Earth and Environmental Sciences

The Circular Economy (CE) plays a crucial role in both transforming cities into inclusive, resilient, sustainable communities and addressing Sustainable Development Goal 11 (SDG11). However, current methods for the evaluation of city circularity are often incomplete due to their lack of multi-dimensional and comprehensive models and processes that encompass the three core pillars of sustainability, social, economic, and environmental, and SDG 11. This study addresses these limitations, by enabling precise hierarchical and network-based assessments and providing a quantitative score of city circularity. This hybrid model incorporates Multi-Criteria Decision-Making (MCDM) methods structurally inspired by Analytic Hierarchy Process (AHP) and Analytic Network Process (ANP), and Machine Learning (ML) models (pre-trained transformers, and Ensemble Learning (EL) techniques). The model operates across four levels: metrics, indicators, sustainability dimensions, and “circularity based on SDG11” (main goal) and measures their links. Semantic assessment and text mining analyze 123 metrics from 59 identified Circular City Indicators (CCIs). An ideal circularity score of 19.57 (equivalent to 100%) is calculated based on the maximum normalized values for all metrics. The proposed model is applied to the current data of Seville (Spain) in 2023, yields a circularity score of 7.25 (37.05%) under the proposed framework. The framework identifies key metrics with the highest impact on the final score while also recognizing secondary metrics with less influence on Seville's circularity score. This framework is a comprehensive tool for measuring city circularity, enabling cross-city comparisons and temporal analyses, while also being a powerful and adaptable instrument for different city levels, helping policymakers develop sustainable strategies.

Tehran, a rapidly urbanizing semi-arid metropolis, faces severe Urban Heat Island (UHI) effects, exacerbating thermal discomfort, health risks, and energy consumption. ​ This study evaluates the potential effectiveness of Tehran's Green Space Master Plan (GSMP) in enhancing ecological performance and mitigating urban heat stress. ​ Using Biotope Area Factor and Urban Cooling Capacity indices, the analysis reveals that the GSMP significantly improves ecological functionality (Biotope Area Factor increase from 0.11 to 0.20) and cooling capacity (Urban Cooling Capacity increase from 0.146 to 0.211). ​ However, the benefits are unevenly distributed, favoring affluent northern districts over densely populated, socioeconomically disadvantaged central and southern districts. ​ A quantitative spatial equity analysis using the Gini coefficient confirms this disparity (Gini BAF: 0.287 to 0.200; Gini UCC: 0.181 to 0.154), with statistically significant improvements in both indices (Wilcoxon signed-rank test, p < 0.001). To address spatial disparities, a modified GSMP scenario incorporating green roofs in high-density neighborhoods was proposed, resulting in substantial improvements in Biotope Area Factor (from 0.06 to 0.35) and Urban Cooling Capacity (from 0.18 to 0.35). ​ The findings underscore the importance of tailored interventions, such as vertical greening and blue infrastructure, to enhance cooling effects, promote environmental justice, and improve urban resilience. ​This study highlights the critical role of integrative urban planning in mitigating UHI effects and advancing equity, offering valuable insights for climate-responsive strategies in arid and semi-arid megacities.

The agricultural highlands of the Galápagos Islands are critical for local food production but exhibit high spatial variability in soil fertility due to differences in geological age, climate, and land-use history. This study provides high-resolution (1 arc-second) soil property maps for these areas, utilizing point samples taken from 130 farms across the islands Isabela, Santa Cruz, San Cristóbal, and Floreana. In a Digital Soil Mapping (DSM) approach, we calibrated Quantile Regression Forest (QRF) models to spatially predict 51 physical and chemical topsoil properties based on gridded environmental covariates representing climate, vegetation, relief, and parent material. Model performance varied by soil property, with best metrices for total nitrogen, water holding capacity (15 bar), aggregate stability, cation exchange capacity, and several macro- and micronutrients (Lin’s Concordance Correlation Coefficient CCC up to 0.90; R 2 up to 0.83). Lower predictive performance was observed for CaCO 3 , phosphorus (Mehlich-3), and PO 4 retention. Spatial uncertainty was visualized via maps of 95 % prediction interval widths, highlighting areas of greater model uncertainty. Predictor importance analysis revealed distance from the volcanic hotspot, precipitation- and temperature-related variables, and lithology as key drivers of the observed spatial patterns. The resulting soil maps provide continuous, up-to-date information to guide sustainable land management, agricultural planning, and conservation in Galápagos. By offering locally calibrated, multi-property soil maps at high spatial resolution, this work overcomes limitations of globally calibrated soil mapping products, thereby supporting informed decision-making in one of the world’s most ecologically sensitive agricultural landscapes.

