Advanced Intelligent Forecasting Technologies in Carbon Emission Reduction and Air Quality Improvement

Dear Colleagues,

The issue of climate warming has become the most serious global environmental problem, and extreme weather phenomena such as drought, heat waves, melting ice, and rising sea levels will continue to emerge as surface temperatures rise. “The fifth climate change assessment report” mentions that multiple mitigation approaches can lead to significant reductions in the coming decades, limiting the temperature rise to 2 °C. Since the industrial revolution, in order to promote the economic and social development, people have consumed a large amount of fossil energy, resulting in the continuous accumulation of pollutants and carbon dioxide emissions, which significantly increased the concentration of carbon dioxide based greenhouse gases and decreased the air quality. Faced with the increasingly severe resource shortage and severe environmental challenges, countries should strengthen the implementation of carbon emission reduction policies and continue to implement air pollution prevention and control actions, so as to ensure people's happy life and realize sustainable development of human and society.Recently, due to the great development of advanced intelligent computing technologies (e.g., quantum computing, chaotic mapping mechanism, cloud mapping mechanism, seasonal mechanism, etc.), many novel hybridized or combined with the carbon emission forecasting and low-carbon economy planning models mentioned above are received many attentions. It is deserved to explore the tendency and development of the modeling methodology by applying these advanced intelligent forecasting technologies.Manuscripts on all aspects of carbon emission forecasting and air quality forecasting with innovative technologies are welcome for this Special Issue.

Prof. Dr. Yi Liang
Prof. Dr. Claudio Carnevale
Dr. Keke Wang
Dr. Xiaolong Yang
Guest Editors

Manuscript Submission Information

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• Carbon emission
• Air quality
• Support vector machine
• Artificial neural network
• Cluster intelligence algorithm