A New Machine Learning Algorithm for Regional Low-Carbon Economic Development Analysis Based on Data Mining

• Published in: Journal of function spaces Vol. 2022; pp. 1 - 8
• Main Author: Liu, Xinlei
• Format: Journal Article
• Language: English
• Published: New York Hindawi 25.08.2022; John Wiley & Sons, Inc; Wiley
• Subjects: Algorithms; Alternative energy sources; Analysis; Carbon; China; Climate change; Climatic changes; Computer terminals; Computers; Continuous improvement; Data mining; Economic analysis; Economic aspects; Economic development; Economic growth; Economic models; Economics; Emissions; Energy consumption; Geographical distribution; Geographical locations; Greenhouse effect; Greenhouse gases; Human influences; Industrialized nations; Information processing; Information services; Information services industry; Information technology; Interdisciplinary subjects; Learning algorithms; Location based services; Machine learning; Modal choice; Natural resources; Pollution; Regional development; Supply and demand; Technology; Urbanization; China
• DOI: 10.1155/2022/5692666
• ISSN: 2314-8896

The development of information technology such as the continuous improvement of mobile Internet infrastructure and the performance of computers has made it easy to process and share information. The huge market demand for location-based information services provides huge impetus to the generation and development of mobile terminal positioning technology. Generally speaking, the main causes of climate change can be summarized into two categories: natural climate fluctuations and the impact of human activities which is a major measure taken by China to actively respond to climate change. This is a successful approach to actively explore the rapid development of China’s industrialization and urbanization, which not only develops the economy and improves people’s livelihood but also responds to climate change and reduces carbon intensity. Firstly, this paper mainly is aimed at the connotation of regional low-carbon economic development mode, studying the basic mode of regional low-carbon economic development, and analyzing the characteristics and applicable conditions of each mode. Secondly, based on the machine learning algorithm of data mining, the main mode selection of regional low-carbon economic development is discussed. Thirdly and finally, when choosing the regional low-carbon economic development mode, comprehensive consideration should be given to the economic development basis, energy structure, resource characteristics, industrial status, development mode, geographic location, and other factors. This paper studies the basic conditions and applicable conditions of regional economic development models. The conclusion shows that from the perspective of regional economic evolution, low-carbon economy can be regarded as the decarbonization process of economic development. It is an economic form combining its own characteristics and an inevitable requirement for the transformation of regional economy from other economic models to low-carbon economic models. And other factors of Selection of regional economic development foundation, energy structure, resource characteristics, industrial status, development mode, geographical location, were also discussed.