Multi-Modality Spatio-Temporal Forecasting (MoSTF) extends traditional spatio-temporal forecasting by incorporating diverse traffic modalities. Despite significant recent strides in spatio-temporal modeling, existing approaches often fail to explicitly model the coupling relationships between different modality variables. Accurate MoSTF is challenging, as it requires modeling (1) temporal dynamic heterogeneity under exogenous influences and (2) heterogeneous spatial dependencies alongside complex cross-variable couplings. To address these challenges, we propose the Dual-Domain Spectral Filtering Network (DSFNet). Our framework employs dual-domain spectral filtering to capture heterogeneous spatial patterns and explicitly model the relationships between variables. Unlike graph-based message passing or dense attention over node-modality pairs, DSFNet factorizes space-modality interactions into feature-domain and spatial-domain spectral operators, enabling scalable modeling of nonlocal dependencies and cross-modality couplings. Furthermore, we introduce an external gating mechanism to adaptively regulate temporal dynamics under external influences. We validate our method through extensive experiments on five representative real-world traffic datasets. Compared with the second-best baselines, DSFNet reduces MAE by 3.21

We evaluated the effectiveness and long-term sustainability of China's low-carbon policies using a comprehensive policy intensity index and satellite-based CO2 emissions. We found that both command-and-control and market-based measures have significantly reduced emissions across China but mainly via scale effects (i.e., contraction of industrial activity) rather than technique effects (i.e., more green invention patents granted and an increase in carbon total factor productivity) or composition effects (i.e., industrial upgrading and clean energy transition). Furthermore, command-and-control policies are associated with less green innovation, while market-based policies lead to limited gains in industrial restructuring and, unexpectedly, also show a negative association with clean energy adoption. Using a unique dataset of millions of business registration records and county-level CO2 emissions, we also uncovered substantial intra-national carbon leakage at the city level, with emissions relocating to provincial border areas where enforcement is weaker, thus exacerbating emission inequality among jurisdictions. Furthermore, our novel transfer learning projections indicate that current policies may lose their efficacy in nearly 47% of cities under foreseeable economic and structural changes, exposing the fragility of contraction-led carbon abatement. These results underscore the need to move beyond the short-term suppression of outputs toward a durable, innovation-driven pathway of decarbonization.

With the rapid evolution of battery technology, China’s new energy vehicle (NEV) sector is undergoing profound shifts in critical material demand and lifecycle dynamics. This paper constructs multiple scenarios from 2025 to 2040, considering emerging battery penetration, lifespan extension, and improved echelon utilization. Results show that in the most advanced scenario, total demand drops by nearly 60% compared to the baseline. Ni, Co, and Mn peak around 2035-2038, while Li demand continues to rise, posing supply risks. The recycling potential is steadily increasing, with the proportion of recycling potential after echelon utilization rising from less than 5% to nearly 50%. Li’s demand coverage stays below 65% in all scenarios, dropping to 40% in the worst. In contrast, Ni, Co, Mn, and graphite may reach full recycling-based coverage by 2038, while Al and Cu still show 10-25% gaps. These findings suggest that future policy should place greater emphasis on addressing the persistent lithium supply gap, improving lifecycle coordination between battery use, echelon utilization, and final recycling, and optimizing collection networks according to regional recycling potential. The study provides quantitative evidence and policy insights to support the sustainable production and consumption transitions in China’s NEV industry.

In February 2022, Southern China experienced a severe compound wet and cold event (CWCE) that caused major socioeconomic impacts on transportation, power supply, and agriculture, ranking among China’s top ten natural disasters of the year. To what extent anthropogenic climate change has already affected such CWCEs remained unclear. This study employs a circulation analogue-based risk attribution framework with observation to quantify contribution of large-scale atmospheric circulation and the thermodynamic effect of climate change to the event, and further used simulations from coupled model intercomparison project (CMIP6) models under different external forcing to assess the anthropogenic influence. The main findings are as follows: atmospheric circulation anomalies were the dominant driver of this CWCE. The contribution of circulation patterns similar to those in February 2022 was estimated at 73% (95% confidence intervals, CIs: 71%, 76%). Thermodynamic effect of climate change reduced the probability of occurrence of this CWCE by 32% (95% CIs: 27%, 36%). Further analysis using CMIP6 models and a risk-based attribution approach confirms that anthropogenic forcing suppresses the occurrence of CWCE, reducing its probability by 21% (95% CIs: 5%, 33%).

