{"query_echo":{"q":"","filters":{"analysis_type":["Methodology"]},"warnings":[]},"total":{"value":240,"relation":"eq"},"page":10,"page_size":20,"self":"https://climateinsightsdirectory.org/api/v1/search?analysis_type=Methodology&page=10","next":"https://climateinsightsdirectory.org/api/v1/search?analysis_type=Methodology&page=11","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":"ember-000349","url":"https://climateinsightsdirectory.org/document/ember-000349","title":"Ember NECP data methodology","publisher":{"name":"Ember","organization":"ember"},"document_type":"guide","language":"en","date_published":null,"source_url":"https://ember-energy.org/app/uploads/2024/09/NECP-Data-Methodology.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","analysis_type"],"summary":{"text":"The document outlines Ember's methodology for extracting and normalizing data from EU National Energy and Climate Plans (NECPs). It specifies the preference for 'with additional measures' (WAM) projections for electricity data, the conversion of energy units (1 mtoe = 11.63 TWh), and the mapping of greenhouse gas emissions and final energy demand from EEA and Eurostat datasets to align with UNFCCC and 'Fit for 55' standards.","ai_generated":true,"unit":"ember-000349#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"Ember prioritizes electricity data from NECP projections that include 'with additional measures' (WAM), which encompass implemented policies and options with a realistic chance of adoption. If WAM projections are unavailable, 'existing measures' (WEM) projections are used.","ai_generated":true,"unit":"ember-000349#insight-1","date_modified":"2026-10-23"},{"text":"To ensure consistency in solar capacity reporting, Ember attempts to present all figures in direct current (DC, gross output), noting that AC capacity differs from DC by a factor of 1.25, although national reporting transparency varies.","ai_generated":true,"unit":"ember-000349#insight-2","date_modified":"2026-10-23"},{"text":"Greenhouse gas emissions data is sourced from the EEA's reports to the UNFCCC and the EU Greenhouse Gas Monitoring Mechanism, covering all sectors including LULUCF but excluding international aviation and navigation, aligning with the EU's 'Fit for 55' target.","ai_generated":true,"unit":"ember-000349#insight-3","date_modified":"2026-10-23"},{"text":"Final energy demand data is derived from Eurostat's 'Complete energy balances' (NRG_BAL_C), specifically using 'Final consumption - energy use' (FC_E) for total demand and its subcategories for fuel and sectoral breakdowns.","ai_generated":true,"unit":"ember-000349#insight-4","date_modified":"2026-10-23"},{"text":"Ember applies a specific conversion factor for energy values reported in million tonnes of oil equivalent (mtoe), assuming 1 mtoe equals 11.63 terawatt hours (TWh).","ai_generated":true,"unit":"ember-000349#insight-5","date_modified":"2026-10-23"}],"climate_tags":["electricity generation","greenhouse gas emissions","Villevocance"],"geography_discussed":[],"frame_perspective":[],"analysis_type":["methodology"]},{"id":"ember-000513","url":"https://climateinsightsdirectory.org/document/ember-000513","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/GER22-Methodology.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The document outlines Ember's methodology for assembling electricity data for 215 countries. It describes the use of multiple sources—including Eurostat, ENTSO-E, the Energy Institute, and the U.S. Energy Information Administration—to reconcile annual and monthly time series. The guide explains how Ember handles data gaps through ARIMA-based projections, disaggregates thermal generation using IEA and EI ratios, and calculates lifecycle emissions using IPCC and UNECE factors. It also provides a comprehensive country-by-country breakdown of the specific sources and adjustments applied to each national dataset.","ai_generated":true,"unit":"ember-000513#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"Ember tracks five core electricity datasets: generation (TWh), net imports (TWh), demand (TWh), installed power generation capacity (GW), and emissions from electricity generation (Mt CO2e).","ai_generated":true,"unit":"ember-000513#insight-1","date_modified":"2026-10-23"},{"text":"To estimate the latest annual generation when data is unavailable, Ember uses monthly data and applies absolute changes by fuel from annualised monthly data to historical annual values.","ai_generated":true,"unit":"ember-000513#insight-2","date_modified":"2026-10-23"},{"text":"For countries that do not report disaggregated fossil fuel generation, Ember estimates the split using ratios from the IEA’s monthly electricity generation statistics or the Energy Institute (EI) statistical review of world energy.","ai_generated":true,"unit":"ember-000513#insight-3","date_modified":"2026-10-23"},{"text":"Ember uses a series of ARIMA models to project incomplete monthly data, using projected GDP from the World Bank as an exogenous variable for demand.","ai_generated":true,"unit":"ember-000513#insight-4","date_modified":"2026-10-23"},{"text":"Lifecycle emissions are calculated by multiplying generation numbers by emissions factors from sources such as UNECE, IPCC AR5 WG3, and Jordaan et al. 2022, covering all gases converted to CO2 equivalent over a 100-year timescale.","ai_generated":true,"unit":"ember-000513#insight-5","date_modified":"2026-10-23"},{"text":"Ember identifies significant data uncertainties regarding bioenergy emissions, noting that the IPCC figure used may be a significant underestimate because it focuses on energy crops rather than woody or forest biomass.","ai_generated":true,"unit":"ember-000513#insight-6","date_modified":"2026-10-23"},{"text":"Installed capacity data is primarily sourced from Global Energy Monitor (GEM) for coal and gas, and the International Renewable Energy Agency (IRENA) for non-fossil generation.","ai_generated":true,"unit":"ember-000513#insight-7","date_modified":"2026-10-23"},{"text":"The methodology notes a consistent challenge with 'captive power'—industrial thermal resources that are often included in national yearly statistics but excluded from monthly TSO-based statistics.","ai_generated":true,"unit":"ember-000513#insight-8","date_modified":"2026-10-23"}],"climate_tags":["electricity generation","electric energy consumption","carbon dioxide emissions","bioenergy","solar energy","wind power","coal","natural gas","nuclear power","hydropower","distributed generation","methane emissions"],"geography_discussed":["European Union","People's Republic of China","United States","India","United Kingdom","Germany","Brazil"],"frame_perspective":[],"analysis_type":["methodology"]},{"id":"fordecl-000099","url":"https://climateinsightsdirectory.org/document/fordecl-000099","title":"FOREST DECLARATION ASSESSMENT","publisher":{"name":"Forest Declaration Assessment","organization":"forest-declaration-assessment"},"document_type":"report","language":"en","date_published":null,"source_url":"https://forestdeclaration.org/wp-content/uploads/2022/10/2022Theme1AnnexesAB.