Search Results (621)

Greenhouse gas (GHG) emission-induced climate change, particularly occurring since the mid-20th century, has been considerably affecting short-term weather conditions, such as increasing weather variability and the incidence of extreme weather-related events. Milk production is sensitive to such changes. In this study, we use spatial panel econometric models, the spatial error model (SEM) and the spatial Durbin model (SDM), with a panel dataset at the state-level varying over seasons, to estimate the relationship between weather indicators and milk productivity, in an effort to reduce the bias of omitted climatic variables that can be time varying and spatially correlated and cannot be directly captured by conventional panel data models. We find an inverse U-shaped effect of summer heat stress on milk production per cow (MPC), indicating that milk production reacts positively to a low-level increase in summer heat stress, and then MPC declines as heat stress continues increasing beyond a threshold value of 72. Additionally, fall precipitation exhibits an inverse U-shaped effect on MPC, showing that milk yield increases at a decreasing rate until fall precipitation rises to 14 inches, and then over that threshold, milk yield declines at an increasing rate. We also find that, relative to conventional panel data models, spatial panel econometric models could improve prediction performance by leading to smaller in-sample and out-sample root mean squared errors. Our study contributes to the literature by exploring the feasibility of promising spatial panel models and resulting in estimating weather influences on milk productivity with high model predicting performance. Full article

Background: In recent years, the advancement of computational chemistry has offered new insights into the rational design of molecularly imprinted polymers (MIPs). From this aspect, our study tried to give quantitative parameters for evaluating imprinting efficiency and exploring the formation mechanism of MIPs by combining simulation and experiments. Methods: The pre-polymerization system of sulfadimethoxine (SDM) was investigated using a combination of quantum chemical (QC) calculations and molecular dynamics (MD) simulations. MIPs were prepared on the surface of silica gel by a surface-initiated supplemental activator and reducing agent atom transfer radical polymerization (SI-SARA ATRP). Results: The results of the QC calculations showed that carboxylic monomers exhibited higher bonding energies with template molecules than carboxylic ester monomers. MD simulations confirmed the hydrogen bonding sites predicted by QC calculations. Furthermore, it was observed that only two molecules of monomers could bind up to one molecule of SDM, even when the functional monomer ratio was up to 10. Two quantitative parameters, namely, the effective binding number (EBN) and the maximum hydrogen bond number (HBN_Max), were defined. Higher values of EBN and HBN_Max indicated a higher effective binding efficiency. Hydrogen bond occupancies and RDF analysis were performed to analyze the hydrogen bond formation between the template and the monomer from different perspectives. Furthermore, under the influence of the EBN and collision probability of the template and the monomers, the experimental results show that the optimal molar ratio of template to monomer is 1:3. Conclusions: The method of monomer screening presented in this study can be extended to future investigations of pre-polymerization systems involving different templates and monomers. Full article

It is very important to enhance the risk resistance of the agricultural sector to realize the modernization transformation of the agricultural industry and strengthen the competitiveness of national agriculture. Based on the relevant spatial data of 30 provincial administrative regions in China from 2013 to 2022, this study constructs a multi-dimensional index framework to comprehensively evaluate the resilience of China’s agricultural economy by comprehensively considering the three key aspects of adaptability, management strategy, and innovation drive. This study adopts several quantitative analysis tools including the Theil index, global and local analysis of the Moran I index, and kernel density estimation (KDE), and further combines with the spatial Durbin model (SDM) to conduct an in-depth spatiotemporal analysis of the resilience of China’s agricultural economy. This study not only reveals the evolution trend of agricultural economic resilience in different times and spaces but also analyzes the differences in resilience among regions and its spread in space. Through these refined analytical tools, we aim to reveal how agricultural economic resilience changes over time, the differences in resilience levels among regions, and the geospatial interactions and diffusion. This study reveals a series of key findings: (1) The resilience of China’s agricultural economy shows a trend of steady improvement. (2) Differences within the three regions are the main factors generating differences in the development of resilience in China’s agricultural economy. (3) The resilience of the agricultural economy in different regions shows obvious spatial correlations. (4) Further analysis shows that the efficiency of agricultural production and the urbanization process have a positive direct impact on the resilience of the agricultural economy, and this impact has a significant positive spatial diffusion effect. Meanwhile, although the level of agricultural mechanization is not significant in its direct impact, it has a positive spatial impact on the enhancement of agricultural economic resilience in other regions. In addition, the restructuring of agricultural cropping has both direct negative impacts and positive spatial spillover effects on the resilience of the agricultural economy. Based on these findings, this paper suggests that agricultural policies should consider regional development differences, implement differentiated agricultural support policies, fully account for the spatial spillover effects of agricultural ecological efficiency, and strengthen the exchange and cooperation of resources between regions. This study deepens the understanding of the spatial and temporal characteristics of the resilience of China’s agricultural economy, reveals its inherent dynamic processes and spatial interactions, and provides valuable references for policymakers and practitioners to better cope with the various challenges encountered in agricultural production, and to jointly promote the sound development of China’s agricultural economy. Full article

