{"query_echo":{"q":"","filters":{"analysis_type":["methodology"]},"warnings":[]},"total":{"value":240,"relation":"eq"},"page":9,"page_size":20,"self":"https://climateinsightsdirectory.org/api/v1/search?analysis_type=methodology&page=9","next":"https://climateinsightsdirectory.org/api/v1/search?analysis_type=methodology&page=10","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":"iisd-001600","url":"https://climateinsightsdirectory.org/document/iisd-001600","title":"IISD Methodology to Assess Government Implementation of the IGF Mining Policy Framework","publisher":{"name":"International Institute for Sustainable Development","organization":"international-institute-for-sustainable-development"},"document_type":"guide","language":"en","date_published":"2014","source_url":"https://www.iisd.org/system/files/publications/iisd-methodology-igf-mining-policy-framework.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The IISD has developed a six-step methodology to evaluate national mining policy frameworks against the IGF Mining Policy Framework (MPF). The process involves desk-based research, the use of a detailed scoring template, stakeholder consultations, and a validation meeting to identify strengths, weaknesses, and gaps in implementation. The methodology is designed to be adaptable to other frameworks, such as the Africa Mining Vision, and aims to inform capacity-building efforts for governments.","ai_generated":true,"unit":"iisd-001600#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The assessment evaluates national mining policy frameworks across six specific dimensions of the MPF: legal and policy framework, financial benefit optimization, socioeconomic benefit optimization, environmental management, post-mining transition, and artisanal and small-scale mining.","ai_generated":true,"unit":"iisd-001600#insight-1","date_modified":"2026-10-23"},{"text":"The methodology employs a detailed Excel-based template that assigns a colour-coded numerical score to each MPF standard: 0 (low-red) for no existing law, 1 (medium-yellow) for a law that exists but is not implemented, and 2 (high-green) for a law that is consistently implemented and enforced.","ai_generated":true,"unit":"iisd-001600#insight-2","date_modified":"2026-10-23"},{"text":"The assessment process consists of six sequential components: desk-based research, confirmation of the assessment template, field visits and stakeholder consultations, preparing the initial assessment, an in-country validation meeting, and the preparation of the draft and final assessment report.","ai_generated":true,"unit":"iisd-001600#insight-3","date_modified":"2026-10-23"},{"text":"In-country assessments typically last 10 to 11 days, including meetings with government agencies, mining companies, and civil society stakeholders, as well as one to two days of field visits to mining regions.","ai_generated":true,"unit":"iisd-001600#insight-4","date_modified":"2026-10-23"},{"text":"The methodology is adaptable for use with other holistic mining policy frameworks, specifically the Africa Mining Vision (AMV) and the Country Mining Vision Guidebook.","ai_generated":true,"unit":"iisd-001600#insight-5","date_modified":"2026-10-23"},{"text":"The process has been applied to assessments in the Dominican Republic (completed March 2014), Uganda (in-country visit June 2014), and Madagascar (October 2014).","ai_generated":true,"unit":"iisd-001600#insight-6","date_modified":"2026-10-23"}],"climate_tags":["sustainable development","capacity building"],"geography_discussed":["Dominican Republic","Uganda","Madagascar"],"frame_perspective":[],"analysis_type":["methodology"]},{"id":"ucs-000747","url":"https://climateinsightsdirectory.org/document/ucs-000747","title":"Half the Oil Plan Methodology and Assumptions","publisher":{"name":"Union of Concerned Scientists","organization":"union-of-concerned-scientists"},"document_type":"research paper","language":"en","date_published":"2013-06","source_url":"https://www.ucs.org/sites/default/files/2019-09/half-the-oil-methodology.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","analysis_type"],"summary":{"text":"The Union of Concerned Scientists (UCS) argues that the U.S. can cut its projected oil use in half by 2035 compared to a 'do-nothing' baseline. By implementing technology and policy advances across transportation, buildings, and industry, the plan estimates a reduction of 11 million barrels per day (mbpd), resulting in gross cost savings of more than $550 billion and the creation of over 1 million net new jobs by 2035.","ai_generated":true,"unit":"ucs-000747#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The analysis establishes a 'do-nothing' baseline where no efficiency improvements or low-carbon alternatives are adopted after 2013, projecting U.S. oil use to reach 22 million barrels per day by 2035.","ai_generated":true,"unit":"ucs-000747#insight-1","date_modified":"2026-10-23"},{"text":"The 'Half the Oil' plan estimates a realistic reduction of 11 million barrels per day (mbpd) by 2035, although the analysis suggests potential savings of up to 12 mbpd.","ai_generated":true,"unit":"ucs-000747#insight-2","date_modified":"2026-10-23"},{"text":"Achieving the 'Half the Oil' target would result in gross oil cost savings of more than $550 billion in 2035, based on a forecasted oil price of $133 per barrel.","ai_generated":true,"unit":"ucs-000747#insight-3","date_modified":"2026-10-23"},{"text":"The plan projects significant emissions reductions across sectors, with passenger cars providing the largest contribution at 902 million metric tons of emissions saved in 2035.","ai_generated":true,"unit":"ucs-000747#insight-4","date_modified":"2026-10-23"},{"text":"The implementation of fuel efficiency and greenhouse gas emissions standards for on-road vehicles is estimated to create more than 1 million net new jobs by 2035.","ai_generated":true,"unit":"ucs-000747#insight-5","date_modified":"2026-10-23"},{"text":"Specific technology and efficiency targets for 2035 include increasing passenger car fuel economy to 34.6 mpg and achieving a 45% market share for electric vehicles.","ai_generated":true,"unit":"ucs-000747#insight-6","date_modified":"2026-10-23"}],"climate_tags":["fuel consumption","energy efficiency","electric vehicle","biofuel","greenhouse gas emissions","transport industry","climate finance","green job"],"geography_discussed":[],"frame_perspective":[],"analysis_type":["methodology","projection","policy assessment"]},{"id":"iisd-001616","url":"https://climateinsightsdirectory.org/document/iisd-001616","title":"Toolkit on Internet Public Policy Dialogue","publisher":{"name":"International Institute for Sustainable Development","organization":"international-institute-for-sustainable-development"},"document_type":"guide","language":"en","date_published":"2012-09","source_url":"https://www.iisd.org/system/files/publications/iisd_toolkit_internet_public_policy_bw.