Report The Panel agreed on the outline of the 2027 IPCC Methodology Report on Carbon Dioxide Removal Technologies, Carbon Capture, Utilization, and Storage for National Greenhouse Gas Inventories (Additional guidance) at its 63rd Session held in Lima, Peru from 27-30 October 2025 (Decision IPCC-LXIII-6). The report will be a single Methodology Report comprising an Overview Chapter and six volumes consistent with the format of the 2006 IPCC Guidelines for National Greenhouse Gas Inventories. The structure of the Methodology Report is consistent with the 2006 IPCC Guidelines so as to make it easier for inventory compilers to use this Methodology Report with the 2006 IPCC Guidelines. Topics that will be addressed include: Transport, injection and sequestering of CO2 in relation to enhanced oil, gas, and coal-bed methane recovery Production of products containing or derived from captured and/or removed CO2 Carbonation of cement and lime-based structures Soil carbon sinks and related emissions enhanced through biochar and weathering and other elements Coastal wetlands carbon dioxide removal types not in previous IPCC Guidelines as well as additional information on mangroves, tidal marshes and seagrass in coastal waters Durable biomass products Carbon dioxide capture from combustion and process gases Direct air capture Carbon dioxide utilisation Carbon dioxide transport including cross border issues Carbon dioxide injection and storage CO2 removal through direct capture of CO2 from water already processed by inland and coastal facilities; and related elements across the range of categories of the IPCC Guidelines. The national greenhouse gas inventory includes sources and sinks occurring within the territory over which a country has jurisdiction. Over 150 experts are expected to participate in the writing process, which will be completed by 2027. The participants will be selected by the Task Force Bureau taking into account scientific and technical expertise, geographical and gender balance to the extent possible in line with Appendix A to the Principles Governing IPCC Work. The First Lead Authors’ meeting will be held in Rome, Italy, in April 2026. Preparatory Work The decision by the Panel to prepare this Methodology Report was informed by the work of experts at the scoping meeting held in Copenhagen, Denmark, from 14-16 October 2024. Prior to the scoping meeting, an expert meeting was held at Vienna, Austria 1-3 July 2024. These meetings considered Carbon Dioxide Removal (CDR) methods mentioned in the AR6 WGIII Report as a starting point for discussion and noted that several CDR activities have been already covered by the existing IPCC Guidelines. More Information The IPCC Secretary has written to national government focal points inviting nominations of authors by 12 December 2025.

Fast Facts Medicaid programs that cover prescription drugs are generally required to cover drugs that are (1) FDA approved and (2) made by a manufacturer that participates in the Medicaid Drug Rebate Program. 13 Medicaid programs didn’t cover Mifeprex and its generic equivalent, Mifepristone Tablets, 200 mg, when required. These drugs are used for medical abortion. We recommended the Centers for Medicare & Medicaid Services ensure Medicaid programs comply with federal requirements for covering Mifepristone Tablets, 200 mg. We also reiterated our 2019 recommendation on Mifeprex, which hasn’t been implemented. White pills spilling from a pill bottle. Skip to Highlights Highlights What GAO Found Medicaid programs that choose to cover outpatient prescription drugs are required to cover all Food and Drug Administration (FDA) approved drugs for their medically accepted indications when those drugs are made by a manufacturer that participates in the Medicaid Drug Rebate Program (MDRP), except as outlined in federal law. The FDA has approved two drugs—Mifeprex in 2000 and its generic equivalent in 2019, referred to as Mifepristone Tablets, 200 mg—for the medical termination of an intrauterine pregnancy, known as a medical abortion. Danco Laboratories and GenBioPro are the exclusive manufacturers of Mifeprex and Mifepristone Tablets, 200 mg, respectively, and both manufacturers participate in the MDRP. Medicaid programs in all 50 states, the District of Columbia, and Puerto Rico cover prescription drugs and participate in the MDRP. According to officials from the Centers for Medicare & Medicaid Services (CMS)—the federal agency within the Department of Health and Human Services (HHS) responsible for ensuring Medicaid programs’ compliance—none of the MDRP’s statutory exceptions apply to Mifeprex or Mifepristone Tablets, 200 mg. Thus, these 52 Medicaid programs must cover these drugs when prescribed for medical abortion in circumstances eligible for federal funding, such as when the pregnancy is the result of rape or incest. GAO identified gaps in Medicaid programs’ coverage of Mifeprex and Mifepristone Tablets, 200 mg. Officials from 35 of the 49 programs who responded to GAO questions said their programs covered Mifeprex and Mifepristone Tablets, 200 mg for medical abortion, as of December 31, 2024. In contrast, officials from 13 programs told GAO their programs did not cover either drug for medical abortion. An official from the remaining program did not specify the medical indications for which its program covered the drugs. Medicaid Programs’ Coverage of Danco Laboratories’ Mifeprex and GenBioPro’s Mifepristone Tablets, 200 mg, as of December 31, 2024 Note: For more details, see fig. 1 in GAO-25-107911. State officials’ responses to GAO’s questions indicated that some