Accurate multinational soil prediction remains a fundamental challenge due to pedogenic heterogeneity, management-induced variability, and sample distribution imbalance that constrain the transferability of conventional global calibration models. To address these limitations, this study introduces the Mixture-of-Algorithmic-Experts (MoAE) framework, an architectural approach that decouples global coverage from local specialization by combining algorithmically specialised expert ensembles with a learned routing mechanism for conditional computation. The framework was evaluated using proximal sensor data (visible near infrared spectroscopy and portable X-ray fluorescence spectrometry) from four pedoclimatically distinct countries (Brazil, France, India, and the USA), MoAE was evaluated across 17 physicochemical soil properties encompassing carbon fractions, particle-size distribution, cation exchange capacity, electrical conductivity, and macro- and micronutrients. In the primary hold-out validation, the framework achieved high validation performance for structurally stable properties, including total carbon (Coefficient of Correlation (R2) = 0.99), total nitrogen (R2 = 0.99), and texture fractions (R2 = 0.95–0.98), with negligible systematic bias, substantially exceeding typical global spectroscopic calibration benchmarks reported in the literature. In contrast, lower performance for management-sensitive nutrients such as available S and P reflected signal limitations inherent to the sensing-feature space rather than architectural failure. Shapley Additive Explanations-based interpretability confirmed that predictions were driven by geochemically meaningful elemental proxies and diagnostically relevant spectral wavelengths, while routing-weight distributions provided an internal confidence indicator for deployment-aware decision support. Unlike monolithic calibration strategies, MoAE enables context-aware computation, dynamically weighting expert contributions according to sample-specific feature representations. By integrating conditional modelling, interpretability, and multi-sensor complementarity within a unified framework, MoAE offers a scalable and responsible foundation for multinational digital soil mapping and next-generation operational soil intelligence systems.

The Middle Ordovician Yijianfang Formation in the Shuntuoguole area of the Tarim Basin is an important ultra-deep carbonate exploration target, but favorable reservoir prediction remains difficult because effective storage space is controlled jointly by depositional architecture, diagenetic modification and strike-slip faulting. In this study, formability refers to the capacity of depositional fabrics to create initial pore space, modifiability refers to the potential for diagenetic processes to preserve or enlarge pores, and connectivity refers to the linkage of pores, vugs and fractures into an effective flow network. To clarify these controls, we integrate seismic interpretation, well-log correlation, core and thin-section observations, carbon–oxygen isotopes, elemental logging data and Dionisos-based 3D forward stratigraphic modeling. The Yijianfang Formation is divided into four third-order sequences (SQ1–SQ4), bounded by sequence boundaries (SB1–SB4) and containing maximum flooding surfaces (MFS1–MFS4). MFS4 is correlated with the regional T 7 4 seismic marker. Depositional systems are dominated by restricted and open carbonate platforms, with intraplatform shoals, intershoal areas and lagoonal settings forming the main facies associations. Thickness differentiation and shoal development were strongest during SQ2–SQ3, indicating that these intervals provided the most favorable depositional basis for reservoir formation. Diagenetic modification was facies dependent. Grain-supported shoal facies commonly developed initial intergranular pores but were prone to cement occlusion, whereas micritic intershoal and lagoonal facies had poorer primary porosity but could be enlarged by dissolution along stylolites and microfractures. Exposure-related dissolution and fracture enhancement were preferentially developed near SB3–SB4 and in adjacent rapid facies-transition zones. Late strike-slip fault activity and related fluids further improved reservoir connectivity within damage zones. Therefore, favorable reservoirs are most likely where SQ2–SQ3 shoal complexes, SB3–SB4-adjacent dissolution-prone intervals and strike-slip fault damage zones overlap. This framework supports reservoir prediction by using facies to locate favorable belts, sequence surfaces to select intervals and faults to evaluate connectivity.

