","date_modified":"2026-10-23"},{"text":"In cities with tiered electricity rates where the EV and house share a single meter, the analysis treats EV charging as the \"last\" electricity used, meaning it incurs the highest-tiered rates.","ai_generated":true,"unit":"ucs-000586#insight-8","date_modified":"2026-10-23"}],"climate_tags":["electric vehicle","greenhouse gas emissions","electrical grid","emission intensity","carbon footprint","electricity price"],"geography_discussed":["California"],"frame_perspective":[],"analysis_type":["technical appendix","methodology","quantitative analysis"]},{"id":"ucs-000608","url":"https://climateinsightsdirectory.org/document/ucs-000608","title":"Microsoft Word - Freight Project Methodology Draft - 2015-03-10.docx","publisher":{"name":"Union of Concerned Scientists","organization":"union-of-concerned-scientists"},"document_type":"report","language":"en","date_published":null,"source_url":"https://www.ucs.org/sites/default/files/attach/2015/03/Engines-For-Change-Methodology.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","analysis_type"],"summary":{"text":"The document outlines a technical framework for calculating fuel use across various truck types—including tractor-trailers, straight trucks, and cargo vans—using data from the Freight Analysis Framework (FAF) and the Vehicle Inventory and Use Survey (VIUS). It establishes baseline fuel economies (e.g., 5.8 mpg for tractor-trailers) and projects improvements by 2025 based on technology packages such as aerodynamic devices and hybrid-electric powertrains. Additionally, it applies these methodologies to estimate the current fuel use and future savings for the fleets of UPS, FedEx, PepsiCo, The Coca-Cola Company, and Walmart.","ai_generated":true,"unit":"ucs-000608#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The analysis identifies specific fuel economy targets and payback periods for different vehicle types to determine technology feasibility: tractor-trailers were targeted for a two-year payback, while vocational and pick-up trucks were targeted for five years. Actual average paybacks achieved were 13 months for tractor-trailers, 47 months for vocational vehicles, and 35 months for pick-up trucks/vans.","ai_generated":true,"unit":"ucs-000608#insight-1","date_modified":"2026-10-23"},{"text":"For sleeper cab tractor-trailers, the methodology assumes a baseline fuel economy of 5.8 mpg, with a potential increase to 11.4 mpg under strong standards. This represents an overall 46 percent improvement in fuel consumption, achievable through technologies such as waste heat recovery, automated manual transmissions, and low rolling-resistance tires.","ai_generated":true,"unit":"ucs-000608#insight-2","date_modified":"2026-10-23"},{"text":"The fuel consumption of trucks is significantly impacted by cargo weight; data from Cummins, Inc. indicates that fuel economy decreases by 1.2 percent for every additional ton of goods transported.","ai_generated":true,"unit":"ucs-000608#insight-3","date_modified":"2026-10-23"},{"text":"The Freight Analysis Framework (FAF) is used to model domestic truck freight, but it is noted that the underlying Commodity Flow Survey (CFS) likely covers less than 75 percent of all annual freight tons in the U.S., primarily missing last-mile delivery and warehouse-to-retail movements.","ai_generated":true,"unit":"ucs-000608#insight-4","date_modified":"2026-10-23"},{"text":"Fleet-specific estimates for UPS indicate a total technology cost differential of $1.20 billion to implement improvements, with a projected payback period of 30 months based on a 5 percent annual discount rate.","ai_generated":true,"unit":"ucs-000608#insight-5","date_modified":"2026-10-23"},{"text":"Walmart's fleet has already achieved a 15 percent reduction in fuel consumption compared to the EPA baseline, resulting in a fuel economy of 6.9 mpg through the use of aerodynamic tractors, auxiliary power units (APUs), and trailer skirts.","ai_generated":true,"unit":"ucs-000608#insight-6","date_modified":"2026-10-23"}],"climate_tags":["fuel economy","large goods vehicle","hybrid electric vehicle","greenhouse gas emissions","compressed natural gas","liquefied natural gas","vulnerability assessment"],"geography_discussed":[],"frame_perspective":[],"analysis_type":["methodology","technical assessment","fleet analysis"]},{"id":"ucs-000627","url":"https://climateinsightsdirectory.org/document/ucs-000627","title":"Freshwater Use by U.S. Power Plants: Electricity’s Thirst for a Precious Resource","publisher":{"name":"Union of Concerned Scientists","organization":"union-of-concerned-scientists"},"document_type":"report","language":"en","date_published":null,"source_url":"https://www.ucs.org/sites/default/files/2019-09/ew3-freshwater-use-by-us-power-plants_apx-a_methodology.