{"query_echo":{"q":"","filters":{"analysis_type":["quantitative mapping"]},"warnings":[{"code":"facet_value_not_in_vocabulary","message":"Value 'quantitative mapping' for facet 'analysis_type' is not in the controlled vocabulary."}]},"total":{"value":3,"relation":"eq"},"page":1,"page_size":20,"self":"https://climateinsightsdirectory.org/api/v1/search?analysis_type=quantitative+mapping","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-001367","url":"https://climateinsightsdirectory.org/document/sei-001367","title":"Five ways climate adaptation finance falls short in Africa","publisher":{"name":"Stockholm Environment Institute","organization":"stockholm-environment-institute"},"document_type":"fact sheet","language":"en","date_published":"2021-10","source_url":"https://www.sei.org/wp-content/uploads/2021/10/five-shortfalls-climate-adaptation-finance-seifactsheet.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"Based on a quantitative mapping of OECD data from 2014 to 2018, the document identifies five primary failures in climate adaptation finance for Africa: insufficient funding levels compared to mitigation and actual needs, a lack of strategic targeting toward the most vulnerable countries, over-concentration in agriculture and water sectors, low disbursement rates of committed funds, and an over-reliance on loans rather than grants.","ai_generated":true,"unit":"sei-001367#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"Adaptation funding for Africa is significantly lower than estimated costs and is overshadowed by mitigation funding. Annual commitments remained under US$5.5 billion (approximately US$5 per person), while estimated costs for 2020 range from US$7 billion to US$15 billion. Between 2014 and 2018, 61% of climate-related finance commitments (US$30.6 billion) targeted mitigation, while only 33% (US$16.5 billion) targeted adaptation.","ai_generated":true,"unit":"sei-001367#insight-1","date_modified":"2026-10-23"},{"text":"The allocation of adaptation finance does not correlate with climate vulnerability or the status of a country as a 'least developed country' (LDC). The research found no obvious difference in per capita funding levels between LDCs—which align with high vulnerability in the ND-GAIN Country Index—and non-LDCs.","ai_generated":true,"unit":"sei-001367#insight-2","date_modified":"2026-10-23"},{"text":"Adaptation funding is heavily concentrated in two sectors, leaving other critical areas underfunded. Half of all commitments were directed toward agriculture and water (supply and sanitation), while funding for biodiversity and basic development sectors like health and education was negligible.","ai_generated":true,"unit":"sei-001367#insight-3","date_modified":"2026-10-23"},{"text":"There is a significant gap between the commitment of adaptation funds and their actual disbursement. For the 2014–2018 period, only 46% of adaptation commitments (US$4.7 billion of US$10.1 billion) were disbursed, excluding multilateral development banks. This is notably lower than the disbursement ratio for mitigation (56%) and general development finance to Africa (96%).","ai_generated":true,"unit":"sei-001367#insight-4","date_modified":"2026-10-23"},{"text":"The financial instruments used for adaptation are skewed toward loans, which the authors argue is problematic from a climate justice perspective. Loans accounted for 57% of funding, while grants accounted for 42%. The document notes that grant-based funding appears to have higher disbursement rates.","ai_generated":true,"unit":"sei-001367#insight-5","date_modified":"2026-10-23"}],"climate_tags":["adaptation finace","climate justice","agriculture","biodiversity","public health","Nationally Determined Contributions"],"geography_discussed":["Uganda","Mali","Malawi","Gabon"],"frame_perspective":[],"analysis_type":["quantitative mapping","financial analysis"]},{"id":"sei-000470","url":"https://climateinsightsdirectory.org/document/sei-000470","title":"Climate Finance for Adaptation in Africa","publisher":{"name":"Stockholm Environment Institute","organization":"stockholm-environment-institute"},"document_type":"research paper","language":"en","date_published":"2021-09-20","source_url":"https://www.sei.org/publications/climate-finance-adaptation-africa","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","analysis_type"],"summary":{"text":"The research finds that climate adaptation finance for Africa is significantly lower than mitigation finance and the actual investment needs of the region. Between 2014 and 2018, adaptation finance was dominated by loans over grants, heavily concentrated in only two sectors, and suffered from low disbursement rates compared to general development finance.","ai_generated":true,"unit":"sei-000470#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"Climate finance for Africa is insufficient relative to the region's high vulnerability and low