The 'Grain for Green' (GFG) project is a key ecological restoration initiative in the Loess Plateau. The land use changes induced by GFG project have the potential to alter the spatial distribution of soil organic carbon (SOC), yet its impact on the lateral loss of SOC was not well understood or insufficiently quantified. This study was to develop a comprehensive framework using a coupled hydro-biological model (SWAT-DayCent) together with incorporating an empirical carbon enrichment coefficient for quantitatively assessing soil and SOC losses in a typical watershed in the Chinese Loess Plateau-the Weihe River Basin (WRB). The results revealed that the GFG project reduced cropland area from 58.64 × 103 km2 in 1995 to 54.55 × 103 km2 in 2020, while forest area expanded from 21.13 × 103 km2 in 1995 to 22.14 × 103 km2 in 2020. Grassland area initially declined from 50.20 × 103 km2 in 1995 to 49.62 × 103 km2 in 2000, before increasing to 51.39 × 103 km2 in 2020. Areas with high soil erosion and SOC loss in the basin are predominantly located in its western and southern regions, while low-value areas are mainly concentrated in the northern and central-eastern regions, exhibiting strong spatial heterogeneity. The GFG project significantly reduced the soil erosion and SOC loss in WRB, thereby enhancing regional soil carbon sequestration capacity. Compared to cropland-to-grassland conversion (CTG), the reduction in soil and SOC losses was more pronounced for cropland-to-forest conversion (CTF). Specifically, under the CTF program, soil erosion decreased from 1179.13 t km-2 yr-1 in the baseline period (1995, before GFG) to 15.24 t km-2 yr-1 (2015, about 99% reduction), and 98% reduction was found for SOC (from 10.29 t km-2 yr-1 to 0.22 t km-2 yr-1). For the CTG program, soil loss decreased by 76% (from 965.29 t km-2 yr-1 to 232.95 t km-2 yr-1), and SOC loss decreased by 74% (from 8.68 t km-2 yr-1 to 2.25 t km-2 yr-1). The findings of this study can be valuable for soil conservation and carbon sink management in the Loess Plateau, and the framework we developed can be potentially applicable in other areas.

Wet deposition of dissolved organic matter (DOM) is important yet understudied for fluvial carbon cycling. To investigate rainwater DOM sources and their contributions to riverine systems in the Chinese Loess Plateau (CLP)-Qinling Mountains (QM) transition zone, this study captured 57 rainwater samples with varying rainfall intensities over one year, accompanying monthly surface water sampling from an adjacent river. Three major fluorescent DOM (FDOM) components were observed in rainwater, including a polycyclic aromatic hydrocarbon-like component (C1), a low-aromaticity humic-like component (C2), and a tryptophan-like proteinaceous component (C3). C1 dominated FDOM and exhibited seasonal variations, highlighting the importance of anthropogenic inputs, especially in summer. Higher aromaticity and humification in autumn and winter reflected the combined effects of anthropogenic inputs and photochemical processes. The results confirmed the influence of CLP dust on rainwater DOM, particularly during spring dust episodes. Backward trajectories revealed the contributions of air masses originating from dust source regions to increased particulate matter and associated organic compound inputs. The high-altitude QM acts as a geographical-climatic barrier regulating DOM transport. Rainwater-derived DOM contributed <1% to surface water directly, highlighting rainstorm-induced terrestrial runoff and in-stream biogeochemical processes for riverine DOM, consistent with the higher humification and aromaticity of riverine FDOM and their smaller seasonal fluctuation. The discrepancies in DOM between rainwater and surface river indicate different dominant source controls and processing pathways, while likely remaining linked through rainfall-runoff and post-precipitation transport. This study highlights the significance of underlying surface characteristics across CLP-QM regions in affecting rainwater DOM and its contribution to terrestrial riverine systems. Rainstorm-induced soil washing and runoff processes deserve attention for quantifying riverine DOM sources in the future.

