Amid environmental pressures and sustainable development challenges at Agricultural Heritage Sites, this study explores the impact mechanism of host–guest value co-creation on tourists’ environmental responsibility behaviors, with a focus on the mediating role of psychological ownership and the moderating effect of emotional inertia. Grounded in Affective Events Theory, hierarchical regression and Bootstrap sampling were employed, analyzing 384 questionnaires from tourists at the Anxi Tieguanyin Tea Culture System. The findings reveal: (1) Host–guest value co-creation directly promotes tourists’ environmental responsibility behaviors; (2) psychological ownership serves as a mediator in the relationship between host–guest value co-creation and tourists’ environmental responsibility behaviors; (3) emotional inertia, an individual characteristic, not only moderates the influence of host–guest value co-creation on psychological ownership but also the transmission effect of psychological ownership. These insights uncover the influencing mechanisms of tourists’ environmental responsibility behaviors at Agricultural Heritage Sites, offering theoretical support for the sustainable management of such sites.

Exploring the interrelationships and influencing factors of the multifunctionality of cultivated land is crucial for achieving its multifunctional protection and sustainable use. In this paper, we take the Min River basin as a case study to construct a multifunctional evaluation system based on “agricultural production, social security, ecological service, and cultural landscape” using multi-source data. We analyze the spatial and temporal characteristics of the multifunctionality of cultivated land through kernel density estimation (KDE) and visual mapping. Subsequently, we assess the trade-off strength between the multifunctional aspects of cultivated land using the root mean square error (RMSD). Finally, we identify the drivers of the multifunctional trade-off intensity of cultivated land and analyze their influencing mechanisms using Geographic Detectors. The results show that (1) from 2010 to 2020, the multifunctional structure of cultivated land in the study area underwent significant changes: the levels of agricultural production, social security, and ecological service functions first increased and then decreased, while the levels of cultural landscape function and comprehensive function continued to increase. The spatial distribution is characterized, respectively, by “high in the east and low in the west”, “high in the west and low in the east”, “high in the north and low in the south”, “high in the whole and sporadically low in the northeast”, and “high in the middle and low in the surroundings”. (2) During the study period, the trade-off strengths related to social security functions increased, while the trade-off strengths of the remaining multifunctional pairs of cultivated land showed a weakening trend, with high values of trade-off strengths among functions particularly prominent in the Nanping Municipal District. (3) Both natural and human factors significantly affect the multifunctional trade-off strength of cultivated land. Among the specific factors, elevation, slope, average annual temperature, and per capita GDP are the key factors influencing the strength of the trade-offs between functions. The results of this study provide empirical support for enriching the understanding of the multifunctionality of cultivated land and offer a decision-making basis for promoting the differentiated management of cultivated land resources and the synergistic development of its multifunctionality.

Improving agricultural eco-efficiency (AEE) plays a critical role in fulfilling agriculture sustainable development goals (SDGs). China’s agriculture-led Rural Industrial Integration (RII) seeks to synergize rural industrialization with agricultural sustainability, yet its impact on AEE remains underexplored. Using a 2008–2022 panel of 285 prefecture-level cities in China, this study uses a series of econometric methods to empirically verify the impact of RII on AEE. The coefficient of RII under the fixed effect model is 0.366, indicating that it has a significant positive impact on AEE, which remains valid after robustness tests such as the instrumental variable method and the use of the “Rural Industrial Integration Development Demonstration County” pilot as a quasi-natural experiment. Mechanism tests show that rural labor transfer, agricultural technology innovation, and agricultural carbon emissions play an important role in mediating the impact of RII on AEE. RII has a negative spatial spillover effect on AEE, with a coefficient of −2.280. In addition, the impact of RII on AEE also varies under the heterogeneity of regions and development models. This study provides new evidence that China’s RII practices can promote sustainable agricultural development, deepens theoretical understanding of the impact of RII on AEE, and provides a reference for future policy implementation.

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.

The increasing demand for tea has resulted in the swift expansion of tea plantations, leading to significant alterations in the local ecosystem. This has garnered considerable attention. However, research on rationally modifying land use structures (LUSs) in specialized tea regions to achieve a balance with ecosystem services (ESs) and enhance their synergistic effects remains limited. In this study, we quantified the values of water yield (WY), soil conservation (SC), carbon storage (CS), and habitat quality (HQ) by the InVEST model, utilized K-means clustering to categorize the LUSs of the village areas in Anxi County from 2010 to 2020, and then investigated the influences of the LUSs on the ESs by the improvement of the constraint line tool, according to which we utilized the ecosystem services trade-off degree (ESTD) and the Pearson correlation coefficient to explore the trade-offs and synergies among ecosystem services. The findings indicated that: (1) LUSs in Anxi County are tea garden structure (TS), forest land-tea garden structure (FTS), and construction land-cropland-tea garden structure (CCTS); (2) In LUSs, to achieve a balance among WY, SC, CS, and HQ, the dominant land use proportions were as follows: 0.5 for tea plantations in TS; a range of 0.55–0.6 for forest land and 0.05–0.1 for tea plantations in FTS; and CCTS, 0.25–0.3 for built-up land, 0–0.1 for cropland, and 0.2 for tea plantations; (3) The trade-offs and synergies of ESs vary across different LUSs. The synergy is particularly evident in FTS. Increasing the quantity of forest area is helpful in enhancing the synergy of ESs. This study offers an analysis of the effect of LUSs on ESs and their interconnections in specialized tea planting regions. It serves as a valuable insight into informing urban planning for future land usage and ecological conservation efforts.

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.