Search Results (80)

Litchi (Litchi chinensis Sonn.) is a pillar of the tropical agricultural economy in southern China, yet its production faces increasing instability due to climate change. Traditional agronomic models often fail to capture the complex, non-linear interactions between meteorological drivers and yield formation in perennial fruit trees. To address this challenge, the study constructed a yield prediction framework using an optimized Random Forest (RF) model integrated with interpretable machine learning (SHAP), based on a comprehensive dataset from 17 major production regions in Hainan Province (2000–2022). The model demonstrated robust predictive capability at the provincial scale (R² = 0.564, RMSE = 2.1 t/ha) and high consistency across regions (R² ranging from 0.51 to 0.94). Feature importance analysis revealed that heat accumulation (specifically growing degree days above 20 °C) is the dominant driver, explaining over 85% of yield variability. Crucially, scenario simulations uncovered asymmetric climate risks across phenological stages: while moderate warming generally enhances yield by promoting vegetative growth and ripening, it acts as a stressor during the Fruit Development stage, where temperatures exceeding 26 °C trigger yield decline. Furthermore, the yield penalty for drought during Flowering (−8.09%) far outweighed the marginal benefits of surplus rainfall, identifying this window as critically sensitive to water deficits. These findings underscore the necessity of phenology-aligned adaptation strategies—specifically, securing irrigation during flowering and deploying cooling interventions during fruit development—providing a data-driven basis for climate-smart management in tropical agriculture. Full article

The thermal influence of Urban Heat Islands (UHIs) is not limited to periods of high temperature but persists throughout the year. The present study utilizes hourly data collected over a period of one year from a network of hygrothermal monitoring stations with a high density, which were deployed across the city of Cáceres (Spain). The network was designed in accordance with the World Meteorological Organization’s guidelines for urban measurements (employing radiation footprints and surface roughness) and ensures representation of each Local Climate Zone (LCZ), characterized by those factors (such as building typology and density, urban fabric, vegetation, and anthropogenic activity, among others) that influence potential solar radiation absorption. The magnitude of the heat island effect in this city has been determined to be approximately 7 °C in summer and winter at the first hours of the morning. In order to assess the energy impact of UHIs, Cooling and Heating Degree Days (CDD and HDD) were calculated for both summer and winter periods across the different LCZs. Following the implementation of rigorous quality control procedures and the utilization of gap-filling techniques, the analysis yielded discrepancies in energy demand of up to 10% between LCZs within the city. The significance of incorporating UHIs into the design of building envelopes and climate control systems is underscored by these findings, with the potential to enhance both energy efficiency and occupant thermal comfort. This methodology is particularly relevant for extrapolation to larger and denser urban environments, where the intensification of UHI effects exerts a direct impact on energy consumption and costs. The following essay will provide a comprehensive overview of the relevant literature on the subject. Full article

Depending on the factors to which the soils are exposed, many properties and engineering parameters may change. In particular, the temperature parameter affects the strength of the soils, the degree of compressibility, permeability, void ratio, Atterberg limits, and many other parameters. In areas where high temperatures occur, such as heat piles and nuclear waste storage areas, alternative soil mixtures are needed that can stabilize or better optimize the behavior of the soils. For this purpose, additives with high heat transfer capacity and symmetry can be used. In this study, aluminum additive, which is known to have high conductivity, was used together with zeolite–bentonite mixtures. Aluminum-added mixtures were kept at different temperatures, and their thermal conductivity values were measured at the end of different periods. Measurements were first carried out at room temperature for all mixtures. Then, measurements were repeated at the end of 1, 3, and 10 days for 55 °C and 80 °C temperature values. At the end of the heating periods, the samples were left to cool to room temperature, and the thermal conductivity values were examined at the end of the heating–cooling cycle. Experimental results showed that thermal conductivity increased as temperature increased when the same period was taken as a basis, but an increase was observed for 1 and 3 day heating periods, while the thermal conductivity values for the 10th day decreased. The initial increase is attributed to the densification of the material due to the removal of free and weakly bound water or to the improvement of solid–solid contact paths. The subsequent decrease is due to microstructural deterioration, such as increased air-filled porosity, drying shrinkage, and microcracking due to thermal stresses, and material degradation caused by prolonged heating. In addition, thermal conductivity values of the mixtures under high temperature were estimated for days 100 and 365 using the DeepSeek method. The results showed that the thermal conductivity coefficients symmetrically decreased with increasing time. Full article

