Search Results (1,069)

Desertification greatly threatens the ecological environment and sustainable development over approximately 30% of global land. In this study, the contributions of climate drivers and human activity in shaping the desertification process from 1984 to 2014 were quantified in the desertification-prone region (DPR) in Northern China (NC) by employing net primary productivity (NPP) as a proxy. The results reveal that 72.74% of the DPR experienced desertification mitigation and 27.26% experienced exacerbation. Climate drivers acted as primary drivers, contributing to both the mitigation (47.2%) and exacerbation (48.5%) of desertification, while human activity also played a crucial role, with contributions of 39.6% to mitigation and 41.0% to exacerbation of desertification. Furthermore, a shift in desertification dynamics emerged around 2000, with climate drivers promoting the mitigation process (66.8%), and precipitation was a dominant climatic factor for the mitigation of desertification after 2000, which was related to internal atmospheric variability. This study highlights changes in the contributions of different factors to desertification, underscoring the need for policy adjustment to attain sustainable land management in NC. Full article

Some of the issues concerning energy security and climate change can be addressed by employing nuclear power (NP) to supply the energy required for the conversion of carbon dioxide (CO₂) into chemicals, products, and materials. Nuclear energy represents a neutral carbon source that can be generated sustainably, reliably, and consistently. Nuclear power plants (NPPs) could supply energy in the form of heat, electricity, and ionizing radiation to drive CO₂ chemical reactions underpinning NP-to-X type of pathways. CO₂ conversion processes are either commercially available or emerging technologies at different developmental maturity stages. This work reviews the published literature (articles and patents) that reports R&D results and the understanding and development of chemical reactions and processes, as well as the efforts in integrating NPPs and chemical processes (CPs). As will be made evident, a new industrial era for the manufacturing of decarbonized chemicals, products, and materials will be possible by developing and implementing new (more energy- and carbon-efficient) processes responding to the NP-to-X pathways. This new decarbonizing platform not only contributes to achieving net zero goals but also broadens the NPP product beyond electricity. Full article

This paper attempts to establish the accurate and timely forecasting model for the total output value of agriculture, forestry, animal husbandry, and fishery (TOVAFAF) in various provinces of China using NPP-VIIRS nighttime light (NTL) remote sensing data and machine learning algorithms. It can provide important data references for timely assessment of agricultural economic development level and policy adjustment. Firstly, multiple NTL indices for provincial-level administrative regions of China were constructed based on NTL images from 2013 to 2023 and various statistics. The results of correlation analysis and significance test show that the constructed total nighttime light index (TNLI), luminous pixel quantity index (LPQI), luminous pixel ratio index (LPRI), and nighttime light squared deviation sum index (NLSDSI) are highly correlated with the TOVAFAF. Subsequently, using the relevant data from 2013 to 2020 as the training set, the four NTL indices were separately taken as single independent variable to establish the linear model, exponential model, logarithmic model, power exponential model, and polynomial model. And all the four NTL indices were taken as the input features together to establish the multiple linear regression (MLR), extreme learning machine (ELM), and particle swarm optimization-ELM (PSO-ELM) models. The relevant data from 2021 to 2022 were taken as the validation set for the adjustment and optimization of the model weight parameters and the preliminary evaluation of the modeling effect. Finally, the established models were employed to forecast the TOVAFAF in 2023. The experimental results show that the ELM and PSO-ELM models can better explore and characterize the potential nonlinear relationship between NTL data and the TOVAFAF than all the models established based on single NTL index and the MLR model, and the PSO-ELM model achieves the best forecasting effect in 2023 with the MRE value for 32.20% and the R² values of the linear relationship between the actual values and the forecasting values for 0.6460. Full article

The aim of this paper is to present the isotopic changes in nuclear fuel during the first reactor cycle of the Korean Advanced Power Reactor 1400 (APR1400). The neutron transport and burnup calculations were performed using the Monte Carlo continuous energy burnup code—MCB. The three-dimensional numerical model consisting of the reactor pressure vessel with core internals was developed using available geometrical and material data as well as the reactor’s operating conditions. The reactor core was divided into 11 axial and 22 radial burnup zones in order to recreate the spatial distribution of the fuel burnup. The isotopic changes due to the nuclear transmutations and decays were calculated in each burnup zone until the desired average burnup of 17.571 GWd/tHM_int was reached. The calculations include changes in the boric acid concentration at defined time steps and the burnout of the gadolinia burnable absorber embedded in the nuclear fuel. This study shows the spatial distribution of minor and major actinides at the end of the reactor cycle as well as the depletion of uranium, the build-up of plutonium, and the formation of neptunium, americium, and curium during the reactor’s operation. Full article

