{"query_echo":{"q":"","filters":{"analysis_type":["technical documentation"]},"warnings":[{"code":"facet_value_not_in_vocabulary","message":"Value 'technical documentation' for facet 'analysis_type' is not in the controlled vocabulary."}]},"total":{"value":6,"relation":"eq"},"page":1,"page_size":20,"self":"https://climateinsightsdirectory.org/api/v1/search?analysis_type=technical+documentation","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":"eninnova-000121","url":"https://climateinsightsdirectory.org/document/eninnova-000121","title":"INDUSTRIAL TRANSFORMATION TECHNOLOGY AND INVESTMENT MODEL DOCUMENTATION","publisher":{"name":"Energy Innovation","organization":"energy-innovation"},"document_type":"guide","language":"en","date_published":"2026-04","source_url":"https://energyinnovation.org/wp-content/uploads/ITTIM-Documentation-v1.1.0.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","analysis_type"],"summary":{"text":"The Industrial Transformation Technology and Investment Model (ITTIM) is an Excel-based tool that estimates energy use, CO2 emissions, and capital investment needs for industrial decarbonization. It employs a six-tier cumulative technology framework—ranging from energy efficiency to clean chemical feedstocks—to mitigate 97% to 99% of industrial emissions. The model currently supports datasets for China, India, Indonesia, Viet Nam, the Philippines, and the United States, allowing users to simulate various scenarios including carbon pricing and different hydrogen cost projections.","ai_generated":true,"unit":"eninnova-000121#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The ITTIM utilizes a cumulative six-tier technology framework to address industrial emissions, which together can mitigate between 97% and 99% of energy-related and cement calcination emissions in each modeled region.","ai_generated":true,"unit":"eninnova-000121#insight-1","date_modified":"2026-10-23"},{"text":"The technology tiers are prioritized by cost, maturity, and practicality: Tier 1 focuses on material and energy efficiency; Tier 2a and 2b cover electrification of low- to high-temperature processes; Tier 3 implements Carbon Capture and Use or Storage (CCUS); Tier 4a replaces remaining fossil fuels with green hydrogen; and Tier 4b transitions to clean chemical feedstocks.","ai_generated":true,"unit":"eninnova-000121#insight-2","date_modified":"2026-10-23"},{"text":"The model's geographic scope includes datasets for China, India, Indonesia, Viet Nam, the Philippines, and the United States (including 48 contiguous states), which collectively represented 64% of global industrial energy-related CO2 emissions in 2019.","ai_generated":true,"unit":"eninnova-000121#insight-3","date_modified":"2026-10-23"},{"text":"The ITTIM estimates both energy costs and capital equipment costs, though it excludes costs for land, buildings, installation, and operations and maintenance.","ai_generated":true,"unit":"eninnova-000121#insight-4","date_modified":"2026-10-23"},{"text":"The model allows users to simulate carbon pricing from $0 to $250 per tonne of CO2, which affects the cost-effectiveness of interventions but does not change the choice of technologies because the tool is not a cost-driven optimization model.","ai_generated":true,"unit":"eninnova-000121#insight-5","date_modified":"2026-10-23"},{"text":"Green hydrogen costs in the 'High Improvement' scenario for 2050 vary by region, ranging from $1.80/kg in China and India to $3.53/kg in the Philippines.","ai_generated":true,"unit":"eninnova-000121#insight-6","date_modified":"2026-10-23"}],"climate_tags":["Industrial decarbonization","energy efficiency","electrification","green hydrogen","blue hydrogen","carbon price"],"geography_discussed":[],"frame_perspective":[],"analysis_type":["technical documentation","user guide","modeling framework"]},{"id":"rmi-000745","url":"https://climateinsightsdirectory.org/document/rmi-000745","title":"Corporate Commitments and Emissions Explorer Technical Documentation","publisher":{"name":"RMI","organization":"rmi"},"document_type":"guide","language":"en","date_published":"2023-07-13","source_url":"https://rmi.org/app/uploads/2023/07/documentation_for_corporate_commitments_and_emissions_explorer.