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The document provides the technical annexes for the Forest Declaration Assessment, detailing the definitions and methodologies used to measure deforestation, forest degradation, and restoration potential. It establishes a 'gross zero' deforestation benchmark for 2030, requiring a linear 10 percent annual reduction in loss relative to a 2018-2020 baseline. The methodology incorporates satellite data and econometric models to estimate GHG emissions from forest loss and identifies 'cost-effective' restoration potential based on a carbon price threshold of USD 100/tCO2eq. A provided table lists the 30 countries with the highest absolute levels of deforestation in 2021, showing varied trends such as a 3% increase in Brazil and a 25% decrease in Indonesia compared to the baseline.","ai_generated":true,"unit":"fordecl-000099#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The assessment utilizes a 'gross zero' interpretation for the 2030 goal to halt and reverse forest loss, requiring a linear reduction pathway where each year requires a \"10 percent reduction in loss relative to the baseline to reach zero gross loss by 2030\".","ai_generated":true,"unit":"fordecl-000099#insight-1","date_modified":"2026-10-23"},{"text":"In 2021, Brazil had the highest absolute level of deforestation at 2.33 million hectares, representing a 3% increase from the 2018-2020 baseline. Other countries with high absolute deforestation in 2021 included Indonesia (0.78 Mha, a 25% decrease) and Bolivia (0.53 Mha, a 6% increase).","ai_generated":true,"unit":"fordecl-000099#insight-2","date_modified":"2026-10-23"},{"text":"Restoration interventions are defined as cost-effective if the cost of mitigating one tonne of CO2 equivalent is up to USD 100, a threshold based on the \"middle of the range for carbon prices in 2030 for a 1.5°C pathway, and at the low end of the range in 2050\".","ai_generated":true,"unit":"fordecl-000099#insight-3","date_modified":"2026-10-23"},{"text":"The Forest Landscape Integrity Index (FLII) is used to track forest degradation on a scale from 0 to 10, combining data on human pressures and losses in forest connectivity. The 2030 target for this indicator is \"zero further degradation (i.e., no further loss in FLII)\".","ai_generated":true,"unit":"fordecl-000099#insight-4","date_modified":"2026-10-23"},{"text":"Gross GHG emissions from global deforestation are estimated by combining carbon stock data with tree cover loss, including aboveground, belowground, deadwood, litter, and soil organic carbon, as well as emissions from forest fires and peat drainage.","ai_generated":true,"unit":"fordecl-000099#insight-5","date_modified":"2026-10-23"}],"climate_tags":["deforestation","forest degradation","greenhouse gas emissions","carbon price","afforestation","reforestation"],"geography_discussed":["Brazil","Indonesia","Bolivia","Democratic Republic of the Congo","Paraguay","Malaysia","Laos","Myanmar","Peru","Colombia","Argentina","Vietnam","Cambodia","United States","Cameroon","Thailand","Mexico","Nicaragua","Madagascar","Philippines","Papua New Guinea","Honduras","Venezuela","Liberia","Angola","Nigeria","Guatemala","Republic of the Congo","Central African Republic","Canada"],"frame_perspective":[],"analysis_type":["methodology","definitions"]},{"id":"issafri-001828","url":"https://climateinsightsdirectory.org/document/issafri-001828","title":"supporting-curriculum-development-for-the-international-institute-for-justice-and-the-rule-of-law_oct-2013_-final-18a979f7807224dd.pdf","publisher":{"name":"Institute for Security Studies","organization":"institute-for-security-studies"},"document_type":"report","language":"en","date_published":null,"source_url":"https://issafrica.s3.amazonaws.com/site/uploads/Supporting-Curriculum-Development-for-the-International-Institute-for-Justice-and-the-Rule-of-Law_Oct-2013_-final.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The document serves as a comprehensive guide for the International Institute for Justice and the Rule of Law to develop its training curricula. It emphasizes the critical role of policymakers and senior officials in driving institutional change and provides a step-by-step methodology for capacity building, including needs assessments and participant selection. Additionally, it advocates for the use of immersive, simulation-based training to recreate the complexity of real-life criminal justice incidents, specifically in the context of counterterrorism.","ai_generated":true,"unit":"issafri-001828#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"Effective capacity development in justice and security requires a combination of 'hard capacities' (technical skills, regulations, and policies) and 'soft capacities' (attitudes, behaviors, relationships, and values).","ai_generated":true,"unit":"issafri-001828#insight-1","date_modified":"2026-10-23"},{"text":"Training alone is rarely an effective capacity-development response because it often lacks sustainable impact, requires prerequisite capacities to be effective, and ignores the environmental context in which practitioners operate.","ai_generated":true,"unit":"issafri-001828#insight-2","date_modified":"2026-10-23"},{"text":"Policymakers and senior officials are essential for the success of rule of law initiatives as they provide the political and operational space necessary for practitioners to deploy new skills and transform behaviors.","ai_generated":true,"unit":"issafri-001828#insight-3","date_modified":"2026-10-23"},{"text":"A successful curriculum development process must begin with a pretraining needs assessment that examines both the specific organization and the macro-level functioning of the justice sector to ensure the training is context-sensitive.","ai_generated":true,"unit":"issafri-001828#insight-4","date_modified":"2026-10-23"},{"text":"Target groups for training can be defined using three approaches: horizontal (homogenous professional groups), chain (representatives from various procedural stages of the judicial sector), or vertical (participants with different seniority ranks).","ai_generated":true,"unit":"issafri-001828#insight-5","date_modified":"2026-10-23"},{"text":"Sustainability in training is achieved through tools such as post-training action plans, training-the-trainers components, and the maintenance of an alumni pool to facilitate the exchange of experiences.","ai_generated":true,"unit":"issafri-001828#insight-6","date_modified":"2026-10-23"},{"text":"Immersive training simulations are more effective than traditional classroom lectures for specialized rule of law training because they recreate the 'complexity, chaos, and challenge of a real life incident' in a safe environment.","ai_generated":true,"unit":"issafri-001828#insight-7","date_modified":"2026-10-23"},{"text":"The 'Karibu Dam' immersive exercise is designed for East Africa to address themes including the intelligence cycle, investigation processes, terrorism financing, and human rights standards.","ai_generated":true,"unit":"issafri-001828#insight-8","date_modified":"2026-10-23"}],"climate_tags":["capacity building","human rights"],"geography_discussed":["East Africa","Brussels","Geneva","Sudan","Pakistan","Iraq"],"frame_perspective":[],"analysis_type":["guidance","methodology"]},{"id":"mearth-000138","url":"https://climateinsightsdirectory.org/document/mearth-000138","title":"Appendix I – Scoring Methodology","publisher":{"name":"Mighty Earth","organization":"mighty-earth"},"document_type":"guide","language":"en","date_published":null,"source_url":"https://www.mightyearth.org/wp-content/uploads/Appendix-I.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The scoring methodology is divided into three categories totaling 100 points: Policy Commitment (38 points), Monitoring & Verification of Compliance (44 points), and Public Disclosure & Reporting (18 points). It assesses whether companies have specific policies to eliminate deforestation and conversion in beef production, implement rigorous supplier monitoring systems, and provide transparent public reporting on supply chain traceability and risk assessments. The framework heavily references the Accountability Framework Initiative's operational guidance for defining environmental protections, human rights, and monitoring standards.","ai_generated":true,"unit":"mearth-000138#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The 'Policy Commitment' category (38 points) evaluates the content, scope, and industry transformation efforts of a company's beef supply chain policy. This includes commitments to 'deforestation and conversion-free production', the protection of degraded landscapes, and the upholding of human rights for workers and Indigenous Peoples, including the requirement for 'Free, Prior, & Informed Consent'.","ai_generated":true,"unit":"mearth-000138#insight-1","date_modified":"2026-10-23"},{"text":"The methodology sets specific requirements for the scope of commitments, including