As a strategic emerging mineral resource, lithium is widely used in new energy, new materials and other emerging industries. There exists a changing trend of the material flow, consumption and evolution of lithium resources in the market. Thus, this research constructed a material flow analysis system for lithium resources based on the trade correlation of the whole life cycle. The study used the material flow analysis method to analyze the supply, flow and stock of lithium resources in China from 2007 to 2020. The research shows that during that timeframe, China’s cumulative consumption of lithium resources equivalent to lithium carbonate reached 309.9348 kiloton (kt). The consumption of lithium in traditional and lithium electric fields increased from 14.3653 kt and 8.08228 kt in 2007 to 49.53125 kt and 90.75866 kt in 2020, respectively. From 2007 to 2011, the consumption of lithium in the traditional sector was greater than that in the lithium electric sector. From 2012 to 2020, with the innovation of network technology and the boom in the New energy vehicles (NEV) market, the market for consumer lithium and power lithium grew rapidly, and the field of lithium with power batteries as the main driving force gradually became the major contributor to the consumption of lithium resources. With the rapid increase in the consumption demand for lithium resources, the supply structure of lithium resources in China has changed from domestic supply to international import. The external dependence of lithium resources has increased from 29.74% in 2007 to 70.75% in 2020. With increasing lithium consumption, the storage of lithium batteries increased from 20.69721 kt in 2007 to 341.6322 kt in 2020. At the same time, the scrap volume of lithium batteries increased rapidly, but the recycling volume of lithium resources was far lower than the scrap volume. The resource recycling potential is huge, and there is still a lot of room for the development of the sequential utilization of waste lithium-ion batteries.

The rapid development of urbanization has led to increasing uncertainties related to urban safety risks, which has brought certain challenges to the sustainable development of cities. The concept of urban resilience has found a new way to improve the ability of a city to absorb and resolve risks. However, the existing literature on the evaluation of urban resilience is mostly developed from a static perspective, lacking a systematic and dynamic understanding of the level of urban resilience. Therefore, this paper takes Hunan Province as the research object, determines the resilience evaluation indicators, collects the data of each indicator by using the observation method and the literature method, then chooses the comprehensive index method and other methods to measure the urban resilience level of Hunan Province in the years of 2010–2021, and observes the dynamic changes in the resilience level. And, we use the GeoDetector model to detect the dominant factors affecting the urban resilience level and the interaction between these factors. The results of this study show that: (1) The level of urban resilience in Hunan Province shows a steady upward trend from 2010 to 2021, but cities with low resilience levels hold a dominant position. Among all subsystems, the level of urban economic resilience is the highest. (2) From 2010 to 2021, the level of urban resilience in Hunan Province indicates a stepwise spatial structure in the spatial pattern, gradually decreasing from east to west. (3) The urban resilience of Hunan Province from 2010 to 2021 has a significant spatial agglomeration effect, mainly manifested as “L-H type” agglomeration and “L-L type” agglomeration. (4) The spatio-temporal differentiation of urban resilience is mainly caused by economic and social factors, while ecological, institutional, and infrastructure factors have a relatively small influence on the level of urban resilience. The interaction of impact factors will have a more significant influence on urban resilience. The research results of this article are of great significance for urban resilience construction in Hunan Province and even the whole country.

The frequent occurrence of accidents in the chemical industry has caused serious economic loss and negative social impact. The chemical accident investigation report is of great value for analyzing the risk factors involved. However, traditional manual analysis is time-consuming and labor-intensive, while existing keyword extraction methods still need to be improved. This study aims to propose an improved text-mining method to analyze a large number of chemical accident reports. A workflow was designed for building and updating lexicons of word segmentation. An improved keyword extraction algorithm was proposed to extract the top 100 keywords from 330 incident reports. A total of 51 safety risk factors was obtained by standardizing these keywords. In all, 294 strong association rules were obtained by Apriori. Based on these rules, a Bayesian network was built to analyze safety risk factors. The mean accuracy and mean recall of the BM25 model in the comparison experiments were 10.5% and 14.38% higher than those of TF-IDF, respectively. The results of association-rule mining and Bayesian network analysis can clearly demonstrate the interrelationship between the safety risk factors. The methodology of this study can quickly and efficiently extract key information from incident reports which can provide managers with new insights and suggestions.