Accurate radar echo extrapolation is fundamental for real-time monitoring and nowcasting severe rainfall events. However, most existing deep learning approaches tend to formulate this task in a simple manner, overlooking the physical mechanisms that govern radar echo evolution. This limitation restricts model interpretability and degrades nowcast reliability under dynamically evolving weather conditions. To this end, this study proposes a physics-informed deep learning framework that explicitly integrates prior hydrometeorological information into the radar echo extrapolation process. The evolution of radar reflectivity is decomposed into two distinct yet complementary components: advection and residual intensity evolution. By coupling both components within a unified neural architecture, the framework captures both large-scale displacement and small-scale intensity evolution of radar echoes. Numerical experiments conducted on multi-temporal radar datasets demonstrate that the proposed method outperforms the conventional data-driven benchmark model in both nowcasting accuracy and spatial consistency. The decomposition method not only reduces prediction errors but also enhances the physical interpretability of deep learning model outputs, allowing a more transparent attribution of echo evolution to dominant hydrometeorological processes. Quantitative verification confirms the MvReNowcast model's systematic superiority over the Baseline. The performance gap widens significantly with time, reaching relative improvements exceeding 120.8 % on CSI30 and 52.9 % on CSI40 at the 120-minute forecast horizon. This study highlights the potential of physics-informed separation strategies in bridging the gap between data-driven and process-based modeling for rainfall nowcasting. The proposed approach provides a physically consistent and computationally efficient framework that can be extended to other hydrometeorological prediction tasks.

Along with global warming, the world's highest large river basin, the Yarlung Zangbo River Basin (YRB) on the Tibetan Plateau, has experienced notable hydroclimatic changes over the past four decades. Meanwhile, the Indian Summer Monsoon (ISM) has weakened, altering meridional wind patterns and associated moisture transport, which in turn has shaped spatiotemporal precipitation variability across the basin. However, the mechanisms linking ISM variability, meridional winds, and regional precipitation trends in the YRB remain unclear. This study systematically analyzes precipitation changes and quantifies the role of meridional wind variability in modulating JJAS (June to September) precipitation across the YRB from 1979 to 2019. Multiple data sources, including observations and reanalysis, are combined with Weather Research and Forecasting (WRF) model simulations. Results reveal pronounced spatial heterogeneity: the downstream YRB shows significant declines in meridional wind and precipitation, while moderate increases occur upstream and midstream. Importantly, net water vapor input accounts for approximately 83 % of precipitation in the downstream, 41 % in the midstream, and 55 % in the upstream, highlighting the dominant role of advected moisture, especially downstream. WRF sensitivity experiments indicate that a 10 % reduction in meridional wind leads to a 4.9 % decrease in downstream precipitation, whereas a 10 % increase results in a 1.3 % increase in the midstream; upstream precipitation is insensitive to meridional wind changes. These findings clarify the hydroclimatic response of the YRB to ISM weakening and highlight the pivotal role of meridional winds in regional precipitation changes. The results provide insights for water resources managements under a changing monsoon regime.

The Belt and Road Initiative promotes international cooperation and economic growth across Eurasia but poses challenges to sustainable development, especially in food, energy, and water security. Here, we use a national-scale Bayesian network model and analyze food-energy-water nexus interactions across 39 Eurasian countries. Our findings identify that the food subsystem, particularly food consumption, is the most sensitive component within resource subsystems: food consumption has the strongest interaction in 69% of countries, and the food subsystem is the dominant resource subsystem in 46% of countries. Further, shifting crop types and optimizing planting structures are more effective strategies to manage the food-energy-water tradeoff than modifying energy systems. Socioeconomic factors have a greater impact on resource security than natural factors. However, uneven benefits may exacerbate regional inequality. International trade acts both as a source of vulnerability and a means to compensate for resource deficits. Our findings offer a comprehensive perspective and support strategic planning for integrated resource governance in Eurasia. In Eurasia, food systems, especially consumption, are the most sensitive part of the food-energy water nexus, and shifting crop types and planting is more effective than changing energy systems for improving resource security, according to an analysis of socioeconomic data.

