{"query_echo":{"q":"","filters":{"analysis_type":["methodological description"]},"warnings":[]},"total":{"value":13,"relation":"eq"},"page":1,"page_size":20,"self":"https://climateinsightsdirectory.org/api/v1/search?analysis_type=methodological+description","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":"iai-000193","url":"https://climateinsightsdirectory.org/document/iai-000193","title":"Planificación del Ordenamiento Territorial Rural","publisher":{"name":"Inter-American Institute for Global Change Research","organization":"inter-american-institute-for-global-change-research"},"document_type":"research paper","language":"es","date_published":"2025-01-15","source_url":"https://iai.int/es/pub_acad/planificacion-del-ordenamiento-territorial-rural/","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","geography_discussed","analysis_type"],"summary":{"text":"The document defines rural land-use planning as a continuous and participatory process designed to align objectives and goals while managing risks and uncertainties. It outlines the methodology's role in decision-making and indicates that the text details the specific steps, activities, resulting products, and common techniques used in this planning process.","ai_generated":true,"unit":"iai-000193#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"Rural land-use planning is characterized as a proactive methodology for decision-making and a prospective, continuous, ordered, participatory, and iterative process. Its primary focus is on solving problems and aligning ends, objectives, and goals to reach a desired future for the territorial system, while accounting for the risks and uncertainties of different intervention options.","ai_generated":true,"unit":"iai-000193#insight-1","date_modified":"2026-10-23"},{"text":"The text provides a description of the primary steps and activities involved in rural land-use planning, the products generated from each stage, and the most frequently used techniques to achieve these results.","ai_generated":true,"unit":"iai-000193#insight-2","date_modified":"2026-10-23"}],"climate_tags":[],"geography_discussed":["Argentina","Chile","Uruguay"],"frame_perspective":[],"analysis_type":["methodological description"]},{"id":"sei-001030","url":"https://climateinsightsdirectory.org/document/sei-001030","title":"Designing Development Interventions","publisher":{"name":"Stockholm Environment Institute","organization":"stockholm-environment-institute"},"document_type":"research paper","language":"en","date_published":"2022-07-31","source_url":"https://www.sei.org/publications/designing-development-interventions/","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The authors demonstrate how combining service design—specifically using archetypes and prototyping—with discrete choice experiments can create effective development interventions, such as Weather Index Insurance for Ugandan farmers, by involving users in the design process and testing core assumptions.","ai_generated":true,"unit":"sei-001030#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"Service design (SD) is identified as a promising tool for designing interventions to address complex development challenges in the Global South because it directly involves citizens in improving services and systems.","ai_generated":true,"unit":"sei-001030#insight-1","date_modified":"2026-10-23"},{"text":"In the development of a Weather Index Insurance (WII) product in Uganda, the authors used archetypes to articulate farmer vulnerabilities and prototype insurance packages, while discrete choice experiments (DCEs) were used to test and triangulate these design features.","ai_generated":true,"unit":"sei-001030#insight-2","date_modified":"2026-10-23"},{"text":"The combined approach of service design and DCEs resulted in a WII product adopted by major insurance providers in Uganda and improved upon traditional qualitative work by allowing participants to challenge core assumptions and participate directly in service design.","ai_generated":true,"unit":"sei-001030#insight-3","date_modified":"2026-10-23"}],"climate_tags":["climate change","biodiversity loss","smallholder"],"geography_discussed":["Global South"],"frame_perspective":[],"analysis_type":["case study","methodological description"]},{"id":"sei-002756","url":"https://climateinsightsdirectory.org/document/sei-002756","title":"Serious Game System – SELECT ECOTECH","publisher":{"name":"Stockholm Environment Institute","organization":"stockholm-environment-institute"},"document_type":"report","language":"en","date_published":"2020-02-14","source_url":"https://www.sei.org/wp-content/uploads/2022/03/select-eco-tech.