RETRACTED ARTICLE: Impact of PM2.5 environmental regulation based on big data for green technology development
China has received widespread attention in recent years due to the severe PM2.5 problem. Although paying close attention to the government, the government felt immense pressure to adopt a range of corrective measures and policies, but the impact of these measures and policies was not clear and citiz...
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Veröffentlicht in: | Arabian journal of geosciences 2021, Vol.14 (7), Article 649 |
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Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | China has received widespread attention in recent years due to the severe PM2.5 problem. Although paying close attention to the government, the government felt immense pressure to adopt a range of corrective measures and policies, but the impact of these measures and policies was not clear and citizens were not happy with the governance status of PM2.5. The biggest issue with PM2.5 management is the lack of environmental law enforcement. Many emerging technologies have arisen as a result of economic growth, and these technologies play a major role in gathering information and processing information and enhancing the quality of information. Therefore, we analyzes and explores the emerging ideas and approaches of these technologies and assesses the evolution of these technologies. It is obvious that the combination of big data technology and industrial green manufacturing technology is slow, and it is necessary to combine industrial green manufacturing enterprises and big data technology with artificial intelligence. In order to improve the current severe environmental problems, we have discussed technical advances and improved business efficiency such as development trend of green technology production based on big data technology, integration and innovation of big data technology and green technology, enriching environmental regulatory forms and participating entities, and PM2.5 environmental regulation strategy and recommendations for green technology advancement based on big data. |
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ISSN: | 1866-7511 1866-7538 |
DOI: | 10.1007/s12517-021-06900-2 |