[Trade-off and Synergy of Ecosystem Services in the Yangtze River Economic Belt and Its Driving Factors].

Ecosystem services (ESs) and their changes are complex processes driven by multiple factors. Understanding the trade-off and synergy between ESs and their driving factors is essential for achieving effective management of ESs and human well-being. Taking the Yangtze River Economic Belt as the research area, this study analyzed the temporal and spatial variation characteristics of four ESs including water yield, soil conservation, carbon sequestration, and food supply from 2000 to 2020. Correlation analysis and geographically weighted regression were used to identify and quantify the trade-off and synergy between ESs. On this basis, the partial least squares structural equation model was used to explore the impact of natural and human activities on ESs, and then the driving mechanism of ESs relationship change was analyzed via GeoDetector. The results showed that:① During the 20 years, the average annual carbon sequestration increased from 946.14 t·km-2 to 1 202.73 t·km-2, and the average food supply increased from 32.73×104 Yuan·km-2 to 127.22×104 Yuan·km-2. Water yield and soil conservation increased to a lesser degree. ② On the whole, carbon sequestration and soil conservation and food supply and water yield showed synergy, and other ESs were trade-offs. The relationship between ESs varied in different regions. ③ Terrain and climate were important driving factors for ESs and the trade-off and synergy of multiple ESs. Among them, structural equation model results showed that climate had a positive impact on water yield (S=0.73), and terrain had a negative impact on food supply (S=-0.57). GeoDetector results revealed that the main driving factors affecting the spatial relationship between carbon sequestration and water yield were elevation (q=0.38) and precipitation (q=0.19). The results of this study can provide a scientific reference for the sustainable management of ESs in the Yangtze River Economic Belt and the realization of the coordinated development of ecological environment protection and social economy in the region.

Comprehensive assessment of refined greenhouse gas emissions from China's livestock sector.

Livestock and poultry products are an essential human food source. However, the rapid development of the livestock sector (LS) has caused it to become a significant source of greenhouse gas (GHG) emissions. Consequently, investigating the spatio-temporal characteristics and evolution of GHG emissions is crucial to facilitate the green development of the LS and achieve "peak carbon and carbon neutrality". This study combined life cycle assessment (LCA) with the IPCC Tier II method to construct a novel GHG emissions inventory. The GHG emissions of 31 provinces in China from 2000 to 2021 were calculated, and their spatio-temporal characteristics were revealed. Then, the stochastic impacts by regression on population, affluence, and technology (STIRPAT) model was used to identify the main driving factors of GHG emissions in six regions of China and explore the emission reduction potential. The results showed that GHG emissions increased and then decreased from 2000 to 2021, following a gradual and steady trend. The peak of 628.55 Mt CO2-eq was reached in 2006. The main GHG-producing segments were enteric fermentation, slaughtering and processing, and manure management, accounting for 45.39 %, 26.34 %, and 23.08 % of total GHG emissions, respectively. Overall, the center of gravity of GHG emissions in China migrated northward, with spatial aggregation observed since 2016. The high emission intensity regions were mainly located west of the "Hu Huanyong line". Economic efficiency and emissions intensity were the main drivers of GHG emissions. Under the baseline scenario, GHG emissions are not projected to peak until 2050. Therefore, urgent action is needed to promote the low-carbon green development of the LS in China. The results can serve as scientific references for the macro-prevention and control of GHG emissions, aiding strategic decision-making. Additionally, they can provide new ideas for GHG accounting in China and other countries around the world.

Exploring the interrelations and driving factors among typical ecosystem services in the Yangtze river economic Belt, China.