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The document details the development of SELECT ECOTECH, a serious game system (SGS) designed as a learning platform for stakeholders in the Baltic Sea Region (BSR). The game aims to foster systemic awareness and adaptive capacity when selecting eco-technologies to reduce nutrient and carbon emissions while maximizing co-benefits. Co-designed with stakeholders from three case study catchments—Fyrisån (Sweden), Vantaanjoki (Finland), and Slupia (Poland)—the game uses a board game format to simulate 'wicked' situations, including system shocks and policy changes. The report outlines a three-phase participatory design process, the game's rules regarding baseline emissions and resource management, and the specific eco-technologies and developments integrated into the system. The findings suggest that the iterative, playful nature of the SGS allows stakeholders to cross knowledge boundaries and experiment with innovative technology constellations beyond technocratic solutions.","ai_generated":true,"unit":"sei-002756#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"SELECT ECOTECH was developed through a participatory co-design process involving stakeholders from three specific river catchments: Fyrisån in Sweden, Vantaanjoki in Finland, and Slupia in Poland.","ai_generated":true,"unit":"sei-002756#insight-1","date_modified":"2026-10-23"},{"text":"The serious game system is designed to help users navigate 'wicked' situations—characterized by high uncertainty, complexity, and controversy—by providing a safe space to explore the systemic impact of different eco-technology constellations.","ai_generated":true,"unit":"sei-002756#insight-2","date_modified":"2026-10-23"},{"text":"The game's primary objectives for players are to reduce emissions from land use systems (urban, forestry, and agriculture), increase system productivity, and enhance adaptive capacity.","ai_generated":true,"unit":"sei-002756#insight-3","date_modified":"2026-10-23"},{"text":"The game mechanics incorporate dynamic elements such as 'shock' cards and policy changes to simulate real-world uncertainties and the impact of the policy environment on eco-technology implementation.","ai_generated":true,"unit":"sei-002756#insight-4","date_modified":"2026-10-23"},{"text":"The co-development process revealed that the learning and knowledge co-production began at the start of the design phase and did not require a fully functional game to be effective.","ai_generated":true,"unit":"sei-002756#insight-5","date_modified":"2026-10-23"}],"climate_tags":["carbon sequestration","water management","adaptive capacity","land use","bioenergy"],"geography_discussed":["Finland","Poland","Vantaa River","Słupia","Uppsala","Helsinki","Słupsk"],"frame_perspective":[],"analysis_type":["methodological description","process evaluation"]},{"id":"wri-001094","url":"https://climateinsightsdirectory.org/document/wri-001094","title":"Aqueduct 3.0: Updated Decision-Relevant Global Water Risk Indicators","publisher":{"name":"World Resources Institute","organization":"world-resources-institute"},"document_type":"research paper","language":"en","date_published":"2019-11-08","source_url":"https://www.wri.org/research/aqueduct-30-updated-decision-relevant-global-water-risk-indicators","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","analysis_type"],"summary":{"text":"The document introduces Aqueduct 3.0, a framework that translates complex hydrological data into intuitive water-related risk indicators. It combines 13 indicators covering quantity, quality, and reputational risks into a composite score to help decision-makers identify high-risk regions or assets. The updated version features a new hydrological model with integrated supply and demand, surface and groundwater modeling, higher spatial resolution, and monthly time series data.","ai_generated":true,"unit":"wri-001094#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"Aqueduct 3.0 utilizes a composite index approach to combine 13 distinct water risk indicators—specifically addressing quantity, quality, and reputational risks—into a single overall water risk score.","ai_generated":true,"unit":"wri-001094#insight-1","date_modified":"2026-10-23"},{"text":"The framework incorporates a new hydrological model characterized by four primary improvements: integrated water supply and demand, combined surface and groundwater modeling, increased spatial resolution, and the use of a monthly time series for selected indicators.","ai_generated":true,"unit":"wri-001094#insight-2","date_modified":"2026-10-23"},{"text":"Because