{"query_echo":{"q":"","filters":{"analysis_type":["index development"]},"warnings":[{"code":"facet_value_not_in_vocabulary","message":"Value 'index development' for facet 'analysis_type' is not in the controlled vocabulary."}]},"total":{"value":5,"relation":"eq"},"page":1,"page_size":20,"self":"https://climateinsightsdirectory.org/api/v1/search?analysis_type=index+development","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":"newclim-000191","url":"https://climateinsightsdirectory.org/document/newclim-000191","title":"The Impacts of Skills Shortages on Global Power Sector Emissions","publisher":{"name":"NewClimate Institute","organization":"newclimate-institute"},"document_type":"research paper","language":"en","date_published":"2025-10-09","source_url":"http://www.newclimate.org/resources/publications/the-impacts-of-skills-shortages-on-global-power-sector-emissions","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","analysis_type"],"summary":{"text":"The study warns that failing to meet 40-80% of new labour demand for the energy transition could result in a shortfall of 6 million workers by 2030, leaving renewable capacity 10% below targets and driving power sector emissions 12% above 2030 pledges. Such a delay could shift the sector's warming pathway from 1.7°C to 2.4°C. The research identifies ageing populations, education gaps, and slow worker transitions from fossil fuels as primary constraints, and proposes the Labour Market Transition Potential Index to evaluate national workforce mobilisation potential.","ai_generated":true,"unit":"newclim-000191#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"Tripling global renewable power capacity by 2030 may require the power generation sector workforce to grow from 12.5 million workers in 2021 to 47 million by 2030, with a focus on manufacturing, installation, and operation.","ai_generated":true,"unit":"newclim-000191#insight-1","date_modified":"2026-10-23"},{"text":"If only 20 to 60 percent of new labour demand is met, a shortfall of up to 6 million workers by 2030 could leave global renewable generation capacity nearly 10 percent below pledged targets.","ai_generated":true,"unit":"newclim-000191#insight-2","date_modified":"2026-10-23"},{"text":"A slower renewable rollout due to labour shortages could drive power sector emissions 12 percent above 2030 pledges and more than double them by 2045, potentially shifting the sector from a 1.7°C warming pathway toward 2.4°C.","ai_generated":true,"unit":"newclim-000191#insight-3","date_modified":"2026-10-23"},{"text":"Workforce availability is constrained by ageing populations, low labour force participation, limited inward migration, and education systems that fail to keep pace with demand, especially in developing countries.","ai_generated":true,"unit":"newclim-000191#insight-4","date_modified":"2026-10-23"},{"text":"Transitions from fossil fuel to renewable energy sectors are limited by mismatches in timing and location, insufficient retraining support, and differing job characteristics, despite overlapping skill sets.","ai_generated":true,"unit":"newclim-000191#insight-5","date_modified":"2026-10-23"}],"climate_tags":["renewable energy","Grid decarbonization","Paris Agreement","climate finance"],"geography_discussed":[],"frame_perspective":[],"analysis_type":["quantitative modelling","index development"]},{"id":"sei-000037","url":"https://climateinsightsdirectory.org/document/sei-000037","title":"Quantifying Transnational Climate Impact Exposure","publisher":{"name":"Stockholm Environment Institute","organization":"stockholm-environment-institute"},"document_type":"research paper","language":"en","date_published":"2018-07-06","source_url":"https://www.sei.org/publications/quantifying-transnational-climate-impact-exposure/","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","analysis_type"],"summary":{"text":"The authors developed a global index of exposure to transnational climate impacts to address the limitation of classic vulnerability indices, which typically focus on national or local estimates and ignore cross-border effects. The findings show that while internal climate risks correlate strongly with geography and economic development, exposure to transnational impacts follows a more complex distribution that cannot be explained by these factors alone. The paper argues that robust adaptation planning requires risk assessments to incorporate both transboundary and far-reaching teleconnected interdependencies.","ai_generated":true,"unit":"sei-000037#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"Traditional climate vulnerability indices are limited because they primarily focus on national or local estimates and fail to account for global interconnections and cross-border effects.","ai_generated":true,"unit":"sei-000037#insight-1","date_modified":"2026-10-23"},{"text":"The distribution of exposure to transnational climate impacts is more complex than internal climate risks; unlike internal risks, transnational exposure is not sufficiently explained by geography or economic development alone.","ai_generated":true,"unit":"sei-000037#insight-2","date_modified":"2026-10-23"},{"text":"To improve adaptation planning, risk assessments must adopt a multidimensional and cross-scale perspective that includes far-reaching teleconnected interdependencies and transboundary risks.","ai_generated":true,"unit":"sei-000037#insight-3","date_modified":"2026-10-23"}],"climate_tags":["climate change adaptation","climate change vulnerability"],"geography_discussed":[],"frame_perspective":[],"analysis_type":["index development","global assessment"]},{"id":"sei-001420","url":"https://climateinsightsdirectory.org/document/sei-001420","title":"Introducing the Transnational Climate Impacts Index: Indicators of country-level exposure – methodology report","publisher":{"name":"Stockholm Environment Institute","organization":"stockholm-environment-institute"},"document_type":"research paper","language":"en","date_published":"2016-06","source_url":"https://www.sei.org/wp-content/uploads/2016/06/introducing-the-transnational-climate-impacts-index-indicators-of-country-level-exposure-methodology-report.