Rapid warming and expanding heat seasons are reshaping electricity demand in cities, with basin-type megacities like Chengdu facing amplified risks due to calm-wind, high-humidity conditions and fast-growing digital infrastructure. This study develops a Transformer-based, multi-model downscaling framework that integrates outputs from 17 CMIP6 global climate models (GCMs), dynamically re-weighted through self-attention to generate city-scale temperature projections. Compared to individual models and simple averaging, the method achieves higher fidelity in reproducing historical variability (correlation ≈ 0.98; RMSD < 0.05 °C), while enabling century-scale projections within seconds on a personal computer. Downscaled results indicate sustained warming and a seasonal expansion of cooling needs: by 2100, Chengdu is projected to warm by ~2–2.5 °C under SSP2-4.5 and ~3.5–4 °C under SSP3-7.0 (relative to a 2015–2024 baseline). Using a transparent, temperature-only Cooling Degree Day (CDD)–load model, we estimate median summer (JJA) electricity demand increases of +12.8% under SSP2-4.5 and +20.1% under SSP3-7.0 by 2085–2094, with upper-quartile peaks reaching +26.2%. Spring and autumn impacts remain modest, concentrating demand growth and operational risk in summer. These findings suggest steeper peak loads and longer high-load durations in the absence of adaptation. We recommend cost-aware resilience strategies for Chengdu, including peaking capacity, energy storage, demand response, and virtual power plants, alongside climate-informed urban planning and enterprise-level scheduling supported by high-resolution forecasts. Future work will incorporate multi-factor and sector-specific models, advancing the integration of climate projections into operational energy planning. This framework provides a scalable pathway from climate signals to power system and industrial cost management in heat-sensitive cities. Full article

The aim of the study is to analyze the impact of climate change on modern low-energy construction. The authors attempted to answer the question whether an existing single-family building that meets the current requirements for a low-energy facility can be called such in terms of ongoing long-term climate changes. Therefore, on the model of the building in question, the ESP-r program analyzed the impact of climate change on energy consumption for both heating and cooling purposes. The SSP2-4.5 scenario (RCP 4.5 according to the IPCC 5th Assessment Report) was adopted, generating future climate parameters for 2050 and 2080, using the HadCM3 model. In order to validate the model, the actual energy consumption values were compared with the values obtained from numerical modeling in the ESP-r program. The final task was to analyze the impact of ongoing climate changes on energy parameters and comfort of use of the facility. Based on the results obtained, the authors concluded that the effect of the changes that take place is the need to introduce an air conditioning system into it in the summer, because the currently existing solution, using a mechanical ventilation system to maintain thermal comfort, is unable to provide the required parameters in the rooms. We are dealing with the phenomenon of excessive temperature increase in the building in the summer. Therefore, the facility currently designed as a low-energy building will require the installation of additional installation systems in the coming years, primarily cooling rooms, which will involve increased energy consumption. Full article

This study investigates the cooling potential of night ventilation and the climate adaptability of local vernacular buildings in the Turpan basin, aiming to identify passive energy-saving design strategies. A rural building with an air-drying shelter was selected for summer indoor environment measurements (two stages: all-day window closure vs. night ventilation), and a numerical model was established to simulate the impacts of window-to-wall ratio and window shading projection factor on the indoor environment. Results indicate that night ventilation introduces cool outdoor air to replace indoor hot air, cools building components, improves thermal comfort, and reduces cooling energy demand. Without additional cooling technology, increasing the window-to-wall ratio lowers nighttime temperatures but increases Degree Discomfort Hours, while appropriately sized shading devices mitigate daytime overheating from larger windows. Benefiting from the high thermal storage capacity of earth-appressed walls, semi-underground rooms offer better comfort with lower temperatures and higher humidity; for aboveground rooms, orientation is critical due to intense solar radiation. The air-drying shelter reduces solar radiant heat absorption and inhibits convective/radiative heat transfer on the roof’s external surface, significantly lowering its temperature from noon to midnight. This leads to notable reductions in the roof’s internal surface temperature (1.02 °C in the sealed stage, 2.09 °C during night ventilation) and the average indoor temperature (1.70 °C). Full article

