This study measures regional circular economy efficiency (CEE) and examines its impact factors of environmental regulations and the digital economy from 2009-2023 in China, yielding the following conclusions: (1) At the national level, environmental regulations exhibit a U-shaped relationship with CEE, initially suppressing it before promoting it.Regionally, the coefficient for the first order term and the coefficient for the second order term of environmental regulation intensity in the central and western regions fail to pass the significance test.In the eastern region, environmental regulations first reduce CEE before enhancing it, confirming a U-shaped relationship.(2) Digital economy can promote CEE in whole regions except western region which is not significant.(3) Economic development can improve CEE in all regions except western region.The industrial structure inhibit CEE in all regions, though the negative impact in the eastern region is not statistically significant.Foreign trade enhances CEE in the eastern region but has a negligible effect in the central and western regions.The impact of resource endowments on CEE is insignificant in national and eastern.However, technological advancements in central and western regions have boosted efficiency in the mining sector, thereby elevating the average energy efficiency of resource rich areas.

Melaleuca forests in the U Minh Thuong ASEAN Heritage Park, the Mekong Delta of Vietnam involve a distinctive ecosystem that contributes significantly to biodiversity conservation and the provision of essential ecosystem services.Despite their importance, methods for accurately estimating carbon stocks in these forests remain insufficiently studied.This study utilizes both RADAR and optical data from ALOS-2, Sentinel-1, Landsat 8, and Sentinel-2 satellites to model and map the spatial distribution of aboveground biomass (AGB) in Melaleuca forests within U Minh Thuong National Park, a tropical forest site in the Mekong Delta of Vietnam.Various models were constructed using the backscatter values and Gray Level Co-occurrence Matrix (GLCM) calculated from RADAR data and normalized difference vegetation index (NDVI) from optical data, and field estimated AGB.For modelling, k-fold cross-validation approach was used.The study also found that the model integrating ALOS-2 PALSAR-2 HV data, GLCM textures, and Sentinel-2 NDVI, showed the best results (R 2 = 0.84, RMSE = 9.59 Mg ha -1 ).The research underscores the significance of remote sensing data in supporting conservation efforts and sustainable forest management practices in tropical ecosystems.The study contributes to advancing biomass estimation methodologies and emphasizes the importance of multi-sensor satellite data integration for accurate and reliable forest biomass mapping, particularly in ecologically sensitive regions like the U Minh Thuong National Park.

The water network (WN) not only plays a part in our daily life, but also partly determines the regional planting structure and ecological pattern.Therefore, using Jilin-1 Gaofen satellite imagery (0.75 m), this study innovatively proposed an improved DeepLabV3 + deep-learning semantic segmentation model to extract WN elements (nodes, arcs, and polygons) in three typical sub-basins of the Erhai Lake Basin, China, for the years 2001, 2008, 2015 and 2023, thereby unveiling the spatiotemporal evolutionary patterns and trends of the WN in the basin.It was found that 1) our improved DeepLabV3 + model could effectively extract complex WN elements, reaching a precision of 98.87%; specifically, it showed robust performance in extracting small objects, effectively suppressed noises from building shades, vegetation occlusions, and complex ground objects; 2) the Northern Three-River Sub-basin it demonstrated the most drastic changes and increasing WN connectivity; in the Cangshan Mountain Sub-basin, the WN experienced less intense variations, and the WN elements grew increasingly independent from each other; WN elements in the Southern Boluo River Sub-basin showed milder variations and declined WN connectivity; 3)as of 2023, the density of WN in the Northern Three-River Sub-basin and Cangshan Mountain Sub-basin has increased, with Sanying Town (19.37) and Xizhou Town (15.70) marking the highest densities, respectively, while the Southern Polo River Sub-basin showed a decreasing density, with Fengyi Town (34.71) having the highest density.