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The document argues that Internet policy must move beyond technical management to include broad societal impacts on rights, security, and sustainable development. It advocates for a multistakeholder model involving government, the private sector, civil society, and academia to ensure legitimacy and effectiveness. The toolkit details eight specific tools—ranging from expert roundtables and policy mapping to online surveys and public forums—designed to generate evidence and foster public consent. It emphasizes 'adaptive policy making' and the principle of subsidiarity, suggesting that policy action should occur at the lowest jurisdictional level consistent with effectiveness.","ai_generated":true,"unit":"iisd-001616#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"Internet public policy is often developed in isolation by technical communities, leading to a gap where ICTs are mainstreamed into sectoral policies as afterthoughts rather than integral factors for achieving development objectives.","ai_generated":true,"unit":"iisd-001616#insight-1","date_modified":"2026-10-23"},{"text":"Effective multistakeholder collaboration must manage four critical risks: legitimacy (ensuring diverse voices are recognized), capture (preventing powerful stakeholders from biasing the process), vested interests (overcoming resistance from privileged lobbyists), and effectiveness (ensuring dialogue leads to tangible outcomes).","ai_generated":true,"unit":"iisd-001616#insight-2","date_modified":"2026-10-23"},{"text":"The toolkit promotes the principle of subsidiarity, which dictates that policy action should be prioritized at the lowest jurisdictional level (local or regional) that remains effective, with international policies adopted only when more effective than national ones.","ai_generated":true,"unit":"iisd-001616#insight-3","date_modified":"2026-10-23"},{"text":"The document identifies a core set of global technical governance institutions, including the ITU, ICANN, W3C, ISOC, and IETF, noting that while some are open, others have high membership costs that can act as barriers to participation.","ai_generated":true,"unit":"iisd-001616#insight-4","date_modified":"2026-10-23"},{"text":"Policy practitioners are advised to use 'adaptive policy making,' a process of continuous learning where policies are dynamic and subject to constant re-evaluation to respond to uncertainties and change.","ai_generated":true,"unit":"iisd-001616#insight-5","date_modified":"2026-10-23"},{"text":"The toolkit categorizes its tools into those that generate evidence (Mapping the ICT landscape, Online surveys, Experts roundtables) and those that generate consent (Background papers, Public multistakeholder events, Focus groups, Scenarios/Backcasting, and Online discussion boards).","ai_generated":true,"unit":"iisd-001616#insight-6","date_modified":"2026-10-23"},{"text":"Online surveys are highlighted as a cost-effective way to create a baseline of stakeholder opinion, but they carry a risk of discriminatory evidence gathering in areas with low Internet penetration.","ai_generated":true,"unit":"iisd-001616#insight-7","date_modified":"2026-10-23"},{"text":"Scenario building and backcasting are presented as tools to help stakeholders imagine possible futures and identify the necessary policy steps to achieve a desired 'best case' outcome while avoiding 'worst case' scenarios.","ai_generated":true,"unit":"iisd-001616#insight-8","date_modified":"2026-10-23"},{"text":"The document provides a case study of the Canadian Internet Forum, where a national survey and six city-based focus groups (Winnipeg, Iqaluit, Toronto, Vancouver, Halifax, and Montreal) were used to identify key policy tracks in digital literacy and economic development.","ai_generated":true,"unit":"iisd-001616#insight-9","date_modified":"2026-10-23"},{"text":"The KICTAnet experience in Kenya demonstrates that online mailing lists can serve as a catalyst for national policy, provided there is strong leadership and a focus on specific thematic areas.","ai_generated":true,"unit":"iisd-001616#insight-10","date_modified":"2026-10-23"}],"climate_tags":["sustainable development","digital divide","public policy","climate change","economic development"],"geography_discussed":["Canada","West Africa","Senegal","Nigeria","Sierra Leone","Togo","Kenya","Winnipeg","Iqaluit","Toronto","Halifax","Montreal","Vancouver"],"frame_perspective":[],"analysis_type":["guide","methodology"]},{"id":"iisd-002740","url":"https://climateinsightsdirectory.org/document/iisd-002740","title":"CRiSTAL User’s Manual","publisher":{"name":"International Institute for Sustainable Development","organization":"international-institute-for-sustainable-development"},"document_type":"guide","language":"en","date_published":"2012","source_url":"https://www.iisd.org/system/files/publications/cristal_user_manual_v5_2012.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The CRiSTAL User's Manual describes a desktop application and methodology for screening development projects to ensure they support climate adaptation. The tool uses a livelihoods-based approach, combining scientific climate data with participatory community consultations to identify climate risks and design adaptation strategies. It guides users through three phases: understanding the livelihoods and climate context, evaluating project implications, and supporting monitoring and evaluation (M&E).","ai_generated":true,"unit":"iisd-002740#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"CRiSTAL is a project planning tool designed to help users design activities that support climate adaptation at the community level by identifying and prioritizing climate risks.","ai_generated":true,"unit":"iisd-002740#insight-1","date_modified":"2026-10-23"},{"text":"The tool is structured into three sequential phases: understanding the livelihoods and climate context, evaluating implications for the project, and supporting the monitoring and evaluation (M&E) of climate adaptation.","ai_generated":true,"unit":"iisd-002740#insight-2","date_modified":"2026-10-23"},{"text":"CRiSTAL utilizes a livelihoods-based approach, categorizing resources into six types: natural, physical, financial, human, social, and political resources.","ai_generated":true,"unit":"iisd-002740#insight-3","date_modified":"2026-10-23"},{"text":"The methodology relies on a combination of secondary scientific data on climate projections and primary data gathered through participatory community consultations, including focus groups separated by gender.","ai_generated":true,"unit":"iisd-002740#insight-4","date_modified":"2026-10-23"},{"text":"The tool enables users to analyze climate risk by identifying direct and indirect impacts of climate hazards on specific livelihood resources and assessing the sustainability of existing response strategies.","ai_generated":true,"unit":"iisd-002740#insight-5","date_modified":"2026-10-23"},{"text":"Users can use CRiSTAL to either screen and revise existing project activities or design new activities from scratch to reduce climate risks.","ai_generated":true,"unit":"iisd-002740#insight-6","date_modified":"2026-10-23"},{"text":"The tool includes a prioritization process for new activities based on weighted evaluation criteria, such as benefit to vulnerable groups, long-term cost effectiveness, and cultural appropriateness.","ai_generated":true,"unit":"iisd-002740#insight-7","date_modified":"2026-10-23"},{"text":"CRiSTAL supports the development of M&E frameworks by helping users identify concrete outcome statements and key contextual factors that influence adaptation success.","ai_generated":true,"unit":"iisd-002740#insight-8","date_modified":"2026-10-23"},{"text":"The tool was developed by a partnership of four NGOs: the International Union for the Conservation of Nature, the International Institute for Sustainable Development, the Stockholm Environment