The temperature-based indicators heating and cooling degree days, are frequently utilized to quantitatively link indoor energy demand and outdoor thermal conditions, especially in the context of climate change. We present a comprehensive study of the heating and cooling degree-days and the degree-days categories for the near past (1976–2005), and the AR5 RCP4.5 and RCP8.5 scenario-driven future (2066–2095) over Southeast Europe based on an elaborated methodology and performed using a 19 combinations of driving global and regional climate models from EURO-CORDEX with horizontal resolution of 0.11°. Alongside the explicit focus of the degree-days categories and the finer grid resolution, the study benefits substantially from the consideration of the monthly, rather than annual, time scale, which allows the assessment of the intra-annual variations of all analyzed parameters. We provide evidences that the EURO-CORDEX ensemble is capable of simulating the spatiotemporal patterns of the degree-days and degree-day categories for the near past period. Generally, we demonstrate also a steady growth in cooling and a decrease in heating degree-days, where the change of the former is larger in relative terms. Additionally, we show an overall shift toward warmer degree-day categories as well as prolongation of the cooling season and shortening of the heating season. As a whole, the magnitude of the projected long-term changes is significantly stronger for the ’pessimistic’ scenario RCP8.5 than the ’realistic’ scenario RCP4.5. These outcomes are consistent with the well-documented general temperature trend in the gradually warming climate of Southeast Europe. The patterns of the projected long-term changes, however, exhibit essential heterogeneity, both in time and space, as well as among the analyzed parameters. This finding is manifested, in particular, in the coexistence of opposite tendencies for some degree-day categories over neighboring parts of the domain and non-negligible month-to-month variations. Most importantly, the present study unequivocally affirms the significance of the anticipated long-term changes of the considered parameters over Southeast Europe in the RCP scenario-driven future with all subsequent and far-reaching effects on the heating, cooling, and ventilation industry. Full article

The Life Cycle Cost Assessment (LCCA) methodology is widely used to determine the optimal thickness of thermal insulation for walls and roofs. The results depend on several factors, such as the degree day calculations method, the ambient or sol–air temperature, base temperature variations, and the heat capacity of the thermal envelope elements. This study aims to analyze the impact of solar radiation on mass walls with different orientations in five cities in northern Chile, which have severe solar climates. The goal is to determine the optimal thickness of expanded polystyrene insulation using the LCCA method, considering solar radiation, a varying base temperature, and validating results by analyzing the energy demand for heating and cooling of a typical house. The findings show that excluding solar radiation in the LCCA methodology can lead to an underestimation of the optimal insulation thickness by 21–39% for walls in northern Chile. It was also found that using variable monthly threshold temperatures for heating and cooling based on the adaptive thermal comfort model results in a slight underestimation (1–3%) of the optimal thickness compared to a constant annual temperature. An energy simulation of a typical house in five cities in northern Chile showed that neglecting the effect of solar radiation when determining the thermal insulation thickness for the studied wall can lead to a minor increase in heating and cooling energy demand, ranging from approximately 1% to 9%. However, this study emphasizes the importance of applying optimal insulation thickness for cities with more continental climates like Santiago and Calama, where the heating demand is higher than cooling. Full article

Weather derivative markets, particularly Chicago Mercantile Exchange (CME) Heating Degree Day (HDD) and Cooling Degree Day (CDD) futures, face challenges from complex temperature dynamics and spatially heterogeneous co-extremes that standard Gaussian models overlook. Using daily data from 13 major U.S. cities (2014–2024), we first construct a two-stage baseline model to extract standardized residuals isolating stochastic temperature deviations. We then estimate the Extreme Value Index (EVI) of HDD/CDD residuals, finding that the nonlinear degree-day transformation amplifies univariate tail risk, notably for warm-winter HDD events in northern cities. To assess multivariate extremes, we compute Tail Dependence Coefficient (TDC), revealing pronounced, geographically clustered tail dependence among HDD residuals and weaker dependence for CDD. Finally, we compare Gaussian, Student’s t, and Regular Vine Copula (R-Vine) copulas via joint VaR–ES backtesting. The R-Vine copula reproduces HDD portfolio tail risk, whereas elliptical copulas misestimate portfolio losses. These findings highlight the necessity of flexible dependence models, particularly R-Vine, to set margins, allocate capital, and hedge effectively in weather derivative markets. Full article

