Environmental and Earth Sciences

Abstract: The Challawa and Jakara rivers of Kano State, Nigeria, receive complex multi-class toxic loads from over 300 tannery, textile, and pharmaceutical industries. Conventional physicochemical monitoring is insufficient to capture the cumulative ecological impact of co-occurring contaminants at the regulatory scale required for Sustainable Development Goal (SDG) 6.3 compliance. We applied a dual metagenomic framework; 16S rRNA V3–V4 amplicon sequencing and shotgun metagenomics to characterize microbial community structure, functional gene repertoires, metal resistance genes (MRGs), and antibiotic resistance genes (ARGs) across three hydrological zones of the corridor: upstream reference (Site-R), industrial confluence (Site-I), and downstream recovery (Site-D). Amplicon sequencing (DADA2; 1847 ASVs) revealed significant community restructuring along the contamination gradient; Shannon diversity declined from H′ = 4.2 ± 0.3 (Site-R) to H′ = 2.8 ± 0.2 (Site-I) (PERMANOVA R2 = 0.68, p = 0.001). Metal-tolerant Cupriavidus metallidurans (6.1%), Pseudomonas spp. (14.2%), and Stenotrophomonas maltophilia (8.7%) were selectively enriched at Site-I, while obligate nitrifiers (Nitrosomonadaceae) were suppressed to 0.9% (p < 0.001). Shotgun metagenomics (DESeq2) identified the czc efflux operon and mer volatilisation operon as 5.4-fold and 4.9-fold enriched, respectively, and strongly correlated with cadmium (Spearman ρ = 0.87) and mercury (ρ = 0.83) concentrations. Metal–antibiotic co-selection was confirmed by a partial Mantel test (ρ = 0.74, p = 0.003), with 47 ARG families at Site-I versus 12 at Site-R. A Random Forest classifier trained on normalized functional gene profiles achieved 91.4 ± 2.1% accuracy (AUC = 0.96), comparable to a full physicochemical panel. This metagenomic biosensor framework provides a scalable, cost-effective, and ecologically meaningful tool for SDG 6.3 compliance monitoring in resource-limited regulatory environments.

Abstract: Post-harvest losses remain a significant challenge in Nigeria's agricultural sector, worsening food insecurity and economic instability. This study examines the impact of climatic stressors on PHL, identifying key climatic drivers and assessing their projected influence under future climate scenarios (SSP5-8.5). Using a combination of spatial analysis, Partial Least Squares Discriminant Analysis (PLS-DA), and Random Forest (RF) modelling, the study quantifies the relationship between temperature, precipitation, humidity, and post-harvest losses across Nigeria's six geopolitical regions. Findings reveal that temperature-related variables, such as maximum temperature (Txx), mean temperature (Tas), and heat days exceeding critical thresholds (HD35, HD40), significantly increase PHL, particularly in the northern regions. Conversely, precipitation (Pr) and relative humidity (Hurs) exhibit mixed effects, reducing losses in arid areas but exacerbating spoilage risks in humid zones. Economic implications are severe, with the North Central, North East, and North West regions accounting for over 80% of total financial losses. Additionally, the COVID-19 pandemic contributed to a 22.55% increase in PHL, highlighting vulnerabilities within Nigeria's food supply chain. Future projections under the SSP5-8.5 climate scenario indicate a continuous rise in losses, surpassing 250,000 tons by 2050. In response, climate-smart agriculture (CSA) is explored as a viable mitigation strategy. The adoption of heat- and drought-tolerant crop varieties, improved post-harvest storage technologies, integrated pest management, and climate-smart advisory services are proposed to enhance resilience against climate-induced losses. This study posits the necessity of integrating climatic variables in the quantification of PHL and also climate-responsive strategies into post-harvest loss mitigation frameworks in order to safeguard food security and agricultural sustainability in Nigeria.