Vegetation net primary productivity (NPP) is a key indicator for assessing vegetation dynamics and carbon cycle balance. Xinjiang is located in an arid and ecologically fragile region in northwest China, but the current understanding of vegetation dynamics in the region is still limited. This study aims to analyze Xinjiang’s NPP spatial and temporal trends, using random forest regression to quantify the extent to which climate change and human activities affect vegetation productivity. CMIP6 (Coupled Model Intercomparison Project Phase 6) climate scenario data help assess vegetation restoration potential and future risks. Our findings indicate that (1) Xinjiang’s NPP exhibits a significant increasing trend from 2001 to 2020, with three-quarters of the region experiencing an increase, 2.64% of the area showing significant decrease (p < 0.05), and the Ili River Basin showing a nonsignificant decreasing trend; (2) precipitation and radiation are major drivers of NPP variations, with contribution ratios of 35.13% and 30.17%, respectively; (3) noteworthy restoration potential exists on the Tian Shan northern slope and the Irtysh River Basin, where average restoration potentials surpass 80% relative to 2020, while the Ili River Basin has the highest future risk. This study explores the factors influencing the current vegetation dynamics in Xinjiang, aiming to provide references for vegetation restoration and future risk mitigation, thereby promoting sustainable ecological development in Xinjiang. Full article

The cycle of carbon and water in ecosystems is likely to be significantly impacted by future climate change, especially in semiarid regions. While a considerable number of investigations have scrutinized the repercussions of impending climatic transformations on either the carbon or water cycles, there is a scarcity of studies delving into the effects of future climate change on the coupled water–carbon process and its interrelationships. Based on this, the Sanchuan River Basin, an ecologically fragile region of the Loess Plateau, was chosen as the research area. General circulation model-projected climate scenarios (SSP1-2.6, SSP2-4.5, SSP5-8.5) and an ecohydrological model were integrated to predict (2021–2100) changes in actual evapotranspiration (ET), surface runoff (Rs), net primary productivity (NPP), and soil organic carbon (SOC). The results indicated that under the impacts of future climatic warming and humidification, ET, Rs, and NPP will increase by 0.17–6.88%, 1.08–42.04%, and 2.18–10.14%, respectively, while SOC will decrease by 3.38–10.39% in the basin. A path analysis showed that precipitation and temperature had significant effects on ET and NPP, Rs was more sensitive to precipitation, and temperature had a significant impact on SOC. Furthermore, all climate scenarios had an average ET-NPP correlation coefficient greater than 0.6, showing that the basin’s water–carbon cycle was tightly coupled. However, under SSP5-8.5, the correlation coefficient of Rs-NPP decreased from −0.35 in the near-future period to −0.44 in the far-future period, which may indicate that the positive effect of increased precipitation on Rs-NPP would barely offset the negative effect of large future temperature increases. As a foundation for achieving sustainable water resource management and ecosystem preservation policies, this study can be utilized to build adaptation methods to manage climate change. Full article

Understanding the relationship between climate, snow cover, and vegetation Net Primary Productivity (NPP) in the Tibetan Plateau (TP) is crucial. However, the role of snow cover in influencing the NPP remains unclear. This study investigates the connection between the NPP and snow phenology (SP) across the TP from 2011 to 2020. Interannual trends were assessed using the Theil–Sen non-parametric regression approach combined with the Mann–Kendall test. Additionally, the pathways through which snow cover affects the NPP, considering various environmental factors, were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). Approximately 10.72% of the TP showed a significant decrease in the NPP, accompanied by advancing trends in the Snow Onset Date (SOD) and Snow End Date (SED), as well as a gradual decrease in the Snow Cover Duration (SCD). The PLS-SEM results reveal that precipitation and soil temperature significantly influenced the NPP, with total effects of 0.309 and 0.206 in the SCD structural equation. Temperature had a relatively strong indirect effect on the NPP through its influence on the SOD and SCD, contributing 16% and 10% to the total effect, respectively. Neglecting the mediating effect of SP underestimates the environmental impact on the NPP. This study highlights how environmental factors influence the NPP through snow cover changes as the biomass increases, thereby enhancing our understanding of SP’s impact on the TP. Full article