Arc lavas with heavier magnesium (Mg) isotope ratios (denoted as δ26Mg) compared to mid-ocean-ridge basalt (MORB) are commonly attributed to the involvement of subducted serpentinite. However, this hypothesis is challenged by the widespread absence of high-δ26Mg serpentinite in the subducting plates beneath Pacific arcs and fails to consider potential δ26Mg heterogeneity in ambient arc mantle. Here, we show that backarc lavas from the Mariana and Ryukyu subduction zones have MORB-like boron (B) isotope compositions and B/Nb ratios, indicative of little subducted serpentinite in their source. Such lavas have similarly high δ26Mg as their arc counterparts, suggesting the ambient arc mantle in this area is intrinsically rich in isotopically heavy Mg. Further analysis of metaserpentinites demonstrates that deserpentinization residues inherit protolith δ26Mg without significant isotope fractionation during dehydration, highlighting the important contribution of deserpentinized peridotite to the high-δ26Mg mantle. We propose that the high-δ26Mg arc lavas are not necessarily related to ongoing serpentinite subduction, especially in fast-spreading ridge systems. On the other hand, long-term recycling of ancient high-δ26Mg serpentinite provides one plausible mechanism for the heavy Mg mantle domain identified beneath the arc systems. This potentially widespread domain in the mantle greatly affects long-term Mg recycling and crust-mantle evolution.

Abstract Wetlands in the U.S. Prairie Pothole Region (USPPR) provide habitat for an estimated 5 million breeding ducks and remain a focal point for North American waterfowl conservation. After decades of investment, almost 20% of the USPPR is protected by some form of wetland or grassland conservation. We synthesized four decades of modeling studies aimed at understanding how environmental change may impact duck populations. We built upon past modeling efforts by applying an integrated scenario-based approach to inform wetland-waterfowl conservation planning. By century’s end, all scenarios projected declines (27%–53%) in breeding duck abundance in the USPPR. Despite these declines, areas in the USPPR with higher rates of protection and wetland density will likely continue to support the most breeding ducks, indicating resilient conservation planning. Our findings underscore the benefits of interdisciplinary efforts that can inform complex conservation decision-making in the face of environmental uncertainty.

Landslide susceptibility assessment acts as a core technical tool for geological disaster governance, ecological protection and long-term risk mitigation strategies. This modeling approach quantifies the possibility of slope-collapse events and delivers objective decision-making support for regional geologic environment supervision. To overcome the low computational efficiency and weak capacity of conventional evaluation frameworks to extract multi-level spatial grid rules, this paper takes Nanning City, the capital and largest city of the Guangxi Zhuang Autonomous Region in southern China, as the research object. Ten types of terrain and geological control factors combined with historical landslide inventory records are adopted to build a two-stage coupled evaluation framework integrating the information value method (IVM), a Bidirectional Temporal Convolutional Network (BiTCN) and Transformer, named IVM-BiTCN–Transformer. The hierarchical framework first adopts IVM to finish preliminary hazard grading and calculate factor contribution weights, then inputs classified grid samples into the BiTCN-Transformer module to realize local terrain feature and global factor fusion, which significantly lifts the overall identification precision. Ten widely adopted landslide evaluation algorithms are selected for contrast simulation, with multiple quantitative metrics adopted to judge model reliability. Experimental outcomes prove that the presented IVM-BiTCN–Transformer framework obtains superior hazard discrimination capacity, which can raise the precision and stability of landslide zoning and offer reliable technical support for targeted regional geological disaster prevention.

The coal chemical industry is a major emitter of carbon and pollutants in China, yet the synergistic potential of decarbonization options remains unclear. This study integrates life-cycle assessment (LCA) and techno-economic analysis (TEA) to evaluate the synergistic reduction potential of substituting conventional coal-based H2/O2 with renewable-powered electrolytic H2/O2 across eight scenarios for 2030 and 2050, explicitly accounting for green H2 supply constraints. We find that full life-cycle emissions reached 1.29 Gt CO2eq and 20.43 Mt of pollutants in 2023 (≈10% of national GHG emissions), projected to rise to 2.49 Gt and 41.46 Mt by 2050. While the theoretical maximum carbon reduction potential reaches 95%, a severe green H2 supply gap limits near-term feasibility: achievable reductions are only 12% (carbon) and 1% (pollutants) by 2030, rising to 42% and 11% by 2050, with abatement costs of –380 billion to 3.6 trillion CNY. The wind- and solar-powered pathways are most cost-effective (marginal abatement costs as low as 195 CNY/t CO2eq). We recommend prioritizing deployment in renewable-rich regions and aligning electrolysis scale-up with grid decarbonization to enable a pragmatic transition toward a green H2-integrated coal chemical industry.