Exploring the spatiotemporal characteristics of ecosystem services (ESs) and their drivers is crucial for managers to develop significant scientific policies that further sustainable development. We used the Yangtze River Economic Belt (YREB) to explore the trends, hotspots, and drivers of water yield (WY), soil conservation (SC), carbon sequestration (CS), and food supply (FS) between 2000 and 2020. Similarly, we analyzed relationships among ESs and drivers of the multiple ecosystem services landscape index (MESLI). We used the self-organizing map method to obtain the types and distribution of the ES bundles, revealing the bundles, trade-offs, and synergies among ESs. The four ESs had an increasing trend, with CS having the highest increase; ES hotspot analysis showed differences among upper, middle, and lower reaches. Constraint lines among ESs and drivers were diverse; the corresponding SC and WY reached thresholds when CS values were 1477.81 and 460.5 t km-2, respectively. When FS values were 67.34 and 86.17 × 104 Yuan·km-2, CS and WY reached their thresholds. All critical drivers of the four ESs were natural factors. The thresholds that the MESLI reached with driver status were 1000 mm (evapotranspiration), 2121 mm (precipitation), 2.42° (slope), 1.46% (soil organic matter), 36.08% (sand), 30.75% (proportion of non-agricultural population), 18.57% (cropland proportion), 1.05 × 104 persons·km-2 (population density), and 84.84% (proportion of non-agricultural industries in total gross domestic product), respectively. FS, water supply, and ecological conservation bundles changed over the 20 years, and trade-offs and synergies among ESs within bundles differed. We revealed the complexity of ESs from multiple perspectives, which will enable the development of ecosystem management and conservation recommendations for the YREB and large-scale economic zones worldwide.

Impact of individual behavioral changes on epidemic spreading in time-varying networks.

Changes in individual behavior often entangle with the dynamic interaction of individuals, which complicates the epidemic process and brings great challenges for the understanding and control of the epidemic. In this work, we consider three kinds of typical behavioral changes in epidemic process, that is, self-quarantine of infected individuals, self-protection of susceptible individuals, and social distancing between them. We connect the behavioral changes with individual's social attributes by the activity-driven network with attractiveness. A mean-field theory is established to derive an analytical estimate of epidemic threshold for susceptible-infected-susceptible models with individual behavioral changes, which depends on the correlations between activity, attractiveness, and the number of generative links in the susceptible and infected states. We find that individual behaviors play different roles in suppressing the epidemic. Although all the behavioral changes could delay the epidemic by increasing the epidemic threshold, self-quarantine and social distancing of infected individuals could effectively decrease the epidemic outbreak size. In addition, simultaneous changes in these behaviors and the timing of implement of them also play a key role in suppressing the epidemic. These results provide helpful significance for understanding the interaction of individual behaviors in the epidemic process.

Modeling of suppression and mitigation interventions in the COVID-19 epidemics.

BACKGROUND: The global spread of the COVID-19 pandemic has become the most fundamental threat to human health. In the absence of vaccines and effective therapeutical solutions, non-pharmaceutic intervention has become a major way for controlling the epidemic. Gentle mitigation interventions are able to slow down the epidemic but not to halt it well. While strict suppression interventions are efficient for controlling the epidemic, long-term measures are likely to have negative impacts on economics and people's daily live. Hence, dynamically balancing suppression and mitigation interventions plays a fundamental role in manipulating the epidemic curve. METHODS: We collected data of the number of infections for several countries during the COVID-19 pandemics and found a clear phenomenon of periodic waves of infection. Based on the observation, by connecting the infection level with the medical resources and a tolerance parameter, we propose a mathematical model to understand impacts of combining intervention measures on the epidemic dynamics. RESULTS: Depending on the parameters of the medical resources, tolerance level, and the starting time of interventions, the combined intervention measure dynamically changes with the infection level, resulting in a periodic wave of infections controlled below an accepted level. The study reveals that, (a) with an immediate, strict suppression, the numbers of infections and deaths are well controlled with a significant reduction in a very short time period; (b) an appropriate, dynamical combination of suppression and mitigation may find a feasible way in reducing the impacts of epidemic on people's live and economics. CONCLUSIONS: While the assumption of interventions deployed with a cycle of period in the model is limited and unrealistic, the phenomenon of periodic waves of infections in reality is captured by our model. These results provide helpful insights for policy-makers to dynamically deploy an appropriate intervention strategy to effectively battle against the COVID-19.