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The document details the construction of the EW3 database, which integrates 2008 Energy Information Administration (EIA) data with NREL and Argonne National Laboratory water coefficients to estimate freshwater withdrawals and consumption. It describes the process of mapping cooling systems to specific generators, using Google Earth to verify cooling types, and applying the Water Supply Stress Index (WaSSI) to identify watersheds where electricity generation significantly increases water stress. The methodology also covers the projection of water use through 2036 using the Regional Energy Deployment System (ReEDS) model and the calculation of water and carbon intensities for the top 15 U.S. electricity providers.","ai_generated":true,"unit":"ucs-000627#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The EW3 database relies primarily on 2008 EIA Form 860 and Form 923 data, though it only includes organic-fueled steam electric plants with capacities greater than 100 MW due to EIA reporting requirements.","ai_generated":true,"unit":"ucs-000627#insight-1","date_modified":"2026-10-23"},{"text":"To improve the accuracy of cooling system data, the authors used Google Earth to manually verify and correct the cooling types for 848 generators, representing 32 percent of electrical output.","ai_generated":true,"unit":"ucs-000627#insight-2","date_modified":"2026-10-23"},{"text":"The analysis identifies 'environmental-compliance cooling' as a distinct category where cooling towers are placed downstream of the boiler condensation loop specifically to reduce the temperature of effluent.","ai_generated":true,"unit":"ucs-000627#insight-3","date_modified":"2026-10-23"},{"text":"Water withdrawals and consumption were calculated by applying water coefficients from the National Renewable Energy Laboratory (NREL) and Argonne National Laboratory (for geothermal plants) to estimated electrical output.","ai_generated":true,"unit":"ucs-000627#insight-4","date_modified":"2026-10-23"},{"text":"The study utilizes the Water Supply Stress Index (WaSSI), defined as the ratio of water demand to water supply, and considers basins with a WaSSI above 0.4 to be highly stressed.","ai_generated":true,"unit":"ucs-000627#insight-5","date_modified":"2026-10-23"},{"text":"Future water use projections to 2036 were modeled using the Regional Energy Deployment System (ReEDS), assuming that new capacity added after 2008 would utilize more water-efficient recirculating-cooling systems.","ai_generated":true,"unit":"ucs-000627#insight-6","date_modified":"2026-10-23"}],"climate_tags":["electricity generation","carbon dioxide emissions","emission intensity","geothermal energy","coal-fired power station"],"geography_discussed":["Texas"],"frame_perspective":[],"analysis_type":["methodology","data compilation","modeling"]},{"id":"ucs-000922","url":"https://climateinsightsdirectory.org/document/ucs-000922","title":"Donuts, Deodorant, Deforestation","publisher":{"name":"Union of Concerned Scientists","organization":"union-of-concerned-scientists"},"document_type":"report","language":"en","date_published":null,"source_url":"https://www.ucs.org/sites/default/files/2019-10/palm-oil-scorecard-methodology.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","analysis_type"],"summary":{"text":"The document details the selection process and scoring criteria used to evaluate 30 companies' palm oil commitments. Companies were selected from the Forbes Global 2000 (2013) and QSR Magazine’s Global 30 (2012) lists. Scoring was based on public data available as of January 16, 2014, and evaluated across five categories: deforestation-free commitments, peat-free commitments, traceability, transparency, and early action. Each category is worth 20 points, with specific rubrics for awarding points based on the strength and specificity of company commitments and reporting.","ai_generated":true,"unit":"ucs-000922#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The study evaluated 30 companies across three sectors: packaged food, personal care, and fast food. For the first two, the top 10 companies