Climate change is a serious hazard that has already harmed urban and natural systems and resulted in over $500 billion in economic losses worldwide.These problems could be partially resolved by artificial intelligence,which incorporates internet resources to provide quick recommendations based on precise climate change projections.Our research revealed that raising energy efficiency can greatly lessen the effects of climate change.Smart manufacturing, in instance, can reduce energy consumption in buildings by 30 to 50 percent.It can also reduce waste and carbon emissions by 30 to 50 percent.Artificial intelligence technologies are used by almost 70% of the world's natural gas industry to improve the precision and dependability of weather forecasts. Artificial intelligence and smart grids can be used to maximise power system efficiency and cut electricity costs by 10–20%.Intelligent transport systems have a 60% reduction in CO2 emissions potential. Furthermore,artificial intelligence may be used to manage natural resources and build resilient cities, further advancing sustainability.
TopIntroduction
Machine learning (ML) is a branch of artificial intelligence (AI) and computer science that focuses on the using data and algorithms to enable AI to imitate the way that humans learn, gradually improving its accuracy. Deep learning is the subset of machine learning methods based on artificial neural networks (ANNs) with representation learning. The adjective “deep” refers to the use of multiple layers in the network. One of the most serious climate issues facing humanity right now is climate change, which is being produced by the carbon dioxide emissions from industrial production. Climate change is strongly associated with rising sea levels, an increase in the frequency of natural disasters, decreased crop productivity, and the loss of biological variety (Shivanna 2022). The significant carbon dioxide emissions are mostly caused by the widespread use of fossil fuels in manufacturing processes (Yue and Gao 2018). To combat climate change, it is crucial to increase energy efficiency, create green energy, and practise energy conservation. Ecological protection may be positively impacted by the shift from a society reliant on fossil fuels to one reliant on electricity (Fang et al. 2023; Farghali et al. 2022). Deep neural networks enable artificial intelligence to automate discovery, distribution, and transmission processes, significantly lowering energy consumption (Farghali et al., 2023).
Artificial intelligence (AI) is frequently promoted as a viable solution for solving the challenges of climate change as the severity of related issues continues to rise. The internet of things (IoT) and renewable energy sources offer the energy sector a growing number of options, and artificial intelligence technology has the ability to seamlessly combine these opportunities. It can be a significant driving force in the energy sector by optimising decision-making procedures, supplying energy, and controlling autonomous software. Artificial intelligence has also been crucial in modelling solar radiation, simulating and optimising renewable energy systems, forecasting urban power loads, and forecasting urban building heat loads (Al-Othman et al., 2022; Jha et al., 2017; Khosravi et al., 2018, Lyu and Liu, 2021; Wang and Srinivasan, 2017). Artificial intelligence can help mitigate climate change in a variety of ways, including improving the forecasting of extreme weather events (McGovern et al. 2017), building energy-efficient and environmentally friendly structures that collect and sense data while anticipating thermal comfort (Ngarambe et al. 2020; Yan et al. 2021), developing crop productivity models to reduce fertiliser use (Elahi et al. 2019b; Zhang et al. 2021), and implementing policies that encourage renewable energy source.