In the current context of the international climate agenda, understanding both the sources of greenhouse gas (GHG) emissions and the mechanisms for their mitigation is a fundamental requirement for low-carbon development strategies. Kazakhstan has pledged to reduce its GHG emissions by 15–25% by 2030, relative to 1990 levels, and to achieve carbon neutrality by 2060. However, there is no unified methodology for comprehensively assessing the national carbon balance, particularly at the regional scale. This study addresses this gap by analyzing GHG emissions and carbon sequestration capacities across Kazakhstan’s regions using a sectoral disaggregation approach and scenario-based modeling aligned with IPCC methods. Emission hotspots were identified in the energy sector (328 MtCO₂-eq), agriculture (118 MtCO₂-eq—primarily from pasturelands), and transport (7 MtCO₂-eq). In contrast, current carbon sinks—mainly forest ecosystems and abandoned pasturelands—account for only 3.97 and 13.9 MtCO₂-eq, respectively. The research evaluates the technical potential for emissions reduction through the best available technologies (BAT), livestock management, partial transition to gas-powered vehicles, and reforestation. A geoengineering scenario combining all measures suggests that Kazakhstan could meet its 2030 climate targets, although full carbon neutrality by 2060 would remain out of reach under current policy trajectories. The Akmola region is examined as a representative case study, demonstrating a possible 35% reduction in net emissions by 2035. This work contributes a regionally nuanced, data-driven framework for integrating ecosystem services into national climate policy and identifies nature-based solutions—especially forest management—as essential components of Kazakhstan’s decarbonization pathway, offering insights for other carbon-intensive economies.

Bentonite materials are widely utilized across various industries due to their unique physicochemical properties. This study presents a qualitative and comparative analysis of mineral substances derived from ben tonite clay deposits in East Kazakhstan. The montmorillonite content in the samples ranges from 75.5% to 88%, with adsorption capacity (determined by the methylene blue method) varying between 119 and 204 mg/g. The pH of the samples lies within the range of 7 to 10. FTIR and XRF analyses confirmed the presence of major components such as silicon and aluminum oxides, while X-ray diffraction identified montmorillonite as the dominant crystalline phase. Textural characterization revealed specific surface areas of 94–104 m²/g, pore volumes of 0.03–0.05 cm³/g, and pore sizes between 0.8 and 1.04 nm. SEM analysis demonstrated a flaky, layered, and porous morphology typical of bentonite. Based on these properties, the bentonite samples exhibit strong potential for industrial use. They are applicable in oil production (as components of drilling fluids) and construction (as insulating and sealing agents). Additionally, their high sorption capacity makes them promising candidates for pharmaceutical applications, particularly as carriers in topical formulations such as ointments and pastes for wound healing and inflammatory skin conditions. Keywords: bentonite clays, montmorillonite, mineral composition, surface area, physico-chemical properties.