Abstract: Agrivoltaics (APV) systems, integrating solar energy generation with agriculture, offer a promising solution for optimizing land use facing a rising energy demand and climate change concerns. However, the impact of APV induced shading on orchards micrometeorology and physiology is not fully understood. This study investigated the effects of simulated APV shading on sensible heat flux, temperature, humidity, wind, CO₂ flux, and evapotranspiration (ET) in deciduous plum and nectarine orchards in northern Israel. Using the eddy covariance (EC) method, we measured CO₂ ​ and water vapor fluxes in adjacent shaded and unshaded (referred to as ‘paneled’ and ‘sunlit’) sections. Principal component analysis (PCA) and linear regression were employed to analyze the relationships between meteorological variables and the measured fluxes. Results showed significantly higher rates of CO₂ ​ flux (absorption) and ET in the paneled sections compared to sunlit sections, particularly during summer peak radiation hours. These findings suggest that partial shading moderates environmental stress (excessive heat, high vapor pressure deficit), improving stomatal function, enhancing photosynthesis, and potentially promoting water use efficiency. This research integrates the EC method with APV system analyses in orchards, providing novel insights into the dynamic interactions under shading and highlighting the potential of APV to enhance agricultural sustainability in semi-arid climates.

Abstract: Universities possess a unique capacity to influence a more sustainable future through both educational content and operational practices. This study examines how students at Cypriot universities experience, perceive, and engage with environmental sustainability and energy efficiency on their campuses. To gain insight into student perspectives, a survey was conducted with 149 students from five public and private higher education institutions, assessing their awareness of and attitudes toward campus sustainability and energy-saving initiatives. Our findings reveal that while students care deeply about environmental issues and support the idea of greener campuses, many are not fully aware of the sustainability efforts, energy management systems, or environmental strategies that their universities have already put in place. Limited communication and the low visibility of these initiatives seem to make it harder for students to get involved or participate meaningfully. Based on the survey, it is evidenced that universities can foster a stronger culture of sustainability by making their efforts more visible, improving communication, and actively involving students in projects around campus. Integrating sustainability into courses, encouraging hands-on participation, and introducing practical tools like real-time energy monitoring could help students develop more environmentally responsible habits.

Abstract: This study analyzes the changes in land use and land cover over time in the Porto Nacional micro-region, situated in the MATOPIBA agricultural frontier, from 1985 to 2055. It emphasizes the increasing anthropogenic pressure on the ecosystems of the Brazilian Cerrado. The primary goal of this research is to understand how the territory has transformed and to project future scenarios related to agricultural expansion, urbanization, and unplanned land use. The methodology incorporates geoprocessing techniques, spatio-temporal analysis, data from the MapBiomas platform, remote sensing imagery, and time series analysis. Seven categories of land use and land cover were examined, with a focus on forest formations, savannas, agricultural areas, and pastures. Descriptive statistics, trend graphs, and regression methods were used to project changes through 2055. The results indicate a significant reduction in native vegetation, particularly in savanna formations, which is closely linked to the expansion of agricultural, livestock, and urban areas. The model used to simulate vegetation changes over time showed high reliability (R² = 0.98), forecasting that human expansion will continue to encroach on native vegetation through 2055. This underscores the urgent need for territorial planning and public policies to ensure the sustainability of the Cerrado.

Abstract: A capacity analysis report (CAR) is an essential document that assists public water system owners in prioritizing water infrastructure and resources for future capital improvements. The United States Environmental Protection Agency states that public water systems should develop a CAR when average daily production approaches or exceeds a certain percentage of the system design capacity. Different states have different CAR requirements; this report is a guide for developing a CAR for drinking water facilities based on State of Florida requirements. This document will also demonstrate how to use a water demand time series predictive model to forecast future demand. An illustration of how to develop two models is provided. Both models use time as the predictor/independent variable and water demand/consumption as the dependent variable. The first model uses full data to determine the linear regression equation, while the second model uses partitioned data to develop the linear regression equation. The model with the lowest Mean Absolute Error (MAE) is selected for future demand predictions. City of Fort Myers Water Treatment Plant data was used to demonstrate how to develop a CAR and demand forecasting models.