To address the issues of limited coverage, low spatiotemporal resolution, and insufficient observational data variables in evaluating the forecasting performance of a weather modification model, this study utilized Moderate Resolution Imaging Spectroradiometer (MODIS) polar-orbiting satellite data from September to October 2018.It assessed the reliability of cloud products from the Himawari-8 geostationary satellite, known for its high spatiotemporal resolution and extensive coverage, which was applied to evaluate the forecasting capability of the Cloud and Precipitation Explicit Forecasting System (CPEFS) model in cloud fields over the Central Region of China.The results showed that the spatial distributions of cloud top temperature (CTT) and cloud water path (CWP) observed by Himawari-8 and MODIS were consistent, with monthly average spatial anomaly correlation coefficients (ACC) ranged from 0.51 to 0.67 for CTT and 0.68 to 0.69 for CWP.The average ACC between CPEFS model forecasts and Himawari-8 observations was 0.23 for CTT and 0.34 for CWP.The forecasting performance of CTT exhibited greater variability between cases compared to CWP.Overall, the reliability of forecasts was low at the initial stage, increased with lead time, and then declined in later stages.In higher-performing cases, the earlystage forecasting reliability improved more rapidly.

Genotype environment interactions represent a major challenge in breeding programs aimed at developing high-yielding, well-adapted cultivars.To achieve more precise selection, it is essential to evaluate both the performance and stability of hybrids across different environments simultaneously.Maize (Zea mays L.) is one of the world's most important cereal crops, valued for its high yield potential, genetic diversity, and multifunctional uses in food, feed, and industry.Climate change exacerbates water scarcity by disrupting rainfall, intensifying droughts, thereby boosting evaporation, threatening agriculture and ecosystems.The study aimed to evaluate performance and yield stability of ten maize hybrid cultivars under different spring sowing dates and irrigation levels in Bazian, Kurdistan region-Iraq in spring, 2024.The experimental design was (CRBD) across six environments E1-E6 (three sowing dates SD1-SD3 two irrigation levels: 2 and 4 L), using 10 hybrid cultivars of both local and introduced species (Kontigos, Koncense, ED 110, DKC 66 64, AGN 720, Jameson, ZPF 59 un 44 52, ZP 505 IK8, Abjaro, Al Nahrayn).Stability analysis using Eberhart and Russell's model identified differences between hybrids were highly significant for all parameters (number of ear per plant, number of kernels per ear, 300 kernel weight, kernel yield, total root dry weight, soil surface wetting area and soil moisture depth) except soil surface wetting area and soil moisture depth which were not significant.Despite low stability C9 had highyielding across all environments (16.088) th 1 , with (bi 1.513 and S 2 di 2.928).The environment with early SD and 4L (E2) consistently produced the highest yields (17.438) th -1 .

As new digital infrastructure propels the development of the digital economy, its role in driving green innovation amidst mandatory low-carbon development requirements warrants in-depth investigation.Leveraging urban information infrastructure data and Chinese listed firm data, this study examines how information infrastructure (II) advances green technological innovation (GTI) under carbon constraints.II boosts corporate GTI by 13.52% (quantity) and 11.88% (quality) while helping enterprises meet low-carbon requirements.Robustness tests such as the placebo test, PSM-DID test, instrumental variables test, and exclusion of particular samples test, confirmed the same findings.Further, the mechanism analysis showed that the II promotes the GTI of enterprises through accelerating corporate digital transformation for energyperformance alignment and easing green financing constraints for eco-innovation investments.The effect is affected by carbon-reduction pressure and enterprise life cycle heterogeneity.This study provides evidence for synergizing digital infrastructure with carbon governance, suggesting II deployment as a strategic lever for achieving dual innovation-carbon goals.