Introduction. Anemia comprises heterogeneous disorders that share reduced hemoglobin yet differ by etiology and morphology. Routine hematology analyzers generate rich, time-varying signals that remain underused for differential diagnosis. Aim. To formalize and test mathematical models and rule-based/algorithmic workflows that discriminate major anemia types using routinely available laboratory data, and to outline a tractable pathway for clinical implementation. Materials and Methods. We analyzed routine hemograms on Sysmex XE-2100 and KX-21N with HGB, HCT, RBC, MCV, MCH, MCHC, RDW, PLT, WBC, and reticulocyte indices Ret%, Ret, IRF%, LFR%, MFR%, HFR%, RET-Y, Ret-He. We modeled treatment dynamics in iron-deficiency and vitamin B12–deficiency anemia using a generalized S-function. Fit metrics were not reported in the sources. Algorithmic differential diagnosis covered normocytic and other forms using WHO thresholds, ferritin, and vitamin B12. Results. In the microspherocytosis study 41 patients were examined, 17 men and 24 women, age 23 to 61 years. RDW was 13.6±0.5% in controls and 17.6±3.9% in the main group with p=0.0001. Thickness of microerythrocytes was 2.3±0.2 µm in controls and 2.9±0.2 µm in the main group with p=0.001. Mean erythrocyte thickness was 2.1±0.2 µm and 2.6±0.3 µm with p=0.005. Microcell sphericity index was 2.8±0.2 and 2.2±0.2 with p=0.003. Erythrocyte sphericity index was 3.7±0.3 and 2.3±0.1 with p=0.005. Mean erythrocyte diameter was 7.5±0.2 µm and 6.6±0.2 µm with p=0.005. The share of microcytes was 12.6±6.7% and 68.7±16.9% with p=0.005. Normocytes were 72.9±7.3% and 30.4±16.5% with p=0.005. Macrocytes were 14.5±11.8% and 1.9±1.2% with p=0.005. In the retrospective routine dataset 364 of 400 records were retained, which is 91%, with 11 hematological indicators. Conclusion. A combined modeling-plus-algorithmic framework built on routine laboratory data can structure differential diagnosis of anemia and prioritize confirmatory tests. The approach is implementable on existing analyzers and amenable to software deployment. Key words: anemia, differential diagnosis, normocytic anemia, iron-deficiency, vitamin B12–deficiency, hereditary spherocytosis. Кіріспе. Анемия гемоглобин деңгейінің төмендеуімен сипатталатын, бірақ этиологиясы мен морфологиясы бойынша әртүрлі болып келетін гетерогенді аурулар тобына жатады. Рутинді гематологиялық анализаторлар уақытқа байланысты өзгеретін ауқымды сигналдар береді, алайда олар дифференциалды диагностикада жеткілікті қолданылмайды. Мақсаты. Қолжетімді зертханалық көрсеткіштер негізінде анемияның негізгі түрлерін ажыратуға арналған математикалық модельдер мен ережеге/алгоритмге негізделген әдістерді әзірлеу және клиникалық тәжірибеге енгізудің ықтимал жолын көрсету. Материалдар мен әдістер. Sysmex XE-2100 және KX-21N анализаторларының деректері пайдаланылды: HGB, HCT, RBC, MCV, MCH, MCHC, RDW, PLT, WBC, сондай-ақ ретикулоцит индекстері (Ret%, Ret, IRF%, LFR%, MFR%, HFR%, RET-Y, Ret-He). Темір тапшылығы және В12 витамині тапшылығы анемияларындағы емдеу динамикасы жалпыланған S-функция арқылы модельденді. Бейімделу метрикалары бастапқы дереккөздерде көрсетілмеген. Дифференциалды диагностика алгоритмі нормоцитарлы және басқа түрлерін ДДСҰ шектері, ферритин мен В12 витамині деңгейлеріне сүйене отырып қамтыды. Нәтижелер. Микросфероцитозбен жүргізілген зерттеуде 41 науқас қаралды (17 ер адам, 24 әйел), жасы 23–61 жас аралығында. Бақылау тобында RDW – 13,6±0,5%, ал негізгі топта – 17,6±3,9% (p=0,0001). Микроэритроцит қалыңдығы – тиісінше 2,3±0,2 мкм және 2,9±0,2 мкм (p=0,001). Орташа эритроцит қалыңдығы – 2,1±0,2 мкм және 2,6±0,3 мкм (p=0,005). Микрожасушалардың сферикалық индексі – 2,8±0,2 және 2,2±0,2 (p=0,003). Эритроциттердің сферикалық индексі – 3,7±0,3 және 2,3±0,1 (p=0,005). Орташа эритроцит диаметрі – 7,5±0,2 мкм және 6,6±0,2 мкм (p=0,005). Микроциттердің үлесі – 12,6±6,7% және 68,7±16,9% (p=0,005). Нормоциттер – 72,9±7,3% және 30,4±16,5 % (p=0,005). Макроциттер – 14,5±11,8% және 1,9±1,2% (p=0,005). Ретроспективті қолжетімді деректер жиынтығында 400 жазбаның 364-і (91%) пайдаланылды, соның ішінде 11-і гематологиялық көрсеткішпен болды. Қорытынды. Қолжетімді зертханалық деректерге негізделген модельдеу мен алгоритмдік әдістерді біріктіру анемияның дифференциалды диагностикасын құрылымдауға және растаушы зерттеулерді басымдықпен таңдауға мүмкіндік береді....