Abstract
In the early development process, my country destroyed the development of the natural environment and the allocation of natural resources in pursuit of the speed of economic development. With the acceleration of development, my country’s economic construction and social development have achieved good results. To achieve sustainable economic and ecological development in the future, green and high-quality development must be achieved to meet the content of the development strategy of realizing the resource-based and economical society in our country. In the long-term development process, many regions rely on resources to create a lot of opportunities for the development of the region, but with the high consumption of resources, many resources are non-renewable or the regeneration process is very slow, resulting in many regions entering the resources. During the period of shortage, it is necessary to advocate green development. This article analyzes the green economic benefits achieved by the innovative development of a certain province in my country by studying the relevant knowledge of machine learning and some important issues in the theoretical development of cloud computing, which can provide a certain reference for the development of economic efficiency theory and provide help for people to formulate green development strategies for cities and regions.
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23 November 2021
This article has been retracted. Please see the Retraction Notice for more detail: https://doi.org/10.1007/s12517-021-09070-3
28 September 2021
An Editorial Expression of Concern to this paper has been published: https://doi.org/10.1007/s12517-021-08471-8
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This article is part of the Topical Collection on Environment and Low Carbon Transportation
This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s12517-021-09070-3
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Jin, Z. RETRACTED ARTICLE: Green city economic efficiency based on cloud computing and machine learning. Arab J Geosci 14, 1007 (2021). https://doi.org/10.1007/s12517-021-07204-1
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DOI: https://doi.org/10.1007/s12517-021-07204-1