Sustainable development has accelerated the transition to electric and lightweight vehicles, increasing global aluminium demand. This trend heightens supply risks due to constrained primary aluminium production. Recycling aluminium from end-of-life (EoL) vehicles is critical to alleviating these supply constraints and supporting sustainable development in China’s automotive sector. This study employs a grey forecasting model to project electric passenger vehicle (EPV) sales and estimates aluminium demand and EoL EPV recycling potential in China from 2024 to 2035, considering three service lifespan scenarios (8, 10, and 12 years) and two lightweighting scenarios (Slight and Deep). By 2035, under the Deep Lightweighting scenario with an 8-year lifespan, EoL EPVs could yield 3.75 million tons of recycled aluminium, offering substantial resource recovery and economic benefits. As a secondary supply source, recycled aluminium significantly supplements primary production, reducing supply constraints. Extending EPV service life through technological improvements and promoting recycled aluminium use among manufacturers are key strategies for advancing sustainable lightweighting and fostering a circular economy.

Heavy precipitation events (HPEs) in the Yangtze River Valley (YRV) pose persistent threats to population safety and infrastructure. This study classifies five population-weighted HPE types (P1-P5) and examines their underlying dynamics using multiscale window transform and canonical energy transfer framework. Each type links to distinct circulation patterns. During 1979-2020, P1 and P2 are associated with Meiyu front systems, with P1 producing stronger precipitation. P2 accounts for 68% of all events, resulting in the greatest population exposure. P3 is driven by landfalling typhoons and represents the second most frequent type. P4 arises from short-wave disturbances that primarily affect northern YRV, whereas P5 is concentrated in the western YRV and influenced by low-vortex systems. Multiscale energy diagnostics show that most HPEs draw energy from the transfer of available potential energy (APE) from background to synoptic circulations, which subsequently convert to kinetic energy (KE). This transfer dominates the maintenance of P1 and P2. In contrast, P3 cases rely more on KE cascades, reflecting their stronger dynamic forcing. P4 and P5 exhibit weaker exchanges and depend more on background circulations. Additionally, future climate projections indicate that warming will amplify convective instability and strengthen APE-to-KE conversions in P1 and P3, but weaken these transfers in P2, P4, and P5. As a result, the currently dominant P2 type is expected to decline, while P1 and P3 become more frequent. By 2100, the population exposed to these type events will increase by more than 20%, posing substantial risks to societal resilience and adaptation.

Extraordinary loads in building structures feature short durations, low occurrence frequencies, and large amplitude variability. These characteristics introduce significant uncertainty into their stochastic evolution. Traditional survey methods rely on subjective human memory, which inevitably introduces epistemic uncertainties. To address these limitations, this study proposes a big data-driven stochastic simulation framework for the probabilistic modeling of extraordinary loads. By integrating virtual reality (VR) property systems with physical furniture databases from e-commerce platforms, the framework executes Monte Carlo simulations (MCS) for two primary transient events, namely temporary furniture stacking and crowd gatherings, to generate representative load amplitude samples. Furthermore, a global sensitivity analysis based on Kullback-Leibler (KL) divergence indicates that the extreme value distribution of the maximum combined load is predominantly governed by the variability of load amplitudes. The influence of occurrence intervals is minor. Based on this finding, the compound Poisson point process is simplified into a stationary binomial process, providing an efficient approach for structural reliability assessment.

