AI exposes greenwashing and ethical risks in global tourism supply chains

Artificial intelligence can transform the way tourism companies detect ethical violations and manage sustainability risks across their global supply chains. New research introduces a data-driven system capable of identifying hidden environmental, social, and governance (ESG) threats in real time, while tackling one of the industry's most pressing challenges - greenwashing.

Published in Sustainability, the study "AI-Driven Responsible Supply Chain Management and Ethical Issue Detection in the Tourism Industry" outlines an AI-powered model that integrates big data analytics, network mapping, and natural language processing (NLP) to create a transparent, predictive, and responsive framework for ESG risk management in tourism. The authors argue that as tourism continues to expand its global footprint, it must adopt advanced digital tools to maintain credibility, comply with emerging sustainability standards, and regain stakeholder trust.

Tourism's ethical blind spot: Hidden ESG risks and greenwashing

The tourism industry remains one of the most influential sectors in the world economy, supporting millions of jobs and generating trillions in revenue. Yet, behind its growth lies a web of suppliers, contractors, and intermediaries whose operations often remain invisible. Hong and Kim identify this lack of visibility as a critical weakness that allows serious ESG risks to persist, ranging from labor exploitation and forced work in supply chains to deforestation, excessive emissions, and corrupt business practices.

According to the study, the tourism supply chain's complexity stems from its multi-tiered structure. Beyond hotels and travel agencies, it encompasses food producers, construction contractors, textile suppliers, and logistics firms operating under varied legal systems and cultural conditions. This fragmentation creates fertile ground for unethical behavior and weakens the ability of firms to ensure compliance across all tiers.

Environmental threats such as carbon emissions, waste generation, and biodiversity loss continue to mount due to tourism's reliance on energy-intensive operations and global transportation networks. On the social side, low wages, unsafe work environments, and human rights abuses remain widespread in tourism-dependent economies with limited oversight. Governance failures, including bribery, opaque sourcing, and lack of accountability, further undermine the industry's sustainability claims.

Adding to these direct challenges is the pervasive practice of greenwashing, where companies exaggerate or falsify their environmental achievements to mislead consumers and investors. The authors describe this as one of the most harmful obstacles to genuine sustainability, eroding public trust and placing truly responsible operators at a disadvantage. The study's AI system directly addresses this issue by cross-verifying public claims with independent data sources to determine their accuracy.

AI-powered detection: Mapping, monitoring, and predicting supply chain risks

The framework introduces a multi-layered system designed to detect, evaluate, and predict ethical risks in real time. Unlike traditional audits, which are static and infrequent, the AI model continuously analyzes information drawn from internal records, financial disclosures, news articles, social media activity, and public databases. By integrating this data, the system delivers a live, holistic view of how supply chains behave and where violations may be emerging.

The framework operates through five analytical modules: network analysis, anomaly detection, natural language processing, predictive modeling, and decision support. Together, these components form a dynamic surveillance mechanism for ESG compliance.

Network analysis creates visual maps of interconnected suppliers, exposing dependencies and identifying key nodes whose disruption could cascade through the supply chain. By analyzing relationships among suppliers, the system highlights weak points and single points of failure.

Anomaly detection applies machine learning models such as Isolation Forests and One-Class Support Vector Machines to flag abnormal patterns, sudden spikes in waste production, irregular labor reports, or suspicious financial movements. These indicators provide early warnings of potential misconduct or systemic inefficiencies before they escalate.

Natural language processing plays a central role in identifying ethical violations and detecting greenwashing. Using advanced transformer-based models similar to BERT and GPT, the system scans corporate sustainability reports, marketing materials, and social media posts to verify whether environmental claims match external evidence from NGOs, regulators, and news outlets. The algorithm assigns each claim a "credibility score," helping managers and investors separate genuine sustainability performance from deceptive communication.

Predictive modeling further extends this capacity by forecasting future ESG risks using historical data and emerging patterns. Algorithms like Long Short-Term Memory (LSTM) networks and XGBoost anticipate which suppliers are most likely to face future ethical breaches, financial instability, or reputational crises. The system's simulations, tested with real data from hotel-related news and social media sources, demonstrated an impressive 0.91 accuracy rate in distinguishing authentic from misleading sustainability claims.

Finally, a decision-support module translates analytical outputs into actionable insights. By aggregating risk scores and ranking suppliers according to vulnerability, it allows tourism companies to prioritize interventions, schedule targeted audits, or restructure supplier relationships based on quantifiable data.

From reactive compliance to proactive governance

Traditional ESG monitoring relies heavily on self-reported questionnaires and third-party certifications, both of which can be manipulated or outdated by the time issues surface. Hong and Kim argue that the tourism sector must replace this backward-looking model with continuous, AI-driven oversight that can identify and respond to risks as they develop.

The authors highlight that the new framework is not only technically feasible but also scalable across industries that rely on complex supply chains. The system's ability to integrate diverse data sources and apply multiple AI techniques ensures that tourism enterprises can adapt to regulatory requirements such as the European Union's Corporate Sustainability Reporting Directive. By using automation, firms can reduce monitoring latency by up to 60% and improve anomaly detection accuracy by up to 40%, compared to conventional audits.

In addition to corporate benefits, the implications extend to policymakers and consumers. For governments, AI-based monitoring systems offer a tool to strengthen ESG compliance and detect misleading marketing, enhancing consumer protection and market integrity. For investors, the model provides a quantifiable measure of ESG reliability, supporting informed decision-making in sustainable finance.

The researchers also acknowledge the challenges of implementation. Data availability, privacy compliance, and the uneven digital maturity of small and medium-sized enterprises remain significant barriers. Ethical considerations such as algorithmic bias and accountability in automated decision-making must also be addressed through robust governance protocols. However, the authors stress that the system incorporates privacy safeguards, anonymization procedures, and transparent data handling to mitigate these concerns.