{"query_echo":{"q":"","filters":{"analysis_type":["Risk Framework"]},"warnings":[{"code":"facet_value_not_in_vocabulary","message":"Value 'Risk Framework' for facet 'analysis_type' is not in the controlled vocabulary."}]},"total":{"value":2,"relation":"eq"},"page":1,"page_size":20,"self":"https://climateinsightsdirectory.org/api/v1/search?analysis_type=Risk+Framework","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":"clipolin-001024","url":"https://climateinsightsdirectory.org/document/clipolin-001024","title":"Assessing Climate Risks to India’s Power Sector: A District-Level and Asset-Level Methodology for Climate-Resilient Power Systems","publisher":{"name":"Climate Policy Initiative","organization":"climate-policy-initiative"},"document_type":"report","language":"en","date_published":"2026-05","source_url":"https://www.climatepolicyinitiative.org/wp-content/uploads/2026/05/Annexure-Assessing-Climate-Risks-to-Indias-Power-Sector.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","analysis_type"],"summary":{"text":"The report details a framework for assessing climate risks to India's power sector, distinguishing between chronic stresses (e.g., temperature, drought) and acute extremes (e.g., floods, cyclones). It introduces the Asset-Specific Climate Risk Factor (ACRF) for spatial screening and Expected Annual Impact (EAI)—measured as Expected Annual Loss (EAL) for generation and Energy Not Served (ENS) for networks—to quantify operational disruptions in MWh. The methodology includes a cost-benefit analysis (CBA) framework to evaluate the economic viability of resilience measures by comparing investment costs against avoided energy losses and repair expenditures.","ai_generated":true,"unit":"clipolin-001024#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The methodology employs a 'hazard–exposure–vulnerability' construct to generate two primary outputs: the Asset-Specific Climate Risk Factor (ACRF), a dimensionless index for spatial risk mapping, and the Expected Annual Impact (EAI), which quantifies energy losses in MWh per year.","ai_generated":true,"unit":"clipolin-001024#insight-1","date_modified":"2026-10-23"},{"text":"Climate hazards are categorized into chronic and acute pathways based on their physical impact mechanisms. Chronic hazards, including temperature, rainfall, wind speed, and drought-induced water stress, cause gradual performance degradation or operational constraints. Acute hazards, such as floods, cyclones, and landslides, cause sudden physical failures and forced outages.","ai_generated":true,"unit":"clipolin-001024#insight-2","date_modified":"2026-10-23"},{"text":"Asset exposure is defined differently across the power sector: generation assets are mapped to a single district based on rated installed capacity; transmission assets are distributed across districts based on line length; and distribution assets are mapped to a single district based on average load served.","ai_generated":true,"unit":"clipolin-001024#insight-3","date_modified":"2026-10-23"},{"text":"The framework quantifies operational impacts through Expected Annual Loss (EAL) for generation assets and Energy Not Served (ENS) for transmission and distribution assets. ENS incorporates a redundancy/load-transfer factor to account for the grid's ability to reroute power, preventing the overestimation of impacts in meshed networks.","ai_generated":true,"unit":"clipolin-001024#insight-4","date_modified":"2026-10-23"},{"text":"A cost-benefit analysis (CBA) framework is provided to evaluate resilience measures, such as flood protection walls or equipment hardening. It compares the present value of lifecycle costs (CAPEX and O&M) against the discounted stream of avoided energy losses and avoided direct repair and replacement expenditures.","ai_generated":true,"unit":"clipolin-001024#insight-5","date_modified":"2026-10-23"}],"climate_tags":["Climate risk assessment","electric power industry","climate resilience","temperature","flood","tropical cyclone","landslide"],"geography_discussed":[],"frame_perspective":[],"analysis_type":["methodology","risk framework","economic appraisal"]},{"id":"cdkn-000534","url":"https://climateinsightsdirectory.org/document/cdkn-000534","title":"Climate Risk Assessment Guide – Central Asia","publisher":{"name":"Climate and Development Knowledge Network","organization":"climate-and-development-knowledge-network"},"document_type":"guide","language":"en","date_published":"2013-07","source_url":"http://cdkn.org/wp-content/uploads/2013/08/Climate-Risk-Assessment-Guide.