With the rapid growth of urban economy and population in China, the output of municipal solid waste (MSW) has dramatically increased becoming a constant threat to residents’ living environment and health. The classification intention of residents plays a pivotal role in solving the problem of MSW disposal. While numerous studies have examined the classification behavior of MSW from the perspective of ordinary residents and households, few studies have attempted to understand young people’s sorting intention. The novelty of this research is to explore the determinants that affect young people’s intention toward municipal solid waste sorting (MSWS) by extending the predictive factors of environmental concern and personal moral obligation into the theory of planned behavior (TPB). A sample of 524 young respondents from Hebei Province in China were used to conduct a structural equation model (SEM) validation. The empirical results revealed that, according to the rankings of significance, personal moral obligation, perceived behavioral control, and subjective norm had positive influences on young people’s intention toward MSWS, while attitude and environmental concern did not. Furthermore, the multi-group comparison showed that, compared with the male and rural group, the intention of female and urban respondents to classify MSW was not affected by subjective norms. Some targeted managerial implications were ultimately proposed to promote young people’s intention toward MSWS. This study contributes to the existing knowledge system of MSWS by revealing the classification intention of young people as a group. The findings and implications provide the government with useful insights for encouraging young people to actively participate in MSWS.

The green retrofitting of existing residential buildings is an important approach to realise the sustainable development of stock buildings. In addition to the new technologies and materials related to green retrofitting, the intention of residents toward the green retrofitting of existing residential buildings must be understood. However, the factors affecting such intentions are still unclear. Hence, this study refers to the extended theory of planed behaviour and constructs a theoretical model of the intention toward the green retrofitting of existing residential buildings. On the basis of the data from 507 questionnaires collected from eastern and western China, the theoretical model is tested via structural equation modelling (SEM). Multigroup SEM is used to analyse the differences in population characteristics and the intention of residents toward the green retrofitting of existing residential buildings in residential areas. Research results reveal the following: (1) the most important factors affecting residents’ intention toward green retrofitting are policy factors, followed by cognition of green retrofitting, behaviour, subjective norms, and perceived behavioural control; (2) policy factors not only directly affect residents’ intention toward green retrofitting but also indirectly affect their intention toward existing residential green retrofitting through perceived behavioural control; (3) residents’ cognition of green retrofitting exerts no significant direct impact on their intention toward green retrofitting, but it does indirectly affect their intention toward green retrofitting through behaviour and subjective norms; (4) behaviour, subjective norms, and perceived behavioural control have direct and significant influences on intention toward green retrofitting; (5) demographic characteristics (gender, age, monthly family income, education level, and occupation) and regional variables (east and west) present significant differences in different influence paths. The conclusion of the study provides a targeted path reference for the promotion of the green retrofitting of existing residential buildings.

The ecological compensation mechanism is a tool for managing regional development and promoting the green economy. This paper proposes a revised model for carbon emissions and absorption based on IPCC, then analyses the spatial and temporal variations of carbon emissions, absorption, and deficit in 31 provinces of China from 2001 to 2019. A model was developed to redistribute carbon deficits and compensate for ecological imbalances, with the aim of eliminating background differences between regions. The concept of ecological compensation sensitivity was proposed, and a redundancy analysis (RDA) was conducted to detect possible influencing factors. Results indicate that: (1) The carbon deficits were relatively substantial in the centre and developed coastal regions, which were the subject regions of China’s ecological compensation. Meanwhile, the northwest and southwest regions were the object regions of ecological compensation, and the compensated object regions tended to extend towards the southeast. The majority of compensated subject areas are generally less sensitive than compensated object regions. (2) The graph of carbon deficit volume over time shows that China’s two carbon targets are closely aligned. The distribution of China’s ecological compensation amounts follows the pattern of provincial economic development levels, with higher compensation amounts concentrated in the central and coastal regions. There is a positive correlation between GDP, year, and population size with carbon emission and carbon deficit. Additionally, there is a positive correlation between year, region, and carbon absorption. (3) This compensation model can strongly incentivise the compensation subject area to proactively adjust its economic development model to cope with the significant compensation pressure. Additionally, it can fully encourage the compensation recipient area to continue adhering to the green economic development model. The conclusions of the study hold significant reference value for promoting the development of a green, low-carbon economy.

The rapid proliferation of generative artificial intelligence (GenAI) in higher education raises critical concerns regarding educational equity. However, t