Against the backdrop of escalating environmental pollution and resource depletion induced by traditional agricultural industry development, the imperative for agricultural efficiency and ecologically friendly practices has become pivotal for global agricultural sustainability. In the digital era, rural digitalization has substantially amplified agricultural production efficiency while notably reducing environmental pollution. This study is based on panel data from 1602 counties in China spanning from 2011 to 2020, measuring the levels of rural digitalization and agricultural green total factor productivity (AGTFP) across these counties, empirically examining their dynamic correlation. The research findings reveal: (1) A significant enhancement of AGTFP with the elevation of rural digitalization; for every 1 % increase in rural digitalization, AGTFP increases by 1.78 %. Robustness checks, including instrumental variables and quasi-natural experiments, affirm the validity of these conclusions. Furthermore, by comparing regional differences in China, it can be observed that the impact of rural digitalization on AGTFP varies across different areas: in the eastern, central, and western regions, for every 1 % increase in rural digitalization, the AGTFP increases by 2.65 %, 1.53 %, and 0.82 %, respectively. (2) Rural digitalization fosters an increase in AGTFP by stimulating rural entrepreneurial activity, with the mediation effect of rural entrepreneurial activity accounting for 3.34 % of the total effect. (3) Utilizing a threshold model, observations indicate that when rural digitalization surpasses the first threshold of 0.3200, the AGTFP coefficient escalates from 0.0462 to 0.7519. However, when rural digitalization exceeds the second threshold of 0.7118, this coefficient declines to 0.2429, signifying a nonlinear trend in the marginal effect of rural digitalization on AGTFP-initial enhancement followed by attenuation. (4) In the spatial model analysis, the study confirms spatial spillover effects of rural digitalization on AGTFP, indicating variations in spatial effects among different matrices. However, the indirect effects of rural digitalization on AGTFP, mainly influenced by the neighboring counties' digitalization levels, contribute more significantly than the direct effects. Based on these conclusions, this paper proposes augmenting investments in rural digitalization, implementing diversified rural digitalization schemes, invigorating rural entrepreneurial activity, and harmonizing regional digital development as novel approaches to enhance AGTFP, thereby fostering rural economic development.

Frequent shifts in economic policies not only inject uncertainty into the economic landscape but also pose significant challenges to corporate endeavors in green technological innovation. Drawing on a dataset of Chinese A-share listed companies spanning 2008 to 2020, this research delves into the repercussions of economic policy uncertainty on the green technological pursuits of manufacturing firms and elucidates the underlying dynamics at play. The empirical evidence underscores a marked reluctance among companies to champion green technological innovation in the face of economic policy ambiguity, a stance that holds water even after rigorous robustness checks. Delving into the mechanisms, the study pinpoints heightened financial constraints and a diminishing risk appetite within the managerial ranks as pivotal deterrents steering firms away from green innovation projects amidst such uncertainty. Intriguingly, the adverse interplay between economic policy uncertainty and green innovation is especially accentuated in firms marked by tenuous government–business affiliations, pronounced monopolistic inclinations, lax intellectual property safeguards, minimal pollution footprints, and a skewed labor-to-capital composition. This investigation augments the scholarly discourse on the nexus between economic policy volatility and corporate green innovation, shedding light on strategic imperatives for emerging economies as they chart out future environmental blueprints and cultivate a conducive milieu for green innovation.

