Search Results (6,674)

Decarbonising the building sector is an energy policy priority due to its major contribution to global energy consumption and related emissions. Accurate energy modelling is crucial, with significant scientific advancements being made in the last decade. As data gathering is a primary bottleneck, the potential of Geographic Information Systems and Remote Sensing for streamlining data acquisition and integrating data sources has gained specific interest. This study aims to identify prevailing trends in scales, inputs, and outputs of energy modelling, focusing on Remote Sensing and Geographic Information Systems applications. A structured literature review was conducted, encompassing screening, textual analysis, and findings synthesis to identify key research trends. The results highlight a predominance of the neighbourhood scale (54%) and the reliance on building geometries as principal input (91% of studies). Remote Sensing, used in 36% of cases, is employed for defining geometric (41%) and non-geometric (45%) attributes, while 17% of studies leverage it to determine climatic variables. EnergyPlus remains the most widespread simulation engine (37%), frequently coupled with construction archetypes (50% of cases) to address data gaps. The increasing integration of these technologies in energy modelling is expected to diversify the number of inputs, ultimately enhancing output accuracy, scalability, and generalisability. Full article

Climatic niche breadth is defined as the range of climatic conditions (e.g., temperature and precipitation) under which a species occurs. However, the relationship between niche breadth variation and climatic factors remains poorly studied, and existing results require more general testing. We studied spatial patterns of variation in climatic niche breadths in lizards of the family Agamidae and compared patterns within and across regions to see if they parallel or differ from each other using geo-referenced occurrence records, climatic data and phylogenetic comparative methods. We found that (1) species in warmer environments have narrower temperature niche breadths; (2) precipitation niche breadths are positively correlated with precipitation niche position, and also with temperature niche breadths; and (3) most of the variation in temperature niche breadths is explained by within-locality variation in climatic conditions, whereas most of the variation in precipitation niche breadths is explained by among-locality variation. The patterns of climatic niche breadth in agamids are consistent across regional and global scales, similar to those in other amphibians and reptiles. This suggests that this is a widespread phenomenon among ectothermic vertebrates. Full article

To address global climate challenges, China’s transition toward a green, low-carbon economy underscores the critical role of green finance (GF) as a key policy instrument. Against this backdrop, clarifying how GF influences green technology innovation (GTI) has become an urgent research priority. Using panel data from 283 Chinese cities (2012–2023), this study estimates a panel threshold model to examine the non-linear relationship between GF and GTI, with environmental regulation (ER) as the threshold variable. The results, validated by robustness and endogeneity tests, reveal the following: (1) GF exerts a double-threshold effect on GTI, with its promoting effect strengthening between thresholds but weakening beyond the second threshold. (2) ER exhibits a significant single-threshold effect; beyond it, GF’s contribution to GTI is substantially enhanced. (3) Three types of heterogeneity analysis are performed based on geographical regions, historical endowments, and whether a city is classified as an innovation-driven city. Overall, the results indicate that the threshold effects are more pronounced in eastern regions, cities with stronger historical endowments, and innovation-driven cities. These findings not only deepen the theoretical understanding of the GF–ER–GTI nexus but also provide empirically grounded insights for designing differentiated GF policies and region-specific environmental regulation strategies, thereby supporting both China’s low-carbon transition and global climate governance efforts. Full article

