23 research outputs found

    Toward the development of subnational hybrid input-output tables in a multiregional framework

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    Environmental input–output analyses can be a useful decision support tool at the subnational level, because of its ability to capture economic and environmental impacts at other geographical levels. Yet, such analyses are hindered by the lack of subnational IO tables. Furthermore, the lack of physical product and waste flows in what is known as a “hybrid” table prevents a range of consumption‐based and circular‐economy‐type analyses. We demonstrate the development of a multiregional hybrid IOT (MRHIOT) along with environmental extensions at the subnational level and exemplify it for the case of Belgium. The development procedure discloses a novel approach of combining national hybrid tables, subnational monetary tables, and physical survey‐based data. Such a combination builds upon a partial‐survey approach that includes a range of techniques for initial estimation and reconciliation within a balancing procedure. For the validation of the approaches, we assessed the magnitude of deviations between the initial and final estimates and analyzed the uncertainties inherent to each initial estimation procedure. Subsequently, we conducted a consumption‐based analysis where we assessed the carbon footprint (CF) at the subnational level and highlighted the CF inherent to the interregional linkages. This study provides methodological and application‐based contributions to the discussion on the relevance of hybrid subnational tables and analyses compared to national ones. The proposed approach could be replicable to some extent for further developing subnational MRHIOT. The study is expected to foster more research toward the development of further subnational MRHIOT as well as its associated wide‐ranging applications.Industrial Ecolog

    Evolution of the public opinion on COVID-19 vaccination in Japan

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    Vaccines are promising tools to control the spread of COVID-19. An effective vaccination campaign requires government policies and community engagement, sharing experiences for social support, and voicing concerns to vaccine safety and efficiency. The increasing use of online social platforms allows us to trace large-scale communication and infer public opinion in real-time. We collected more than 100 million vaccine-related tweets posted by 8 million users and used the Latent Dirichlet Allocation model to perform automated topic modeling of tweet texts during the vaccination campaign in Japan. We identified 15 topics grouped into 4 themes on Personal issue, Breaking news, Politics, and Conspiracy and humour. The evolution of the popularity of themes revealed a shift in public opinion, initially sharing the attention over personal issues (individual aspect), collecting information from the news (knowledge acquisition), and government criticisms, towards personal experiences once confidence in the vaccination campaign was established. An interrupted time series regression analysis showed that the Tokyo Olympic Games affected public opinion more than other critical events but not the course of the vaccination. Public opinion on politics was significantly affected by various events, positively shifting the attention in the early stages of the vaccination campaign and negatively later. Tweets about personal issues were mostly retweeted when the vaccination reached the younger population. The associations between the vaccination campaign stages and tweet themes suggest that the public engagement in the social platform contributed to speedup vaccine uptake by reducing anxiety via social learning and support
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