Search Results (527)

This paper aims to conduct a gap analysis and explore the potential for greenhouse gas (GHG) emissions reduction in the tourism sector of Chiang Mai province, with the goal of promoting sustainable tourism. Chiang Mai is a major tourism hub in Thailand, located in the Northern Economic Corridor (NEC). The gap analysis of small- and medium-sized tourism enterprises will be examined across four dimensions: (1) management, (2) socio-economy, (3) cultural, and (4) environmental. In 2024, Chiang Mai’s tourism revenue accounted for 46.97% of the northern region’s total tourism revenue and 3.73% of Thailand’s total tourism revenue. Given this economic significance, the development of sustainable tourism should be accelerated to meet the expectations of new tourists who are increasingly concerned about the environment. To address this need, this study analyzes the gaps in small- and medium-sized tourism enterprises and assesses GHG emissions through interviews and surveys of 90 tourism-related establishments across nine sectors: hotels, restaurants and beverages, tour agencies, transportation, souvenirs, attractions and activities, spas and wellness, community-based tourism, and farm tourism. The total GHG emissions from these establishments were found to be 15,303.72 tCO₂eq. Moreover, if renewable energy from solar power were adopted, an installation capacity of 21,866.84 kWp would be required. Such a transition would not only reduce emissions, but also support low-carbon development in small- and medium-sized tourism enterprises and ultimately contribute to achieving net-zero tourism. Finally, this study contributes to the advancement of STGs 1–17, adapted from the SDGs 1–17, with particular emphasis on SDG 7 on clean energy and SDG 13 on climate change. Full article

Ecosystems are nonlinear systems that can shift between multiple stable states. Ecosystem service bundles (ESBs) integrate the supply and trade-offs of multiple services, yet the conditions for achieving high-supply and balanced states remain unclear from a nonlinear, threshold-based perspective. In this study, six representative ecosystem services in Fujian Province were quantified, and ESBs were identified using a Self-Organizing Map (SOM). By integrating the Multiclass Explainable Boosting Machine (MC-EBM) with the API interpretable algorithm, we propose a framework for exploring ESB driving mechanisms from a nonlinear, threshold-based perspective, addressing two key questions: (1) Which factors dominate ESB formation? (2) What thresholds of these factors promote high-supply, balanced ESBs? Results show that (i) the proportion of water bodies, distance to construction land, annual solar radiation, annual precipitation, population density, and GDP density are the primary driving factors; (ii) higher proportions of water bodies enhance and balance multiple services, whereas intensified human activities significantly reduce supply levels, and ESBs are highly sensitive to climatic variables; (iii) at the 1 km × 1 km grid scale, optimal threshold ranges of the dominant factors substantially increase the likelihood of forming high-supply, balanced ESBs. The MC-EBM effectively reveals ESB formation mechanisms, significantly outperforming multinomial logistic regression in predictive accuracy and demonstrating strong generalizability. The proposed approach provides methodological guidance for multi-service coordination across regions and scales. Corresponding land management strategies are also proposed, which deepen understanding of ESB formation and offer practical references for enhancing ecosystem service supply and reducing trade-offs. Full article

In addition to air temperature and personal factors, other weather quantities govern the outdoor human thermal perception. This study provides a new targeted approach for the evaluation of extreme events based on a specific multivariable bioclimate index. Heat waves (HWs) and outdoor human thermal stress (OHTS) events that occurred in downtown Rome (Italy) over the years 2018–2023 are identified, characterized, and compared through appropriate indices based on the air temperature for HWs and the Mediterranean Outdoor Comfort Index (MOCI) for OHTS events. The overlap between the two types of events is evaluated for each year through the hit (HR) and false alarm rates. The outcomes reveal severe traits for HWs and OHTS events and higher values of HR (minimum of 66%) with OHTS as a predictor of extreme conditions. This pilot investigation confirms that the use of air temperature threshold underestimates human physiological stress, revealing the importance of including multiple parameters, such as weather variables (temperature, wind speed, humidity, and solar radiation) and personal factors, in the assessment of hazards for the population living in a specific geographical region. This type of approach reveals increasingly critical facets and can provide key strategies to establish safe outdoor conditions for occupational and leisure activities. Full article