Access to clean energy and poverty reduction remain linked challenges in many low-income regions. Solar power programs often aim to address both goals, yet evidence on their broader economic and environmental effects remains limited. Here, we assess China’s photovoltaic poverty alleviation program, which supports households with low incomes through small-scale solar electricity generation. We exploit a natural experiment created by uneven program rollout across counties and combine satellite observations of air pollution with county-level economic data from 2010 to 2020. We find that counties covered by the program show higher economic output, with gross domestic product rising by about 3%, and lower sulfur dioxide concentrations, which fall by about four micrograms per cubic meter. These effects appear stronger in areas with greater industrial activity and lower income levels. The results indicate that targeted solar programs can support economic development and improve air quality, while long-term financial viability depends on policy design. China’s photovoltaic poverty programs raised economic output by about three percent and reduced sulfur dioxide concentrations by about four micrograms per cubic meter, according to a natural experiment using remote sensing and county-level analysis.

Accurate mapping of thermokarst lakes in the Qinghai–Tibet Engineering Corridor is important for understanding permafrost degradation and assessing environmental risks to major infrastructure. However, thermokarst lake extraction from medium-resolution satellite imagery remains challenging because these lakes are often small, fragmented, seasonally variable, and spectrally confused with wetlands, shadows, and other surface water bodies. In this study, Sentinel-2 imagery from the 2020 thaw season was used to map thermokarst lakes in the Qinghai–Tibet Engineering Corridor. A 16-feature dataset was constructed by integrating spectral bands, water indices, texture features, and topographic variables, and a convolutional neural network (CNN) was compared with five conventional machine learning classifiers. In the pixel-based validation, the CNN slightly outperformed the other evaluated models, achieving the overall accuracy of 98.04% and an F1-score of 97.18%. Independent polygon-based validation using the Jilin-1 visual interpretation reference showed that the final CNN-derived inventory achieved an IoU of 0.79, with omission and commission ratios of 0.15 and 0.09, respectively. The CNN more effectively suppressed salt-and-pepper noise, reduced fragmented and serrated lake boundaries, and improved the spatial continuity of mapped water bodies compared with traditional machine learning classifiers. SHAP-based attribution suggested that CNN predictions were more strongly associated with water indices and texture features, whereas Random Forest predictions were mainly associated with near-infrared and shortwave-infrared bands. Thermokarst lakes showed higher lake area proportions and densities in areas with relatively high ground ice content, gentle slopes, thicker active layers, warmer permafrost, and unstable permafrost conditions. These results demonstrate the potential of Sentinel-2 imagery and convolutional models for regional thermokarst lake mapping and permafrost degradation monitoring.

Acoustic agglomeration has emerged as an efficient, non-chemical method for enhancing particle growth and facilitating removal. In this study, an experimental investigation was conducted to examine the agglomeration behavior of droplets and aerosols in a turbulent airflow subjected to low-frequency acoustic fields (80-120 Hz, 110-120 dB). A customized low-speed wind tunnel equipped with a perforated plate generated homogeneous turbulence, and droplet size distributions were measured using a laser particle size analyzer. Results demonstrated that turbulence significantly enhances the effectiveness of acoustic agglomeration by increasing collision frequency and promoting the growth of large droplets, while small particle concentration declined correspondingly. Agglomeration efficiency increased with sound pressure level, indicating a positive intensity dependence. Theoretical analysis based on Stokes numbers and Kolmogorov scales revealed that turbulenceinduced particle nonuniformity reduces the acoustic energy required for effective coagulation. These findings provide new insights into the coupling mechanism between turbulence and sound fields and suggest a potential strategy for aerosol mitigation, fire smoke suppression, and optimizing acoustic rainfall stimulation technologies.

