Carbon emissions are the main cause of climate change. The increase in atmospheric carbon dioxide concentration has broken the balance between the original layers of the earth, resulting in the occurrence of ecological and environmental effects such as climate zone changes, terrestrial ecosystem evolution, and ocean acidification. These ecological and environmental changes affect the social and environmental determinants of health – clean air, safe drinking water, sufficient food, and secure shelter. Increasing carbon emissions are a global threat that needs to be taken into account in climate negotiations, environmental policies, ecological civilization, economic society, and even international politics. In this regard, there are two effective ways to control climate change: one is to reduce carbon emissions from the source, and the other is to increase carbon absorption. As climate change intensifies the devastation from storms, wildfires, and droughts, artificial intelligence (AI) and digital tools are used to predict and limit the impacts of those climate events.
Governments, tech firms, and investors are showing a growing interest in artificial intelligence (AI)-based learning systems that use algorithms to identify patterns in data sets and make predictions, recommendations, or decisions in real or virtual settings about the change in environmental patterns. Predictive AI can forecast future carbon emissions based on current data on carbon footprint. By providing detailed insight into every aspect of carbon emission, AI tools and data optimization techniques can improve efficiency in production, transportation, etc. thereby reducing carbon emissions and cutting costs. AI applications could also help design more energy-efficient buildings, improve power storage and optimize renewable energy deployment by feeding solar and wind power into the electricity grid as needed. From electricity grids to smart appliances, data and AI-driven software can be integral to predicting market behavior, balancing operations in real-time, and maximizing energy yield. AI can rapidly analyze dynamic data, such as obtained from weather forecasting systems and create a simulation that scientists can use for decision making. Therefore, this Research Topic aims to collect research on novel AI applications in carbon emission monitoring, and forecasting air quality patterns, with a focus on techniques mitigating the effects of climate change for a sustainable environment.
Potential topics of interest include but are not limited to the following:
• AI in monitoring and predicting patterns of carbon emissions
• AI in environmental monitoring, including changes in pollutants concentration in air
• AI in air quality assessment and monitoring
• AI in organic and inorganic materials monitoring
• AI in climate technologies for a sustainable environment
• AI in monitoring carbon emission on roads and seas
• AI applications for climate change decision making
Carbon emissions are the main cause of climate change. The increase in atmospheric carbon dioxide concentration has broken the balance between the original layers of the earth, resulting in the occurrence of ecological and environmental effects such as climate zone changes, terrestrial ecosystem evolution, and ocean acidification. These ecological and environmental changes affect the social and environmental determinants of health – clean air, safe drinking water, sufficient food, and secure shelter. Increasing carbon emissions are a global threat that needs to be taken into account in climate negotiations, environmental policies, ecological civilization, economic society, and even international politics. In this regard, there are two effective ways to control climate change: one is to reduce carbon emissions from the source, and the other is to increase carbon absorption. As climate change intensifies the devastation from storms, wildfires, and droughts, artificial intelligence (AI) and digital tools are used to predict and limit the impacts of those climate events.
Governments, tech firms, and investors are showing a growing interest in artificial intelligence (AI)-based learning systems that use algorithms to identify patterns in data sets and make predictions, recommendations, or decisions in real or virtual settings about the change in environmental patterns. Predictive AI can forecast future carbon emissions based on current data on carbon footprint. By providing detailed insight into every aspect of carbon emission, AI tools and data optimization techniques can improve efficiency in production, transportation, etc. thereby reducing carbon emissions and cutting costs. AI applications could also help design more energy-efficient buildings, improve power storage and optimize renewable energy deployment by feeding solar and wind power into the electricity grid as needed. From electricity grids to smart appliances, data and AI-driven software can be integral to predicting market behavior, balancing operations in real-time, and maximizing energy yield. AI can rapidly analyze dynamic data, such as obtained from weather forecasting systems and create a simulation that scientists can use for decision making. Therefore, this Research Topic aims to collect research on novel AI applications in carbon emission monitoring, and forecasting air quality patterns, with a focus on techniques mitigating the effects of climate change for a sustainable environment.
Potential topics of interest include but are not limited to the following:
• AI in monitoring and predicting patterns of carbon emissions
• AI in environmental monitoring, including changes in pollutants concentration in air
• AI in air quality assessment and monitoring
• AI in organic and inorganic materials monitoring
• AI in climate technologies for a sustainable environment
• AI in monitoring carbon emission on roads and seas
• AI applications for climate change decision making