Institute, and Helvetas Swiss Intercooperation.","ai_generated":true,"unit":"iisd-002740#insight-9","date_modified":"2026-10-23"}],"climate_tags":["climate change adaptation","climate risk management","climate variability","climate change","vulnerability assessment"],"geography_discussed":["Ghana","Ethiopia","Fiji Islands"],"frame_perspective":[],"analysis_type":["guide","methodology"]},{"id":"natjust-000132","url":"https://climateinsightsdirectory.org/document/natjust-000132","title":"Protocolos Comunitarios Bioculturales: Kit de Herramientas para Facilitadores Comunitarios","publisher":{"name":"Natural Justice","organization":"natural-justice"},"document_type":"guide","language":"es","date_published":"2012","source_url":"https://naturaljustice.org/wp-content/uploads/2012/05/BCP-Toolkit-Espanol.pdf","generator":"Climate Insights Directory","generated_fields":["summary","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The document outlines a methodology for creating Biocultural Community Protocols (BCPs), which are community-defined instruments used to communicate local values, customary laws, and priorities to external actors. It provides a structured approach across four parts: understanding the toolkit and the role of the facilitator, documenting the community's biocultural foundations, implementing the protocol through dialogue and negotiation, and reflecting on outcomes to revise the protocol. The guide emphasizes endogenous development, legal empowerment, and participatory methods—such as community mapping, participatory video, and 'Forum Theatre'—to reduce power asymmetries between local communities and entities like governments, corporations, and NGOs.","ai_generated":true,"unit":"natjust-000132#summary","date_modified":"2026-10-23"},"key_insights":[],"climate_tags":["indigenous rights","biodiversity conservation","land tenure","natural resource management","Free, Prior and Informed Consent","environmental impact assessment","climate change adaptation"],"geography_discussed":["Guyana"],"frame_perspective":[],"analysis_type":["guide","methodology","toolkit"]},{"id":"rmi-000828","url":"https://climateinsightsdirectory.org/document/rmi-000828","title":"GREEN FOOTSTEP: CALCULATIONS AND DATA SOURCES","publisher":{"name":"RMI","organization":"rmi"},"document_type":"research paper","language":"en","date_published":"2011-07","source_url":"https://rmi.org/app/uploads/2017/05/RMI_Document_Repository_Public-Reprts_2011-11_GreenFootstep.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","analysis_type"],"summary":{"text":"The document explains the methodology of the Green Footstep tool, which calculates the net carbon flow of building projects by analyzing site development, construction, and operation. It defines carbon neutrality as achieving a net carbon storage level equal to or greater than the site's native state. The paper provides specific formulas for calculating native site carbon storage, emissions from site development, and operational emissions. For construction emissions, it details an Economic Input-Output Life Cycle Assessment (EIO LCA) approach based on 1997 US economic data, offering coefficients for residential, multifamily, manufacturing/industrial, and commercial/institutional sectors. It also includes a historical validation comparing 1997 energy intensity coefficients with 1967 data, noting a general decrease in energy intensity for most sectors except commercial and institutional buildings.","ai_generated":true,"unit":"rmi-000828#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"Green Footstep defines a building project as \"carbon neutral\" if, by the end of the analysis period, the net amount of carbon stored on the site is equal to or greater than the amount stored in its native state.","ai_generated":true,"unit":"rmi-000828#insight-1","date_modified":"2026-10-23"},{"text":"The tool calculates net carbon flow by accounting for three primary sources: site development (removal or addition of vegetation), construction (raw material extraction, manufacture, transport, and assembly), and operation (Scope 1 and 2 emissions from electricity, natural gas, and other fuels, with optional Scope 3 emissions like commuting).","ai_generated":true,"unit":"rmi-000828#insight-2","date_modified":"2026-10-23"},{"text":"Native site carbon storage is calculated by multiplying the site area by the carbon intensity of the native ecological zone, using IPCC data for forests and grasslands.","ai_generated":true,"unit":"rmi-000828#insight-3","date_modified":"2026-10-23"},{"text":"For construction emissions, the tool provides an EIO LCA option based on 1997 US National Purchaser Price model data, which assigns carbon intensity per US dollar of construction activity across four sectors: 1-unit Residential, Multifamily, Manufacturing and industrial buildings, and Commercial and institutional buildings.","ai_generated":true,"unit":"rmi-000828#insight-4","date_modified":"2026-10-23"},{"text":"The document derives carbon intensity coefficients per square foot for new construction based on 1997 data, finding that Commercial and institutional buildings have the highest carbon intensity at 120 kg CO2e/ft2, followed by Manufacturing and industrial buildings at 49 kg CO2e/ft2, 1-unit Residential at 45 kg CO2e/ft2, and Multifamily at 42 kg CO2e/ft2.","ai_generated":true,"unit":"rmi-000828#insight-5","date_modified":"2026-10-23"},{"text":"A historical comparison between 1967 and 1997 data indicates that energy intensity in building construction decreased by approximately 30% for 1-unit residential, multifamily, and manufacturing/industrial sectors, while it increased by 15% for commercial and institutional buildings.","ai_generated":true,"unit":"rmi-000828#insight-6","date_modified":"2026-10-23"}],"climate_tags":["carbon dioxide emissions","embedded emissions","carbon neutrality","construction of residential and non-residential buildings","carbon capture and storage","GHG Protocol"],"geography_discussed":[],"frame_perspective":[],"analysis_type":["methodology","comparative analysis"]},{"id":"wri-001371","url":"https://climateinsightsdirectory.org/document/wri-001371","title":"Reefs at Risk Revisited","publisher":{"name":"World Resources Institute","organization":"world-resources-institute"},"document_type":"research paper","language":"en","date_published":"2011","source_url":"https://wriorg.s3.amazonaws.com/s3fs-public/docs/reefs_at_risk_revisited_technical_notes.