Path, Method and Theory of Carbon Neutralization in the Context of Artificial Intelligence

As the global warming situation gets worse, more and more people are paying attention to the realization of carbon neutrality. The key to achieving carbon neutrality is energy conservation and emission reduction. Afforestation alone is difficult to offset carbon dioxide emissions, and it is urgent to adopt advanced technologies to reduce emissions. Artificial intelligence (AI) technology can use a large amount of data from different sources, visually discover unnoticed correlations, and give appropriate action recommendations based on conclusions. It is expected not only to improve the efficiency of global energy production, but also to reduce total greenhouse gas emissions. For example, companies can use AI-driven data engineering to track their carbon footprints, collecting data from operations, travel, and all parts of the value chain. Then AI can be used to generate approximations of missing data to improve monitoring accuracy. AI has three key applications for energy conservation and emission reduction: monitoring emissions, predicting emissions, and reducing emissions. The ways in which AI-powered data analytics can help reduce emissions are applicable across sectors such as industrial products, transportation, pharmaceuticals, FMCG, energy, and utilities.

Therefore, to achieve carbon neutrality goals based on advanced technologies, there is an urgent need for researchers and practitioners to discuss AI techniques and case studies related to carbon neutrality. This Research Topic aims to collect paths, methods and theories for AI to achieve carbon neutrality, which presents a relevant opportunity for all scholars to share their knowledge from the multidisciplinary community across the world, including Information scientists, environmental scientists, and ecological scientists.

This Research Topic welcomes high-quality Original Research and Review Articles related to the following topics of interest:

? Artificial intelligence methods for monitoring, forecasting, and optimizing carbon emissions;

? Artificial intelligence methods and theories for sustainable environment-building decisions;

? AI models for recommending carbon reduction strategies;

? Holistic AI design approaches related to carbon neutrality;

? Sustainable artificial intelligence technology;

? Research on practical carbon neutrality cases related to artificial intelligence;

? Artificial intelligence methods and theories for carbon capture, storage and utilization;

? Artificial intelligence methods and theories for energy efficient building design;

? Research on air quality assessment and monitoring methods based on artificial intelligence.

Keywords: artificial intelligence, carbon neutrality, machine learning, environmental monitoring, sustainable development, environmental governance decision-making