The Solar Magnetic Field Telescope (SMFT) at Huairou Solar Observing Station (HSOS) has conducted continuous observations of solar vector magnetic fields for nearly four decades, and while the primary optical system remains unchanged, critical components—including filters, polarizers, and detectors—have undergone multiple upgrades and replacements. Maintaining data consistency is essential for reliable long-term studies of magnetic field evolution and solar activity, as well as current helicity. In this study, we systematically analyze background bias and noise levels in SMFT observations from 1988 to 2019. Our dataset comprises 12,281 vector magnetograms of 1484 active regions. To quantify background bias, we computed mean values of Stokes $Q/I$, $U/I$ and $V/I$ over each entire magnetogram. The background bias of Stokes $V/I$ is small for the whole dataset. The background biases of Stokes $Q/I$ and $U/I$ fluctuate around zero during 1988–2000. From 2001 to 2011, however, the fluctuations in the background bias of both $Q/I$ and $U/I$ become significantly larger, exhibiting mixed positive and negative values. Between 2012 and 2019, the background biases shift to predominantly positive values for both Stokes $Q/I$ and $U/I$ parameters. To address this issue, we propose a potential method for removing the background bias and further discuss its impact on the estimation of current helicity. For each magnetogram, we quantify measurement noise by calculating the standard deviation ($\sigma$) of the longitudinal ($B_{\mathrm{l}}$) and transverse ($B_{\mathrm{t}}$) magnetic field components within a quiet-Sun region. The noise levels for $B_l$ and $B_{\mathrm{t}}$ components were approximately 15 Gauss (G) and 87 G, respectively, during 1988–2011. Since 2012, these values decreased significantly to ∼6 G for $B_{\mathrm{l}}$ and ∼55 G for $B_{\mathrm{t}}$, likely due to the installation of a new filter. Full article

Climate-relevant technological innovation in renewable energy sources, such as solar energy, is essential for mitigating climate change and achieving sustainable development. The recent literature highlights substantial patent activity in China’s solar energy industry, which may contribute to the sector’s success in international markets. This study examines the relationship between patent activity and corporate financial performance in China’s solar energy industry from 2012 to 2022 using panel data analysis. The results indicate that patent applications positively impact firms’ corporate performance, showing a time-lag of approximately 6–7 years. Notably, this positive impact is particularly pronounced for firms located in the eastern region and state-owned enterprises. Additionally, we investigate whether CEO duality affects the relationship between patent applications and firms’ corporate performance, seeking to reveal unique development pathways within the industry. These findings are important for understanding how China’s solar energy sector can advance along sustainable development pathways amid the challenges of climate change. Full article

Agrivoltaics (APV) mitigates land-use competition between photovoltaic installations and agricultural activities, thereby supporting multifaceted policy objectives in energy transition and sustainability. The availability of organic residuals from agrifood practices may also open the way to their energy valorization. This paper examines a small-scale farm in the Basilicata Region, southern Italy, to investigate the potential installation of an APV plant or a combined APV and bioenergy system to meet the electrical needs of the existing processing machinery. A dynamic numerical analysis is performed over an annual cycle to properly size the storage system under three distinct APV configurations. The panel shadowing effects on the underlying crops are quantified by evaluating the reduction in incident solar irradiance during daylight and the consequent agricultural yield differentials over the life period of each crop. The integration of APV and a biomass-powered cogenerator is then considered to explore the possible off-grid farm operation. In the sole APV case, the single-axis tracking configuration achieves the highest performance, with 45.83% self-consumption, a land equivalent ratio (LER) of 1.7, and a payback period of 2.77 years. For APV and bioenergy, integration with a 20 kW cogeneration unit achieves over 99% grid independence by utilizing a 97.57 kWh storage system. The CO₂ emission reduction is 49.6% for APV alone and 100% with biomass integration. Full article

This study investigates the climatic dynamics of Odesa, Ukraine, by integrating over 200 years of archival meteorological records with recent observations from the Davis Vantage Pro2 weather station and advanced machine learning techniques. The results reveal a distinct warming trend since 1985, with average annual temperatures projected by a CNN–LSTM model to rise by more than 6–7 °C above the mid-20th-century baseline by 2029, indicating an exceptionally rapid regional climatic shift. Spatial analysis of the July 2024 heatwave demonstrated pronounced thermal gradients, with the strongest overheating observed inland and the moderating influence of the Black Sea reducing temperature extremes in coastal areas. Precipitation analysis (1985–2024) showed an overall statistically insignificant increase; however, the summer months exhibited drying tendencies, a trend reinforced by model forecasts. Solar radiation dynamics (2012–2024) highlighted significant local variability shaped primarily by atmospheric conditions rather than solar activity, with notable monthly increases in October, November, and February. The novelty of this research lies in combining long-term datasets with deep learning methods to produce localized climate scenarios for Odesa, offering new insights into the city’s transition toward extreme warming, shifting precipitation patterns, and evolving solar energy potential. The findings have direct implications for environmental modeling, energy efficiency, and the development of climate change adaptation strategies in urbanized coastal regions. Full article