To address a core bottleneck in the development of Urban Air Mobility (UAM), namely the physical simulation of complex, dynamic low-altitude wind fields, this study successfully developed and validated the large-scale integrated simulation facility named “Wind-Matrix.” Through three integrated designs oriented toward low-altitude aircraft testing, the facility extends the application scope of existing multi-fan wind tunnels and extreme-wind-field simulators: (1) It uses a horizontal wind wall composed of 81 independently programmable high-speed axial-flow permanent magnet synchronous fans, enabling precise and rapid (sub-second) control of wind speed, wind shear, and gusts. (2) It integrates a movable vertical jet, a bottom swirl flow system, and the horizontal wind wall into a synchronized facility, allowing the stable reproduction of the complete evolution of extreme wind fields—such as tornadoes and downbursts—within a single experiment. (3) It enables precise generation of complex three-dimensional non-uniform flow fields, including sinusoidal distributions, through independent coordinated control of all 81 fans, with dynamic wind-field adjustment based on real-time feedback. Experimental results demonstrate that the platform can accurately generate maximum wind speeds of 55 m/s, customizable turbulence spectra, wind shear gradients, transient gusts, tornadoes and downbursts, successfully simulating multidimensional urban wind environments ranging from steady flows to unsteady composite flows. This work helps narrow the critical “data gap” between numerical simulation and real flight, providing an essential data foundation for aerodynamic testing, control system verification, and the establishment of safety standards for aircraft. It is expected to significantly advance the safety and engineering maturity of UAM technologies. Chen Zhao and colleagues developed and validated Wind-Matrix, a large-scale low-altitude Urban Air Mobility wind simulator with 81 programmable fans, vertical jet, and swirl system. It generates 55 m/s max winds, turbulence spectra, shear, gusts, tornadoes, downbursts, and 3D non-uniform flows, bridging the critical sim-to-flight data gap and supporting aerodynamic testing, control system verification, and safety standards.

The weight of existing buildings is a critical parameter in various structural engineering applications, including seismic assessment, uneven settlement evaluation, structural vibration control, building relocation, and demolition operations. While current practice typically estimates this value by multiplying floor area multiplied by an empirical unit weight coefficient. This approach faces limitations when the original design details are unavailable, making total floor area difficult to determine. To address this challenge, this study develops predictive models for estimating the weight of existing reinforce concrete (RC) buildings using easily accessible structural parameters, such as structural height, plan dimensions, number of stories, and fundamental period. A database comprising the weights and related design parameters of 732 RC buildings was developed through an extensive literature search. The maximum information coefficient and Kruskal–Wallis analysis of variance were used to identify factors that significantly influence building weight. Subsequently, regression formulas for building weight, incorporating structural height, plan dimensions of a standard floor, fundamental period, and structural type were established. These prediction formulas were applied to five building examples, and the results were compared with actual values. The comparison shows that the weight prediction formulas have good accuracy and can be used in state assessment of existing buildings and parametric modeling in disaster prevention analysis of urban buildings. Finally, the predictive models have been deployed on an online web page for the convenience of users.

Enhancing greenspace cooling efficiency (GCE) is a cost-effective nature-based solution to improve the urban thermal environment. The spatiotemporal patterns of GCE and their driving factors have been investigated mainly based on land surface temperature in a spatial comparison perspective. However, the diurnal change in GCE based on air temperature (AT) and its non-linear responses to meteorological factors are far from thoroughly understood. Taking the subtropical Chinese city of Changsha as an example, we quantified the hourly GCE based on AT in the hottest month of 2020, investigated its diurnal changes, and uncovered its non-linear responses to meteorological change using the Generalized Additive Model. The results showed that (1) the hourly GCE displayed a U-shaped temporal pattern with an average of 0.0128 °C%−1. The nighttime GCE (0.0134 °C%−1) was significantly higher than the daytime GCE (0.012 °C%−1). (2) Meteorological factors (i.e., temperature, relative humidity, and wind speed) significantly and non-linearly impacted GCE. (3) The responses of GCE to changes in relative humidity and wind speed followed an inverted U-shaped pattern, with the maximum values appearing at a relative humidity of 70% and a wind speed of 6m/s, respectively. GCE responded to temperature change more complexly, i.e., a negative response (<28 °C), then a positive response (30–35 °C), and finally a negative response (>35 °C). These findings extend our understanding of the diurnal variations of GCE and the non-linear responses to meteorological change and can help effective urban greenspace planning and management in Changsha, China, and other cities with similar climates in an era of rapid climate change. For example, expanding greenspace coverage as well as optimizing greenspace spatial configuration should be a priority action in areas where the AT is higher than 35 °C currently and will be in the future.