states may not be complying with the MDRP requirements for covering Mifeprex and Mifepristone Tablets, 200 mg. However, CMS has not determined the extent to which states comply with the MDRP requirements for these drugs. CMS officials told GAO they were not aware of the following: Nine programs did not cover Mifeprex and Mifepristone Tablets, 200 mg for any medical indication, as of December 31, 2024; GAO reported four of these programs did not cover Mifeprex in 2019. Mifepristone Tablets, 200 mg was not available at the time of GAO’s 2019 report. Four additional Medicaid programs did not cover either drug when prescribed for medical abortion, as of December 31, 2024. CMS was not aware of these coverage gaps, in part, because it had not implemented GAO’s 2019 recommendation to take actions to ensure Medicaid programs comply with MDRP requirements to cover Mifeprex. CMS also has not taken actions related to the coverage of Mifepristone Tablets, 200 mg, as of August 2025. Without such actions, CMS lacks assurance that Medicaid programs comply with MDRP requirements and Medicaid beneficiaries may lack access to these drugs when appropriate. Why GAO Did This Study GAO was asked to describe Medicaid programs’ coverage of mifepristone. This report examines Medicaid programs’ coverage of Mifeprex and Mifepristone Tablets, 200 mg, among other things. GAO reviewed laws and CMS guidance on the MDRP, and coverage of Mifeprex and Mifepristone Tablets, 200 mg. GAO also sent written questions to officials from the 52 Medicaid programs that participate in the MDRP regarding their coverage of these drugs, and reviewed officials’ responses from the 49 programs that provided GAO information. Recommendations GAO reiterates its 2019 recommendation that CMS take actions to ensure states’ compliance with MDRP requirements to cover Mifeprex. GAO also recommends that CMS determine the extent to which states comply with federal Medicaid requirements regarding coverage of GenBioPro’s Mifepristone Tablets, 200 mg, and take actions, as appropriate, to ensure compliance. In response to the recommendation, HHS noted it is reviewing applicable law and will determine the best course of action to address it moving forward. Recommendations for Executive Action Agency Affected Recommendation Status Centers for Medicare & Medicaid Services The Administrator of CMS should determine the extent to which states comply with federal Medicaid requirements regarding coverage of GenBioPro's Mifepristone Tablets, 200 mg, and take actions, as appropriate, to ensure compliance. (Recommendation 1) Open Actions to satisfy the intent of the recommendation have not been taken or are being planned. When we confirm what actions the agency has taken in response to this recommendation, we will provide updated information. Full Report Full Report (11 pages)

05.12.2025 – The European Scientific Advisory Board on Climate Change, established under the European Climate Law, will continue to be supported in its second term (2026-2030) by Ottmar Edenhofer. The Director of the Potsdam Institute for Climate Impact Research (PIK) has now been appointed by the Management Board of the European Environment Agency in Copenhagen for another four-year term on the Advisory Board, beginning on 24 March 2026. Advising EU policymakers on the path to the declared goal of climate neutrality: PIK Director Ottmar Edenhofer. Photo: PIK/Karkow The Advisory Board gives independent advice and produces reports on EU policies, and their coherence with the Climate Law and the EU’s commitments under the Paris Agreement. It consists of 15 high-level scientific experts covering a wide range of relevant fields. Edenhofer is serving as the Advisory Board’s current Chair during its first term (2022-2026). Highlights during this period have included scientific recommendations for an ambitious EU climate target for 2040, an analysis of the action needed to achieve climate neutrality, and a study on scaling up atmospheric carbon removals. “I am very thankful for the great opportunity to continue supporting EU climate policy in this service role for the next four years,” says Edenhofer, who is also Professor for The Economics and Politics of Climate Change at the Technische Universität Berlin. “The European Union has taken some important steps in recent years towards its declared goal of climate neutrality by 2050. It remains important to make climate policy cost-effective, socially balanced and consistent with the requirements of an internationally competitive economy. As a member of the Advisory Board, I will do my best to provide scientific advice to policymakers on this task.” The composition of the Advisory Board for the next four-year term has now been decided through an open, fair and transparent selection process lasting several months. The decision on who will chair the body in future is not expected until beginning of the second term. The other members of the Advisory Board in the second term are: • Annela Anger-Kraavi – University of Cambridge • Constantinos Cartalis – National and Kapodistrian University of Athens • Suraje Dessai – University of Leeds’ School of Earth, Environment, and Sustainability • Laura Díaz Anadón – University of Cambridge • Vera Eory – Scotland’s Rural College • Lena Kitzing - Technical University of Denmark • Kati Kulovesi – University of Eastern Finland • Lars J. Nilsson – Lund University • Åsa Persson – KTH Royal Institute of Technology’s Climate Action Centre • Keywan Riahi – International Institute for Applied Systems Analysis • Jean-François Soussana – French National Research Institute for Agriculture, Food and the Environment • Giorgio Vacchiano – University of Milan • Detlef van Vuuren – PBL Netherlands Environmental Assessment Agency • Zinta Zommers – University of Toronto