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The report develops the Transnational Climate Impacts (TCI) Index to measure how countries are exposed to climate risks originating outside their borders. Using a framework of four risk pathways—biophysical, finance, people, and trade—alongside a global context indicator, the authors identify nine indicators of exposure. The resulting index reveals that many high-income, small European nations are highly exposed to transnational risks, contrasting with traditional vulnerability indices that primarily highlight low-income nations. The authors present this as a tool to raise awareness for national adaptation planning, though they note it is a preliminary instrument not yet intended for direct decision-making.","ai_generated":true,"unit":"sei-001420#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The authors define transnational climate impacts as those that \"occur in one place as a consequence of climate impacts somewhere else,\" distinguishing them from direct impacts that occur within a country's own borders.","ai_generated":true,"unit":"sei-001420#insight-1","date_modified":"2026-10-23"},{"text":"The framework identifies four primary pathways for the transmission of transnational climate risks: biophysical (e.g., transboundary river basins), finance (e.g., foreign direct investment and remittances), people (e.g., asylum and migration), and trade (e.g., supply chain disruptions and food import dependency).","ai_generated":true,"unit":"sei-001420#insight-2","date_modified":"2026-10-23"},{"text":"The TCI Index utilizes nine indicators: transboundary water dependency, bilateral climate-weighted foreign direct investment, remittance flows, openness to asylum, migration from climate vulnerable countries, trade openness, cereal import dependency ratio, embedded water risk, and the KOF Globalization Index.","ai_generated":true,"unit":"sei-001420#insight-3","date_modified":"2026-10-23"},{"text":"Analysis of the TCI Index shows that small industrialized nations in Europe—including the Netherlands, Belgium, Luxembourg, Portugal, Montenegro, Malta, Austria, and Lithuania—exhibit high exposure to transnational impacts, despite often ranking low on direct vulnerability indices like ND-GAIN.","ai_generated":true,"unit":"sei-001420#insight-4","date_modified":"2026-10-23"},{"text":"The four highest-scoring countries on the TCI Index are located in the Middle East and North Africa (MENA) region: Jordan, Lebanon, Kuwait, and the United Arab Emirates.","ai_generated":true,"unit":"sei-001420#insight-5","date_modified":"2026-10-23"},{"text":"The TCI Index results demonstrate significantly less correlation with national wealth or human development than traditional climate vulnerability indices, suggesting that transnational risk is driven by complex, country-specific interdependencies rather than just poverty.","ai_generated":true,"unit":"sei-001420#insight-6","date_modified":"2026-10-23"},{"text":"The 'embedded water risk' indicator, developed using the IOTA model, reveals that countries like Afghanistan, China, India, Turkey, Nepal, Pakistan, and Central Asian states are highly dependent on commodities grown in water-stressed areas.","ai_generated":true,"unit":"sei-001420#insight-7","date_modified":"2026-10-23"},{"text":"The authors caution that the TCI Index is a \"crude tool\" intended to provoke discussion and raise awareness rather than to serve as a definitive basis for immediate policy decision-making.","ai_generated":true,"unit":"sei-001420#insight-8","date_modified":"2026-10-23"}],"climate_tags":["climate change adaptation","Climate risk assessment","food security","climate migration","climate change vulnerability"],"geography_discussed":["Europe","sub-Saharan Africa","Middle East and North Africa","Sweden"],"frame_perspective":[],"analysis_type":["methodology report","quantitative analysis","index development"]},{"id":"iisd-001888","url":"https://climateinsightsdirectory.org/document/iisd-001888","title":"Indicators of Adaptive Capacity to Climate Change for Agriculture in the Prairie Region of Canada","publisher":{"name":"International Institute for Sustainable Development","organization":"international-institute-for-sustainable-development"},"document_type":"research paper","language":"en","date_published":"2009-07","source_url":"https://www.iisd.org/system/files/publications/pcr_adaptive_cap_ag.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The research establishes a GIS-based index of adaptive capacity for the Canadian Prairies, using six determinants: economic resources, technology, information/skills/management, infrastructure, institutions/networks, and equity. The analysis finds that adaptive capacity is generally higher near large urban centres and lower along the northern boundaries of the agricultural region. While the six determinants align with producer perspectives, the study concludes that aggregated census data only accurately represent a fraction of the specific factors producers identify as critical for adaptation, emphasizing the need for site-specific research to inform policy.","ai_generated":true,"unit":"iisd-001888#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"Adaptive capacity in the Prairie region is spatially clustered, with the highest levels found near large urban centres in three main corridors: Winnipeg (extending west to Brandon and south to the U.S. border), Saskatoon (extending southeast to Regina and west to the Alberta border), and Calgary (extending