Nursery habitats play a significant role in completing fish life cycles, and they are now recognized as essential habitats. Monthly variations in nursery ground distributions of Larimichthys polyactis were investigated in the southern Yellow Sea in 2019. Bayesian hierarchical models with integrated nested Laplace approximation were utilized to model the preferential nursery habitats of L. polyactis larvae. The study analyzed the spatial and temporal distributions of the larvae and juveniles based on three environmental variables: sea surface temperature, sea surface salinity, and depth. Additionally, this study examined the utilization of habitats by different fish life stages and ontogenetic shifts. A total of 3240 individuals were collected from April to June, with the peak occurring in May (0.05 ind./m³), and the distribution areas varied between different months. The prediction of the model reveals the ecological adaptability of L. polyactis to temperature variations. The optimal temperature for L. polyactis density ranges from 12.5 °C to 16.5 °C in April and 16.5 °C to 17.5 °C in May, demonstrating a broad temperature tolerance for L. polyactis survival. In addition, there are variations in distribution patterns among different developmental stages. Larimichthys polyactis spawn in the inshore and nearshore waters, and after hatching, larvae in the pre-flexion stage tend to remain aggregated near the spawning beds. However, larvae in the advanced development stage (post-flexion) and juveniles move towards the sandy ridge habitats along the coast and start to migrate offshore in June. This study provides valuable insights for the effective management of fishery resources in the area and can be utilized to identify marine areas with specific habitat features that require conservation. Full article

Species distribution models (SDMs) have been widely used to project how species respond to future climate changes as forecasted by global climate models (GCMs). While uncertainties in GCMs specific to the Tibetan Plateau have been acknowledged, their impacts on species distribution modeling needs to be explored. Here, we employed ten algorithms to evaluate the uncertainties of SDMs across four GCMs (ACCESS-CM2, CMCC-ESM2, MPI-ESM1-2-HR, and UKESM1-0-LL) under two shared socioeconomic pathways (SSP2-4.5 and SSP5-8.5) at two time stages. We selected two endemic species of the Tibetan Plateau, Gentiana yunnanensis and G. siphonantha, distributed in the Hengduan Mountain regions of the southeast plateau and northeast plateau regions, respectively, as case studies. Under the two SSPs and two time periods, there are significant differences in the distribution areas of G. yunnanensis predicted by different GCMs, with some showing increases and others showing decreases. In contrast, the distribution range trends for G. siphonantha predicted by different GCMs are consistent, initially increasing and then decreasing. The CMCC-ESM2 model predicted the largest increase in the distribution range of G. yunnanensis, while the UKESM1-0-LL model predicted the greatest decrease in the distribution range of G. siphonantha. Our findings highlight that the four selected GCMs still lead to some variations in the final outcome despite the existence of similar trends. We recommend employing the average values from the four selected GCMs to simulate species potential distribution under future climate change scenarios to mitigate uncertainties among GCMs. Full article

Species distribution models (SDMs) have been used to forecast the impact of climate change on species’ potential distribution, with results that might support decisions for conservation and biodiversity management. Despite their vulnerability to parameterization and data quality input, SDM use has been increasing in the last decades. In fact, inappropriate inputs and the lack of awareness about the effects of methodological decisions on results can lead to potential unreliability in results, a problem that might gain relevance when SDMs are used to predict climate change impacts on species-suitable areas. Aiming to assess how far such a topic is considered, an analysis of the calibration data and methodological decisions was conducted for recent publications (2018 to 2022) that include SDMs in this context, aiming to identify putative deviations from the consensual best practices. Results show that the parameters presented more consistently are the algorithm in use (MaxEnt was used in 98% of the studies), the accuracy measures, and the time windows. But many papers fail to specify other parameters, limiting the reproducibility of the studies. Some papers fail to provide information about calibration procedures, others consider only a fraction of the species’ range, and others provide no justification for including specific variables in the model. These options can decrease reliability in predictions under future scenarios, since data provided to the model are inaccurate from the start or there is insufficient information for output discussion. Full article