Increasing the carbon sink function of forests is critical for achieving carbon (C) neutrality in the context of global climate change. Past studies have focused on the estimation of forest biomass or C storage, while those on forest C sink potential remain limited. In particular, there remain few systematic investigations to define the forest C sink, to characterize the synergistic influencing factors, and to develop related quantitative analysis methods. The development of scientific C enhancement strategies requires the construction of C density-age models integrating multiple stand factors. These models allow accurate quantification of the gap (∆C) between actual and maximum C sequestration capacity. This study used permanent sample plot data to develop and validate a novel multi-model assessment approach for quantifying the C sink potential of Larix olgensis plantations in Heilongjiang Province, China, and to translate the results into precise management tools. An Average-Level Model (ALM) was established to define baseline C sequestration. Three innovative potential assessment models were then proposed: (1) the Empirical Upper Boundary Model (PLM1); (2) the Dummy Variable Model (PLM2); and (3) the Quantile Regression Model (PLM3). These models define the maximum C sequestration capacity from distinct perspectives. PLM1 (R² = 0.7910) characterized the theoretical upper limit of C sink potential (79.86 Mg·ha⁻¹), making it suitable for macro-strategic goal setting, though it is somewhat dependent on extreme data points. PLM2 (R² = 0.7943) achieved the best fit, and when combined with measurable stand conditions (site class index [SCI] > 16 m, stand density index [SDI] > 800 trees·ha⁻¹), it provides clear guidance for management practices. Although PLM3 showed a lower goodness-of-fit (R² = 0.1056), it provided reasonable parameter estimates and robust predictions, offering a reliable upper-bound reference for C sink project planning and risk control. At a stand age of 60 years (yr), the C sink enhancement potentials (“∆” C) corresponding to the three models were 15.73, 14.48, and 13.26 Mg·ha⁻¹, representing increases of 24.53%, 22.58%, and 20.68%, respectively, over the average level (64.13 Mg·ha⁻¹); the peak C sequestration rates of the models were 104.3%, 82.7%, and 60.5% higher than that of the ALM, with peak times occurring earlier at 9, 7, and 11 yr, respectively, underscoring the importance of the early management. The multi-model assessment approach developed here facilitates “precision carbon enhancement” by quantifying C sink potential across its theoretical, achievable, and robust upper-bound dimensions. This quantification provides both mechanistic insights into C sequestration processes and a critical link between theoretical understanding and practical forest management. This work holds significant value for advancing forestry C sinks in service of national strategies. Full article

In response to global climate change, low-carbon transition in the industrial sector has become essential for emission reduction. Industrial parks, as concentrated centers of production, are major sources of urban energy use and carbon emissions. Whether park-based policy interventions can generate broader decarbonization effects remains unclear. This study conceptualizes China’s National Low-Carbon Industrial Park Pilot Policy (NLCIPP) as a meso-level systemic intervention and examines its impact on urban carbon intensity (UCI). Using panel data for 282 Chinese cities from 2006 to 2020, causal effects are identified through a multi-period DID framework combined with a synthetic DID approach. The results show that the NLCIPP significantly reduces UCI, indicating that energy-oriented interventions at the industrial park level can induce broader decarbonization outcomes. The policy effect mainly works via reduced energy consumption and enhanced green technological capability, while the contribution of industrial structural upgrading is relatively limited. Stronger impacts appear in central regions, cities with stricter environmental regulation, and non-resource-based cities, highlighting the context-dependent effectiveness of energy transition policies. These findings provide empirical evidence for designing effective industrial energy policies to promote low-carbon transition. Full article

Ecosystem transpiration (T) is the core process in terrestrial water and carbon cycles. Accurately estimating T is critical to improving evapotranspiration (ET) models and understanding global ecosystem responses to climate change. In this study, we evaluated four ET partitioning methods (TEA, Z16, L19, and Y21) using 368 global eddy covariance (EC) sites and 15 sap flow sites. Intercomparison results showed that TEA, Z16, and Y21 maintained good consistency, whereas L19 exhibited lower agreement, primarily due to its high sensitivity to energy closure errors and poor non-linear fitting accuracy under extreme conditions. Validation against sap flow data indicated that Z16 performed best (R² = 0.45, KGE = 0.52), followed by Y21, while TEA had the lowest accuracy due to systematic overestimation driven by unremoved persistent background soil evaporation in its training dataset. Global analysis revealed that mean annual T ranged from 213 mm yr⁻¹ (Z16) to 294 mm yr⁻¹ (TEA), with annual T/ET varying between 0.45 (Z16) and 0.63 (TEA). Trend analysis further showed consistent increasing trends across all four methods for both annual T (0.33–0.83 mm·yr⁻²) and annual T/ET (0.0015–0.0019 yr⁻¹). Additionally, a notably stronger relationship was found between gross primary productivity (GPP) and T than between GPP and ET. Despite substantial differences in model structures, these methods effectively capture the temporal dynamics of T and the coupled relationships between ecosystem carbon and water fluxes. Our findings provide critical benchmarks for terrestrial water cycle modeling and sustainable water resource management under a changing climate. Full article

