Search Results (926)

The North China Plain, a major grain production base in China, is facing the chronic threat of climate-change-induced delays in winter wheat sowing, with late sowing constraining yields by shortening the pre-winter growth period, and soil moisture at sowing potentially serving as a key factor to alleviate late-sowing losses. However, previous studies have mostly independently analyzed the effects of sowing time or water stress, and there is still a lack of systematic quantitative evaluation on how the interaction effects between the two determine long-term yield potential and risk. To fill this gap, this study aims to quantify, in the context of long-term climate change, the independent and interactive effects of different sowing dates and baseline soil moisture on the growth, yield, and production risk of winter wheat in the North China Plain, and to propose regionally adaptive management strategies. We selected three representative stations—Beijing (BJ), Wuqiao (WQ), and Zhengzhou (ZZ)—and, using long-term meteorological data (1981–2025) and field trial data, undertook local calibration and validation of the APSIM-Wheat model. Based on the validated model, we simulated 20 management scenarios comprising four sowing dates and five baseline soil moisture levels to examine the responses of phenology, aboveground dry matter, and yield, and further defined yield-reduction risk probability and expected yield loss indicators to assess long-term production risk. The results show that the APSIM-Wheat model can reliably simulate the winter wheat growing period (RMSE 4.6 days), yield (RMSE 727.1 kg ha⁻¹), and soil moisture dynamics for the North China Plain. Long-term trend analysis indicates that cumulative rainfall and the number of rainy days within the conventional sowing window have risen at all three sites. Delayed sowing leads to substantial yield reductions; specifically, compared with S1, the S4 treatment yields about 6.9%, 16.2%, and 16.0% less at BJ, WQ, and ZZ, respectively. Moreover, increasing the baseline soil moisture can effectively compensate for the losses caused by late sowing, although the effect is regionally heterogeneous. In BJ and WQ, raising the baseline moisture to a high level (P85) continues to promote biomass accumulation, whereas in ZZ this promotion diminishes as growth progresses. The risk assessment indicates that increasing baseline moisture can notably reduce the probability of yield loss; for example, in BJ under S4, elevating the baseline moisture from P45 to P85 can reduce risk from 93.2% to 0%. However, in ZZ, even the optimal management (S1P85) still carries a 22.7% risk of yield reduction, and under late sowing (S4P85) the risk reaches 68.2%, suggesting that moisture management alone cannot fully overcome late-sowing constraints in this region. Optimizing baseline soil moisture management is an effective adaptive strategy to mitigate late-sowing losses in winter wheat across the North China Plain, but the optimal approach must be region-specific: for BJ and WQ, irrigation should raise baseline moisture to high levels (P75-P85); for ZZ, the key lies in ensuring baseline moisture crosses a critical threshold (P65) and should be coupled with cultivar selection and fertilizer management to stabilize yields. The study thus provides a scientific basis for regionally differentiated adaptation of winter wheat in the North China Plain to address climate change and achieve stable production gains. Full article

Although bamboo holds great promise as a sustainable construction material in industry, its susceptibility to cracking during drying compromises its mechanical performance and limits its structural applications. This study aims to develop a predictive model for bamboo cracking and investigate effective mitigation strategies. A crack evaluation model for round bamboo was established based on an analysis of tangential stress and validated experimentally in a climate chamber. The model demonstrated a prediction accuracy of 75–80% with a built-in safety margin, while analysis revealed that outer surface strain, inner surface strain, radial elastic modulus, and culm outer diameter all positively correlated with tangential stress, highlighting the importance of controlling these factors to prevent cracking. Moreover, a surface-bonded palm fiber wrapping method was proposed and tested, which significantly enhanced the crack resistance and delayed crack initiation. The effect was most pronounced in 1-year-old bamboo, while culms aged 3, 5, and 7 years remained crack-free until moisture content fell below 5%. The proposed model accurately predicts cracking behavior in bamboo, offering theoretical support for its structural use and practical insights for crack prevention. Full article