a global extension across all cattle-producing regions and a target implementation date for deforestation-free sourcing no later than 2025. It also defines strict cut-off dates for deforestation: 2009 for direct and 2019 for indirect suppliers in the Amazon biome, and 2021 or earlier for all other biomes.","ai_generated":true,"unit":"mearth-000138#insight-2","date_modified":"2026-10-23"},{"text":"The 'Monitoring & Verification of Compliance' category (44 points) focuses on internal procedures and supplier requirements. Companies are scored on conducting risk assessments, implementing a 'engagement, retention, suspension, and then exclusion' protocol for non-compliance, and requiring suppliers to disclose the 'Farm-level point of origin' and the percentage of properties with traceability to the farm level.","ai_generated":true,"unit":"mearth-000138#insight-3","date_modified":"2026-10-23"},{"text":"The 'Public Disclosure & Reporting' category (18 points) requires annual reporting on progress using standardized metrics. This includes disclosing the volumes of conversion-free products, the degree of traceability (including country of origin and volumes traceable to slaughterhouses and farms in high-risk regions), and the public disclosure of all 'tier 1 beef suppliers' in high-risk sourcing regions.","ai_generated":true,"unit":"mearth-000138#insight-4","date_modified":"2026-10-23"}],"climate_tags":["deforestation","human rights","indigenous rights","nature restoration"],"geography_discussed":["Amazon rainforest"],"frame_perspective":[],"analysis_type":["methodology"]},{"id":"natjust-000343","url":"https://climateinsightsdirectory.org/document/natjust-000343","title":"Developing and Using Community Protocols","publisher":{"name":"Natural Justice","organization":"natural-justice"},"document_type":"guide","language":"en","date_published":null,"source_url":"https://naturaljustice.org/wp-content/uploads/2016/05/toolbox_3.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The document outlines the process of creating community protocols—codified rules and procedures based on customary law—to help communities manage interactions with external parties like governments and investors. It details the role of facilitators, the common elements of a protocol (such as identity, values, and governance), and strategies for using these documents for advocacy, negotiation, and conflict resolution. A significant focus is placed on ensuring Free, Prior and Informed Consent (FPIC) and challenging top-down impact assessments through community-led evidence.","ai_generated":true,"unit":"natjust-000343#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"Community protocols serve as a means to articulate customary rules and procedures in forms that external parties can understand, moving away from top-down approaches that disregard traditional laws.","ai_generated":true,"unit":"natjust-000343#insight-1","date_modified":"2026-10-23"},{"text":"Protocols can be developed for either aspirational purposes, such as ensuring equitable benefit sharing from desired infrastructure, or defensive purposes, such as opposing projects detrimental to territories and wellbeing.","ai_generated":true,"unit":"natjust-000343#insight-2","date_modified":"2026-10-23"},{"text":"The development process is community-driven and typically involves identifying the community, training catalysts, documenting information through workshops, and finalizing the protocol for approval and publication.","ai_generated":true,"unit":"natjust-000343#insight-3","date_modified":"2026-10-23"},{"text":"Communities can use protocols to 'put external actors on notice' by proactively informing them of their rights and expectations, which can encourage accountability and prevent conflict.","ai_generated":true,"unit":"natjust-000343#insight-4","date_modified":"2026-10-23"},{"text":"Community protocols can be used to challenge the accuracy of professional impact assessments by providing factual evidence of land use, governance structures, and health impacts.","ai_generated":true,"unit":"natjust-000343#insight-5","date_modified":"2026-10-23"},{"text":"Free, Prior and Informed Consent (FPIC) is a minimum international law standard requiring that communities be free from pressure, informed in their own language, and have the right to say 'no' at any stage of a project.","ai_generated":true,"unit":"natjust-000343#insight-6","date_modified":"2026-10-23"},{"text":"Practical applications of protocols include the Tanchara community in Ghana using a Community Health Impact Assessment Tool to engage government authorities regarding gold mining, and 30 Afro-Colombian communities in Choco using a protocol to guide dialogue with mining companies.","ai_generated":true,"unit":"natjust-000343#insight-7","date_modified":"2026-10-23"}],"climate_tags":["extractive industry","Free, Prior and Informed Consent","environmental impact assessment","land tenure","indigenous people","natural resource management"],"geography_discussed":["Argentina","Kenya","Ghana","Colombia","Chocó Department","Tanchera"],"frame_perspective":[],"analysis_type":["guide","methodology"]},{"id":"natjust-000346","url":"https://climateinsightsdirectory.org/document/natjust-000346","title":"COMMUNITY PROTOCOLS TOOLBOX","publisher":{"name":"Natural Justice","organization":"natural-justice"},"document_type":"guide","language":"en","date_published":null,"source_url":"https://naturaljustice.org/wp-content/uploads/2016/05/toolbox_complete.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The Community Protocols Toolbox is a guide designed for NGOs and civil society to support local communities in documenting their identity, customary laws, and development priorities. It emphasizes a participatory, community-led process to enable meaningful engagement and free, prior and informed consent (FPIC) when facing industrial investment projects. The toolbox includes practical methodologies—textual, visual, verbal, and dramatic—to map territories, analyze power dynamics, and navigate legal landscapes across national and international frameworks.","ai_generated":true,"unit":"natjust-000346#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"Community protocols serve as a mechanism for communities to collectively document their identity, link customary laws with national and international law, and engage with external parties, particularly regarding industrial investment projects.","ai_generated":true,"unit":"natjust-000346#insight-1","date_modified":"2026-10-23"},{"text":"The development of a protocol is a community-led process that can be either 'aspirational'—seeking opportunities and equitable benefit sharing—or 'defensive'—opposing harmful activities or asserting rights to consultation and consent.","ai_generated":true,"unit":"natjust-000346#insight-2","date_modified":"2026-10-23"},{"text":"Facilitators are encouraged to use a variety of participatory tools to ensure inclusivity, including textual, visual, verbal, and dramatic methods, to overcome barriers such as literacy or language.","ai_generated":true,"unit":"natjust-000346#insight-3","date_modified":"2026-10-23"},{"text":"Free, Prior and Informed Consent (FPIC) is identified as a minimum international legal standard, requiring that communities be free from coercion, informed in a language they understand, and have the right to say 'no' at any stage of a project.","ai_generated":true,"unit":"natjust-000346#insight-4","date_modified":"2026-10-23"},{"text":"In the context of extractive industries, communities face significant legal challenges, including the lack of recognition of customary land rights and the fact that states often reserve ownership of sub-soil mineral resources regardless of surface land ownership.","ai_generated":true,"unit":"natjust-000346#insight-5","date_modified":"2026-10-23"},{"text":"The toolbox identifies a five-stage mining life cycle—exploration, feasibility and planning, construction, operation, and closure and reclamation—and suggests that protocols are most effective when developed at the