Using the results of numerical simulations and solar observations, this study shows that the transition from deterministic chaos to hard turbulence in the magnetic field generated by the emerging small-scale, near-surface (within the Sun’s outer 5–10% convection zone) solar MHD dynamos occurs through a randomization process. This randomization process has been described using the concept of distributed chaos, and the main parameter of distributed chaos $\beta$ has been employed to quantify the degree of randomization (the wavenumber spectrum characterising distributed chaos has a stretched exponential form $E\left(k\right)\propto exp-{(k/k_{\beta })}^{\beta }$). The dissipative (Loitsianskii and Birkhoff–Saffman integrals) and ideal (magnetic helicity) magnetohydrodynamic invariants govern the randomization process and determine the degree of randomization $0<\beta \le 1$ at various stages of the emerging MHD dynamos, directly or through Kolmogorov–Iroshnikov phenomenology (the magnetoinertial range of scales as a precursor of hard turbulence). Despite the considerable differences in the scales and physical parameters, the results of numerical simulations are in quantitative agreement with solar observations (magnetograms) within this framework. The Hall magnetohydrodynamic dynamo is also briefly discussed in this context. Full article

The Xinjiang Province of China, characterized as a typical arid to semi-arid region, is increasingly facing severe issues related to soil salinization. Timely and accurate estimation of soil salinization in this region is crucial for the sustainable development of agriculture and food security. However, current methods for detecting soil salinization primarily rely on various environmental covariates, which assess the extent of soil salinization by analyzing the relationship between environmental factors and the accumulation of soil salts. Nonetheless, these conventional environmental covariates often suffer from response delays, making it challenging to promptly reflect the dynamic changes in soil salinity. Solar-induced chlorophyll fluorescence (SIF) has been widely used to assess vegetation photosynthetic efficiency and is considered a direct indicator of plant photosynthetic activity. In contrast, SIF provides a timely means of monitoring the status of plant photosynthesis, indirectly reflecting the impact of soil salinization on plant growth. However, the spatial resolution of SIF products derived from satellites is typically low, which significantly limits the accurate estimation of soil salinization in Xinjiang. This study proposes a novel method for monitoring soil salinization, based on SIF data. The approach employs a Stacking ensemble learning model to downscale SIF data, thereby improving the spatial resolution of soil salinity monitoring. Using the GOSIF dataset, combined with environmental covariates, such as MODIS, the Stacking framework facilitates the fine-scale downscaling of SIF data, generating high-resolution SIF products, ranging from 0.05° to 0.005°, with a spatial resolution of 30 m. This refined SIF data is then used to predict soil electrical conductivity (EC). The experimental results demonstrate that: (1) the proposed Stacking-based SIF downscaling method is highly effective, with a high degree of fit to reference SIF data (R² > 0.85); (2) the high-resolution SIF data, after downscaling, more accurately reflects the spatial heterogeneity of soil salinization, especially in shallow soils (r < −0.6); and (3) models combining SIF and environmental covariates exhibit superior accuracy compared to models that rely solely on SIF or traditional environmental covariates (R² > 0.65). This research provides new data support and methodological advancements for precision agriculture and ecological environmental monitoring. Full article

As the peak of Solar Cycle 25 approaches, space weather events such as Equatorial Plasma Bubble (EPBs) and geomagnetic storms are expected to become more frequent. While EPBs are a primary source of scintillation, geomagnetic storms can either enhance or suppress this activity depending on storm timing, intensity, and induced electric field effects, thereby causing significant ionospheric disturbances that degrade Global Navigation Satellite System (GNSS) signal reception performance. This study presents a novel, systematic evaluation of GPS + Galileo Precise Point Positioning (PPP) performance under intense ionospheric scintillation during the rising phase of Solar Cycle 25 using datasets from globally distributed stations. More than twenty months of data have been systematically analysed, with a focus on stations located in equatorial regions, which are the most affected by strong scintillation. PPP processing was performed using final products from the European Space Agency (ESA) with Multi-GNSS Experiment (MGEX) products employed as backups when ESA data were unavailable. It is shown that under severe scintillation the accuracy of the final PPP solution is severely reduced, with errors more than doubled with respect to calm days. In this respect, frequent cycle slips and anomalies in the input observations are detected. A comparative analysis of GPS-only and GPS + Galileo PPP solutions confirms that integrating Galileo not only mitigates the impact of scintillation but also improves the reliability and accuracy of positioning in challenging space weather conditions. Full article

