Carbon neutrality refers to net-zero carbon emissions. It can be achieved by reducing carbon emissions or increasing carbon adsorption. The popularity of social media including Twitter, YouTube, and LinkedIn provides good channels for sharing relevant information and promoting sustainable carbon-neutral living styles. For example, a motor company has launched social media challenges with Korean pop stars to raise awareness of carbon neutrality. Social media provides real-time information. In Jakarta, flood-related tweet intensity during a flood peaked at about 900 tweets a minute during the floods of 2015. The tweets created real-time maps that people sent a minute before. Regarding government policies, identification of problem areas is needed to help policymakers to resolve problems. Machine learning can monitor these events by studying past records to know how countries and governments performed in a high-risk event or environmental crisis and this data can be used to provide future recommendations to governments for policy making. Additionally, AI can analyze online unstructured data, and predict various scenarios of carbon emissions and adsorptions using structured data. The world of social media is a huge data source still yet to be fully optimized in science.
While social media is often used in industry, scientific research on these findings is scarce. This Research Topic aims to address carbon neutrality from social media, big data, and artificial intelligence perspectives. We welcome articles on themes including, but not limited to:
• Carbon neutrality modeling via Artificial Intelligence/econometrics
• Cabron finance modeling
• Carbon neutrality, social media, big data analysis.
• Natural language processing for carbon neutrality text in social media e.g. YouTube, Twitter, and Facebook.
• Text mining for carbon-neutral social media information related to different stages in the built environment.
• Prediction of carbon dioxide emissions via machine learning, deep learning, etc.
• AI sentiment analysis on carbon neutrality expressions in social media.
• Computer vision for analyzing figures related to carbon emissions.
• Prediction of coral changes due to increases in carbon emissions.
Carbon neutrality refers to net-zero carbon emissions. It can be achieved by reducing carbon emissions or increasing carbon adsorption. The popularity of social media including Twitter, YouTube, and LinkedIn provides good channels for sharing relevant information and promoting sustainable carbon-neutral living styles. For example, a motor company has launched social media challenges with Korean pop stars to raise awareness of carbon neutrality. Social media provides real-time information. In Jakarta, flood-related tweet intensity during a flood peaked at about 900 tweets a minute during the floods of 2015. The tweets created real-time maps that people sent a minute before. Regarding government policies, identification of problem areas is needed to help policymakers to resolve problems. Machine learning can monitor these events by studying past records to know how countries and governments performed in a high-risk event or environmental crisis and this data can be used to provide future recommendations to governments for policy making. Additionally, AI can analyze online unstructured data, and predict various scenarios of carbon emissions and adsorptions using structured data. The world of social media is a huge data source still yet to be fully optimized in science.
While social media is often used in industry, scientific research on these findings is scarce. This Research Topic aims to address carbon neutrality from social media, big data, and artificial intelligence perspectives. We welcome articles on themes including, but not limited to:
• Carbon neutrality modeling via Artificial Intelligence/econometrics
• Cabron finance modeling
• Carbon neutrality, social media, big data analysis.
• Natural language processing for carbon neutrality text in social media e.g. YouTube, Twitter, and Facebook.
• Text mining for carbon-neutral social media information related to different stages in the built environment.
• Prediction of carbon dioxide emissions via machine learning, deep learning, etc.
• AI sentiment analysis on carbon neutrality expressions in social media.
• Computer vision for analyzing figures related to carbon emissions.
• Prediction of coral changes due to increases in carbon emissions.
