Waste source separation is the fundamental premise to ensure effective waste recycling. Whether the entire waste recycling and reduction process can be effectively carried out depends on the waste source separation. Clarifying the driving mechanism of waste separation behavior plays an important role in effectively guiding the urban residents’ waste separation behavior and achieving waste recycling. In this study, qualitative analysis was used to explore the driving mechanism of waste separation behavior. Through the open coding, axial coding and selective coding of the in-depth interview data collected from 323 Chinese urban residents, the study has proposed and verified the four-dimensional structure of waste separation behavior, namely, waste separation behavior of habit, decision, relationship, and citizen. The main driving factors of urban residents’ waste separation behavior have been clarified. On this basis, a theoretical model for the driving mechanism of waste separation behavior was constructed in this study. Ten main categories of factors have been presented, namely, value orientation, cognition of separation, regulatory focus, preferences for comfort, perception of separation empowerment, policy and standards, products and facilities, group norms, links trustworthiness, and social demography variables. Moreover, four typical relationship structures were proposed. Finally, the intervention policy suggestions were made to effectively guide the urban residents’ waste separation behavior.

The quest for carbon neutrality raises challenges in most sectors. In coal mining, overcapacity cutting is the major concern at this time, and the increase in the number of abandoned mine shafts is a pervasive issue. Pumped storage hydropower (PSH) plants built in abandoned mine shafts can convert intermittent electricity into useful energy. However, studies on basic theories and key technologies are a pressing issue. Six key scientific problems have been identified in PSH development in abandoned mine shafts that are relevant to China’s national conditions, current resource structure, and relative status of energy storage technologies in China and other countries. It is proposed that the research on pumped storage should move closer to the direction of intelligence, stabilization, and greening, and the construction and development should gradually realize integration, completion, and coordination. The goal is to realize integrated, complete, and coordinated development of PSH in abandoned mine shafts, streamline national policies concerning PSH, drive the co-development of industry, education and research, and achieve the carbon neutrality targets set by China.

Different coals were used as raw material for the preparation of carbonization precursors and coal-based activated carbons. The physicochemical structure and adsorption performance of the samples were tested. Results show that the carbonization and activation process greatly changed the molecular structure of raw coal, and a large number of organic functional groups disappeared. The carbonization process has enriched the pore structure of coal by thermal ablation, and it has a pore expansion effect on all the pores in coal, while the activation process is more conducive to micropore generation. The calculated mean isosteric heat of adsorption showed that the activated carbon needs to release more heat in the adsorption process as the same equilibrium pressure increased due to the adsorption capacity of the prepared activated carbon being far more than that of the raw coal. Adsorption processes of activated carbons are more sensitive to temperature changes, providing a certain guiding significance for the temperature swing adsorption and pressure swing adsorption.

Effective risk prevention and management in deep coal mines can reduce the occurrences of outburst accidents and casualties. To address the low accuracy and inefficiency of coal–gas outburst prediction in deep coal mines, this study proposes a deep coal–gas outburst risk prediction method based on kernal principal component analysis (KPCA) and an improved extreme learning machine (SAPSO-ELM) algorithm. Firstly, high-dimensional nonlinear raw data were processed by KPCA. Secondly, the extracted sequence of outburst-causing indicator principal components were used as the input variables for the simulated annealing particle swarm algorithm (SAPSO), which was proposed to optimize the input layer weights and implied layer thresholds of the ELM. Finally, a coal and gas outburst risk prediction model for a deep coal mine based on the SAPSO-ELM algorithm was developed. The research results show that, compared with the ELM and PSO-ELM algorithms, the SAPSO-ELM optimization algorithm significantly improved the accuracy of risk prediction for coal–gas outbursts in deep coal mines, and the accuracy rate was as high as 100%. This study enriches the theory and methods of safety management in deep coal mines, and effectively helps coal mine enterprises in improving their ability to manage coal–gas outburst risks.