Based on panel data from 265 Chinese prefecture-level cities covering the period from 2008 to 2023, this study utilizes China's Low-Carbon Transportation System (LCTS) pilot policy as a quasi-natural experiment and applies a staggered difference-in-differences (DID) model to empirically examine the impact of urban transportation green transition on low-carbon technology innovation (LCTI).The results indicate that the LCTS pilot policy has a statistically significant and positive effect on promoting urban LCTI.Mechanism analysis indicates that the policy exerts its incentive effect mainly through two channels: government-provided green financial support, which alleviates the funding constraints for low-carbon technology R&D, and the demand-pull effect due to heightened public environmental awareness.Furthermore, heterogeneity analysis shows that the positive impact of the LCTS pilot policy on LCTI is more pronounced in cities with higher marketization levels, lower energy cleanliness (higher coal dependence), and stronger intellectual property protection.This study not only enriches the theoretical understanding of the relationship between transportation green transition and technological innovation but also provides empirical evidence and policy implications for formulating differentiated policies to advance urban low-carbon development and achieve the "dual carbon" goals.

To comprehensively understand the toxic effects and ecological risks of microplastics on major economic tree species, a pot experiment was conducted using polylactic acid (mPLA) microplastics as the test object to explore the effects of different concentrations (0.1%, 0.5%, 1%, 5%, 10%, w/w, mass fraction) of microplastics on the growth and physiological characteristics of mulberry trees.The study results showed that, compared with the control group, the biomass, total chlorophyll content, and net photosynthetic rate of mulberry trees in the mPLA treatment group were significantly reduced under high concentration (10%) treatment; the activities of SOD and CAT and the MDA content were significantly increased by 50.00%, 47.83%, and 60.87%, respectively, at a 10% concentration.The results indicate that the toxic effects of microplastic addition on mulberry trees are related to the type and concentration of microplastics.High concentrations of mPLA can damage the photosynthetic system of plants, affecting photosynthesis, causing oxidative damage and thus inhibiting the growth of mulberry plants.

Policymakers and scholars around the world have grappled with the difficulties of reducing rural poverty.For China, although absolute poverty has been eradicated, considerable efforts are still required to address relative poverty and boost sustainable income growth among farmers.This study examines Ganzi Prefecture in Sichuan Province as a representative of China's relatively poor areas, analyzing the spatiotemporal dynamics of farmers' income and exploring the interactions between income growth, the natural environment, socio-economic progress, and rural support measures.The results indicate that, since 2014, both the internal income disparities of farmers in Ganzi and the income gap relative to the national average have converged.Our analysis provides robust evidence that socioeconomic development and rural support have significantly increased farmers' income.The discussion of heterogeneity reveals that these positive effects vary across sub-income types and county categories.However, the impact of agricultural resources is not constant, and a "resource curse" effect is observed in more challenging counties, particularly in terms of hindering wage and salary income growth.Our findings advance scientific understanding of income growth challenges among farmers in relatively poor areas and highlight an integrated natural-social-economic perspective for analyzing this issue.These insights would provide new information for policymakers to formulate targeted follow-up assistance policies in the post-poverty era.

This research aims to explore the validity of the Environmental Kuznets Curve (EKC) hypothesis and analyzes the roles of irrigation investments and research and development expenditures (R&D) expenditures in mitigating agricultural carbon emissions in the conventional resource-dependent areas of China from 2001 to 2021.A multivariable empirical framework is employed, incorporating the Lee and Strazicich (LS) and Fourier Lagrange multiplier (LM) unit root tests, Engle-Granger and Phillips-Ouliaris single equation cointegration tests, auto regressive distributed lag (ARDL)methodology, fully modified ordinary least squares (FMOLS) and dynamic ordinary least squares (DOLS) robustness estimators, and the Granger causality test to accomplish this objective.The research reveals that the EKC hypothesis is evident for carbon emissions related to agriculture across all provinces in the sample.Additionally, irrigation investments and R&D expenditures exhibit negative consequences for carbon emissions in the agricultural sector, both in the short and long term.Furthermore, there exists one-way causal relationships originating from irrigation investments and R&D expenditures to agricultural carbon emissions.Finally, this study highlights policy implications of digital and intelligent irrigation technology for local governments on both inhibiting carbon emissions and promoting sustainable agricultural development in China's conventional resource-dependent areas.