{"query_echo":{"q":"","filters":{"analysis_type":["cluster analysis"]},"warnings":[{"code":"facet_value_not_in_vocabulary","message":"Value 'cluster analysis' 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=cluster+analysis","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":"iai-000072","url":"https://climateinsightsdirectory.org/document/iai-000072","title":"Regionalization of climate over the Argentine Pampas","publisher":{"name":"Inter-American Institute for Global Change Research","organization":"inter-american-institute-for-global-change-research"},"document_type":"research paper","language":"en","date_published":"2025-01-13","source_url":"https://iai.int/es/pub_acad/regionalization-of-climate-over-the-argentine-pampas/","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","geography_discussed","analysis_type"],"summary":{"text":"The study used data from 33 National Meteorological Service stations (1960-2010) and cluster analysis to divide the Argentine Pampas into eight climatic subregions based on temperature, precipitation, humidity, and wind patterns.","ai_generated":true,"unit":"iai-000072#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The study identified eight climatic subregions by applying a hierarchical Ward cluster analysis to data from 33 stations between 1960 and 2010, using the standard precipitation index to determine wet and dry cycles.","ai_generated":true,"unit":"iai-000072#insight-1","date_modified":"2026-10-23"},{"text":"Climatic patterns in the region are defined by specific gradients: temperature follows a north-south gradient influenced by the Pampean hills in Córdoba and the Tandilia and Ventania hills in Buenos Aires Province, while precipitation follows a northeast-southwest gradient.","ai_generated":true,"unit":"iai-000072#insight-2","date_modified":"2026-10-23"},{"text":"Regional climate characteristics include a semi-arid environment created by the arid southwest diagonal, humidity levels determined by proximity to the sea, and high-intensity winds occurring in the south and west.","ai_generated":true,"unit":"iai-000072#insight-3","date_modified":"2026-10-23"}],"climate_tags":[],"geography_discussed":["Buenos Aires Province","Córdoba Province"],"frame_perspective":[],"analysis_type":["cluster analysis"]},{"id":"climstra-000037","url":"https://climateinsightsdirectory.org/document/climstra-000037","title":"Carbon pricing moves in clusters: when is the right time to catch it?","publisher":{"name":"Climate Strategies","organization":"climate-strategies"},"document_type":"research paper","language":"en","date_published":"2019-09-16","source_url":"https://climatestrategies.org/carbon-pricing-moves-in-clusters-when-is-the-right-time-to-catch-it/","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The research identifies 66 carbon pricing policies globally, categorising adopting polities into five clusters: early adopters, North-American states and provinces, Chinese pilot provinces, second-wave developed polities, and second-wave developing polities. The authors conclude that there is no 'one-size-fits-all' approach to promoting carbon pricing, as different economic, political, and policy variables drive adoption across different clusters. However, they identify general facilitators, including economic crises, UNFCCC commitments, regional learning, and a preference for emissions trading in carbon-intensive economies.","ai_generated":true,"unit":"climstra-000037#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"Carbon pricing is now a global mainstream policy instrument, with 66 policies (including taxes and emissions trading systems) adopted across diverse regions, income levels, and political systems.","ai_generated":true,"unit":"climstra-000037#insight-1","date_modified":"2026-10-23"},{"text":"Polities adopting carbon pricing fall into five specific clusters: early adopters, North-American states and provinces, Chinese pilot provinces, second-wave developed polities, and second-wave developing polities.","ai_generated":true,"unit":"climstra-000037#insight-2","date_modified":"2026-10-23"},{"text":"There is no universal set of variables that drive the adoption of carbon pricing across all cases; instead, each cluster is influenced by distinct combinations of economic, political, carbon-intensity, and policy diffusion variables.","ai_generated":true,"unit":"climstra-000037#insight-3","date_modified":"2026-10-23"},{"text":"General factors that facilitate the promotion of carbon pricing include economic and fiscal crises, UNFCCC commitments, financial and technical assistance, and regional learning between polities.","ai_generated":true,"unit":"climstra-000037#insight-4","date_modified":"2026-10-23"},{"text":"Economies with high carbon intensity generally prefer emissions trading systems over carbon taxes.","ai_generated":true,"unit":"climstra-000037#insight-5","date_modified":"2026-10-23"}],"climate_tags":["carbon price","carbon tax","Emissions trading","United Nations Framework Convention on Climate Change","climate policy"],"geography_discussed":["France","Washington","European Union","Finland","Colombia","South Africa","Chile"],"frame_perspective":[],"analysis_type":["cluster analysis"]},{"id":"iisd-003676","url":"https://climateinsightsdirectory.org/document/iisd-003676","title":"Transforming Agriculture in Africa & Asia: What are the policy priorities?","publisher":{"name":"International Institute for Sustainable Development","organization":"international-institute-for-sustainable-development"},"document_type":"report","language":"en","date_published":"2019-09","source_url":"https://www.iisd.org/system/files/publications/transforming-agriculture-africa-asia.