A core aspect of agile governance is effectively managing communications between a government and its citizens. However, doing so during an emergencyparticularly a pandemic-is often complex and challenging. In this article, we examine how various levels of the Chinese government (central, provincial, and municipal) communicated with the public in response to the COVID-19 pandemic. Analyzing government social media posts during the COVID-19 outbreak in Wuhan ("text as data"), we conduct topic modeling analysis and identify four strategies that characterize Chinese governments' responses to a variety of issues at the ground level, which we label instructing information, adjusting information, advocacy, and bolstering. The results show that local government agencies predominantly used the first two strategies, whereas the central government mainly relied on the last two. These strategies explain how various levels of government engaged in agile governance through their communication with citizens, highlight the coordination and control work undertaken by governments at all levels, and demonstrate how these methods shielded the central government from blame for the pandemic.

This paper proposes a new power grid investment prediction model based on the deep restricted Boltzmann machine (DRBM) optimized by the Lion algorithm (LA). Firstly, two factors including transmission and distribution price reform (TDPR) and 5G station construction were comprehensively incorporated into the consideration of influencing factors, and the fuzzy threshold method was used to screen out critical influencing factors. Then, the LA was used to optimize the parameters of the DRBM model to improve the model's prediction accuracy, and the model was trained with the selected influencing factors and investment. Finally, the LA-DRBM model was used to predict the investment of a power grid enterprise, and the final prediction result was obtained by modifying the initial result with the modifying factors. The LA-DRBM model compensates for the deficiency of the single model, and greatly improves the investment prediction accuracy of the power grid. In this study, a power grid enterprise was taken as an example to carry out an empirical analysis to prove the validity of the model, and a comparison with the RBM, support vector machine (SVM), back propagation neural network (BPNN), and regression model was conducted to verify the superiority of the model. The conclusion indicates that the proposed model has a strong generalization ability and good robustness, is able to abstract the combination of low-level features into high-level features, and can improve the efficiency of the model's calculations for investment prediction of power grid enterprises.

The article examines the role of social media in mitigating information asymmetry and coordination problems during COVID-19 epidemic crisis. We use "Sisters-Fight-Epidemic" online volunteering project during the outbreak of COVID-19 in Wuhan, China, as a case to demonstrate how social media plays a role as a mechanism in linking multiple stakeholders and shaping their actions during the epidemic response. We show that social media facilitates the self-organizing processes of volunteers and develops the emergency information networks, therefore enabling a relatively efficient relief responses to the needs of epidemic victims particularly female medical workers. This article also identifies spontaneous online volunteering project as a new form of nonprofit organization and as a new emergent response group that can leverage the strengths of social media in disaster responses to enable effective coordination, initiate advocacy, and improve transparency of relief efforts.

As the key to digital transformation, artificial intelligence is believed to help achieve the goal of government as a platform and the agile development of digital services. Yet we know little about its potential role in local governance, especially the advances that AI-supported services for the public sector in local governance have ventured and the public value they have created. Combining the digital transformation concepts and public value theory, we fill the gap by examining artificial intelligence (AI) deployment in the public sector of a pilot city of digital transformation in China. Using a mixed-method approach, we show how AI configurations facilitate public value creation in the digital era and identify four dimensions of AI deployment in the public sector: data integration, policy innovation, smart application, and collaboration. Our case analysis on these four dimensions demonstrates two roles that AI technology plays in local governance—“AI cage” and “AI colleague.” The former builds the technology infrastructure and platform in each stage of service delivery, regulating the behaviors of frontline workers, while the latter helps frontline workers make decisions, thus improving the agility of public service provision.

In the actual operation process of PPP(Public-Private Partnership) projects, due to inaccurate estimation, lack of accuracy of submitted data and asymmetric information of all parties involved, problems such as asset falsification often occur when assets are formed. In order to solve this problem, we have properly integrated the concept of asset life-cycle management (ALM) into the whole life cycle of PPP projects, and established an ALM-PPP model to analyze the asset management of PPP projects. We select the construction phase of PPP project as the research scope, and take tracking audit as the core to audit the actual value of PPP project, so as to improve the management efficiency of PPP project and ensure the public interest.