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The document details the GIS-based modeling approach used to assess threats to global coral reefs, categorizing them into local pressures (coastal development, watershed-based pollution, marine-based pollution, and overfishing) and global threats (thermal stress and ocean acidification). It explains the proxy indicators, distance-based rules, and data sources used to create an integrated threat index, while acknowledging limitations such as the use of population as a proxy for overfishing and the exclusion of coral disease and storm intensity due to uncertainty.","ai_generated":true,"unit":"wri-001371#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The analysis categorizes human pressures on coral reefs into 'local' threats—including coastal development, watershed-based pollution, marine-based pollution and damage, and overfishing and destructive fishing—and 'global' threats, specifically thermal stress and ocean acidification.","ai_generated":true,"unit":"wri-001371#insight-1","date_modified":"2026-10-23"},{"text":"Local threats were modeled using proxy indicators and distance-based rules; for example, coastal development threat is based on the size of cities, ports, and airports, as well as coastal population density, growth, and tourism growth.","ai_generated":true,"unit":"wri-001371#insight-2","date_modified":"2026-10-23"},{"text":"Watershed-based pollution was modeled using a simplified version of the Revised Universal Soil Loss Equation (RUSLE) to estimate relative erosion rates, which serve as a proxy for sediment and pollutant delivery to reefs.","ai_generated":true,"unit":"wri-001371#insight-3","date_modified":"2026-10-23"},{"text":"Overfishing threat is estimated based on coastal population density within 30 km of a reef, adjusted for the available shallow shelf and reef area, and the proximity to market centers.","ai_generated":true,"unit":"wri-001371#insight-4","date_modified":"2026-10-23"},{"text":"The model incorporates the effectiveness of Marine Protected Areas (MPAs) to downgrade overfishing threat levels; reefs within 'effectively managed' MPAs are downgraded to low threat, while those in 'partially effective' MPAs are downgraded by one threat level.","ai_generated":true,"unit":"wri-001371#insight-5","date_modified":"2026-10-23"},{"text":"Global threats are evaluated using external expert data: past thermal stress (1998–2007) combines satellite sea surface temperature and bleaching observations, while future thermal stress and ocean acidification are projected for 2030 and 2050 based on 'business-as-usual' scenarios.","ai_generated":true,"unit":"wri-001371#insight-6","date_modified":"2026-10-23"},{"text":"The analysis produces an integrated local threat index by summing four local threats (scored 0 to 2), which is then combined with past and future climate data to create a final threat classification ranging from low to 'critical'.","ai_generated":true,"unit":"wri-001371#insight-7","date_modified":"2026-10-23"}],"climate_tags":["coral bleaching","ocean acidification","climate change","coastal management","sea surface temperature"],"geography_discussed":["Australia","Great Barrier Reef"],"frame_perspective":[],"analysis_type":["methodology","threat assessment","spatial modeling"]},{"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":"caad-000050","url":"https://climateinsightsdirectory.org/document/caad-000050","title":"COP28 Pre-Summit Report: Annex 1 (Methodology)","publisher":{"name":"Climate Action Against Disinformation","organization":"climate-action-against-disinformation"},"document_type":"report","language":"en","date_published":null,"source_url":"https://caad.info/wp-content/uploads/2023/11/Deny-Deceive-Delay-Vol.-3-Annex-1-Methodology-FINAL.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The document details the methodology for four distinct research areas: the analysis of the #ClimateScam hashtag and X's auto-complete function; the identification of 15 monetized websites sharing climate disinformation and the ad exchanges supporting them; the monitoring of Russian state media and diplomatic accounts on Facebook across four languages; and the collection of political advertising from major fossil fuel companies and petrostates via the Meta Ad Library.","ai_generated":true,"unit":"caad-000050#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"To analyze the #ClimateScam hashtag, researchers collected all original posts containing #ClimateScam, #ClimateCrisis, or #ClimateEmergency between 1 July 2022 and 31 October 2023 on X, Facebook, and Instagram.","ai_generated":true,"unit":"caad-000050#insight-1","date_modified":"2026-10-23"},{"text":"Testing of X's search function revealed that typing only \"#Cl\" produced an auto-complete suggestion of #ClimateScam during a test period from 9 to 13 November 2023.","ai_generated":true,"unit":"caad-000050#insight-2","date_modified":"2026-10-23"}],"climate_tags":["climate change disinformation","Fossil fuel advertising","offshore wind power","Paris Agreement"],"geography_discussed":["United States","Australia","Canada","New Zealand","United Kingdom","Germany","sub-Saharan Africa","Latin America","Russia","Saudi Arabia","United Arab Emirates","Nigeria","Venezuela","Norway","Qatar","Azerbaijan","People's Republic of China","India"],"frame_perspective":[],"analysis_type":["methodology"]},{"id":"catf-000503","url":"https://climateinsightsdirectory.org/document/catf-000503","title":"Methodology Note for Impacts of Continued Operations of Select Coal-Fired Power Plants and Generating Units","publisher":{"name":"Clean Air Task Force","organization":"clean-air-task-force"},"document_type":"guide","language":"en","date_published":null,"source_url":"https://cdn.catf.us/wp-content/uploads/2026/08/27122703/2024-toll-from-coal-methodology-continued-operations.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","analysis_type"],"summary":{"text":"The methodology note describes how the Clean Air Task Force (CATF) models unit-level emissions and resulting health impacts for coal plants slated for retirement between 2025 and 2028. It details the data sources used, the cumulative nature of the map results, and the user-adjustable emission levels provided in the tool.","ai_generated":true,"unit":"catf-000503#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The modeling uses data from the January 2026 Monthly Electric Generator Inventory (Form EIA 860M) to identify coal-fired powerplants with reported retirement dates between 2025 and 2028. For plants slated for closure in 2025, actual reported emissions from 2025 are used; for those closing between 2026 and 2028, emissions from 2024 are used as the base.","ai_generated":true,"unit":"catf-000503#insight-1","date_modified":"2026-10-23"},{"text":"The results are presented cumulatively across a series of maps. The final map aggregates four years of impacts for 2025 facilities, three years for 2026 facilities, two years for 2027 facilities, and one year for 2028 facilities.","ai_generated":true,"unit":"catf-000503#insight-2","date_modified":"2026-10-23"}],"climate_tags":["coal-fired power station","air pollution","effects of climate change on human health"],"geography_discussed":[],"frame_perspective":[],"analysis_type":["methodology"]},{"id":"catf-000866","url":"https://climateinsightsdirectory.org/document/catf-000866","title":"Deaths by Dirty Diesel Map: Health Impacts Methodology","publisher":{"name":"Clean Air Task Force","organization":"clean-air-task-force"},"document_type":"report","language":"en","date_published":null,"source_url":"https://cdn.catf.us/wp-content/uploads/2022/01/06091832/deaths-dirty-diesel-methodology.