known to use palm oil were selected from the Forbes Global 2000 list for 2013. For the fast food sector, the top 10 palm oil users were selected from QSR Magazine’s Global 30 list (2012), with KFC and Pizza Hut combined as they are both owned by Yum! Brands.","ai_generated":true,"unit":"ucs-000922#insight-1","date_modified":"2026-10-23"},{"text":"Companies were scored on five criteria, each worth 20 points: Deforestation-Free, Peat-Free, Traceability, Transparency, and Early Action. Full points for 'Deforestation-Free' require a commitment to protect all forests, including \"secondary, disturbed, or high carbon stock forests\". Full points for 'Peat-Free' require a commitment to avoid new conversion of peatlands and ensure existing plantations use management practices at least as strong as those of the RSPO.","ai_generated":true,"unit":"ucs-000922#insight-2","date_modified":"2026-10-23"},{"text":"The Traceability criterion (20 points) awards 15 points if a company commits to tracing palm oil to the plantation level and 5 points for asking suppliers about the GHG footprint of production. The Transparency criterion (20 points) awards 10 points for annual reporting of progress, including the percentage of palm oil meeting deforestation-free or peat-free criteria, 5 points for time-bound sourcing goals with intermediate targets, and 5 points for establishing a verification process.","ai_generated":true,"unit":"ucs-000922#insight-3","date_modified":"2026-10-23"},{"text":"The 'Early Action' score is calculated using a weighted average of the company's current purchases of crude palm oil (CPO), palm kernel oil (PKO), and palm oil derivatives (POD). Deforestation- and peat-free oils receive full weight, physically sourced CSPO or sustainable oils receive a weight of 0.5, and CPO sourced through GreenPalm (CPOGP) receives a weight of 0.25.","ai_generated":true,"unit":"ucs-000922#insight-4","date_modified":"2026-10-23"}],"climate_tags":["deforestation","peatland","greenhouse gas emissions"],"geography_discussed":[],"frame_perspective":[],"analysis_type":["methodology"]},{"id":"wri-000010","url":"https://climateinsightsdirectory.org/document/wri-000010","title":"Climate Watch NDC Tracker: Estimating global and national GHG emissions based on emissions reduction targets in countries’ Nationally Determined Contributions","publisher":{"name":"World Resources Institute","organization":"world-resources-institute"},"document_type":"briefing","language":"en","date_published":null,"source_url":"https://www.wri.org/research/climate-watch-ndc-tracker-estimating-global-and-national-ghg-emissions-based-emissions","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","analysis_type"],"summary":{"text":"The document outlines the technical approach for estimating economy-wide GHG emissions for countries and the global aggregate, assuming full achievement of NDC targets. It explains the use of supplemental data for sectors or gases not covered by NDCs and the inclusion of emissions like international bunkers to determine global trajectories. These estimates are used by the Climate Watch NDC Tracker to track progress and identify gaps in global emissions pathways.","ai_generated":true,"unit":"wri-000010#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"Climate Watch estimates future economy-wide GHG emissions for countries by assuming that the targets communicated in their Nationally Determined Contributions (NDCs) are fully achieved, utilizing supplemental data for any sectors or GHGs not explicitly covered by those NDCs.","ai_generated":true,"unit":"wri-000010#insight-1","date_modified":"2026-10-23"}],"climate_tags":["greenhouse gas emissions","Nationally Determined Contributions","climate target"],"geography_discussed":[],"frame_perspective":[],"analysis_type":["methodology"]},{"id":"wri-000145","url":"https://climateinsightsdirectory.org/document/wri-000145","title":"ACCC: Renewables Accelerator State Overviews: Summary Of Methods And Data","publisher":{"name":"World Resources Institute","organization":"world-resources-institute"},"document_type":"guide","language":"en","date_published":null,"source_url":"https://www.wri.org/research/accc-renewables-accelerator-state-overviews-summary-methods-and-data","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","analysis_type"],"summary":{"text":"The document outlines the methodology for the State Overview resource developed by the World Resources Institute as part of the American