{"query_echo":{"q":"","filters":{"analysis_type":["Technical Report"]},"warnings":[]},"total":{"value":26,"relation":"eq"},"page":2,"page_size":20,"self":"https://climateinsightsdirectory.org/api/v1/search?analysis_type=Technical+Report&page=2","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-001663","url":"https://climateinsightsdirectory.org/document/sei-001663","title":"CRED V.1.3 TECHNICAL REPORT","publisher":{"name":"Stockholm Environment Institute","organization":"stockholm-environment-institute"},"document_type":"report","language":"en","date_published":"2012-01-25","source_url":"https://www.sei.org/mediamanager/documents/Publications/Climate-mitigation-adaptation/Economics_of_climate_policy/SEI-CRED13-Tech-Report.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The CRED v.1.3 model is an integrated assessment tool designed to project global climate and development scenarios over a 300-year period. It focuses on the distribution of climate damages and policy costs across nine global regions, utilizing a Cobb-Douglas production function and a vulnerability index to allocate damages. The model incorporates updated population data, four selectable damage functions, and abatement cost curves based on McKinsey data to inform global climate negotiations.","ai_generated":true,"unit":"sei-001663#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"CRED v.1.3 is an integrated assessment model that projects global climate and development scenarios at 10-year intervals over a 300-year time span, using a 2005 base year.","ai_generated":true,"unit":"sei-001663#insight-1","date_modified":"2026-10-23"},{"text":"The model divides the world into nine regions: three high-income (United States, Europe, and Other high-income) and six developing (Latin America and the Caribbean, Middle East, Russia and non-EU Eastern Europe, Africa, China, and Other developing Asia).","ai_generated":true,"unit":"sei-001663#insight-2","date_modified":"2026-10-23"},{"text":"The economy module employs a Cobb-Douglas production function where mitigation investment ('green capital') is assumed to be half as productive of income as standard investment (s = 0.5).","ai_generated":true,"unit":"sei-001663#insight-3","date_modified":"2026-10-23"},{"text":"CRED v.1.3 offers four damage function options (NN, HN, NW, HW) that combine different low-temperature and high-temperature estimates from Nordhaus, Hanemann, and Weitzman.","ai_generated":true,"unit":"sei-001663#insight-4","date_modified":"2026-10-23"},{"text":"Global damages are distributed among regions using a vulnerability index based on three factors: the proportion of GDP in agriculture and tourism, the share of the population living below 5-meter elevation, and freshwater resources per person.","ai_generated":true,"unit":"sei-001663#insight-5","date_modified":"2026-10-23"},{"text":"Abatement costs and potentials are derived from McKinsey 2030 cost curves, with the model assuming that 100 percent abatement of industrial emissions becomes possible in every region by 2105.","ai_generated":true,"unit":"sei-001663#insight-6","date_modified":"2026-10-23"},{"text":"The model's optimization process maximizes a global utility function based on the discounted sum of the logarithms of regional per capita consumption, using a default pure time preference rate of 0.1 percent per year.","ai_generated":true,"unit":"sei-001663#insight-7","date_modified":"2026-10-23"}],"climate_tags":["climate change mitigation","carbon price","climate change vulnerability","sustainable finance","carbon dioxide emissions"],"geography_discussed":["United States","Europe","Latin America and the Caribbean","Middle East","Russia","Africa","People's Republic of China"],"frame_perspective":[],"analysis_type":["technical report","methodological description"]},{"id":"sei-000791","url":"https://climateinsightsdirectory.org/document/sei-000791","title":"A Material Flow Analysis and Ecological Footprint of York","publisher":{"name":"Stockholm Environment Institute","organization":"stockholm-environment-institute"},"document_type":"report","language":"en","date_published":"2002-01-01","source_url":"https://www.sei.org/publications/a-material-flow-analysis-and-ecological-footprint-of-york/","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The provided text is a list of publications from the Stockholm Environment Institute. It includes a 2002 technical report titled 'A Material Flow Analysis and Ecological Footprint of York' and summaries of more recent works concerning UK supply chain impacts on deforestation and water stress, the necessity of combining territorial and supply chain metrics for deforestation monitoring, and the communication of ecological citizenship.","ai_generated":true,"unit":"sei-000791#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"A 2002 technical report was produced by Barrett, J., Vallack, H., Haq, G. and