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The Clean Air Task Force (CATF) used the U.S. EPA's CO-Benefits Risk Assessment (COBRA) version 4.0 to model the health effects of 2023 diesel emissions from highway and off-highway sectors. The methodology employs a damage function approach to translate emissions of fine particulate matter (PM2.5), sulfur dioxide, and nitrogen oxide into premature mortality, hospital admissions, and economic costs. For 2023, the model estimated premature deaths from diesel exhaust to be between 3,900 and 8,800, a decrease from the 2016 range of 6,300 to 14,200. Additionally, using a 2015 California Air Resources Board risk factor, CATF estimated that 2023 diesel particulate concentrations of 0.26 ug/m3 result in a national risk of 228 lifetime cancers per million people, totaling nearly 80,000 cancer cases.","ai_generated":true,"unit":"catf-000866#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The analysis utilizes the U.S. EPA's CO-Benefits Risk Assessment (COBRA) screening model to estimate health impacts from 2023 diesel engine emissions in both highway and off-highway sectors, focusing on the contribution of direct PM, sulfur dioxide, and nitrogen oxide to ambient PM2.5 levels.","ai_generated":true,"unit":"catf-000866#insight-1","date_modified":"2026-10-23"},{"text":"Premature mortality attributed to diesel exhaust decreased from a 2016 range of 6,300 to 14,200 deaths to a 2023 range of 3,900 to 8,800 deaths. The variation in these ranges depends on whether the Krewski or Lepeule concentration-response functions are used.","ai_generated":true,"unit":"catf-000866#insight-2","date_modified":"2026-10-23"},{"text":"The COBRA model employs a three-step damage function approach: calculating the impact of emissions on ambient PM2.5 levels, estimating adverse health impacts using epidemiological concentration-response functions, and assigning monetized economic values to those effects.","ai_generated":true,"unit":"catf-000866#insight-3","date_modified":"2026-10-23"},{"text":"Economic damages are calculated using different unit values based on the health endpoint: cost of illness (COI) for hospital admissions, willingness to pay (WTP) for asthma exacerbation, and value of statistical life (VSL) for premature mortality.","ai_generated":true,"unit":"catf-000866#insight-4","date_modified":"2026-10-23"},{"text":"Based on a 2015 California Air Resources Board (CARB) risk factor and projected 2023 concentrations of 0.26 ug/m3, the national cancer risk from diesel particulate matter is estimated at 228 lifetime cancers per million people, which equates to nearly 80,000 cases across the nation.","ai_generated":true,"unit":"catf-000866#insight-5","date_modified":"2026-10-23"}],"climate_tags":["air pollution","public health"],"geography_discussed":["California"],"frame_perspective":[],"analysis_type":["methodology","health impact assessment","economic valuation"]},{"id":"catf-001153","url":"https://climateinsightsdirectory.org/document/catf-001153","title":"Pulp and Paper Tool Scoring Methodology","publisher":{"name":"Clean Air Task Force","organization":"clean-air-task-force"},"document_type":"guide","language":"en","date_published":null,"source_url":"https://cdn.catf.us/wp-content/uploads/2025/12/02170030/pulp-paper-tool-methodology.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","analysis_type"],"summary":{"text":"The Clean Air Task Force developed a tool to score U.S. pulp and paper facilities on their potential for carbon management based on five criteria: capture cost, capture potential, storage cost, cluster potential, and co-benefits potential. The tool uses GIS analysis, spatial statistics, and cost modeling to identify high-opportunity facilities for decarbonization, though the authors note that plant-specific engineering analysis is still required for feasibility determinations.","ai_generated":true,"unit":"catf-001153#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The mapping tool evaluates pulp and paper facilities using five specific criteria: cost of capture, capture potential/volume, storage cost, clustered infrastructure, and environmental and social co-benefits.","ai_generated":true,"unit":"catf-001153#insight-1","date_modified":"2026-10-23"},{"text":"Capture costs are scored from 1 to 3, with the highest score (3) assigned to facilities with costs less than or equal to $85/MT, and the lowest score (1) for costs exceeding $120/MT.","ai_generated":true,"unit":"catf-001153#insight-2","date_modified":"2026-10-23"},{"text":"Capture potential is scored from 0.5 to 3 based on total capturable emissions from 2023 EPA Greenhouse Gas Reporting Program data, with the highest score given to facilities emitting more than 2,000,000 MT.","ai_generated":true,"unit":"catf-001153#insight-3","date_modified":"2026-10-23"},{"text":"Storage costs are scored from 0 to 3 based on the Livermore Lab Foundation’s Roads to Removal report, where costs less than $9/MT receive the highest score.","ai_generated":true,"unit":"catf-001153#insight-4","date_modified":"2026-10-23"},{"text":"Cluster potential is determined using the Getis-Ord Gi* spatial statistic to measure the clustering of other industrial facilities that qualify for the 45Q tax credit.","ai_generated":true,"unit":"catf-001153#insight-5","date_modified":"2026-10-23"},{"text":"Co-benefits potential is scored from 0.5 to 3 based on county-level indices for geologic storage and BiCRS (dry waste and woody biomass carbon removal and storage) feedstock, considering factors like employment, air quality, and land ownership.","ai_generated":true,"unit":"catf-001153#insight-6","date_modified":"2026-10-23"}],"climate_tags":["carbon capture and storage","decarbonization"],"geography_discussed":[],"frame_perspective":[],"analysis_type":["methodology"]},{"id":"chanmar-000242","url":"https://climateinsightsdirectory.org/document/chanmar-000242","title":"ANNEX Methodology - Spinning Greenwash.docx","publisher":{"name":"Changing Markets Foundation","organization":"changing-markets-foundation"},"document_type":"report","language":"en","date_published":null,"source_url":"https://changingmarkets.org/wp-content/uploads/2025/12/Methodology.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","analysis_type"],"summary":{"text":"The methodology employs two internationally recognised testing systems—the GyroWash (ISO 105-C06) and the Wascator (ISO 6330)—to measure microfibre shedding from 51 garments made of cotton, polyester, recycled polyester, polyamide, and recycled polyamide. The GyroWash uses standardised 4 x 10 cm fabric samples to provide precise fibre counts and length measurements, while the Wascator simulates full-garment domestic laundering to determine total shed mass. To ensure consistency, both systems used identical temperature (40 °C ± 2 °C), duration (30 minutes), and mechanical action without detergents. The study included blank controls and specific sample preparation, such as sealing cut edges with adhesive, to prevent contamination and ensure that fibre release resulted solely from mechanical and thermal stresses.","ai_generated":true,"unit":"chanmar-000242#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The study utilised two distinct testing systems to capture different dimensions of microfibre shedding: the GyroWash for quantifying the number and size of fibres from standardised samples, and the Wascator for determining the total mass of fibres released from whole garments.","ai_generated":true,"unit":"chanmar-000242#insight-1","date_modified":"2026-10-23"},{"text":"Testing was conducted on 51 garments composed of cotton, polyester, recycled polyester, polyamide, and recycled polyamide. While 40 garments underwent both GyroWash and Wascator tests, 11 garments with printed areas or structural variations were tested only in the Wascator to account for material heterogeneity.","ai_generated":true,"unit":"chanmar-000242#insight-2","date_modified":"2026-10-23"},{"text":"The experimental parameters were standardised across both systems to represent mild domestic laundering, specifically using a temperature of 40 °C ± 2 °C for 30 minutes without the use of detergents or auxiliary chemicals.","ai_generated":true,"unit":"chanmar-000242#insight-3","date_modified":"2026-10-23"}],"climate_tags":[],"geography_discussed":[],"frame_perspective":[],"analysis_type":["methodology"]},{"id":"clibonds-000885","url":"https://climateinsightsdirectory.org/document/clibonds-000885","title":"Climate Bonds Resilience Taxonomy (CBRT)","publisher":{"name":"Climate Bonds Initiative","organization":"climate-bonds-initiative"},"document_type":"report","language":"en","date_published":null,"source_url":"https://www.climatebonds.net/files/documents/Updated-Climate-Bonds-Resilience-Taxonomy-Methodology.