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The report analyzes agricultural transformation across 117 countries from 1970 to 2015, identifying six phases of transformation and five structural contexts based on land fertility, availability, and population dynamics. It argues that successful transformation requires a tailored mix of 'push' strategies (increasing agricultural productivity) or 'pull' strategies (growing non-agricultural sectors), depending on a country's endowments. Key drivers of success include removing 'anti-agricultural bias' in price policies, strategic public investment in R&D and extension services, and critical institutional reforms such as land redistribution. The report provides specific policy recommendations for 13 focus countries in Africa and Asia, matching them with historically successful 'analogue' countries based on their 2015 structural contexts.","ai_generated":true,"unit":"iisd-003676#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"Agricultural transformation is heavily influenced by a country's structural endowments; those with abundant, fertile land and high birth rates (e.g., Brazil, Colombia) typically prioritize a 'push' strategy to increase agricultural productivity, while those with scarce land and high birth rates (e.g., China, Malaysia, Vietnam) prioritize a 'pull' strategy to grow non-agricultural productivity.","ai_generated":true,"unit":"iisd-003676#insight-1","date_modified":"2026-10-23"},{"text":"The removal of 'anti-agricultural bias'—where price policies provide negative assistance to farmers relative to the rest of the economy—was a common factor in all studied successful transformations, including those in Brazil, Indonesia, South Korea, and Vietnam.","ai_generated":true,"unit":"iisd-003676#insight-2","date_modified":"2026-10-23"},{"text":"While public investment is critical, its composition and quality are more important than the total amount. Investments in research and development (R&D) and extension services are the most impactful, especially when complemented by rural infrastructure like electrification, irrigation, and roads.","ai_generated":true,"unit":"iisd-003676#insight-3","date_modified":"2026-10-23"},{"text":"Institutional and legal reforms, particularly land reform, are essential for jumpstarting productivity and facilitating structural change, as seen in the cases of China, Vietnam, and South Korea.","ai_generated":true,"unit":"iisd-003676#insight-4","date_modified":"2026-10-23"},{"text":"No single policy measure is sufficient for success; transformation requires a complementary combination of policies and public investments tailored to the specific juncture of development.","ai_generated":true,"unit":"iisd-003676#insight-5","date_modified":"2026-10-23"},{"text":"For focus countries with high birth rates and scarce land (Ethiopia, India, Kenya, Rwanda), the top priority should be economic diversification and the removal of anti-agricultural bias to allow rural workers to transition into non-agricultural sectors.","ai_generated":true,"unit":"iisd-003676#insight-6","date_modified":"2026-10-23"},{"text":"Focus countries with abundant and fertile land (Mali, Mozambique, Zimbabwe) should prioritize public investment in R&D and extension services to push productivity, as current spending in some cases (e.g., Mali) is too heavily focused on input subsidies.","ai_generated":true,"unit":"iisd-003676#insight-7","date_modified":"2026-10-23"},{"text":"Gender inequality remains a persistent barrier to transformation; for example, women-led farms in some regions are 23 per cent less productive than those run by men due to lower-quality extension support.","ai_generated":true,"unit":"iisd-003676#insight-8","date_modified":"2026-10-23"}],"climate_tags":["agricultural productivity","climate change","agricultural irrigation","economic diversity","food security","gender equality"],"geography_discussed":["Africa","Asia","Latin America","Brazil","Colombia","People's Republic of China","Malaysia","Vietnam","Ethiopia","Malawi","Togo","Uganda","India","Kenya","Rwanda","Mali","Mozambique","Zimbabwe","Zambia","Burkina Faso","Tanzania"],"frame_perspective":[],"analysis_type":["comparative analysis","cluster analysis","literature review","policy assessment"]},{"id":"sei-002501","url":"https://climateinsightsdirectory.org/document/sei-002501","title":"Piloting the SDG Synergies approach in Mongolia","publisher":{"name":"Stockholm Environment Institute","organization":"stockholm-environment-institute"},"document_type":"report","language":"en","date_published":"2019-07","source_url":"https://www.sei.org/wp-content/uploads/2019/07/sei-2019-sdg-synergies-mongolia.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The report details a pilot project in Mongolia using the SDG Synergies approach to identify interactions between 17 targets from the Sustainable Development Vision (SDV) 2030. Centered on integrated water governance, the process involved a stakeholder workshop in Ulaanbaatar to map first-order interactions, followed by SEI's second-order and cluster analyses. The findings highlight the critical role of accountable governance and urban planning in driving sustainability, while identifying strong self-reinforcing synergies within the agriculture and food sectors. The project resulted in the Mongolian Cabinet approving guidelines for using the approach in sectoral policy development.","ai_generated":true,"unit":"sei-002501#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The first-order interaction analysis revealed that governance targets are the most influential. Specifically, Target 2.4.i (Accountable governance structures) had the highest net influence