Diurnally asymmetric warming under global climate change is reshaping terrestrial ecosystems, with important implications for vegetation productivity, biodiversity, and carbon sequestration. However, the mechanisms underlying the delayed and differentiated vegetation responses to daytime and nighttime warming, particularly under interacting precipitation regimes, remain insufficiently understood, limiting accurate assessments of ecosystem resilience under future climate scenarios. Clarifying how vegetation responds dynamically to asymmetric temperature changes and precipitation, including their lagged effects, is therefore essential. Here, we analyzed the spatiotemporal evolution of growing-season Normalized Difference Vegetation Index (NDVI) across the Yellow River Basin from 2001 to 2022 using Theil–Sen median trend estimation and the Mann–Kendall test. We further quantified the lagged responses of NDVI to daytime maximum temperature (Tmax), nighttime minimum temperature (Tmin), and precipitation, and identified their dominant controls using partial correlation analysis and an XGBoost–SHAP framework. Results show that (1) growing-season climate in the YRB experienced pronounced diurnal warming asymmetry: Tmax, Tmin, and precipitation all increased, but Tmin rose substantially faster than Tmax. (2) NDVI exhibited an overall increasing trend, with declines confined to only 2.72% of the basin, mainly in Inner Mongolia, Ningxia, and Qinghai. (3) NDVI responded to Tmax, Tmin, and precipitation with distinct lag times, averaging 43, 16, and 42 days, respectively. (4) Lag times were strongly modulated by topography, soil properties, and hydro-climatic background. Specifically, Tmax lag time shortened with increasing elevation, soil silt content, and slope, while showing a decrease-then-increase pattern with potential evapotranspiration. Tmin lag time lengthened with elevation, soil sand content, and soil pH, but shortened with higher potential evapotranspiration. Precipitation lag time increased with soil silt content and net primary productivity, decreased with soil pH, and varied nonlinearly with elevation (decrease then increase). By explicitly linking diurnal warming asymmetry to vegetation response lags and their environmental controls, this study advances process-based understanding of climate–vegetation interactions in arid and semi-arid regions. The findings provide a transferable framework for improving ecosystem vulnerability assessments and informing adaptive vegetation management and conservation strategies under ongoing asymmetric warming. Full article

Nuclear Power Plant (NPP) site preparation in tropical regions faces significant schedule and cost risks due to rainfall, which are often addressed with inadequate and unspecified contingencies. This study develops an integrated framework to address these issues by converting multi-year daily rainfall data into auditable seasonal risk inputs for project simulations. The methodology involves synthesizing rainfall data from multiple stations with quality weighting, mapping rainfall to Lost Time Hours (LTH) using a double logistic function, and applying time–cost co-sampling analysis in Primavera Risk Analysis. Applied to the Indonesian case study, the framework predicts an increase in P80 duration of 36 days, or 10.17%, and an increase in cost of USD 64,809, or 8.41%. This analysis reveals that the raw rainfall index is only weakly correlated with delays and cost overruns at the project level, because the network structure and monthly usage levels filter out the weather signal; this weak correlation and the systematic time–cost decoupling encourage comprehensive network simulations rather than simply accounting for uniform weather allowances. This methodology has potential applications for site preparation activities and other types of infrastructure. However, validation on external datasets and calibration to local climate and operational contexts remain critical future steps. This framework provides a transparent and replicable approach to converting local climate data into project-specific contingency data, improving schedule reliability and cost control for construction projects in tropical regions. Full article

Climate variability plays a crucial role in shaping drinking water quality, yet the quantitative links between meteorological factors and microbiological contamination remain underexplored in temperate continental climates. A secondary data analysis was conducted on 15,394 microbiological water quality test results collected between 2015 and 2024, including heterotrophic plate counts (22 °C and 37 °C), coliform bacteria, and Escherichia coli, and their associations with local meteorological conditions across groundwater wells and piped distribution networks were examined. A clear distinction emerged: groundwater wells showed higher vulnerability to primary microbial contamination (coliforms and E. coli), whereas distribution networks exhibited higher odds of heterotrophic plate count exceedances, indicating greater susceptibility to post-treatment microbial regrowth. In groundwater wells, temperature showed strong positive associations with all microbial indicators (p < 0.001), with pronounced summer peaks in coliforms and E. coli, while precipitation triggered short-term contamination spikes characterized by a 2-day lag. In contrast, piped networks exhibited weaker and more delayed meteorological responses. These results highlight the need for a shift from climate-responsive to climate-pre-emptive water quality monitoring by incorporating meteorological forecasts, especially for non-chlorinated groundwater sources. Full article