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The Climate Risk Assessment Guide – Central Asia is a practical manual designed to assess the impacts of climate-related events on lives and livelihoods in Central Asia. It provides a structured methodology to downscale climate risk understanding to sub-national and community levels, bridging the gap between long-term climate change modeling and immediate disaster risk assessment. The guide emphasizes a combination of impact-based assessments (using monetary damage and livelihood capital scoring) and perception-based surveys to inform sustainable risk management and policy development.","ai_generated":true,"unit":"cdkn-000534#summary","date_modified":"2026-08-17"},"key_insights":[{"text":"The guide identifies a critical need to 'downscale the understanding of climate impacts' to sub-national and community levels because existing national reports are often too broad to guide practical local action and resource prioritization.","ai_generated":true,"unit":"cdkn-000534#insight-1","date_modified":"2026-08-17"},{"text":"Climate-related hazards in Central Asia are categorized into three types: Direct (e.g., heavy rainfall, extreme heat), Consequent (e.g., flooding, landslides, glacial lake outbursts), and Contributory (e.g., food and water shortages, resource-related conflict).","ai_generated":true,"unit":"cdkn-000534#insight-2","date_modified":"2026-08-17"},{"text":"The methodology utilizes a modified Sustainable Livelihoods Framework to assess impacts across six types of capital: Human, Natural, Social, Financial, Physical, and Political. Political capital was specifically added to account for the role of governance in risk management.","ai_generated":true,"unit":"cdkn-000534#insight-3","date_modified":"2026-08-17"},{"text":"Risk is quantified through two primary lenses: monetary value of expected physical damage per capita for specific hazard frequencies, and a qualitative 'livelihood impact score' derived from a consensus-based adaptation of the Delphi technique.","ai_generated":true,"unit":"cdkn-000534#insight-4","date_modified":"2026-08-17"},{"text":"The guide stresses the importance of perception-based assessments, noting that gaps between objective data and local perceptions can lead to risk management efforts that lack local support or fail to address actual societal concerns.","ai_generated":true,"unit":"cdkn-000534#insight-5","date_modified":"2026-08-17"},{"text":"Cross-cutting issues such as age and gender are integrated into the process, requiring that data be disaggregated to identify how risks differently affect children, the elderly, men, and women.","ai_generated":true,"unit":"cdkn-000534#insight-6","date_modified":"2026-08-17"},{"text":"The guide employs a 'six-pack approach' to data collection, prioritizing only data clearly relevant to specific risk parameters to maintain efficiency given the limitations of data availability and resources in Central Asia.","ai_generated":true,"unit":"cdkn-000534#insight-7","date_modified":"2026-08-17"},{"text":"Geographic Information Systems (GIS) are used as the analytical platform to overlay layers of baseline, hazard, damage, and socio-economic data, though the accuracy is limited by the smallest scale of the data used (approximately 10 km for land use).","ai_generated":true,"unit":"cdkn-000534#insight-8","date_modified":"2026-08-17"},{"text":"Future risk outcomes are projected using simple scenario-based calculations in spreadsheets, such as comparing a baseline of population-linked damage growth against scenarios of successful damage reduction.","ai_generated":true,"unit":"cdkn-000534#insight-9","date_modified":"2026-08-17"},{"text":"A 'willingness to pay' survey is used to gauge the perceived importance of climate risks; preliminary results from Kyrgyzstan indicated that many respondents prioritize other needs (e.g., livestock, education) over climate risk management.","ai_generated":true,"unit":"cdkn-000534#insight-10","date_modified":"2026-08-17"}],"climate_tags":["climate risk assessment","central asia","disaster risk management","sustainable livelihoods","sub-national planning","climate adaptation"],"geography_discussed":["Batken Oblast","Chui Oblast","Talas Oblast"],"frame_perspective":[],"analysis_type":["Methodological Guide","Risk Framework","Capacity Building"]}]}