Sustainable Development of Green Tourism Supply Chain Considering Blockchain Traceability and Government Subsidies

Q1 Decision Sciences

Annals of Data Science Pub Date : 2025-04-21 DOI:10.1007/s40745-025-00588-x

Jixia Zheng, Rui Chen, Qinggen Zeng, Yanan Chen, Qianlin Ye

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Abstract

This study examines the issue of green information distortion and its impact on tourists’ purchasing decisions, as well as the associated high transaction costs within the green tourism supply chain. By selecting a green tourism supply chain with varying government subsidy schemes as the focus of the research, the objective is to explore optimal subsidy strategies and assess the implications of blockchain integration. A three-level Stackelberg game model is established, featuring the government as the leader and a scenic spot (SS) and travel agency (TA) as participants. Key findings include: (1) Production subsidies are more effective in boosting market demand than environmental investment subsidies, particularly when tourist green trust and preferences are high. (2) Blockchain enhances greenness, market demand, and social welfare, positively influencing the green tourism supply chain (GTSC). (3) Tourist green preference and trust significantly affect GTSC optimization, especially as preferences increase. Additionally, a cost-sharing smart contract mechanism is designed to mitigate environmental investment's negative impact and optimize social welfare and product greenness.

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考虑可追溯性和政府补贴的绿色旅游供应链可持续发展

本研究探讨绿色资讯扭曲问题及其对游客购买决策的影响，以及绿色旅游供应链中相关的高交易成本。本研究选取具有不同政府补贴方案的绿色旅游供应链作为研究重点，探讨绿色旅游供应链的最优补贴策略，并评估绿色旅游供应链一体化的影响。建立了以政府为主导，景区（SS）和旅行社（TA）为参与者的三级Stackelberg博弈模型。主要发现包括：(1)生产补贴比环境投资补贴更有效地促进了市场需求，特别是在游客绿色信任和偏好高的情况下。(2)区块链提高绿色度、市场需求和社会福利，正向影响绿色旅游供应链（GTSC）。(3)游客绿色偏好和信任显著影响GTSC优化，尤其是随着偏好的增加。此外，设计了成本分担智能合约机制，以减轻环境投资的负面影响，优化社会福利和产品绿色度。

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来源期刊

Annals of Data Science Decision Sciences-Statistics, Probability and Uncertainty

CiteScore

6.50

自引率

0.00%

发文量

期刊介绍： Annals of Data Science (ADS) publishes cutting-edge research findings, experimental results and case studies of data science. Although Data Science is regarded as an interdisciplinary field of using mathematics, statistics, databases, data mining, high-performance computing, knowledge management and virtualization to discover knowledge from Big Data, it should have its own scientific contents, such as axioms, laws and rules, which are fundamentally important for experts in different fields to explore their own interests from Big Data. ADS encourages contributors to address such challenging problems at this exchange platform. At present, how to discover knowledge from heterogeneous data under Big Data environment needs to be addressed. ADS is a series of volumes edited by either the editorial office or guest editors. Guest editors will be responsible for call-for-papers and the review process for high-quality contributions in their volumes.