Climate change and irregular precipitation patterns have increasingly threatened groundwater sustainability in semi-arid regions like the Eastern Mediterranean. Specifically, in coastal Syria, the lack of quantitative understanding regarding aquifer recharge mechanisms hinders effective water resource management. To address this, this study investigates the dynamic relationship between rainfall and groundwater levels in the Al-Hsain Basin coastal plain using 48 months of monitoring data (2020–2024) from 35 wells. We employed a unified analytical framework combining statistical methods (correlation, regression) with advanced time–frequency techniques (Wavelet Coherence) to capture recharge behavior across diverse Quaternary, Neogene, and Cretaceous strata. The results indicate strong climatic control on groundwater dynamics, particularly in shallow Quaternary wells, which exhibit rapid recharge responses (lag < 1 month). In contrast, deeper aquifers showed delayed and buffered responses. A dual-variable model incorporating temperature significantly improved prediction accuracy (R² = 0.97), highlighting the role of evapotranspiration. These findings provide a transferable diagnostic framework for identifying recharge zones and supporting adaptive groundwater governance in data-scarce semi-arid environments. Full article

Leptospirosis remains a significant public health and economic burden in Southeast Asia, particularly in low- and middle-income countries where environmental, occupational, and socioeconomic factors contribute to its endemicity. Transmission is driven by close interactions between humans and infected animal reservoirs, alongside climatic conditions such as heavy rainfall and flooding. The region’s high but variable seroprevalence reflects inconsistencies in diagnostic methodologies and surveillance systems, complicating disease burden estimation. Major gaps persist in diagnostic capabilities, with current tools often unsuitable for resource-limited settings, leading to underdiagnosis and delayed treatment. Environmental modeling and spatial epidemiology are underutilized due to limited interdisciplinary data integration and predictive capacity. Addressing these challenges requires a One Health approach that integrates human, animal, and environmental health sectors. Key policy recommendations include harmonized surveillance, standardized and validated diagnostics, expanded vaccination programs, improved animal husbandry, and targeted public education. Urban infrastructure improvements and early warning systems are also critical, particularly in disaster-prone areas. Strengthened governance, cross-sectoral collaboration, and investment in research and innovation are essential for sustainable leptospirosis control. Implementing these measures will enhance preparedness, reduce disease transmission, and contribute to improved public health outcomes in all sectors across the region. Full article

In Japan, the flowering of the red spider lily (Lycoris radiata) marks the autumn equinox. To evaluate the effect of climate change on Japanese people’s sense of seasons and this cultural ecosystem service, we examined the spatiotemporal variability of the flowering day (FD) of red spider lily at 9 sites (Maebashi, Choshi, Nagano, Kanazawa, Shizuoka, Tsu, Nara, Wakayama, and Okayama) over the past 60 to 70 years through its relationship with the autumn equinox. (1) Delaying trends were statistically significant (0.12–0.16 days per year) at 4 sites (Nagano, Tsu, Nara, and Wakayama). (2) Bayesian inference analysis with a beta distribution showed that the probability of FD being later than the autumn equinox has increased in the 2010s at all sites. (3) The year-to-year variability of FD was positively correlated with average temperature during the period of flower stalk elongation (late August to mid-September) at 7 sites (except Nagano and Shizuoka). These results suggest that the probability of FD being later than the autumn equinox will increase under further warming during the period of flower stalk elongation, thus affecting people’s sense of seasons and this cultural ecosystem service. Full article