earliest opportunity to define engagement terms.","ai_generated":true,"unit":"natjust-000346#insight-6","date_modified":"2026-10-23"},{"text":"Community protocols can be used as evidence in legal or administrative processes to contest the legality of impact assessments, dispute claims that land is 'vacant', or prove that meaningful participation did not occur.","ai_generated":true,"unit":"natjust-000346#insight-7","date_modified":"2026-10-23"},{"text":"The guide warns of 'red-flags' in the protocol process, such as the risk of 'elite capture' where a small influential group protects their own interests, or the potential for documentation of sensitive information to attract predatory external interest in lucrative resources.","ai_generated":true,"unit":"natjust-000346#insight-8","date_modified":"2026-10-23"}],"climate_tags":["extractive industry","Free, Prior and Informed Consent","land tenure","environmental impact assessment","indigenous people","human rights"],"geography_discussed":["Argentina","India","Kenya","Zimbabwe","Ghana","Colombia"],"frame_perspective":[],"analysis_type":["guide","methodology"]},{"id":"prio-001849","url":"https://climateinsightsdirectory.org/document/prio-001849","title":"Water-Related Intrastate Conflict and Cooperation (WARICC): A New Event Dataset Codebook","publisher":{"name":"Peace Research Institute Oslo","organization":"peace-research-institute-oslo"},"document_type":"guide","language":"en","date_published":null,"source_url":"https://cdn.cloud.prio.org/files/4a847ac9-c852-4955-8741-e5f601a6626a/WARICC codebook v10.pdf?inline=true","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The WARICC codebook details the construction of a dataset designed to analyze the frequency and intensity of domestic water-related conflict and cooperation. Using BBC Monitoring as its primary source, the project employs an 11-point Water-Events Scale (WES) to categorize events from substantial national improvements (+5) to overt physical violence or national deterioration (-5). The methodology addresses potential data biases related to urban areas, regime types, and extreme events, while providing a comprehensive system of country and actor codes for standardized reporting.","ai_generated":true,"unit":"prio-001849#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The dataset focuses on the time-period 1997–2009 and covers all riparian countries of the Mediterranean Sea and all countries in the Sahel.","ai_generated":true,"unit":"prio-001849#insight-1","date_modified":"2026-10-23"},{"text":"The researchers selected BBC Monitoring over Factiva as the primary news source because Factiva produced an unmanageable volume of irrelevant hits and exhibited language bias due to limited translation of foreign-language articles.","ai_generated":true,"unit":"prio-001849#insight-2","date_modified":"2026-10-23"},{"text":"The Water-Events Scale (WES) is an 11-point system where +5 represents substantial national improvements in water quality or quantity, and -5 represents national-level deterioration or physical violence over water resources.","ai_generated":true,"unit":"prio-001849#insight-3","date_modified":"2026-10-23"},{"text":"The methodology identifies three primary sources of potential data error: urban bias (over-reporting of high-density areas), regime type bias (state censorship in non-democratic regimes), and extreme event bias (disproportionate coverage of very large projects or severe conflicts).","ai_generated":true,"unit":"prio-001849#insight-4","date_modified":"2026-10-23"},{"text":"The dataset distinguishes between the source level of action (scale) and the target level of action (impact), ranging from grass-roots individuals (1) to national governments (4).","ai_generated":true,"unit":"prio-001849#insight-5","date_modified":"2026-10-23"}],"climate_tags":["water management","climate change","water quality"],"geography_discussed":["Spain","Ethiopia","Albania","North Macedonia","Gaza Strip","Somalia"],"frame_perspective":[],"analysis_type":["methodology","dataset documentation"]},{"id":"reclfin-000701","url":"https://climateinsightsdirectory.org/document/reclfin-000701","title":"ARE EUROPEAN OIL AND GAS MAJORS ON TRACK FOR 1.5°C?","publisher":{"name":"Reclaim Finance","organization":"reclaim-finance"},"document_type":"report","language":"en","date_published":null,"source_url":"https://reclaimfinance.org/site/wp-content/uploads/2022/03/Reclaim-Finance-Majors-project-Methodology-FINAL.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The methodology focuses on five key indicators to evaluate the climate credibility of European oil and gas majors: alignment of decarbonization targets with a 1.5°C carbon budget, reliance on offset mechanisms, asset expansion plans, trends in oil and gas production growth, and the forecasted energy mix for 2030. The analysis employs conservative hypotheses, assuming companies will meet their pledged targets and maintain current market shares in line with the IEA Net Zero scenario to establish a 'low-estimate' of cumulative emissions.","ai_generated":true,"unit":"reclfin-000701#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"Reclaim Finance uses a 'low-estimate' approach to calculate cumulative emissions, which is considered conservative because it assumes companies will both meet their decarbonization targets and align their production with the IEA 1.5°C reference scenario.","ai_generated":true,"unit":"reclfin-000701#insight-1","date_modified":"2026-10-23"},{"text":"The analysis indicates that for all six companies covered, more than 70% of their long-term carbon budget is consumed by 2030, suggesting an urgent need for immediate emission reductions.","ai_generated":true,"unit":"reclfin-000701#insight-2","date_modified":"2026-10-23"},{"text":"The methodology assesses reliance on offsets by calculating the ratio of pledged carbon capture or nature-based solutions to the ambitioned absolute emissions reduction, forecasting the need for these offsets through 2050.","ai_generated":true,"unit":"reclfin-000701#insight-3","date_modified":"2026-10-23"},{"text":"To evaluate expansion plans, the document categorizes assets into four stages: undiscovered, not yet committed/discovered, future development/expansion, and developed/producing, noting that assets under development are highly likely to be exploited to avoid financial loss.","ai_generated":true,"unit":"reclfin-000701#insight-4","date_modified":"2026-10-23"},{"text":"The analysis tracks oil and gas production growth across three timeframes: historical growth since the 2016 Paris Agreement, forecasted short-term growth to 2024, and potential mid-term growth to 2030.","ai_generated":true,"unit":"reclfin-000701#insight-5","date_modified":"2026-10-23"}],"climate_tags":["carbon budget","emission intensity","nature-based solutions","carbon offset","petroleum industry","hydraulic fracturing","oil sands"],"geography_discussed":["World","Australia"],"frame_perspective":[],"analysis_type":["methodology"]},{"id":"rmi-000736","url":"https://climateinsightsdirectory.org/document/rmi-000736","title":"Clean Economy Graphics Data Sources and Documentation Overview","publisher":{"name":"RMI","organization":"rmi"},"document_type":"guide","language":"en","date_published":null,"source_url":"https://rmi.org/app/uploads/2023/10/clean_economy_graphics_documentation.