ISSN 2512-6660 (Online) ISSN 1478-9906 (Print)
CN 10-1192/N
CN 10-1192/N
Driven by different promotion pressures, different decisions made by government officials may change the development path of cities and directly affect the ability to cope with crises, thus playing an all-encompassing and sustained role in urban economic resilience (UER). Considering that the COVID-19 pandemic that occurred at the end of 2019 is a large external shock, which may cause a large disturbance to economic resilience, this article tests the impact of official promotion pressure (OPP) on UER using data from 265 cities in China from 2004 to 2019. This paper also explores the role of the “National Civilized City” (NCC) selection mechanism in the process. The findings indicate a positive correlation and spatial spillover effect between OPP and UER. Moreover, the impact of both civilization status and civilization intensity on OPP is negative, which means that obtaining the title weakens OPP, and the positive effect on UER is weakened. And this effect becomes increasingly obvious with the increase in the duration of the title of NCC. Furthermore, the heterogeneity analysis yields rich findings, which provide new perspectives for the policy recommendations in this paper.
Interval-valued functional principal component analysis (IFPCA) is a comprehensive evaluation method that can effectively handle continuous high-frequency data. However, most existing IFPCA methods assume that samples within intervals follow a uniform distribution, which may overlook the actual distribution of samples within intervals. This assumption may result in the omission of key features in samples, thereby affecting the accuracy of analyses. To address this issue, this study considers the internal distributional information of intervals using means and standard deviations to reflect the centralized location and discrete changes of intervals under the general distribution. The current time-varying distance function does not fully utilize this distributional information, necessitating an extension to accommodate the general distribution. Building on this, an IFPCA based on the time-varying distance function under the general distribution is proposed. This new IFPCA better utilizes the known internal information within intervals, uncovering intrinsic features of data. Simulation studies demonstrate the effectiveness of the IFPCA under the general distribution. An empirical application further confirms that the new IFPCA is superior to existing IFPCA methods.