This study presents a three-dimensional numerical analysis of natural convection during the postharvest storage of corn and wheat in a galvanized steel silo with a conical roof and floor, measuring 3 m in radius and 18.7 m in height, located in the Bajío region of Mexico. Simulations were carried out specifically for December, a period characterized by cold ambient temperatures (10–20 °C) and comparatively lower solar radiation than in warmer months, yet still sufficient to induce significant heating of the silo’s metallic surfaces. The governing conservation equations of mass, momentum, energy, and species were solved using the finite volume method under the Boussinesq approximation. The model included grain–air sorption equilibrium via sorption isotherms, as well as metabolic heat generation: for wheat, a constant respiration rate was assumed due to limited biochemical data, whereas for corn, respiration heat was modeled as a function of grain temperature and moisture, thereby more realistically representing metabolic activity. The results, obtained for December storage conditions, reveal distinct thermal and hygroscopic responses between the two grains. Corn, with higher thermal diffusivity, developed a central thermal core reaching 32 °C, whereas wheat, with lower diffusivity, retained heat in the upper region, peaking at 29 °C. Radial temperature profiles showed progressive transitions: the silo core exhibited a delayed response relative to ambient temperature fluctuations, reflecting the insulating effect of grain. In contrast, grain at 1 m from the wall displayed intermediate amplitudes. In contrast, zones adjacent to the wall reached 40–41 °C during solar exposure. In comparison, shaded regions exhibited minimum temperatures close to 15 °C, confirming that wall heating is governed primarily by solar radiation and metal conductivity. Axial gradients further emphasized critical zones, as roof-adjacent grain heated rapidly to 38–40 °C during midday before cooling sharply at night. Relative humidity levels exceeded 70% along roof and wall surfaces, leading to condensation risks, while core moisture remained stable (~14.0% for corn and ~13.9% for wheat). Despite the cold ambient temperatures typical of December, neither temperature nor relative humidity remained within recommended safe storage ranges (10–15 °C; 65–75%). These findings demonstrate that external climatic conditions and solar radiation, even at reduced levels in December, dominate the thermal and hygroscopic behavior of the silo, independent of grain type. The identification of unstable zones near the roof and walls underscores the need for passive conservation strategies, such as grain redistribution and selective ventilation, to mitigate fungal proliferation and storage losses under non-aerated conditions. Full article

One of the most important applications of solar energy is electricity generation using photovoltaic (PV) panels. Yet, as the temperature of PV modules rises, both their efficiency and service life decline. A common approach to mitigate this issue is cooling with fins, a design that is now widely adopted. However, traditional fin-based cooling systems often fail to deliver adequate performance in hot regions with strong solar radiation. In particular, passive cooling alone shows limited effectiveness under conditions of high ambient temperatures and intense sunlight, such as those typical in Malaysia. To address this limitation, hybrid cooling strategies, especially those integrating both air and water, have emerged as promising solutions for enhancing PV performance. In this study, an experimental and economic investigations were carried out on a PV cooling system combining copper tubes and aluminium fins, tested under Malaysian climatic conditions. The economic feasibility was evaluated using the Simple Payback Period (SPP) method. An outdoor test was conducted over four consecutive days (10–13 June 2024), comparing a conventional PV module with one fitted with the hybrid cooling system (active and passive). The cooled module achieved noticeable surface temperature reductions of 2.56 °C, 2.15 °C, 2.08 °C, and 2.58 °C across the four days. The system also delivered a peak power gain of 66.85 W, corresponding to a 2.82% efficiency improvement. Economic analysis showed that the system’s payback period is 4.52 years, with the total energy value increasing by USD 477.88, representing about a 2.81% improvement compared to the reference panel. In summary, the hybrid cooling method demonstrates clear advantages in lowering panel temperature, enhancing electrical output, and ensuring favorable economic performance. Full article