As a measure of the accumulated heat deficit during the growing season transition, cooling degree days (CDDs) play a crucial role in regulating vegetation phenology and ecosystem dynamics. However, systematic analyses of CDD trends and their driving mechanisms remain limited, particularly in high-altitude regions where climate variability is pronounced. This study investigated the spatiotemporal variability in CDDs from 1982 to 2022 in alpine grasslands on the Qinghai–Tibetan Plateau (TP) and quantified the contributions of key climatic factors. The results indicate that lower CDD values (<350 °C-days) were predominantly found in warm, arid regions, whereas higher CDD values (>600 °C-days) were concentrated in colder, wetter areas. Temporally, area-averaged CDDs exhibited a significant decline, decreasing from 490.9 °C-days in 1982 to 495.8 °C-days in 2022 at a rate of 3.8 °C-days per year. Elevation plays a critical role in shaping CDD patterns, displaying a nonlinear relationship: CDDs decrease as elevation increases up to 4300 m, beyond which they increase, suggesting a transition from global climate-driven warming at lower elevations to local environmental controls at higher elevations, where snow–albedo feedback, topographic effects, and atmospheric circulation patterns regulate temperature dynamics. Tmax was identified as the dominant climatic driver of CDD variation, particularly above 4300 m, while radiation showed a consistent positive influence across elevations. In contrast, precipitation had a limited and spatially inconsistent effect. These findings emphasize the complex interactions between elevation, temperature, radiation, and precipitation in regulating CDD trends. By providing a long-term perspective on CDD variations and their climatic drivers, this study enhances our understanding of vegetation–climate interactions in alpine ecosystems. The results offer a scientific basis for modeling late-season phenological changes, ecosystem resilience, and land-use planning under ongoing climate change. Full article

Accurate energy demand forecasting is vital for optimizing resource allocation and energy efficiency. Despite advancements in various prediction models, existing approaches often struggle to capture the complex, nonlinear relationships between temperature variations and electricity consumption. To address this issue, this paper introduces a novel hybrid deep learning model that integrates Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks with an attention mechanism designed to forecast day-ahead electricity demand in Australia. This research aims to enhance the accuracy of electricity demand predictions by effectively modeling the impact of heating degree days (HDDs) and cooling degree days (CDDs) on energy usage. The HDDs and CDDs capture extreme weather conditions. They are critical for understanding spikes in energy consumption for heating and cooling needs. The proposed model leverages the strengths of CNNs in extracting spatial features in HDDs and CDDs, LSTMs in capturing temporal dependencies, and the attention mechanism in focusing on the most relevant aspects of the data. This study compares the CNN-LSTM-Attention model with traditional methods, including Deep Neural Networks, and demonstrates superior performance. The results show a significant reduction in both Mean Absolute Error and Mean Absolute Percentage Error, confirming the model’s effectiveness. The primary contribution of this paper lies in the novel integration of CDD and HDD data within the CNN-LSTM framework, which has not been extensively explored in prior studies. This approach offers a robust solution for energy management, particularly in climates with significant temperature fluctuations. Full article

The ongoing and projected future climate change impacts the heating, cooling, and air-conditioning sectors both directly and indirectly. The consideration of heating, cooling, and energy degree-days is a consistent, robust, and widely used approach for quantitatively estimating the energy demand of closed environments based on outdoor thermal conditions. Hence, the spatial distribution and the long-term changes in this demand depend on on the quantity of final users for such services; it is essential to consider demographic data in the assessment. The paper presents a comprehensive analysis of the population-weighted degree-days for the near past and the Sixth Assessment Report (AR6) of the Intergovernmental Panel on Climate Change (IPCC) scenario-driven future over Southeast Europe for all four ‘Tier 1’ Shared Socioeconomic Pathways (SSPs) based on the methodology of the United Kingdom Meteorological Office and performed using large NEX-GDDP CMIP6 ensemble of global circulation models (GCMs) and up to date population dynamics data from the NASA’s SEDAC. As an expression of regional warming tendencies, the study reveals an overall reduction in heating and an increase in cooling degree-days, confirming the leading role of the climate. We also provide evidences for the influence of the population factor, which significantly alters the region’s degree-day climatology in both space and time. The resulting overall picture on country-wide and regional level is complex; in some cases, the population dynamics is projected to outbalance the thermal-induced changes. Full article

