{"query_echo":{"q":"","filters":{"analysis_type":["machine learning"]},"warnings":[{"code":"facet_value_not_in_vocabulary","message":"Value 'machine learning' for facet 'analysis_type' is not in the controlled vocabulary."}]},"total":{"value":4,"relation":"eq"},"page":1,"page_size":20,"self":"https://climateinsightsdirectory.org/api/v1/search?analysis_type=machine+learning","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-000991","url":"https://climateinsightsdirectory.org/document/clipolin-000991","title":"Challenges of Rural Insurance in the Context of Climate Change: The Case of Soybeans","publisher":{"name":"Climate Policy Initiative","organization":"climate-policy-initiative"},"document_type":"report","language":"en","date_published":"2023-04-10","source_url":"https://www.climatepolicyinitiative.org/publication/challenges-of-rural-insurance-in-the-context-of-climate-change-the-case-of-soybeans/","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The report examines the gap between soybean production distribution and rural insurance coverage in Brazil, highlighting how climate change—manifesting as severe droughts, hail, and frost—has led to unprecedented insurance claim payments. Using LASSO machine learning models, researchers found that while insurance data is insufficient to predict losses in the North and Northeast regions, agricultural yield data from PAM/IBGE can effectively identify climate variables (precipitation, fire risk, and wind) that correlate with losses. The authors recommend expanding insurance supply to vulnerable regions like MATOPIBA, utilizing production history for pricing in underserved areas, and strengthening the reinsurance market and public-private funds to ensure insurer liquidity.","ai_generated":true,"unit":"clipolin-000991#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"Climate change is increasing the frequency of extreme weather events in Brazil, leading to a surge in rural insurance claims. In the 2021/2022 agricultural year, severe drought caused claim payments to grow more than four times compared to the previous season, with total claims reaching R$ 8.8 billion, exceeding the total premiums collected.","ai_generated":true,"unit":"clipolin-000991#insight-1","date_modified":"2026-10-23"},{"text":"There is a significant mismatch between where soybeans are produced and where rural insurance is available. The Southern region holds 60% of policies and 43% of premium value, despite representing only 37% of soybean production value (2006-2018). Conversely, the Midwest accounts for 44% of production value but only 20% of insurance policies.","ai_generated":true,"unit":"clipolin-000991#insight-2","date_modified":"2026-10-23"},{"text":"The MATOPIBA region (Maranhão, Tocantins, Piauí, and Bahia) has seen an increase in soybean production share, rising from 10% in 2010 to 14% in 2020, yet insurance coverage has not grown proportionally, leaving the area vulnerable to high climate risk.","ai_generated":true,"unit":"clipolin-000991#insight-3","date_modified":"2026-10-23"},{"text":"Machine learning models (LASSO) identify precipitation, fire risk, and wind as the most relevant climate variables for explaining agricultural losses across Brazil. These variables are strongly correlated with droughts and storms, while temperature variables have lower individual explanatory power.","ai_generated":true,"unit":"clipolin-000991#insight-4","date_modified":"2026-10-23"},{"text":"Predictive models based on insurance claims data perform poorly in the North and Northeast due to limited policy history. However, when using average soybean yield data from PAM/IBGE, the models can effectively explain losses in the Northeast, suggesting that the lack of insurance is due to data scarcity and high risk rather than an inability to model the climate.","ai_generated":true,"unit":"clipolin-000991#insight-5","date_modified":"2026-10-23"},{"text":"The Brazilian reinsurance market is expanding but remains highly concentrated. While IRB Brasil held 89% of premiums in the 2011/12 crop, its share fell to 38% by 2021/22 as foreign companies increased their participation.","ai_generated":true,"unit":"clipolin-000991#insight-6","date_modified":"2026-10-23"}],"climate_tags":["climate change","drought","climate finance"],"geography_discussed":["MATOPIBA","South","Midwest","Northeast","North","Southeast","São Paulo","Santa Catarina","Rio Grande do Sul","Paraná","Piauí","Bahia"],"frame_perspective":[],"analysis_type":["econometric analysis","machine learning","policy assessment"]},{"id":"iisd-002637","url":"https://climateinsightsdirectory.org/document/iisd-002637","title":"Sustainable Solutions to End Hunger","publisher":{"name":"International Institute for Sustainable Development","organization":"international-institute-for-sustainable-development"},"document_type":"report","language":"en","date_published":"2020","source_url":"https://ceres2030.iisd.org/wp-content/uploads/2021/07/ceres2030-nature-portfolio-.