Cities Climate Challenge: Renewables Accelerator. This resource is designed to help U.S. cities understand regulatory frameworks and renewable electricity procurement options through dedicated webpages for all 50 states and the District of Columbia.","ai_generated":true,"unit":"wri-000145#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The State Overview online resource consists of webpages for 50 states and DC that detail regulatory and market information along with state policies specific to procurement options.","ai_generated":true,"unit":"wri-000145#insight-1","date_modified":"2026-10-23"},{"text":"The World Resources Institute is supporting U.S. cities of various sizes and energy mixes in navigating their regulatory frameworks and options for procuring renewable electricity.","ai_generated":true,"unit":"wri-000145#insight-2","date_modified":"2026-10-23"}],"climate_tags":[],"geography_discussed":[],"frame_perspective":[],"analysis_type":["methodology"]},{"id":"wri-000182","url":"https://climateinsightsdirectory.org/document/wri-000182","title":"Aqueduct Global Maps 2.1: Constructing Decision-Relevant Global Water Risk Indicators","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/aqueduct-global-maps-21-indicators","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","analysis_type"],"summary":{"text":"The document describes the construction of the Aqueduct Water Risk Atlas, which uses a composite index approach to map 12 water-risk indicators globally. Six indicators were generated using a combination of time series estimators, spatial regression, and a sparse hydrological model, while the other six were adapted from existing publications. The authors note that model accuracy is primarily constrained by the lack of public data on major infrastructure, in-situ water quality, and river gauge measurements, and that overall index scores cannot be validated due to a lack of public datasets on water-related losses.","ai_generated":true,"unit":"wri-000182#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The Aqueduct framework utilizes 12 indicators to identify spatial variations in water risks; six of these were developed using an ensemble of time series estimators, spatial regression, and a sparse hydrological model, while the other six were adapted from existing publications.","ai_generated":true,"unit":"wri-000182#insight-1","date_modified":"2026-10-23"},{"text":"The accuracy of the hydrological models is primarily limited by the availability of data regarding river gauge measurements, in-situ water quality, and major infrastructure such as inter-basin transfers.","ai_generated":true,"unit":"wri-000182#insight-2","date_modified":"2026-10-23"},{"text":"The authors are unable to validate the overall index scores because there are no public datasets available that track water-related losses.","ai_generated":true,"unit":"wri-000182#insight-3","date_modified":"2026-10-23"}],"climate_tags":["water quality","climate change"],"geography_discussed":[],"frame_perspective":[],"analysis_type":["methodology"]},{"id":"wri-000237","url":"https://climateinsightsdirectory.org/document/wri-000237","title":"ACCC: Renewables Accelerator Procurement Guidance: Summary of Methods and Data","publisher":{"name":"World Resources Institute","organization":"world-resources-institute"},"document_type":"briefing","language":"en","date_published":null,"source_url":"https://www.wri.org/research/accc-renewables-accelerator-procurement-guidance-summary-methods-and-data","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","analysis_type"],"summary":{"text":"The document outlines the technical framework and curation methodology for the American Cities Climate Challenge: Renewables Accelerator website, a collaborative resource by the World Resources Institute and Rocky Mountain Institute designed to support city sustainability staff in the procurement of renewable energy.","ai_generated":true,"unit":"wri-000237#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The American Cities Climate Challenge: Renewables Accelerator website was created through a partnership between the World Resources Institute and the Rocky Mountain Institute to support cities in the procurement of renewable electricity.","ai_generated":true,"unit":"wri-000237#insight-1","date_modified":"2026-10-23"},{"text":"The procurement guidance section of the website is intended to provide city sustainability staff with resources and critical considerations