key elements like overall water risk cannot be directly measured or validated, the tool is intended for prioritization and should be supplemented with local and regional detailed analyses.","ai_generated":true,"unit":"wri-001094#insight-3","date_modified":"2026-10-23"}],"climate_tags":["water quality","climate change"],"geography_discussed":[],"frame_perspective":[],"analysis_type":["methodological description"]},{"id":"sei-001725","url":"https://climateinsightsdirectory.org/document/sei-001725","title":"Getting the picture: using data visualization to make complex models more accessible to policy-makers","publisher":{"name":"Stockholm Environment Institute","organization":"stockholm-environment-institute"},"document_type":"fact sheet","language":"en","date_published":"2015","source_url":"https://www.sei.org/mediamanager/documents/Publications/SEI-FS-2015-Data-visualization-complex-models.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The Stockholm Environment Institute (SEI) utilizes a Robust Decision Support (RDS) approach that integrates water systems modelling, participatory techniques, and interactive data visualization via Tableau. This method addresses the challenge of communicating vast amounts of data from complex models, such as the Water Evaluation and Planning (WEAP) system, to non-scientists. By using multi-field graphics and interactive filters, stakeholders can evaluate system vulnerability across numerous scenarios and assess the trade-offs of different management strategies, such as dam construction, to identify robust solutions for an uncertain future.","ai_generated":true,"unit":"sei-001725#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The Robust Decision Support (RDS) approach combines water systems modelling, data visualization, and participatory techniques to help stakeholders identify strategies that provide satisfactory outcomes across a wide range of possible future scenarios.","ai_generated":true,"unit":"sei-001725#insight-1","date_modified":"2026-10-23"},{"text":"SEI uses the Water Evaluation and Planning (WEAP) system for water management models, but because these models produce enormous amounts of data that can obscure insights, SEI employs Tableau software to create interactive visualizations that bridge the communication gap between scientists and decision-makers.","ai_generated":true,"unit":"sei-001725#insight-2","date_modified":"2026-10-23"},{"text":"In the Peruvian Chira-Piura watershed case study, interactive dashboards allowed users to filter data across 13 system performance metrics and 11 strategies, revealing that while a strategy like building a dam might improve hydropower generation, it could negatively impact other sectors or objectives.","ai_generated":true,"unit":"sei-001725#insight-3","date_modified":"2026-10-23"},{"text":"Effective communication of complex model results requires a balance between presenting nuance and maintaining clarity to avoid overwhelming decision-makers; visualization software is a tool that must be used thoughtfully alongside close interaction with key actors.","ai_generated":true,"unit":"sei-001725#insight-4","date_modified":"2026-10-23"}],"climate_tags":["water management","climate change","vulnerability assessment"],"geography_discussed":["Peru","Chira-Piura watershed"],"frame_perspective":[],"analysis_type":["methodological description","case study"]},{"id":"ucs-000117","url":"https://climateinsightsdirectory.org/document/ucs-000117","title":"Technical appendix for the report Confronting Climate Change in the Great Lakes Region: Impacts on Our Communities and Ecosystems","publisher":{"name":"Union of Concerned Scientists","organization":"union-of-concerned-scientists"},"document_type":"report","language":"en","date_published":"2014-06-20","source_url":"https://www.ucs.org/resources/great-lakes-report-technical-appendix-climate-change-projections","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","analysis_type"],"summary":{"text":"The document outlines the use of HadCM3 and Parallel Climate Model (PCM) data, combined with IPCC 2000 emission scenarios and a century of historical records, to project climate changes in the Great Lakes region. The analysis indicates greater temperature increases, especially in summer, and general drying during summer and fall compared to previous assessments.","ai_generated":true,"unit":"ucs-000117#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The study utilized two three-dimensional climate models—the HadCM3 and the Parallel Climate Model (PCM)—alongside IPCC 2000 standard emission scenarios ('high', 'medium', and 