Shared decision-making (SDM) is an essential component of patient-centered healthcare and disease management. However, the association of SDM with healthcare resource utilization and patient-reported outcomes among multimorbid individuals is not well understood. This study sought to evaluate the association of SDM with healthcare resource utilization and patient-reported outcomes among United States (US) adults with multimorbidity. Data were collected from the 2020 Medical Expenditure Panel Survey (MEPS) for this cross-sectional study. Eligible participants were US adults with two or more comorbidities. The predictor variable was SDM (optimal versus not optimal). The outcome variables were healthcare resource utilization and patient-reported outcomes. Logistic regression models, adjusted for demographic characteristics, assessed associations with SDM for each healthcare resource utilization and patient-reported outcome variable. The analysis maintained the complex survey data and was weighted to produce nationally representative estimates. Individuals who reported optimal SDM in adjusted analyses utilized more healthcare resources compared to those who reported not optimal SDM. Individuals with optimal SDM had more than one outpatient visit (odds ratio OR = 1.23, 95% CI = 1.03–1.47), emergency room visit (OR = 1.55, 95% CI = 1.17–2.06), and inpatient discharge (OR = 1.44, 95% CI = 1.05–1.96). Additionally, these individuals had higher odds of reporting limitations in their ability to work or engage in other activities due to their physical health in the past four weeks (OR = 1.27, 95% CI = 1.01–1.60). This study indicated evidence of increased healthcare resource utilization among patients who participate in SDM with their providers, which should be explored in future studies. Full article

Climate change is altering the geographical distribution and abundance of species. Abundant genetic variation generally indicates a stronger adaptability and evolutionary potentiality, especially in case of sharply changing climates or environments. With the past global climate fluctuations, especially the climate oscillation since the Quaternary, the global temperature changes related to glaciation, many relict plant species have formed possible refugia in humid subtropical/warm temperate forests, thus retaining a high level of genetic diversity patterns. Based on the contraction and expansion of the geographical distribution of Taxus wallichiana Zucc. in the past driven by climate change, combined with the contemporary genetic diversity modeling, the distribution performance of Taxus wallichiana Zucc. in future climate change was predicted. The areas of highly suitable habitat will increase with climate change in the future. There were continuous and stable high suitable areas of T. wallichiana in the southeastern Tibet and northwestern Yunnan as long-term stable climate refugia. We made the genetic landscape surface of T. wallichiana complex and discovered geographical barriers against gene flow. Genetic barriers spatially isolated the center of genetic diversity into three regions: west (east Himalaya), middle (Yunnan plateau, Sichuan basin, Shennongjia, and the junction of Guizhou and Guangxi provinces), and east (Mt. Huangshan and Fujian). Southern Tibet was isolated from other populations. The central and western Yunnan, the Sichuan basin, and surrounding mountains were isolated from the southern China populations. We found that the positive correlationships between the present species genetic diversity and suitability index during LGM, MH, and 2070. This infers that T. wallichiana has provisioned certain genetic diversity and has strong evolutionary potential under conditions of climate change. Full article

The lower course of the Yellow River is a “hanging river” across the hinterland of China, and the safety of its flood control measures/systems is closely tied to the stability of the nation. Ensuring high-quality, sustainable development of the lower Yellow River floodplain while maintaining flood safety is crucial for the entire Yellow River Basin. Previous studies have primarily focused on the overall development of the Yellow River Basin or the economic–ecological coupling development of cities along the river, often neglecting the flood safety development of the floodplain. This study optimizes the socio-economic–flood-safety–ecological (SFE) system of the typical downstream Landong floodplain within the Yellow River Basin. The system dynamics model (SDM) can simulate the dynamic behavior of SFE systems by constructing mathematical models that incorporate feedback loops and time delays. The primary components include causal loop modules and stock-flow modules. Then, a coupling coordination degree model for the Landong floodplain is established using a comprehensive subjective and objective weighting method, assessing the SFE system’s coordination under five scenarios: inertial development, economic development, environmental protection, flood safety, and sustainable development. The results of historical and validity tests indicate that the SDM can effectively simulate the coupling coordination degree of the SFE system. The study results suggest that the coupling coordination degree increases the most under the sustainable development scenario, indicating that the development of the Landong floodplain should not only focus on socio-economic growth, but should also consider flood safety and ecological concerns. In addition, comprehensive regulation from socio-economic, flood safety, and ecological environment indicators are necessary to achieve high-quality, coordinated development. This study has significant implications for policy formulation and management to achieve high-quality and sustainable development in the downstream floodplain of the Yellow River. Full article

