According to the seventh national census conducted in 2020, the number of megacities in China has reached 21, and the GDP of these cities accounted for 32.9% of the national GDP. In the next few years, several cities will join the ranks of megacities or megalopolis, which means that their contributions will probably reach 40%, or more than half of the national GDP.

Emphasis placed on national level

With huge population volumes, resources, information, and traffic, megacities are important municipal hubs in China and throughout the world. Once afflicted with sudden risks, megalopolises may face huge challenges, triggering chain reactions on regional and even national and global scales.

In the Communist Party of China (CPC) Central Committee’s proposals for formulating the “14th Five-Year Plan (2021-2025) for National Economic and Social Development and the Long-Range Objectives Through the Year 2035,” it is mentioned that risk prevention and control of urban governance should be strengthened. It also proposes building “sponge cities” and “resilient cities” to reduce the impacts and uncertainties brought by risks.

In the context of newly emerged technologies, cities emphasize the importance of applying modern information technology in dealing with all kinds of risks, and many cities have started exploration of smart city and big data governance. Application of big data in risk prevention and control is an important part of this exploration. In recent years, big data has indeed played a positive role in the risk management of different cities.

Root causes of data failures

However, although big data was pinned upon high hopes, its applications have become obscure and it has not played its due role. Why does it fail in risk management? Preliminary explanations fall into three categories.

The first category begins with the concept of governance. The development of big data has provided favorable conditions for the modernization of governance. However, many cities still lack the concept of digital intelligence, only collecting data year after year in the traditional sense, by backward means and with repetitive work. Some cities are keen on using a “gridding method,” and large amounts of data become dead numbers on the platform after being collected. Some cites, though they have have set up big data platforms, are not capable of truly utilizing them. Once risks occur, these data sets are rejected and left in a disused state.

The next area for focus is data structure. To truly serve risk prevention and regulation of megacities, big data must be structured rather than scattered. Some megacities possess big data, but not in a holistic way, with the data distributed through different institutions and dispersed between sectors. There is no united, large data platform which can well serve risk prevention and control. As a result, big data is merely applied to separate links of different risks in an individual way, rather than applied throughout the whole process and in all cases.

The third research focus is governance structure. Governance structure and big data do not integrate well, which is essentially an incompatibility between administrative power and data power. In the extremely heavy flood that struck Zhengzhou City, Henan Province, on July 20, 2021, the meteorological departments issued a number of “red alerts” to warn against heavy rainstorm, and the news that “Metro Line 4’s tunnels are completely submerged in water” frequently appeared online as headlines. However, staff who were working at the frontlines failed to take necessary measures immediately. This liability should not only be attributed to the staff’s failure while on duty, but also to poor connections between the whole risk management system and big data. The consequence was that even though employees were aware of the importance of big data, they did not know how to give early warnings by means of this data, let alone how to make data-based decisions.

Given the fact that data within megacities is usually distributed in different fields and sectors, and possessed by different departments, we commonly see “information silos” and “information blind-spots.” As a result, data platform obstacles are difficult to breach. Once the risks occur, it is thus difficult to conduct effective data calculations, which is not conducive to risk prevention and control. For the governments of megacities, it is therefore important to solve the problem of data connectivity in a timely fashion and establish platforms with shared information. The governments should also devolve appropriate power to risk management and urban governance decision-makers who work on different procedures, and clarify their rights and responsibilities.

Wu Xiaolin is a research fellow from the Research Center of China’s Development and Government Management at Nankai University.