Frequent droughts pose serious threats to terrestrial ecosystems under global climate change. However, the spatial patterns of drought risk in China over the past few decades and the mechanisms of vegetation response to drought remain unclear, especially on large watershed scales. Here, we applied a cluster analysis method that captures the multidimensional characteristics of drought indicated by the self-calibrating Palmer Drought Severity Index (scPDSI) to quantitatively assess drought risk in China from 1965 to 2018. Furthermore, we assessed the direct and legacy impacts of drought on vegetation dynamics across major river basins in China and identified the major drivers using random forest method. Results showed that high-risk drought areas in China, accounting for similar to 17.1%, were mainly located at the junctions of the Inland River and Yellow River basins, and the Yangtze River and Pearl River basins. Vegetation dynamics in northern China were more sensitive to drought, particularly in the Yellow River and the Hai River basins, with growing-season detrended NDVI and scPDSI correlation coefficients of 0.68 and 0.73, respectively, during 1982-2015. The contribution of precipitation to vegetation growth was higher during droughts, particularly in high-risk drought areas where the coupling between vegetation growth and precipitation increased with intensified drought. Limited by soil moisture availability, pre-growing season drought in northern China exhibited legacy effects on spring phenology, particularly evident in the Hai River basin, where it resulted in an average decrease of 0.32 in spring NDVI anomalies. Overall, this study highlights the vulnerability of vegetation activities to drought stress in arid and semi-arid regions.

The interaction between macroinvertebrates and environmental factors has long been a question of great interest across a wide range of fields. However, our understanding of the impacts of environmental factors on macroinvertebrates remains vague. Moreover, the study of the distribution drivers of macroinvertebrates in mountainous rivers has been rarely undertaken, primarily due to the sampling difficulties and the inherent complexity of the terrestrial ecosystems in these regions. This study applied a partial least squares structural equation modelling (PLS-SEM) to investigate the interactions between specific environmental factors, namely, hydrometeorology, water temperature, nutrient levels and substrate composition, and their effects on macroinvertebrates. Five stations along the Lai Chi Wo River in the Northwest New Territories of Hong Kong, China, were selected for data monitoring. The model results reveal site-specific environmental influences on macroinvertebrates. The furthest downstream station, close to the sea, is vulnerable to seawater intrusion during the dry season, indicating a hydrometeorological-driven model as freshwater macroinvertebrates are intolerant to seawater. Two stations, one downstream and the other upstream, are occupied by rocks that stir river water and increase dissolved oxygen, forming a nutrient-driven model. The remaining two stations, located at small ponds next to man-made weirs, experienced accumulated sedimentation and debris, which are conducive to macroinvertebrates, resulting in a substrate-driven model. Furthermore, this study demonstrated that PLS-SEM provides a more precise understanding of the direct and indirect effects of environmental factors on macroinvertebrates compared to linear fitting and principal component analysis methods.

As a frequent extreme event under global climate change, drought significantly threatens the net primary productivity (NPP) of terrestrial ecosystems. Although numerous studies have reported drought-induced declines in NPP, the hydroclimatic mechanisms behind remain insufficiently understood. The strong interdependence among temperature, precipitation, and water availability has made it challenging to quantify their independent effects on vegetation productivity under drought stress. Here, we assessed basin-scale NPP responses to drought across mainland China from 1982 to 2018 and applied a ridge regression method to disentangle the individual impacts of multiple hydroclimatic drivers on NPP variability, which overcomes the limitations of conventional correlation-based analyses by accounting for multicollinearity, thereby allowing a more robust identification of the dominant controls on drought-induced NPP variations. Our results revealed pronounced spatial heterogeneity in NPP responses among major basins, with the strongest productivity losses in the Songliao River basin and the Yellow River basin (12.41 g & sdot;C & sdot;m-2 & sdot;yr-1 and 11.71 g & sdot;C & sdot;m-2 & sdot;yr-1, respectively). Ridge-derived coefficients indicated that water availability was the primary driver of NPP sensitivity to drought, and stronger wateravailability control was typically associated with greater NPP losses during drought in water-limited basins. By integrating basin-scale analysis with a multivariate attribution framework, this study isolated the hydroclimatic controls of vegetation productivity under drought. The findings can improve understanding of terrestrial carbon dynamics and be informative for enhancing drought resilience and optimizing water-carbon management strategies in terrestrial ecosystems.