for procuring renewable energy.","ai_generated":true,"unit":"wri-000237#insight-2","date_modified":"2026-10-23"}],"climate_tags":["renewable electricity","power purchase agreement"],"geography_discussed":[],"frame_perspective":[],"analysis_type":["methodology"]},{"id":"wri-000241","url":"https://climateinsightsdirectory.org/document/wri-000241","title":"Bottom-up Climate Finance Requirements Tracker: Data and Methodology","publisher":{"name":"World Resources Institute","organization":"world-resources-institute"},"document_type":"guide","language":"en","date_published":null,"source_url":"https://www.wri.org/research/bottom-climate-finance-requirements-tracker-data-and-methodology","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","analysis_type"],"summary":{"text":"The document outlines the methodology for the Climate Finance Requirements Tracker, a tool that systematically tracks quantitative climate finance data from all rounds of nationally determined contributions (NDCs). Since 2021, the World Resources Institute has used this process to distinguish between international and domestic funding requirements for mitigation and adaptation, while also compiling data on the economic impacts of climate-related loss and damage.","ai_generated":true,"unit":"wri-000241#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"Since 2021, the World Resources Institute has systematically tracked quantitative climate finance data from all rounds of nationally determined contributions (NDCs), covering both mitigation and adaptation requirements.","ai_generated":true,"unit":"wri-000241#insight-1","date_modified":"2026-10-23"},{"text":"The tracker distinguishes between funding expected from international sources and funding anticipated to be provided through domestic finance.","ai_generated":true,"unit":"wri-000241#insight-2","date_modified":"2026-10-23"},{"text":"The World Resources Institute has compiled data on the current and projected economic impacts of climate-related loss and damage based on all available NDC submissions.","ai_generated":true,"unit":"wri-000241#insight-3","date_modified":"2026-10-23"}],"climate_tags":["climate finance","Nationally Determined Contributions","climate change mitigation","climate change adaptation","loss and damage"],"geography_discussed":[],"frame_perspective":[],"analysis_type":["methodology"]},{"id":"wri-000254","url":"https://climateinsightsdirectory.org/document/wri-000254","title":"Aqueduct Country and River Basin Rankings: A Weighted Aggregation of Spatially Distinct Hydrological Indicators","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/aqueduct-country-and-river-basin-rankings","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","analysis_type"],"summary":{"text":"The document presents a framework for aggregating localized water-risk scores into national and river basin-level rankings to support administrative decision-making and financial portfolio evaluation. It focuses on five objective physical indicators: baseline water stress, inter-annual variability, seasonal variability, flood occurrence (1985-2011), and drought severity (1901-2008). The rankings exclude subjective factors such as water governance, investment, or conservation efforts.","ai_generated":true,"unit":"wri-000254#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The rankings provide data for 180 countries and two sets of 100 river basins—the largest by area and the most populous—across five specific measures of water supply and demand.","ai_generated":true,"unit":"wri-000254#insight-1","date_modified":"2026-10-23"},{"text":"The analysis relies exclusively on objective natural factors and does not account for subjective elements such as water governance, conservation efforts, or investment.","ai_generated":true,"unit":"wri-000254#insight-2","date_modified":"2026-10-23"},{"text":"The five physical water quantity risk indicators used are baseline water stress, inter-annual variability, seasonal variability, flood occurrence (recorded from 1985 to 2011), and drought severity (calculated from 1901 to 2008).","ai_generated":true,"unit":"wri-000254#insight-3","date_modified":"2026-10-23"}],"climate_tags":[],"geography_discussed":[],"frame_perspective":[],"analysis_type":["methodology","ranking"]},{"id":"wri-000283","url":"https://climateinsightsdirectory.org/document/wri-000283","title":"Making Climate Data Accessible: Methods for Producing NEX-GDDP and LOCA Downscaled Climate