Abstract: Brick manufacturing is among the oldest industries that play a crucial role in socio-economic development, particularly in developing countries i.e. Pakistan. Pakistan is the 3rd largest brick producer in the world with 70 billion annual brick production and 20,000 kilns, after China and India. In Pakistan, a high percentage of kilns are more than a century old while a fraction of kilns shifted to zig-zag technology and the remaining kilns are working on the older technologies like Fixed Chimney Bull's Trench Kiln (FCBTK). Additionally, low-quality fuels are used, and no safety measures are there. Due to such reasons, brick kilns significantly impact air quality and health in Pakistan, which is already experiencing severe air quality problems. Brick kilns emit a huge number of pollutants into the environment including sulfur dioxide, carbon oxides (CO and CO2), nitrogen oxides, particulate matter, carcinogenic dioxins, fluoride compounds, H2S (hydrogen sulfide), polycyclic aromatic hydrocarbons (PAHs) and carbon black etc. Direct inhalation of pollutants causes respiratory diseases, nervous system diseases, cardiovascular diseases, cancer, skin diseases, and reproduction problems. Workers and nearby communities suffer more than others. These emissions also pose indirect impacts by polluting the environment, ozone depletion, acid rain, smog, global warming, and climate change. We need to shift to cleaner production and the modern technologies of brick manufacturing to overcome environmental and health challenges. For example, we can use fly ash, waste glass powder and plastic to produce our bricks rather than using clay. It not only cuts down emissions but also will be helpful to cope with health challenges.

Abstract: The Tomarrazón-Camarones River (La Guajira, Colombia) is characterized by frequent, widespread flooding and, anthropogenically, by intense instream sediment mining. Mapping flood hazard is hence essential to develop effective flood management plans; and a knowledge of the water regime (duration curves) is also essential to estimate sediment transport and carry out sediment budgets to inform on the impacts and sus-tainability of the mining activity. However, neither water levels nor discharges are monitored by official gauging stations, and only a few rainfall gauging stations are available in the area, with daily records often affected by data gaps. Therefore, a first challenge is to reconstruct flowrate time series by an affordable effort, scaled to the fi-nancial-labor resources available in that challenging context. This paper presents an integrated approach that combines satellite-derived rainfall data with ground observa-tions. A semi-distributed hydrological model (HEC-HMS) is used to reconstruct the full flow-rate time series once calibrated and validated with data derived from automatic sensors and field measurements. The model is fed with hourly data derived from daily data at ground gauging stations temporally downscaled by adopting the spatially dis-tributed hourly rainfall patterns obtained from satellite records. Before that, observed water levels in three stations equipped with water level sensors are to be traduced into discharge time series using analytical relationships based on field-measured geometric and physical characteristics. Then, these event-based hydrographs are used to calibrate and validate the model. Results show good agreement with observations, supporting a reasonable confidence in the approach. The calibrated model is then applied to long term datasets to retrieve duration curves and return periods of peak discharges.

Abstract: Agri-food byproducts are increasingly recognized as sustainable feedstocks for high-value bioactive compounds, but their practical valorization requires integrated evidence on recovery conditions, chemical composition, bioactivity, and application readiness. This review critically examines green recovery strategies and chemical profiling platforms for bioactive compounds recovered from peels, pomace, seed residues, hulls, vegetation waters, and pruning waste. Emphasis is placed on how extraction variables shape chemical profiles, extract quality, and reported biological activities. Ultrasound- and microwave-assisted extraction, enzyme- and fermentation-assisted recovery, supercritical fluid extraction, pressurized liquid extraction, pulsed electric field-assisted pretreatment, and green solvent-based extraction are discussed in terms of target-compound selectivity, solvent and energy demand, process safety, scalability, and sustainability-related evidence. Chromatographic, mass-spectrometric, spectroscopic, and metabolomics-based profiling approaches are evaluated for identification, annotation, quantification, fingerprinting, quality-marker selection, and standardization, with confidence levels distinguished according to authentic-standard matching, tandem mass spectrometry evidence, spectral libraries, or fingerprint-level evidence. Circular valorization pathways in food, nutraceutical, cosmetic, pharmaceutical-oriented, and biopesticide-related applications are further considered with attention to feedstock heterogeneity, process standardization, stability, safety, regulatory feasibility, scalability, and techno-economic feasibility. Overall, this review provides a linkage-oriented framework for developing standardized, application-readiness-oriented bioactive candidates from agri-food byproducts.