Understanding the severe flood erosion and subsequent sedimentation in arid mountainous regions is crucial for assessing future flood risks under the pressure of global warming and human activities. It has long been hypothesized that a shift toward a more arid climate in an arid environment could enhance flood erosion despite the decreased discharge in rivers. However, the scarcity of long and reliable flood records makes testing this hypothesis difficult, thus limiting understanding of flood erosion during climate aridification. Here, we reconstruct a 1000-year-long extreme flooding record by exploring original Chinese historical archives and by analyzing sediment cores from the semi-arid mountainous catchment-Daling River estuary, NE Asia, based on the observation that instrumental floods normally cause coarse particle enrichment in the estuary. Our data reveal that on the centennial scale, extreme flooding mainly coincided with periods of climate aridification. The frequent rainstorms in this high variable topography catchment, coupled with reduced vegetation coverage as the climate shifts toward a more arid condition, have primarily contributed to the heightened flood erosion. Additionally, our record highlights the significant impact of accelerated reservoir construction and vegetation restoration in the river catchment since 1960 CE. These human activities have led to a noticeable reduction in coarse particle contents and sediment flux reaching the estuary which supports the previous viewpoints that human activities in Asia have greatly decreased river load entering the coastal oceans.

Increasing the proportion of lightweight materials in automobile manufacturing materials can effectively reduce carbon emissions. Consequently, there exists a close coupling relationship between materials and energy. This study aims to analyze the development of lightweight materials in the future, obtain the material stock and carbon emissions of vehicles during the use phase, and further illustrate the comprehensive impact of lightweight on resources and energy. This study focuses on analyzing the development of several different vehicle models (fuel vehicle, battery electric vehicles, and plug-in hybrid electric vehicles), while discussing the varying degrees of development of several lightweight materials (Advanced and High-Strength Steels, Aluminium, Magnesium and plastics and plastic composites). The results indicate that the use of lightweight materials can achieve carbon peak earlier, but it also brings about an increase in resource demand. In order to alleviate resource pressure, the continuous supply of lightweight materials and the development of recycling technology need to be given special attention. This paper has policy reference significance for the collaborative management of energy and material utilization. At the same time, it can provide theoretical and data support for energy and materials sustainable development planning.

Ecosystem dynamics and ecological disturbances manifest as breakpoints in long-term multispectral remote sensing time series. Typically, these breakpoints are captured using univariate methods applied individually to each band, with subsequent integration of the results. However, multivariate analysis provides a promising way to fully incorporate the multispectral bands into breakpoints detection methods, but it has been rarely applied in monitoring ecosystem dynamics and detecting ecological disturbances. In this research, we developed a multivariate algorithm, named breakpoints-Detection algoRithm using MultivAriate Time series (DRMAT). DRMAT can fully use multispectral bands simultaneously with the consideration of the inter-correlation among bands. It decomposes a multivariate time series into trend, seasonality, and noise, iteratively segmenting the detrended/ de-seasonalized signals. We quantitatively evaluated DRMAT using both simulated multivariate data and randomly sampled real-world data, including subtle land cover changes caused by forest disturbances (depletions) and recovery (return of vegetation), as well as subtle changes over a broad range of land cover types. We also qualitatively assessed DRMAT in mapping real-world disturbances. For simulated data with prescribed breakpoints in both trend and seasonality, DRMAT detected breakpoints in trend with an F1 score of 85.5 % and in seasonality with an F1 score of 91.7 %. For real-world data in forested land cover, DRMAT unveiled both disturbances and subsequent recovery with an F1 score of 95.1 % for disturbances and 77.1 % for recovery. It detected disturbances in broader land cover types with an F1 score of 84.0 %. We demonstrated that using allband data was more accurate than using selected bands in breakpoint detection. The inclusion of vegetation indices as model inputs did not improve accuracy unless the original input bands lacked the specific band information in the vegetation indices. As a multivariate approach, DRMAT leverages the full information in the multispectral data and avoids the necessity of integrating results derived from individual bands.