'low') and a historical climate database spanning over 100 years to project regional changes.","ai_generated":true,"unit":"ucs-000117#insight-1","date_modified":"2026-10-23"},{"text":"The current projections show a higher degree of agreement between different models than previous assessments, such as the U.S. National Assessment, and predict more significant temperature increases (particularly in summer) and increased drying during summer and fall across the region.","ai_generated":true,"unit":"ucs-000117#insight-2","date_modified":"2026-10-23"}],"climate_tags":["precipitation"],"geography_discussed":[],"frame_perspective":[],"analysis_type":["methodological description","comparative analysis"]},{"id":"sei-001662","url":"https://climateinsightsdirectory.org/document/sei-001662","title":"CRED v.1.4 Technical Report","publisher":{"name":"Stockholm Environment Institute","organization":"stockholm-environment-institute"},"document_type":"report","language":"en","date_published":"2012-08","source_url":"https://www.sei.org/mediamanager/documents/Publications/Climate/SEI-CRED-1.4-Technical-Report.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The CRED v.1.4 technical report details an integrated assessment model designed to optimize the pace and distribution of emissions mitigation investments across 16 global regions. The model utilizes a Cobb-Douglas production function that accounts for the productivity of 'green capital' and employs a vulnerability index to apportion global climate damages. Key updates in version 1.4 include a 2010 base year, expanded regional disaggregation, and a climate module recalibrated to match the MAGICC5.3 model. The model's optimization process maximizes a global utility function based on regional per capita consumption, with an optional feature to calculate the social cost of carbon.","ai_generated":true,"unit":"sei-001662#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"CRED v.1.4 expands its geographic granularity by disaggregating the world into 16 regions, an increase from the 9 regions used in previous versions.","ai_generated":true,"unit":"sei-001662#insight-1","date_modified":"2026-10-23"},{"text":"The model's economy module assumes that 'green' (emission-reducing) investment is half as productive of income as standard investment, assigning a fixed parameter of s = 0.5 to represent this relative productivity.","ai_generated":true,"unit":"sei-001662#insight-2","date_modified":"2026-10-23"},{"text":"Global climate damages are distributed among regions using a vulnerability index based on three factors: the proportion of GDP in agriculture and tourism, the share of the population living below 5 meters above sea level, and the inverse of freshwater resources per person.","ai_generated":true,"unit":"sei-001662#insight-3","date_modified":"2026-10-23"},{"text":"The model provides four options for global damage functions, ranging from the N-N function (equivalent to DICE 2007) to the H-W function, which incorporates Weitzman's estimates of higher losses at higher temperatures, such as a 50 percent loss of output at 6°C.","ai_generated":true,"unit":"sei-001662#insight-4","date_modified":"2026-10-23"},{"text":"The climate module in v.1.4 was recalibrated to align with the MAGICC5.3 model and uses a default climate sensitivity parameter of 3.0°C for the temperature increase resulting from a doubling of atmospheric CO2 concentrations.","ai_generated":true,"unit":"sei-001662#insight-5","date_modified":"2026-10-23"},{"text":"Abatement costs and potentials are derived from McKinsey Climate Desk Version 2.1 data, with the model assuming that technological progress will make 100 percent abatement of industrial emissions possible in every region by the year 2100.","ai_generated":true,"unit":"sei-001662#insight-6","date_modified":"2026-10-23"}],"climate_tags":["climate change mitigation","carbon dioxide emissions","climate finance"],"geography_discussed":["United States","Japan","Brazil","Mexico","South Africa","People's Republic of China","India"],"frame_perspective":[],"analysis_type":["technical report","methodological description"]},{"id":"sei-001663","url":"https://climateinsightsdirectory.org/document/sei-001663","title":"CRED V.1.3 TECHNICAL REPORT","publisher":{"name":"Stockholm Environment Institute","organization":"stockholm-environment-institute"},"document_type":"report","language":"en","date_published":"2012-01-25","source_url":"https://www.sei.org/mediamanager/documents/Publications/Climate-mitigation-adaptation/Economics_of_climate_policy/SEI-CRED13-Tech-Report.