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The calculation of tourism carbon footprint is of great significance to low-carbon tourism. This study estimates the tourism carbon footprint in Heilongjiang Province from 2009 to 2018 by using tourism carbon footprint and tourism carbon capacity models. The results show that the total tourism carbon footprint of Heilongjiang Province increased fast to 2.97 times from 5.926million tons in 2009 to 21.13million tons in 2018, while its per capita tourism carbon footprint increased from 53.9 kg to 116.0 kg. During the same period, tourism carbon capacity continued to grow steadily from 15.18billion tons to 21.96billion tons, and the growth rate was 50% of the growth rate of tourism carbon footprint. Tourism carbon emissions can be absorbed by environmental capacity, and the risk of carbon deficit is relatively small.

Fatty liver disease (FLD) is a common liver disease, which poses a great threat to people's health, but there is still no optimal method that can be used on a large-scale screening. This research is based on machine learning algorithms, using electronic physical examination records in the health database as data support, to a predictive model for FLD. The model has shown good predictive ability on the test set, with its AUC reaching 0.89. Since there are a large number of electronic physical examination records in most of health database, this model might be used as a non-invasive diagnostic tool for FLD for large-scale screening.

This study analyses the composition and evolution of carbon dioxide emissions from the tourism industry in Heilongjiang Province and its 12 regions by the tourism consumption stripping coefficient method and calculates the decoupling relationship between the carbon dioxide emissions and economic growth of tourism from 2010 to 2019. The empirical results are as follows. (1) From 2010 to 2019, carbon dioxide emissions from Heilongjiang Province’s tourism industry and its subsector increased steadily, of which the tourism industry accounted for a relatively large amount of carbon dioxide emissions in “Transport, Storage, and Post.” (2) Time series analysis reveals that the carbon dioxide emissions of tourism basically show an increasing trend and there are still multiple decoupling relationships with economic growth. Expansive decoupling and weak decoupling have occurred more frequently. (3) Spatial analysis reveals that the carbon dioxide emissions of the regional tourism industry show a fluctuating upward trend. The tourism industry in Harbin has significantly higher carbon dioxide emissions than in other regions. In addition, this study provides feasible suggestions and countermeasures for low-carbon tourism development in Heilongjiang Province. The findings are considered useful in future planning of energy conservation and emission reduction in Heilongjiang Province and the regional tourism industry.