The quality of nighttime light (NTL) data is an important factor affecting the estimation of gross domestic product (GDP), but most studies do not use the latest NPP/VIIRS V2 annual composite product, and there is a lack of China’s GDP estimation products in recent years. To address this problem, this paper studies the NPP/VIIRS remote sensing estimation method for the GDP in mainland China from 2013 to 2023. First, the remote sensing data are preprocessed, and the noise masking method is used to remove outliers. The total amount of NTL, average NTL value, and comprehensive NTL index data are extracted. Combined with the GDP data from the Statistical Yearbook, a fitting model of the GDP and NTL index is constructed. The differences between different GDP estimation models are compared and analyzed, and the optimal model is selected as the estimation model. In addition, through the optimal fitting model, GDP spatial estimation products from 2013 to 2023 are produced. Moreover, the spatiotemporal variation characteristics of the GDP in mainland China are analyzed, with a focus on the spatiotemporal variation of GDP decline regions and the changes in the GDP rankings of provinces and cities. The main conclusions include the following: (1) In the time regression analysis, the linear model MNL has a strong correlation with the GDP, with an R² of 0.972. This model is selected as the optimal fitting model to calculate the spatial data of the GDP. (2) The spatial distribution of the GDP in mainland China is high in the east and low in the west, and it shows a characteristic of extending from the provincial capital to the surrounding cities. The connectivity between adjacent high-GDP areas continues to increase. (3) From 2013 to 2023, the GDP in most parts of China showed an upward trend, with 98.56% of pixels growing and only 0.99% of pixels declining. The declining pixels are mainly distributed in heavy industrial cities supported by fossil fuel resources, such as Ordos, Daqing, Aksu, etc. (4) Compared with statistical data, the overall difference of the GDP estimated by NTL data is not large, and the relative error is between 0.04% and 1.95%. From the perspective of the GDP ranking of each province, the ranking of most provinces is not much different, fluctuating between ±2. A small number of provinces have large ranking differences due to reasons such as dominant industries and power supply. By spatializing the GDP data of mainland China in the past 11 years, the spatiotemporal changes of the GDP within mainland China were analyzed. The research results can provide support for government economic decisions such as urban development. Full article

Neuropathic pain (NPP) is a devastating and unbearable painful condition. As prevailing treatment strategies have failed to mitigate its complications, there remains a demand for effective therapies. Electroacupuncture (EA) has proved a potent remedial strategy in NPP management in humans and mammals. However, past studies have investigated the underlying mechanism of the analgesic effects of EA on NPP, focusing primarily on adenosine receptors in peripheral tissues. Herein, we elucidate the role of the adenosine (Adora-3) signaling pathway in mediating pain relief through EA in the central nervous system, which is obscure in the literature and needs exploration. Specific pathogen-free (SPF) male adult mice (C57BL/6 J) were utilized to investigate the effect of EA on adenosine metabolism (CD73, ADA) and its receptor activation (Adora-3), as potential mechanisms to mitigate NPP in the central nervous system. NPP was induced via spared nerve injury (SNI). EA treatment was administered seven times post-SNI surgery, and lumber (L4–L6) spinal cord was collected to determine the molecular expression of mRNA and protein levels. In the spinal cord of mice, following EA application, the expression results revealed that EA upregulated (p < 0.05) Adora-3 and CD73 by inhibiting ADA expression. In addition, EA triggered the release of adenosine (ADO), which modulated the nociceptive responses and enhanced neuronal activation. Meanwhile, the interplay between ADO levels and EA-induced antinociception, using an Adora-3 agonist and antagonist, showed that the Adora-3 agonist IB-MECA significantly increased (p < 0.05) nociceptive thresholds and expression levels. In contrast, the antagonist MRS1523 exacerbated neuropathic pain. Furthermore, an upregulated effect of EA on Adora-3 expression was inferred when the Adora-3 antagonist was administered, and the EA treatment increased the fluorescent intensity of Adora-3 in the spinal cord. Taken together, EA effectively modulates NPP by regulating the Adora-3 signaling pathway under induced pain conditions. These findings enhance our understanding of NPP management and offer potential avenues for innovative therapeutic interventions. Full article

