From waste to Watts: Causal evidence on AI-enabled waste-to-energy and financial performance in tourism and hospitality using an empirics-first investigation

This study examines whether artificial intelligence enabled waste to energy adoption improves financial performance in tourism, travel, and hospitality firms and identifies the mechanisms and conditions shaping these effects. Using an empirics first mixed methods design, we combine interviews with 41 managers and a firm year panel of United Kingdom listed tourism, travel, and hospitality firms from 2015 to 2025. Difference in differences models with firm and year fixed effects show that adoption is associated with significantly higher financial performance, particularly at greater levels of adoption intensity. The findings indicate that energy cost savings and operational efficiency partially explain these gains. Performance benefits are also stronger in firms with higher baseline waste intensity and during periods of elevated energy prices. The study demonstrates that AI enabled waste to energy functions as an operational capability that can generate measurable economic value when integrated into organisational routines and governance systems.

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Publication Details

Journal
Tourism Management
Published
2026-08-26
DOI
https://doi.org/10.1016/j.tourman.2026.105517
Primary Topic
Food Waste Reduction and Sustainability
Type
article
Field-Weighted Citation Impact
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article

From waste to Watts: Causal evidence on AI-enabled waste-to-energy and financial performance in tourism and hospitality using an empirics-first investigation

Gomaa Agag
Tourism Management
Food Waste Reduction and Sustainability
article

From waste to Watts: Causal evidence on AI-enabled waste-to-energy and financial performance in tourism and hospitality using an empirics-first investigation

Gomaa Agag
article en

Abstract

This study examines whether artificial intelligence enabled waste to energy adoption improves financial performance in tourism, travel, and hospitality firms and identifies the mechanisms and conditions shaping these effects. Using an empirics first mixed methods design, we combine interviews with 41 managers and a firm year panel of United Kingdom listed tourism, travel, and hospitality firms from 2015 to 2025. Difference in differences models with firm and year fixed effects show that adoption is associated with significantly higher financial performance, particularly at greater levels of adoption intensity. The findings indicate that energy cost savings and operational efficiency partially explain these gains. Performance benefits are also stronger in firms with higher baseline waste intensity and during periods of elevated energy prices. The study demonstrates that AI enabled waste to energy functions as an operational capability that can generate measurable economic value when integrated into organisational routines and governance systems.

Tourism ManagementVol. 119
Nottingham Trent University (GB)
Affordable and clean energy
Openalex Percentile: Top 13%
Food Waste Reduction and Sustainability
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