The positive or negative δ¹³C shifts between total organic carbon (TOC) in surface soils and the corresponding overlying plants/vegetation have been widely reported and are thought to result from various factors, such as atmospheric, climatic, and soil changes. Conducting studies on a larger spatial scale can better reveal the dominant factors. In this study, 107 surface soil and herbivore fecal samples were collected across several thousand kilometers of inland China. δ¹³C analyses were performed on surface soil TOC, bulk plant litter separated from surface soil (PL), bulk herbivore feces (HF), and α-cellulose extracted from both surface soil plant litter and herbivore feces. Data analysis showed that the δ¹³C shift between surface soil TOC and bulk plant litter (Δ¹³C_TOC-PL) and the δ¹³C shift between bulk herbivore feces and bulk plant litter in surface soils (Δ¹³C_HF-PL) both exhibited a negative correlation with δ¹³C_PL. Similarly, the δ¹³C shift between herbivore feces α-cellulose and surface soil plant α-cellulose (Δ¹³C_HF-PL-cell) also showed a negative correlation with δ¹³C_PL-cell. The most reasonable explanation for these negative correlations is that during decomposition in surface soils and digestion within herbivores, C₄ plants decompose/digest faster than C₃ plants. The findings suggest that the relative abundance of C₄ plants, estimated based on modern surface soils, sediments, and animal remains δ¹³C data, may have been commonly underestimated, and this should be appropriately considered and evaluated in future studies.

Continuous global warming has been affecting the structure and function of forest ecosystems, and forest productivity is strongly restricted by large-scale extreme drought and shows significant spatial heterogeneity. Patterns of changes in radial growth dynamics of coniferous species and responses to climatic factors since the beginning of the 20th century were identified by examining growth trends and assessing climatic drivers based on the tree-ring data from the 257 sampling sites in arid and semi-arid areas of northern China. The running average results indicated that radial growth of coniferous species in Xinjiang and northwest China showed a continuous increase, and radial growth of coniferous species showed a fluctuating increase in northern China, and a decline in northeast China was detected after the mid-20th century. Moisture has a positive effect on radial growth of coniferous species in all regions, and increased evapotranspiration induced by warmer temperatures will inhibit radial growth to some extent. The effect of growing season changes on radial growth in northwest China benefits from the thermal effects of warming and humidification phenomena. Climatological analyses showed that atmospheric circulation factors have some vital influences on regional precipitation generated by the Asian monsoon system by regulating global SST and baroclinic intensity changes on interannual to interdecadal scales, and potentially affecting radial growth of coniferous species in arid and semi-arid areas of northern China. CMIP5 data and VS-Lite modeling revealed impacts under future climate change scenarios, the extended growing season due to global warming promotes regional radial growth of coniferous species, and however, drought stress due to less precipitation still have some limiting effects on radial growth, especially in western China.

The Three-River-Source National Park, located in the hinterland of the Qinghai-Tibet Plateau, possesses unique climate and abundant genetic species. However, this region is facing serious ecological problems due to climate change and human activities. Accurate monitoring of spatiotemporal variations in net primary productivity (NPP) in the Three-River-Source region is crucial for promoting ecological conservation and environment improvement. Model simulation is an important approach in terrestrial ecosystem research, but it inherently remains uncertain. Multi-model integration techniques can enhance the accuracy of NPP simulation and provide a better estimation of NPP variations for environmental governance. In this study, we used four process-based ecosystem models (i.e., CLM, DALEC, CEVSA, and GLOPEM-CEVSA) and a multi-model integration analysis method to examine the spatiotemporal changes in NPP in the Three-River-Source National Park from 2000 to 2018 and to investigate the effect of climatic factors on NPP variations. The results show that NPP exhibited a decreasing trend from southeast to northwest, with an average annual NPP of 251.17 gC m^-2 a^-1 during 2000-2018. Ecosystems in the Lancang River Source Park had the highest NPP (267.24 gC m^-2 a^-1), followed by the Yellow River Source Park (198.81 gC m^-2 a^-1) and Yangtze River Source Park (121.88 gC m^-2 a^-1. The average NPP in the Three-River-Source region ranged from 222.00 to 298.02 gC m^-2 a^-1 and had a significant increasing trend with the rate of 9.8 gC m^-2 10a^-1. The attributions of NPP variation to climatic factors were far different among regions. It was primarily affected by temperature and radiation in the Yangtze River Source Park and Yellow River Source Park, but was also significantly influenced by precipitation in the Lancang River Source Park. The findings of this study could provide technical support and decision-making basis for assessing the effectiveness of ecological conservation and ecological management in the Three-River-Source National Park.