Under the context of global climate change, the frequent occurrence of extreme low-temperature events poses a severe challenge to plant distribution and ecosystem stability. The arid and semi-arid regions of Northwestern China, as a sensitive response area to global change, have proven to possess significant development potential with their unique desert vegetation systems. This study focuses on the ecological adaptability mechanisms of Nitraria tangutorum Bobr., a key species of the desert ecosystem in Northwestern China, and systematically analyzes the evolution patterns of its geographical distribution under the coupled effects of climate change and human activities through a MaxEnt model. The research conclusions are as follows: (i) This study constructs a Human Footprint-MaxEnt (HF-MaxEnt) coupling model. After incorporating human footprint variables, the AUC value of the model increases to 0.914 (from 0.888), demonstrating higher accuracy and reliability. (ii) After incorporating human footprint variables, the predicted area of the model decreases from 2,248,000 km² to 1,976,000 km², with the High Suitability experiencing a particularly sharp decline of up to 79.4%, highlighting the significant negative impact of human disturbance on Nitraria tangutorum. (iii) Under the current climate baseline period, solar radiation, precipitation during the wettest season, and mean temperature of the coldest month are the core driving factors for suitable areas of Nitraria tangutorum. (iv) Under future climate scenarios, the potential distribution area of Nitraria tangutorum is significantly positively correlated with carbon emission levels. Under the SSP370 and SSP585 emission pathways, the area of potential distribution reaches 172.24% and 161.3% of that in the current climate baseline period. (v) Under future climate scenarios, the distribution center of potential suitable areas for Nitraria tangutorum shows a dual migration characteristic of “west–south” and “high altitude”, and the mean temperature of the hottest month will become the core constraint factor in the future. This study provides theoretical support and data backing for the delineation of habitat protection areas, population restoration, resource management, and future development prospects for Nitraria tangutorum. Full article

Emerging lighting technology aims to enhance indoor light quality while conserving energy through control systems that integrate with natural light. In related technologies, it is crucial to identify quickly and accurately indoor light environments that are constantly changing due to natural light. Consequently, a large number of sensors must be installed, but installing multiple sensors would cause an increasing data processing load and inconvenience to users’ activities. Some have attempted to calculate natural light characteristics, such as solar radiation and color temperature cycles, and implement natural light lighting technology by applying deep learning technology. However, there are only a few cases of using deep learning to analyze indoor illuminance, which is essential for commercializing natural light lighting technology. Research on minimizing the number of sensors is also lacking. This paper proposes a method for generating a detailed indoor illuminance map using deep learning, which calculates the illuminance values of the entire indoor area with a single illuminance sensor. A dataset was constructed by collecting dynamically changing indoor illuminance and the position of the sun, and a single sensor was selected through analysis. Then, a DNN model was built to calculate the illuminance of every region of an indoor space by inputting the illuminance measured by a single sensor and the position of the sun, and it was applied to generate a detailed indoor illuminance map. Research has demonstrated that calculating the illuminance levels across an entire indoor area is feasible. Specifically, on clear days with a color temperature anomaly of about 1%, a detailed illuminance map of the indoor space was created, achieving an average MAE of 2.0 Lux or an MAPE of 2.5%. Full article

Nitrogen dioxide (NO₂) plays a crucial role in environmental processes and public health. In recent years, NO₂ pollution has been monitored using a combination of in situ measurements and satellite remote sensing, supported by the development of advanced retrieval algorithms. With advancements in satellite technology, large-scale NO₂ monitoring is now feasible through instruments such as GOME-2C and TROPOMI. However, the fixed local overpass times of polar-orbiting satellites limit their ability to capture the complete diurnal cycle of NO₂, introducing uncertainties in emission estimation and pollution trend analysis. In this study, we evaluated differences in NO₂ observations between GOME-2C (morning overpass at ~09:30 LT) and TROPOMI (afternoon overpass at ~13:30 LT) across three representative regions—East Asia, Central Africa, and Europe—that exhibit distinct emission sources and atmospheric conditions. By comparing satellite-derived tropospheric NO₂ column densities with ground-based measurements from the Pandora network, we analyzed spatial distribution patterns and seasonal variability in NO₂ concentrations. Our results show that East Asia experiences the highest NO₂ concentrations in densely populated urban and industrial areas. During winter, lower boundary layer heights and weakened photolysis processes lead to stronger accumulation of NO₂ in the morning. In Central Africa, where biomass burning is the dominant emission source, afternoon fire activity is significantly higher, resulting in a substantial difference (1.01 × 10¹⁶ molecules/cm²) between GOME-2C and TROPOMI observations. Over Europe, NO₂ pollution is primarily concentrated in Western Europe and along the Mediterranean coast, with seasonal peaks in winter. In high-latitude regions, weaker solar radiation limits the photochemical removal of NO₂, causing concentrations to continue rising into the afternoon. These findings demonstrate that differences in polar-orbiting satellite overpass times can significantly affect the interpretation of daily NO₂ variability, especially in regions with strong diurnal emissions or meteorological patterns. This study highlights the observational limitations of fixed-time satellites and offers an important reference for the future development of geostationary satellite missions, contributing to improved strategies for NO₂ pollution monitoring and control. Full article