Assessing the sustainability of urban development, including road changes, is increasing from year to year and requires clear indicators for robust decision-making tools to gain knowledge across regions. This study conducts the selection of transportation routes over a longer period as an example to evaluate the sustainability of historical official routes in achieving economically cost-efficient operation and maintenance. Official ways in the Qiantang River Basin connected the Jiangnan region, the economic center of China, with surrounding provinces were assessed. During the past six hundred years, the official road network in this area gradually simplified, evolving from valley roads to river banks, which covered longer distances. However, this transformation lacks a systematic explanation. By applying the analytic hierarchy process (AHP) with geographic information system (GIS), quantitative analysis was gained and the results are as follows: (1) Among the influencing factors, the weights of transportation cost and population related to economic needs are 39.54% and 29.52% respectively, with a combined total of 69.06%. (2) The official road network is often designed for governing the people, but in places such as the Qiantang River Basin, economic logic superseded political imperatives, becoming the dominant factor in reshaping the official ways. (3) In the pre-industrial era characterized by limited technological capacity, the physical environment had a greater impact on economic costs, ultimately reshaping the spatial configuration of official route networks. Overall, the evolution of official routes reflects the decline in their military-political function, driven by sustained peace and long-term decline in strategic position. The evolution of the official ways in the Qiantang River Basin reveals the importance of economic benefits in road selection.

The 1991 Gulf War contaminated more than 49 km2 of Kuwaiti desert with hydrocarbon spills, a persistent threat to soil resources, infrastructure and the United Nations Sustainable Development Goals embedded in Kuwait Vision 2035. Managing these legacy lands calls for predictive tools that capture spatial variability while remaining computationally tractable and statistically defensible at the small sample sizes typical of post-conflict monitoring. This study develops a multi-resolution physics-informed neural network that combines wavelet-based parameter encoding, scale-dependent regularisation and a progressive upsampling training protocol. The framework is evaluated on nine trial-pit observations at a single depth of 30 cm in the Al-Ahmadi field, where the contaminated pits show a mean internal friction angle of 26.8° compared with 36.0° at co-located control pits sampled at the same time. Generalisation is assessed by leave-one-out cross-validation across the nine locations. The framework attains a friction-angle root-mean-square error of 1.29°. Under the same data and compute budget, ordinary kriging and a standard physics-informed neural network remain statistically competitive. This outcome indicates that the physics residual acts as a mass-conservation-consistent smoothness regulariser rather than a site-calibrated transport predictor. A multi-objective remediation workflow produces a cost-versus-residual-risk Pareto front for a scenario-specific 1–2 km2 case, presented as an illustrative decision-support envelope pending external pilot calibration. A projected pathway from these outcomes to six Sustainable Development Goals and two pillars of Kuwait Vision 2035 is also discussed; quantitative attribution at this sample size is beyond scope. The small-sample, single-depth and single-locality limitations that bound the admissible inference are stated explicitly.