southeast to the U.S. border).","ai_generated":true,"unit":"iisd-001888#insight-1","date_modified":"2026-10-23"},{"text":"The lowest relative adaptive capacity is typically found along the northern boundaries of the Prairie agricultural region, with the exception of southwest and southeast Saskatchewan.","ai_generated":true,"unit":"iisd-001888#insight-2","date_modified":"2026-10-23"},{"text":"The Census Division around Morden and Winkler in southern Manitoba is the highest-ranking for adaptive capacity in the entire Prairie region, despite not being a large urban centre.","ai_generated":true,"unit":"iisd-001888#insight-3","date_modified":"2026-10-23"},{"text":"The lowest-ranking Census Division in the entire region is Manitoba’s Interlake region, which is situated between Lake Winnipeg and Lake Manitoba.","ai_generated":true,"unit":"iisd-001888#insight-4","date_modified":"2026-10-23"},{"text":"Proximity to urban centres is positively correlated with adaptive capacity because it provides better access to off-farm earnings, diverse employment, computer technology, transportation networks, internet/email, and agricultural education institutions.","ai_generated":true,"unit":"iisd-001888#insight-5","date_modified":"2026-10-23"},{"text":"Comparison with field interviews shows that while the six chosen determinants of adaptive capacity are a good reflection of producer experiences, only 9 of the 15 reflective aspects were supported by indicators that accurately represented what producers reported as important.","ai_generated":true,"unit":"iisd-001888#insight-6","date_modified":"2026-10-23"}],"climate_tags":["adaptive capacity","climate change adaptation","agriculture","climate change vulnerability","water management","climate resilience"],"geography_discussed":["Winnipeg","Brandon","Saskatoon","Regina","Calgary","Morden","Winkler","Lethbridge","Medicine Hat","Grand Prairie"],"frame_perspective":[],"analysis_type":["spatial analysis","index development","comparative study","qualitative validation"]},{"id":"saiia-001170","url":"https://climateinsightsdirectory.org/document/saiia-001170","title":"Measuring Economic Vulnerability and Resilience to Climate Change","publisher":{"name":"South African Institute of International Affairs","organization":"south-african-institute-of-international-affairs"},"document_type":"research paper","language":"en","date_published":null,"source_url":"https://saiia.org.za/wp-content/uploads/2024/02/SAIIA_CoMPRA_PI_29_MeasuringEconVulnerability-1.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The South African Institute of International Affairs (SAIIA) developed the Climate Change and Economic Vulnerability Index (CEVI) and the Climate Change and Economic Resilience Index (CERI) to address the misalignment of climate finance. Using data from 160 countries, the indices reveal that low-income economies, particularly in Africa, face the highest vulnerability and lowest resilience. An analysis of 2021 OECD adaptation finance flows shows that funds are often misallocated, with more resilient and less vulnerable countries sometimes receiving higher per-person funding than those in the most critical need. The paper recommends that international financial institutions mainstream these indices to ensure needs-based allocation of climate support.","ai_generated":true,"unit":"saiia-001170#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"SAIIA developed two quantitative tools, the Climate Change and Economic Vulnerability Index (CEVI) and the Climate Change and Economic Resilience Index (CERI), to facilitate needs-based decision-making for climate fund allocations. The CEVI uses seven indicators focusing on economic exposure, such as dependence on agriculture and food security, while the CERI uses five indicators measuring efficiency, diversification, and government effectiveness.","ai_generated":true,"unit":"saiia-001170#insight-1","date_modified":"2026-10-23"},{"text":"There is a strong inverse relationship between climate vulnerability and economic resilience, with a Pearson correlation coefficient of -0.7. Low-income economies, especially those in Africa, typically exhibit high vulnerability and low resilience, whereas high-income Western economies generally show low vulnerability and high resilience.","ai_generated":true,"unit":"saiia-001170#insight-2","date_modified":"2026-10-23"},{"text":"African countries are disproportionately affected, with an average vulnerability score of 0.48 compared to the global average of 0.4. Nine of the 10 most vulnerable countries globally are African, a situation driven largely by the continent's dependence on agriculture and food security challenges.","ai_generated":true,"unit":"saiia-001170#insight-3","date_modified":"2026-10-23"},{"text":"Analysis of 2021 OECD adaptation finance flows reveals a misalignment where the most vulnerable and least resilient countries often receive less funding per person than less vulnerable or more resilient countries. For example, the three most vulnerable countries—Madagascar, Haiti, and Uganda—received approximately $11 per person, while less vulnerable countries like Costa Rica received $53.9 per person.","ai_generated":true,"unit":"saiia-001170#insight-4","date_modified":"2026-10-23"},{"text":"The least resilient country globally is Libya, followed by Guinea-Bissau, Iraq, and Turkmenistan, demonstrating that income level is not the sole determinant of economic resilience.","ai_generated":true,"unit":"saiia-001170#insight-5","date_modified":"2026-10-23"}],"climate_tags":["climate finance","climate change adaptation","agriculture","food security","Loss and Damage Fund"],"geography_discussed":["Africa","Madagascar","Haiti","Uganda","Libya","Denmark","Luxembourg","Costa Rica"],"frame_perspective":[],"analysis_type":["quantitative analysis","index development","comparative study"]}]}