The Intensive Supervision Mechanism (hereafter referred to as ISM) is one of the most important institutional management innovations for air pollution control in China, but there is currently no consensus on the effects of the ISM on air quality improvement. In this study, a reliable quantitative model based on the Difference-in-Differences (DID) analysis was designed to evaluate the impacts of ISM on air quality (as indicated by good air quality days (hereafter referred to as GAD) and the concentrations of six major air pollutants (i.e. PM_2.5, PM₁₀, O₃_8H, NO₂, SO₂, and CO)), in China with focuses on the implementation cities of Henan Province. To optimize the model design, six meteorological factors, five socio-economic indicators, and VIIRS (Visible Infrared Imaging Radiometer Suite) data were also considered as alternative control variables for more comprehensive and effective results. In addition, the redundancy analysis (RDA) and Monte Carlo simulation were conducted to determine the optimal combination of those control variables which can best reflect the changes in explanatory variables. The main findings are as follows: (1) the statistical model applied in this study can well evaluate the impacts of ISM; (2) the implementation of ISM can significantly reduce the concentrations of SO_2, CO, and NO₂, but the improvements for PM_2.5, PM₁₀, GAD and O₃_8H were not significant. (3) the potential for air quality improvement due to ISM tends to be reduced over time, and thus the positive effects of ISM at its second stage were not increased significantly compared with those observed during its first stage. In general, those results not only demonstrate the effectiveness of ISM on air quality improvement, but also provide insights into how the ISM can be optimized to gain a sustained improvement of the ambient air quality in the future.

Kobresia pygmaea Willd. dominates the alpine meadow ecosystem on the Qinghai-Tibet Plateau. Knowledge of this species' distribution and ecological environment could provide valuable insights into the alpine ecosystem and key species living there, support species and ecosystem conservation in alpine regions, and build on species origin and evolutionary research. To avoid modelling uncertainty encountered in a single approach, four species distribution model algorithms (Surface Range Envelope (SRE), Generalized Linear Model (GLM), Generalized Boosted Regression (GBM) and Maximum Entropy (MAXENT)), were used to simulate the distribution of K. pygmaea based on occurrence samples that were verified using DNA sequencing techniques. Species distribution modelling revealed a vast distribution region of K. pygmaea in the northern Tibetan Highlands and alpine meadows in the southern and eastern declivity of the plateau. A high evaluation performance was found for the GLM, GBM and MAXENT models. Different potential range size patterns for the four models were found between 374340–482605 km² (average = 421591 km²). Precipitation during growing seasons was found to be the dominant factor accounting for the distribution, consistent with patterns of heat and water patterns conditions of alpine ecosystems on the plateau. Species distribution models provide a simple and reliable approach to simulating the spatial patterns of species inhabiting the Qinghai-Tibet Plateau.

How to meet food demands with limited cropland has become a serious problem worldwide, especially in China. Global and national food trade can alleviate regional food unbalance among different countries or regions to some extent. The embedded virtual land also flows with food trade, and reasonable food trade can save global croplands and protect local ecosystems. Therefore, it is of great importance to study how trade influences land use as well as its associated environmental consequences. Recent studies have mainly focused on global food trade and its associated virtual land flow; however, only a few have focused on national food trade and its associated virtual land flow. Thus, this study aims to explore the domestic wheat trade and its associated virtual land flows in China during the period 2010–2015, based on the CHINAGRO model and previous studies. The Huang-Huai-Hai Plain and its surrounding regions were the main producers and consumers of wheat and virtual wheat. Without wheat stocks and international wheat trade, the annual domestic wheat trade accounted for 16.43% of the total national wheat production in China. Anhui was the largest net exporter of wheat, followed by Henan, while the developed areas such as Beijing, Guangdong, and Shanghai were the main net importers. Additionally, as an important transfer region, Jiangsu was the largest exporter and importer of wheat virtual cropland, but it finally presented as a net exporter of wheat cropland. During this period, domestic wheat trade led to a total land savings of 2.62 Mha/a, accounting for 10.80% of Chinese wheat cropland. However, compared with the year 2010, Shandong, Hebei, and Sichuan turned into wheat net importers in 2015, which was due to different reasons such as national agriculture polices and economic development. In addition, the net virtual cropland per capita has been greatly influenced by local economic development.

Rice is an important staple food for more than half of the global population and one of the largest water consumers on earth. Improving the efficiency of water embedded in rice production and supply could have great implications for food and water security. This study starts from Yunnan, a traditional rice producing and consuming province in southwest China, and analyses its rice supply structure and dynamics, together with embedded water footprints (WFs) of three other regions: Northeast China, South and Southwest China and Southeast Asia. The results show that Yunnan has been through drastic food change in the past decades, leading to an increasing production and supply gap. Yunnan is found to have the least WF (778.2 m3/t) for rice production across the study regions, while Northeast China consumes the highest blue WF (364.6 m3/t) and blue to total WF ratio (97.7%). The study indicates that Northeast China is at risk of groundwater deficit due to rice production and export and the current rice production and consumption pattern is inefficient. The study suggests that policies for groundwater extraction, water resource price and international trade need to be in place to ensure sustainable food supply and water use at regional and national levels.