The harmonious coexistence of humanity and nature stands as a paramount objective in the global endeavor towards ecological civilization. By vigorously promoting eco-friendly recreational activities within World Agricultural Heritage sites, we can evoke tourists’ emotional understanding and empathy towards environmental protection. This approach presents an optimal solution to the challenges faced in realizing the ecological value of these heritage sites, thereby fostering an even deeper harmony between people and nature. For this study, the Anxi Tie Guan Yin Tea Cultural System, a site of global agricultural heritage significance, was chosen as the case study. Employing structural equation modeling (SEM) analysis, a survey was administered to 452 local tourists for empirical testing. The research reveals several key findings: (1) Tourists’ environmental concern exerts a notably positive influence on their pro-environmental behaviors; (2) both nature empathy and ecological anxiety serve as mediators in the relationship between environmental concern and tourists’ pro-environmental behaviors; (3) furthermore, nature empathy and ecological anxiety exhibit a sequential, or chain, mediation effect between environmental concern and pro-environmental behaviors; and (4) lastly, the impact of nature empathy and ecological anxiety on tourists’ pro-environmental behaviors differs according to gender. Consequently, managers of agricultural heritage sites should aim to evoke tourists’ sense of nature empathy while appropriately stimulating their ecological anxiety, thereby providing guidance for ensuring a harmonious blend of human activities and the ecological environment and ultimately fostering the sustainable development of World Agricultural Heritage sites.

In the context of global climate change and the deepening of ecological civilization construction, forestry, as an ecological security barrier and green economic engine, faces many challenges to the enhancement of its ecological total factor productivity in the traditional development model. As a new type of production factor, the data factor provides a new path to crack the bottleneck of forestry eco-efficiency improvement. Based on China’s provincial annual panel data from 2014 to 2022, this study systematically examines the impact and mechanism of data factors on forestry ecological total factor productivity by using the SBM-GML model and dual machine learning model. It was found that data factors have a significant contribution to forestry ecological total factor productivity, a conclusion that passes a series of robustness tests and endogeneity tests. The analysis of the mechanism shows that the data factor enhances the total factor productivity of forestry ecology mainly through three paths: promoting the progress of forestry technology and promoting the rationalization and advanced structure of the forestry industry. Further analysis showed that the promotional effect of data elements is more obvious in regions with a high level of green finance development, high intensity of environmental regulation, and strong financial autonomy. It is recommended to systematically promote the in-depth application of data elements in forestry, build a data element-driven innovation system for the whole chain of forestry, and implement regionally differentiated data element-enabling strategies.

Accelerating farmers’ adoption of postharvest green technologies is critical for achieving sustainable agricultural transitions, yet the behavioral and cognitive foundations underlying farmers’ adoption decisions remain insufficiently understood. Anchored in capital structure theory, the theory of planned behavior (TPB), and institutional fit theory, this study develops an integrated analytical framework linking structural conditions, cognitive mediation, and institutional adaptation to explain adoption behavior in the context of air-source heat pump drying technology. Drawing on primary survey data collected from 439 moderate-scale farmers in Gutian County, Ningde City, Fujian Province, China, between July and September 2023, a binary logistic regression model and causal mediation effect analysis (CMA) were employed to test the proposed hypotheses. The results reveal that economic capital exerts the strongest positive effect on adoption (β = 0.645, P < .01), followed by cultural capital (β = 0.568, P < .01) and social capital (β = 0.501, P < .01), which jointly constitute the structural foundation of adoption propensity. Perceived value—comprising economic, functional, ecological, and social dimensions—serves as a significant cognitive mediator between capital endowment and behavioral intention, with economic and functional value dimensions being the most influential. Furthermore, incentive-based and constraint-based policy instruments significantly moderate the relationship between perceived value and adoption likelihood, particularly when policy incentives align with farmers’ cognitive appraisals. The findings demonstrate that green technology adoption is neither purely resource-driven nor policy-imposed, but embedded within a dynamic interplay of capital asymmetries, cognitive processing, and institutional design. This study contributes to the literature by clarifying the microlevel behavioral mechanisms of sustainable technology diffusion and providing practical insights into designing policy instruments aligned with farmers’ decision logic in postharvest green processing contexts.