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The Climate Bonds Resilience Taxonomy (CBRT) establishes a science-based classification system to direct capital toward climate resilience. It categorizes investments into four types—adapting measures, adapted activities, enabling measures, and enabling activities—and organizes them across seven thematic areas. To prevent greenwashing, the CBRT requires investments to demonstrate a 'Substantial Contribution' to resilience, manage maladaptation risks, and 'Do No Significant Harm' (DNSH) to climate mitigation and other environmental goals. The methodology emphasizes a graduated screening approach, ranging from automatically eligible investments to complex local threshold assessments, and ensures interoperability with the EU Taxonomy and MDB Joint Methodology.","ai_generated":true,"unit":"clibonds-000885#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The CBRT identifies four distinct investment types to ensure a comprehensive coverage of resilience efforts: adapting measures (interventions within an activity), adapted activities (resilient economic activities), enabling measures (interventions that make other activities resilient), and enabling activities (production of resilience solutions).","ai_generated":true,"unit":"clibonds-000885#insight-1","date_modified":"2026-10-23"},{"text":"Eligible costs for resilience financing differ by investment type: for adapting measures, adapted activities, and enabling measures, only the costs directly associated with the resilience intervention are eligible; however, for enabling activities, the entire cost of the activity is eligible.","ai_generated":true,"unit":"clibonds-000885#insight-2","date_modified":"2026-10-23"},{"text":"The taxonomy is organized into seven Climate Resilience Themes: Resilient Agrifood Systems, Resilient Cities & Settlements, Resilient Health Systems, Resilient Industry & Commerce, Resilient Infrastructure, Resilient Natural Systems, and Resilient Social Systems, further divided into 27 sectors and 69 subsectors.","ai_generated":true,"unit":"clibonds-000885#insight-3","date_modified":"2026-10-23"},{"text":"To qualify as a resilience investment, a project must meet three core requirements: it must make a 'Substantial Contribution' to climate resilience (defined as a 'discernible reduction in vulnerability to climate impacts in the investment context'), manage maladaptation risks, and 'Do No Significant Harm' (DNSH) to mitigation and other environmental objectives.","ai_generated":true,"unit":"clibonds-000885#insight-4","date_modified":"2026-10-23"},{"text":"The CBRT employs a graduated screening approach for adapting measures to reduce the assessment burden: some are 'automatically eligible' based on expert judgment, others require a 'technical specification check', and the most complex require 'investment-level assessment' against either global or local thresholds.","ai_generated":true,"unit":"clibonds-000885#insight-5","date_modified":"2026-10-23"},{"text":"Maladaptation is specifically defined as the risk of an unintended measurable increase in vulnerability or exposure within the investment context or the wider system, often resulting from physical climate risk uncertainty or miscalculation of system boundaries.","ai_generated":true,"unit":"clibonds-000885#insight-6","date_modified":"2026-10-23"},{"text":"The CBRT integrates gender as a cross-cutting element, using 'Gender-responsive' tags for investments intentionally designed to address gender-specific needs and 'Gender-relevant' tags for those with potential gender equality co-benefits.","ai_generated":true,"unit":"clibonds-000885#insight-7","date_modified":"2026-10-23"},{"text":"The framework is designed for interoperability with other major standards, including the EU Sustainable Finance Taxonomy (specifically the 2021 Climate Delegated Act), the MDB Joint Methodology for Tracking Climate Change Adaptation Finance, and the Common Framework of Sustainable Finance Taxonomies for Latin America and the Caribbean.","ai_generated":true,"unit":"clibonds-000885#insight-8","date_modified":"2026-10-23"}],"climate_tags":["climate resilience","climate change adaptation","climate bond","maladaptation","climate finance","climate change vulnerability"],"geography_discussed":["European Union","Netherlands","Latin America and the Caribbean"],"frame_perspective":[],"analysis_type":["methodology","framework"]},{"id":"ember-000221","url":"https://climateinsightsdirectory.org/document/ember-000221","title":"China Cleantech Exports Data Methodology","publisher":{"name":"Ember","organization":"ember"},"document_type":"guide","language":"en","date_published":null,"source_url":"https://ember-energy.org/app/uploads/2025/10/China-Cleantech-Export-Data-Explorer-Data-Methodology.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","analysis_type"],"summary":{"text":"The document specifies that Ember's China Cleantech Exports Data Explorer utilizes data from the General Administration of Customs of the People’s Republic of China (GACC), categorized into six main clean tech groups: Solar PV, EVs, Batteries, Heating and cooling systems, Grid, and Wind. It provides the specific HS codes used for sub-categories, such as solar cells (85414300) and lithium ion batteries (850760), while noting that the reported export values may be lower than the total value chain as they exclude certain parts and raw materials.","ai_generated":true,"unit":"ember-000221#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"Ember sources its clean technology export data from the General Administration of Customs of the People’s Republic of China (GACC) and organizes it into six primary categories: Solar PV, EVs, Batteries, Heating and cooling systems, Grid, and Wind.","ai_generated":true,"unit":"ember-000221#insight-1","date_modified":"2026-10-23"},{"text":"The methodology employs specific HS commodity codes for detailed tracking, including solar cells (85414300), solar modules (85414200 and 85414020), solar wafers (38180019), lithium ion batteries (850760), and wind generating sets (850231).","ai_generated":true,"unit":"ember-000221#insight-2","date_modified":"2026-10-23"},{"text":"The reported export values are intended to be precise but likely underrepresent the full scale of the clean tech value chain because they do not include all associated raw materials and parts.","ai_generated":true,"unit":"ember-000221#insight-3","date_modified":"2026-10-23"}],"climate_tags":["photovoltaics","electric vehicle","battery","heat pump","wind power"],"geography_discussed":[],"frame_perspective":[],"analysis_type":["methodology"]},{"id":"ember-000269","url":"https://climateinsightsdirectory.org/document/ember-000269","title":"Ember China Solar Export Data Methodology","publisher":{"name":"Ember","organization":"ember"},"document_type":"guide","language":"en","date_published":null,"source_url":"https://files.ember-energy.org/public-downloads/ember_china_solar_exports_methodology_v4.