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","analysis_type"],"summary":{"text":"The Corporate Commitments and Emissions Explorer uses a formula-based approach to estimate corporate emissions trajectories, incorporating revenue growth, climate targets, and sectoral intensities. It offers five preset outlooks—Current, Climate-Aligned Action, Herd Catches Leaders, Laggards Catch Herd, and No Further Action—to simulate different levels of corporate ambition. The tool focuses on Scope 1 and 2 emissions, utilizing data from CDP and the Net Zero Tracker, while excluding Scope 3 emissions due to data quality and double-counting risks. Companies are categorized as ambitious, somewhat ambitious, or least ambitious based on their disclosure habits, emission intensities, and the nature of their climate commitments.","ai_generated":true,"unit":"rmi-000745#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The Explorer utilizes five preset outlooks to generate emission pathway scenarios: 'Current Outlook' applies existing public commitments; 'Climate-Aligned Action' assumes a 2050 net-zero target and a 50% reduction by 2030; 'Herd Catches Leaders' simulates laggards ramping intensity down to the minimum; 'Laggards Catch Herd' applies net-zero by 2050 to companies without public commitments; and 'No Further Action' assumes no action beyond currently published targets.","ai_generated":true,"unit":"rmi-000745#insight-1","date_modified":"2026-10-23"},{"text":"Emission estimations are calculated using a baseline formula that multiplies start year emissions (2022) by revenue growth, which can be further modified by multipliers for end targets, interim targets, partial target achievement, and carbon offsets.","ai_generated":true,"unit":"rmi-000745#insight-2","date_modified":"2026-10-23"},{"text":"Start year emissions for 2022 are sourced from CDP disclosures; for companies that do not disclose to CDP, emissions are estimated by multiplying the company's revenue by the minimum, median, or maximum emission intensity for its respective sector.","ai_generated":true,"unit":"rmi-000745#insight-3","date_modified":"2026-10-23"},{"text":"The tool exclusively includes Scope 1 and Scope 2 emissions measured in MTCO2e, intentionally omitting Scope 3, supply chain, embodied, or lifecycle emissions because of low data quality and consistency and the high risk of double counting.","ai_generated":true,"unit":"rmi-000745#insight-4","date_modified":"2026-10-23"},{"text":"Companies are classified into three ambition levels: 'ambitious' (disclose to CDP, below median intensity, include Scope 3, and have zero emissions targets), 'somewhat ambitious' (e.g., disclose to CDP with above-median intensity but have long-term commitments), and 'least ambitious' (no long-term commitments and either do not disclose to CDP or have above-median intensity).","ai_generated":true,"unit":"rmi-000745#insight-5","date_modified":"2026-10-23"}],"climate_tags":["Scope 2","Scope 3","Net-zero emissions","emission intensity","carbon offset","carbon accounting"],"geography_discussed":[],"frame_perspective":[],"analysis_type":["technical documentation","methodology"]},{"id":"sei-001961","url":"https://climateinsightsdirectory.org/document/sei-001961","title":"Free access to functioning SWAT application of the three river basins","publisher":{"name":"Stockholm Environment Institute","organization":"stockholm-environment-institute"},"document_type":"report","language":"en","date_published":"2020-02-29","source_url":"https://www.sei.org/wp-content/uploads/2022/03/swat-application-of-the-three-river-basins-1.