{"query_echo":{"q":"","filters":{"analysis_type":["stochastic modelling"]},"warnings":[{"code":"facet_value_not_in_vocabulary","message":"Value 'stochastic modelling' 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=stochastic+modelling","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-001233","url":"https://climateinsightsdirectory.org/document/clipolin-001233","title":"Reaching India’s Renewable Energy Targets Cost-Effectively: A Foreign Exchange Hedging Facility","publisher":{"name":"Climate Policy Initiative","organization":"climate-policy-initiative"},"document_type":"research paper","language":"en","date_published":"2015-06","source_url":"https://www.climatepolicyinitiative.org/wp-content/uploads/2015/06/Reaching-Indias-Renewable-Energy-Targets-Foreign-Exchange-Hedging-Facility_Technical-Paper.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The paper argues that high currency hedging costs in India negate the benefits of cheaper foreign loans, increasing renewable energy costs by 30% compared to the US. Using a stochastic model, the authors find that a government-sponsored FXHF could provide a 10-year currency hedge at an expected cost of 3.5 percentage points, which is 50% lower than market rates. Such a facility could reduce the delivered cost of renewable energy by up to 19% and government support costs by up to 54%, provided the government manages extreme currency risks. To achieve a credit rating equivalent to India's sovereign rating of BBB-, the paper estimates a cumulative capital buffer of approximately 30% of the underlying loan would be required.","ai_generated":true,"unit":"clipolin-001233#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"Domestic debt in India is characterized by high costs, short tenors, and variable interest rates, which increases the cost of renewable energy by 30% relative to the United States.","ai_generated":true,"unit":"clipolin-001233#insight-1","date_modified":"2026-10-23"},{"text":"Market-based currency hedging in India typically costs around 7% per year, making foreign loans nearly as expensive as domestic loans.","ai_generated":true,"unit":"clipolin-001233#insight-2","date_modified":"2026-10-23"},{"text":"A government-sponsored foreign exchange hedging facility (FXHF) is estimated to have an expected cost of 3.5 percentage points for a 10-year hedge, representing a 50% reduction compared to market hedging costs.","ai_generated":true,"unit":"clipolin-001233#insight-3","date_modified":"2026-10-23"},{"text":"If the government bears the expected cost of the FXHF, the delivered cost of renewable energy could decrease by 19% and the cost of government support could fall by 54%.","ai_generated":true,"unit":"clipolin-001233#insight-4","date_modified":"2026-10-23"},{"text":"If the expected cost of the FXHF is passed to the project developer or off-taker, the delivered cost of renewable energy would be reduced by approximately 9% and government support costs by 33%.","ai_generated":true,"unit":"clipolin-001233#insight-5","date_modified":"2026-10-23"},{"text":"To ensure the FXHF achieves a credit rating equivalent to India's sovereign rating of BBB- (requiring a 10-year default probability of less than 5%), a cumulative capital buffer of approximately 30% of the underlying loan is necessary.","ai_generated":true,"unit":"clipolin-001233#insight-6","date_modified":"2026-10-23"},{"text":"The gap between market hedging costs (7 percentage points) and the FXHF expected cost (3.5 percentage points) is primarily explained by a risk-premium of 2.76 percentage points, with the remainder attributed to liquidity and regulation costs.","ai_generated":true,"unit":"clipolin-001233#insight-7","date_modified":"2026-10-23"}],"climate_tags":["transition finance","power purchase agreement","climate finance"],"geography_discussed":["United States"],"frame_perspective":[],"analysis_type":["stochastic modelling","financial analysis","policy assessment"]},{"id":"bc3res-000229","url":"https://climateinsightsdirectory.org/document/bc3res-000229","title":"URBAN RESEARCH AT BC3","publisher":{"name":"Basque Centre for Climate Change","organization":"basque-centre-for-climate-change"},"document_type":"fact sheet","language":"en","date_published":null,"source_url":"http://info.bc3research.org/wp-content/uploads/2016/04/Urban_BC3.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The document presents a portfolio of BC3's urban research, focusing on the socio-economic and ecological implications of climate change. Key studies include an analysis of Bilbao's Sustainable Energy Action Plan, flood-risk assessments in Bilbao, Zurich, and Zaragoza, and the evaluation of heat-health warning systems and green roofs in Madrid.","ai_generated":true,"unit":"bc3res-000229#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"An analysis of the city of Bilbao's Sustainable Energy Action Plan (SEAP) indicates that while one euro invested produces €1.27 in outcome and €0.18 in income, focusing only on additional measures reduces the output increase to €0.26 per euro and results in a €-0.02 decrease in income.","ai_generated":true,"unit":"bc3res-000229#insight-1","date_modified":"2026-10-23"},{"text":"In Bilbao, the opening of the Deusto Canal to turn the Zorrotzaurre peninsula into an island is identified as a \"no-regret adaptation measure\" to reduce current and future flood-risk.","ai_generated":true,"unit":"bc3res-000229#insight-2","date_modified":"2026-10-23"},{"text":"A case study in Madrid suggests that heat health watch warning systems (HHWWS) require periodical evaluation of threshold temperatures to remain effective, as incorrect settings can increase implementation costs and decrease the system's ability to reduce health impacts.","ai_generated":true,"unit":"bc3res-000229#insight-3","date_modified":"2026-10-23"},{"text":"Research in the lower Sihl valley and the city of Zurich used a spatially explicit Bayesian network model to determine that improved early warning systems provide the most significant benefits in terms of avoided human impacts during major flood events.","ai_generated":true,"unit":"bc3res-000229#insight-4","date_modified":"2026-10-23"},{"text":"Flood-risk analysis for the town of Plentzia in the Basque Country indicates that under the most pessimistic sea-level rise scenario, some areas could be affected by 2030, with damage costs potentially increasing by 15 times between 2030 and 2100.","ai_generated":true,"unit":"bc3res-000229#insight-5","date_modified":"2026-10-23"},{"text":"A study on extensive green roofs in Madrid highlights significant uncertainty regarding their deployment as an urban adaptation strategy, emphasizing that performance estimates must account for epidemiological processes and urban heat island regulation to avoid bias.","ai_generated":true,"unit":"bc3res-000229#insight-6","date_modified":"2026-10-23"},{"text":"Spatial assessment of flood risk in Zaragoza demonstrated that infrastructural measures can transfer risks to downstream floodplains that were previously less densely populated, necessitating coordinated institutional and environmental prevention measures.","ai_generated":true,"unit":"bc3res-000229#insight-7","date_modified":"2026-10-23"}],"climate_tags":["energy efficiency","carbon dioxide emissions","climate change adaptation","sea level rise","heat wave","green infrastructure","urban heat island","nature-based solutions","public health","early warning system","urban planning"],"geography_discussed":["Bilbao","Madrid","Zurich","Switzerland","Plentzia","Spain","Zaragoza"],"frame_perspective":[],"analysis_type":["case study","economic impact assessment","stochastic modelling","Bayesian network model","participatory mapping"]}]}