Abstract Self‐supervised learning (SSL) provides an innovative paradigm for pretraining without involving any new data or labels. However, its potential has not yet been evaluated in the field of typhoon raincloud nowcasting. Therefore, we explore two different SSL approaches: autoencoder (AE) and masked autoencoder (MAE). Both are then paired with a subsequent adaptation strategy, namely fine‐tuning (FT). We evaluate the performance gains from different combinations using the popular U‐Net backbone across multiple evaluation metrics. The results show that both AE‐FT and MAE‐FT outperform control experiments (Ctrl), where the U‐Net is trained from scratch without SSL. The AE‐FT yields a root mean square error of 6.56, around 2.6% improvement compared to Ctrl (6.72), whereas the MAE‐FT excels in extreme event detection, achieving a Heidke Skill Score of 0.12 at the 40 dBZ threshold, 50.7% higher than Ctrl (0.08). Comparison with rotation‐based data augmentation further validates the merits of SSL for pretraining, which stem from enrichment of learned representations in general and geospatial sharpness induced by masking strategies. However, the choice of loss function used in SSL greatly impacts its effectiveness, and a perceptually sensitive loss function like structure similarity (SSIM) loss is more desirable than the traditional mean square error (MSE) loss.

Soil organic carbon (SOC) is a key component of the terrestrial carbon cycle and is essential for soil fertility, directly influencing climate change and human well-being. However, it remains unclear how different spectral data sources and machine learning (ML) models can jointly influence SOC prediction performance, especially across large and heterogeneous agricultural landscapes. This study systematically evaluates combinations of spectral data sources, feature-selection methods, and ML models for SOC prediction in the farmland of the Loess Plateau (LP), a region characterized by fragmented croplands and limited carbon stock data. Based on large-scale field sampling of topsoil (0–5 cm) and measurements of SOC content and reflectance spectra from 460 samples, we evaluated 52 combinations of ML models and three categories of input data, including hyperspectral data, resampled multispectral data, and multispectral data combined with environmental variables. The results show that the hyperspectral-based model achieved the highest accuracy (R2 = 0.97 in validation), but its reliance on regional-scale hyperspectral datasets restricts its practical applications. In contrast, the multispectral-environmental integration model delivered scalable performance, offering a feasible pathway for regional SOC mapping. The mapping results in this case study show that the SOC content in the farmland of the LP ranges from 0.002 to 22.26 g·kg–1, with low SOC levels predominantly distributed in the loess hilly gully region and the central parts of the sandy and agricultural irrigation region. This study establishes a systematic framework for SOC mapping in heterogeneous agricultural landscapes and offers practical support for carbon management in agroecosystems.

Upholding stable terrestrial ecosystems is integral to supporting climate regulation and planetary security. Yet, while aboveground ecosystem stability is widely described, global-scale patterns in belowground ecosystem stability and how it connects to aboveground stability remain virtually unknown. Here, we assembled a global dataset including high-resolution information on annual estimates of soil respiration from 4,544 communities and associated aboveground ecosystem productivity over the past four decades (1985-2018). We found that ecosystems with greater stability in aboveground productivity had greater long-term stability in soil respiration, with a positive and significant connection between above- and belowground stability being especially strong in arid environments. Stable temperatures played a crucial role in reinforcing the stability and coupling of above- and belowground ecosystems. Our work provides new evidence of, and insights into, the local to global connections of stability of above- and belowground biological activity, and identifies a fundamental role of temperature stability in maintaining this stability under a changing climate.

Vegetation restoration represents a highly effective strategy for offsetting carbon emissions, mitigating climate change, and enhancing the quality of the eco-environment. Despite the ecological benefits, the large-scale 'Grain-for-Green' program in China has caused unintended hydrological consequences, threatening the sustainability of regional water resources in this arid and semi-arid region. Therefore, it is crucial to understand the competing water demands of ecosystems and humans. In this study, we investigated the effects of vegetation change on the water cycle and the vegetation carrying capacity in a semi-arid loess-gully basin using our previously modified Soil and Water Assessment Tool (SWAT), which focuses on simulating forest growth from young to mature stages. Model validation demonstrated that the improved SWAT model can well simulate eco-hydrological processes, including leaf area index (LAI), streamflow, evapotranspiration (ET), and soil water content. Simulations under various vegetation restoration scenarios revealed that low- and medium-intensity revegetation (converting sloping farmland (slope > 15 degrees) to grass or forest) resulted in slight changes (<= 4.84 %) in mean annual streamflow, soil water, and ET. In contrast, high-intensity revegetation caused significant water stress, with streamflow and soil moisture reduced by 19.35 % and 15.14 %, respectively. In addition, the vegetation carrying capacity of the watershed was evaluated, indicating that the basin can sustainably support a maximum LAI increase of 0.20 (15.63 %). Overall, this study can be valuable for vegetation restoration and forest management in the Chinese Loess Plateau, and the proposed methods can be applicable in other areas.

