Purpose: This study investigated employees’ career planning in preparation for the impact of manufacturing transformation triggered by automation technology. Built on career construction theory, the purpose of this paper is to conceptualize career planning as an attempt to integrate oneself into the social environment. In this process of integration, career adaptability is a critical psychological resource for adaptation to anticipated changes.Design/methodology/approach: Through an online survey, 476 participants answered questions regarding the following aspects: perceptions of the threats and opportunities posed by automation technology; career adaptability, that is, career-related concern, control, curiosity, and confidence in adapting to occupational transitions; and career plans and actions to address the challenge, including short-term job crafting behaviors and long-term career adjustment plan.Findings: The results showed that opportunity and threat perceptions were associated with one’s job crafting behavior and long-term career adjustment plan and such relationships were moderated by career adaptability and work experience relevant to automation technologies. Specifically, career adaptability is a psychological resource helping individuals deal with perceived challenges, while relevant work experience moderated one’s strategies to catch opportunities.Originality/value: This study contributes to the understanding of psychosocial determinants for better career planning in the midst of the industrial revolution. Policies that aim to prepare workers for the upcoming social transition may benefit from this study to leverage adaptive and proactive behaviors at a societal level.

The first task for eradicating poverty is accurate poverty identification. Deep poverty identification is conducive to investing resources to help deeply poor populations achieve prosperity, one of the most challenging tasks in poverty eradication. This study constructs a deep poverty identification model utilizing explainable artificial intelligence (XAI) to identify deeply poor households based on the data of 23,307 poor households in rural areas in China. For comparison, a logistic regression-based model and an income-based model are developed as well. We found that our XAI-based model achieves a higher identification performance in terms of the area under the ROC curve than both the logistic regression-based model and the income-based model. For each rural household, the odds of being identified as deeply poor are obtained. Additionally, multidimensional household characteristics associated with deep poverty are specified and ranked for each poor household, while ordinary feature ranking methods can only provide ranking results for poor households as a whole. Taking all poor households into consideration, we found that common important characteristics that can be used to identify deeply poor households include household income, disability, village attributes, lack of funds, labor force, disease, and number of household members, which are validated by mutual information analysis. In conclusion, our XAI-based model can be used to identify deep poverty and specify key household characteristics associated with deep poverty for individual households, facilitating the development of new targeted poverty reduction strategies.

The quality of the living environment is a critical determinant of residents’ health and urban sustainability. To mitigate the environmental pollution induced by the traditional urbanization model, China launched the New-Type Urbanization Pilot Policy (NTUPP) as a nationwide experimental reform. Yet whether the NTUPP has improved the living environment remains underexplored. Drawing on a balanced panel dataset of 273 prefectures in China from 2011 to 2023, this study applies a staggered difference-in-differences approach to examine the NTUPP’s impact. We find that the NTUPP significantly improves the quality of the living environment, while its spatial spillovers to neighboring prefectures remain limited. Mechanistic analysis suggests that these gains are driven by stronger environmental regulation and increased investment in environmental governance. The policy’s effects are more pronounced in northwestern prefectures, prefectures in the servitization stage and the middle stage of urbanization, and small or medium-sized prefectures.

Agricultural development is vital to rural economies in developing countries. Rural e-commerce has emerged as an important instrument for promoting rural economic transformation, yet its impact on agricultural development remains underexplored. Using panel data for 1345 counties in China from 2010 to 2022, this study exploits a quasi-natural experiment created by the phased implementation of the Rural E-Commerce Demonstration County (REDC) program and applies a staggered difference-in-differences approach to identify its causal effects on agricultural development. The results show that the REDC program significantly promotes agricultural development, mainly through expanded consumer demand and greater social investment. Its effects are particularly evident in regions with balanced production and consumption and in economically developed counties. Moreover, the REDC program shows no evidence of a significant negative siphon effect within 50 km of pilot counties but generates strong positive spillovers in the 50–200 km surrounding range. Taken together, these findings provide empirical evidence supporting the advancement of rural e-commerce and agricultural scaling in China, while also offering policy implications for other developing countries seeking to promote rural e-commerce.