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The BONUS RETURN project has released three baseline SWAT (Soil and Water Assessment Tool) model applications for the Vantaanjoki (Finland), Fyrisån (Sweden), and Słupia (Poland) river basins. These models, which have undergone calibration and validation, are hosted in the Finnish Environment Institute (SYKE) open data repository to allow researchers and practitioners to utilize existing parameterizations or develop new scenarios for these and similar target areas.","ai_generated":true,"unit":"sei-001961#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The BONUS RETURN project developed three baseline SWAT applications for specific case study catchments: Vantaanjoki in Finland, Fyrisån in Sweden, and Slupia in Poland.","ai_generated":true,"unit":"sei-001961#insight-1","date_modified":"2026-10-23"},{"text":"The SWAT model applications are designed to help other modelers by providing ready-made parameterizations for areas similar to the case studies, reducing the laborious process of building a model from scratch.","ai_generated":true,"unit":"sei-001961#insight-2","date_modified":"2026-10-23"},{"text":"The three model applications have been compressed into zip-files and made available through the Finnish Environment Institute (SYKE) open data repository.","ai_generated":true,"unit":"sei-001961#insight-3","date_modified":"2026-10-23"},{"text":"The Vantaanjoki catchment in Finland covers 1,688 km2 and utilized data from sources including the National Land Survey of Finland (MML), the Finnish Meteorological Institute (FMI), and the Finnish Geological Survey (GTK).","ai_generated":true,"unit":"sei-001961#insight-4","date_modified":"2026-10-23"},{"text":"The Fyrisån catchment in Sweden covers 2,005 km2 and incorporated data from Lantmäteriet, the Swedish Meteorological and Hydrological Institute (SMHI), and the Swedish Board of Agriculture (SBoA).","ai_generated":true,"unit":"sei-001961#insight-5","date_modified":"2026-10-23"},{"text":"The Słupia catchment in Poland covers 1,621 km2 and used data from the Centre for Geodetic and Cartographic Data (CODGIK), the Institute of Meteorology and Water Management (IMGW), and the Regional Water Management Authority (RZGW).","ai_generated":true,"unit":"sei-001961#insight-6","date_modified":"2026-10-23"}],"climate_tags":["water management","bioenergy"],"geography_discussed":["Finland","Sweden","Poland","Vantaa River","Fyris","Słupia"],"frame_perspective":[],"analysis_type":["technical documentation","data repository guide"]},{"id":"catf-000738","url":"https://climateinsightsdirectory.org/document/catf-000738","title":"Technical Support Document for the Powerplant Impact Estimator Software Tool","publisher":{"name":"Clean Air Task Force","organization":"clean-air-task-force"},"document_type":"report","language":"en","date_published":"2010-07","source_url":"https://cdn.catf.us/wp-content/uploads/2010/07/21094100/CATF_Pub_AbtTechnicalSupportDoc.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The Powerplant Impact Estimator (PIE) is a software tool designed to quantify the human health and economic benefits of reducing fine particulate matter (PM2.5) emissions from US electric generating units. It employs a three-step damage function approach: modeling changes in ambient air quality using a Source-Receptor (S-R) Matrix, calculating reductions in adverse health effects via epidemiological concentration-response functions, and assigning economic values to these avoided effects using cost-of-illness and willingness-to-pay metrics.","ai_generated":true,"unit":"catf-000738#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The PIE tool utilizes a three-step damage function approach to estimate benefits: first, it models changes in ambient air pollution levels; second, it calculates the associated change in adverse health effects; and third, it assigns an economic value to those effects.","ai_generated":true,"unit":"catf-000738#insight-1","date_modified":"2026-10-23"},{"text":"To estimate ambient PM2.5 levels, PIE uses a Source-Receptor (S-R) Matrix that combines an emissions inventory of SO2, NOx, direct PM2.5, NH3, and VOCs with transfer coefficients to determine the impact of emissions on each county in the continental United States.","ai_generated":true,"unit":"catf-000738#insight-2","date_modified":"2026-10-23"},{"text":"The tool's air chemistry is simplified, assuming that sulfate forms ammonium sulfate and that remaining ammonium reacts with nitrate to produce ammonium nitrate, a process the document notes is \"reasonably consistent with more complicated air quality models\".","ai_generated":true,"unit":"catf-000738#insight-3","date_modified":"2026-10-23"},{"text":"Health impact functions in PIE are derived from peer-reviewed epidemiological studies, using effect estimates, baseline