Extreme weather events such as higher temperatures, droughts, and soil salinization are projected to increase as atmospheric CO₂ concentrations rise and climate change progresses. These factors have a negative impact on global food security, the water supply, and ecosystem productivity. The focus of this review is on modern concepts, comparative studies, and our data on the mechanisms of adaptation of halophytes and glycophytes with different types of photosynthetic metabolism (C₃, C₄) to the individual and combined effects of climatic factors. The analysis revealed that C₃ and C₄ species and C₄-NAD-ME and C₄-NADP-ME species differ in terms of stability and photosynthetic plasticity. Under drought conditions, both individually and in combination with other factors, C₄ halophytes demonstrate the advantages of efficient photosynthesis and salt tolerance. Halophytes with C₄-NADP-ME are characterized by uniquely high levels of plasticity and variability in photosynthetic metabolism. This is reflected in their ability to mitigate the negative effects of elevated temperatures and drought through the use of elevated CO₂ (eCO₂). The mitigating effect of eCO₂ on photosynthesis at elevated temperatures was not detected in halophytes, regardless of photosynthesis type. Halophytes possess an augmented capacity for heat tolerance. Integrating fundamental scientific knowledge with urgent practical needs will enable us to predict changes in ecosystems and create new, sustainable agricultural systems. Full article

The guinea fowl (Numida meleagris), a thermo-tolerant and disease-resilient poultry species, holds great potential for sustainable poultry production in climate-vulnerable regions. The genomic aspects of this species remain largely understudied. The present study aims to delineate the patterns of domestication and understand the evolutionary dynamics of guinea fowl populations (wild and domestic) across three continents, utilizing whole-genome sequencing data from 122 genomes. The population structure analyses (ADMIXTURE, PCA, phylogeny, F_ST, LD, and MAF) revealed that Indian guinea fowl (CARI) shared close ancestry with Iranian (IRAD) and Chinese (CHID) domesticated populations while remaining distinct from wild lineages. The runs of homozygosity (ROH) identified 49,088 segments, with short fragments (ROHs) preponderant in Indian and domestic populations, reflecting historical inbreeding and effects of domestication cum selection. Copy number variation (CNV) analysis revealed 105,178 CNVs concatenated into 40,067 CNV regions (CNVRs) across 11 populations, establishing the first CNV atlas for guinea fowl at the global level. Gene annotation of overlapping ROH and CNVRs revealed 1080 common candidates across Asian guinea fowl populations, i.e., the Indian guinea fowl (CARI), IRAD, and CHID, including FOS, EPAS1, CD74, and CSF1R. These genes have earlier been associated with immune regulation, stress response, and thermal adaptation. Selection signature scans, integrating intra-population (iHS) and inter-population (XP-EHH) approaches, uncovered genes under positive selection linked to immune response (like BCL11B, IL18, and GPC3), thermo-tolerance (like TRPV4 and BAG3), lipid metabolism (like AACS and ELOVL4), and pigmentation (BCO2). These signatures highlight the molecular basis of resilience in guinea fowl and their potential to withstand climate-induced stresses. This study presents the first global CNV atlas for guinea fowl and provides the first comprehensive genomic characterization of the Indian domestic population, integrating ROH, CNV, and selection signature analyses. It offers a comprehensive assessment of guinea fowl genomes (wild and domesticated) across three continents, offering insights into domestication, evolutionary dynamics, and the genetic basis of their adaptation and resilience. Full article

Under the strategic constraints of global carbon emission targets, how supply chain management can effectively drive enterprises’ low-carbon transformation has become an important issue. Based on China’s Supply Chain Innovation and Application Pilot Program (SCIAPP), this paper approaches it as a quasi-natural experiment to empirically investigate how supply chain management affects enterprises’ low-carbon technological innovation (LCTI). This paper uses the data from publicly listed companies in China. and the difference-in-differences approach to empirically test the policy effect of SCIAPP and determine its influencing path. The study finds that first, SCIAPP significantly enhances enterprises’ LCTI level by approximately 14.2%. Second, SCIAPP mainly achieves this through three mechanisms, including strengthening enterprises’ green management, promoting digital transformation, and improving operational efficiency. Third, the impact effect is stronger in enterprises with more robust environmental management systems, fewer financing constraints and higher capital intensity. Additionally, the LCTI driven by SCIAPP can further positively impact the supply chain resilience. This study innovatively incorporates pilot policies, supply chain management, and LCTI for analysis, providing theoretical evidence and empirical support for the government to optimize supply chain governance and achieve climate goals. Full article

