China taps AI to cut emissions and rethink tourism sustainability

In an era marked by climate urgency and digital transformation, artificial intelligence (AI) is reshaping the path toward sustainability in tourism. A new academic study explores how AI can serve as both a strategic tool and a catalyst for environmentally responsible tourism growth. Amid increasing scrutiny on the sector’s carbon footprint, the research presents empirical evidence that AI significantly contributes to improving Tourism Carbon Efficiency (TCE).

The study, titled “Artificial Intelligence as a Catalyst for Sustainable Tourism: A Case Study from China” by Dandan Song and Hongwen Chen, published in Systems, examines AI’s role in promoting sustainable tourism practices across China. It draws attention to how intelligent technologies, backed by supportive government frameworks, enhance TCE by optimizing resource usage, mitigating emissions, and improving policy responsiveness.

How Is AI Transforming Tourism Carbon Efficiency?

The study focuses on a concept called Tourism Carbon Efficiency, the ratio of tourism-generated economic output to its carbon emissions. China's tourism sector has experienced significant economic expansion, but that growth has also brought rising environmental costs. The study highlights how AI systems can mitigate this imbalance.

AI contributes to higher TCE by enabling data-driven decision-making, reducing waste, and enhancing operational efficiency. Smart transportation systems, predictive analytics for visitor flow, and automated resource management reduce energy use across accommodation, transport, and attractions. These measures collectively minimize emissions while maintaining or improving service quality.

In tourism zones where AI-powered platforms have been implemented, local authorities report improved monitoring of energy-intensive activities and more agile responses to carbon-intensive operations. The study positions AI as a structural enabler in aligning China’s tourism development with its broader decarbonization commitments.

What Institutional Strategies Support AI’s Role in Sustainable Tourism?

The study emphasizes that AI’s effectiveness is not solely a technical matter - it depends on institutional readiness and strategic integration. In China, the government has played a formative role in embedding AI into tourism governance frameworks, particularly in its efforts to achieve carbon neutrality goals.

Policy support for AI integration includes the establishment of smart tourism demonstration areas, fiscal incentives for green tech investment, and the promotion of digital infrastructure in rural tourism zones. These initiatives not only promote sustainability but also distribute AI benefits more evenly across regions.

Government-backed platforms aggregate multisource data, environmental, economic, and behavioral, into integrated dashboards that inform emissions management strategies. The result is a measurable improvement in the tourism sector’s environmental performance, especially in ecologically fragile destinations.

How Does AI Enable Systemic Carbon Efficiency?

Beyond local optimizations, the study finds that AI’s impact on TCE is most pronounced when applied to system-level tourism planning. Through machine learning and big data analytics, AI tools forecast demand peaks, assess carbon impacts of visitor activities, and simulate alternative routing to distribute environmental load more evenly.

AI-driven decision support systems have enabled tourism managers to introduce low-carbon itineraries and real-time emissions tracking, giving both authorities and tourists actionable information. AI recommendation engines also nudge tourists toward off-peak travel times and eco-friendly accommodations - tactics that reduce crowding and associated emissions.

The research underscores that improving TCE is not about suppressing tourism demand, but about reengineering how that demand is met - smarter logistics, better-informed consumers, and adaptive resource use.

What Challenges Persist in AI-Driven Carbon Reduction?

Despite promising results, the study highlights several challenges that temper the effectiveness of AI in driving TCE improvements.

First, data fragmentation and quality issues undermine AI’s accuracy in carbon measurement. Inconsistent metrics and incomplete emissions data limit the ability to track performance at scale. Second, infrastructural inequality creates barriers in deploying AI uniformly across urban and rural regions. Smaller operators often lack access to AI tools and expertise, which risks concentrating gains in well-funded destinations.

Moreover, ethical concerns arise in the use of surveillance-based technologies, such as facial recognition or location tracking, to optimize operations. These methods may increase efficiency but raise questions about privacy and consent.

The authors call for the development of inclusive, ethically grounded AI frameworks that protect rights while maximizing carbon efficiency benefits.

How Can Other Countries Replicate China’s AI-TCE Model?

China’s case offers a roadmap, but the study cautions against a one-size-fits-all approach. Key strategies for replicability include:

• Establishing standardized carbon accounting systems across the tourism sector to ensure reliable AI modeling.
• Expanding public-private collaborations to enable SME access to AI and emissions management tools.
• Investing in data infrastructure and interoperability, allowing seamless integration across tourism subsectors.
• Embedding TCE metrics in national sustainability goals to guide long-term AI investment decisions.

These strategies help other nations localize the China model based on their technological readiness, regulatory environments, and tourism structures.