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Artificial intelligence-based solutions for climate change: a review.
- Source :
- Environmental Chemistry Letters; Oct2023, Vol. 21 Issue 5, p2525-2557, 33p
- Publication Year :
- 2023
-
Abstract
- Climate change is a major threat already causing system damage to urban and natural systems, and inducing global economic losses of over $500 billion. These issues may be partly solved by artificial intelligence because artificial intelligence integrates internet resources to make prompt suggestions based on accurate climate change predictions. Here we review recent research and applications of artificial intelligence in mitigating the adverse effects of climate change, with a focus on energy efficiency, carbon sequestration and storage, weather and renewable energy forecasting, grid management, building design, transportation, precision agriculture, industrial processes, reducing deforestation, and resilient cities. We found that enhancing energy efficiency can significantly contribute to reducing the impact of climate change. Smart manufacturing can reduce energy consumption, waste, and carbon emissions by 30–50% and, in particular, can reduce energy consumption in buildings by 30–50%. About 70% of the global natural gas industry utilizes artificial intelligence technologies to enhance the accuracy and reliability of weather forecasts. Combining smart grids with artificial intelligence can optimize the efficiency of power systems, thereby reducing electricity bills by 10–20%. Intelligent transportation systems can reduce carbon dioxide emissions by approximately 60%. Moreover, the management of natural resources and the design of resilient cities through the application of artificial intelligence can further promote sustainability. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 16103653
- Volume :
- 21
- Issue :
- 5
- Database :
- Complementary Index
- Journal :
- Environmental Chemistry Letters
- Publication Type :
- Academic Journal
- Accession number :
- 171309663
- Full Text :
- https://doi.org/10.1007/s10311-023-01617-y