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The global energy transition is a process without historical experience that affects all participants in the technological chain of energy management, citizens and business entities. This increasingly dynamic process is aimed at decarbonisation of the entire economy, social stability and human well-being. We are witnessing the rapid development of more energy-efficient technologies, clean energy sources and stricter rules regarding the greenhouse gas (GHG) emissions. The data clearly show changes in the Earth’s climate system. Their consequences represent the most urgent threat to the longevity of tourism, which is one of the five most threatened economic sectors. At the same time, tourism continues to have a significant contribution to climate change due to growing GHG emissions, primarily from transport and accommodation facilities.

This chapter seeks to provide an overview of the drivers of GHG emissions and societal responses aimed at addressing tourism-related carbon emissions. It also offers an overview of climate and energy public policies and possible solutions towards a net-zero carbon future for the tourism industry. The purpose of this review is to empower tourism practitioners with current knowledge funded in global and European decarbonisation strategies and encourage them to reflect and create a new and more effective solution.

The outbreak of COVID-19 has become a major public health emergency worldwide. How to effectively guide public opinion and implement precise prevention and control is a hot topic in current research. Mining the spatiotemporal coupling between online public opinion and offline epidemics can provide decision support for the precise management and control of future emergencies.

This study focuses on analyzing the spatiotemporal coupling relationship between public opinion and the epidemic. First, based on Weibo information and confirmed case information, a field framework is constructed using field theory. Second, SnowNLP is used for sentiment mining and LDA is utilized for topic extraction to analyze the topic evolution and the sentiment evolution of public opinion in each coupling stage. Finally, the spatial model is used to explore the coupling relationship between public opinion and the epidemic in space.

The findings show that there is a certain coupling between online public opinion sentiment and offline epidemics, with a significant coupling relationship in the time dimension, while there is no remarkable coupling relationship in space. In addition, the core topics of public concern are different at different coupling stages.

This study deeply explores the spatiotemporal coupling relationship between online public opinion and offline epidemics, adding a new research perspective to related research. The result can help the government and relevant departments understand the dynamic development of epidemic events and achieve precise control while mastering the dynamics of online public opinion.