Digital technologies could cut global transport emissions by 34% by 2050

AI emerges as the most advanced of the three, driving breakthroughs in traffic prediction, route optimization, and fleet coordination. Machine learning algorithms now underpin systems that forecast congestion, adapt signal timings, and reroute vehicles dynamically to cut idle times. In logistics, AI-powered predictive analytics allow for energy-efficient scheduling of deliveries and the reduction of empty runs.

In a recent analysis of emerging technologies shaping the global mobility landscape, researchers have called for a systemic overhaul of transport infrastructure guided by artificial intelligence (AI), the Internet of Things (IoT), and blockchain. Their work published in the journal Future Transportation underscores that sustainability in transportation now depends on the seamless fusion of these digital tools to reduce emissions, optimize logistics, and ensure equitable mobility access.

The study, titled “Digitalization in Sustainable Transportation Operations: A Systematic Review of AI, IoT, and Blockchain Applications for Future Mobility,” presents a comprehensive evaluation of 104 peer-reviewed studies that together chart how data-driven systems are transforming urban transport, supply chains, and traffic management.

Rethinking mobility: From smart traffic to sustainable logistics

The authors identify three key technological pillars defining future mobility, AI for intelligent decision-making, IoT for connected infrastructure, and blockchain for secure data governance. Their findings reveal how these systems are converging to tackle long-standing inefficiencies in urban and intercity transport.

AI emerges as the most advanced of the three, driving breakthroughs in traffic prediction, route optimization, and fleet coordination. Machine learning algorithms now underpin systems that forecast congestion, adapt signal timings, and reroute vehicles dynamically to cut idle times. In logistics, AI-powered predictive analytics allow for energy-efficient scheduling of deliveries and the reduction of empty runs.

IoT’s role, though equally transformative, faces hurdles of interoperability and standardization. The study points out that connected sensors embedded in roads, vehicles, and infrastructure form the backbone of smart transport ecosystems, but fragmented data formats and lack of common standards limit scalability. Still, IoT-driven monitoring has already proven effective in reducing congestion and emissions in cities such as Singapore and Barcelona, where adaptive traffic systems process live data from thousands of sensors.

Blockchain, though the least mature, introduces new possibilities for transparency and trust across decentralized transport networks. It supports verifiable data sharing among multiple actors, municipal authorities, logistics companies, and energy suppliers, reducing administrative friction and fraud. Smart contracts enable automated tolling, carbon credit trading, and supply-chain traceability, aligning digital infrastructure with broader sustainability goals.

How technology lowers emissions and costs

The study’s synthesis of case studies and cited reports shows that digitalization is not only modernizing transport management but also measurably reducing carbon footprints. AI-driven optimization of public transport schedules has cut average trip times and fuel consumption in pilot cities. IoT-based predictive maintenance systems in electric bus fleets are extending vehicle lifespans and minimizing downtime. Blockchain’s ability to eliminate paper-based documentation in shipping logistics translates into a near-total reduction of administrative emissions, over 99 percent, according to referenced analyses.

The researchers note that the combined application of AI, IoT, and blockchain could reduce global transport-related greenhouse gas emissions by up to 34 percent by 2050. IoT alone, they report, holds the potential for gigaton-scale carbon reductions by 2030, if integrated effectively with AI optimization and clean energy policies. Beyond environmental benefits, the technologies promise cost savings through automation and efficiency gains, creating the foundation for sustainable transport economies.

However, the paper warns against over-reliance on individual technologies without system-wide coordination. While AI can optimize logistics, it cannot guarantee transparency without blockchain-based audit trails. Similarly, IoT sensors generate vast datasets, but without robust AI interpretation, such data remain underutilized. The authors argue for an ecosystem approach, an interlinked digital mobility framework where each component reinforces the others.

The road ahead: Bridging technology, policy and equity

Despite their transformative promise, the authors caution that the digitalization of transport must be guided by inclusivity and sound governance. Their systematic review finds that much of the existing research focuses on technical efficiency rather than social equity or regulatory alignment. Without frameworks to ensure fair data access and citizen participation, digital transport systems risk deepening existing inequalities.

To close this gap, the researchers propose a research roadmap centered on interoperability standards, privacy-preserving data protocols, and ethical design principles. They call for stronger collaborations between technologists, policymakers, and social scientists to align digital mobility solutions with sustainability targets and human well-being. The authors also stress the need for city-scale pilot projects that measure real-world emission reductions and test the integration of AI, IoT, and blockchain under live conditions.

Another major recommendation is the establishment of transparent data-sharing agreements among stakeholders. This would allow cities to move from fragmented, pilot-based deployments to holistic digital mobility ecosystems. Such integration could help countries meet climate commitments while enhancing public safety, traffic flow, and accessibility for marginalized groups.