Post-disaster recovery is a multi-year, multi-dimensional process, yet most remote-sensing assessments rely on single indicators and are hard to apply in data-sparse regions—limiting their value for sustainable, evidence-based reconstruction. We develop a Google Earth Engine (GEE)-based multi-sensor Recovery-Activity Index (RAI), built entirely from free satellite data, and apply it to the 2023 Al Haouz earthquake (Mw 6.8) in the High Atlas, Morocco. The index is framed explicitly as an observed recovery-activity monitoring proxy, not a direct measure of welfare or resilience capacity. Monthly VIIRS nighttime-light (NTL) anomalies, Dynamic World built-up probability, and precipitation-corrected Sentinel-2 NDVI were extracted for a 30 km rural core zone (January 2022–May 2026), deseasonalized, standardized, and integrated. NTL anomalies rose after the earthquake (post-event mean +18%) and appeared to precede built-up anomalies by about two months; because monthly series are short and autocorrelated, we tested this lead with block-bootstrap and block-permutation methods and report it as a reproducible but modest early-activity lead (r = 0.65, p = 0.02; p = 0.14 after correction) that is not an artefact of optical data gaps. NDVI was governed mainly by precipitation (R2 = 0.61) with negligible earthquake-attributable change, so vegetation signals do not confound the index. The integrated RAI peaked in December 2024 and proved robust to indicator weighting (pairwise r ≥ 0.97), baseline choice (r = 0.88), and spatial domain (&lt;9% variation), with a genuinely multi-sensor peak (NTL 62%, built-up 43%). Province-level analysis revealed an uneven recovery hierarchy (Chichaoua &gt; Al Haouz &gt; Taroudannt) driven by differences in physical-rebuilding signal rather than baseline luminosity. Running in minutes server-side at no cost, the RAI offers data- and resource-limited administrations a scalable, reproducible tool to flag where reconstruction activity lags and to prioritize targeted ground verification—supporting more equitable, sustainability-oriented recovery governance—rather than serving as a stand-alone, validated recovery measure.

The carried-out microfield model research was aimed at identifying patterns in the dynamics of soil CO2 effluxes depending on the locally occurring hydrothermal regimes of regenerated lawn ecosystems on peat–sand substrates with different peat contents. Monitoring was carried out every ten days from 21 April 2019 to 30 October 2019 and included measurements of soil and air temperature, soil moisture, and CO2 efflux every 3 h during the day. The weather conditions of the 2019 growing season in Moscow, with air temperature close to the annual average and increased precipitation, made it possible to clarify quantitative patterns of the temperature influence on CO2 efflux from lawn soils in case of their pronounced dynamics without real soil moisture deficit. To study relationships between CO2 efflux and soil and air temperatures, three empirical CO2 efflux models (Exponential, Raich–Hashimoto and Lloyd–Taylor) were used with comparative assessment of their results. The conducted investigation showed that both peat content, local hydrothermal regime, and type of vegetation cover play a significant role in efflux modulation, with the temperature factor dominating on both seasonal (72% impact) and intraday (51–94% impact) scales. The lawn substrate factor accounts for up to 10% of CO2 efflux variability on the intraday scale. The lawn vegetation cover (with the lower and higher diversity) significantly affects the soil hydrothermal regime depending on the peat content (a higher impact with a lower peat content due to the soil pH difference). The denser vegetation reduces the soil temperature, providing better protection, and at the same time reduces soil moisture by transpiration, which provides the combined effect on the CO2 efflux reduction (up to 1 g CO2 m−2 day−1 reduction for the lower-pH soils).

This study investigated vegetation frontline dynamics, fractional vegetation cover (FVC), and community succession in the tidal-flat wetlands of the Liaohe Estuary. The eastern bank of the Liaohe River within the Shuangtaihe National Nature Reserve was selected as the study area, and six periods of Landsat and Gaofen-1 (GF-1) imagery from 2000 to 2025 were used. Remote-sensing preprocessing, normalized difference vegetation index (NDVI)-based FVC inversion, vegetation frontline extraction, Digital Shoreline Analysis System (DSAS)-based rate calculation, land-cover classification, and spatial correlation analysis were integrated to characterize wetland spatiotemporal dynamics and succession patterns. The results showed that the linear regression rate (LRR) and end point rate (EPR) effectively captured the long-term trend and five short-term fluctuations in vegetation frontline migration. FVC fluctuated markedly over the 25-year period, whereas the weighted average (WA) of the five FVC classes remained generally stable and effectively summarized overall vegetation growth. Vegetation frontline migration was spatially associated with annual FVC change (ΔFVC); both LRR and ΔFVC showed significant positive spatial autocorrelation and evident spatial clustering. In addition, the conversion among mudflats, Suaeda salsa, Phragmites australis, and water bodies was closely coupled with frontline migration. These findings provide a scientific basis for quantifying coastal wetland sustainability and for designing spatially targeted restoration strategies in the Liaohe Estuary. The proposed coupling analysis framework also offers a transferable remote sensing approach for monitoring wetland sustainability under changing environmental conditions.

