Dynamic evolution and driving mechanisms of tourism carbon reduction networks
A TERGM-based study
Dados Bibliográficos
| ID | 21699720 |
|---|---|
| Autores | Huifang Liu (0000-0001-5357-4938, Tianjin University), Weidong Chen (0009-0004-8504-7840, Tianjin University), Pengwei Yuan (0000-0003-3172-5752, University of Jinan, autor correspondente), Xiaoqing Dong (0000-0002-3965-0655, University of Jinan) |
| Ano | 2026 |
| Volume | 29 |
| Fascículo | 9 |
| Páginas | 1790-1814 |
| Data de publicação | 2026-05-03 |
| Peer Reviewed | Sim |
| Open Access | Não |
| Tipo | ARTICLE |
| Periódico | Current Issues in Tourism (JOURNAL) |
| Identificadores do periódico | ISSN: 1368-3500 • E-ISSN: 1747-7603 |
| Editora | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/13683500.2025.2488034 |
| OpenAlex | W4409295639 |
| Idioma | EN |
| Citações recebidas | 3 |
| Referências citadas | 43 |
Based on energy inputs and CO2 outputs, the carbon reduction efficiency of provincial tourism in China from 2006–2022 is quantified using SBM-DEA. A modified gravity model and social network analysis (SNA) examine the spatial topology of the tourism carbon reduction efficiency network. The temporal exponential random graph model explores TCREN’s dynamics. Key findings include: (1) Provincial tourism carbon reduction efficiency (TCRE) has improved, with TCREN showing low density, long average shortest path, and moderate efficiency. (2) In-degree and out-degree distributions show long-tail and normal characteristics, respectively. (3) TCREN demonstrates significant connectivity and disassortativity; motif analysis confirms reciprocity, while core–periphery and block models reveal hierarchical and clustering features. (4) Variables like labour force, industrialisation, openness, urbanisation, and R&D intensity facilitate inter-provincial cooperation. Similar technological innovation and government intervention also influence carbon reduction relationships. Informatization promotes, while transportation infrastructure inhibits, TCREN formation. This research recapitulates the evolution of TCREN, provides new insights into the factors affecting CO2 reduction in the context of sustainable tourism, and guides the development of future policies and strategies aimed at improving the carbon efficiency of China’s tourism industry
Business · Economic geography · Economics · Environmental resource management · Geography · Natural resource economics · Tourism · Climate Change Policy and Economics · Diverse Aspects of Tourism Research · Energy, Environment, Economic Growth · Mathematics
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Measurement and determinants of smart destinations’ sustainable performance
Spatial spillover and determinants of tourism efficiency
Eco-efficiency, eco-productivity and tourism growth in China
Reciprocity and the structural determinants of the international sanctions network
Evaluating provincial tourism competitiveness in China
Nonlinear Impact of Labor Market Integration on Tourism Development
Challenges of tourism in a low‐carbon economy
Exploring energy and tourism economy growth nexus with DEA-based index systems
Eco-efficiency and its determinants at a tourism destination
Factors influencing the co-occurrence of visits to attractions
Carbon tax, tourism CO 2 emissions and economic welfare
Social Structure from Multiple Networks. I. Blockmodels of Roles and Positions
| Obras citantes distintas | 3 |
|---|---|
| Citações por ano | 3 |
| Intervalo de citações | 2025 - 2026 (2) |
| Velocidade de citação | current |
| Altamente citado | Não |
| Tipos de citação | Neutras: 2 |