Chytridiomycosis is one of the greatest threats to the diversity of amphibians worldwide. Caused by the chytrid fungus Batrachochytrium salamandrivorans (Bsal), it plays a decisive role in species declines. Bsal is particularly harmful to the European fire salamander (Salamandra salamandra), causing ulcerations, anorexia and ataxia, which ultimately lead to death. While most studies have focused on the geographic expansion of the pathogen, there is little high-resolution information available. Therefore, we chose a three-step approach in this study: We (I) used a mechanistic distribution model to project the microclimatic growth rate of Bsal within its invasive range on a spatially very high resolution (25 m). We (II) used a correlative distribution model to predict the potential distribution of S. salamandra and (III) applied n-dimensional hypervolumes to quantify the realized microclimatic niches of both species and examine their overlaps. We estimated future trends based on comparisons among three climate scenarios, the current microclimatic conditions and a +2 °C and +4 °C global mean temperature scenario. We demonstrated that Bsal finds suitable growth conditions everywhere within our study area, thus putting S. salamandra at high risk. However, climate change could lead to less suitable thermal conditions for Bsal, possibly providing a loophole for S. salamandra. Full article

Urban development relies on the promotion of innovation, while sustainable development is an inevitable requirement for green urban development. As the primary carrier of innovation and sustainable development, cities have seen the construction of smart cities become a hotspot topic of public concern against the backdrop of rapid advancements in information technology. Based on the Chinese smart-city pilot policies, this paper selects data from 278 prefecture-level cities between 2007 and 2020, employing difference-in-difference (DID), epsilon-based measures and global Malmquist–Luenberger index (EBM-GLM), and the Spatial Durbin Model (SDM) to analyze the direct impact, spatial effects, and regional differences of smart-city construction on urban innovation capacity and sustainable development. The research results indicate the following: (1) the implementation of smart-city policies significantly enhances the urban innovation capacity ($UCI$), but its impact on green total-factor productivity ($GTFP$) is unstable and even insignificant; (2) the $UCI$ and $GTFP$ of smart cities have spillover effects, and the implementation of policies may inhibit the improvement of $UCI$ and $GTFP$ in neighboring cities; (3) the impact of smart-city construction varies across different regions, with a more significant promotion effect on the innovation capacity of economically developed cities. Full article

Climate change promotes variations in distribution ranges, potentially leading to biodiversity loss and increased extinction risks for species. It is crucial to investigate these variations under future climate change scenarios for effective biodiversity conservation. Here, we studied the future distribution ranges of 268 Testudoformes species under climate change using habitat models, specifically species distribution models (SDMs), to assess their conservation status. Our results have indicated that over half of species are projected to experience declines in their potential distribution ranges under two scenarios. In particular, we found that three critically endangered species—Three-striped roofed turtle (Batagur dhongoka), Durango mud turtle (Kinosternon durangoense), and Colombian mud turtle (Kinosternon dunni)—displayed extraction of their distribution ranges and faced extinction under global climate change. Additionally, our analysis revealed that the potential distribution ranges of some species might increase under future climate scenarios. However, these findings must be interpreted with caution as they do not account for other significant factors such as biological invasions, population structure, land-use change, anthropogenic disturbances, and inter-organism interrelationships. Future studies should incorporate these factors to provide a more comprehensive assessment of extinction risks. Our findings suggest that climate change, in conjunction with habitat degradation and human activities, must be considered when assessing the extinction risks of Testudoformes. Full article