Jones, A. titled 'A Material Flow Analysis and Ecological Footprint of York'.","ai_generated":true,"unit":"sei-000791#insight-1","date_modified":"2026-10-23"},{"text":"Analysis of UK supply chains indicates international consequences regarding wildlife extinction risks, deforestation-related carbon emissions, and water stress.","ai_generated":true,"unit":"sei-000791#insight-2","date_modified":"2026-10-23"},{"text":"Effective deforestation monitoring requires policymakers to integrate supply chain metrics with territorial metrics to account for future risks and past impacts.","ai_generated":true,"unit":"sei-000791#insight-3","date_modified":"2026-10-23"}],"climate_tags":["supply chain","deforestation","carbon dioxide emissions","biodiversity loss","public policy","climate change adaptation"],"geography_discussed":["United Kingdom"],"frame_perspective":[],"analysis_type":["technical report","press release","perspective","journal article"]},{"id":"cer-001292","url":"https://climateinsightsdirectory.org/document/cer-001292","title":"record-13.1-pg-37-51-e5c649172c49ed28.pdf","publisher":{"name":"Centre for Environmental Rights","organization":"centre-for-environmental-rights"},"document_type":"report","language":"en","date_published":null,"source_url":"https://cer.org.za/wp-content/uploads/2016/05/Record-13.1-pg-37-51.pdf?x21779","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"This document is an excerpt from Eskom's Air Quality Strategy (Unique Identifier: 32-1143), detailing the company's approach to ambient air quality monitoring, atmospheric dispersion modelling, stakeholder engagement, and research. It outlines specific emission targets for existing power stations, a retrofit plan to meet Minimum Emission Standards, and a strategy for coal beneficiation to reduce emissions.","ai_generated":true,"unit":"cer-001292#summary","date_modified":"2026-08-17"},"key_insights":[{"text":"Eskom has established specific emission targets for existing power stations: particulate matter must be less than 50 mg/Nm³ for stations under 35 years old; sulphur dioxide must be less than 3500 mg/Nm³; and oxides of nitrogen should be less than the minimum NOx emission standard for existing plant (1100 mg/Nm³) for stations under 35 years old, where technically feasible.","ai_generated":true,"unit":"cer-001292#insight-1","date_modified":"2026-08-17"},{"text":"To comply with new Minimum Emission Standards, Eskom is implementing a phased retrofit plan. This includes installing fabric filter plants at Tutuka, Kriel, Matla, Grootvlei Units 2, 3, 4, Duvha Units 4-6, Lethabo, and Matimba; flue gas desulphurisation at Medupi and Kendal; and low NOx burners and over-fire air at Majuba, Duvha, Tutuka, and Kriel.","ai_generated":true,"unit":"cer-001292#insight-2","date_modified":"2026-08-17"},{"text":"The financial and resource requirements for retrofitting six stations with fabric filter plants, four with low NOx burners, and one with FGD are estimated at a CAPEX of R28 billion (in 2009 real terms) and an annual OPEX of R0.4 billion once operational. These retrofits will require 500,000 tons of sorbent per annum and result in a capacity loss of 160 MW.","ai_generated":true,"unit":"cer-001292#insight-3","date_modified":"2026-08-17"},{"text":"Eskom's ambient air quality monitoring strategy includes maintaining SANAS accreditation and ensuring real-time data availability. The network is reviewed every three years, and stations may be decommissioned if no significant impact from a source is measured for three consecutive years. The cost to purchase equipment and housing for a station measuring SO2, NO, and PM10 is approximately R600,000, with annual operating costs of R400,000.","ai_generated":true,"unit":"cer-001292#insight-4","date_modified":"2026-08-17"},{"text":"Eskom is exploring coal beneficiation—specifically removing the \"stone\" fraction (>2 g/cm³)—as a cost-effective alternative to retrofitting boilers. This process is intended to improve combustion efficiencies and reduce emissions of SO2, NOx, CO, and particulates, as demonstrated at Lethabo Power Station.","ai_generated":true,"unit":"cer-001292#insight-5","date_modified":"2026-08-17"},{"text":"The air quality research programme, which had a budget of R8.3 million in the 2009/10 financial year (excluding coal beneficiation and research monitoring stations), focuses on emission control technologies, atmospheric dispersion modelling, and atmospheric deposition. A key focus is developing a framework to monitor the long-term impact of emissions on soils, surface waters, and ecosystems in the Lephalale region and the Highveld.","ai_generated":true,"unit":"cer-001292#insight-6","date_modified":"2026-08-17"}],"climate_tags":["energy transition","air quality","emission reduction","coal beneficiation","pollution abatement"],"geography_discussed":["Highveld","Waterberg","Vaal","Lephalale"],"frame_perspective":[],"analysis_type":["strategic