journal article·2025-05-19Open access

The impact of spatial perception at agricultural heritage sites on tourists’ carbon reduction behavior

Shuaijun Lin, Hongfeng Zhang, Xiaowei Wang, Johnny F. I. Lam

npj Heritage Science· Volume 13· Issue 1

Abstract

With “Carbon Neutral” goals and rural revitalization strategies, developing low-carbon tourism at agricultural heritage sites has become academically significant. Existing research overlooks how agricultural traditions, human-land relationships, and cultural diversity influence tourists’ carbon-reducing behaviors at heritage sites. Using XGBoost-SHAP machine learning models, this study explores how spatial perception influences tourists’ carbon reduction behaviors. Findings reveal that land use coordination perception and cultural heritage value recognition are dominant factors, contributing 58.267% and 57.810% to transportation and recreational behaviors, respectively. Different spatial perception elements show differentiated impacts: immediate carbon reduction behaviors are primarily influenced by land use coordination and traditional agricultural features, while sustained behaviors depend on traffic accessibility and interpretation system performance. Significant synergistic effects exist among spatial elements, particularly between land use coordination and cultural heritage value recognition. This study reveals nonlinear relationships between spatial perception and carbon reduction behaviors, providing theoretical guidance for heritage site optimization.
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Research context

OpenAlex topics: Diverse Aspects of Tourism Research · Land Use and Ecosystem Services

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References (46)