The green and low-carbon transformation of land use (GLTLU) is a pressing global issue that requires urgent attention. The digital economy has emerged as a new driver for the GLTLU. However, current research mainly focuses on the measurement and environmental effects of the digital economy, with less exploration of how the digital economy influences the spatial effects and regulatory mechanisms of GLTLU, particularly regarding the differential impacts and specific mechanisms at the regional level. This study uses panel data from 283 cities in China from 2011 to 2019, employing the spatial Durbin model (SDM) and the panel threshold model to examine the spatial and regulatory mechanisms of the digital economy’s impact on GLTLU. The findings reveal that digital economy promotes GLTLU not only within cities but also in surrounding regions. Robustness analyses support this conclusion. Notably, the digital economy’s positive impact on GLTLU in surrounding areas is confined to the central region of China. In contrast, the Yangtze River Delta urban agglomeration experiences a significant negative impact on GLTLU in nearby regions due to the digital economy. The study also identifies that the positive spatial spillover effect of the digital economy on GLTLU reaches its peak at a distance of 450 km. Additionally, the digital economy’s ability to promote GLTLU is contingent upon financial agglomeration levels exceeding 9.1728. Moreover, the local government’s emphasis on the digital economy and intellectual property protection enhances the digital economy’s impact on GLTLU. The promotion effect is maximized when these factors surpass the thresholds of 27.8054 and 3.5189, respectively. Overall, this study contributes to the understanding of how the digital economy influences sustainable land development, highlighting the critical role of regional factors and regulatory mechanisms in amplifying the digital economy’s positive effects on GLTLU. Full article

The common myna bird (Acridotheres tristis Linnaeus, 1766) is widely recognized as one of the most formidable invasive avian species globally. The bird poses significant challenges due to its ability to outcompete a variety of native cavity-nesting birds. Additionally, the common myna is a notable agricultural pest and a substantial threat to indigenous biodiversity. The current study is focused on understanding the distribution pattern of the common myna (Acridotheres tristis Linnaeus, 1766) in Egypt and the significant favorable conditions to predict the invasion scale of the bird to the Egyptian fauna. To determine the environmental variables influencing the invasion range of the common myna in Egypt, a Species Distribution Model (SDM) was employed. The current work documented 117 invasion sites of the species from February to December 2023. The predicted habitats are mainly concentrated close to the Nile Delta of Egypt, the Suez Canal region, North and South Sinai, in addition to scattered areas on the Red Sea coast, along the riverbanks of Upper Egypt, in addition to a few northwestern areas of the Western Desert. The most significant environmental factors affecting the establishment were the Minimum Temperature of the Coldest Month, the Mean Temperature of the Coldest Quarter, and Elevation. The current invaded areas comprise about 0.8% of Egypt (8240 km² out of roughly one million km²). We found that this is significant and of concern due to the expectation of increasingly favourable conditions due to global warming; this will turn this invasive species into a real threat to Egyptian ecosystems due to its aggressive competition with native cavity-nesting birds, its impact as an agricultural pest, and its potential to disrupt local biodiversity. Full article

Can government environmental policy harmonize environmental protection with economic output? We explore this issue from the perspective of forestry eco-efficiency, using China’s National Ecological Civilization Pilot Zone Policy (NECP), an environmental policy promulgated by the government of China, as the subject of this study. The study introduces forestry eco-efficiency as an indicator to assess the balance between economic development in the forestry sector and environmental conservation. The indicator, grounded in sustainable development theory, employs a super-efficiency SBM model that includes undesirable outputs to evaluate efficiency. Additionally, we empirically analyze the impact of NECP on forestry eco-efficiency by using the difference-in-difference (DID) model with provincial panel data from 2011 to 2020. Ultimately, we analyze the effects of spatial spillover by employing the spatial Durbin model (SDM). Our study yields the following conclusions. (1) In this paper, through hotspot clustering analysis, forestry eco-efficiency in each province is categorized into three categories: effective, semi-effective and ineffective. Our findings suggest that China’s average forestry eco-efficiency falls into the ineffective category, highlighting the need to optimize resource allocation within the sector. (2) NECP significantly enhances forestry eco-efficiency, with robust findings across various stability tests. Thus, implementing government environmental policies can have a multiplier effect on forestry, i.e., it can synergize its economic development with environmental protection. (3) In provinces with a strong ecological foundation, the NECP significantly enhances forestry eco-efficiency. However, in other provinces, the improvement is only moderate. Furthermore, while the NECP has a substantial positive impact in the eastern region, it has yet to show a discernible effect in other regions. (4) The positive impacts of NECP implementation on forestry eco-efficiency have spatial spillover effects due to demonstration effects and comparative advantages. Full article