Abstract: Remote sensing-based change detection for infrastructure monitoring demands methods that are simultaneously accurate, robust to severe class imbalance, and transparent in their decision logic. This study proposes MS-HySAN, a hybrid change-detection framework that addresses these requirements through three coordinated design decisions: (i) a truncated, attention-augmented Siamese encoder that serves as a frozen feature extractor rather than an end-to-end pixel classifier, (ii) a latent–physical fusion strategy that concatenates multi-scale CNN difference features with physically interpretable spectral-index differences, and (iii) a LightGBM classifier that performs internal sparse feature selection and exposes gradient-based SHAP attributions for post-hoc analysis. The framework is evaluated on high-resolution PlanetScope imagery (4-band and 8-band) over a national highway construction corridor in Indore, India, using 21 acquisitions from 2022–2025 with geographic k-fold cross-validation to enforce spatial independence. Experimental results show that the proposed hybrid model consistently outperforms conventional deep learning baselines including U-Net and Siamese U-Net across bi-temporal multi-class change-detection tasks, and competes with bi-temporal architectures (ChangeFormer, SNUNet, BIT) under the same training conditions. A SHAP interpretability analysis reveals complementary and physically meaningful contributions from the learned deep features and the handcrafted spectral indices, validating the fusion strategy. In the best-case setting, MS-HySAN (bi-temporal, indices + reflectance) achieves an overall mean F1-score of 0.95 (Kappa: 0.90), outperforming the corresponding deep baseline by +6 F1 points while maintaining stable cross-fold performance.

Abstract: Coastlines possess significant ecological and resource values, which are intricately associated with marine ecological civilization, the marine green economy, and coastal well-being. Comprehending the spatiotemporal variations and driving mechanisms of coastlines is of great significance for their effective protection, rational utilization, and sustainable development. In this study, we employed ArcGIS to extract the coastline vectors of Huizhou in 1973, 1988, 2003, and 2019 based on multi-source remote sensing and unmanned aerial vehicle (UAV) images. The coastline location and type (CLT) model was proposed to differentiate four coastline types, namely the sets of coastline segments with invariant locations and types (SCA), the sets of coastline segments with invariant locations but altered types (SCB), the sets of coastline segments with changed locations but invariant types (SCC), and the sets of coastline segments with both changed locations and types (SCD). Subsequently, the spatio-temporal evolution and disturbance factors of these coastline types were analyzed, offering a diversified foundation for quantitative coastline analysis. The results indicate that total length of Huizhou coastlines increased from 248.75 km in 1973 to 260.82 km in 2019, with natural coastlines decreasing by 62.86 km and artificial coastlines increasing by 75.21 km. The length and proportion of SCA exhibited a continuous decline, decreasing from 79.66% in the initial stage to 58% in the final stage. Conversely, the lengths of SCB, SCC, and SCD all witnessed a continuous increase. The coastline disturbance index (CDI) of Huizhou exhibited a continuous upward trend, escalating from 20.34% to 30.95% and further to 42.00%. This phenomenon was primarily propelled by land reclamation and aquaculture enclosures, accompanied by distinct regional disparities. The coastline alterations were concentrated in regions such as the Daya Bay Petrochemical Zone, Fanhe Port, Kaozhouyang Bay, Xiaogui Village, and Quanwan Port. Meanwhile, the CDI of aquaculture reclamation witnessed a continuous decline, whereas the CDI of land reclamation showed a continuous increase. The natural environment of Huizhou, including its topographical, geomorphological, and hydrological characteristics, serves as the basis for coastline evolution. Meanwhile, social and economic development, along with policies, are significant driving forces for coastline evolution. These findings offer a solid scientific foundation for the management of coastal zones in Huizhou.