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The document outlines the data sources and calculation methods for Ember's China solar export dataset. It details the use of General Administration of Customs of the People's Republic of China (GACC) data for export amounts and InfoLink Consulting Group for pricing. It explains how capacity in megawatts (MW) is estimated for panels, cells, and wafers, and notes that GACC data may record initial export destinations rather than final destinations.","ai_generated":true,"unit":"ember-000269#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"Ember's China solar export dataset tracks panels, cells, and wafers using data from the General Administration of Customs of the People's Republic of China (GACC), with panel data available from 2017 and cell and wafer data available from 2022.","ai_generated":true,"unit":"ember-000269#insight-1","date_modified":"2026-10-23"},{"text":"Because the GACC does not report capacity in megawatts (MW), Ember estimates it by combining GACC export values with average monthly prices from InfoLink Consulting Group for panels and cells, and by using Ministry of Industry and Information Technology (MIIT) reporting for wafers.","ai_generated":true,"unit":"ember-000269#insight-2","date_modified":"2026-10-23"},{"text":"The dataset contains a caveat that GACC data records the first country of export, which may not be the final destination, specifically noting The Netherlands as a major re-export hub for Europe.","ai_generated":true,"unit":"ember-000269#insight-3","date_modified":"2026-10-23"}],"climate_tags":["photovoltaics"],"geography_discussed":["Netherlands"],"frame_perspective":[],"analysis_type":["methodology"]},{"id":"ember-000272","url":"https://climateinsightsdirectory.org/document/ember-000272","title":"Monthly Wind and Solar Capacity Data Methodology","publisher":{"name":"Ember","organization":"ember"},"document_type":"guide","language":"en","date_published":null,"source_url":"https://files.ember-energy.org/public-downloads/capacity/outputs/ember_monthly_capacity_methodology.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 calculating and harmonising monthly wind and solar capacity data. It defines 'installed capacity' as the maximum potential output and explains the process for converting solar data between AC and DC values, using a default conversion factor of 1.3. The methodology describes how onshore and offshore wind are distinguished, often relying on IRENA data when primary sources lack a breakdown. It also specifies the publication schedule (twice monthly) and the use of proxies and linear interpolation to estimate missing data for global aggregates and specific countries. Detailed country-specific sections list data sources, publication lags, and specific adjustments for 25 territories, including the US, China, and the EU.","ai_generated":true,"unit":"ember-000272#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"As of March 20, 2026, the dataset covers 25 countries and/or regions, representing approximately 91% of global wind capacity and 92% of global solar capacity.","ai_generated":true,"unit":"ember-000272#insight-1","date_modified":"2026-10-23"},{"text":"To ensure comparability, solar capacity is harmonised to GWDC; where a source only provides one value, Ember calculates the alternate using a default AC/DC conversion factor of 1.3.","ai_generated":true,"unit":"ember-000272#insight-2","date_modified":"2026-10-23"},{"text":"Wind capacity is reported as onshore and offshore separately when sources allow; if a source does not differentiate, Ember uses IRENA data to determine if offshore capacity exists, otherwise classifying it as 'Unknown wind' or onshore.","ai_generated":true,"unit":"ember-000272#insight-3","date_modified":"2026-10-23"},{"text":"Global and regional aggregates (EU, G7, G20, OECD) are estimated by scaling monthly data from reporting countries based on their share of annual totals, using China solar export trackers as a proxy for missing solar data.","ai_generated":true,"unit":"ember-000272#insight-4","date_modified":"2026-10-23"},{"text":"The dataset is updated twice monthly, specifically on the 5th and 27th (or the first working day thereafter), with the second update delayed until major capacities like the USA and China publish their data.","ai_generated":true,"unit":"ember-000272#insight-5","date_modified":"2026-10-23"},{"text":"For China, solar data from April to November 2016 was linearly interpolated due to missing publications, and offshore wind is modeled using GEM’s Global Wind Power Tracker to match NEA quarterly totals.","ai_generated":true,"unit":"ember-000272#insight-6","date_modified":"2026-10-23"}],"climate_tags":["photovoltaics","offshore wind power","energy transition"],"geography_discussed":["Argentina","Australia","Belgium","Brazil","Chile","People's Republic of China","Denmark","Finland","France","Germany","Hungary","India","Italy","Japan","Netherlands","Poland","Portugal","South Africa","South Korea","Spain","Taiwan (China)","Philippines","Turkey","United Kingdom","United States","European Union"],"frame_perspective":[],"analysis_type":["methodology"]},{"id":"ember-000273","url":"https://climateinsightsdirectory.org/document/ember-000273","title":"Ember Subnational Data Methodology","publisher":{"name":"Ember","organization":"ember"},"document_type":"guide","language":"en","date_published":null,"source_url":"https://files.ember-energy.org/public-downloads/ember_subnational_data_methodology.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The Ember Subnational Data Methodology describes the data sources and calculation methods for electricity metrics across 36 states/union territories in India and 50 states in the US. It details the mapping of nine fuel types and the application of IPCC 5th Assessment Report emissions factors to determine carbon intensity, while noting caveats regarding fuel quality, methane potency, and plant efficiency.","ai_generated":true,"unit":"ember-000273#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The subnational dataset provides monthly and yearly data on electricity generation (GWh), power generation capacity (MW), emissions from electricity generation (ktCO2e), and carbon intensity (gCO2 per KWh). As of November 17, 2022, the scope includes 36 states/union territories in India (from 2019) and 50 states in the United States (from 2015), though capacity data is not available for the US.","ai_generated":true,"unit":"ember-000273#insight-1","date_modified":"2026-10-23"},{"text":"Ember maps fuel data into nine categories: Bioenergy, Coal, Gas, Hydro, Nuclear, Other Fossil, Other Renewables, Solar, and Wind. For India, specific mappings include 'Other Fossil' as diesel, and 'Bioenergy' as a total including bagasse, non-bagasse biomass cogeneration, and waste-to-energy.","ai_generated":true,"unit":"ember-000273#insight-2","date_modified":"2026-10-23"},{"text":"Emissions are calculated by multiplying generation numbers by factors from the IPCC 5th Assessment Report Annex 3 (2014), which cover full lifecycle emissions including upstream methane and manufacturing. The assigned intensities (gCO2eqkWh-1) range from 11 for Wind to 820 for Coal.","ai_generated":true,"unit":"ember-000273#insight-3","date_modified":"2026-10-23"},{"text":"The methodology acknowledges several caveats that may cause IPCC emissions factors to deviate from reality, including regional variations in fuel quality (e.g., lignite vs. anthracite), the underestimation of short-term methane potency, and improvements in thermal power plant efficiency and renewable energy manufacturing.","ai_generated":true,"unit":"ember-000273#insight-4","date_modified":"2026-10-23"}],"climate_tags":["electricity generation","emission intensity","greenhouse gas emissions","bioenergy","coal","natural gas","hydroelectricity","nuclear power","solar energy","wind power","methane emissions"],"geography_discussed":["People's Republic of China"],"frame_perspective":[],"analysis_type":["methodology"]},{"id":"ember-000343","url":"https://climateinsightsdirectory.org/document/ember-000343","title":"Data Methodology","publisher":{"name":"Ember","organization":"ember"},"document_type":"guide","language":"en","date_published":null,"source_url":"https://ember-energy.org/app/uploads/2022/03/Methodology-GER22-PDF-844-KB.