Show source-provided references
1. Wang, C. Z. Several major issues in constructing the evaluation index system of Chinese-style modernization. Sci. Social. 5, 4–11 (2023).
2. Jones, C. The carbon footprint of regional tourism trips: insights from environmentally extended regional input output analysis. J. Sustain. Tourism 32, 1605–1620 (2024).10.1080/09669582.2023.2254949 ↗
3. Wang, L., Wang, J., Shen, P., Liu, S. & Zhang, S. Low-carbon travel behavior in daily residence and tourism destination: Based on TPB-ABC integrated model. Sustainability 15, 14349 (2023).10.3390/su151914349 ↗
4. Lin, H. H. et al. Can low-carbon tourism awareness promote rural and ecological development, create safe leisure spaces, and increase public happiness? A discussion from the perspective of different stakeholders. Water 14, 3557 (2022).10.3390/w14213557 ↗
5. Dolnicar, S. & Demeter, C. Why targeting attitudes often fails to elicit sustainable tourist behaviour. Int. J. Contemp. Hosp. Manag. 36, 730–742 (2024).10.1108/IJCHM-07-2022-0828 ↗
6. Zhang, A., Yang, Y., Chen, T., Liu, J. & Hu, Y. Exploration of spatial differentiation patterns and related influencing factors for National Key Villages for rural tourism in China in the context of a rural revitalization strategy, using GIS-based overlay analysis. Arab. J. Geosci. 14, 1–15 (2021).10.1007/s12517-020-06304-8 ↗
7. Lee, K. J. & Lee, S. Y. Cognitive appraisal theory, memorable tourism experiences, and family cohesion in rural travel. J. Travel. Tourism Mark. 38, 399–412 (2021).10.1080/10548408.2021.1921094 ↗
8. Yang, C., Tan, S., Zhou, H., & Zeng, W. Towards sustainable rural development: assessment spatio-temporal evolution of rural ecosystem health through integrating ecosystem integrity and SDGs. Land, 13, 1672 (2024).10.3390/land13101672 ↗
9. Liu, Y., Li, X. & Guo, Y. Exploring land system reform for demographic transition in rural China. Land Use Policy 147, 107355 (2024).10.1016/j.landusepol.2024.107355 ↗
10. Kim, S., Whitford, M., & Arcodia, C. Development of intangible cultural heritage as a sustainable tourism resource: the intangible cultural heritage practitioners’ perspectives. in Authenticity and Authentication of Heritage (pp. 34–47). Routledge.10.4324/9781003130253-4 ↗
11. Zhou, L., Zhao, Y., Yang, X., He, J. & Wang, H. Identification of important terraced visual landscapes based on a sensitivity-subjectivity preference matrix for agricultural cultural heritage in the southwestern china. Ecol. Indicat. 154, 110573 (2023).10.1016/j.ecolind.2023.110573 ↗
12. Wang, N., Li, J. & Zhou, Z. Landscape pattern optimization approach to protect rice terrace Agroecosystem: case of GIAHS site Jiache Valley, Guizhou, southwest China. Ecol. Indic. 129, 107958 (2021).10.1016/j.ecolind.2021.107958 ↗
13. Su, M. M., Dong, Y., Wall, G. & Sun, Y. A value-based analysis of the tourism use of agricultural heritage systems: Duotian Agrosystem, Jiangsu Province, China. J. Sustain. Tour. 28, 2136–2155 (2020).10.1080/09669582.2020.1795184 ↗
14. Zhu, J. et al. Evaluating the sustainability of rural complex ecosystems during the development of traditional farming villages into tourism destinations: a diachronic emergy approach. J. Rural Studies 86, 473–484 (2021).10.1016/j.jrurstud.2021.07.010 ↗
15. Shen, J. & Chou, R. J. Cultural landscape development integrated with rural revitalization: a case study of Songkou ancient town. Land 10, 406 (2021).10.3390/land10040406 ↗
16. Wang, L., & Huang, W. Visitors’ consistent stay behavior patterns within free-roaming scenic architectural complexes: considering impacts of temporal, spatial, and environmental factors. Front. Architect. Res. 13, 990–1008 (2024).10.1016/j.foar.2024.02.005 ↗
17. Mandić, A., Knight, D. W., Vuković, M., & Thomsen, B. Place attachment, awareness of environmental responsibility and pro-environmental behaviour of visitors in protected natural areas. Tour. Plan. Dev. 1–26 (2024).10.1080/21568316.2024.2353596 ↗
18. Nasir, N. A. M., Manaf, M. A. A., Hashim, N. H. M. & Hashim, N. I. Place attachment and its impacts towards pro-environmental behavior intention among urban park visitors. Semarak Int. J. Appl. Psychol. 1, 51–60 (2024).10.37934/sijap.1.1.5160 ↗
19. Liu, H., Gao, C. & Tsai, H. Spatial spillover and determinants of tourism efficiency: a low carbon emission perspective. Tour. Econ. 30, 543–566 (2024).10.1177/13548166231167045 ↗
20. Yang, W., Fan, B., Tan, J., Lin, J., & Shao, T. The spatial perception and spatial feature of rural cultural landscape in the context of rural tourism. Sustainability 14, 4370 (2022).10.3390/su14074370 ↗
21. Zhang, Y., Zheng, Q., Tang, C., Liu, H. & Cui, M. Spatial characteristics and restructuring model of the agro-cultural heritage site in the context of culture and tourism integration. Heliyon 10, e30227 (2024).10.1016/j.heliyon.2024.e30227 ↗