plan","technical report"]},{"id":"cer-001429","url":"https://climateinsightsdirectory.org/document/cer-001429","title":"unit-1-correlations-report-summary-40fc0ae4d90696f8.pdf","publisher":{"name":"Centre for Environmental Rights","organization":"centre-for-environmental-rights"},"document_type":"report","language":"en","date_published":null,"source_url":"https://cer.org.za/wp-content/uploads/2017/07/Unit-1-Correlations-report-Summary.pdf?x21779","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","analysis_type"],"summary":{"text":"This document provides a results summary of particulate emission monitor and air-to-gas flow correlations for Unit No. 1 at Duvha Power Station, based on February 2016 data. It confirms compliance with ISO 10155 and ESKOM standards and recommends the use of specific correlation functions for reporting emissions to authorities.","ai_generated":true,"unit":"cer-001429#summary","date_modified":"2026-08-17"},"key_insights":[{"text":"The particulate emission monitor and air-to-gas flow correlations for Unit No. 1 at Duvha Power Station, conducted in February 2016, meet all minimum requirements specified in ISO 10155 and the ESKOM Standard for Emission Monitoring and Reporting (420-56242363).","ai_generated":true,"unit":"cer-001429#insight-1","date_modified":"2026-08-17"},{"text":"The report recommends that particulate emissions from Unit No. 1 reported to authorities be based on the correlation functions presented in Figures 1, 2, and 3 of the report.","ai_generated":true,"unit":"cer-001429#insight-2","date_modified":"2026-08-17"},{"text":"The document recommends that new compliant correlations for these units be performed during the first quarter of 2018.","ai_generated":true,"unit":"cer-001429#insight-3","date_modified":"2026-08-17"}],"climate_tags":["energy transition","emissions monitoring","particulate matter"],"geography_discussed":[],"frame_perspective":[],"analysis_type":["technical report","compliance audit"]},{"id":"cer-001498","url":"https://climateinsightsdirectory.org/document/cer-001498","title":"SECTORAL EMISSION TARGETS Technical Report","publisher":{"name":"Centre for Environmental Rights","organization":"centre-for-environmental-rights"},"document_type":"report","language":"en","date_published":null,"source_url":"https://cer.org.za/wp-content/uploads/2024/07/Annexure-D-SETs-Technical-Report1.pdf?x21779","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","analysis_type"],"summary":{"text":"The report details a bottom-up integrated model developed in Analytica to project greenhouse gas emissions in South Africa to 2050. It incorporates a Mitigation Potential Analysis (MPA), GHG emission pathways, and a socio-economic model linked via a Social Accounting Matrix (SAM). The model assesses various sectors—including energy, industry, transport, waste, and AFOLU—applying specific mitigation measures and policies such as the Carbon Tax Act of 2019 and the IRP 2023. It provides a framework for evaluating the socio-economic impacts of decarbonisation pathways on employment, output, and household consumption across different income deciles.","ai_generated":true,"unit":"cer-001498#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The energy sector is the largest source of emissions in South Africa, accounting for 86% of national emissions in 2022, with the electricity subsector contributing 87% of that total.","ai_generated":true,"unit":"cer-001498#insight-1","date_modified":"2026-10-23"},{"text":"The transport sector was the second largest emissions contributor in 2022, representing 12% of national emissions, with road transport accounting for 11.75% of the total.","ai_generated":true,"unit":"cer-001498#insight-2","date_modified":"2026-10-23"},{"text":"The industry sector accounted for 13% of national emissions in 2022, with more than half of these being process emissions and 47% resulting from energy emissions.","ai_generated":true,"unit":"cer-001498#insight-3","date_modified":"2026-10-23"},{"text":"The Integrated Model utilizes a carbon tax based on the Carbon Tax Act of 2019, with a 2023 rate of R 159 per tonne of CO2eq, aiming to reach at least $30 by 2030.","ai_generated":true,"unit":"cer-001498#insight-4","date_modified":"2026-10-23"},{"text":"For the electricity sector, the model uses the IRP 2023 build plan for Horizon 1 (up to 2030) and offers six potential scenarios for Horizon 2 (2031 to 2050), including 'Renewable energy' and 'Renewable energy and nuclear'.","ai_generated":true,"unit":"cer-001498#insight-5","date_modified":"2026-10-23"},{"text":"The socio-economic model is a demand-driven simulation based on a Social Accounting Matrix (SAM) that estimates impacts on employment, average wages, investment, and prices across household income deciles.","ai_generated":true,"unit":"cer-001498#insight-6","date_modified":"2026-10-23"},{"text":"In