score of +31 and a positive influence on all other targets, followed by Target 2.4.ii (Leadership for civil service).","ai_generated":true,"unit":"sei-002501#insight-1","date_modified":"2026-10-23"},{"text":"Urban planning and waste management (Target 2.3.3.ii) was identified as a highly influential target, scoring second in the first-order analysis with a net score of +26. This is attributed to the rapid growth of Ulaanbaatar and the resulting challenges in providing basic infrastructure and social services.","ai_generated":true,"unit":"sei-002501#insight-2","date_modified":"2026-10-23"},{"text":"Second-order analysis, which accounts for indirect system effects, slightly altered the ranking of influential targets. Target 2.4.ii (Leadership of civil service organizations) rose to the second position, while targets related to social welfare, water resource protection, and biodiversity preservation fell in rank.","ai_generated":true,"unit":"sei-002501#insight-3","date_modified":"2026-10-23"},{"text":"Cluster analysis identified four groups of positively linked targets. The 'Agriculture/food' cluster (comprising targets 2.1.1.ii, 2.1.1.iii, and 2.1.3.iii) demonstrated a strong, self-reinforcing 'loop of influence' where investment in one target cascades to the others.","ai_generated":true,"unit":"sei-002501#insight-4","date_modified":"2026-10-23"},{"text":"The 'Industry' cluster was found to consist of targets 2.1.3.ii (chemical industrial sector), 2.1.5.ii (renewable energy), and 2.1.4.ii (extractive industries). The analysis suggests significant gains could be achieved through formalized cross-sectoral cooperation within the Industry and Agricultural clusters.","ai_generated":true,"unit":"sei-002501#insight-5","date_modified":"2026-10-23"},{"text":"The pilot project led to institutionalization of the method in Mongolia; the National Development Agency developed guidelines for the SDG Synergies approach, which were approved by the Cabinet in October 2018, mandating sectoral ministries to use the approach in policy development.","ai_generated":true,"unit":"sei-002501#insight-6","date_modified":"2026-10-23"}],"climate_tags":["Integrated Water Resources Management","renewable energy","climate change adaptation","biodiversity","Sustainable Development Goals","urban planning","extractive industry","agriculture"],"geography_discussed":["Ulaanbaatar","Bangkok","Colombia","Sri Lanka"],"frame_perspective":[],"analysis_type":["first-order interactions analysis","second-order interactions analysis","cluster analysis","capacity-building"]},{"id":"clipolin-000398","url":"https://climateinsightsdirectory.org/document/clipolin-000398","title":"Role of Carbon Capture, Utilization, and Storage in Decarbonizing India’s Steel Sector","publisher":{"name":"Climate Policy Initiative","organization":"climate-policy-initiative"},"document_type":"report","language":"en","date_published":null,"source_url":"https://www.climatepolicyinitiative.org/wp-content/uploads/2025/12/CCUS-Report-Annexure-Document.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The document identifies specific steel clusters in India—including Angul, Rourkela, Raigarh, Raipur, Jamshedpur, Durgapur, and Vizag—and assesses their CO2 emissions against available storage capacities in saline aquifers, coal fields, and basalt formations. It provides a comparative list of global operational CCUS projects and outlines the regulatory and financial support mechanisms for CCUS in the United States, European Union, United Kingdom, and China.","ai_generated":true,"unit":"clipolin-000398#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The Jamshedpur cluster in Jharkhand has a total crude steel production capacity of 21 mtpa, resulting in CO2 emissions of nearly 48 mtpa, with 28.8 mtpa amenable for capture and a 30-year storage requirement of 0.86 Gt.","ai_generated":true,"unit":"clipolin-000398#insight-1","date_modified":"2026-10-23"},{"text":"The Vizag Steel plant has a crude steel production capacity of ~7.3 mtpa and CO2 emissions of nearly 15.5 mtpa, with 9.3 mtpa amenable for capture and a 30-year storage requirement of ~0.3 Gt. Storage opportunities within 150 km total 6.43 Gt, primarily in the Krishna-Godavari basin.","ai_generated":true,"unit":"clipolin-000398#insight-2","date_modified":"2026-10-23"},{"text":"The United States utilizes the Inflation Reduction Act and Bipartisan Infrastructure Law to fund CCS, with Section 45Q and 45V credits subsidizing operating costs and the EPA granting Class VI underground injection permits.","ai_generated":true,"unit":"clipolin-000398#insight-3","date_modified":"2026-10-23"},{"text":"The United Kingdom is developing three large CCUS clusters with an investment of USD 25 billion, aiming to capture 20–30 million tonnes of CO2 per year by 2030 and over 50 million tonnes by 2035.","ai_generated":true,"unit":"clipolin-000398#insight-4","date_modified":"2026-10-23"},{"text":"China's National Development and Reform Commission (NDRC) announced six CCUS-related initiatives in April 2024, including projects for Baotou Steel in Inner Mongolia and a coal-fired power plant project by Huaneng.","ai_generated":true,"unit":"clipolin-000398#insight-5","date_modified":"2026-10-23"}],"climate_tags":["iron and steel industry","decarbonization","carbon capture and storage","climate finance","Carbon Border Adjustment Mechanism","emission trading"],"geography_discussed":["Odisha","Chhattisgarh","Jharkhand","West Bengal","United States","European Union","United Kingdom","People's Republic of China","Norway","Canada","United Arab Emirates","Saudi Arabia"],"frame_perspective":[],"analysis_type":["source-sink mapping","cluster analysis","policy review"]}]}