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In 1995, the Kobe earthquake occurred in the second largest economic region of Japan, and its economic damages were accounted around 10 trillion yen. This paper presents an empirical investigation of long-run economic effects of the event based on a time-series data. The results indicate that the event had created statistically significant deviations from the pre-earthquake growth path of Kobe. In addition, the comparison with the projected pre-event growth path revealed that the long-run effects have resulted in a steady decline of per capita GRP, while the short-run impacts led to some positive impacts from recovery and reconstruction during the first several years.

This paper presents an in-depth research analysis on the evaluation of the development quality of regional economy through an improved convolutional neural network algorithm, and uses it to design a fuzzy comprehensive evaluation model for the practical process. Based on the measured indices of different variables, a spatial econometric model is constructed and provincial panel data are selected to empirically analyze the impact and spatial spillover effects of financial agglomeration and technological innovation on regional economic quality development from both static and dynamic aspects and to examine the spatial correlation of the factors. A new serial data flow model is adopted, which optimizes the control of data flow in convolutional computation, reduces the percentage of clock cycles used to read memory data, and increases the computational efficiency. At the same time, with dynamic data caching, a convolutional computation can be completed in one clock cycle, reducing the memory capacity required for caching intermediate data. The effectiveness of the evaluation system constructed in this paper is further tested. Most of the indicators have a significant positive or negative impact on the quality level of economic development, and the direction of the impact is consistent with the positive and negative attributes of the indicators in this study, which verifies the validity of the evaluation indicator system constructed in this paper. In summary of the study, effective suggestions are made in terms of human capital investment, reasonable allocation of fiscal expenditure, enhancing regional greening development and improving risk prevention measures.

This chapter presents the results of research on the condition of social entrepreneurship and the potential of its involvement in solving the problems of sustainable development of the Rostov Region. Diagnostics of institutional conditions for implementing social-oriented and environmental business projects were also carried out. The social entrepreneurship sector is identified and determined considering the compliance of economic activities with the federal list, namely: (1) “education,” (2) “activities in the field of health and social services,” (3) “activities in the field of culture, sports, leisure and entertainment,” and (4) “activities in the field of information and communication.” The second group of industries closely related to eco-entrepreneurship and environmental protection is also analyzed separately.

Talents are an important and valuable resource for regional economic development. As of the end of 2021, the total population of Liaoning Province has reached 42.295 million, ranking 14th in China. However, according to the provincial panel data from 2008 to 2018, the dual problems of population loss and the outflow of talent are plaguing the economic development of Liaoning Province. According to the Gray Relation Analysis (GRA), one can construct the talent structure and regional economic index system of Liaoning Province, find out the talent elements that stimulate the regional economic growth of Liaoning Province through the empirical analysis of coupling correlation degree and put forward four suggestions to balance the talent structure and help the balanced development of the regional economy of Liaoning Province.

This study presents an integrated model to estimate the economic impact of tourism in the Central Region of Portugal. In order to accomplish this objective a visitor survey was undertaken and 2,876 responses were collected to measure tourist expenditures. The tourist multipliers (output, household income and employment) were obtained through a regional input–output model. The model developed in this study was used to estimate the different types of economic impacts (direct, indirect and induced) and to identify the segments of greatest economic value to Central Region of Portugal. The results obtained provide useful information for the definition of marketing and development strategies in the region.