Indicators","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/making-climate-data-accessible","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","analysis_type"],"summary":{"text":"The document outlines an effort to make complex global climate data more accessible by converting it into simple indicators and open geospatial formats. This process aims to reduce barriers for users who need to anticipate trends in agriculture, energy demand, disaster risk, and water management to inform adaptation strategies. The resulting data are hosted on PREPdata and Resource Watch, with the source code available on GitHub.","ai_generated":true,"unit":"wri-000283#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"Climate data is currently underutilized because it is difficult for users to access, process, and utilize.","ai_generated":true,"unit":"wri-000283#insight-1","date_modified":"2026-10-23"},{"text":"The authors developed a process to convert global climate data into simple indicators and open geospatial datasets to lower barriers for decision-makers adapting to climate change.","ai_generated":true,"unit":"wri-000283#insight-2","date_modified":"2026-10-23"}],"climate_tags":["climate data","climate change adaptation"],"geography_discussed":[],"frame_perspective":[],"analysis_type":["methodology"]},{"id":"wri-000303","url":"https://climateinsightsdirectory.org/document/wri-000303","title":"Determining Impacts of NDC Enhancement on Country-Level Emissions","publisher":{"name":"World Resources Institute","organization":"world-resources-institute"},"document_type":"briefing","language":"en","date_published":null,"source_url":"https://www.wri.org/research/determining-impacts-ndc-enhancement-country-level-emissions","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","analysis_type"],"summary":{"text":"The document outlines a technical approach to determine the extent of NDC enhancement by comparing estimated target-year greenhouse gas emissions under a Party's previous NDC against those under its subsequent NDC. The method covers both NDCs with explicit GHG reduction targets and those based on non-GHG targets and policies.","ai_generated":true,"unit":"wri-000303#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The methodology determines NDC enhancement by estimating a Party's target-year greenhouse gas emissions under its previous NDC (usually the First NDC) and comparing them to emissions under its subsequent NDC (typically an Updated First or Second NDC).","ai_generated":true,"unit":"wri-000303#insight-1","date_modified":"2026-10-23"},{"text":"The approach is designed to identify if a subsequent NDC reduces emissions relative to the previous one and, when possible, quantify that reduction, applying to both GHG-specific targets and non-GHG targets and policies.","ai_generated":true,"unit":"wri-000303#insight-2","date_modified":"2026-10-23"}],"climate_tags":["Nationally Determined Contributions","Paris Agreement"],"geography_discussed":[],"frame_perspective":[],"analysis_type":["methodology"]},{"id":"wri-000318","url":"https://climateinsightsdirectory.org/document/wri-000318","title":"Mapping Cocoa and Assessing Deforestation Risk for the Cocoa Sector in Côte d’Ivoire and Ghana","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/mapping-cocoa-assessing-deforestation-risk-cocoa-cote-divoire-ghana","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The World Resources Institute, in partnership with the World Cocoa Foundation and Climate Focus, developed two tools to support deforestation-free cocoa commitments in West Africa. The West Africa Cocoa Dataset aggregates data from 19 companies, identifying 840,000 unique plots covering approximately 1.5 million hectares. To protect farmer privacy, this dataset is shared as a heat map rather than raw plot data. The Cocoa Deforestation Risk Assessment provides a harmonized method to prioritize deforestation prevention; it found that recent forest loss significantly influences risk levels, while legal protections in areas like Taï National Park and Bia National Park correlate with lower priority rankings due to a lack of recent loss.","ai_generated":true,"unit":"wri-000318#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The West Africa Cocoa Dataset was developed from 1.2 million contributed polygons, resulting in 840,000 unique plots. These plots typically range from 1 to 2 hectares, covering a total land area of roughly 1.5 million