The Tibetan Plateau, with its great hydrothermal gradients and diverse ecosystems, is considered vulnerable to climate change. Extreme drought can have detrimental effects on carbon sequestration in terrestrial ecosystems by disrupting plant eco-hydrological processes. Such effects are presumed to vary and depend on the vegetation types, environmental factors and drought properties. The drought timing has been widely highlighted in drought studies at both regional and site scales. However, the systematic insight into the impact of drought timing on the ecosystem functioning over the Tibetan Plateau remains unclear. In this study, we investigated the responses of vegetation greenness to meteorological drought and attributed them to the drought properties, climatic and edaphic factors. We found that the timing of drought plays a predominant role in regulating vegetation drought responses on the Tibetan Plateau. Notably, we observed significant differences in vegetation responses between late growing season drought and non-late growing season drought. In addition to drought timing, soil moisture and long-term hydrothermal conditions also played a significant role. Furthermore, our study revealed that alpine grassland was more sensitive to the drought timing, soil moisture and sand content than woody plants. We discovered a significant interplay between rainfall at hottest quarter and drought timing, with the role of drought timing weakening as the rainfall at hottest quarter increases. These findings underscore the crucial role of drought timing in shaping ecosystem functioning in response to the changing climate regime over the Tibetan Plateau and provide crucial insights into the improvement of land surface models. Global warming is leading to increased drought stress worldwide. The intensification of extreme droughts has adverse effects on vegetation growth in large vegetated regions. The timing of drought events is especially important when vegetation growth exhibits high seasonality. However, the responses of vegetation growth to the timing of extreme droughts and the underlying drivers are poorly understood on the Tibetan Plateau, despite being a hotspot region experiencing marked warming. Here, we quantified the impacts of drought on the vegetation growth across diverse ecosystems on the Tibetan Plateau based on remote-sensing observations. Our results pointed out that drought timing is the dominant factor regulating the responses of vegetation to meteorological drought. Specifically, we found that alpine grassland exhibits greater sensitivity to the timing of drought events compared to forest and bushwood. Moreover, our study revealed that vegetation in regions with higher summer rainfall is less sensitive to drought timing. These findings underscore the vital role of drought timing in regulating ecosystem functioning across the Tibetan Plateau under future climate change. The occurrence of extreme drought events decreases significantly and temporally in the westerlies and transition domains Drought timing plays a dominant role in determining the vegetation responses to extreme drought The sensitivity of vegetation growth to drought timing strongly depends on rainfall at the hottest quarter

The growing demand for new energy vehicles (NEVs) has resulted in a corresponding increase in demand for cobalt as a critical material. It is crucial to estimate the cobalt resource recycling potential of China’s NEV industry to ensure a balance between the supply and demand for cobalt metal minerals. This article is based on using the historical data of the new energy passenger vehicle (NEPV) sales volume from 2013 to 2022 to estimate the NEPV sales volume from 2023 to 2035. On this basis, the Weibull distribution was used to analyse the different sales scenarios (low sales and high sales) of NEPVs in China, and the recycling potential of cobalt metal in NEPVs was evaluated under three battery life scenarios (8, 10 and 12 years) from 2023 to 2035. Based on the above scenarios, in 2035, the greatest recycling potential of cobalt is predicted to be 166.9 kilotonnes, with economic values of CNY 49.01–94.60 billion. Moreover, the extent to which the recycling potential of cobalt can cover the market demand for NEPVs was analysed. Our analysis concluded that recycling cobalt as a secondary supply has emerged as a necessary solution to supplement the primary supply, which can make a significant contribution to alleviating the pressure of the supply and demand.

Understanding crop responses to climate change is crucial for ensuring food security. Here, we reviewed similar to 230 statistical crop modeling studies for major crops and summarized recent progress in estimating climate change impacts on crop yields. Evidence was strong that increasing temperatures reduce crop yields. A 1 degrees C warming decreased the yields by 7.5 +/- 5.3% (maize), 6.0 +/- 3.3% (wheat), 6.8 +/- 5.9% (soybean), and 1.2 +/- 5.2% (rice) across the world, but spatial heterogeneity was noticeable, due partly to asymmetric nonlinear crop responses to temperature (e.g., warming-induced gains in cold regions). Yield responses to precipitation were not consistent across the studies or geographical areas. On average, climate explained 37% of yield variability. We also observed a methodological shift from linear regression to machine learning (e.g., explainable AI and interpretable machine learning), which on average reduced predictve errors by 44%. Furthermore, we discussed the opportunities and challenges facing statistical crop modeling, such as ensemble modeling, physics-informed machine learning, spatiotemporal heterogeneity in crop responses, climate extremes, extrapolation under novel climates, and the confounding from technology, management, CO2, and O-3.

