Based on survey data from 579 farmer households in major non-timber forest product (NTFP) regions of Zhejiang Province, this study comprehensively employs binary logit/ordered probit and machine learning methods for cross-validation. It systematically examines the effects of multiple factors, including perceived property rights security, technical training, village rules and regulations, ecological awareness, and economic incentives, on forest farmers’ adoption of green production technologies. The cross-validation between econometric and machine learning approaches enhances the reliability of the findings. Results show that perceived property rights security is robustly and positively associated with green production behavior, while village rules and regulations and ecological awareness emerge as the two most critical driving factors. These associations exhibit significant NTFP-type heterogeneity: the impacts of technical training and forestry subsidies vary in direction depending on the crop cultivated, rendering traditional one-size-fits-all policies ineffective. This study highlights the crucial role of informal institutions and environmental awareness in the green transition, offering empirical evidence for designing differentiated training programs, optimizing penalty gradients, and implementing targeted subsidy policies.

This study investigates the drivers of green production practices among forest-cultivated ginseng growers in Jilin Province, China, by integrating the Theory of Planned Behavior (TPB) and the Technology–Organization–Environment (TOE) framework. Based on survey data from 369 households in the major production regions of Tonghua, Baishan, and Yanbian areas, an Ordered Probit model and a Light Gradient Boosting Machine (LGBM) algorithm are employed for cross-validation. The results indicate that growers’ cognitive traits (awareness of green production standards and ecological/quality safety) and willingness (acceptance of price premiums for green products) are the most stable and critical drivers. Policy incentives (e.g., certification subsidies and outreach) not only directly promote green practices but also exhibit synergistic effects through interactions with resource endowments and psychological cognition. Regional heterogeneity is evident: Tonghua shows policy–market co-drive, Baishan is dominated by ecological constraints and safeguard policies, while Yanbian relies more on education and individual resources. Accordingly, this study proposes a differentiated policy system based on diagnosis–intervention–evaluation to support the high-quality development of forest-cultivated ginseng industry and ecological-economic synergies.

This paper uses a young fruiting body harvesting experiment to examine how uncertainty affects improper behavior and how two nudges, prior selection and consequence viewing, can mitigate or shape this behavior. Uncertainty promotes improper harvesting, with harvesting rates higher under ambiguity than under risk. It also induces belief bias, yet this bias drives behavior only when consequences are observable. Prior selection reduces harvesting through an anchoring mechanism, whereas consequence viewing alone has little effect. Notably, combining the two nudges produces a boomerang effect: harvesting rates rise above those observed under prior selection alone. These findings inform behavioral interventions for natural resource management.