hectares.","ai_generated":true,"unit":"wri-000318#insight-1","date_modified":"2026-10-23"},{"text":"Due to legal and ethics reviews regarding farmer privacy, the West Africa Cocoa dataset is not public; it is instead distributed as a heat map to show cocoa presence without revealing exact plot boundaries.","ai_generated":true,"unit":"wri-000318#insight-2","date_modified":"2026-10-23"},{"text":"The Cocoa Deforestation Risk Assessment indicates that recent forest loss is a significant variable, sometimes marking regions as high risk even without mapped cocoa production. Conversely, the lack of recent loss in protected areas like Ghana's Bia National Park and Côte d’Ivoire's Taï National Park resulted in low priority rankings, highlighting the effectiveness of legal forest protections.","ai_generated":true,"unit":"wri-000318#insight-3","date_modified":"2026-10-23"}],"climate_tags":["deforestation","remote sensing","forest conservation","land use"],"geography_discussed":["European Union","Taï National Park","Bia National Park"],"frame_perspective":[],"analysis_type":["methodology","risk assessment"]},{"id":"wri-000352","url":"https://climateinsightsdirectory.org/document/wri-000352","title":"Forest Footprint for Cities: Methods for Estimating Deforestation and Associated CO2 Emissions Embodied in Products Consumed in Cities","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/forest-footprint-cities-methods-estimating-deforestation-and-associated-co2-emissions","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","analysis_type"],"summary":{"text":"The document introduces the 'Forest Footprint for Cities' methodology, which connects city-level consumption and international trade to global tropical and subtropical deforestation. By quantifying the hectares of forest lost and the resulting CO2 emissions embodied in urban products, the method aims to make the external impacts of city governance and sustainability planning visible, enabling cities to join national and private sector efforts in reducing deforestation.","ai_generated":true,"unit":"wri-000352#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The methodology functions by linking global estimates of tropical and subtropical deforestation caused by agricultural production to city consumption and commodity-specific international trade.","ai_generated":true,"unit":"wri-000352#insight-1","date_modified":"2026-10-23"}],"climate_tags":["deforestation","carbon dioxide emissions","agricultural production","international trade"],"geography_discussed":[],"frame_perspective":[],"analysis_type":["methodology"]},{"id":"wri-000382","url":"https://climateinsightsdirectory.org/document/wri-000382","title":"Climate Watch Country Greenhouse Gas Emissions Data and Methodology","publisher":{"name":"World Resources Institute","organization":"world-resources-institute"},"document_type":"guide","language":"en","date_published":null,"source_url":"https://www.wri.org/research/climate-watch-country-greenhouse-gas-emissions-data-and-methodology","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The Climate Watch Country Greenhouse Gas Emissions Data provides comprehensive, comparable GHG emissions data for 193 countries and the European Union from 1990 onwards, covering all GHG types and sectors including energy, industrial processes, agriculture, waste, and LULUCF. The dataset typically operates with a three-year reporting lag, such as reporting 2020 data in 2023, and aims to help countries identify mitigation opportunities and compare emission profiles.","ai_generated":true,"unit":"wri-000382#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The Climate Watch dataset provides comparable greenhouse gas emissions data for 193 countries and the European Union, with records beginning in 1990.","ai_generated":true,"unit":"wri-000382#insight-1","date_modified":"2026-10-23"},{"text":"The emissions data covers all greenhouse gases across five specific sectors: energy, industrial processes, agriculture, waste, and land use, land-use change and forestry (LULUCF).","ai_generated":true,"unit":"wri-000382#insight-2","date_modified":"2026-10-23"},{"text":"There is typically a three-year lag in the availability of the latest data; for instance, data from 2020 was reported in 2023.","ai_generated":true,"unit":"wri-000382#insight-3","date_modified":"2026-10-23"}],"climate_tags":["greenhouse gas emissions","agriculture"],"geography_discussed":["European Union"],"frame_perspective":[],"analysis_type":["methodology"]}]}