Multi-junction solar cells are vital in developing reliable, green, sustainable solar cells. Consequently, the computational optimization of solar cell architecture has the potential to profoundly expedite the process of discovering high-efficiency solar cells. Copper indium gallium selenide (CIGS)-based solar cells exhibit substantial performance compared to those utilizing cadmium sulfide (CdS). Likewise, CIGS-based devices are more efficient according to their device performance, environmentally benign nature, and thus, reduced cost. Therefore, the paper introduces an optimization process of three-layered n-CdS/p-CIGS/p-GaAs (NPP)) solar cell architecture based on thickness and carrier charge density. An in-depth investigation of the numerical analysis for homojunction PPN-junction with the ’GaAs’ layer structure along with n-ZnO front contact was simulated using the Solar Cells Capacitance Simulator (SCAPS-1D) software. Subsequently, various computational optimization techniques for evaluating the effect of the thickness and the carrier density on the performance of the PPN layer on solar cell architecture were examined. The electronic characteristics by adding the GaAs layer on the top of the conventional (PN) junction further led to optimized values of the power conversion efficiency (PCE), open-circuit voltage (VOC), fill factor (FF), and short-circuit current density (JSC) of the solar cell. Lastly, the paper concludes by highlighting the most promising results of our study, showcasing the impact of adding the GaAs layer. Hence, using the optimized values from the analysis, thickness of 5 (μm) and carrier density of $1\times {10}^{20}$ (1/cm) resulted in the maximum PCE, VOC, FF, and JSC of 45.7%, 1.16 V, 89.52%, and 43.88 $\left({\mathrm{mA}/\mathrm{m}}^2\right)$, respectively, for the proposed solar cell architecture. The outcomes of the study aim to pave the path for highly efficient, optimized, and robust multi-junction solar cells. Full article

Approximately 86% of the total carbon emissions are generated by energy consumption, and the study of the variation of energy consumption carbon emissions (ECCE) is of vital significance to regional sustainable development and energy conservation. Currently, carbon emissions accounting mainly focuses on large and medium-scale statistics, but at smaller scales (district and county level), it still remains unclear. Due to the high correlation between nighttime light (NTL) data and ECCE, this study combines “energy inventory statistics” with NTL data to estimate ECCE at smaller scales. First, we obtained city-level statistics on ECCE and corrected the NTL data by applying the VANUI index to the original NTL data from NPP-VIIRS. Second, an analysis was conducted on the correlation between the two variables, and a model was created to fit the relationship between them. Under the assumption that ECCE will be consistent within a given region, we utilized the model to estimate ECCE in districts and counties, eventually obtaining correct results at the county-level. We estimated the ECCE in each district and county of Jiangsu Province from 2013 to 2022 using the above-proposed approach, and we examined the variations in these emissions both spatially and temporally across the districts and counties. The results revealed a significant degree of correlation between the two variables, with the R² of the fitting models exceeding 0.8. Furthermore, ECCE in Jiangsu Province fluctuated upward during this period, with clear regional clustering characteristics. The study’s conclusions provide information about how carbon emissions from small-scale energy use are estimated. They also serve as a foundation for the creation of regional energy conservation and emission reduction policies, as well as a small-scale assessment of the present state. Full article

A large number of the thin-film organic structures (polyimides, 2-cyclooctylarnino-5-nitropyridine, N-(4-nitrophenyl)-(L)-prolinol, 2-(n-Prolinol)-5-nitropyridine) sensitized with the different types of the nano-objects (fullerenes, carbon nanotubes, quantum dots, shungites, reduced graphene oxides) are presented, which are studied using the holographic technique under the Raman–Nath diffraction conditions. Pulsed laser irradiation testing of these materials predicts a dramatic increase of the laser-induced refractive index, which is in several orders of the magnitude greater compared to pure materials. The estimated nonlinear refraction coefficients and the cubic nonlinearities for the materials studied are close to or larger than those known for volumetric inorganic crystals. The role of the intermolecular charge transfer complex formation is considered as the essential in the refractivity increase in nano-objects-doped organics. As a new idea, the shift of charge from the intramolecular donor fragment to the intermolecular acceptors can be proposed as the development of Janus particles. The energy losses via diffraction are considered as an additional mechanism to explain the nonlinear attenuation of the laser beam. Full article