Abstract: The desert-oasis ecotone is a critical ecological buffer in arid regions, and its evapo-transpiration (ET) process is vital for local water cycling and ecosystem stability. However, due to sparse meteorological stations and the coarse spatial resolution of satellite remote sensing, traditional methods struggle to accurately capture the highly heterogeneous spatial patterns of ET in these transition zones. This study focuses on typical desert-oasis ecotones in the Hexi Corridor and proposes a novel method for high-resolution ET estimation by integrating unmanned aerial vehicle (UAV)-based thermal infrared remote sensing with a Three-Temperature (3T) model. A UAV equipped with a thermal infrared camera was used to acquire land surface tempera-ture (LST) data at meter-scale resolution. Combined with meteorological data and vegetation parameters, the 3T model was constructed and solved to produce high-precision ET maps. The model's performance was validated spatially against the Surface Energy Balance Algorithm for Land (SEBAL) model and at the point scale against a two -source model. The results show that: 1) The 3T model effectively cap-tured the spatial gradient of decreasing ET from cropland, through shelterbelts, to de-sert areas, with ET values ranging from 0.12 to 10.69 mm d⁻¹; 2) The model performed well in validation, with coefficients of determination (R²) of 0.80–0.98, indices of agreement (IOA) of 0.83–0.99 against SEBAL, and a mean absolute error of 0.38 mm d⁻¹ against the two-source model; 3) The model performed best in cropland areas (R²=0.92, RMSE=0.24 mm d⁻¹), while a slight overestimation was observed in structurally com-plex shelterbelts. This study demonstrates the effectiveness of combining UAV thermal infrared data with the 3T model for high-resolution ET simulation in complex ecologi-cal transition zones, providing a reliable technical approach for detailed ecohydrolog-ical monitoring and optimized water resource allocation in arid regions.

Abstract: This article examines how Rondônia's territory changed in the early 21st century, focusing on “matogrossization,” a process reflecting the spread of Mato Grosso's productive practices due to agribusiness expansion, especially soybean and corn monocultures. This led to a major reorganization of the regional agrarian space, with commodity exports sparking a land rush and intensive deforestation. The process caused conflicts over land, resistance from local peasants and Amazonian peoples, rural depopulation, and urban growth, driven by rural-to-urban migration. “Matogrossization” explains these socio-spatial shifts, linking Rondônia to global agribusiness and its environmental impacts.

Abstract: Crumb rubber, a recycled tire product used in artificial turf fields, alters soil physical properties and releases chemical contaminants, but few studies have examined its effects on plant physiology and resource allocation. We conducted a greenhouse experiment to evaluate the responses of soybeans (Glycine max) grown in soils containing increasing proportions of crumb rubber (0%, 16.6%, 33.3%, and 50% by weight). Germination, plant growth, chlorophyll content, soil respiration, biomass production, water loss, root allocation, and elemental composition of soils and plant tissues were measured over a 21-day period. Most indicators of plant performance (germination, plant height, chlorophyll content, soil respiration, and total biomass) were not significantly affected by crumb rubber additions. However, crumb rubber significantly reduced cumulative water loss and increased both root biomass and root-to-shoot ratios at moderate and high concentrations. Soil and plant tissue analyses revealed substantial increases in zinc concentrations across the crumb rubber gradient, with leaf zinc concentrations exceeding sufficiency ranges at higher treatments. These results demonstrate that crumb rubber contamination can alter soil water dynamics, plant resource allocation, and trace metal accumulation even when aboveground growth responses remain limited.