22. Dai, T. & Zheng, X. Understanding how multi-sensory spatial experience influences atmosphere, affective city image and behavioural intention. Environ. Impact Assess. Rev. 89, 106595 (2021).10.1016/j.eiar.2021.106595 ↗
23. McKercher, B., Prideaux, B. & Thompson, M. The impact of changing seasons on in-destination tourist behaviour. Tour. Rev. 79, 1197–1209 (2024).10.1108/TR-06-2023-0420 ↗
24. Weng, L., Liang, Z. & Bao, J. The effect of tour interpretation on perceived heritage values: a comparison of tourists with and without tour guiding interpretation at a heritage destination. J. Destin. Mark. Manag. 16, 100431 (2020).
25. Pai, C. K., Liu, Y., Kang, S. & Dai, A. The role of perceived smart tourism technology experience for tourist satisfaction, happiness and revisit intention. Sustainability 12, 6592 (2020).10.3390/su12166592 ↗
26. Albayrak, T. et al. How does perceived crowding moderate tourist shopping experience and satisfaction relationship? Int. J. Tour. Cities 7, 46–62 (2021).10.1108/IJTC-02-2020-0028 ↗
27. Zha, J., Fan, R., Yao, Y., He, L. & Meng, Y. Framework for accounting for tourism carbon emissions in China: an industrial linkage perspective. Tour. Econ. 27, 1430–1460 (2021).10.1177/1354816620924891 ↗
28. Cao, R., Mo, Y. & Ma, J. Carbon footprint analysis of tourism life cycle: the case of Guilin from 2011 to 2022. Sustainability 15, 7124 (2023).10.3390/su15097124 ↗
29. Rao, X., Qiu, H., Morrison, A. M., Wei, W. & Zhang, X. Predicting private and public pro-environmental behaviors in rural tourism contexts using SEM and fsQCA: the role of destination image and relationship quality. Land 11, 448 (2022).10.3390/land11030448 ↗
30. Vázquez-Parra, J. C., Henao-Rodriguez, L. C., Lis-Gutiérrez, J. P., Castillo-Martínez, I. M. & Suarez-Brito, P. eComplexity: validation of a complex thinking instrument from a structural equation model. Front. Educ. 9, 1334834 (2024).10.3389/feduc.2024.1334834 ↗
31. Gao, Y. et al. The spatial cognition of a forest landscape and its relationship with tourist viewing intention in different walking passage stages. Urban For. Urban Green. 58, 126975 (2021).10.1016/j.ufug.2020.126975 ↗
32. Kline, R. B. Principles and practice of structural equation modeling. (Guilford Press, 2023).
33. Darlington, R. B. & Hayes, A. F. Regression analysis and linear models: concepts, applications, and implementation (Guilford Press, 2016).
34. Yuan, J., Gao, H., Shen, Y. & Ma, G. Spatial differentiation of ecotourist perceptions based on the random forest model: the case of the Gansu Section of the Yellow River Basin. Land 13, 560 (2024).10.3390/land13040560 ↗
35. Yin, X. & Jung, T. Analysing the causes of tourists’ emotional experience related to tourist attractions from a binary emotions perspective utilising machine learning models. Asia Pac. J. Tour. Res. 29, 699–718 (2024).10.1080/10941665.2024.2343077 ↗
36. Rudin, C. et al. Interpretable machine learning: Fundamental principles and 10 grand challenges. Stat. Surv. 16, 1–85 (2022).10.1214/21-SS133 ↗
37. Chen, T. Xgboost: extreme gradient boosting. R package version 0.4-2. (2015).
38. Chen, T., & Guestrin, C. Xgboost: a scalable tree boosting system. In Proc. 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 785–794 (2016).10.1145/2939672.2939785 ↗
39. Sagi, O. & Rokach, L. Approximating XGBoost with an interpretable decision tree. Inform. Sci. 572, 522–542 (2021).10.1016/j.ins.2021.05.055 ↗
40. Jas, K. & Dodagoudar, G. R. Explainable machine learning model for liquefaction potential assessment of soils using XGBoost-SHAP. Soil Dyn. Earthq. Eng. 165, 107662 (2023).10.1016/j.soildyn.2022.107662 ↗
41. Wang, M. et al. An XGBoost-SHAP approach to quantifying morphological impact on urban flooding susceptibility. Ecol. Indicat. 156, 111137 (2023).10.1016/j.ecolind.2023.111137 ↗
42. Gupta, A., Gowda, S., Tiwari, A. & Gupta, A. K. XGBoost-SHAP framework for asphalt pavement condition evaluation. Construct. Building Mater. 426, 136182 (2024).10.1016/j.conbuildmat.2024.136182 ↗
43. Sun, Z. et al. Understanding key contributing factors on the severity of traffic violations by elderly drivers: a hybrid approach of latent class analysis and XGBoost based SHAP. Int. J. Injury Control Saf. Promotion 31, 273–293 (2024).10.1080/17457300.2023.2300479 ↗
44. Li, L. et al. Integrated machine learning reveals aquatic biological integrity patterns in semi-arid watersheds. J. Environ. Manag. 359, 121054 (2024).10.1016/j.jenvman.2024.121054 ↗
45. Ma, S. et al. Exploring nonlinear and synergistic effects of street environment on the spirit of place in historic districts: Using multi-source data and XGBoost. Sustainability 16, 5182 (2024).10.3390/su16125182 ↗
46. Yang, Y., Ma, X., Ding, W., Wen, H. & Sun, D. A novel dataset replenishment strategy integrating time-series InSAR for refined landslide susceptibility mapping in Karst regions. Water 16, 2414 (2024).10.3390/w16172414 ↗