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","analysis_type"],"summary":{"text":"The document outlines the technical framework for RMI's State Clean Energy Eco graphics, which project the scale of energy transformation required for the US to reach net zero emissions by 2050. It details the use of the Princeton Net Zero America (NZA) project as the primary data source, supplemented by the Energy Policy Simulator (EPS), NREL Cambium scenarios, and population projections. The methodology covers specific metrics across electricity, industry, transportation, buildings, and human impact, including a detailed approach for downscaling potential federal investments from the Inflation Reduction Act (IRA) to the state level. To account for uncertainty in 2050 projections, metrics are generally rounded to one significant figure.","ai_generated":true,"unit":"rmi-000736#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"RMI utilized several primary data sources to estimate 2050 metrics for a net zero US economy, with the Princeton Net Zero America (NZA) project serving as the main source using the \"E+RE+\" (higher electrification, higher renewable energy) scenario.","ai_generated":true,"unit":"rmi-000736#insight-1","date_modified":"2026-10-23"},{"text":"To estimate potential federal investment from the Inflation Reduction Act (IRA) by state, RMI used a combination of downscaling national values from the NZA \"E+RE+\" scenario for tax credits, downscaling Congressional Budget Office scores for non-tax credit provisions, and using official formula funding allocations where available.","ai_generated":true,"unit":"rmi-000736#insight-2","date_modified":"2026-10-23"},{"text":"The graphics include specific 2050 projections for infrastructure and technology deployment, such as the number of new wind turbines (assuming 3 megawatts per turbine), new solar panels (assuming 320 watts per panel), and the stock of electric light-duty vehicles.","ai_generated":true,"unit":"rmi-000736#insight-3","date_modified":"2026-10-23"},{"text":"Human impact metrics include projections for avoided asthma attacks and premature deaths due to reduced air pollution, with the latter monetized using an assumed Value of Statistical Life of $10 million (converted to 2023$).","ai_generated":true,"unit":"rmi-000736#insight-4","date_modified":"2026-10-23"},{"text":"Current greenhouse gas emissions for the electricity, industry, transportation, and buildings sectors were retrieved from state EPS models for the year 2021.","ai_generated":true,"unit":"rmi-000736#insight-5","date_modified":"2026-10-23"}],"climate_tags":["carbon neutrality","energy transition","electrical grid","electric power transmission","solar energy","wind power","hydrogen","Industrial decarbonization","electric vehicle","electric vehicle charging network","public transport","heat pump","house-building","air pollution","Inflation Reduction Act of 2022","climate finance"],"geography_discussed":[],"frame_perspective":[],"analysis_type":["methodology","data documentation"]},{"id":"rmi-000744","url":"https://climateinsightsdirectory.org/document/rmi-000744","title":"Demand Growth Solutions Methodology Solution Source Notes","publisher":{"name":"RMI","organization":"rmi"},"document_type":"fact sheet","language":"en","date_published":null,"source_url":"https://rmi.org/app/uploads/2024/09/demand_growth_methodology.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","analysis_type"],"summary":{"text":"The document details the data sources and specific capacity estimates used by RMI for energy efficiency, virtual power plants, grid enhancing technologies, and clean repowering. It highlights a projected 53GW reduction from energy efficiency and 60GW of dispatchable capacity from flexible loads and storage by 2030, while adopting a conservative 20GW estimate for grid enhancing technologies and 14GW for clean repowering via generator replacement.","ai_generated":true,"unit":"rmi-000744#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"RMI estimates that energy efficiency measures alone will result in a 53GW reduction in peak demand, contrasting with a DOE projection of 68GW in peak demand savings by 2030 from a combination of energy efficiency and demand flexibility (which includes 15GW from demand flexibility).","ai_generated":true,"unit":"rmi-000744#insight-1","date_modified":"2026-10-23"},{"text":"By 2030, RMI estimates approximately 60GW of peak coincident dispatchable capacity can be achieved through flexible residential and commercial demand, behind-the-meter battery storage, and flexible EV load.","ai_generated":true,"unit":"rmi-000744#insight-2","date_modified":"2026-10-23"},{"text":"While the DOE suggests full deployment of advanced grid solutions like Grid Enhancing Technologies (GETs) and advanced conductors could unlock 20-100GW of peak demand capacity, RMI uses a conservative estimate of 20GW for its analysis.","ai_generated":true,"unit":"rmi-000744#insight-3","date_modified":"2026-10-23"},{"text":"RMI identifies a 14GW opportunity for clean repowering specifically through generator replacement at fossil plants that would otherwise retire, utilizing existing points of interconnection incentivized by the Inflation Reduction Act (IRA).","ai_generated":true,"unit":"rmi-000744#insight-4","date_modified":"2026-10-23"}],"climate_tags":["energy efficiency","Demand flexibility","virtual power plant","demand response","electric vehicle","battery energy storage system","renewable energy","Inflation Reduction Act of 2022"],"geography_discussed":[],"frame_perspective":[],"analysis_type":["methodology","capacity estimation"]},{"id":"rmi-000759","url":"https://climateinsightsdirectory.org/document/rmi-000759","title":"Lower Bills, Cleaner Air: Heat Pump Benefits for Homes Relying on Delivered Fuels","publisher":{"name":"RMI","organization":"rmi"},"document_type":"fact sheet","language":"en","date_published":null,"source_url":"https://rmi.org/wp-content/uploads/2025/04/Heat-Pump-vs-Delivered-Fuel-Analysis-Assumptions.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","analysis_type"],"summary":{"text":"The document details the methodology and assumptions for a comparative analysis of heat pumps versus delivered fuels in US homes. It specifies the use of 2020 EIA RECS microdata for building archetypes, 2024 state-average energy prices, and specific efficiency ratings (HSPF2, SEER2, and UEF) for air source heat pumps (ASHP) and heat pump water heaters (HPWH) across different climate zones. Hourly energy load profiles were sourced from NREL's ResStock, with ASHP performance curves based on TMY3 temperature data and the Northeast Energy Efficiency Partnerships' database.","ai_generated":true,"unit":"rmi-000759#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The analysis uses building stock archetypes based on 2020 US Energy Information Administration (EIA) Residential Energy Consumption (RECS) microdata, accounting for building type, year, heating fuel, AC type, and household income across state climate zones.","ai_generated":true,"unit":"rmi-000759#insight-1","date_modified":"2026-10-23"},{"text":"Energy price assumptions are based on 2024 state-average volumetric prices for electricity and delivered fuels, with electric prices derived by subtracting utility fixed charge revenue from total revenue and dividing by residential sales.","ai_generated":true,"unit":"rmi-000759#insight-2","date_modified":"2026-10-23"},{"text":"Heat pump efficiency assumptions vary by equipment type and climate zone: ducted ASHPs have an HSPF2 of 8 and SEER2 of 17 in zones 1–4, and HSPF2 of 9 and SEER2 of 16 in zones 5 and above; ductless ASHPs have an HSPF2 of 8.5 and SEER2 of 19 in zones 1–4, and HSPF2 of 9.5 and SEER2 of 18 in zones 5 and above. Heat pump water heaters (HPWH) are assigned a uniform energy factor (UEF) of 3.7.","ai_generated":true,"unit":"rmi-000759#insight-3","date_modified":"2026-10-23"},{"text":"Hourly energy load profiles were sourced from NREL's ResStock, while cold climate ASHP performance curves were derived from an average subset of 100 common models in the Northeast Energy Efficiency Partnerships' database.","ai_generated":true,"unit":"rmi-000759#insight-4","date_modified":"2026-10-23"}],"climate_tags":["energy efficiency"],"geography_discussed":[],"frame_perspective":[],"analysis_type":["technical assumptions","methodology"]},{"id":"rmi-000773","url":"https://climateinsightsdirectory.org/document/rmi-000773","title":"Illinois Clean Transportation Strategy Analysis Executive Summary","publisher":{"name":"RMI","organization":"rmi"},"document_type":"briefing","language":"en","date_published":null,"source_url":"https://rmi.org/app/uploads/2024/04/key_assumptions_IL_comp_analysis.