Sustainable development in the tourism sector: The impact of environmental, social, and governance performance on operational efficiency—A multilevel analytical approach
This study uses panel data from 274 listed companies (2009 to 2022) and a multi-level analytical framework that integrates econometric models with machine learning techniques to explore the complex relationship between Environmental, Social, and Governance (ESG) performance and operational efficienc...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-12-01
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| Series: | Sustainable Futures |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666188825004885 |
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| _version_ | 1849420865779793920 |
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| author | Yu Zhang Hon Tat Huam Zi Rui Zhang |
| author_facet | Yu Zhang Hon Tat Huam Zi Rui Zhang |
| author_sort | Yu Zhang |
| collection | DOAJ |
| description | This study uses panel data from 274 listed companies (2009 to 2022) and a multi-level analytical framework that integrates econometric models with machine learning techniques to explore the complex relationship between Environmental, Social, and Governance (ESG) performance and operational efficiency in China's tourism industry. The main findings reveal a dual narrative: employment level is a robust predictor of ESG performance, with scaled employment growth significantly enhancing ESG outcomes; meanwhile, lagged Tobin's Q shows a distinct negative correlation, highlighting the trade-offs between short-term financial valuation and long-term ESG commitment. The industry heterogeneity analysis based on random forest and clustering models reveals differences in ESG-employment dynamics across sub-sectors. The pure tourism segment shows the strongest linkage between ESG performance and employment, outperforming accommodation and aviation sectors—likely due to its labor-intensive structure and direct consumer engagement. Small and medium enterprises (SMEs) in accommodation and aviation sectors with lower ESG scores exhibit potential to improve financial health and employment through sustainable practices, whereas large firms capitalize on economies of scale to create a virtuous cycle of ESG integration and business expansion. ARIMA time series forecasts indicate a moderate upward trend in employment in the accommodation industry by 2032, with these insights elucidating nonlinear ESG impact pathways. For stakeholders, the study proposes actionable strategies: tailored ESG subsidies for SMEs, integrating ESG metrics into wage policies, and governance reforms aligning financial markets with long-term sustainability goals. In summary, these findings chart a roadmap for the tourism industry to efficiently achieve sustainable transformation while balancing operational resilience with environmental and social responsibility. |
| format | Article |
| id | doaj-art-a07acd510ab843e4b1ddd94702d9f362 |
| institution | Kabale University |
| issn | 2666-1888 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Sustainable Futures |
| spelling | doaj-art-a07acd510ab843e4b1ddd94702d9f3622025-08-20T03:31:37ZengElsevierSustainable Futures2666-18882025-12-011010092310.1016/j.sftr.2025.100923Sustainable development in the tourism sector: The impact of environmental, social, and governance performance on operational efficiency—A multilevel analytical approachYu Zhang0Hon Tat Huam1Zi Rui Zhang2Faculty of Business, City University of Macau, Avenida Padre Tomás Pereira, Taipa, Macau; Corresponding author.Faculty of Business, City University of Macau, Avenida Padre Tomás Pereira, Taipa, MacauHangzhou No.2 High School of Zhejiang Province, ChinaThis study uses panel data from 274 listed companies (2009 to 2022) and a multi-level analytical framework that integrates econometric models with machine learning techniques to explore the complex relationship between Environmental, Social, and Governance (ESG) performance and operational efficiency in China's tourism industry. The main findings reveal a dual narrative: employment level is a robust predictor of ESG performance, with scaled employment growth significantly enhancing ESG outcomes; meanwhile, lagged Tobin's Q shows a distinct negative correlation, highlighting the trade-offs between short-term financial valuation and long-term ESG commitment. The industry heterogeneity analysis based on random forest and clustering models reveals differences in ESG-employment dynamics across sub-sectors. The pure tourism segment shows the strongest linkage between ESG performance and employment, outperforming accommodation and aviation sectors—likely due to its labor-intensive structure and direct consumer engagement. Small and medium enterprises (SMEs) in accommodation and aviation sectors with lower ESG scores exhibit potential to improve financial health and employment through sustainable practices, whereas large firms capitalize on economies of scale to create a virtuous cycle of ESG integration and business expansion. ARIMA time series forecasts indicate a moderate upward trend in employment in the accommodation industry by 2032, with these insights elucidating nonlinear ESG impact pathways. For stakeholders, the study proposes actionable strategies: tailored ESG subsidies for SMEs, integrating ESG metrics into wage policies, and governance reforms aligning financial markets with long-term sustainability goals. In summary, these findings chart a roadmap for the tourism industry to efficiently achieve sustainable transformation while balancing operational resilience with environmental and social responsibility.http://www.sciencedirect.com/science/article/pii/S2666188825004885ESGTourismOLSMachine learningSustainable development |
| spellingShingle | Yu Zhang Hon Tat Huam Zi Rui Zhang Sustainable development in the tourism sector: The impact of environmental, social, and governance performance on operational efficiency—A multilevel analytical approach Sustainable Futures ESG Tourism OLS Machine learning Sustainable development |
| title | Sustainable development in the tourism sector: The impact of environmental, social, and governance performance on operational efficiency—A multilevel analytical approach |
| title_full | Sustainable development in the tourism sector: The impact of environmental, social, and governance performance on operational efficiency—A multilevel analytical approach |
| title_fullStr | Sustainable development in the tourism sector: The impact of environmental, social, and governance performance on operational efficiency—A multilevel analytical approach |
| title_full_unstemmed | Sustainable development in the tourism sector: The impact of environmental, social, and governance performance on operational efficiency—A multilevel analytical approach |
| title_short | Sustainable development in the tourism sector: The impact of environmental, social, and governance performance on operational efficiency—A multilevel analytical approach |
| title_sort | sustainable development in the tourism sector the impact of environmental social and governance performance on operational efficiency a multilevel analytical approach |
| topic | ESG Tourism OLS Machine learning Sustainable development |
| url | http://www.sciencedirect.com/science/article/pii/S2666188825004885 |
| work_keys_str_mv | AT yuzhang sustainabledevelopmentinthetourismsectortheimpactofenvironmentalsocialandgovernanceperformanceonoperationalefficiencyamultilevelanalyticalapproach AT hontathuam sustainabledevelopmentinthetourismsectortheimpactofenvironmentalsocialandgovernanceperformanceonoperationalefficiencyamultilevelanalyticalapproach AT ziruizhang sustainabledevelopmentinthetourismsectortheimpactofenvironmentalsocialandgovernanceperformanceonoperationalefficiencyamultilevelanalyticalapproach |