Vector-borne diseases are strongly influenced by climate, yet the magnitude and temporal variability of climate–disease relationships remain poorly quantified. Outbreaks occur abruptly, and responses are typically delayed, underscoring the need for predictive tools that can support proactive interventions. This study applies Temporal Fusion Transformers (TFTs) to long-term, high-resolution climate datasets and to weekly malaria and dengue case records from South Africa and Vietnam to assess predictive performance and identify key environmental drivers. The models incorporated diverse climatic predictors and large-scale climate indices and were trained using multi-horizon forecasting with novel loss functions and physics-based constraints. The best malaria model achieved an R² of 0.95 and an MAE of 4.98, while leading dengue models reached R² values up to 0.90. Variable-importance analyses derived from model-learned weights showed that extreme temperature and rainfall metrics were consistently the strongest predictors, with ENSO (El Niño Southern Oscillation) and IOD (Indian Ocean Dipole) improving longer-range malaria forecasts. Furthermore, climate–disease risk dynamics were explored, revealing specific temperature and rainfall thresholds associated with elevated transmission and highlighting non-stationary relationships across decades. These findings demonstrate accurate, interpretable forecasting offered by TFTs and represent a valuable tool for early warning and understanding of complex climate–disease interactions. Full article

Greece’s National Climate Law, enacted under L. 4936, mandates the development of Municipal Emission Reduction Plans (MERPs) by local authorities. Publicly available MERP procurement data contains valuable information that can be utilized to provide an overview and insights into MERP procurement and development. The main objective of this study is to perform a comparative analysis of Greek MERP procurement data and identify patterns in the contract cost estimation of mitigation action plans in Greek municipalities. For this purpose, MERP procurement data was collected from the official procurement register, KIMDIS, and subsequently analyzed through a bivariate approach comparing the collected data with selected independent variables. The results are stratified by population range and official municipal classification to enable comparison between different sizes and types of municipalities. The results indicate that a total of 44% of municipalities in Greece procured their MERP, with significant delays in adherence to official deadlines and only after the MERP became a prerequisite for funding-related matters. Additionally, the procurement process was highly characterized by single bidding. Average contract duration ranged from 110 to 220 days, with an average contract value between EUR 18,000 and EUR 33,000. The difference between tender budget and contract value averaged between 0 and 5%. Full article

Voluntary carbon markets (VCMs) are growing rapidly but remain structurally fragmented due to verification delays, lifecycle opacity, inconsistent metadata, and capital mobilisation bottlenecks. While blockchain is often proposed as a digitalisation layer to improve transparency and traceability, this paper reframes tokenisation as a sector-aware financial infrastructure capturing the full lifecycle of carbon credits. Rather than treating it as a digital overlay, this study argues that tokenisation functions as a modular, automated architecture capable of absorbing sector-specific frictions within VCMs. Drawing on 1495 registry-compliant projects from the Berkeley Voluntary Offsets Database (BVOD v2025-06), the study develops the sector tokenisation opportunity matrix (STOM). This diagnostic framework maps registry-derived indicators—issuance volume, credit retirement ratio, and average credits per project—to three tokenisation functions: market expansion, retirement acceleration, and structuring for scale and fragmentation. STOM reveals how tokenisation can address VCM fragmentation by mobilising capital, reinforcing lifecycle integrity, and enabling assets to be packaged across diverse project types. By linking friction diagnostics to governance-sensitive infrastructure design, the research proposes a sector-aware blueprint for climate finance infrastructure and positions tokenisation as a strategic tool for scaling high-integrity climate action. Full article

Agrivoltaics (APV) offers a promising dual land-use solution for food and energy production, yet empirical data regarding its impact on leguminous crops in tropical monsoon climates remain limited. This study evaluated the microclimate, growth, and yield of soybean (Glycine max) under an APV system compared to an open-field control during the wet and dry seasons in Bogor, Indonesia. The APV structure reduced incident solar radiation by approximately 35%, significantly lowering soil temperatures and maintaining higher soil moisture across both seasons. In the wet season, the APV treatment significantly increased grain yield (3528.8 vs. 1708.3 kg ha⁻¹, +106%) relative to the open field by mitigating excessive heat and radiative loads, which enhanced pod retention. In the dry season, APV maintained a yield advantage (2025.6 vs. 1724.4 kg ha⁻¹, +17%), driven by improved water conservation and a higher harvest index. Notably, shading did not delay phenological development or hinder vegetative growth in either season. These findings demonstrate that APV systems can contribute to sustainably higher yields and stability in tropical environments by buffering against season-specific environmental stresses, suggesting a viable pathway for sustainable agricultural intensification in equatorial regions. Full article