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","analysis_type"],"summary":{"text":"The analysis employs RMI's Energy Policy Simulator (EPS) and Smarter MODES tools to model the impact of three specific policy interventions in Illinois: electric vehicle sales standards, GHG planning in infrastructure, and increased transit investment. The EPS model assumes the adoption of Advanced Clean Cars 2 and Advanced Clean Trucks starting in 2029, targeting 100% zero-emission passenger cars and SUVs, 58% of commercial trucks, and 33% of buses by 2035. Simultaneously, the Smarter MODES calculator models a 20% per capita reduction in vehicle miles traveled (VMT) by 2050 compared to 2019 levels, alongside an 80% electric light-duty vehicle stock by 2050.","ai_generated":true,"unit":"rmi-000773#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The analysis models the adoption of Advanced Clean Cars 2 (ACCII) and Advanced Clean Trucks (ACT) starting in model year 2029, with specific zero-emission sales targets for 2035: 100% for passenger cars and SUVs, 58% for light, medium, and heavy commercial trucks, and 33% for buses.","ai_generated":true,"unit":"rmi-000773#insight-1","date_modified":"2026-10-23"},{"text":"The methodology assumes that through transit investments and GHG planning standards, Illinois will achieve a 20% per capita reduction in vehicle miles traveled (VMT) by 2050 relative to 2019 levels, while reaching approximately 80% electric light-duty vehicle stock by 2050.","ai_generated":true,"unit":"rmi-000773#insight-2","date_modified":"2026-10-23"}],"climate_tags":["zero-emissions vehicle"],"geography_discussed":[],"frame_perspective":[],"analysis_type":["methodology"]},{"id":"rmi-000774","url":"https://climateinsightsdirectory.org/document/rmi-000774","title":"Why State Land Use Reform Should Be a Priority Climate Lever for America","publisher":{"name":"RMI","organization":"rmi"},"document_type":"report","language":"en","date_published":null,"source_url":"https://rmi.org/app/uploads/2024/02/land_use_reform_methodology.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The document outlines a methodology for estimating GHG emissions reductions achievable through land use reform in the US. It projects that over 13 million new housing units will be needed by 2033 to address population growth and existing shortages. By comparing a 'business as usual' scenario with a 'low VMT' scenario—where 90% of new housing is placed in the 90th percentile lowest VMT areas of each state—the analysis calculates potential savings in direct tailpipe emissions, vehicle lifecycle emissions, and indirect emissions from building energy, embodied carbon, and the loss of natural land carbon sinks.","ai_generated":true,"unit":"rmi-000774#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The analysis projects that more than 13 million new housing units will be added nationally by 2033 if states eliminate existing housing shortages and accommodate projected population growth.","ai_generated":true,"unit":"rmi-000774#insight-1","date_modified":"2026-10-23"},{"text":"The 'low VMT' scenario assumes that 90% of new housing is associated with the 90th percentile lowest VMT per capita already achievable in each state, while the remaining 10% corresponds to the current state average.","ai_generated":true,"unit":"rmi-000774#insight-2","date_modified":"2026-10-23"},{"text":"Indirect emissions sources—including building energy, embodied carbon, and the loss of land sinks—can almost double the total GHG impact of car-oriented communities compared to direct tailpipe emissions alone.","ai_generated":true,"unit":"rmi-000774#insight-3","date_modified":"2026-10-23"},{"text":"The analysis identifies specific indirect GHG contributors: building energy (4% to ~10%), housing embodied carbon (3%), and the loss of natural land sinks (at least 7%) relative to national direct tailpipe emissions of 1,100 million tons CO2e/year.","ai_generated":true,"unit":"rmi-000774#insight-4","date_modified":"2026-10-23"},{"text":"For states committed to the Advanced Clean Cars II (ACCII) standard—including California, Vermont, New York, Washington, Oregon, Rhode Island, New Jersey, and Maryland—the electric vehicle (EV) stock percentage is projected to reach 33% by 2033.","ai_generated":true,"unit":"rmi-000774#insight-5","date_modified":"2026-10-23"},{"text":"Based on the provided data table, Texas (TX) has the highest potential gross rough lifecycle GHG savings at 12.4 MMT CO2e/Year, followed by California (CA) at 8.3 MMT CO2e/Year and Florida (FL) at 7.0 MMT CO2e/Year.","ai_generated":true,"unit":"rmi-000774#insight-6","date_modified":"2026-10-23"}],"climate_tags":["greenhouse gas emissions","electric vehicle","embedded emissions"],"geography_discussed":["World","Alaska","California","Washington","New York","Colorado","District of Columbia"],"frame_perspective":[],"analysis_type":["methodology","technical supplement","projection"]},{"id":"rmi-000778","url":"https://climateinsightsdirectory.org/document/rmi-000778","title":"Benefits of the Inflation Reduction Act by State","publisher":{"name":"RMI","organization":"rmi"},"document_type":"report","language":"en","date_published":null,"source_url":"https://rmi.org/app/uploads/2023/02/methodology-ira.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","analysis_type"],"summary":{"text":"The document describes a methodology for downscaling national Inflation Reduction Act (IRA) funding and co-benefits to the US state level. For tax credits, RMI uses 'Climate-Aligned Targets' based on the Net Zero America study and RMI State Scorecards to estimate potential investment in electric vehicles, renewable energy, heat pumps, and carbon sequestration. For non-tax credit funding (rebates, grants, loans), the analysis downscales Congressional Budget Office (CBO) estimates using state-specific metrics such as methane emissions, fuel consumption, or population. Public health and jobs benefits are downscaled from Energy Innovation's national modeling based on state population relative to the United States.","ai_generated":true,"unit":"rmi-000778#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"Funding for IRA tax credits is calculated by determining a 'Climate-Aligned Target' for 2030 per state, often by downscaling national values from the Net Zero America study's \"E+RE+\" scenario, and multiplying the required capacity or unit increase by the credit's dollar value.","ai_generated":true,"unit":"rmi-000778#insight-1","date_modified":"2026-10-23"},{"text":"For provisions not funded via tax credits, RMI typically downscales the Congressional Budget Office (CBO) national budget score to the state level using parameters related to the specific provision, or uses federal formula funding allocations where available.","ai_generated":true,"unit":"rmi-000778#insight-2","date_modified":"2026-10-23"},{"text":"Electric vehicle (EV) funding estimates are based on the difference between 2022 EV levels (from the state Energy Policy Simulator) and a 2030 target aimed at slashing emissions in half relative to 2005 levels, with a 0.30 multiplier applied to the new EV tax credit (Section 13401) to reflect expected qualification rates.","ai_generated":true,"unit":"rmi-000778#insight-3","date_modified":"2026-10-23"},{"text":"Estimates for clean electricity production (Section 13701) and investment (Section 13702) tax credits use weighted averages of credit rates based on the likelihood of state utilities meeting criteria such as prevailing wage requirements.","ai_generated":true,"unit":"rmi-000778#insight-4","date_modified":"2026-10-23"},{"text":"Public health and employment benefits are derived by downscaling Energy Innovation's national 'Moderate Scenario' modeling to the state level based on population.","ai_generated":true,"unit":"rmi-000778#insight-5","date_modified":"2026-10-23"}],"climate_tags":["Inflation Reduction Act of 2022","tax credit","electric vehicle","renewable energy","heat pump","carbon sequestration","energy efficiency","climate finance","green job"],"geography_discussed":[],"frame_perspective":[],"analysis_type":["methodology"]},{"id":"rmi-000779","url":"https://climateinsightsdirectory.org/document/rmi-000779","title":"Minnesotans would save up to $91 billion from climate-smart transportation","publisher":{"name":"RMI","organization":"rmi"},"document_type":"report","language":"en","date_published":null,"source_url":"https://rmi.org/app/uploads/2023/04/minnesota-dot-methodology.