{"query_echo":{"q":"","filters":{"geography":["Si Sa Ket"]},"warnings":[{"code":"facet_value_not_in_vocabulary","message":"Value 'Si Sa Ket' for facet 'geography' is not in the controlled vocabulary."}]},"total":{"value":1,"relation":"eq"},"page":1,"page_size":20,"self":"https://climateinsightsdirectory.org/api/v1/search?geography=Si+Sa+Ket","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-002166","url":"https://climateinsightsdirectory.org/document/sei-002166","title":"Assessing Water-related Poverty Using the Sustainable Livelihoods Framework","publisher":{"name":"Stockholm Environment Institute","organization":"stockholm-environment-institute"},"document_type":"research paper","language":"en","date_published":"2009","source_url":"https://www.sei.org/mediamanager/documents/Publications/Sustainable-livelihoods/assessing-water-related-poverty.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The document presents a method for analysing water-related poverty by integrating the Sustainable Livelihoods (SL) framework with Bayesian networks (BNs). This approach addresses the 'fuzziness' of livelihood systems—caused by variability, diversity, and uncertainty—by using probability distributions rather than fixed values. The authors introduce a novel way to represent institutions as 'categorical inequality' (e.g., gender) and a 'latent variable' method where indicators serve as indirect evidence for assets rather than direct proxies. The model was applied to data from Si Sa Ket province in Northeast Thailand, demonstrating that locally-originated water supply schemes, such as water tanks and drill wells, can be more effective for poor households than unreliable large-scale irrigation projects.","ai_generated":true,"unit":"sei-002166#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The authors propose a conceptual framework for water-related poverty that integrates the Sustainable Livelihoods (SL) framework with a Bayesian network to account for variability, diversity, and uncertainty in livelihood systems.","ai_generated":true,"unit":"sei-002166#insight-1","date_modified":"2026-10-23"},{"text":"To represent the complex and often fluid nature of institutions, the paper suggests focusing on the observable effects of 'categorical inequality'—such as gender, race, or class—on how resources are distributed and converted into outcomes.","ai_generated":true,"unit":"sei-002166#insight-2","date_modified":"2026-10-23"},{"text":"The paper advocates for a 'latent variable' approach to indicators, where data (like education levels) are treated as indirect evidence of an underlying asset (like human capital) rather than as a direct proxy, which prevents overestimating the strength of the relationship.","ai_generated":true,"unit":"sei-002166#insight-3","date_modified":"2026-10-23"},{"text":"The researchers developed a 'Bayes Table Generator' software tool and an elicitation algorithm to simplify the creation of conditional probability tables (CPTs), reducing the number of required input parameters from hundreds to a small set of influence weights.","ai_generated":true,"unit":"sei-002166#insight-4","date_modified":"2026-10-23"},{"text":"In the application to Si Sa Ket province, the model indicated that for poor households, access to water tanks or drill wells provided more consistent livelihood benefits than irrigation, which was often unreliable.","ai_generated":true,"unit":"sei-002166#insight-5","date_modified":"2026-10-23"},{"text":"The authors conclude that while Bayesian networks are effective for early assessment and engagement with technical experts, they are less suitable for direct engagement with field informants, for whom agent-based models or role-playing simulations are preferred.","ai_generated":true,"unit":"sei-002166#insight-6","date_modified":"2026-10-23"}],"climate_tags":["water distribution system","climate variability","natural capital"],"geography_discussed":["Khon Kaen","Maha Sarakham","Ubon Ratchathani"],"frame_perspective":[],"analysis_type":["conceptual framework development","probabilistic modelling","case study"]}]}