IntroductionSystemic building design is a critical starting point for enabling Circular Economy (CE) transitions in the Building Construction Industry (BCI) and advancing modern methods such as Industrialised Construction (IC). However, in practice, design approaches promoting circularity and those supporting manufacturing-led construction are often pursued separately, creating misalignments that limit both sustainability and productivity outcomes. Design for Circular Manufacturing and Assembly (DfCMA) is proposed as an integrative framework aligning Design for Circularity (DfC) with Design for Manufacturing and Assembly (DfMA). Proponents of DfC aim at both contributing to and benefiting from worldwide CE thrusts, while advocates of DfMA separately point to multiple benefits in boosting productivity, quality and reliability in particular. This paper presents a case for synergising these two major thrusts in the BCI, by focusing on their shared and complementary values, factoring in inevitably competing values and operational protocols that could result in complex interactions and inter-dependencies.MethodsSystems Dynamics Modelling (SDM) was therefore adopted to capture the complexities, model their dynamics and next help identify ways forward following this integrated modelling of the DfCMA constructs identified in the study underling this paper. Accordingly, this study develops a Causal Loop Diagram (CLD) as the conceptual modelling foundation for SDM to reveal the systemic relationships governing DfCMA. The structural architecture of these constructs was examined through methodological triangulation, combining qualitative insights from a Delphi study with twelve BCI experts and quantitative validation through a questionnaire survey analysed using Partial Least Squares–Structural Equation Modelling (PLS-SEM).Results and discussionThe empirically-supported structural relationships among eighteen DfMA variables, eighteen DfC variables, and six performance variables were mapped through developing the CLD. The resulting CLD captures the dynamism and complexity of integrating CE principles and practices of modular building design owing to the stakeholders that co-create and co-evaluate at varying degrees of connectivity across the construction value chain and the intricacy of the product itself: for example, materials and components in buildings have their own life cycles and functions, while dynamically interacting with each other over space and time, thereby also influencing the state and performance levels of each other, over the lifespan of a building.

Freshwater ecosystems are closely related to human lives, especially in rural areas, necessitating advanced tools for monitoring sediment dynamics, which are critical for water quality and aquatic health. Turbidity, as one of the key indicators of suspended sediments, is widely adopted to monitor the sediment dynamics. This paper addresses the urgent need for robust turbidity prediction in Hong Kong's Lai Chi Wo (LCW) catchment, as a case study where rural communities rely on riverine water resources. Four DL models namely Long Short-Term Memory (LSTM), Encoder-Decoder LSTM (ED-LSTM), Temporal Convolutional Network (TCN), and Informer are employed for multi-step turbidity forecasting using high-resolution (5-minute interval) on-site hydrological data. Our results demonstrate Informer's superior short-term (ST) prediction skill (NSE = 0.83, KGE = 0.85), closely followed by ED-LSTM (NSE = 0.80, KGE = 0.80). Both models exhibit temporal dependency modeling, outperforming LSTM and TCN, particularly in long-term (LT) forecasts. Moreover, the insight of temporal modeling is uncovered by element-wise saliency maps, a model explainable tool, revealing turbidity autoregression and rainfall triggers as dominant drivers. The merit of predictions of ED-LSTM and Informer originates from their capacity in capturing fading temporal dependencies and identifying the physical trigger in models. This work advances DL applications in hydrology by bridging the gap between prediction performance and mechanistic interpretation, offering new insights for predictive modeling.