Energy-efficient building envelope design is essential for minimizing cooling loads and reducing energy consumption, particularly in hot desert climates. This study presents a model that optimizes insulation thickness by taking into account climate-specific conditions and economic factors. The model employs a life-cycle cost analysis framework, incorporating energy savings, insulation costs, and payback periods across various climatic zones. A typical wall is considered with three commonly applied insulation materials. The optimization is validated by energy modeling. A key contribution of this study is the introduction of a correction factor based on average humidity for each city, which adjusts the conduction-based model to account for latent heat effects from moisture-dependent insulation degradation. Unlike existing building codes, which prescribe fixed insulation requirements regardless of regional climate conditions, our approach dynamically adapts insulation thickness based on Cooling Degree Days (CDDs) and economic feasibility. The results reveal significant variations in optimal insulation thickness across different cities, demonstrating the necessity of climate-responsive insulation strategies. The analysis indicates that locations with higher CDD, such as Jeddah and Dhahran, require thicker insulation to reduce cooling loads effectively, whereas cities with lower cooling demand, such as Khamis Mushait, necessitate thinner insulation for economic viability. The results show that polystyrene (K = 0.034 W/m.K) has the least cost, whereas polyurethane (K = 0.24 W/m.K) records the least thickness in Saudi Arabia. This study presents a model that optimizes insulation thickness by taking into account climate-specific conditions and economic factors. Full article

The rapid urbanization of developing cities has intensified the challenge of maintaining thermal comfort in buildings, particularly in hot–humid climates. This study investigates the impact of floor level on airflow patterns and indoor temperatures in multi-purpose mid-rise buildings in Onitsha, Nigeria, where increasing urban density and frequent power outages necessitate effective passive cooling strategies. Through a mixed-method approach combining empirical measurements, computational fluid dynamics (CFD) simulations, and thermal performance analysis, the research examined variations in ventilation rates and temperature distributions across different floor levels of a six-story educational building over an annual cycle, focusing on the hottest (27 February), coldest (28 December), most windy (3 April), and least windy (17 September) days. Results revealed distinct floor-level ventilation patterns: upper floors (fourth–fifth) achieved 39–40 air changes per hour (ACH) during hot periods while maintaining temperatures of 30–35 degrees Celsius (°C); middle floors (second–third) showed moderate ventilation (15–22 ACH) but experienced heat accumulation (35–42 °C); and lower floors reached 20 ACH during windy conditions. Temperature stratification varied from 15 °C between floors across the entire building during peak conditions to 7 °C during windy periods. Stack-driven ventilation in upper floors contributed to temperature reductions of up to 3 °C, while wind-driven ventilation promoted uniform temperature distribution across all levels. These findings informed floor-specific design recommendations: hybrid ventilation systems with automated controls, strategic architectural features including a minimum floor level area of 15% for the central atrium, and comprehensive monitoring systems with six temperature sensors per floor. This study provides evidence-based strategies for optimizing thermal comfort in tropical urban environments, particularly valuable for designing energy-efficient buildings in rapidly developing cities with hot-humid climates. Full article

Brick is a common construction material but often ends up as waste due to suboptimal quality. In Ecuador, artisanal brick production results in inconsistent properties for construction. This research aims to repurpose discarded bricks through geopolymerization to create a sustainable building material. The geopolymerization process was carried out using sodium hydroxide as the alkaline activator, followed by structural and chemical characterization, including X-Ray Diffraction (XRD) and X-Ray Fluorescence (XRF) to determine composition and crystalline phases. The recycled material underwent extensive testing of its physical and mechanical properties, such as density, porosity, and compressive strength. Its application as facade cladding for housing was also analyzed. The results showed that the geopolymerized material significantly reduced heating and cooling demand when used in building envelopes. A case study in Loja demonstrated a notable decrease in heating and cooling degree days, contributing to improved thermal comfort. This research highlights the potential for recycled bricks in sustainable construction, presenting viable alternatives to conventional construction materials and advancing knowledge in eco-friendly building practices. Full article