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The Ceres2030 project identifies a funding gap of USD 33 billion per year to achieve SDG 2 targets, with USD 14 billion coming from donors and USD 19 billion from recipient countries. The report emphasizes that most existing agricultural research ignores the needs of smallholders, who represent over 80% of the world's farms. Key recommendations include investing in extension services, climate-resilient crops, social protection, and supporting small and medium enterprises (SMEs) in the value chain. The project utilizes a complex general equilibrium model and machine-learning-assisted evidence synthesis to identify a portfolio of 14 policy instruments across three categories: empowering the excluded, on-farm interventions, and food on the move.","ai_generated":true,"unit":"iisd-002637#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"To end hunger and double the incomes of small-scale producers in low- and middle-income countries by 2030, an additional USD 33 billion per year is needed, consisting of USD 14 billion from donors and USD 19 billion from the countries themselves through increased taxation.","ai_generated":true,"unit":"iisd-002637#insight-1","date_modified":"2026-10-23"},{"text":"The current body of agricultural research is largely irrelevant to smallholder farmers; more than 95% of assessed studies were not relevant to the needs of smallholders and their families, and less than 2% of the available evidence base is pertinent to the questions donors typically investigate, such as intervention costs.","ai_generated":true,"unit":"iisd-002637#insight-2","date_modified":"2026-10-23"},{"text":"Small-scale producers represent over 80% of the world's farms, and those with less than 2 hectares produce between 30% and 34% of the global food supply.","ai_generated":true,"unit":"iisd-002637#insight-3","date_modified":"2026-10-23"},{"text":"The adoption of climate-resilient crops is most strongly determined by the availability and effectiveness of extension services, followed by the education level of the household head and access to inputs like seeds and fertilizers.","ai_generated":true,"unit":"iisd-002637#insight-4","date_modified":"2026-10-23"},{"text":"Membership in farmers' organizations is associated with positive income effects in 57% of reviewed cases, primarily by strengthening producers' market power.","ai_generated":true,"unit":"iisd-002637#insight-5","date_modified":"2026-10-23"},{"text":"Small and medium enterprises (SMEs) in the value chain, particularly in Africa, are correlated with higher technology adoption and productivity among small-scale producers by providing complementary services such as credit, training, and market links.","ai_generated":true,"unit":"iisd-002637#insight-6","date_modified":"2026-10-23"},{"text":"Post-harvest loss reduction is dominated by storage technology research, with 83% of studies focusing on this area, while there is a critical lack of evidence on training, finance, policy, and infrastructure interventions.","ai_generated":true,"unit":"iisd-002637#insight-7","date_modified":"2026-10-23"},{"text":"Social protection programs are most effective when they create a bridge to productive employment and remove barriers to accessing markets, education, and credit.","ai_generated":true,"unit":"iisd-002637#insight-8","date_modified":"2026-10-23"},{"text":"In water-scarce regions of low- and middle-income countries, less than 37.2% of small-scale farms are irrigated, compared to 43.2% of non-small-scale farms.","ai_generated":true,"unit":"iisd-002637#insight-9","date_modified":"2026-10-23"},{"text":"The project's economic model indicates that the additional public spending of USD 33 billion per year will prevent 490 million people from experiencing hunger and double the incomes of 545 million producers and their