In 2025, the Asia Pacific has steadily built momentum for climate action amid a fraught political backdrop of geopolitical conflict, rising energy demand and high inflation. Already subscribed? Sign in To continue reading, subscribe to Eco‑Business. There's something for everyone. We offer a range of subscription plans. Subscribe now → Access our stories and receive our Insights Weekly newsletter with the free EB Member plan. Unlock unlimited access to our content and archive with EB Circle. Publish your content with EB Premium. Most countries in the region maintained low-carbon ambitions despite anti-climate signals from the United States, with renewable energy capacity set to grow almost twofold by the end of the decade. It is this rapid expansion of clean power that is expected to support cross-border electricity trade in Southeast Asia set to gain more traction in the coming year. Over the past twelve months, the inter of technology and climate has also emerged as a key theme. Asia’s artificial intelligence (AI) sector is poised for robust growth in the coming year, driven by hyperscaler investments in data centres. Eco-Business rounds up five trends that could influence sustainability in Asia in 2026, as the region positions itself in a world reeling from a politicised environmental, social and governance (ESG) backlash that continues to stir markets. 1. Renewable energy boom to fuel APG Asia’s clean energy surge is expected to help advance the long-delayed Asean Power Grid (APG), a project that has remained largely unrealised for nearly three decades but aims to enable cross-border electricity trade within the bloc. The APG regained traction this year when Southeast Asian leaders vowed to make it a top priority to boost interoperability between member states’ electricity grids. Solar power will lead APG’s capacity additions, with 24 gigawatts (GW) potential in Indonesia’s Riau islands and Malaysia’s Sarawak. The Philippines aims to utilise offshore wind to contribute to the regional grid, aiming to tap 50 GW of the resource by 2050. Elsewhere in Asia, new capacity additions are projected to expand by 670 GW from 2025 to 2030, with solar photovoltaic sources from India accounting for nearly three-quarters of the total. But renewable energy sources alone will not be able to support a regional grid, warned Kitty Bu, vice president for Southeast Asia at Global Energy Alliance for People and Planet (GEAPP). Southeast Asia still relies heavily on fossil fuels for up to 80 per cent of its primary energy supply, with coal and natural gas dominating electricity generation. For large-scale regeneration to support the APG, battery storage is a critical technology, Bu told Eco-Business. A battery energy storage system (BESS) in a 500-megawatt (MW) solar farm in Maharashtra, India. Image: Global Energy Alliance But the deployment of battery energy storage systems (BESS) in Asia faces key barriers such as high costs and technical gaps. Bu said GEAPP is working with the Asian Development Bank (ADB) to pool philanthropic and development finance to de-risk BESS projects. They are also providing technical assistance, which includes grid integration studies, procurement guidelines and AI-optimised business models for scalable pilots. “Without battery storage, the intermittent nature of the renewables will become a constraint, rather than an enabler in this Asean power grid,” she said. “This growth only translates into grid reliability. If we solve the storage challenge, then we solve the grid challenge.” 2. De-risking facility could boost geothermal Geothermal energy is expected to be another major resource in ramping up the regional grid, said Smile Yu, lawyer for resources, energy and mobility for Japan and Southeast Asia at environmental law nonprofit ClientEarth. The trend is especially visible in Indonesia and the Philippines, which hold some of the world’s largest reserves and can offer steady, renewable power into a regional grid, she told Eco-Business. Indonesia boasts of the world’s largest resource potential at 24 to 29 GW, while the US and Philippines have 30 to 39 GW and 4 GW, respectively, though much remains untapped. A 112.5 MW geothermal plant in Negros Oriental, Philippines, managed by Energy Development Corporation. Image: EDC The Philippines could address its untapped potential through the US$170 million geothermal de-risking facility launched in December, which seeks to accelerate early-stage geothermal exploration. Through the Philippine Geothermal Resource De-Risking Facility (PGRDF), companies can tap into the fund, with the government bearing at least half of the cost for geothermal exploration or drilling through a conditionally repayable grant. The facility was funded through a sovereign loan to the Philippine government from the ADB. “There is growing interest in how improvements in drilling techniques could make geothermal development more efficient and potentially expand the types of sites that can be explored,” said Yu. “Although geothermal is seen as a mature technology, the advancement of new drilling technologies are prompting renewed discussion about geothermal’s long-term role not just in the region but globally as well.” 3. Stricter measures to curb impacts of data centre boom As artificial intelligence (AI) adoption grows, so will the pressure to curb its environmental impact, said Andrew Young, founder and managing director