The Beautiful China Initiative (BCI) is a vivid embodiment of the harmonious coexistence between humans and nature during modernization. Implementing the BCI is an effective method for achieving the goals of building a beautiful China, while offering a “Chinese solution” to global sustainable development. This article summarizes the progress and main experiences of the BCI, as well as analyzing the primary challenges facing its future development. Finally, five policy recommendations are proposed, which emphasize the importance of top-level design, coordinated planning, and a robust support system in the implementation of the BCI.

Fencing enclosures play an important role in improving ecological quality. There is a direct impact of implementing fencing enclosures on the change in soil quality. The soil quality index was used to examine the effects of fencing enclosures for different years (7 and 11 years) on soil quality in Biru County of Qinghai–Tibet Plateau, China. The fencing enclosure significantly increased soil water content, non-capillary porosity, soil organic matter, total nitrogen, total phosphorus, and alkali-hydrolyzable nitrogen, and significantly decreased the soil bulk density. The soil quality gradually improved as the fencing enclosure time length increased, probably due to the increase of vegetation coverage and biomass under the fencing enclosure. The minimum data set was composed of soil organic matter, capillary porosity, total potassium, and non-capillary porosity. The minimum data set was significantly correlated with the total data set and could replace the total data set for soil quality evaluation in the fencing enclosure project area. In summary, our study reflects that fencing enclosures significantly improve soil quality, and the implementation of the fencing enclosure project will effectively curb land degradation in Biru County of the Qinghai–Tibet Plateau, China.

Rapidly predicting and revealing the spatiotemporal characteristics and driving factors of land-use changes in carbon storage within megacities under different scenarios is crucial to achieving sustainable development. In this study, Jinan City (JNC) is taken as the study area, and the Markov-FLUS-InVEST model is utilized to predict and analyze the spatiotemporal variation in carbon storage in 2030 under three scenarios, namely, the natural development scenario (S1), the ecological conservation scenario (S2), and the economic development scenario (S3). The drivers of carbon storage changes were identified using an optimal parameter-based geographic detection (OPGD) model. The findings indicate that (1) land use from 2010 to 2018 shows a trend of continuous expansion of construction land and reduction in arable land. (2) The main types of carbon pools were cropland, forest, and grassland, accounting for more than 96% of the total amount. Carbon storage showed a decreasing trend from 2010 to 2018, and the main type of carbon pool that decreased was cropland. The center of gravity of carbon storage increases and decreases was located in the southern Lixia District, and the center of gravity of increase and decrease moved to the southwest by 3057.48 m and 1478.57 m, respectively. (3) From 2018 to 2030, the reductions in carbon stocks were 3.20 × 10⁶ t (S1), 2.60 × 10⁶ t (S2), and 4.26 × 10⁶ t (S3), and the carbon release was about 9 times (S1), 4 times (S2), and 10 times (S3) that of the carbon sink. (4) The contribution of slope (A2) ∩ nighttime light index (B6) and elevation (A1) ∩ nighttime light index (B6) to the regional heterogeneity of carbon stocks was the largest among the interaction drivers. To sum up, this study deepens the simulation of spatial and temporal dynamics of carbon storage under land-use changes in megacities and the related driving mechanism, which can provide the basis for scientific decision-making for cities to conduct territorial spatial planning and ecological protection and restoration.

As a developing country, China is facing serious water pollution and scarcity, which indicates the need for integrated water-resource and environmental regulations. Zoning policies have undergone significant advancements to enhance water-resource utilization in China. However, conflicts and overlaps still exist among these policies. To integrate these zoning policies and regulations, the “Three Lines One Permit” (TLOP) water-environment policy was formulated as a new framework, which included the goal for water quality, upper limits on water-resource utilization, and a permit list. This study presents the main achievements of the TLOP as a case-study in Jinan. The territories of Jinan were divided into 158 water-environment control-units (WECUs) and classified into two types of protected zones, three types of pollution-control zones, and ordinary zones. The total maximum pollutant-loads in the 158 WECUs, and 138 townships were calculated. The water-resource-utilization indicator values and ecological demand of key rivers were specified. The permit lists for the water environment at macroscale, mesoscale, and microscale were compiled from four perspectives: spatial constraints, emissions control, risk prevention, and resource utilization. Finally, suggestions were proposed to promote a more scientific and efficient TLOP policy to enhance human–water harmony.

Assessing the spatiotemporal evolution characteristics of habitat quality, human footprint, and coupling coordination between two systems in continuous cycles and on national scales is of great significance to maintaining biodiversity and sustainable development. This study took China as an example, based on land-use data from 2000 to 2020, using the Integrated Valuation of Ecosystem Services and Tradeoffs—Habitat Quality (InVEST-HQ) model and the human footprint framework, coupling trend analysis methods such as Theil–Sen Median Analysis, Mann–Kendall Test, and Grid Transition Matrix (GTM) Method and combining the four-quadrant model and the coupling coordination degree model (CCDM) to reveal the spatiotemporal evolution characteristics of habitat quality, human footprint, and CCDM in China for 21 consecutive years and the response relationship between the two systems of habitat quality and human footprint. The results show that the land cover change area from 2000 to 2020 accounted for 4.2% of the total area. Both habitat quality and human footprints exhibit apparent spatial heterogeneity along the “Hu Line” and generally fall into two evolutionary stages: “degradation–improvement”. The proportions of degradation and improvement were 14.37% and 8.36%, respectively, and the mutation point was in the year 2013; the average human footprint increased by 16.75%, and the increased and decreased area proportions were 63.40% and 21.53%, respectively. The mutation occurred in 2014. The right side of the “Hu Line” primarily hosts areas with high values of the coordinated coupling index of human footprints and habitat quality systems. The four quadrants generally have the following characteristics: “quadrant IV on the right is dominant, quadrants II and III on the left are dominant, and quadrant I is located in the transition zone of the ‘Hu Line’”. The coupling coordination degree (CCD) and human footprints have a weak, nonlinear “inverted U-shaped” relationship. This study provides compelling evidence for the spatiotemporal evolution and coupling relationship between habitat quality and human footprint in China, provides scientific decision-making support for biodiversity protection and sustainable economic development, and maintains the bottom line of ecological security for a beautiful China.

As bisphenol A (BPA) has gradually become restricted in production scenarios, the ecological risk level of its main replacement chemicals, i.e., bisphenol S (BPS) and bisphenol F (BPF), should be noted. To overcome the limitations of toxicity data, two kinds of in silico toxicology models (quantitative structure–activity relationship (QSAR) and interspecies correlation estimation (ICE) models) were used to predict enough toxicity data for multiple species. The accuracy of the coupled in silico toxicology models was verified by comparing experimental and predicted data results. Reliable predicted no-effect concentrations (PNECs) of 8.04, 35.2, and 34.2 μg/L were derived for BPA, BPS, and BPF, respectively, using species sensitivity distribution (SSD). Accordingly, the ecological risk quotient (RQ) values of BPA, BPS, and BPF for aquatic organisms were assessed in 32 major Chinese surface waters; they ranged from nearly 0 to 1.86, but were <0.1 in most cases, which indicated that the overall ecological risk level of BPA and its alternatives was low. However, in some cases, the ecological risks posed by BPA alternatives have reached equivalent levels to those posed by BPA (e.g., Liuxi River, Taihu Lake, and Pearl River), which requires further attention. This study provides evidence that the application of coupled in silico toxicology models can effectively predict toxicity data for new chemicals, avoiding time-consuming and laborious animal experiments. The main findings of this study can support environmental risk assessment and management for new chemicals that lack toxicity data.