Climate variability significantly impacts plant growth, making it crucial to monitor ecosystem performance for optimal carbon sequestration, especially in the context of rising atmospheric CO₂ levels. Net Primary Productivity (NPP), which measures the net carbon flux between the atmosphere and plants, serves as a key indicator. This study uses the CASA (Carnegie–Ames–Stanford Approach) model, a radiation use efficiency method, to assess the spatio-temporal dynamics of NPP in Togo from 1987 to 2022 and its climatic drivers. The average annual NPP over 36 years is 4565.31 Kg C ha⁻¹, with notable extremes in 2017 (6312.26 Kg C ha⁻¹) and 1996 (3394.29 Kg C ha⁻¹). Productivity in natural formations increased between 2000 and 2022. While climate change and land use negatively affect Total Production (PT) from 2000 to 2022, they individually enhance NPP variation (58.28% and 188.63%, respectively). NPP shows a strong positive correlation with light use efficiency (r² = 0.75) and a moderate one with actual evapotranspiration (r² = 0.43). Precipitation and potential evapotranspiration have weaker correlations (r² = 0.20; 0.10), and temperature shows almost none (r² = 0.05). These findings contribute to understanding ecosystem performance, supporting Togo’s climate commitments. Full article

Net Primary Productivity (NPP) is a crucial indicator of ecological environment quality. To better understand the carbon absorption and carbon cycling capabilities of Henan Province, this study investigates the trends and driving factors of NPP across different climatic zones and land use types. The Theil–Sen Median trend analysis method and the Mann–Kendall trend test are employed to monitor NPP changes from 2001 to 2020. The average annual NPP in Henan Province during this period was 414.61 gC·m⁻²·year⁻¹, showing a significant increasing trend with a growth rate of 3.73 gC·m⁻²·year⁻¹. Spatially, both the annual average NPP and its increase rate were higher in the western part of Henan compared to the eastern part, and NPP variability was more stable in the southern region than in the northern region. By classifying climatic zones and using the Geodetector method to assess NPP sensitivity to natural factors, the results show that climate and vegetation factors jointly influence NPP variations, with annual precipitation being the primary natural factor affecting NPP trends in Henan Province from 2001 to 2020. By analyzing the NPP gain and loss matrix, the impact of land use changes on NPP was evaluated. Forests had the highest average annual NPP at 483.52 gC·m⁻²·year⁻¹, and the conversion of arable land to urban areas was identified as the primary land change type leading to NPP reductions. In the subtropical zone of Henan, forests, croplands, and grasslands exhibited higher NPP values and increase rates compared to those in the warm belt. This study provides new insights into the spatial variation of NPP caused by changes in climatic zones and land use types. Full article

Enhancing regional ecosystem stability and managing land resources effectively requires identifying ecological function zones and understanding the factors that influence them. However, most current studies have primarily focused on ecosystem service bundles, paying less attention to the trade-offs, synergies, and ecological sensitivity, leading to a more uniform approach to functional zoning. This study aimed to analyze and describe the spatial and temporal patterns of four essential ecosystem services, including water yield (WY), net primary productivity (NPP), soil conservation (SC), and habitat quality (HQ), in the Sichuan-Yunnan ecological buffer area over the period from 2005 to 2019. Spatial overlay analysis was used to assess ecological sensitivity, trade-offs, synergies, and ecosystem service bundles to define ecological functional zones. Geographic detectors were then applied to identify the primary drivers of spatial variation in these zones. The findings showed a progressive improvement in ecosystem service functions within the Sichuan-Yunnan ecological buffer zone. Between 2005 and 2019, NPP, soil conservation, and water yield all demonstrated positive trends, while HQ displayed a declining trend. There was significant spatial heterogeneity and distinct regional patterns in ecosystem service functions, with a general decrease from southwest to northeast, particularly in NPP and HQ. Trade-offs were evident in most ecosystem services, with the most significant between WY and HQ and most in the northeast and east regions. Ecological sensitivity decreased from southwest to northeast. Regions with a higher ecological sensitivity were primarily situated in the southwestern region, and their spatial distribution pattern was comparable to that of high habitat quality. The spatial overlay analysis categorized areas into various types, including human production and settlement zones, ecologically vulnerable zones, ecological transition zones, and ecological conservation zones, accounting for 17.28%, 22.30%, 7.41%, and 53.01% of the total area, respectively. The primary environmental factor affecting ecological function zoning was identified as precipitation, while the main social variables were human activity and population density. This study enhances the understanding of ecological functions and supports sustainable development in the Sichuan-Yunnan ecological buffer area, offering important guidance for ecological zoning. Full article