Highway corridors are increasingly being discussed not only as zones of ecological disturbance but also as components of regional green infrastructure with potential carbon sequestration functions, yet their long-term evolutionary characteristics and multi-scale associated factors remain insufficiently understood. Using multi-source time-series data from 2000 to 2023, we developed an analytical framework integrating the CASA model, Random Forest, and geographically weighted regression (GWR). To ensure methodological rigor, we implemented a Spatial K-fold Cross-Validation strategy and incorporated Partial Dependence Analysis (PDA) to identify non-linear thresholds. The results indicate that: (1) Vegetation carbon sequestration within Shandong’s highway corridors increased significantly, with total sequestration rising from 5.54 × 106 t in 2000 to 1.55 × 107 t in 2023, representing an average annual growth rate of approximately 5.0%. This growth transitioned from a relatively stable phase to a more rapid growth phase. (2) A clear distance-related ecological pattern was observed. Statistical tests (Kruskal–Wallis H test) confirmed that vegetation carbon sequestration exhibited a significant non-monotonic gradient (p&lt;0.05), with a stable peak zone observed 50–100 m from the roadbed. This peak zone is associated with a spatial “trade-off” pattern between the attenuation of traffic-related stressors and roadside ecological management. (3) The observed spatial pattern was associated with a nonlinear coupling of natural background conditions and human disturbance. Precipitation and temperature were the dominant associated factors, while PDA further identified a critical precipitation threshold (~750 mm) and localized tipping points for human interference, with a distinct road-disturbance-sensitive zone evident within 200–500 m. The results suggest that high-standard ecological design and active restoration measures are associated with lower ecological disturbance and higher vegetation carbon sequestration performance in some highway corridors. However, these relationships should be interpreted cautiously, as they may also be influenced by differences in climate background, topography, land-use context, and road construction history. These findings provide empirical evidence to inform differentiated ecological restoration and low-carbon management of traffic corridors.

Climate change politics has been largely analyzed through the lenses of a liberal international order. This is the most favorable approach, because liberalism contains a powerful universalistic strand, defends the rights of people, and engages in multilateral negotiations and agreements, which are important to deal with a global issue that requires intra- and intergenerational solidarity. Yet despite robust scientific consensus and decades of international multilateral agreements under the United Nations, global greenhouse-gas atmospheric concentrations continue to increase, and high fossil-fuel dependence persists. One may say that without those negotiations, the situation would be worse, but humanity is increasingly distant from complying with the objective of the United Nations Framework Convention on climate change (UNFCCC). The present work addresses climate change politics under liberal and neorealist international orders and follows the Mearsheimer hypothesis of a transition from a unipolar liberal order to a bipolar neorealistic bounded orders dominated by the US and China. The effect of international orders on sustainability and, more specifically, on climate change politics is analyzed with a methodology based on three structural determinants: (1) the world evolution of climate change variables; (2) primary-energy sources and critical minerals, and (3) climate change responses—mitigation, adaptation and climate geoengineering. The distinct energy and climate policies of the US and China are discussed using these structural determinants. US climate change policy appears to be less driven by climate observation, science and the severity of harmful impacts of climate change than by the vested interests of the fossil-fuel industry. It is argued that solar radiation manipulation (SRM) is a technological fix involving negative side-effects, uncertainties, risks and geopolitical implications, while lacking an agreed international governance framework. Potential deployment is more likely under a neorealistic international order, although it adds further uncertainty and risks without solving the climate change challenge.

Climate policies are becoming increasingly politicized, and decisions often do not survive changes in governments 1 . This undermines the achievement of international agreements on climate change, because effective climate governance requires stability and predictability for businesses and other actors. Surveys show that a majority of the global population demands intensified political action on climate—and underestimate the willingness of their fellow citizens to act on climate change 2 . Even though support tends to drop with increasing costs 3 , this calls for a mechanism for overseeing that long-term climate policies are trustworthy and can be trusted to be honored by new governments. Such a stable arm’s length distance institutional structure has existed in most Western countries for many years, but in a different domain: monetary policy. Following Helm et al. 4 , we suggest that climate policy could benefit substantially from similar institutional structures. In this Commentary, we explore similarities and differences between monetary and climate management and suggest institutional options for reaching emission targets more efficiently. We argue that there are climate policy lessons to be learned from the central bank principle of arm’s length distance between policy management and politics. We focus on Sweden as a case study, but the arguments should be relevant for many other countries as well.