incidence rates, affected populations, and estimated changes in pollutant concentrations.","ai_generated":true,"unit":"catf-000738#insight-4","date_modified":"2026-10-23"},{"text":"The analysis assumes there are no population-level thresholds for PM2.5-related health effects, although it provides results based on a hypothetical threshold of 10 ug/m3 to remain consistent with some EPA analyses.","ai_generated":true,"unit":"catf-000738#insight-5","date_modified":"2026-10-23"},{"text":"Economic valuation of avoided health effects uses three primary methods: Value of Statistical Life (VSL) for premature mortality, Cost of Illness (COI) for hospital admissions, and Willingness to Pay (WTP) for asthma exacerbations.","ai_generated":true,"unit":"catf-000738#insight-6","date_modified":"2026-10-23"},{"text":"To account for inflation, the tool adjusts 2000 $ values to 2006 $ using three indices: the All Goods Index (1.171), the Medical Cost Index (1.289), and the Wage Index (1.191).","ai_generated":true,"unit":"catf-000738#insight-7","date_modified":"2026-10-23"},{"text":"The model incorporates a 20-year lag structure for premature mortality, assuming 30% of deaths occur in the first year, 50% in years 2-5, and 20% in years 6-20, with a 3% discount rate applied to future benefits.","ai_generated":true,"unit":"catf-000738#insight-8","date_modified":"2026-10-23"},{"text":"For the year 2010, the estimated national economic damages from existing EGUs are $97 billion for adult mortality based on the Pope study and $250 billion based on the Laden study.","ai_generated":true,"unit":"catf-000738#insight-9","date_modified":"2026-10-23"},{"text":"The tool's S-R Matrix is calibrated using 2001 emission inventories and 2002 monitor data from Federal Reference Method (FRM) and EPA’s Speciation Network (STN) sites to improve accuracy.","ai_generated":true,"unit":"catf-000738#insight-10","date_modified":"2026-10-23"},{"text":"Population forecasts for the years 2010, 2015, and 2020 are based on economic forecasting models from Woods & Poole, adjusted using 2000 U.S. Census data.","ai_generated":true,"unit":"catf-000738#insight-11","date_modified":"2026-10-23"}],"climate_tags":["particulates","Green ammonia"],"geography_discussed":["Canada"],"frame_perspective":[],"analysis_type":["methodology","technical documentation","impact assessment"]},{"id":"sei-001259","url":"https://climateinsightsdirectory.org/document/sei-001259","title":"Calculations for the Greenhouse Development Rights Calculator","publisher":{"name":"Stockholm Environment Institute","organization":"stockholm-environment-institute"},"document_type":"research paper","language":"en","date_published":"2008-09-24","source_url":"https://www.sei.org/mediamanager/documents/Publications/Climate/calculations-gdr-calculator.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","analysis_type"],"summary":{"text":"The document details the calculations for the Greenhouse Development Rights (GDRs) calculator, a tool designed to explore burden-sharing for climate change. It establishes a methodology that uses the individual as the unit of analysis, assuming lognormal income distributions within countries. The paper defines how to calculate individual and national 'capacity' (financial ability to pay) and 'responsibility' (based on emissions), which are then combined into a Responsibility-Capacity Indicator (RCI) to allocate a global tax. It also describes the use of the Suits index to measure the progressivity of this tax system on a global scale.","ai_generated":true,"unit":"sei-001259#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The GDRs framework utilizes the individual as the primary unit of analysis, requiring data on instantaneous income distributions within countries to calculate burden-sharing.","ai_generated":true,"unit":"sei-001259#insight-1","date_modified":"2026-10-23"},{"text":"Income within countries is assumed to follow a lognormal distribution, characterized by mean income and the standard deviation of the log of income, the latter of which relates to the Gini coefficient.","ai_generated":true,"unit":"sei-001259#insight-2","date_modified":"2026-10-23"},{"text":"The calculator assumes that individual per capita emissions