families on average.","ai_generated":true,"unit":"iisd-002637#insight-10","date_modified":"2026-10-23"}],"climate_tags":["sustainable agriculture","climate finance","greenhouse gas emissions","water scarcity","agroforestry","smallholder","social protection","Paris Agreement"],"geography_discussed":["Africa","South Asia","India","Ethiopia","Kenya","Uganda","Ghana","Tanzania","Burkina Faso"],"frame_perspective":[],"analysis_type":["economic modelling","evidence synthesis","scoping review","systematic review","machine learning"]},{"id":"isdglo-000102","url":"https://climateinsightsdirectory.org/document/isdglo-000102","title":"The Online Ecosystem of the German Far-Right","publisher":{"name":"Institute for Strategic Dialogue","organization":"institute-for-strategic-dialogue"},"document_type":"report","language":"en","date_published":"2020","source_url":"https://www.isdglobal.org/wp-content/uploads/2020/02/ISD-The-Online-Ecosystem-of-the-German-Far-Right-English-Draft-11.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","geography_discussed","analysis_type"],"summary":{"text":"The report maps the 'online ecosystem' of the German far-right, identifying 379 channels across ten alternative platforms. It finds that while these platforms provide safe havens for extremist discourse, they generally have a much smaller reach than mainstream platforms. The study suggests that banning extremists from mainstream sites reduces their overall reach and does not lead to a direct mass migration to alternative platforms. It also highlights the role of 'alt-media' in amplifying far-right narratives, such as the 'great replacement' myth, which are rarely prominent in mainstream media.","ai_generated":true,"unit":"isdglo-000102#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The research identified 379 far-right and right-wing populist channels across ten alternative platforms. The platforms with the highest presence were Telegram (129 channels), VK (115 groups), and BitChute (79). The total number of German-speaking individuals with far-right beliefs using these platforms is estimated to be between 15,000 and 50,000.","ai_generated":true,"unit":"isdglo-000102#insight-1","date_modified":"2026-10-23"},{"text":"Different far-right ideologies vary in their reach; while anti-Muslim and neo-Nazi channels are more numerous, 'Identitarian' groups have the largest reach, with the largest Identitarian channel exceeding 35,000 followers, compared to 18,000 for the largest anti-Muslim channel and approximately 10,000 for the largest neo-Nazi channel.","ai_generated":true,"unit":"isdglo-000102#insight-2","date_modified":"2026-10-23"},{"text":"User motivations for joining alternative platforms differ by site: Gab users are primarily driven by freedom of speech grievances and anti-left resentment (54%), 4chan /pol/ users are motivated by anti-minority hatred (46%) and entertainment (22%), and members of the Discord group Reconquista Germanica seek political change (39%) and community belonging (41%).","ai_generated":true,"unit":"isdglo-000102#insight-3","date_modified":"2026-10-23"},{"text":"Immigration is the most discussed topic across the sampled platforms, with 60% of immigration-related posts focusing on the illegal behaviour of refugees or migrants and 12% referencing conspiracy theories regarding the demographic replacement of 'native Europeans'.","ai_generated":true,"unit":"isdglo-000102#insight-4","date_modified":"2026-10-23"},{"text":"An algorithmic analysis of 4chan's Kraut/pol/ threads revealed that 56.9% of mentions of Jewish people contained clearly antisemitic narratives, moving beyond simple slurs to include allegations of Jewish control over tech firms, media, and democratic institutions.","ai_generated":true,"unit":"isdglo-000102#insight-5","date_modified":"2026-10-23"},{"text":"Banning far-right groups from mainstream platforms reduces their overall reach and does not appear to cause a direct displacement of users to alternative platforms. A sample of 25 groups showed that those with a presence on mainstream platforms actually had higher follower numbers on alternative platforms than those who had been banned.","ai_generated":true,"unit":"isdglo-000102#insight-6","date_modified":"2026-10-23"},{"text":"Far-right themes are disproportionately overrepresented in alternative media compared to mainstream media. Concepts like 'great replacement' and 'Islamisation' appear in 