Abstract: Reprocessing historical lead–zinc (Pb–Zn) slag offers a circular-economy pathway for secondary metal recovery, yet it can remobilize legacy contaminants where con-tainment is inadequate, transferring risk to the surrounding land. Sustainable man-agement of such sites requires frameworks that link contamination assessment to ac-tionable remediation. We integrated ICP-OES geochemistry, native-plant biomonitor-ing, and US EPA RAGS-based risk modeling at an active Pb–Zn slag reprocessing site in Shymkent, Southern Kazakhstan. Twenty-four soil samples along four cardinal transects, two reference samples, and four composite plant samples (Centaurea pseu-dosquarrosa + Plantago lanceolata) were analyzed for ten metals by ICP-OES. UCC-referenced indices classified six metals as geoaccumulation Class 6 at most points (enrichment factors up to 90,871, confirming an exclusively anthropogenic origin). Peak concentrations reached 9,350 mg·kg⁻¹ Pb, 290 mg·kg⁻¹ Cd, and 10,900 mg·kg⁻¹ As — exceeding Kazakhstan MPC by 72×, 290×, and 5,450×. Worst-case carcinogenic risk reached 4.3 × 10⁻³ (43× above the US EPA threshold), driven almost entirely by arsenic (93%); ecosystem risk (RCRtotal = 223) was dominated by cadmium (43%), arsenic (27%), and mercury (16%) — a disconnect between mass-based and toxicity-based prioritiza-tion. On this basis we propose a three-tier remediation framework (engineered con-tainment, phytostabilization, monitored attenuation) that couples resource recovery with contamination control, is transferable to analogous Pb–Zn legacy sites, and sup-ports sustainable land use, urban resilience, and responsible secondary-resource use.

Abstract: This study evaluated the effect of mechanical homogenization on the structure and performance of nopal mucilage in a real cyanidation barren solution. The aqueous ex-tract obtained from Opuntia ficus-indica cladodes was processed with a household blender for 0, 30, and 60 s before spray drying, yielding powders designated as CA, CB, and CC, respectively. These powders were characterized by physicochemical, thermal, microstructural, and FTIR analyses, and were reconstituted in water to evaluate hy-drodynamic size, ζ potential, rheology, and coagulant flocculant capacity. Homogeni-zation reduced water activity, decreased the hydrodynamic size of reconstituted mu-cilage from 1.8 to 1.3 μm, and significantly modified the ζ potential. Rheological anal-ysis showed a frequency dependent viscoelastic response, consistent with changes in chain association and hydrocolloid network continuity. The reconstituted mucilages removed more than 98% of Pb, Ni, and As at all evaluated concentrations, including 200 mg·L⁻¹, whereas Cd showed more variable removal. FTIR analysis of recovered flocs revealed a hybrid matrix with a signal near 2104 cm⁻¹ compatible with C≡N groups, suggesting retention of cyanide related species. Overall, these findings show that reconstituted nopal mucilage can act as a sustainable hydrocolloid coagulant for real, chemically complex cyanidation effluents, while mechanical homogenization primarily modified its structural, colloidal, and rheological features.

Abstract: Municipal solid waste (MSW) management is a critical challenge to advancing recycling and circular economic approaches. This review provides a comprehensive overview of MSW management, encompassing sourcing, policy frameworks, characterization techniques, separation technologies, preprocessing strategies, and utilization pathways. First, generation patterns and sourcing mechanisms are discussed in both U.S. and global contexts, with emphasis on the influence of policy frameworks on waste reduction and diversion. Second, characterization techniques are evaluated, focusing on physical and chemical analysis for material recyclability. Third, sorting technologies are critically re-viewed, covering conventional methods and emerging sensor-based approaches. Pre-processing techniques are then evaluated for their role in improving downstream con-version efficiency. Finally, valorization pathways such as waste-to-syngas, waste-to-biochar, and waste-to-sustainable aviation fuel (SAF) are assessed in terms of their role in climate mitigation and the circular economy. It is anticipated that this re-view provides a foundational reference for researchers, policymakers, and industry stakeholders aiming to strengthen the recyclability infrastructure and maximize the efficiency of MSW management systems in the framework of the circular economy.