The Vietnam Mekong River Delta (VMRD) is a climate-sensitive region characterized by diverse ecosystems, including extensive mangrove forests that protect against sea-level rise and contribute to global carbon sequestration. Accurate land cover classification in the VMRD is essential but remains challenging due to complex landscapes and dynamic environmental conditions. The primary objective of this study is to propose a semi-supervised deep learning framework that integrates satellite indices with multi-temporal remote sensing data to address key classification challenges, particularly in situations where ground truth data is limited, as compared to unsupervised and supervised machine learning methods. Our comparative analysis across different sample sizes (500 to 6000 ground-truth data points) reveals critical insights into model performance and scalability. Supervised models, including Random Forest (RF), Support Vector Machine (SVM), and Convolutional Neural Network (CNN), demonstrated strong performance when sufficient labeled data were available, with CNN achieving the highest accuracy (0.97 at 6000 samples). However, at minimal sample sizes (500 sample points), these supervised approaches exhibited substantial limitations, with accuracies dropping dramatically (RF: 0.75, SVM: 0.80, CNN: 0.81). Supervised models also showed overfitting tendencies compared to official land cover statistics. In contrast, the semi-supervised approach (SoC4SS-FGVC) achieves remarkably high performance at small sample sizes (0.92 accuracy with 500 sample points), demonstrating strength under minimal data availability. The framework also showed improved capability in distinguishing spectrally similar land-cover classes and detecting environmentally sensitive types such as mangrove forests. Cross-validation with official statistics confirmed the semi-supervised model’s superior effectiveness in delineating paddy rice fields and its resistance to overfitting. The performance analysis demonstrates that SoC4SS-FGVC provides a practical and cost-effective solution for land cover mapping, particularly in regions where extensive ground-truth data collection is prohibitively expensive or logistically challenging. Full article

Kazakhstan is one of the world’s major wheat producers and exporters, playing an important role in regional and global food security. However, increasing quality requirements in domestic and export markets have exposed limitations in the country’s capacity to consistently supply high-quality spring bread wheat (Triticum aestivum L.). This review aims to assess the current status of spring wheat grain quality in Northern Kazakhstan, identify the main factors driving its variation, and outline pathways for quality improvement. The analysis is based on published literature, official statistics, national quality standards, and recent data on wheat production, grading, breeding systems, agronomic practices, and trade patterns. The review reveals that wheat production is dominated by medium-quality grain (primarily class 3), while high-quality classes suitable for premium and improver markets represent a small share. Compared with major exporters such as Canada, the United States, and Australia, Kazakh wheat is generally inferior across key quality parameters. Structural constraints include the limited integration of quality assessments within breeding programs, insufficient laboratory infrastructure, weak agroecological zoning by quality classes, and suboptimal agronomic management, particularly regarding nitrogen use. Environmental heterogeneity and climate change further influence the yield–quality balance. Overall, the findings suggest that improving wheat grain quality in Kazakhstan will require coordinated advances in breeding, agronomy, institutional capacity, and market alignment, enabling a gradual shift toward a more competitive, quality-oriented wheat production system. Full article

Botswana’s semi-arid climate makes it vulnerable to climate change, particularly drought, which threatens agricultural productivity. This study assesses drought impact on Botswana’s agricultural sector using Climate Hazards Center Infrared Precipitation with Station (CHIRPS) rainfall data and Climate Hazards Center Infrared Temperature with Station (CHIRTS) temperature data (25 km) to compute the Standardized Precipitation Index (SPI), Standardized Temperature Condition Index (STCI) and Standardized Precipitation Evapotranspiration Index (SPEI) at seasonal/annual time scales (1, 3, 6 and 12 months). The indices are used to assess their ability to predict crop yields using national data during Botswana’s rainy season, while employing univariate and multivariate statistical models. Statistical models also linked historical drought patterns to yield variability with the Percentage Area Affected (PAA) by drought, identifying key predictors. A majority of the crops (sunflower, maize, sorghum and pulses) showed variability which was best explained by SPEI 6 more particularly under the PAA multivariate models, with the highest and moderate explanatory power (R²) found in sunflower (0.48) and maize (0.43). However, variability in millet was best explained by SPI-3, although the R² was low (0.26). Other crops displayed positive coefficients within the models, which may be attributed to the varieties grown being drought tolerant. Nevertheless, the impacts from drought, which resulted in low yields, were shown by the negative coefficients across most crops. For a more holistic approach, the study also employed questionnaire data to capture first-hand local knowledge. The results showed drought to be among the indicators of climate change that were mostly perceived as well as its effects, in which yield decline, crop damage and crop pests and diseases were among the most perceived effects. Overall, this highlighted the sector’s vulnerability to the changes in climate. The study therefore underscores the need for integrated drought early warning systems, adaptive agricultural/water management and insights for policymakers to enhance drought resilience in Botswana, aligning with global sustainability goals. Full article

