In China, a high input of chemical fertilizers is currently required for a relatively low increase in agricultural production, and this has resulted in prominent nonpoint source pollution and problems related to the quality of agricultural products. These phenomena threaten China’s implementation of United Nations Sustainable Development Goal 2 (SDG-2). To explore agricultural chemical fertilizer efficiency and the factors driving the growth in chemical fertilizer use in China, as based on an international comparative analysis of China’s chemical fertilizer input, the development trend in the application level and the efficiency in the use of chemical fertilizer in China were subject to time series analysis, and the factors influencing change were identified and analyzed using the Logarithmic Mean Divisia Index (LMDI). The results show that: (1) The gap in agricultural chemical fertilizer efficiency is still large when comparing China with modern agricultural countries, and excessive fertilizer input is still a major problem. (2) The continuous growth in the total amount of chemical fertilizer applied in China during the past 18 years has contributed to the increase in chemical fertilizer application intensity, which provided a cumulative contribution of 85.52%, with smaller contributions from the planting structure and crop sown area. Based on the analysis of fertilizer application, the chemical fertilizer application intensity of the main grain crops was the most significant factor, accounting for about a 40.00% cumulative contribution. (3) Since 2015, the total amount of chemical fertilizers has been reduced through gradually improving the utilization rate of chemical fertilizers, reducing the application intensity of chemical fertilizers, and implementing the fallow rotation system and other measures. Of these, the reduction in application intensity was the most effective at reducing the overall amount of applied fertilizer. To meet the target for achieving sustainable agricultural development, China must still reduce its use of chemical fertilizers by at least 21.80 million tons. Based on the results of current measures and international experience, some suggestions for reducing fertilizer usage are provided.

Excessive use of pesticides and fertilisers has been a key issue limiting sustainable agricultural development. China is a typical pesticide- and chemical-fertiliser-dependent agricultural production area. We have matched the target indicators related to sustainable agricultural development (SDG1 and SDG2) and analysed the gap between China and four developed countries in terms of fertiliser and pesticide use intensity and efficiency from 2002 to 2016. We have used an improved Logarithmic Mean Divisia Index model and cluster analysis to identify the factors and effects driving increased pesticide and fertiliser inputs in China, and we discuss the exploratory effects of different provinces in reducing pesticide and fertiliser application and increasing efficiency. The findings reveal that (1) China is a typical pesticide- and fertiliser-dependent agricultural production area. The average combined fertiliser application efficiency in China from 2002 to 2016 was only 28% of that of the Netherlands, and the country’s average combined pesticide application efficiency was only 35% of that of the USA. (2) The most important of the three main drivers of the increase in pesticide and fertiliser inputs in China is the value added of the primary industry, contributing 56% for the period 2007–2016. (3) Further analysis at the provincial level according to four types—high-intensity high-yield type, high-intensity low-yield type, low-intensity high-yield type, and low-intensity low-yield type—clarified the provinces that should be focused on at the national level in terms of pesticide and fertiliser application reduction and efficiency increase in the future.

The scientific assessment of the health level of county ecosystems is the basis for formulating county-based sustainable development strategies. In this paper, we take the county areas of Sichuan Province as the evaluation objects and combine the SDGs (the Sustainable Development Goals) to establish a county ecosystem health evaluation index system based on the VORS (Vigor–Organization–Resilience–Service) model. On this basis, we used the entropy weight method, the Moran index method, and the obstacle degree model to analyze the ecosystem health level, spatial distribution characteristics, and obstacles of 183 counties in Sichuan Province. The main results were as follows: (1) A total of 80.87% of the counties in Sichuan Province were at sub-healthy and healthy levels, concentrated in the southeastern part of Sichuan, and 19.13% of the counties were at an unhealthy level, mainly in the Aba, Ganzi, and Liangshan areas. (2) The health levels of county ecosystems in Sichuan Province had high spatial autocorrelation characteristics. The H–H (High–High) agglomeration area and the L–L (Low–Low) agglomeration area had significant agglomeration characteristics, which were distributed in the Cheng-Mian area and the northwestern Sichuan area, respectively. (3) The key indicators restricting the healthy development of urban ecosystems in Sichuan counties are economic vitality, economic resilience, and quality of life, all of which belong to the economic subsystems, with obstacles reaching 17.25%, 16.68%, and 13.52%, respectively. This study can provide theoretical and methodological support for research into ecosystem health evaluations at the county level, and provide a decision-making basis for promoting the health of county ecosystems and coordinating regional development in Sichuan Province.

In the context of increasing ecological scarcity, maintaining the balance between natural and artificial capital has become a popular research topic in the field of ecosystem health. From the perspective of coordinating natural and artificial capital and maintaining the balance between human systems and the Earth’s ecosystem, the Ecosystem Health Index (EHI) was developed on the basis of the Sustainable Development Goals (SDGs). The EHI consists of the Social Progress Index (SPI), Economic Development Index (EDI), Natural Environment Index (NEI), and a pressure adjustment coefficient. Comprehensive indicator assessment models were used to analyze the spatial and temporal evolution of the EHIs in 30 of China’s provinces from 2013 to 2019. A three-dimensional judgment matrix was used to classify the 30 provinces into four basic types. The results show the following: (1) From 2013 to 2019, the EHIs of all provinces improved to different degrees, with 19 provinces achieving a healthy state. (2) Spatially, the EHI showed some regional aggregation in 2013. Provinces with high EHIs were concentrated in the west, followed by those in the east, and those in the central provinces had the lowest EHIs. However, the differences between regions had narrowed by 2019. (3) The spatial distribution patterns of the NEI and the EDI varied widely, and most provinces did not reach a high level of coordination between natural and artificial capital. (4) The environmental pressure in all provinces, except Liaoning, decreased over time. In some cases, excessive pressure decreased the pressure-adjusted EHI, regardless of the EHI value. (5) According to the results of the ecosystem health classification in each province, the factors that hinder ecosystem health vary from place to place.

Beautiful City construction is a core initiative to realize the Beautiful China vision and advance China’s progress toward the Sustainable Development Goals (SDGs). This study develops a unified evaluation framework aligned with official Chinese assessment guidelines and global sustainability objectives. Based on a 2015–2022 dataset of 126 prefecture-level cities in the Yangtze River Economic Belt (YREB), we adopt Dagum’s Gini coefficient decomposition, kernel density estimation (KDE), and Moran’s I to investigate the spatiotemporal characteristics of beautiful city construction. The results indicate that the regional Beautiful City Construction Index (BI) increased steadily at both the regional and subregional levels, with all sample cities achieving continuous improvement. A consistent development gradient of lower reach > middle reach > upper reach was identified, with gradually narrowing inter-regional gaps. Importantly, the primary source of regional disparity shifted from inter-subregional differences to intra-subregional differences around 2021. Statistically significant positive spatial clustering was observed, with high-value clusters concentrated in downstream areas and low-value clusters in upstream areas, while the overall clustering effect gradually weakened. This study provides empirical evidence for targeted urban governance and regional coordinated development, offering a typical Chinese practice for global SDG research.