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The CRED v.1.3 model is an integrated assessment tool designed to project global climate and development scenarios over a 300-year period. It focuses on the distribution of climate damages and policy costs across nine global regions, utilizing a Cobb-Douglas production function and a vulnerability index to allocate damages. The model incorporates updated population data, four selectable damage functions, and abatement cost curves based on McKinsey data to inform global climate negotiations.","ai_generated":true,"unit":"sei-001663#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"CRED v.1.3 is an integrated assessment model that projects global climate and development scenarios at 10-year intervals over a 300-year time span, using a 2005 base year.","ai_generated":true,"unit":"sei-001663#insight-1","date_modified":"2026-10-23"},{"text":"The model divides the world into nine regions: three high-income (United States, Europe, and Other high-income) and six developing (Latin America and the Caribbean, Middle East, Russia and non-EU Eastern Europe, Africa, China, and Other developing Asia).","ai_generated":true,"unit":"sei-001663#insight-2","date_modified":"2026-10-23"},{"text":"The economy module employs a Cobb-Douglas production function where mitigation investment ('green capital') is assumed to be half as productive of income as standard investment (s = 0.5).","ai_generated":true,"unit":"sei-001663#insight-3","date_modified":"2026-10-23"},{"text":"CRED v.1.3 offers four damage function options (NN, HN, NW, HW) that combine different low-temperature and high-temperature estimates from Nordhaus, Hanemann, and Weitzman.","ai_generated":true,"unit":"sei-001663#insight-4","date_modified":"2026-10-23"},{"text":"Global damages are distributed among regions using a vulnerability index based on three factors: the proportion of GDP in agriculture and tourism, the share of the population living below 5-meter elevation, and freshwater resources per person.","ai_generated":true,"unit":"sei-001663#insight-5","date_modified":"2026-10-23"},{"text":"Abatement costs and potentials are derived from McKinsey 2030 cost curves, with the model assuming that 100 percent abatement of industrial emissions becomes possible in every region by 2105.","ai_generated":true,"unit":"sei-001663#insight-6","date_modified":"2026-10-23"},{"text":"The model's optimization process maximizes a global utility function based on the discounted sum of the logarithms of regional per capita consumption, using a default pure time preference rate of 0.1 percent per year.","ai_generated":true,"unit":"sei-001663#insight-7","date_modified":"2026-10-23"}],"climate_tags":["climate change mitigation","carbon price","climate change vulnerability","sustainable finance","carbon dioxide emissions"],"geography_discussed":["United States","Europe","Latin America and the Caribbean","Middle East","Russia","Africa","People's Republic of China"],"frame_perspective":[],"analysis_type":["technical report","methodological description"]},{"id":"sei-001421","url":"https://climateinsightsdirectory.org/document/sei-001421","title":"Introducing the Resources and Energy Analysis Programme (REAP)","publisher":{"name":"Stockholm Environment Institute","organization":"stockholm-environment-institute"},"document_type":"research paper","language":"en","date_published":"2010","source_url":"https://www.sei.org/mediamanager/documents/Publications/introducing_reap_100216_web.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The Resources and Energy Analysis Programme (REAP) is a software tool developed by the Stockholm Environment Institute (SEI) that uses an environmentally extended input-output model to track greenhouse gas emissions, air pollutants, and ecological footprints based on consumption rather than just production. By distinguishing between domestic and imported goods across 178 sectors, REAP allows users to create quantifiable scenarios based on the IPAT formula (Population, Affluence, and Technology) to test policy interventions. The tool has been applied to various UK sectors, including the NHS and tourism, and used in regional planning, such as housing scenarios in the Tees Valley. SEI is expanding the tool's application to EU-27 members, Sweden, and the Basque Country.","ai_generated":true,"unit":"sei-001421#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The consumption-based accounting approach reveals that UK consumer emissions are higher than producer emissions; for example, in 2004, UK consumer emissions were approximately 730Mt CO2, which was 20 per cent higher than producer