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Article analysis:

The article provides a comprehensive overview of China's material stocks and flows account from 1978 to 2018, with an emphasis on its potential applications for sustainability analyses and assessments. The authors have made use of various official statistical reports, yearbooks, socio-economic databases, expert judgements, etc., to create the Provincial Material Stocks and Flow Database (PMSFD).

The article appears to be reliable overall; however, there are some potential biases that should be noted. For example, the authors have assumed that there is a directly proportional relationship between power consumption and the amount of industrial machines when estimating metal stocks in industrial machinery. This assumption may not always hold true in all cases, as other factors such as technological advancements could also play a role in determining this relationship. Additionally, when extrapolating data for passenger cars and trucks before 2001 due to limited availability of official reporting data, it is unclear how accurate these estimates are likely to be given that they are based on 2002 proportions only.

Furthermore, while the authors have provided detailed information about their estimation approach for material stocks and flows inventory construction, they do not provide any information about how they verified or validated their results. This lack of evidence makes it difficult to assess the accuracy or reliability of their findings. Additionally, while the authors have discussed some potential applications for their database such as retrospective analyses and prospective forecasts of stocks and flows for sustainable development purposes, they do not provide any evidence or examples demonstrating how this database has been used in practice or what kind of insights it has yielded so far.

In conclusion, while this article provides a comprehensive overview of China's material stocks and flows account from 1978 to 2018 with an emphasis on its potential applications for sustainability analyses and assessments, there are some potential biases that should be noted such as assumptions made when estimating metal stocks in industrial machinery or extrapolating data for passenger cars before 2001 due to limited availability of official