This essay examines the policy response of the federal and regional governments in federations to the COVID-19 crisis. We theorize that the COVID-19 policy response in federations is an outcome of strategic interaction among the federal and regional incumbents in the shadow of their varying accountability for health and the repercussions from the disruptive consequences of public health measures. Using the data from the COVID-19 Public Health Protective Policy Index Project, we study how the variables suggested by our theory correlate with the overall stringency of public health measures in federations as well as the contribution of the federal government to the making of these policies. Our results suggest that the public health measures taken in federations are at least as stringent as those in non-federations, and there is a cluster of federations on which a bulk of crisis policy making is carried by subnational governments. We find that the contribution of the federal government is, on average, higher in parliamentary systems; it appears to decline with the proximity of the next election in presidential republics, and to increase with the fragmentation of the legislative party system in parliamentary systems. Our analysis also suggests that when the federal government carries a significant share of responsibility for healthcare provision, it also tends to play a higher role in taking non-medical steps in response to the pandemic.

At present, the traditional control strategy of heating systems is still unable to achieve building heating on demand, which enhances the energy consumption of heating and affects the thermal comfort of buildings. Therefore, this study puts forward a novel data-driven MPC for building thermal inlet, which allows the optimal operation of the district heating system and has been verified by simulation with three public buildings. In this method, the indoor temperature at the next moment reaches the temperature set value by changing the current flow rate. First, based on the energy consumption monitoring platform and the measured data of the buildings, the building indoor temperature prediction model at the next moment is established by using long short-term memory (LSTM). Compared with subspace model identification (SMI), LSTM has higher prediction accuracy, and the R² was about 0.9 in three buildings. Second, the particle generated by particle swarm optimization, which represents the flow variation, is input to the trained LSTM to predict the indoor temperature. By minimizing the objective function, the optimal flow change at the current time can be calculated. The results showed that the MPC based on a data-driven model can adjust the flow rate in time to maintain a stable indoor temperature with ±0.5 °C error. In addition, when the temperature setting needs to be changed, the indoor temperature can reach the new set value in 3 h, which outperforms the PID control. The method proposed in this paper can greatly reduce the influence of regulation lag by adjusting the flow in advance.