Carbon Capture, Utilization, and Storage (CCUS) represents a vital technology for addressing pressing global challenges such as climate change and carbon emissions. This research aims to explore the relationship between the CCUS capability and carbon emissions in the United States considering thirteen predictors of CCUS and carbon emissions. Incorporating these predictors, we aim to offer policymakers insights to enhance CCUS capabilities and reduce carbon emissions. We utilize diverse econometric techniques: OLS, Lasso, Ridge, Elastic Net, Generalized Method of Moments, and Seemingly Unrelated Regression. Elastic Net outperforms the other models in explaining CCUS, while OLS is effective for carbon emissions. We observe positive impacts of the number of projects and foreign direct investment on the CCUS capacity, but limited influence from the CCUS technology level. However, the relationship between the CCUS capacity and carbon emissions remains limited. Our study highlights the importance of incentivizing projects to increase CCUS capabilities and recognizes the critical role of legal and regulatory frameworks in facilitating effective CCUS implementation in the US. Moreover, we emphasize that achieving decarbonization goals necessitates the development of affordable green alternatives. It is essential to view CCUS as a complementary, rather than a sole, solution for emission reduction as we work towards achieving net-zero emission targets.

This paper addresses the problem of multi-criteria recommendation in the hotel industry. The main focus is to analyze user preferences from different aspects based on multi-criteria ratings and develop a new multi-criteria collaborative filtering method for hotel recommendations. Particularly, the proposed recommendation system integrates matrix factorization into a deep learning model to predict the multi-criteria ratings, and then the evidential reasoning approach is adopted to model the uncertainty of those ratings represented as mass functions in Dempster-Shafer theory of evidence. Finally, Dempster’s rule of combination is utilized to aggregate those multi-criteria ratings to obtain the overall rating for recommendation. Extensive experiments conducted on a real-world dataset demonstrate the effectiveness and efficiency of the proposed method compared with other multi-criteria collaborative filtering methods.

The unprecedented challenges of the COVID-19 pandemic have put human and food security at risk. Currently, the literature on its impacts and implications on the agricultural sector towards United Nations Sustainable Development Goals (UN SDGs) is limited. This study aims to expand the existing knowledge by assessing COVID-19 impacts on sustainable agriculture value chain development in a major global hub of food supplies, Southeast Asia, particularly in the context of regional cooperation for the Association of Southeast Asia Nations (ASEAN). This study employs an empirical qualitative research design to collect primary data from 31 in-depth key-informant interviews with multilateral stakeholders. We also reviewed the latest literature for the secondary data to advance our limited knowledge in this realm. Our study provides a macro-analytical outlook of COVID-19 impacts on the agricultural sector for sustainable development in Thailand and ASEAN, using a SWOT analysis and sustainability framework (i.e., socio-economic and environmental dimensions) with SDGs mapping. Our findings address critical sustainability issues about agriculture and food value chains for food security and post-COVID-19 recovery. Our study also suggests various opportunities and policy recommendations for transformative regional sustainability strategies for sustainable agriculture to achieve the UN SDGs and a sustainable future.