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The document outlines the technical processes Ember uses to assemble electricity data for 215 countries from 2000 to 2021. It describes the use of multiple sources—including the EIA, Eurostat, and IRENA—to reconcile annual and monthly data, disaggregate thermal generation, and estimate emissions using IPCC factors. The guide also provides a detailed country-by-country breakdown of the specific sources and adjustments applied to each territory's data.","ai_generated":true,"unit":"ember-000343#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"Ember's core datasets cover five primary metrics: electricity generation, net imports, electricity demand (calculated as the sum of production and net imports), installed power generation capacity, and emissions from electricity generation.","ai_generated":true,"unit":"ember-000343#insight-1","date_modified":"2026-10-23"},{"text":"Global fuel data is categorized into nine types: Bioenergy, Coal, Gas, Hydro, Nuclear, Other Fossil, Other Renewables, Solar, and Wind. In European datasets, Coal is further subdivided into Hard Coal and Lignite.","ai_generated":true,"unit":"ember-000343#insight-2","date_modified":"2026-10-23"},{"text":"To estimate the latest annual generation when data is unavailable, Ember applies absolute changes from available annualised monthly data to historical annual values, using 2019 as a reference point due to COVID-19 pandemic anomalies.","ai_generated":true,"unit":"ember-000343#insight-3","date_modified":"2026-10-23"},{"text":"Thermal generation is disaggregated using a hierarchy of sources: first using IEA monthly electricity generation statistics ratios, then BP’s statistical review of world energy, and finally capacity ratios for remaining countries.","ai_generated":true,"unit":"ember-000343#insight-4","date_modified":"2026-10-23"},{"text":"Installed capacity data is primarily sourced from Global Energy Monitor (GEM) for coal and gas, the International Renewable Energy Agency (IRENA) for non-fossil generation, and the World Resources Institute (WRI) for certain Other Fossil capacity data.","ai_generated":true,"unit":"ember-000343#insight-5","date_modified":"2026-10-23"},{"text":"Global emissions figures are calculated using IPCC 5th Assessment Report Annex 3 (2014) factors, which include full lifecycle emissions and upstream methane, converted to CO2 equivalent over a 100-year timescale.","ai_generated":true,"unit":"ember-000343#insight-6","date_modified":"2026-10-23"},{"text":"Ember identifies several caveats in emissions intensity factors, noting that IPCC figures may not capture regional fuel quality differences (e.g., lignite vs anthracite), recent efficiency improvements in Chinese coal plants, or the high-carbon risk of certain woody biomass feedstocks in bioenergy.","ai_generated":true,"unit":"ember-000343#insight-7","date_modified":"2026-10-23"}],"climate_tags":["electricity generation","electric energy consumption","greenhouse gas emissions","emission intensity","bioenergy","coal","natural gas","solar energy","wind power","hydropower","nuclear power","methane emissions"],"geography_discussed":["Europe","People's Republic of China","United States","India","Russia","Brazil"],"frame_perspective":[],"analysis_type":["methodology"]},{"id":"ember-000344","url":"https://climateinsightsdirectory.org/document/ember-000344","title":"Indian States’ Electricity Transition","publisher":{"name":"Ember","organization":"ember"},"document_type":"report","language":"en","date_published":null,"source_url":"https://ember-energy.org/app/uploads/2024/10/Methodology-Indian-States-Electricity-Transition-by-IEEFA-Ember.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The document describes the methodology for the Indian States’ Electricity Transition analysis, which evaluates 16 states representing approximately 90% of India's annual power requirement. The analysis uses 17 parameters across four dimensions: Decarbonisation, Performance of the Power System, Readiness of the Power Ecosystem, and Policies and Political Commitments. Data for financial year 2021-22 was primarily sourced from government entities such as the Central Electricity Authority (CEA) and NITI Aayog, then normalised and weighted by sectoral experts to produce state-wise scores.","ai_generated":true,"unit":"ember-000344#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The analysis focuses on 16 specific Indian states and Union Territories because their combined electricity demand accounted for roughly 90% of the country's annual power requirement over the previous three financial years. The selected entities are Delhi, Maharashtra, Uttar Pradesh, Gujarat, Chhattisgarh, Tamil Nadu, Rajasthan, Madhya Pradesh, Karnataka, Telangana, Andhra Pradesh, Punjab, Haryana, West Bengal, Odisha, and Bihar.","ai_generated":true,"unit":"ember-000344#insight-1","date_modified":"2026-10-23"},{"text":"The SET scores are based on four broad dimensions containing 17 total parameters: Decarbonisation, Performance of the Power System, Readiness of the Power Ecosystem, and Policies and Political Commitments.","ai_generated":true,"unit":"ember-000344#insight-2","date_modified":"2026-10-23"},{"text":"The methodology prioritizes data from financial year (FY) 2021-22, utilizing official government sources where possible and private sources when official data was unavailable. Qualitative data for the 'Policies and Political Commitments' dimension was captured using specific data rubrics.","ai_generated":true,"unit":"ember-000344#insight-3","date_modified":"2026-10-23"},{"text":"The scoring process involved normalising parameters to a standard scale and applying weightages determined by sectoral experts from organisations such as Tata Power, CEEW, Wood-Mackenzie, and Ember to calculate weighted average scores for each dimension.","ai_generated":true,"unit":"ember-000344#insight-4","date_modified":"2026-10-23"}],"climate_tags":["decarbonization","renewable energy","coal power","battery energy storage system","circular economy","energy efficiency"],"geography_discussed":["Delhi","Maharashtra","Uttar Pradesh","Gujarat","Chhattisgarh","Tamil Nadu","Rajasthan","Madhya Pradesh","Karnataka","Telangana","Andhra Pradesh","Punjab","Haryana","West Bengal","Odisha","Bihar"],"frame_perspective":[],"analysis_type":["methodology"]}]}