Understanding the intricate changes in land use and land cover (LULC) transformations, as well as accurately quantifying the ecosystem services value (ESV), holds paramount importance in achieving sustainable development goals. However, previous studies have notably neglected conducting empirical evaluations across various LULC prediction models under identical conditions within the same geographical region. Additionally, the majority of relevant studies primarily concentrate on local scales. In this study, we used CA-Markov, Future Land Use Simulation (FLUS) and Patch-generating Land Use Simulation (PLUS) models to simulate the dynamics of LULC, respectively. Subsequently, we successfully projected the future LULC patterns and corresponding ESV within the Harbin-Changchun Urban Agglomeration (HCUA), China. During the period spanning from 2000 to 2020, a persistent reduction of 2067 km2 in farmland was witnessed within the HCUA, while an accompanying expansion of 4081 km2 in built-up land occurred concurrently. The ESV in HCUA experienced a fluctuating trend. There was an initial decline of 7.443 x 109 yuan during the first decade, which was subsequently followed by an increase of 4.615 x 109 yuan during the latter decade. PLUS, FLUS, and CA-Markov models can all simulate the LULC of HCUA, but in decreasing order of accuracy and ease of use, with OA values of 0.8987, 0.8944 and 0.8651, and Kappa values of 0.8217, 0.8150 and 0.7860, respectively. The PLUS model was chosen to predict the future LULC. Then four development scenarios were set. Our optimization results indicate that it is advisable to restrict the area of built-up land within 17000 km2 in 2030. Moreover, under the economic and ecological balance (EEB) scenario, both ESV and economic benefits are projected to increase compared to the business as usual (BAU) scenario. This study offers valuable insights and serves as a significant reference for future research endeavors focused on optimizing land use.

Understanding the changes of wetlands on the Tibetan Plateau (TP) is important for action to ensure ecosystem resilience in Asia. However, mapping long-term changes of wetlands at high resolutions remains challenging. Here, we quantify the spatio-temporal changes of TP wetlands from 1990 to 2019, by combining Landsat imagery with deep learning to map TP wetlands. The deep learning model combined with transfer learning strategies achieves high classification performance using a few class samples. The validation results show that the user’s accuracy is 95.5% and the producer's accuracy is 90.1% for wetland extraction, satisfying with subsequent analysis of wetland spatio-temporal changes. Based on the wetland extraction model, we have created annual wetland map in the TP for the first time. We find that the areal extent of TP wetlands has increased by 31.2 ± 6.6 % over the past 30 years. The growth is particularly noticeable (by 22.5 ± 6.2 %) during 2015–2019. Spatially, the wetland areal extent on the Qiangtang Plateau (in the inner part of TP and as habitats of various birds and rare wild animals) and the source region of Yangtze River show the largest expansions by 55.3 ± 9.3 % and 44.0 ± 8.9 %, respectively. Such rapid wetland expansions are associated with increasing rainfall and temperature which have heterogeneous influences on wetland changes across the TP. Our findings provide evidence for the impact of climate change on wetland area. The marked wetland changes highlight that climate mitigation is a priority for high-latitude ecosystems.

The Tibetan Plateau(TP),known as the"Third Pole"of the Earth and"Asian Water Tower",is the magnifier of global climate change and birthplace of many large rivers in Asia.There are unique alpine wetlands on the TP,accounting for 20％of Chinese wetlands area,and the lakes alone constitute half of the national lake area.Wet-lands are critical to human survival and development as one of the three major ecosystems[1].As an ideal natural environment for sequestration and storage of carbon dioxide(CO2)from the atmosphere,wetland ecosystem plays an important role in global carbon cycles[2].Understanding the changes and drivers of wet-lands on the TP is important for action to ensure ecosystem resili-ence like vegetation cover and species diversity in Asia.Yet,measuring the long-term dynamics of wetlands remains a chal-lenge due to uncertainty of wetland boundaries,complexity of spectral and texture characteristics as well as the lack of the label-ing wetland data on the TP.To our knowledge,the spatial distribu-tion,inter-annual variation and multi-decadal trends of the TP wetlands remain very limited,and consequently their responses to climate variability remain little known.Previous maps and data-sets on the TP wetlands are developed either for a specific year[3-5]or at coarse spatial resolutions(e.g.,1 km)[6].Such coarse res-olutions and time incompleteness cannot capture the complexities of spatio-temporal dynamics of wetlands as well as how climate affects wetland changes.