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The document details the analytical approach used to estimate up to $91 billion in savings for Minnesota by comparing 'business-as-usual' transportation scenarios against targets for VMT reduction and net-zero emissions. It describes the monetization of avoided crash fatalities, air quality improvements, fuel and operating costs, and the social cost of carbon.","ai_generated":true,"unit":"rmi-000779#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"RMI compared two Minnesota Department of Transportation (MnDOT) scenarios for Vehicle Miles Traveled (VMT) by 2050: a 'business-as-usual' case projecting 69.7 billion miles and a target scenario of 56.0 billion miles, representing a 20% per capita reduction.","ai_generated":true,"unit":"rmi-000779#insight-1","date_modified":"2026-10-23"},{"text":"To monetize crash fatality and injury savings, the analysis assumes crashes decrease in proportion to VMT reduction, using fatality data from the Fatality Analysis Reporting System (FARS) for 2000-2009 and 2021 U.S. DOT guidance for the statistical value of life.","ai_generated":true,"unit":"rmi-000779#insight-2","date_modified":"2026-10-23"},{"text":"Air quality fatality savings were calculated using the EPA’s COBRA tool, focusing on avoided all-cause mortality from PM2.5, NOx, and SO2, while assuming an ambitious electric vehicle (EV) adoption rate based on the 'Nationally Determined Contribution' (NDC) scenario.","ai_generated":true,"unit":"rmi-000779#insight-3","date_modified":"2026-10-23"},{"text":"The analysis calculates the social cost of carbon (SCC) at $190 per metric ton of CO2, utilizing a 2% near-term Ramsey discount rate as legally adopted by the state of Minnesota in February 2023 via Senate File 4.","ai_generated":true,"unit":"rmi-000779#insight-4","date_modified":"2026-10-23"},{"text":"Fuel and operating cost savings were estimated using 2020 dollars based on EIA and Kelly Blue Book data, assuming that registered private vehicles in Minnesota grow in proportion to the state's population growth through 2050.","ai_generated":true,"unit":"rmi-000779#insight-5","date_modified":"2026-10-23"}],"climate_tags":["decarbonization","electric vehicle","air quality","active mobility","Net-zero emissions"],"geography_discussed":["United States","Portland"],"frame_perspective":[],"analysis_type":["methodology","cost-benefit analysis"]},{"id":"rmi-000801","url":"https://climateinsightsdirectory.org/document/rmi-000801","title":"rmi-electrification-rate-design-analysis-assumptions-66a0b39b031ddf3e.pdf","publisher":{"name":"RMI","organization":"rmi"},"document_type":"report","language":"en","date_published":null,"source_url":"https://rmi.org/app/uploads/2026/06/RMI-Electrification-Rate-Design-Analysis-Assumptions.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","analysis_type"],"summary":{"text":"The document describes a framework for calculating residential utility bills by combining NREL ResStock building load data with electric and gas tariff data from Genability and RateAcuity. It details specific adjustments made to tariffs for Xcel Energy (Minnesota), Pepco (Maryland), Central Maine Power (Maine), and Eversource Energy (Massachusetts) to ensure analytical consistency. The analysis specifically models upgrades to high-efficiency cold-climate air-to-air heat pumps and evaluates the impact of 'electrification-friendly' rate designs on customer costs.","ai_generated":true,"unit":"rmi-000801#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"RMI's analytical framework estimates residential bill changes by combining NREL ResStock's 2024.2 release load profiles—specifically 'upgrade scenario 2' for high efficiency cold-climate air-to-air heat pumps with electric backup—with tariff data from Genability and RateAcuity and EIA state-average fuel prices.","ai_generated":true,"unit":"rmi-000801#insight-1","date_modified":"2026-10-23"},{"text":"For Xcel Energy in Minnesota, RMI manually adjusted percent-based interim rate adjustment surcharges to prevent overapplication, excluding components such as the Conservation Improvement Program Adjustment, Environmental Improvement Charge, Mercury Cost Recovery, Renewable Development Fund, Affordability Surcharge, and Renewable Energy Standard Adjustment.","ai_generated":true,"unit":"rmi-000801#insight-2","date_modified":"2026-10-23"},{"text":"In the analysis for Pepco Maryland, RMI created a hypothetical 'default' flat rate with consistent volumetric charges to serve as a reference point against the existing 'electrification-friendly' rate, which features seasonally differentiated volumetric charges.","ai_generated":true,"unit":"rmi-000801#insight-3","date_modified":"2026-10-23"},{"text":"RMI estimated heat pump enrollment for Central Maine Power by applying the ratio of the utility's customers to all state electricity customers against total state heat pump data, assuming 10,000 customers are enrolled in the opt-in rate as of 2026.","ai_generated":true,"unit":"rmi-000801#insight-4","date_modified":"2026-10-23"}],"climate_tags":["heat pump"],"geography_discussed":[],"frame_perspective":[],"analysis_type":["methodology","technical assumptions"]},{"id":"rmi-000829","url":"https://climateinsightsdirectory.org/document/rmi-000829","title":"Reinventing Fire Industry Sector Methodology","publisher":{"name":"RMI","organization":"rmi"},"document_type":"guide","language":"en","date_published":null,"source_url":"https://rmi.org/app/uploads/2017/05/RMI_Document_Repository_Public-Reprts_2011-15_RFindustrysectormethodology.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","analysis_type"],"summary":{"text":"The Reinventing Fire industry model uses a stock-flow framework and EIA Annual Energy Outlook 2010 data to simulate the adoption of energy-saving measures in the U.S. industrial sector from 2010 to 2050. It tracks energy consumption, fuel expenditures, and capital investment across various manufacturing and non-manufacturing industries. The model incorporates efficient technologies, combined heat and power (CHP), waste-heat-to-electricity, and integrative design for machine drives, while accounting for refining transformations as demand shifts toward biofuels and hydrogen.","ai_generated":true,"unit":"rmi-000829#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The industry model utilizes a stock-flow framework based on EIA's Annual Energy Outlook 2010 (AEO) to simulate a 'business-as-usual' (BAU) baseline for industrial production capacity turnover and energy consumption from 2010 to 2050. It disaggregates data across numerous manufacturing sectors (such as iron and steel, cement, and chemicals) and non-manufacturing sectors (including mining, construction, and refining).","ai_generated":true,"unit":"rmi-000829#insight-1","date_modified":"2026-10-23"},{"text":"Energy efficiency and CHP measures are modeled with the assumption that real costs and relative savings remain constant from 2010 to 2050, based on the premise that energy efficiency is not a diminishing resource and continues to grow through innovation.","ai_generated":true,"unit":"rmi-000829#insight-2","date_modified":"2026-10-23"},{"text":"The model estimates a 17.3% savings potential for integrative design in machine drive systems. This figure is derived from a universal 80% savings opportunity in fluid-handling systems (pumps, fans, and compressed air), which is then reduced by subtracting AEO spontaneous efficiency savings (19.4%) and LBNL motor measures savings (10%).","ai_generated":true,"unit":"rmi-000829#insight-3","date_modified":"2026-10-23"},{"text":"Technology adoption for energy efficiency is