adaptation capacity, with mitigation funding nearly doubling adaptation funding, which totaled US$16.5 billion compared to US$30.6 billion for mitigation.","ai_generated":true,"unit":"sei-000470#insight-1","date_modified":"2026-10-23"},{"text":"Adaptation-related finance in Africa is primarily delivered as loans (57%) rather than grants (42%) and is highly concentrated, with half of the funding directed toward only two sectors: water supply and sanitation, and agriculture.","ai_generated":true,"unit":"sei-000470#insight-2","date_modified":"2026-10-23"},{"text":"The disbursement ratio for adaptation finance in Africa is 46%, which is substantially lower than the 96% disbursement ratio for total development finance in the region.","ai_generated":true,"unit":"sei-000470#insight-3","date_modified":"2026-10-23"}],"climate_tags":["climate finance","climate change adaptation","climate change mitigation","agriculture","United Nations Framework Convention on Climate Change"],"geography_discussed":[],"frame_perspective":[],"analysis_type":["quantitative mapping"]},{"id":"seforall-000351","url":"https://climateinsightsdirectory.org/document/seforall-000351","title":"Switching Gears","publisher":{"name":"Sustainable Energy for All","organization":"sustainable-energy-for-all"},"document_type":"report","language":"en","date_published":null,"source_url":"https://www.seforall.org/system/files/2020-02/SEforALL-Switching_Gears.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The report argues that energy and mobility sectors must move from silos to synergies to achieve the Sustainable Development Goals (SDGs). It identifies 19 priority countries and 260 cities, categorizing them into four types: Efficiency Opportunists, Digital Disruptors, Electric Vehicle Leapfroggers, and Renewable Drivers. The document proposes three primary solutions: integrated energy, land-use, and mobility planning; demand-side targeting and management using digital tools; and the deployment of electric mobility (specifically buses and two-wheelers). It highlights a significant financing gap for sustainable transport in small cities and recommends cross-sector alliances and improved spatial data to attract investment.","ai_generated":true,"unit":"seforall-000351#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The report identifies 19 priority countries and 260 fast-growing small-to-medium-sized cities (population 300,000 to 1,000,000) as high-potential areas for sustainable urban mobility interventions. These are grouped into four types: 'Efficiency Opportunists' (predominantly in India), 'Digital Disruptors' (diverse global locations), 'Electric Vehicle Leapfroggers' (notably in the Philippines and Namibia), and 'Renewable Drivers' (led by Nigeria, Ethiopia, and Zambia).","ai_generated":true,"unit":"seforall-000351#insight-1","date_modified":"2026-10-23"},{"text":"The 'Avoid/Shift/Improve' framework is proposed to unite energy and mobility. 'Avoid' focuses on integrated land-use and energy planning to reduce unnecessary travel; 'Shift' encourages moving from private cars to mass transit; and 'Improve' focuses on lowering the carbon intensity of fuels through electrification and renewables.","ai_generated":true,"unit":"seforall-000351#insight-2","date_modified":"2026-10-23"},{"text":"Digitalization and spatial mapping (GIS) are identified as critical tools for 'Demand-side targeting and management.' These tools can identify 'spatial mismatch' where low-income households are far from jobs, and can be used to provide services (like health and education) closer to the user to reduce the need for travel.","ai_generated":true,"unit":"seforall-000351#insight-3","date_modified":"2026-10-23"},{"text":"There is a significant financing gap for sustainable urban mobility in secondary cities. Multilateral climate finance for mobility projects has only reached cities at a rate of 11 percent. The report notes that while the total cost of ownership for EVs is often lower, high upfront costs and a lack of credit lines remain major barriers for the poor.","ai_generated":true,"unit":"seforall-000351#insight-4","date_modified":"2026-10-23"},{"text":"The report emphasizes that sustainable mobility intersects with multiple SDGs, including health (reducing air pollution and road fatalities), education (improving access for children and teachers), and gender equality (reducing time spent on care-related trips).","ai_generated":true,"unit":"seforall-000351#insight-5","date_modified":"2026-10-23"}],"climate_tags":["electric vehicle","renewable energy","land-use planning","climate finance","air quality","greenhouse gas emissions","demand-side management","biofuel","hydrogen","energy efficiency"],"geography_discussed":["India","Nigeria","Ethiopia","Zambia","Malawi","Ghana","Nepal","Kenya","Rwanda","Bhubaneswar"],"frame_perspective":[],"analysis_type":["scoping report","quantitative mapping","policy analysis"]}]}