The excessive use of fertilizer has resulted in serious environmental degradation and a high health cost in China. Understanding the reasons for the overuse of fertilizer is critical to the sustainable development of Chinese agriculture, and large-scale operation is considered as one of the measures to deal with the excessive fertilizer use. Under the premise of fully considering the resource endowment and heterogeneity of large-scale farmers and small-scale farmers in production and management, different production decision-making frameworks were constructed. Based on the 300 large-scale farmers and 480 small-scale farmers in eight provinces of northern China wheat region, we analyzed the optimal fertilizer use amount and its deviation as well as the influencing factors of small-scale and large-scale farmers, then further clarified whether the development of scale management could solve the problem of excessive fertilizer use. The empirical results show that: (1) both small-scale farmers and large-scale farmers deviated from the optimal fertilizer application amount, where the deviation degree of optimal fertilizer application of small-scale farmers is significantly higher than that of large-scale farmers, with a deviation degree of 35.43% and 23.69% for small and large scale farmers, respectively; (2) not all wheat growers in North China had the problem of excessive use of chemical fertilizer, as the optimal level of chemical fertilizer application in Heilongjiang and Inner Mongolia are 346.5 kgha⁻¹ and 335.25 kgha⁻¹, while the actual fertilizer use amount was 337.2 kgha⁻¹ and 324.6 kgha⁻¹, respectively; and (3) the higher the risk aversion level, farmers tended to apply more fertilizer to ensure grain output. Therefore, increasing farm size should be integrated into actions such as improving technological innovation and providing better information transfer to achieve the goal of zero-increase in Chinese fertilizer use.

Green development of agriculture is inevitable to meet the objective demand of rural ecological environment protection and high-quality agricultural development. Livelihood capital is the basic condition for farmers to carry out the activities of production and management, while ecological cognition is the direct motivation for their behaviors. Based on field research data from 436 farm households in 4 counties of Langao, Fuping, Mian, and Yaozhou in Shaanxi Province, China, the study conducted the double-hurdle model (DHM) to empirically analyze the effects of livelihood capital and ecological cognition on farmer’s decision on green production and the degree of green production. The results show that (1) farmers’ livelihood capital is the basic condition that significantly affects farmers’ green production. Specifically, human capital and social capital have a positive contribution to farmers’ green production decision and degree of green production; natural capital and financial capital do not have a significant effect on green production decision, but have a significant positive effect on the degree of green production behavior; physical capital, as farmers’ stock capital, does not have a significant effect on green production behavior. (2) Ecological cognition plays a positive moderating role in farmers’ decisions on green production, but as a subjective cognition, its contribution to the degree of green production is not significant. This paper explores the relationship between livelihood capital, ecological cognition, and farmers’ green production behavior, and provides suggestions for improving farmers’ participation in green production.

Given the significant impact of Generative AI (GenAI) in the workplace, there are surprisingly few empirical studies examining how employees’ GenAI awareness shapes differently oriented job crafting behaviors. In organizations, understanding this is important because GenAI is unlikely to fully replace employees; instead, it requires them to adopt adaptive strategies to work alongside GenAI. If employees engage in avoidance crafting behavior, it could have negative consequences for the organization. Based on sensemaking theory, we develop a theoretical model to explore how employees’ GenAI awareness affects their job crafting behavior, as well as the mediating mechanisms and boundary conditions of its influence. Using self-evaluation data from 316 employees at three time points, the results of our hypothesis testing reveal that when employees perceive high internal Corporate Social Responsibility (CSR), their GenAI awareness triggers harmonious work passion and leads to approach job crafting; conversely, when employees perceive high external Corporate Social Responsibility, their GenAI awareness triggers obsessive work passion and leads to avoidance job crafting. Finally, the theoretical and empirical implications of our findings are discussed.

This study investigates how entrepreneurship enhances organizational resilience through AI-driven technological innovation under dynamic environments. Using panel data from 3975 Chinese listed firms (2013–2022) and a moderated mediation regression framework, we examine both direct and indirect effects. AI-driven innovation is operationalized through digital patent text analysis, capturing firms’ technological advancement. The results show that entrepreneurship positively affects organizational resilience (p < 0.01) and promotes AI-driven innovation (p < 0.01). AI-driven innovation further mediates the relationship between entrepreneurship and resilience (p < 0.05). Environmental dynamics strengthen the effects of digital innovation on resilience (p < 0.05) and the mediated pathway. These findings provide empirical evidence on how entrepreneurial capabilities and digital innovation jointly support organizational resilience and sustainable development.

The ongoing progress of industrialization and urbanization has exacerbated the imbalance between carbon emissions and absorption, leading to heightened risks of climate change, such as frequent occurrences of extreme weather events. Clarifying the driving forces and temporal–spatial evolution characteristics of China’s carbon balance holds significant theoretical value in understanding the systemic nature and patterns of interaction between carbon emissions and absorption. We utilize provincial panel data from 2005 to 2021 in China and a spatial Durbin model to explore the spatial spillover effects of carbon imbalance and its influencing factors. The results indicate a gradual exacerbation of carbon imbalance in China over time. There exists a spatially positive correlation pattern in provincial carbon imbalance distribution. From 2005 to 2010, intra-regional differences in carbon imbalance levels were a significant contributor to China’s overall carbon imbalance disparity, while from 2011 to 2019, inter-regional differences played a more substantial role. Given the apparent phenomena of population aggregation, industrial concentration, and economic interdependence among provinces, changes in population size, economic growth, and industrial structure exacerbate the level of carbon imbalance in spatially correlated regions. Conversely, due to knowledge and technology spillovers, improvements in energy efficiency facilitated by the flow of production factors like capital aid in the governance of carbon imbalance in spatially associated areas. We emphasize that local governments should focus on a regional integration perspective in carbon imbalance governance and strategically coordinate with neighboring provinces and cities to advance carbon imbalance governance. The findings provide theoretical support for understanding and effectively managing the situation of carbon imbalance in China.

This study investigates the impact of Machiavellian leadership on team creativity through the mediating role of cross-understanding and the moderating effect of task interdependence. While prior research has emphasized the negative consequences of Machiavellian tendencies, we argue that in highly interdependent team settings—such as project-based groups in technology, manufacturing, and financial enterprises—such leaders may foster constructive processes that enhance innovation. Drawing on social learning and trait activation theories, we conducted a multi-source survey of 86 teams (379 employees) in Chinese organizations. Team members assessed task interdependence and cross-understanding, while leaders reported their own Machiavellian tendencies and rated team creativity. Results show that Machiavellian leadership predicts team creativity indirectly through cross-understanding, with task interdependence strengthening this pathway. Theoretically, this study enriches leadership and creativity research by providing a nuanced view of how dark traits can stimulate team-level creativity through cognitive interaction mechanisms and by identifying task interdependence as a boundary condition. Practically, the findings suggest that organizations should recognize the creative potential of Machiavellian leaders in high-interdependence contexts, channel their ambition toward innovation goals, and design workflows that promote cross-understanding and collaboration.