AI and big data boost cities’ carbon unlocking efficiency

The global race to build data centers, expand digital networks, and harness artificial intelligence (AI) is transforming modern economies. Having said that, cities remain under mounting pressure to cut emissions and break long-standing reliance on carbon-intensive growth models. The intersection of these two transitions is fast becoming a defining policy challenge.

In the study “Can Big Data Policy Promote Urban Carbon Unlocking Efficiency?” published in Sustainability, researchers analyze whether national big data pilot initiatives, using China as an example, can improve cities’ capacity to escape carbon lock-in and strengthen low-carbon development pathways.

Digital infrastructure as a catalyst for industrial restructuring

In many economies, especially those undergoing rapid urbanization or industrialization, carbon emissions are closely tied to heavy manufacturing, energy-intensive production, and outdated supply chains. Industrial composition becomes a structural barrier to decarbonization.

The study finds that structured big data policy can facilitate industrial structure rationalization. By improving information flow, optimizing resource allocation, and enabling digital upgrading of traditional sectors, pilot zones help shift economic activity toward higher-value, knowledge-intensive, and service-oriented industries with lower carbon footprints.

Data-driven management reduces inefficiencies in production processes, enhances supply chain coordination, and lowers energy waste. At the same time, digital ecosystems attract innovative firms and green industries. This combination weakens dependence on high-carbon sectors and strengthens economic resilience.

Although China serves as the empirical example, the broader implication is that digital policy can act as an indirect climate tool by reshaping industrial incentives and accelerating structural modernization.

Optimizing energy systems via data and artificial intelligence

A second key mechanism involves energy structure optimization. Energy systems sit at the core of carbon lock-in. Fossil fuel infrastructure, grid rigidity, and inefficient dispatch mechanisms often slow the transition toward renewable energy.

The study shows that big data pilot programs strengthen the integration of clean energy through intelligent monitoring and real-time management systems. Data analytics improve forecasting, enhance coordination between supply and demand, and reduce unnecessary consumption. Artificial intelligence applications support more efficient energy dispatch and carbon market operations.

In the Chinese context, pilot zones promoted cleaner energy integration and refined management of high-energy-consuming sectors. More broadly, the findings suggest that digital platforms and AI-enabled energy management can increase renewable energy utilization rates and reduce carbon intensity without sacrificing growth.

The research highlights that policy design matters. Incentives tied to clean energy adoption, support for smart grid infrastructure, and integration of carbon trading systems into digital platforms amplify the impact. Digital policy becomes effective climate policy when embedded within supportive institutional frameworks.

Digital innovation and AI as drivers of low-carbon governance

The study further identifies digital technological innovation and AI adoption as core drivers of carbon unlocking. Pilot zones increase patent activity in digital technologies and stimulate research and development related to the digital economy.

Digital innovation enhances manufacturing precision, reduces material waste, and accelerates green technology deployment. AI systems optimize industrial processes, support environmental monitoring, and improve investment decision-making. In carbon markets, AI tools enhance transparency and pricing efficiency.

The researchers measure AI penetration by analyzing corporate disclosures and aggregating data at the city level. Results show that pilot zones significantly increase AI adoption, which in turn strengthens carbon unlocking efficiency.

The broader message extends beyond China. As economies digitize, AI and big data systems can serve as governance infrastructure for climate management. When properly directed, they support pollution tracking, predictive modeling, efficient resource allocation, and policy evaluation.

Uneven effects and the importance of regional context

The study also finds that policy impacts vary across regions. In China, cross-regional pilot zones with strong coordination mechanisms and integrated industrial ecosystems show the strongest gains. Areas already less dependent on heavy industry benefit more rapidly from digital integration. Highly industrialized regions face greater structural inertia.

These findings reflect a universal challenge. Digital transformation does not automatically produce decarbonization. Its climate benefits depend on local industrial composition, governance capacity, and policy coordination. Countries seeking to replicate such strategies must tailor digital policies to regional economic structures.

Another notable finding is the presence of spatial spillover effects. Improvements in carbon unlocking efficiency in pilot cities positively influence neighboring areas. Knowledge diffusion, shared infrastructure, and policy emulation extend the impact beyond designated zones. This suggests that digital climate strategies can generate regional multiplier effects when designed within networked governance systems.