of Singapore-based Envirosolutions and Consulting. “Although power and water are significant hurdles to the growth of hyperscalers, these can be overcome. With the right [regulatory] encouragement, a cleaner, more sustainable industry might arise,” he told Eco-Business. Malaysia started introducing strict water use regulations this year, driven by water shortages related to new data centre demand. The country’s regulators have increasingly conditioned approvals on sustainable design and resource use, with some projects in Johor reportedly rejected when operators failed to show credible plans to reduce power and water footprints. Malaysia has been leading the region’s data centre boom as tech giants like Microsoft and Google have invested about US$2 billion each in it, including a US$236 million deal awarded to local construction firm Gamuda for further expansion. Together with Vietnam, Malaysia has also introduced data‑sovereignty and localisation rules that drive domestic build‑out, prompting authorities to stress that new facilities must meet modern energy‑efficiency and resilience standards. Thailand’s regulators have also started to combine cyber and data rules with infrastructure expectations, signalling that future data-centre growth must align with power‑system stability and national climate targets, even as detailed green metrics are still evolving. Singapore was the first to put a moratorium on data centre development in 2019, primarily due to its demand for power and water outstripping what could be sustainably supplied at the time. Southeast Asia is emerging as a hotspot where large AI players are investing about US$2.3 billion in cloud services and data centres. In the wider region, hyperscaler investment has been projected to rise sharply in 2026, reaching US$552 billion, according to research by Goldman Sachs. Tech giants have been fueling AI infrastructure growth in Asia, positioning the region for major expansion in the coming year. Google is investing US$15 billion in a data hub in India, while Taiwan’s Foxconn is expected to spend up to US$1.37 billion to procure equipment for an AI compute cluster and a supercomputing centre. 4. West–China rivalry intensifies over global critical minerals At a meeting in the Canadian city of Toronto in October, the Group of Seven’s (G7) energy ministers agreed to establish a critical minerals production alliance, countering China’s overwhelming global share of minerals like nickel, a key mineral used for electric vehicle battery manufacturing. G7, which consists of Canada, France, Germany, Italy, Japan, the United Kingdom and the United States, agreed to channel up to US$6.4 billion of critical minerals projects, including 26 new investments, partnerships and research initiatives. G7 leaders have been focused on improving the traceability of minerals and stabilising these supply chains, primarily following Russia’s war with Ukraine and China’s efforts to impose export controls on minerals and rare earth elements. Canada’s energy minister Tim Hodgson has reportedly said that the alliance is intended to “secure transparent, democratic, and sustainable supply chains” by drawing in private capital from across the group. The China Pavilion at COP30. The global clean-energy transition hinges on critical minerals, yet opaque reporting, weak community engagement and uneven ESG compliance remain persistent risks for Chinese-led overseas mining projects. Image: UNclimatechange, CC BY-SA 3.0, via Flickr Tae‑Yoon Kim, acting head of the critical minerals division of the International Energy Agency, described the gathering as a “major opportunity … to start shifting market power,” underscoring worries that China’s state‑directed system can manipulate prices, build strategic stockpiles and influence global material flows. However, Abigail Hunter, executive director of Washington-based research institute Center for Critical Minerals Strategy, warned in a rare earths platform that true progress requires traceability and transparency to “box out” opaque Chinese entities. The platform noted: “Whether the G7 can align around strict traceability rules remains uncertain. Without them, any alliance may look more symbolic than structural. The market’s imbalance rooted in decades of Chinese industrial planning won’t be reversed by declarations alone.” 5. Uneven progress in ESG reporting timelines Southeast Asia’s ESG reporting landscape highlights stark differences in commitment and enforcement as the region enters the new year. This comes amid a global pushback against sustainability reporting requirements. Earlier this year, the European Union moved to ease its Corporate Sustainability Reporting Directive (CSRD) requirements and later, its deforestation-free regulations (EUDR). Singapore, a regional sustainability leader, said it would delay International Sustainability Standards Board (ISSB)-aligned climate reporting requirements for smaller listed firms by up to five years. Singapore’s revised rules now put the city-state, which was among the first in Asia to propose mandatory ISSB-aligned reporting for companies in 2023, behind Malaysia in mandatory sustainability reporting requirements. Listed and large non-listed firms in the neighbouring Southeast Asian nation are expected to start ISSB-aligned reporting, including on Scope 3 emissions, by 2027. It has vowed aggressive action against non-compliant firms under its updated Bursa Malaysia rules, enforcing phased reporting from 2024 to 2026 with penalties for laggards. The Philippines also announced early this year that it will begin implementing mandatory sustainability reporting by 2026, as it aligns itself with the ISSB regulations under a “transitional or phased approach” to mandate sustainability reporting from companies, allowing them to smoothly adjust to new reporting requirements. Related to this story Topics Carbon & Climate Corporate Responsibility Energy Policy & Finance Water Regions Global Southeast Asia Tags ASEAN batteries clean energy data centres emissions energy efficiency ESG fossil fuels geothermal regulation renewable energy reporting solar water security wind energy energy transition sustainable finance Scope 3 artificial intelligence SDGs 7. Energy 17. Partnerships