vary with income according to a specific elasticity (gamma), which is consistent across all countries and typically ranges between 0.7 and 1.0.","ai_generated":true,"unit":"sei-001259#insight-3","date_modified":"2026-10-23"},{"text":"Capacity and responsibility are calculated using income thresholds; income below a lower threshold (yl) is exempt from both contributing to the capacity to pay and the responsibility for emissions.","ai_generated":true,"unit":"sei-001259#insight-4","date_modified":"2026-10-23"},{"text":"The Responsibility-Capacity Indicator (RCI) is the central metric for allocating burden-sharing, calculated as a weighted sum of a nation's share of global responsibility and its share of global capacity.","ai_generated":true,"unit":"sei-001259#insight-5","date_modified":"2026-10-23"},{"text":"Burden-sharing is modeled as a tax (T) representing a fraction of the gross world product (GWP), with national and individual tax obligations determined by the RCI.","ai_generated":true,"unit":"sei-001259#insight-6","date_modified":"2026-10-23"},{"text":"The calculator can optionally employ the Suits index to evaluate the progressivity or regressivity of the resulting global tax system by comparing the cumulative share of total tax against cumulative income.","ai_generated":true,"unit":"sei-001259#insight-7","date_modified":"2026-10-23"}],"climate_tags":["climate finance"],"geography_discussed":[],"frame_perspective":[],"analysis_type":["mathematical modeling","technical documentation"]},{"id":"wri-000061","url":"https://climateinsightsdirectory.org/document/wri-000061","title":"Land Emissions and Removals Navigator (LEARN) Tool: Data Sources and Calculation Methods, Version 1.1","publisher":{"name":"World Resources Institute","organization":"world-resources-institute"},"document_type":"guide","language":"en","date_published":null,"source_url":"https://www.wri.org/research/learn-tool-land-emissions-and-removals-navigator-technical-note","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","analysis_type"],"summary":{"text":"The Land Emissions and Removals Navigator (LEARN) is a free, interactive web tool designed to help U.S. cities, counties, and municipalities analyze the greenhouse gas (GHG) impacts of forests and trees at a community scale. Developed by the World Resources Institute, ICLEI, and partners, the tool provides automated spatial analysis to support data-driven planning and monitoring of nature-based solutions. It utilizes national datasets from the U.S. Forest Service and U.S. Geological Survey to estimate carbon emissions and removals resulting from land cover change, forest disturbances, and changes to tree canopy on non-forest lands. The tool is intended to complement ICLEI's U.S. Community Protocol’s Appendix J: Forests and Trees to assist local leaders in aligning their actions with state and national mitigation targets.","ai_generated":true,"unit":"wri-000061#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The LEARN tool provides automated spatial analysis of greenhouse gas (GHG) impacts for forests and trees within a specific community or area of interest, allowing land managers to create data-driven plans and monitor impacts over time.","ai_generated":true,"unit":"wri-000061#insight-1","date_modified":"2026-10-23"},{"text":"To derive spatially explicit estimates of carbon emissions and removals, the tool analyzes national datasets provided by the U.S. Geological Survey and the U.S. Forest Service, focusing on forest disturbances, land cover change, and tree canopy changes on non-forest lands.","ai_generated":true,"unit":"wri-000061#insight-2","date_modified":"2026-10-23"},{"text":"The tool is designed to work alongside ICLEI's U.S. Community Protocol’s Appendix J: Forests and Trees to support local-scale climate action planning and demonstrate how local efforts contribute to national and state mitigation targets.","ai_generated":true,"unit":"wri-000061#insight-3","date_modified":"2026-10-23"}],"climate_tags":["greenhouse gas inventory","nature-based solutions","climate change mitigation","carbon dioxide emissions","carbon dioxide removal"],"geography_discussed":[],"frame_perspective":[],"analysis_type":["technical documentation"]}]}