The intricate interplay between hydrological and biogeochemical cycles underpins the sustainability of watershed resources, making it essential to comprehend their climate responses for adaptive strategies. Although climate change significantly influences the dynamics of the water-carbon cycle, understanding hydrobiogeochemical responses to climate change remains limited. In this study, we utilized the coupled hydrobiogeochemical model (SWAT-DayCent), known for its robust simulation of hydrological and biogeochemical processes, to evaluate how climate change influences water-carbon dynamics in the Weihe River Basin (WHRB), the largest tributary of the Yellow River. We further predicted the hydro-biogeochemical consequences using climate scenarios derived from four General Circulation Models under three Representative Concentration Pathways (low, medium, and high emissions pathways), with uncertainty analysis of future predictions. The results indicate that the net primary productivity (NPP) would rise under low and medium emissions pathway scenarios with rising temperatures and precipitation. Moreover, the WHRB shows that NPP and soil organic carbon (SOC) are more prominent in the southern parts and less in the northern parts. It is noteworthy that the continued air temperature rise could trigger a decline in SOC in the late century (2070-2099) under the high emissions scenario, though slight increments in precipitation and NPP might partially counterbalance this adverse effect. In summary, this study highlights the need for adaptive management strategies, especially under high emission scenarios, where rising temperatures may diminish SOC, necessitating policies that could enhance soil carbon sequestration and mitigate adverse climate impacts.

Fractional Vegetation Cover (FVC) and Land Surface Temperature (LST) are critical indicators for assessing grassland ecosystems. Based on global remote sensing data for FVC and LST from 2001 to 2022, this study employs the Mann–Kendall trend test and Spearman correlation analysis to explore the dynamic changes in and spatial distribution patterns of both variables. The results indicate that the FVC is increasing in regions such as Europe, the eastern southern Sahara, western India, eastern South America, western and southern North America, and central China. However, it is decreasing in southern Canada, the central United States, and northern Australia. Significant increases in LST are observed in subarctic regions and the Tibetan Plateau, attributed to polar warming effects associated with global climate change. Conversely, the LST is decreasing in central China, eastern coastal Australia, and southern Africa. The global FVC–LST relationship exhibits the following four distinct spatial distribution patterns: (1) FVC increase and LST increase (Type 1), (2) FVC increase and LST decrease (Type 2), (3) FVC decrease and LST increase (Type 3), and (4) FVC decrease and LST decrease (Type 4). Type 1, covering 33.72%, is primarily found in high-latitude and high-altitude areas, such as subarctic regions and the Tibetan Plateau. Type 2, the largest group (46.98%), is mainly located in eastern North America, eastern South America, and southern Africa. Type 3, which comprises 18.72%, is concentrated in arid and semi-arid regions, while Type 4, representing only 0.59%, lacks clear spatial distribution patterns.

Rapid urbanisation and population growth call for more Industrialised Construction (IC) as a swifter, safer, higher-quality and affordable means of delivering housing and infrastructure. Meanwhile, rising global temperatures and extreme weather patterns call for immediate action to combat environmental degradation. The Building Construction Industry (BCI) is a leading contributor to global resource extraction and waste generation, posing a significant threat to our environment and planet. Design for Circular Manufacturing and Assembly (DfCMA) is an overarching design framework that synergises circularity (Design for Circularity (DfC)) and modularity (Design for Manufacturing and Assembly (DfMA)) by enhancing their shared values. This study explores the functional apparatus of DfCMA by identifying 21 DfMA constructs and 20 DfC constructs in the BCI through a rigorous literature review, first analysed descriptively, followed by Exploratory Factor Analysis (EFA) and Fuzzy Synthetic Evaluation (FSE) of the initial findings from a suitably focused questionnaire survey. The study findings confirm the significance of applying the 41 constructs above in advancing the concept of DfCMA in the BCI. This study thus adds value to research and practice, exploring the underlying mechanism of this novel DfCMA concept, which synergises two imperatives, promoting a Circular Economy (CE) and DfMA principles and practices in IC.