Data-driven decarbonisation pathways for reducing life cycle GHG emissions from food waste in the hospitality and food service sectors.

The Hospitality and Food Service (HaFS) sectors are notoriously known for their contribution to the food waste problem. Hence, there is an urgent need to devise strategies to reduce food waste in the HaFS sectors and to decarbonise their operation to help fight hunger, achieve food security, improve nutrition and mitigate climate change. This study proposes three streams to decarbonise the staff cafeteria operation in an integrated resort in Macau. These include upstream optimisation to reduce unserved food waste, midstream education to raise awareness amongst staff about the impact of food choices on the climate and health, and finally downstream recognition to reduce edible plate waste using a state-of-the-art computer vision system. Technology can be an effective medium to facilitate desired behavioural change through nudging, much like how speed cameras can cause people to slow down and help save lives. The holistic and data-driven approach taken revealed great potential for organisations or institutions that offer catering services to reduce their food waste and associated carbon footprint whilst educating individuals about the intricate link between food, climate and well-being.

Evaluation of Twitter data for an emerging crisis: an application to the first wave of COVID-19 in the UK.

In the absence of nationwide mass testing for an emerging health crisis, alternative approaches could provide necessary information efficiently to aid policy makers and health bodies when dealing with a pandemic. The following work presents a methodology by which Twitter data surrounding the first wave of the COVID-19 pandemic in the UK is harvested and analysed using two main approaches. The first is an investigation into localized outbreak predictions by developing a prototype early-warning system using the distribution of total tweet volume. The temporal lag between the rises in the number of COVID-19 related tweets and officially reported deaths by Public Health England (PHE) is observed to be 6-27 days for various UK cities which matches the temporal lag values found in the literature. To better understand the topics of discussion and attitudes of people surrounding the pandemic, the second approach is an in-depth behavioural analysis assessing the public opinion and response to government policies such as the introduction of face-coverings. Using topic modelling, nine distinct topics are identified within the corpus of COVID-19 tweets, of which the themes ranged from retail to government bodies. Sentiment analysis on a subset of mask related tweets revealed sentiment spikes corresponding to major news and announcements. A Named Entity Recognition (NER) algorithm is trained and applied in a semi-supervised manner to recognise tweets containing location keywords within the unlabelled corpus and achieved a precision of 81.6%. Overall, these approaches allowed extraction of temporal trends relating to PHE case numbers, popular locations in relation to the use of face-coverings, and attitudes towards face-coverings, vaccines and the national 'Test and Trace' scheme.