Abstract Flash floods endanger communities and ecosystems in rugged regions, but precise prediction is difficult due to environmental complexity. This study evaluates six machine learning algorithms for flash flood mapping in Iran’s Dez Basin, a region growing more vulnerable to climate extremes. We developed an integrated geospatial database incorporating 32 climatic, anthropogenic, and physiographic parameters, validated through extensive field surveys documenting historical flood events. The dataset (70% training, 30% validation) was analyzed using: (1) H2O Deep Learning framework, (2) Random Forest (RF), and (3) four boosting methods (AdaBoost, XGBoost, LightGBM, CatBoost). The RF model achieved exceptional predictive performance (AUC = 0.89, accuracy = 95%), outperforming other techniques by 6–12% in classification metrics. Sensitivity analysis identified precipitation intensity (β = 0.34, p  0.85) to enhance community resilience. Data availability The data are available upon request from the corresponding author. References Holton, J. R., Pyle, J. A. & Curry, J. A. Encyclopedia of Atmospheric Sciences. Second Edition. (Academic Press, 2015). Georgakakos, K. P. Analytical results for operational flash flood guidance. J. Hydrol. 317 (1–2), 81–103. https://doi.org/10.1016/j.jhydrol.2005.05.009 (2006). Google Scholar Norbiato, D., Borga, M., Esposti, D., Gaume, S., Anquetin, E. & S Flash flood warning based on rainfall depth-duration thresholds and soil moisture conditions: an assessment for gauged and ungauged basins. J. Hydrol. 362 (3–4), 274–290. https://doi.org/10.1016/j.jhydrol.2008.08.023 (2008). Google Scholar Costache, R. et al. Flash-flood hazard using deep learning based on H2O R package and fuzzy-multicriteria decision-making analysis. J. Hydrol. 609, 127747. https://doi.org/10.1016/j.jhydrol.2022.127747 (2022). Google Scholar Youssef, A. M., Pradhan, B. & Sefry, S. A. Flash flood susceptibility assessment in Jeddah City (Kingdom of Saudi Arabia) using bivariate and multivariate statistical models. Environ. Earth Sci. 75 (1), 12. https://doi.org/10.1007/s12665-015-4830-8 (2016). Google Scholar Habibi, A., Delavar, M. R., Sadeghian, M. S. & Nazari, B. Flood susceptibility mapping and assessment using regularized Random forest and naïve bayes algorithms. ISPRS Annals Photogrammetry Remote Sens. Spat. Inform. Sci., 10, 241–248. https://doi.org/10.5194/isprs-annals-X-4-W1-2022-241-2023 (2023). Ren, H. et al. Flood susceptibility assessment with random sampling strategy in ensemble learning (RF and XGBoost). Remote Sens. 16 (2), 320. https://doi.org/10.3390/rs16020320 (2024). Google Scholar SELLAMI, E. M. & Rhinane, H. Google Earth engine and machine learning for flash flood exposure Mapping—Case study: Tetouan, Morocco. Geosciences 14 (6), 152. https://doi.org/10.3390/geosciences14060152 (2024). Google Scholar Elghouat, A. et al. Integrated approaches for flash flood susceptibility mapping: Spatial modeling and comparative analysis of statistical and machine learning models. A case study of the Rheraya watershed, Morocco. J. Water Clim. Change. 15 (8), 3624–3646. https://doi.org/10.2166/wcc.2024.726 (2024). Google Scholar Al-Kindi, K. M. & Alabri, Z. Investigating the role of the key conditioning factors in flood susceptibility mapping through machine learning approaches. Earth Syst. Environ. 8 (1), 63–81. https://doi.org/10.1007/s41748-023-00369-7 (2024). Google Scholar Wahba, M. et al. Forecasting of flash flood susceptibility mapping using Random forest regression model and geographic information systems. Heliyon https://doi.org/10.1016/j.heliyon.2024.e33982 (2024). Google Scholar Rutledge, D. N. & Barros, A. S. Durbin–Watson statistic as a morphological estimator of information content. Anal. Chim. Acta. 454 (2), 277–295. https://doi.org/10.1016/S0003-2670(01)01555-0 (2002). Google Scholar Salas, J. D. Analysis and modeling of hydrological time series. In: Maidment DR, editor. Handbook of hydrology. (McGraw-Hill, 1993). Mohammadzadeh, A. & Massoudzadegan, S. Forecasting daily volatility and value at risk with high frequency data. Dev. Transformation Manage. Q. 8 (27), 63–74 (2015). Google Scholar Choubin, B. et al. Regional groundwater potential analysis using classification and regression trees. In Spatial modeling in GIS and R for earth and environmental sciences. 485–498 https://doi.org/10.1016/B978-0-12-815226-3.00022-3 (2019). Monfared, B., Najafabadi, M. & Nafarzadegan, A. R., Flood zoning and identification of effective factors in flood occurrence: A case study of the urban watershed of Bastak. Master’s thesis, Department of Desert Management and Control. (Hormozgan University, 2021). Eisfelder, C. et al. Cropland and crop type classification with Sentinel-1 and Sentinel-2 time series using Google Earth engine for agricultural monitoring in Ethiopia. Remote Sens. 16 (5), 866. https://doi.org/10.3390/rs16050866 (2024). Google Scholar Rasti, S., Mahdavifardnh, M., Shaykh Ghaderi, H., Nasiri, A. & Taktaz, N. Z. Improving classification accuracy by combining multi-season images of Sentinel 1 and 2 in order to prepare a landuse map in the cloud space of Google Earth engine (case study: Guilan province). Geogr. Hum. Relations. 