Carbon footprints: Uncovering multilevel spatiotemporal changes of ship emissions during 2019-2021 in the U.S.

Quantifying and understanding changes in carbon emissions is essential for the U.S. shipping industry to reduce carbon emissions, especially after its return to the Paris Agreement. We estimated carbon emissions from 48,321 ships in the U.S. Exclusive Economic Zone (EEZ) using the power-based method based on 3.6 billion Automatic Identification System (AIS) reports. We explored the characterization of carbon emissions from the national, regional, and port levels during 2019-2021 by allocating emissions on a 1 km*1 km grid through an activity-weighted method. The results show: (1) Due to the COVID-19 pandemic, emissions within the EEZ show a temporal trend of decreased and then rebound, specifically from 32.628 Tg in 2019 to 30.741 Tg in 2020 and then bounced to 31.786 Tg in 2021. The spatial differences in emissions show significant heterogeneity; (2) There are significant differences in emissions by vessel type, flag, and operational mode for the four regions of the U.S. (Great Lakes, Gulf Coast, Pacific Coast, and Atlantic Coast). Thus, emissions in these regions show different variability patterns over three years. Notably, "port congestion" led to record high emissions on the Pacific Coast; (3) Containerized cargo contributes the most to port core area emissions, so most ports with higher throughputs have higher emissions, with Long Beach and Los Angeles having the highest. Emissions from coastal ports are high and volatile, while inland ports are low and stable. This study provides the U.S. with a high spatiotemporal resolution inventory of carbon emissions from ships, and the findings are expected to provide some reference for controlling ship emissions.

Chinese tourists in Nordic countries: An analysis of spatio-temporal behavior using geo-located travel blog data.

Geo-located travel blogs, a new data source, enable to achieve more detailed analysis of tourists' spatio-temporal behavior. Taking Chinese tourists in Nordic countries as the research object, this paper focuses on their behavior, seasonal patterns and complex network effects by using geo-located travel blog data collected from Qunar.com. The results show that: (1) Chinese tourists visiting Nordic countries are often experienced in traveling. The local climate during the cold season does not prevent them from pursuing the aurora scenery. (2) The travel behavior of Chinese tourists is spatially heterogeneous. The network analysis reveals that Iceland showcases stronger, compared to the other Nordic countries, community independence and small world effect. (3) During the warm season, Chinese tourists choose a variety of destinations, while in cold season, they tend to choose destinations with higher chances for spotting the northern lights. These results provide helpful information for the tourism management departments of Nordic countries to improve their marketing and development efforts directed for Chinese tourists.