Abstract: Ship detection in synthetic aperture radar (SAR) imagery, an indispensable all-weather technology for marine engineering and coastal safety, remains challenging in complex nearshore scenes due to coupled speckle noise, sea-land clutter, large scale variation, and extreme class imbalance. Existing decoupled pipelines fail to jointly mitigate these degradations, leading to high false alarm rates and poor generalization. We propose DN-AnchorNet, an end-to-end unified framework integrating a detection-oriented structure-preserving enhancement branch, a scale-adaptive anchor mechanism, and an adaptive weighted Smooth L1 loss. The detection-guided enhancement branch operates without paired clean data to preserve critical ship structures. The scale-adaptive anchor design enhances matching for small, elongated, and arbitrarily oriented ships, while the tailored loss improves robustness against hard samples and scale errors under class imbalance. Extensive experiments on challenging nearshore subsets of RSDD-SAR and SSDD+ show that DN-AnchorNet achieves the best overall performance among all compared representative oriented object detectors, with AP₅₀ values of 0.699 and 0.610, and F1-scores of 0.757 and 0.689, respectively. A strict zero-shot cross-dataset evaluation on HRSID further demonstrates strong generalization to unseen marine SAR conditions. These results confirm that joint optimization achieves a favorable accuracy-false alarm balance for practical coastal monitoring applications. Code is available at: https://github.com/yongqi011210/Dn-anchornet.

Abstract: To mitigate compression heat loss during the operation of compressed air energy storage(CAES) power stations, this study proposed a design that coupled a CAES system with a heat pump system for high-temperature steam production. Focusing on waste heat utilization in a 100 MW CAES system, a tiered compression heat utilization strategy was adopted: part of the compression heat serves as a reheating source for the energy storage system’s expansion stage, while the remainder acts as a low-grade heat source for the heat pump steam generation system. This approach effectively enhances the comprehensive energy utilization efficiency of the system. Research findings demonstrated that the novel waste heat utilization system integrating a modified CAES system with a heat pump can convert 11.64 MW of secondary low-grade compressed heat into high-temperature steam. Under specific feedwater parameters(eg.,flow rate, pressure, and temperature), the system generated steam with matched operating conditions, while the heat pump subsystem achieves a Coefficient of Performance(COP) of 1.55. Analysis of variable operating conditions revealed that when the heat load of the flash-high/low-temperature regenerators remained constant, both the steam flow rate and flash rate increased with rising feedwater temperature and decreased with increasing feedwater flow rate. Furthermore, higher the feedwater flow rates combined with the lower feedwater temperature yield higher the steam temperature, peaking at 314.02 °C under optimal operating parameters; Environmental analysis indicated that the system produced substantial high-temperature steam during annual operation, achieving significant reductions in multiple pollutants emissions compared to coal-fired industrial boilers and thus providing a valuable technical reference for relevant fields.

Abstract: Decarbonizing the transportation sector depends not only on the scale of mitigation programs, but also on whether financing systems are capable of generating measurable emission reductions. In Indonesia, climate finance allocation remains substantially below the level required to achieve the transportation-sector target under the Enhanced Nationally Determined Contribution (ENDC). At the same time, mitigation planning rarely establishes a clear relationship between financial expenditure and verified greenhouse gas (GHG) reduction outcomes, making policy effectiveness difficult to assess. This study examines the relationship between climate finance and mitigation outcomes in Indonesia’s transportation sector using verified emission reduction data and realized mitigation expenditures during 2018–2022. A cost-based assessment approach was applied to estimate the financing required to reduce one ton of CO2e across direct and indirect mitigation actions. The analysis identified 33 mitigation actions categorized under the Avoid–Shift–Improve (ASI) framework and evaluated their contribution to sectoral emission reduction. The results indicate substantial variation in mitigation costs among intervention types. Direct mitigation actions, particularly mass public transportation expansion, generated larger emission reductions at relatively lower costs than enabling or indirect measures. On average, reducing 1 tCO2e in Indonesia’s transportation sector requires approximately USD 184–305 (IDR 3–5 million). Based on the transportation-sector ENDC target, the estimated financing requirement by 2030 ranges from USD 3–17 billion (IDR 42–69 trillion). The findings suggest that climate finance policies should move beyond expenditure-oriented approaches toward financing frameworks that explicitly connect investment allocation with verified mitigation performance.