2019
DOI: 10.1016/s2542-5196(19)30192-5
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The carbon footprint of the Chinese health-care system: an environmentally extended input–output and structural path analysis study

Abstract: Background The health sector plays an important part in adapting to climate change; however, the sector is also responsible for significant greenhouse gas (GHG) emissions. In high-income countries, the carbon footprints of health-care systems have been estimated to be 3-10% of the total national GHG emissions, but no in-depth investigation has been done for China. This study aims to examine the carbon footprint of the Chinese health-care system and identify emission hotspots. MethodsEnvironmentally extended in… Show more

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Cited by 77 publications
(66 citation statements)
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“…Due to the hybrid approach combining multiple bottomup datasets with top-down MRIO-based results, it was not feasible to conduct a comprehensive uncertainty analysis, such as the Monte Carlo simulation-based analyses done for MRIO-only studies of health-care emissions. 5,6 Bottom-up datasets used for this analysis, including building energy use and anaesthetic gas and MDI use, did not have reported error values. For the MRIO-based results, previous work can provide context: an uncertainty analysis done for an earlier version of the UK MRIO model found that total UK consumer greenhouse gas emissions carried a relative SE of approximately 4-6%; previous health-care footprinting analyses found an SD of less than 5% globally and relative SEs of approximately 15% for China and 28% for Australia.…”
Section: Uncertaintymentioning
confidence: 99%
See 2 more Smart Citations
“…Due to the hybrid approach combining multiple bottomup datasets with top-down MRIO-based results, it was not feasible to conduct a comprehensive uncertainty analysis, such as the Monte Carlo simulation-based analyses done for MRIO-only studies of health-care emissions. 5,6 Bottom-up datasets used for this analysis, including building energy use and anaesthetic gas and MDI use, did not have reported error values. For the MRIO-based results, previous work can provide context: an uncertainty analysis done for an earlier version of the UK MRIO model found that total UK consumer greenhouse gas emissions carried a relative SE of approximately 4-6%; previous health-care footprinting analyses found an SD of less than 5% globally and relative SEs of approximately 15% for China and 28% for Australia.…”
Section: Uncertaintymentioning
confidence: 99%
“…For the MRIO-based results, previous work can provide context: an uncertainty analysis done for an earlier version of the UK MRIO model found that total UK consumer greenhouse gas emissions carried a relative SE of approximately 4-6%; previous health-care footprinting analyses found an SD of less than 5% globally and relative SEs of approximately 15% for China and 28% for Australia. 5,6,30 Qualitative sources of uncertainty are elaborated on in the Discussion.…”
Section: Uncertaintymentioning
confidence: 99%
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“…There are numerous ways to dissect the global PM footprint into underlying constituents. Here we extract some significant findings for countries with detailed healthcare and inputoutput information (appendix pp [4][5][6][7][8][9][10][11][12][13][14], in absolute terms (figure 3A, C) and per capita (figure 3B, D). PM footprints do not necessarily follow healthcare expenditure; despite their healthcare expen diture being lower, China and India have higher footprints than Japan (figure 3A).…”
Section: Figure 1: Environmental Footprints Of Health Care For 2015mentioning
confidence: 99%
“…Greenhouse gas emissions and climate change are an important pathway of the negative health impact resulting from the environmental footprint of health care. The carbon footprint of health care has been calculated previously in specific countries, including the UK, 6 the USA, 7 Australia, 8 Canada, 9 China, 10 Japan, 11 and in inter national comparisons. 12,13 To the best of our knowledge, this is the first assessment of the global environmental footprint of health care, based on available data, and using a panel of indicators that are relevant to health outcomes and that enable a cycle of adverse feedback to be captured.…”
Section: Introductionmentioning
confidence: 99%