emissions.","ai_generated":true,"unit":"sei-001421#insight-1","date_modified":"2026-10-23"},{"text":"REAP utilizes an environmentally extended input-output model that tracks the flow of goods and services between the UK and the 'rest of the world' across 178 individual sectors, calculating the impact per pound (£) spent for 356 product groups and over 50 consumption categories.","ai_generated":true,"unit":"sei-001421#insight-2","date_modified":"2026-10-23"},{"text":"The REAP scenario editor is based on the IPAT equation, allowing users to alter over 200 variables across three determinants: Population (size and number of households), Affluence (per-capita expenditure and consumption baskets), and Technology (energy mix and industrial energy demand).","ai_generated":true,"unit":"sei-001421#insight-3","date_modified":"2026-10-23"},{"text":"SEI has developed a 'family' of models based on REAP's methodology to target specific sectors, including tools for the UK education sector and the National Health Service (NHS). For the NHS, 60 per cent of emissions are linked to the manufacture and transportation of purchased goods and services.","ai_generated":true,"unit":"sei-001421#insight-4","date_modified":"2026-10-23"},{"text":"In a case study of the Tees Valley, a 'Business as Usual' scenario predicted a 1.8 per cent increase in CO2 emissions from household fuel and electricity over 20 years, which equates to a 5.5 per cent increase per person due to projected population decline.","ai_generated":true,"unit":"sei-001421#insight-5","date_modified":"2026-10-23"},{"text":"The Tees Valley workshop demonstrated that combining measures to increase renewable energy, improving energy efficiency in existing homes, and raising energy performance requirements for new homes could reduce the housing carbon footprint by at least 10 per cent over 20 years.","ai_generated":true,"unit":"sei-001421#insight-6","date_modified":"2026-10-23"}],"climate_tags":["greenhouse gas emissions","carbon footprint","energy efficiency","renewable energy","sustainable development","Transportation emission"],"geography_discussed":["Tees Valley","European Union","Sweden","Basque Country","Spain"],"frame_perspective":[],"analysis_type":["methodological description","case study","policy analysis"]},{"id":"iai-001621","url":"https://climateinsightsdirectory.org/document/iai-001621","title":"Impactos del cambio climático en la biodiversidad de los Andes tropicales: Riesgo climático, vulnerabilidad y herramientas de toma de decisiones para la planificación de la conservación","publisher":{"name":"Inter-American Institute for Global Change Research","organization":"inter-american-institute-for-global-change-research"},"document_type":"report","language":"es","date_published":null,"source_url":"https://iai.int/admin/site/sites/default/files/uploads/Site-1_Clima.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The project addresses the lack of long-term climate data in the tropical Andes by combining dendrochronology, improved hydrometeorological networks, in-situ digital sensors, and global circulation models. These diverse data streams—including tree-ring chronologies spanning 100 to 200 years and simulations for 2011-2030 and 2045-2065—are integrated to reduce uncertainty in climate projections and inform the NatureServe vulnerability index for conservation management.","ai_generated":true,"unit":"iai-001621#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"Due to the limited nature of historical hydrometeorological records, the project used classical dendrochronology (tree-ring analysis) to reconstruct past climate conditions over 100 to 200 years. Key species were selected from páramo, High-Andean forest, and upper cloud forest environments, specifically using Weinmannia cochensis and Ocotea sericea in the northern Ecuadorian Andes, and Polylepis pepei and Pseudolmedia rigida in the border region between Bolivia and Peru.","ai_generated":true,"unit":"iai-001621#insight-1","date_modified":"2026-10-23"},{"text":"To improve data density and quality, the project implemented statistical tools via the RClimdex package to homogenize historical series and installed digital sensors at approximately 500-meter altitudinal intervals across gradients of 4,500 meters to collect hourly temperature, humidity, and dew point data.","ai_generated":true,"unit":"iai-001621#insight-2","date_modified":"2026-10-23"},{"text":"Future climate conditions were estimated using global circulation models (including ECHAM4.5, GFDL, CCM3v6, and MRI) and reanalysis data from NOAA NCEP-DOE. Prospective simulations focused on short-term (2011-2030) and medium-term (2045-2065) horizons, incorporating