constrained by BAU capacity stock turnover, utilizing a linear ramp-up from 2011 to 2030 to reach a default adoption rate of 85%. In contrast, waste-heat-to-electricity measures are adopted at half the rate of efficiency measures and are capped at approximately 40% of the technical potential estimated by LBNL.","ai_generated":true,"unit":"rmi-000829#insight-4","date_modified":"2026-10-23"},{"text":"Refining transformations in the model are driven by shifting liquid fuel demands, where transportation shifts toward biofuels (based on NREL's cellulosic ethanol from corn stover process) and hydrogen (based on H2Gen distributed natural gas reforming).","ai_generated":true,"unit":"rmi-000829#insight-5","date_modified":"2026-10-23"}],"climate_tags":["energy efficiency","Industrial decarbonization","biofuel","energy modeling"],"geography_discussed":[],"frame_perspective":[],"analysis_type":["methodology","financial modeling","technical assessment"]},{"id":"rmi-000936","url":"https://climateinsightsdirectory.org/document/rmi-000936","title":"Reinventing Fire Electricity Sector Methodology","publisher":{"name":"RMI","organization":"rmi"},"document_type":"report","language":"en","date_published":null,"source_url":"https://rmi.org/app/uploads/2017/05/RMI_Document_Repository_Public-Reprts_Reinventing-Fire-Electricity-Sector-Methodology.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","analysis_type"],"summary":{"text":"The document describes the use of NREL's Regional Energy Deployment System (ReEDS) and RMI's internal dispatch model to evaluate the technical feasibility and cost of four electricity scenarios: Maintain, Migrate, Renew, and Transform. It outlines spatial and temporal resolutions, generation cost assumptions based on learning curve theory, and specific modifications made to approximate distributed energy resources in the Transform case.","ai_generated":true,"unit":"rmi-000936#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"RMI utilized two primary models for its analysis: the National Renewable Energy Laboratory's (NREL) Regional Energy Deployment System (ReEDS) for bulk power system technical feasibility and cost, and an internally developed hourly, least-cost dispatch model to analyze variable resources and distributed generation.","ai_generated":true,"unit":"rmi-000936#insight-1","date_modified":"2026-10-23"},{"text":"The analysis evaluates four distinct scenarios for the U.S. electricity sector by 2050: 'Maintain' (business-as-usual), 'Migrate' (shift to nuclear and coal with carbon capture), 'Renew' (at least 80% centralized renewables), and 'Transform' (heavy reliance on distributed sources like rooftop solar and CHP).","ai_generated":true,"unit":"rmi-000936#insight-2","date_modified":"2026-10-23"},{"text":"The ReEDS model employs a highly discretized spatial structure, including 3 major grid interconnects, 13 NERC subregions, 21 reserve-sharing groups, 134 balancing authorities, and 356 resource regions for wind and concentrated solar power (CSP).","ai_generated":true,"unit":"rmi-000936#insight-3","date_modified":"2026-10-23"},{"text":"To project technology costs over 40 years, RMI applied learning curve theory, which posits that costs decrease as cumulative experience and deployment scale increase, creating a \"virtuous cycle\" of demand and production.","ai_generated":true,"unit":"rmi-000936#insight-4","date_modified":"2026-10-23"},{"text":"For the Transform case, RMI exogenously specified several distributed resources to account for the distribution network's role: distributed wind (10% of total load by 2050), distributed solar (26% of total load by 2050), and industrial CHP (415 TWh annually by 2050).","ai_generated":true,"unit":"rmi-000936#insight-5","date_modified":"2026-10-23"},{"text":"The methodology incorporates specific financial adjustments, including a risk adjustment factor added to the cost of equity and debt for all coal-fired technologies to account for potential carbon regulation.","ai_generated":true,"unit":"rmi-000936#insight-6","date_modified":"2026-10-23"},{"text":"The ReEDS model's temporal resolution segments electricity demand into 17 timeslices per year, covering different times of day across four seasons, plus a specific slice for the 40 highest demand hours of summer peak.","ai_generated":true,"unit":"rmi-000936#insight-7","date_modified":"2026-10-23"},{"text":"The RMI dispatch model follows a four-step process to determine the least-cost generation mix: applying energy efficiency measures, dispatching minimum generation and variable supply, utilizing hydropower and demand-shifting resources, and finally dispatching remaining dispatchable resources based on marginal cost.","ai_generated":true,"unit":"rmi-000936#insight-8","date_modified":"2026-10-23"}],"climate_tags":["electric power industry","renewable energy","distributed generation","carbon capture and storage","energy efficiency","electric vehicle","smart grid","wind power","photovoltaics","geothermal energy","hydropower","energy storage","demand response","electric power transmission","Renewable portfolio standard"],"geography_discussed":[],"frame_perspective":[],"analysis_type":["methodology","scenario analysis","cost modeling"]},{"id":"rmi-000990","url":"https://climateinsightsdirectory.org/document/rmi-000990","title":"The Incredible Inefficiency of the Fossil Energy System","publisher":{"name":"RMI","organization":"rmi"},"document_type":"report","language":"en","date_published":null,"source_url":"https://rmi.org/wp-content/uploads/2024/05/appendix_the_incredible_inefficiency_of_fossils.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","analysis_type"],"summary":{"text":"The document details a bottom-up methodology for sizing global energy waste using 2019 IEA World Energy Balance data and IIASA's PFUDB dataset. It estimates that of $12 trillion in gross global energy spending, approximately $4.6 trillion (40%) is spent on fuels that are wasted before becoming useful energy. The authors note that while the second law of thermodynamics prevents total elimination of waste, this $4.6 trillion figure represents a significant upper bound for efficiency opportunities.","ai_generated":true,"unit":"rmi-000990#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The authors estimate that global gross spending on energy is approximately $12 trillion per year, of which $4.6 trillion is spent on fuels that are wasted and do not become useful energy, representing roughly 40% of total energy spending.","ai_generated":true,"unit":"rmi-000990#insight-1","date_modified":"2026-10-23"},{"text":"The methodology for sizing energy waste utilizes 2019 IEA World Energy Balance (WEB) data and the IIASA PFUDB dataset to determine final-to-useful losses. The authors adjusted IEA Final Energy Consumption (FEC) figures downward by 7 EJ to account for hidden energy transport waste, such as pipeline consumption and fossil fuel shipping.","ai_generated":true,"unit":"rmi-000990#insight-2","date_modified":"2026-10-23"},{"text":"The calculated waste of 379 EJ and $4.6 trillion serves as an upper bound for efficiency opportunities. While the second law of thermodynamics means not all waste can be eliminated, the document cites research from Cambridge University suggesting that 85% of 2005 energy demand could be practically avoided with existing knowledge and technology.","ai_generated":true,"unit":"rmi-000990#insight-3","date_modified":"2026-10-23"},{"text":"To calculate wasted spending, the authors applied global average wholesale prices for supply losses and retail prices for demand losses. Assumed prices included $100/t for coal, $60/bbl for wholesale crude oil, $150/bbl for final oil products, $5/million BTU wholesale for gas, $21/million BTU consumer price for gas, and $60/MWh wholesale versus $120/MWh retail for electricity.","ai_generated":true,"unit":"rmi-000990#insight-4","date_modified":"2026-10-23"}],"climate_tags":["energy efficiency","fossil fuel"],"geography_discussed":[],"frame_perspective":[],"analysis_type":["methodology","quantitative assessment"]}]}