A new track for government digital governance: How power grids generate user "health codes"

At the end of 2018, the monitoring work of "scattered and messy pollution" sites assisted by electricity load data was effective. Therefore, the local government further promoted the inclusion of water data in 2019 and handed it over to the data analysis team of the power department to coordinate the data modeling of both parties. Afterwards, the Guangzhou Power Supply Bureau signed data confidentiality agreements with 37 local water supply companies.

Although the water supply enterprises in the region are relatively small, they also face the problem of cross-border data integration. The government's administrative coordination ability has partially addressed this problem. "If it weren't for the government departments taking the lead, it would be basically difficult for the power sector to use water data." Industry insiders said.

However, in this case, telecommunications data was never integrated.

The issue of water data access rights has been resolved, but the new problem is the low quality of the data. Taking Guangzhou as an example, there are 37 water supply companies, including both state-owned enterprises and private water supply companies, and there are obvious differences in management models and reform pace among them.

It is understood that the meter reading cycle of some water supply companies is 2-3 months, and some areas have not yet completed the "one household, one meter" transformation. At the village and town level, there is even a situation of "one village sharing one meter". In the absence of unified management standards, the frequency and accuracy of water data collection are insufficient, making it difficult to achieve the established screening effect in some areas.

In addition, the industrial and information technology and environmental protection departments of other cities expressed their desire to introduce Guangzhou's "scattered, disorderly and polluted" data governance model, but the relevant data supply companies in various places failed to reach a consensus on this, and ultimately the model migration could not be achieved.

In resolving the contradictions of cross-industry data barriers, the unilateral ideas of industry authorities cannot promote "collaborative governance" based on data integration, and the coordination of relevant government departments is required. In promoting the construction of cross-border data platforms, the "connection" role that the government can play is irreplaceable.

In April, the "Opinions of the Central Committee of the Communist Party of China and the State Council on Building a More Perfect System and Mechanism for Market-Based Allocation of Factors" was issued, which mentioned accelerating the market-oriented reform of factor prices, including data, and fully reflecting the value of factors such as technology, knowledge, management, and data.

This is not enough. The level of intelligent transformation of relevant physical industries and the degree of data standardization are the prerequisites and objective constraints that affect whether "beautiful ideas can be realized in reality". This requires the government and industry authorities to make clear provisions in the standardization and regularization of data collection to meet the effective connection of cross-border data applications.

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“Big data is not Thor’s hammer”

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Of course, relying solely on basic data such as electricity to accurately locate and assist in searching and screening cannot be foolproof. This is not only related to the richness of the data dimensions, but also involves the limitations of big data applications themselves.

For example, big data cannot predict systemic risks and unexpected impacts of force majeure factors; the setting of data models is related to the depth of cognition. When there are blind spots in cognition and insufficient information is available, the modeling optimization methods are limited; in addition, problems such as data distortion, easy manipulability, algorithm collusion, and inability to reflect causal relationships may also be encountered.

"Big data is not Thor's hammer," Yuan Chao reminded. "It is impossible to be 100% accurate. It is just a preliminary suggestion for screening."

It is understood that in the case of "scattered and disorderly pollution" treatment, some "scattered and disorderly pollution" sites that are not on the power data inspection list were indeed found. "It is normal to have deviations, and we also open channels to add other information to the system." Yuan Chao said.

Just as the health code has been used in epidemic screening, "misjudgments" have occurred due to differences in standards across the country, and there has been helplessness in the face of asymptomatic infections.

"The health code can only tell you that they have done some preliminary screening. In fact, there is no way to screen asymptomatic infections. This cannot be said to be a loophole in the health code, but only a blind spot in the screening of the health code." Zhang Wenhong, director of the Department of Infectious Diseases at Huashan Hospital Affiliated to Fudan University, mentioned his opinion on this in a public interview.

Zhang Wenhong added, "The health code can cover the discovery of most cases. What if we miss a small number of asymptomatic infections? We need to set up some other mechanisms." In his view, nucleic acid monitoring is one of the technical means to screen asymptomatic virus carriers.

The governance of "scattered and disorderly pollution" has also designed another "offline" supervision mechanism in addition to big data screening, that is, through the environmental protection information collection function on WeChat, grassroots personnel will conduct on-site entry of "scattered and disorderly pollution" places outside the list to achieve the aggregation of information from different channels and places.

Whether it is the health code or the "power data portrait", it is more of an "assist" for government governance. On the basis of the top-down government governance framework, it supplements the new tool of bottom-up collaborative governance.