altitudinal corrections to account for the Andean orography.","ai_generated":true,"unit":"iai-001621#insight-3","date_modified":"2026-10-23"},{"text":"The project integrated tree-ring chronologies, long-term climate indices, global circulation model simulations, reanalysis data, and local altitudinal temperature/humidity patterns to develop short- and medium-term scenarios for the climate exposure module of the NatureServe vulnerability index.","ai_generated":true,"unit":"iai-001621#insight-4","date_modified":"2026-10-23"}],"climate_tags":["climate modeling","biodiversity conservation"],"geography_discussed":["Ecuador","Bolivia","Peru"],"frame_perspective":[],"analysis_type":["methodological description","climate reconstruction","climate projection"]},{"id":"wri-000004","url":"https://climateinsightsdirectory.org/document/wri-000004","title":"Balancing Water Demands and Increasing Climate Resilience: Establishing a Baseline Water Risk Assessment Model in Ethiopia","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/water-demands-climate-resilience-risk-ethiopia","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The World Resources Institute developed a baseline water risk model for Ethiopia using 2015 data to help decision-makers incorporate water-related climate risk into sectoral planning. The model utilizes geospatially-explicit estimates for irrigation, livestock, domestic, and industrial water use, combined with 36 years of remotely-sensed renewable water resources data. It generates four key indicators: baseline water stress, months of water scarcity, seasonal variability, and interannual variability. The model is intended for national-level scenario analysis and monitoring of Sustainable Development Goal target 6.4.2, though it excludes water storage infrastructure, environmental flow requirements, and transboundary water obligations.","ai_generated":true,"unit":"wri-000004#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The baseline water risk model generates four specific indicators to assess water security: baseline water stress (total annual withdrawal relative to available resources), months of water scarcity (months with extremely high water stress), interannual variability (variation between years), and seasonal variability (variation between months).","ai_generated":true,"unit":"wri-000004#insight-1","date_modified":"2026-10-23"},{"text":"To address the lack of a comprehensive location dataset for industries, the model used NASA Nighttime Lights as a proxy for industrial activity, attributing industrial water demand to subbasins based on light brightness. This approach attributed 55% of industrial water demand to the Awash Basin, which is comparable to a SIWI study's estimate of 60%.","ai_generated":true,"unit":"wri-000004#insight-2","date_modified":"2026-10-23"},{"text":"The model's accuracy is limited by significant data gaps and the use of proxies. For example, domestic water demand was estimated using Growth and Transformation (GTP II) targets for 2015-2020, and irrigation efficiency was based on a 44.3% average from the Awash Basin rather than country-wide data.","ai_generated":true,"unit":"wri-000004#insight-3","date_modified":"2026-10-23"},{"text":"Several critical factors were excluded from the model to maintain uniformity or due to lack of data, including environmental flow requirements, transboundary water obligations, and water storage and conveyance infrastructure.","ai_generated":true,"unit":"wri-000004#insight-4","date_modified":"2026-10-23"},{"text":"The model focuses on physical water availability and sectoral competition, meaning it does not account for economic water scarcity caused by insufficient investment in infrastructure or institutional capacity.","ai_generated":true,"unit":"wri-000004#insight-5","date_modified":"2026-10-23"}],"climate_tags":["climate resilience","agricultural irrigation","remote sensing"],"geography_discussed":["Awash Basin","Abbay Basin","Afar Region","Somali Region"],"frame_perspective":[],"analysis_type":["methodological description","baseline assessment"]},{"id":"wri-000351","url":"https://climateinsightsdirectory.org/document/wri-000351","title":"Comparison of Annual GHG Reduction Rates from 2020-2030 Based on Submitted or Reported Climate Commitments (2020 Pledges and INDCs) for Canada, EU, Japan and US","publisher":{"name":"World Resources Institute","organization":"world-resources-institute"},"document_type":"fact sheet","language":"en","date_published":null,"source_url":"https://www.wri.org/data/comparison-annual-ghg-reduction-rates-2020-2030-based-submitted-or-reported-climate","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","analysis_type"],"summary":{"text":"The