Soil organic carbon (SOC) is a key indicator reflecting soil quality and management level. Understanding its spatiotemporal dynamics in cropland is necessary for sustainable land management. Revealing the relationship patterns between SOC (Sampling resolution: 1 km²; analysis resolution: 4 km²) and environmental factors in one period allows inferring SOC distribution in unsampled years, partly compensating for temporal data gaps. This study introduces a season-based crop rotation system (Winter wheat in the first season and summer corn in the next) as independent variables in a machine learning model innovatively, enriching variable selection in SOC inference and improving understanding of SOC accumulation. The Beijing–Tianjin–Hebei (BTH) region, characterized by a typical winter wheat–summer corn rotation system, was selected for analysis. The results show that in 2000, the average SOC was relatively low compared with global levels. Climatic variables were negatively correlated with SOC below the 0.8 quantile but positive above it, which corresponds to the upper 20% of the observed range of each climatic variable. Winter-wheat growth is more important on SOC distribution than summer-corn growth (two annual peaks of NDVI and EVI), showing a positive correlation with SOC, while corn showed a weak correlation and became negative above the 0.8 quantile. In the inferred results, the differences between observed and inferred mean values and their confidence intervals were approximately 0.1. This research provides a reference method for evaluating regional-scale SOC distribution patterns under data-limited conditions by integrating environmental factors and crop rotation characteristics. Full article

Plant functional traits, as indicators of community responses to disturbances, are key drivers of ecosystem service multifunctionality (ESMF). However, the relative contribution of these traits to ESMF across different steppe types remains unclear. Using data from 101 sampling sites across Inner Mongolia’s meadow steppe (MS), typical steppe (TS), and desert steppe (DS), we examine the contributions and driving mechanisms of abiotic (climate and soil) and biotic factors (23 community-weighted mean functional traits and diversity indices) to ESMF across different steppe types. Our results show significant differences in ESMF across steppe types, with a decreasing trend from MS to TS to DS. Crucially, the driving factors of ESMF shift fundamentally across steppe types. In MS, ESMF is primarily driven by biotic factors (e.g., stem N:P ratio), whereas as aridity increases, abiotic factors (e.g., aridity and soil clay content) become more influential, ultimately dominating ESMF in DS. This shift from niche differentiation to environmental filtering as the dominant mechanism provides a crucial framework for predicting ecosystem service responses to global change. It highlights the importance of context-dependent grassland conservation strategies, advocating for location-specific management based on environmental gradients. Full article

Identifying suitable habitats for ecosystem restoration is critical for conserving globally threatened biodiversity. Baseline data on the distribution and range of biogenic habitat-forming species at high spatial resolution are essential for informing habitat management strategies and preserving ecosystem integrity. We identified suitable sites for habitat restoration by integrating community ecological data for Quercus mongolica Fisch. ex Turcz., a valuable restoration tree species, with insights from ensemble modeling. Habitat suitability was predicted using an ensemble species distribution model. A total of 89 occurrence records and nine environmental variables were used to develop the single algorithm models. Model performance was assessed using the Receiver Operating Characteristic (ROC) curve and the True Skill Statistic (TSS). Future habitat suitability was evaluated using projected climate change scenarios. Under more extreme climate change scenarios, the future suitable habitat of Q. mongolica is projected to gradually contract toward the high-altitude areas of Mt. Gariwang. The primary environmental variable is elevation, and rising temperatures due to climate change negatively impact habitat suitability for Q. mongolica. Therefore, adaptation measures must be established to mitigate these impacts, such as protecting the reference ecosystems of Q. mongolica. This integrated approach offers a nature-based solution for guiding climate change-integrated restoration programs in Mt. Gariwang and globally. Full article