0.5-6.0% of stories in alt-media, whereas mainstream media coverage of these themes is primarily event-driven, such as in response to the Christchurch attack.","ai_generated":true,"unit":"isdglo-000102#insight-7","date_modified":"2026-10-23"},{"text":"The right-wing populist party Alternative for Deutschland (AfD) has a minimal and largely inactive presence on alternative platforms; only 31 communities were identified, 29 of which are on VK, with the largest recently active group having only 414 followers.","ai_generated":true,"unit":"isdglo-000102#insight-8","date_modified":"2026-10-23"}],"climate_tags":[],"geography_discussed":["United States","Russia","United Kingdom","France","New Zealand"],"frame_perspective":[],"analysis_type":["digital ethnography","content analysis","machine learning","natural language processing","comparative analysis"]},{"id":"prio-001533","url":"https://climateinsightsdirectory.org/document/prio-001533","title":"Rainfall Variability and Violent, State-Based Conflict","publisher":{"name":"Peace Research Institute Oslo","organization":"peace-research-institute-oslo"},"document_type":"research paper","language":"en","date_published":"2020","source_url":"https://cdn.cloud.prio.org/files/52a825cc-c6c8-4c4e-9d38-a3e84afd59af/MA_thesis_Mjelva_2020.pdf?inline=true","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The research examines how the effect of anomalous rainfall (excess and scarce) on violent, state-based conflict varies based on societal vulnerability factors. Using a causal forest model on global grid-cell data, the author finds that while rainfall variability generally has a small effect on conflict, this effect is context-specific. Scarce rainfall is more likely to increase conflict risk in communities with high agricultural employment and poor governance (low SHDI and GDP). Excess rainfall's effect is more complex, showing higher risk in areas with high population density, low globalization, low democracy, and low GDP, but contrary to expectations, it showed smaller effects in areas with high political exclusion or greater distance to the capital.","ai_generated":true,"unit":"prio-001533#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The effect of rainfall variability on violent, state-based conflict is generally small across all studied contexts, but it is confirmed to be context-specific and dependent on the vulnerability of the community.","ai_generated":true,"unit":"prio-001533#insight-1","date_modified":"2026-10-23"},{"text":"Scarce rainfall is most likely to increase the probability of violent, state-based conflict in communities characterized by high agricultural employment and low levels of good governance, specifically low Subnational Human Development Index (SHDI) and GDP.","ai_generated":true,"unit":"prio-001533#insight-2","date_modified":"2026-10-23"},{"text":"The factors that modify the relationship between scarce rainfall and conflict differ from those that modify the relationship between excess rainfall and conflict, suggesting they operate through separate mechanisms.","ai_generated":true,"unit":"prio-001533#insight-3","date_modified":"2026-10-23"},{"text":"Contrary to theoretical expectations, high levels of political exclusion and greater distance to the capital were associated with smaller effects of excess rainfall on conflict.","ai_generated":true,"unit":"prio-001533#insight-4","date_modified":"2026-10-23"},{"text":"The analysis identified that some vulnerability factors, such as temperature and the prevalence of non-state conflict, did not have a significant impact on the treatment effect in either the scarce or excess rainfall models.","ai_generated":true,"unit":"prio-001533#insight-5","date_modified":"2026-10-23"},{"text":"The study encountered a significant violation of the overlap assumption, where propensity scores for receiving the treatment (SPEI3) were heavily skewed toward zero, necessitating the exclusion of a large portion of the data to avoid biased estimates.","ai_generated":true,"unit":"prio-001533#insight-6","date_modified":"2026-10-23"}],"climate_tags":["rain","drought","flood","climate change adaptation"],"geography_discussed":["Ethiopia","Syria","sub-Saharan Africa","South Asia","Southeast Asia","Latin America","Afghanistan"],"frame_perspective":[],"analysis_type":["causal forest","machine learning","heterogeneous treatment effects","causal inference","non-parametric analysis"]}]}