document outlines the data sources and assumptions used to calculate annual GHG reduction rates for Canada, the EU-28, Japan, and the US between 2020 and 2030. It specifies that estimates are provided both with and without the land use, land-use change, and forestry (LULUCF) sector to ensure comparability. The calculations rely on historical emissions data from national inventory reports submitted to the UNFCCC and specific target percentages for 2020 and 2030 (or 2025/2050 for the US) relative to various base years.","ai_generated":true,"unit":"wri-000351#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The analysis provides two sets of decarbonization rate estimates for the 2020-2030 period: one that excludes and one that includes the land use, land-use change, and forestry (LULUCF) sector, because the treatment of this sector varies across the four analyzed entities.","ai_generated":true,"unit":"wri-000351#insight-1","date_modified":"2026-10-23"},{"text":"The 2020 GHG reduction targets used for the estimates are a 17% reduction from 2005 levels for Canada and the United States, a 20% reduction from 1990 levels for the EU-28, and a 3.8% reduction from 2005 levels for Japan.","ai_generated":true,"unit":"wri-000351#insight-2","date_modified":"2026-10-23"},{"text":"The 2030 GHG reduction targets used for the estimates are a 30% reduction from 2005 levels for Canada, a 40% reduction from 1990 levels for the EU-28, and a 26% reduction from 2013 levels for Japan.","ai_generated":true,"unit":"wri-000351#insight-3","date_modified":"2026-10-23"},{"text":"For the United States, the 2030 emissions levels are interpolated based on a 2025 target of a 27% reduction from 2005 levels and a 2050 target of an 80% reduction from 2005 levels.","ai_generated":true,"unit":"wri-000351#insight-4","date_modified":"2026-10-23"}],"climate_tags":["greenhouse gas emissions"],"geography_discussed":[],"frame_perspective":[],"analysis_type":["comparative analysis","methodological description"]},{"id":"wri-000470","url":"https://climateinsightsdirectory.org/document/wri-000470","title":"Baseline Water Stress: China","publisher":{"name":"World Resources Institute","organization":"world-resources-institute"},"document_type":"briefing","language":"en","date_published":null,"source_url":"https://www.wri.org/research/baseline-water-stress-china","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The World Resources Institute (WRI) developed BWS-China to provide more granular water risk mapping by using prefecture-level water withdrawal data for over 300 cities. While BWS-China shares similar spatial patterns with Aqueduct's global indicator, the use of detailed country-specific data reveals more accurate spatial patterns and sector-specific risks in certain catchments. This approach serves as a model for other countries to develop detailed baseline water stress indicators to support corporate investment decisions and government policymaking.","ai_generated":true,"unit":"wri-000470#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"WRI developed the BWS-China indicator to address the need for more granular mapping by utilizing water withdrawal data at the prefecture level for more than 300 cities, which captures spatial patterns that are typically lost in aggregated country-level statistics.","ai_generated":true,"unit":"wri-000470#insight-1","date_modified":"2026-10-23"},{"text":"While the BWS-China maps and the global Aqueduct baseline water stress indicator show similar overall spatial patterns, the more detailed sector-specific withdrawal data in BWS-China reveal differences and new spatial patterns in some catchments.","ai_generated":true,"unit":"wri-000470#insight-2","date_modified":"2026-10-23"},{"text":"The BWS-China project represents the first integration of detailed, country-specific water withdrawal data into the global Aqueduct indicator, establishing a model for other nations and stakeholders to create their own baseline water stress indicators.","ai_generated":true,"unit":"wri-000470#insight-3","date_modified":"2026-10-23"},{"text":"BWS-China is designed for use by Chinese government officials and national or international companies to evaluate supply chain risks, investment opportunities, and sector-induced water stress at local or central levels.","ai_generated":true,"unit":"wri-000470#insight-4","date_modified":"2026-10-23"}],"climate_tags":["water management"],"geography_discussed":["World"],"frame_perspective":[],"analysis_type":["methodological description","comparative analysis"]}]}