Optimizing the growing conditions of Anemone coronaria and Ranunculus asiaticus for cut-flower production under northern greenhouse conditions requires a better understanding of the environmental and cultivation practices influencing emergence, flowering, and flower quality. This study evaluated the effect of storage organ reuse, along with vernalization conditions, growth temperature, growing season, and planting method (in-ground vs. containers) on plant phenology and flower yield and quality. Flower quantity and quality were unaffected by storage organ age, confirming that these organs can be stored and reused the following season. Vernalization at temperatures of 7 °C or 10 °C advanced flowering compared to warmer vernalization in all cultivars, and increased flower yield compared to non-vernalization. Growth under cool conditions (15/10 °C day/night) extended the production period and improved floral quality by promoting longer stems and delaying senescence. Short to moderate photoperiods (11–13 h in the winter vs. 15 h in the spring) and low light intensity, typical of winter, promoted stem elongation and marketable flower yield, whereas increasing photoperiod and temperature in late spring shorten the flowering period. Ground beds provided cooler and more buffered soil conditions, improving flowering duration and yield compared to container-grown plants during springtime. These findings highlight the importance of integrating temperature management, vernalization, and tailored cultivation practices to enhance flower quality, prolong the production, and improve sustainability of cut-flower production under northern climates in both species. Full article

The cotton bollworm, Helicoverpa armigera (Hübner), a cosmopolitan agricultural pest, inflicts severe impacts on global agriculture. As a poikilotherm, it was highly susceptible to climate change, yet critical gaps persist in understanding how its sensitivity interacts with climatic shifts—knowledge essential for integrated pest management (IPM). We, therefore, analyzed H. armigera’s susceptibility to temperature variations using long-term pest population and meteorological data from Maigaiti and Bachu Counties (southern Xinjiang) and Shawan County (northern Xinjiang). The results showed H. armigera populations increased overall, with reduced interannual fluctuation magnitude. The main meteorological factors influencing the interannual population changes of H. armigera in Maigaiti, Bachu, and Shawan were T_max difference in winter (98.0%), T_min difference in May (80.7%), and T_min difference in July (99.4%), respectively. Higher winter temperature (particularly February) reduced the spring population sizes across all three regions, with only the population in Bachu showing a significant correlation. For annual populations, warmer winter caused a significant decline in Bachu, a marked increase in Maigaiti, and a non-significant rise in Shawan. Summer temperature below 33 °C boosted populations in all regions; above 33 °C, the Maigaiti population declined non-significantly, while the Bachu population dropped significantly. Climate warming advanced the pest’s first appearance, delayed its disappearance, and extended its active period, increasing population size—a trend projected to intensify in the future. Maigaiti and Shawan populations were governed by T_max in winter and T_min in July, respectively, whereas the Bachu population was constrained by temperature differences during multiple key growth and development periods throughout the year. These divergent regulatory patterns and climatic responses reflect varying vulnerability levels, providing a theoretical basis for targeted H. armigera control. Full article

As the world’s leading carbon emitter, China’s ability to reach its pledged carbon peak by 2030 is pivotal for its own green transition and global climate governance. This research proposes a novel integration of spatial proximity neural networks with attention-enhanced geographically weighted neural network regression. This new model integrates spatial dependencies and an attention mechanism into the traditional geographically weighted neural network regression framework. The model demonstrates good performance in forecasting carbon emissions (coefficient determination = 0.904, root mean square error = 48.927). Using this model, alongside population, GDP, total energy consumption, and other influencing factors, the research integrated scenario forecasting to project China’s total carbon emissions from 2023 to 2040. Three policy-relevant scenarios—baseline, low-carbon, and extensive development—were set to forecast and analyze various potential outcomes under uncertain conditions. Under the baseline scenario, China’s emissions peak in 2029 at 9926.26 Mt; the low-carbon scenario advances the peak to 2027 at 9688.88 Mt; whereas an extensive growth path delays the peak to 2032 at 10,347.70 Mt. These findings underscore the urgency of optimizing energy structure, curbing fossil fuel dependence, and balancing economic growth with the deep decoupling of emissions. This research offers policymakers a robust, spatially explicit tool for evaluating future trajectories under diverse development pathways. Full article