{"query_echo":{"q":"","filters":{"analysis_type":["principal-agent modeling"]},"warnings":[{"code":"facet_value_not_in_vocabulary","message":"Value 'principal-agent modeling' for facet 'analysis_type' is not in the controlled vocabulary."}]},"total":{"value":1,"relation":"eq"},"page":1,"page_size":20,"self":"https://climateinsightsdirectory.org/api/v1/search?analysis_type=principal-agent+modeling","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-000670","url":"https://climateinsightsdirectory.org/document/clipolin-000670","title":"Misreporting in the Deployment of Wind Power: Evidence from Brazil","publisher":{"name":"Climate Policy Initiative","organization":"climate-policy-initiative"},"document_type":"research paper","language":"en","date_published":"2014-05","source_url":"https://www.climatepolicyinitiative.org/wp-content/uploads/2014/05/Misreporting-in-Wind-Power-Contracts-Technical-Paper.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The research analyzes wind power contracts under Brazil's PROINFA feed-in tariff scheme, finding that a nonlinear payment structure led wind farms to systematically misreport capacity factors (CFs). By comparing reported and realized CFs for 41 operational projects, the authors demonstrate that reported values clustered around price schedule cutoffs, while realized values did not. This misreporting resulted in wind farms being contracted for 14% more energy than delivered and 12% of first-year payments being over-payments. The paper concludes that a simple 1% interest rate penalty on overpayments would have effectively eliminated the incentive to misreport.","ai_generated":true,"unit":"clipolin-000670#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The PROINFA feed-in tariff used a nonlinear payment schedule where wind farms reporting capacity factors (CFs) below 32.4% received R$204.3 per MWh, while those above 41.9% received R$180.2 per MWh, with a downward sloping price curve in between.","ai_generated":true,"unit":"clipolin-000670#insight-1","date_modified":"2026-10-23"},{"text":"There is evidence of systematic misreporting of capacity factors, as reported CFs clustered around the price schedule cutoffs, whereas realized CFs followed a smooth distribution without such bunching.","ai_generated":true,"unit":"clipolin-000670#insight-2","date_modified":"2026-10-23"},{"text":"Misreporting led to significant production and financial discrepancies: contracted wind farms were expected to produce 14% more MWh than they actually delivered, and 12% of total first-year payments were over-payments for energy not delivered.","ai_generated":true,"unit":"clipolin-000670#insight-3","date_modified":"2026-10-23"},{"text":"The average estimated capacity factor for the analyzed projects was 35.4%, which was systematically higher than the average realized capacity factor of 31.1%.","ai_generated":true,"unit":"clipolin-000670#insight-4","date_modified":"2026-10-23"},{"text":"A counterfactual simulation suggests that replacing the nonlinear price schedule with a constant unit price (R$192) would have increased the gap between expected and actual energy output from 14% to 28.5%.","ai_generated":true,"unit":"clipolin-000670#insight-5","date_modified":"2026-10-23"},{"text":"The study finds that implementing an interest rate penalty of 1% on overpayments would practically eliminate the incentives for wind farms to misreport their capacity factors.","ai_generated":true,"unit":"clipolin-000670#insight-6","date_modified":"2026-10-23"}],"climate_tags":["wind power","renewable energy","feed-in tariff"],"geography_discussed":["Ceará","Rio Grande do Sul","Santa Catarina"],"frame_perspective":[],"analysis_type":["empirical analysis","counterfactual simulation","principal-agent modeling"]}]}