In the context of the increasingly severe global food crisis caused by multiple external factors, building a more productive, nutritious, resilient, and sustainable food security system to promote the transformation of the agricultural and food system had become more crucial, particularly because it is essential for stabilizing China's overall economic and social development. This paper was based on the perspective of the Nature-Society-Economy complex system. It divided the food security system into three levels: resources and environment, production and supply, and distribution and consumption. This paper constructed an index system based on the three-dimensional framework of Pressure-State-Response, employing provincial panel data from 2000 to 2021 to analyze the spatial and temporal spatial evolution characteristics of China's food security system transformation. A dynamic panel model was adopted to quantify the various driving factors related to the food security system transformation. The research results indicated the following: (1) The comprehensive index of food security system transformation from 2000 to 2021 increased by 97.90%, showing a two-stage change characteristic with a moderate rise from 2000 to 2012 and a rapid growth from 2013 to 2021. The transformation index of the resources and environment and the production and supply subsystems were consistent with the evolution of the comprehensive index of food security system transformation, also exhibiting a two-stage upward trend. The transformation index of the distribution and consumption subsystem showed a downward and then an upward trend. (2) The comprehensive index of provincial food security system transformation increased continuously from 2000 to 2021. The provinces that exceeded China's average shifted from a balanced distribution across the eastern, central, and western regions in 2000 to clustering in the main grain-producing areas in 2021. (3) The food security system transformation is an ongoing endeavor. The rate of urbanization, agricultural financing policies, birth rate, and dietary diversity index can significantly promote the transformation of the food security system. The transformation of the food security system can be seriously hampered by industrialization, relative returns from agricultural production, GDP growth rate, and per capita disposable income.

This study considered 30 provincial-level administrative units in China as the research object. The network SBM-DEA model and meta-frontier model were used to measure the efficiency of the interprovincial WEF system in China from 1997 to 2021 under the meta-frontier and group-frontier. The improved GTWR model was used to explore the influencing factors. The study produced several results: (1) There are significant differences in the efficiency of WEF systems under different frontiers. The average technology gap ratio of WEF system efficiency in the eastern region was 0.961, whereas that of the central and western regions was 0.673 and 0.455, respectively. (2) In previous studies, there were many overlapping spatial coordinates in the GTWR model, causing the final evaluation results of the model to be close to the linear regression results assessed in the time dimension. To compensate for this deficiency, this study constructed dynamic spatial coordinates and verified the applicability of the model. (3) In terms of temporal change, the GTWR model results showed that the fitting coefficients of energy production, industrial structure rationalization index, proportion of clean energy production, and R&D investment intensity displayed a trend of increasing first and then gradually stabilizing. The fitting coefficient of energy consumption per unit GDP revealed a downward trend year by year, while the change trend of the industrial energy consumption intensity fitting coefficient showed the opposite trend. The fitting coefficients of the remaining influencing factors showed a trend of stability after some fluctuations. (4) In terms of spatial variation, the fitting results of the GTWR model showed that the influencing factors were significantly different between regions and that the influence degrees of per capita water resources, water consumption per unit of agricultural GDP, and gray water footprint load coefficient were all greater in the northern region than in the southern region. The fitting coefficients of energy production and clean energy production were both positive, and the impact in southwest China was large. High fitting coefficient areas under the influences of the industrial structure rationalization index, industrial energy consumption intensity, and R&D investment intensity are mainly distributed along the eastern coast. The fitting coefficients of energy consumption per unit GDP were all negative, and the degree of influence decreased to the west. The fitting coefficients of traffic density were all positive, and the degree of influence showed certain regional characteristics.