5 (3), 357–373. https://doi.org/10.22034/gahr.2022.336692.1696 (2022). Google Scholar Yoothong, K., Moncharoen, L., Vijarnson, P. & Eswaran, H. Clay mineralogy of Thai soils. Appl. Clay Sci. 11 (5–6), 357–371. https://doi.org/10.1016/S0169-1317(96)00033-6 (1997). Google Scholar Kariuki, P. C., Woldai, T. & Van Der Meer, F. Effectiveness of spectroscopy in identification of swelling indicator clay minerals. Int. J. Remote Sens. 25 (2), 455–469. https://doi.org/10.1080/0143116031000084314 (2004). Google Scholar Stenberg, B., Rossel, R. A. V., Mouazen, A. M. & Wetterlind, J. Visible and near infrared spectroscopy in soil science. Adv. Agron. 107, 163–215. https://doi.org/10.1016/S0065-2113(10)07005-7 (2010). Google Scholar Danoedoro, P. & Zukhrufiyati, A. Integrating spectral indices and geostatistics based on Landsat-8 imagery for surface clay content mapping in Gunung Kidul area, Yogyakarta, Indonesia. In: Proceedings of the 36th Asian Conference on Remote Sensing; ; Asia Quezon, Metro Manila, Philippines. (2015). Sabins, F. F. Remote sensing for mineral exploration. Ore Geol. Rev. 14, 157–183. https://doi.org/10.1016/S0169-1368(99)00007-4 (1999). Google Scholar Khan, N. M., Rastoskuev, V. V., Sato, Y. & Shiozawa, S. Assessment of hydrosaline land degradation by using a simple approach of remote sensing indicators. Agric. Water Manag. 77, 96–109. https://doi.org/10.1016/j.agwat.2004.09.038 (2005). Google Scholar Asfaw, E., Suryabhagavan, K. V. & Argaw, M. Soil salinity modeling and mapping using remote sensing and GIS: the case of Wonji sugar cane irrigation farm, Ethiopia. J. Saudi Soc. Agric. Sci. 17, 250–258. https://doi.org/10.1016/j.jssas.2016.05.003 (2018). Google Scholar Caloz, R., Abednego, B. & Collet, C. The Normalisation of a Soil Brightness Index for the Study of Changes in Soil Conditions. In: Proceedings of the 4th International Colloquium on Spectral Signatures of Objects in Remote Sensing. 18–22 (1988). Bousbih, S. et al. Soil texture Estimation using radar and optical data from Sentinel-1 and Sentinel-2. Remote Sens. 11 (13), 1520. https://doi.org/10.3390/rs11131520 (2019). Google Scholar Parvaresh, A., Mahdavi, R., Melkian, A., Ismailpour, Y. & Halisaz, A. Prioritizing the flood potential of sub-watersheds in Sokhon. Hormozgan using fuzzy TOPSIS and ELECTRE methods. Doctoral dissertation in Watershed Sciences and Engineering. (Hormozgan University, 2018). Moore, I. D. & Grayson, R. B. Landson. Digital terrain modeling: A review of hydrological, Geomorphological and biological application. Modelling Hydrology. 5, 3–30. https://doi.org/10.1002/hyp.3360050103 (1991). Google Scholar Moore, I. D. & Burch, G. J. Sediment transport capacity of sheet and Rill flow: application of unit stream power theory. Water Resour. Res. 22 (8), 1350–1360. https://doi.org/10.1029/WR022i008p01350 (1986). Google Scholar Nookaratnam, K., Srivastava, Y. K., Venkateswarao, V., Amminedu, E. & Murthy, K. S. R. Check dam positioning by prioritization of micro watersheds using SYI model and morphometric analysis remote sensing and GIS perspective. J. Indian Soc. Remote Sens. 33 (1), 25–28. https://doi.org/10.1007/BF02989988 (2005). Google Scholar Schumn, S. A. Evolution of drainage basins and slopes in bund land of Peth Amboy, new Jersey. Bull. Geol. Soc. Am. 67, 597–646 (1956). Google Scholar Horton, R. E. Erosional development of streams and their drainage basins; hydrophysical approach to quantitative morphology. Geol. Soc. Am. Bull. 56, 275–370. https://doi.org/10.1130/0016-7606%281945%2956%5B275%3AEDOSAT%5C2.0.CO%3B2 (1945). Miller, V. C. A Quantitative Geomorphic Study of Drainage Basin Characteristics on the Clinch Mountain Area, Virgina and Tennessee, Proj. 389–402 (Columbia University, 1953). Strahler, A. N. Quantitative analysis of watershed geomorphology. Eos Trans. Am. Geophys. Union. 38, 913–920 (1957). Google Scholar Strahler, A. N. & Part, I. I. Quantitative geomorphology of drainage basins and channel networks. In Handbook of Applied Hydrology 4–39 (McGraw-Hill, 1964). Google Scholar Schumm, S. A. Evolution of drainage systems and slopes in badlands at Perth Amboy, New Jersey. Geol. Soc. Am. Bull. 67, 597–646. https://doi.org/10.1130/0016-7606%281956%2967%5B597%3AEODSAS%5C2.0.CO%3B2 (1956). LeCun, Y. & Yoshua, B. Deep learning. Nature 521(7553), 436–444. https://doi.org/10.1038/nature14539 (2015). Google Scholar Schmidhuber, J. Deep Learning in Neural Network: An Overview. Neural Networks 61, 85–117. https://doi.org/10.1016/j.neunet.2014.09.003 (2015). Google Scholar Choi, J. et al. An optimal boosting algorithm based on nonlinear conjugate gradient method. J. Korean Soc. Industr. Appl. Mathemat. 22(1), 1–13 (2018). Google Scholar Divakar, K. & Chitharanjan, K. Performance evaluation of credit card fraud transactions using boosting algorithms. Int. J. Electron. Commun. Comput. Eng. IJECCE. 10 (6), 262–270 (2019). Google Scholar Freund, Y. & Schapire, R. E. A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55 (1), 119–139. https://doi.org/10.1006/jcss.1997.1504 (1997). Google Scholar Iban, M. C. & Bilgilioglu, S. S. Snow avalanche susceptibility mapping using novel tree-based machine learning algorithms (XGBoost, NGBoost, and LightGBM) with eXplainable artificial intelligence (XAI) approach. Stoch. Env. Res. Risk Assess. 