the AFOLU sector, the model identifies several restoration and conservation measures, such as restoring sub-tropical thickets (500,000 ha) and broadleaf woodland (300,000 ha).","ai_generated":true,"unit":"cer-001498#insight-7","date_modified":"2026-10-23"}],"climate_tags":["greenhouse gas emissions","carbon tax","energy transition","electricity generation","renewable energy","industrial emission","road transport","rail transport","aviation","water transport","waste management","carbon sequestration","energy efficiency","hydrofluorocarbon"],"geography_discussed":[],"frame_perspective":[],"analysis_type":["technical report","modelling","policy assessment"]},{"id":"wwf-000615","url":"https://climateinsightsdirectory.org/document/wwf-000615","title":"AN AERIAL SURVEY OF ELEPHANTS AND OTHER LARGE HERBIVORES IN THE KAVANGO ZAMBEZI TRANSFRONTIER CONSERVATION AREA","publisher":{"name":"WWF International","organization":"wwf-international"},"document_type":"report","language":"en","date_published":null,"source_url":"https://www.worldwildlife.org/documents/598/5si83x57zx_1._KAZA_Elephant_Survey_Volume_I.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The 2022 KAZA Elephant Survey estimated a total population of 227,900 elephants across the KAZA TFCA, with the majority located in Botswana (58%) and Zimbabwe (29%). The survey also identified 26,641 elephant carcasses, resulting in an all-carcass ratio of 10.47%, which the authors suggest may be a \"warning sign for the health and stability of the elephant population.\" Beyond elephants, the survey estimated populations for various wildlife species and domestic livestock, noting a ratio of 1.16 wild animals to 1 domestic animal in the survey area. The report details the technical execution of the survey, including the use of systematic transect sampling across 179 strata and the implementation of CITES MIKE Aerial Survey Standards.","ai_generated":true,"unit":"wwf-000615#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The total estimated population of African savanna elephants in the KAZA TFCA survey area is 227,900 (±16,743), achieving a percentage relative precision (PRP) of 7.34%.","ai_generated":true,"unit":"wwf-000615#insight-1","date_modified":"2026-10-23"},{"text":"The survey estimated 26,641 (±1,645) elephant carcasses, leading to an overall all-carcass ratio (CR14) of 10.47%, which is considered a high level of mortality that may signal instability in the population.","ai_generated":true,"unit":"wwf-000615#insight-2","date_modified":"2026-10-23"},{"text":"Elephant distribution is highly uneven across the five partner states: Botswana holds 58% of the population, Zimbabwe 29%, Namibia 9%, and Zambia and Angola combined account for the remaining 4%.","ai_generated":true,"unit":"wwf-000615#insight-3","date_modified":"2026-10-23"},{"text":"The survey estimated populations for several other wildlife species in the KAZA TFCA survey area: impala (100,028), zebra (88,250), buffalo (78,264), red lechwe (137,959), and giraffe (12,771).","ai_generated":true,"unit":"wwf-000615#insight-4","date_modified":"2026-10-23"},{"text":"Domestic livestock in the survey area was estimated at 736,426 animals, consisting of 73% cattle and 24% sheep and goats, creating a ratio of 1.16 wild animals for every 1 domestic animal.","ai_generated":true,"unit":"wwf-000615#insight-5","date_modified":"2026-10-23"},{"text":"Recent mortality, measured by the fresh and recent carcass ratio (CR12), was estimated at 0.51% for the KAZA TFCA, with the highest ratios found in Botswana (0.72%), Angola (0.57%), and the Kavango Zambezi zone (0.49%).","ai_generated":true,"unit":"wwf-000615#insight-6","date_modified":"2026-10-23"},{"text":"The survey observed a pattern of spatial segregation where elephants are generally absent from regions with high densities of human settlements and livestock.","ai_generated":true,"unit":"wwf-000615#insight-7","date_modified":"2026-10-23"},{"text":"The survey covered 60% of the KAZA TFCA (310,865 km²), utilizing 195 flights across 179 strata with an overall sampling intensity of 6.9%.","ai_generated":true,"unit":"wwf-000615#insight-8","date_modified":"2026-10-23"},{"text":"Photo interpretation of observed groups led to a 2.2% increase in the final estimated population of elephants in the KAZA TFCA compared to raw observer data.","ai_generated":true,"unit":"wwf-000615#insight-9","date_modified":"2026-10-23"},{"text":"The survey adhered to CITES MIKE Aerial Survey Standards Version 3.0, with a mean flight height of 91.8 m AGL and a mean ground speed of 171.5 km.hr-1.","ai_generated":true,"unit":"wwf-000615#insight-10","date_modified":"2026-10-23"}],"climate_tags":["biodiversity"],"geography_discussed":["South Africa","France"],"frame_perspective":[],"analysis_type":["population survey","spatial analysis","technical report"]}]}