Coupling coordination analysis with data-driven technology for disaster–economy–ecology system: an empirical study in China

Abstract

Nowadays, frequent meteorological disasters that cause huge economic losses and ecological damages have swept the world. Thus, research investigates how to overcome the adverse impacts of storm debris flow by exploring sustainable interaction among disaster, economy, and ecology. To achieve this goal, the study analyzes coupling coordination for disaster-economy-ecology system through data-driven technology named Scrapy engine. To be specific, a comprehensive index system of disaster-economy-ecology is established. Accordingly, a projection pursuit method is used to reduce the dimensions of data involved in the system. Then, an integrated weighting method of interval-valued hesitant fuzzy entropy and maximum deviation of weight is utilized. For further analysis of the internal laws in disaster-economy-ecology system, a coupling coordination model based on order preference by similarity to ideal solution is proposed. Moreover, a back-propagation artificial neural network is designed to identify the key influencing factors in disaster-economy-ecology system. Finally, an empirical study is carried out using the panel data related to storm debris flow of 31 provincial areas in China within 11 years to illustrate the study. The study results show that the overall sustainable development of disaster, economy, and ecology in China does not achieve an ideal status. Various measures based on local conditions are required to improve the imbalanced development of disaster-economy-ecology system in different areas of China. At last, strategic suggestions for sustainable development of disaster-economy-ecology system are provided.

Evaluation of City–Industry Integration Development and Regional Differences under the New Urbanization: A Case Study of Sichuan

“…To fully reflect the development of city-industry integration, social services, ecology and environment, and science and technology were integrated into the discussion of city-industry integration. We combined web crawler with literature research to build an index system based on multi-source data [49]. Because AHP cannot improve consistency after a given judgment matrix, the PSO algorithm was introduced to optimize its weights [57], and the EM model was added to obtain real and credible weights.…”

“…This paper used crawler technology to access the index database named after urban development in the regional decision-making template for the China Economic and Social Big Data Research Platform (https://data.cnki.net (accessed on 24 January 2022)) [49]. A test tool named Selenium in Python was used to simulate launching the Chrome browser, opening the web page, and clicking to select [50], as shown in Figure 2.…”