37 (6), 2243–2270. https://doi.org/10.1007/s00477-023-02392-6 (2023). Google Scholar Friedman, J. H. Greedy function approximation: a gradient boosting machine. Ann. Stat. https://doi.org/10.1214/aos/1013203451 (2001). Google Scholar Du, J., Fang, J., Xu, W. & Shi, P. alysis of dry/wet conditions using the standardized precipitation index and its potential usefulness for drought/flood monitoring in Hunan Province, China. Stochastic Environ. Res. Risk Assess. 27, 377–387. https://doi.org/10.1007/s00477-012-0589-6 (2013). Google Scholar Hajizadeh, H., Farhang, M. & Vafaie Sadr, A. Searching for cosmic strings in Planck data using image processing tools and machine learning. Master’s thesis in Physics. (Shahid Beheshti University, 2020). Chen, T. & Guestrin, C. Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining. 785–794 https://doi.org/10.1145/2939672.2939785 (2016). Brownlee, J. Imbalanced Classification with Python: Better metrics, Balance Skewed classes, cost-sensitive Learning. (Machine Learning Mastery, 2020). Truong, V. H., Papazafeiropoulos, G., Vu, Q. V., Pham, V. T. & Kong, Z. Predicting the patch load resistance of stiffened plate girders using machine learning algorithms. Ocean Eng. 240, 109886. https://doi.org/10.1016/j.oceaneng.2021.109886 (2021). Google Scholar Liang, Y. et al. Product marketing prediction based on XGboost and LightGBM algorithm, In: 2nd International Conference on Artificial Intelligence and Pattern Recognition. 150–153 https://doi.org/10.1145/3357254.3357290 (2019). Ke, G. et al. Lightgbm: A highly efficient gradient boosting decision tree. Adv. Neural. Inf. Process. Syst., 30. (2017). Dorogush, A. V., Ershov, V. & Gulin, A. CatBoost: gradient boosting with categorical features support. ArXiv Preprint. https://doi.org/10.48550/arXiv.1810.11363 (2018). arXiv:1810.11363. Google Scholar Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V. & Gulin, A. CatBoost: unbiased boosting with categorical features. Adv. Neural. Inf. Process. Syst. 31. (2018). Saber, M. et al. Enhancing flood risk assessment through integration of ensemble learning approaches and physical-based hydrological modeling. Geomatics Nat. Hazards Risk. 14 (1), 2203798. https://doi.org/10.1080/19475705.2023.2203798 (2023). Google Scholar Mahdavi, M. Applied hydrology 8th edn, Vol. 2, 437 (Tehran University, 2013). Google Scholar Talikhoshk, S., Mohseni Saravi, M., Vafakhah, M. & Khalighi Sigaroodi, S. Comparison of neuro-fuzzy and SCS methods in prioritizing sub-watersheds for watershed management actions: A case study of the Talghan watershed. Scientific-Research J. Rangel. Watershed Manage. 68 (2), 213–225. https://doi.org/10.22059/jrwm.2015.54922 (2015). Google Scholar Hosseini, Y. Comparison of SCS unit hydrograph and uniform methods in estimating the maximum flood discharge of the Amoughin basin. Hydrogeomorphology 21 (6), 87–107 (2019). Google Scholar Esfandiari, F., Pourganji, Z., Mostafazadeh, R. & Aghaei, M. Comparison of methods for converting effective precipitation to surface runoff in simulating flood hydrographs in the Naneh Karan basin, ardabil Province. Hydrogeomorphology 9 (32), 63–86. https://doi.org/10.22034/hyd.2022.50000.1624 (2022). Google Scholar Soleimani, K., Shokrian, F., Abdoli, S. & Sabri, E. Prioritizing flood risk potential in the Talhar watershed using. Geographic Inform. Syst. Ecohydrology 8(3), 749–762. https://doi.org/10.22059/ije.2021.324244.1509 (2021). Google Scholar Haghizadeh, A., Mohammadlou, M. & Noori, F. Simulation of rainfall-runoff processes using artificial neural networks, adaptive neuro-fuzzy systems, and multivariate regression: A case study of the Khorramabad watershed. Hydrogeomorphology 2 (2), 233–243. https://doi.org/10.22059/ije.2015.56243 (2015). Google Scholar Zema, D. A., Parhizkar, M., Plaza-Alvarez, P. A., Xu, X. & Lucas-Borja, M. E. Using random forest and multiple-regression models to predict changes in surface runoff and soil erosion after prescribed fire. Model. Earth Syst. Environ. 10 (1), 1215–1228. https://doi.org/10.1007/s40808-023-01838-8 (2024). Google Scholar Hasnaoui, Y. et al. Enhanced machine learning models development for flash flood mapping using Geospatial data. Euro-Mediterranean J. Environ. Integr. 9 (3), 1087–1107 (2024). Google Scholar Xu, K., Han, Z., Xu, H. & Bin, L. Rapid prediction model for urban floods based on a light gradient boosting machine approach and Hydrological–Hydraulic model. Int. J. Disaster Risk Sci. 14 (1), 79–97. https://doi.org/10.1007/s13753-023-00465-2 (2023). Google Scholar Hasnaoui, Y. et al. Integrated Remote Sensing and Deep Learning Models for Flash Flood Detection Based on Spatio-temporal Land Use and Cover Changes in the Mediterranean Region 1–23 (Environmental modeling & assessment, 2025). Abedi, R., Costache, R., Shafizadeh-Moghadam, H. & Pham, Q. B. Flash-flood susceptibility mapping based on XGBoost, random forest and boosted regression trees. Geocarto Int. 37 (19), 5479–5496. https://doi.org/10.1080/10106049.2021.1920636 (2022). Google Scholar Janizadeh, S., Vafakhah, M., Kapelan, Z. & Mobarghaee Dinan, N. Hybrid XGboost model with various bayesian hyperparameter optimization algorithms for flood hazard susceptibility modeling. Geocarto Int. 37 (25), 8273–8292. https://doi.org/10.1080/10106049.2021.1996641 (2022). Google Scholar Vafakhah, M., Nasiri Khiavi, A., Janizadeh, S. & Ganjkhanlo, H. Evaluating different machine learning algorithms for snow water equivalent prediction. Earth Sci. Inf. 15 (4), 2431–2445. https://doi.org/10.1007/s12145-022-00846-z (2022). Google Scholar Moharrami, M., Attarchi, S., Gloaguen, R. & Alavipanah, S. K. Integration of Sentinel-1 and Sentinel-2 data for ground