{"query_echo":{"q":"","filters":{"climate_tags":["decision support tools"]},"warnings":[{"code":"facet_value_not_in_vocabulary","message":"Value 'decision support tools' for facet 'climate_tags' is not in the controlled vocabulary."}]},"total":{"value":2,"relation":"eq"},"page":1,"page_size":20,"self":"https://climateinsightsdirectory.org/api/v1/search?climate_tags=decision+support+tools","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":"sei-001744","url":"https://climateinsightsdirectory.org/document/sei-001744","title":"Layout 1","publisher":{"name":"Stockholm Environment Institute","organization":"stockholm-environment-institute"},"document_type":"policy brief","language":"en","date_published":null,"source_url":"https://www.sei.org/mediamanager/documents/Publications/sei-mediation-briefing2-cost-effective-analysis.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"This policy brief from the MEDIATION project evaluates the use of Cost-Effectiveness Analysis (CEA) as a decision support tool for climate change adaptation. While CEA is a primary tool for mitigation, the document highlights that its application to adaptation is more challenging due to the lack of a single common metric and the critical need to account for uncertainty.","ai_generated":true,"unit":"sei-001744#summary","date_modified":"2026-08-17"},"key_insights":[{"text":"Cost-Effectiveness Analysis (CEA) is a relative measure used to rank options by assessing the cost per unit of benefit delivered, identifying the most cost-effective choices. It can be used to create marginal abatement cost (MAC) curves to identify the least-cost path to achieve pre-defined policy targets.","ai_generated":true,"unit":"sei-001744#insight-1","date_modified":"2026-08-17"},{"text":"Applying CEA to adaptation is significantly more demanding than applying it to mitigation. Mitigation relies on a single global metric (tonnes of GHG emissions), whereas adaptation responds to diverse local, regional, or national impacts across different sectors, meaning there is no single common metric for cross-sectoral analysis.","ai_generated":true,"unit":"sei-001744#insight-2","date_modified":"2026-08-17"},{"text":"A primary strength of CEA is its ability to avoid the monetary valuation of economic benefits, making it useful for non-market sectors like ecosystems where valuation is contentious. However, it often focuses on technical options and may omit capacity building, non-technical 'soft' measures, and the inter-linkages found in portfolios of options.","ai_generated":true,"unit":"sei-001744#insight-3","date_modified":"2026-08-17"},{"text":"Most CEA applications fail to consider uncertainty, often relying on single cost curves or central estimates for future climate change, which the MEDIATION project warns can provide misleading results. To address this, the document recommends using multiple cost curve analysis and sampling across various socio-economic and climate model projections.","ai_generated":true,"unit":"sei-001744#insight-4","date_modified":"2026-08-17"},{"text":"CEA is most effective for near-term assessments and identifying 'low and no regret' options, particularly in areas with a clear headline indicator and low climate uncertainty. For long-term use, it should be integrated into an iterative adaptive management plan rather than used as a standalone tool.","ai_generated":true,"unit":"sei-001744#insight-5","date_modified":"2026-08-17"},{"text":"The MEDIATION project conducted a case study on biodiversity in Finland focusing on grassland butterflies. The study found that while cattle grazing is the most efficient measure for butterflies, buffer zones are the most cost-effective Agri-Environmental Scheme (AES) measure when costs are considered.","ai_generated":true,"unit":"sei-001744#insight-6","date_modified":"2026-08-17"}],"climate_tags":["climate change adaptation","cost-effectiveness analysis","decision support tools","biodiversity","uncertainty"],"geography_discussed":["Finland","England"],"frame_perspective":[],"analysis_type":["policy brief","methodological review","case study"]},{"id":"sei-001745","url":"https://climateinsightsdirectory.org/document/sei-001745","title":"Layout 1","publisher":{"name":"Stockholm Environment Institute","organization":"stockholm-environment-institute"},"document_type":"policy brief","language":"en","date_published":null,"source_url":"https://www.sei.org/mediamanager/documents/Publications/sei-mediation-briefing3-robust-decision-making.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"This policy brief, produced as part of the European Commission FP7 funded MEDIATION project, evaluates Robust Decision Making (RDM) as a decision support tool for climate change adaptation. RDM focuses on identifying strategies that perform well across a wide range of plausible futures—prioritizing 'robustness' over 'optimality'—particularly in situations of 'deep uncertainty' where probabilistic data is scarce.","ai_generated":true,"unit":"sei-001745#summary","date_modified":"2026-08-17"},"key_insights":[{"text":"Robust Decision Making (RDM) is designed for decision-making under 'deep uncertainty,' which occurs when there is little to no probabilistic information available. Unlike traditional cost-benefit analyses that seek optimal options based on economic efficiency, RDM identifies robust strategies that perform acceptably across a wide range of plausible future states.","ai_generated":true,"unit":"sei-001745#insight-1","date_modified":"2026-08-17"},{"text":"The formal application of RDM is a resource-intensive, computer-guided process. It uses data sampling and mining algorithms to 'stress test' strategies against thousands or millions of simulated scenarios to identify vulnerabilities. However, a 'lighter-touch' informal application can be used to test options specifically against climate uncertainty alone when resources are limited.","ai_generated":true,"unit":"sei-001745#insight-2","date_modified":"2026-08-17"},{"text":"RDM is highly applicable to climate adaptation because it can utilize both physical and economic metrics. This flexibility allows it to be used in non-market sectors, such as ecosystems, where traditional economic valuation is difficult or contentious.","ai_generated":true,"unit":"sei-001745#insight-3","date_modified":"2026-08-17"},{"text":"A primary strength of RDM is its ability to identify 'low and no regret' options—near-term strategies that enhance long-term resilience regardless of the specific climate future that unfolds.","ai_generated":true,"unit":"sei-001745#insight-4","date_modified":"2026-08-17"},{"text":"A formal RDM study for the Riverside County Inland Empire Utilities Agency (IEUA) in Southern California found that adaptive strategies were superior to static ones. By expanding groundwater banking and recycling programs while monitoring supply and demand, the agency could eliminate over 80% of initially identified high-cost outcomes.","ai_generated":true,"unit":"sei-001745#insight-5","date_modified":"2026-08-17"},{"text":"An application of RDM in the East Suffolk and Essex Water Resource Zone in England demonstrated that the region's existing water plan was robust because it had already incorporated one of the drier available climate models. The study identified that the regional climate response was the largest source of uncertainty in adaptation planning.","ai_generated":true,"unit":"sei-001745#insight-6","date_modified":"2026-08-17"}],"climate_tags":["climate change adaptation","decision support tools","robust decision making","deep uncertainty","water management"],"geography_discussed":["Southern California","England"],"frame_perspective":[],"analysis_type":["literature review","case study analysis"]}]}