Objective: This study investigated the factors influencing depressive symptoms in women with somatic pain during the perimenopausal period in China and established and validated a nomogram prediction model.Methods: The predictive model is based on data from the China Health and Retirement Longitudinal Study (CHARLS), which focused on individuals aged 45–59 years with somatic pain during the perimenopausal period. The study utilized participants from the CHARLS 2018 wave, 30 factors including individual characteristics, health behaviors, living environment, family economic status, and social participation, were analyzed in this study. To ensure the model’s reliability, the study cohort was randomly split into a training set (80%) and a validation set (20%). The χ² tests and a Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis were used to identify the most effective predictors of the model. The logistic regression model was employed to investigate the factors associated with depressive symptoms in perimenopausal women with somatic pain. A nomogram was constructed to develop a prediction model, and calibration curves were used to assess the accuracy of the nomogram model. The model’s performance was evaluated using the area under the curve (AUC) and decision curve analysis (DCA).Results: In total, 2,265 perimenopausal women were included in the final analysis, of whom 1,402 (61.90%) experienced somatic pain. Multifactorial logistic regression identified marital status, pain distress, self-perceived general health, activities of daily living (ADL), sleep deprivation, life satisfaction, and air quality satisfaction, as predictive risk factors for perimenopausal women with somatic pain. The predictive model achieved an AUC of 0.7010 (95%CI = 0.677–0.725) in the training set and 0.7015 (95%CI = 0.653–0.749) in the validation set. The nomogram showed excellent predictive ability according to receiver operating characteristic (ROC) and DCA, and the model may help in the early detection of high-risk depression symptoms in perimenopausal women with somatic pain, thereby enabling the timely initiation of appropriate treatment interventions.Conclusion: The nomogram constructed in this study can be used to identify the factors related to depression in women with perimenopausal somatic pain.

Introduction: Entrepreneurs’ attitudes toward risk is one of the most critical factors influencing business growth and economic development. Therefore, addressing the influencing factors and formation mechanisms of entrepreneurs’ risk attitudes has become a crucial research endeavor. In this paper, we examine how contract performance rates affect entrepreneurs’ risk attitudes through the mediating effect of subjective well-being as well as assess the moderating effect of the regional business environment on this relationship.Methods: The ordered probit regression technique was employed to analyze the data obtained from 3,660 sampled respondents from the 2019 China Household Finance Survey. All analysis was performed using Stata 15.0.Results: The empirical results show that contract performance rates have a substantial positive indirect effect on entrepreneurs’ degree of risk aversion through improved subjective well-being. The regional business environment plays a negative regulatory role in the relationship between contract performance rates and entrepreneurs’ risk aversion. Furthermore, urban–rural heterogeneity appears to consistently determine the extent of the influence of contract performance rates on entrepreneurs’ risk attitudes.Conclusion: To reduce entrepreneurs’ risk aversion and enhance social and economic activity, the government should improve regional business environments by taking specific measures. Our study contributes to the empirical understanding of entrepreneurs’ investment decisions in the context of urban and rural environments.

Promoting rural industrial integration development (RIID) to boost farmers’ income is a core strategy for rural revitalization and common prosperity. A reasonable and objective evaluation of RIID efficiency that jointly accounts for farmers’ income growth and the food-security and environmental constraints entailed by integration is critical for advancing sustainable rural development. This paper applies the super-efficiency SBM model with undesirable outputs and the Malmquist index to measure China’s RIID efficiency across 30 provinces from 2012 to 2023, analyzing its spatio-temporal evolution. The results show that the national annual average RIID efficiency fluctuated between 0.423 and 0.663, exhibiting a three-stage trajectory of “steady rise—fluctuating bottoming-out—accelerated climb” alongside a descriptively observed east–central–west gradient (eastern leading, central following, western lagging) that does not attain statistical significance. The annual average Malmquist index reached 1.060, corresponding to an average annual growth rate of 6.0%, and efficiency catch-up became the dominant driver of productivity growth in the later stage. The exploratory regression further reveals that rural human capital and transportation infrastructure are significantly and positively associated with RIID efficiency. This study integrates farmers’ income growth, farmland non-grain conversion, and agricultural non-point source pollution into the efficiency evaluation system of rural industrial integration development. It also provides new empirical evidence regarding the spatio-temporal evolution characteristics and driving factors of rural industrial integration, which can support the formulation of differentiated and welfare-oriented rural development policies in China and other developing economies.