truth sample migration for multi-temporal land cover mapping. Remote Sens. ; 16(9):1566. https://doi.org/10.3390/rs16091566 (2024). Mullissa, A. et al. LUCA: A Sentinel-1 SAR-Based global forest landuse change alert. Remote Sens. 16 (12), 2151. https://doi.org/10.3390/rs16122151 (2024). Google Scholar Download references Author information Authors and Affiliations Department of Watershed Management Engineering, Faculty of Natural Resources, Lorestan University, Khorramabad, Iran Hafez Mirzapour, Ali Haghizadeh & Mahdi Soleimani Motlagh Authors Hafez Mirzapour View author publications Search author on:PubMed Google Scholar Ali Haghizadeh View author publications Search author on:PubMed Google Scholar Mahdi Soleimani Motlagh View author publications Search author on:PubMed Google Scholar Contributions AH, HM; Methodology: AH, HM; Formal analysis and investigation: AH, HM, MS; Writing—original draft preparation: AH, HM; Writing— review and editing: AH, HM, MS; Supervision: AH. All authors read and approved the final manuscript. Corresponding author Correspondence to Ali Haghizadeh. Ethics declarations Competing interests The authors declare no competing interests. Additional information Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Rights and permissions Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/. Reprints and permissions About this article Cite this article Mirzapour, H., Haghizadeh, A. & Motlagh, M.S. Evaluating machine learning efficiency and accuracy for real time flash flood mapping. Sci Rep (2025). https://doi.org/10.1038/s41598-025-34037-9 Download citation Received: 06 August 2025 Accepted: 24 December 2025 Published: 30 December 2025 DOI: https://doi.org/10.1038/s41598-025-34037-9 Share this article Anyone you share the following link with will be able to read this content:Get shareable link Sorry, a shareable link is not currently available for this article. Copy shareable link to clipboard Provided by the Springer Nature SharedIt content-sharing initiative Keywords Flash flood susceptibility Machine learning comparison Geospatial modeling Climate adaptation Dez Basin Subjects Climate sciences Environmental sciences Hydrology Natural hazards

A new method to capture carbon dioxide from the air has been developed at the University of Helsinki's chemistry department. The method developed by Postdoctoral Researcher Zahra Eshaghi Gorji is based on a compound of superbase and alcohol. Tests done in professor Timo Repo’s group show that the compound appears promising: one gram of the compound can absorb 156 milligrams of carbon dioxide directly from untreated ambient air. However, the compound does not react with nitrogen, oxygen or other atmospheric gases. Capasity clearly outperforms the CO2 capture methods currently in use. The CO2 captured by the compound can be released by heating the compound at 70 °C in 30 minutes. Clean CO2 is recovered and can be recycled. The ease of releasing CO2 is the key advantage of the new compound. In current compounds, releasing CO2 typically requires heat above 900 degrees Celsius. – In addition, the compound can be used multiple times: the compound retained 75 percent of its original capacity after 50 cycles, and 50 percent after 100 cycles. Non-toxic and cost-effective The new compound was discovered by experimenting with a number of bases in different compounds, says Eshagi Gorji. The experiments lasted more than a year in total. The most promising base proved to be 1,5,7-triazabicyclo [4.3.0] non-6-ene (TBN), developed at in the professor Ilkka Kilpeläinen’s group, which was combined with benzyl alcohol to produce the final compound. – None of the components is expensive to produce, Eshaghi Gorji points out. In addition, the fluid is non-toxic. The compound will now be tested in pilot plants at a near-industrial scale, rather than in grams. A solid version of the liquid compound must be made for this purpose. – The idea is to bind the compound to compounds such as silica and graphene oxide, which promotes the interaction with carbon dioxide. Link to article https://pubs.acs.org/doi/10.1021/acs.est.5c13908 Journal Environmental Science & Technology DOI 10.1021/acs.est.5c13908 Method of Research Experimental study Subject of Research Not applicable Article Title https://pubs.acs.org/doi/10.1021/acs.est.5c13908 Article Publication Date 4-Dec-2025

Conventional approaches to policy design often neglect the plasticity of citizens’ beliefs and values upon which policy effectiveness and political sustainability depend. A consequence, by way of illustration, is that environmental policies may crowd out pre-existing green values. Our representative survey of 3,306 Germans finds that enforced restrictions to promote carbon-neutral lifestyles would trigger strong negative responses because they ‘restrict freedom’. This is true even among those who would adopt green lifestyles when voluntary, thus possibly undermining support for green political movements. These results combined with the long-term political consequences of the polarizing reactions to Covid mandates motivate a new approach to climate policy design. We set aside the conventional economic model assuming self-interested citizens, in which there could be no green values to crowd out. Instead, we propose a dynamic approach recognizing that (1) to succeed, essential policies including bans, carbon taxes and the promotion of new technologies must be both implementable and politically sustainable, entailing (2) a critical role for citizens’ green values, which (3) may be either diminished or cultivated, depending on policy design.