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The carbon footprint of global tourism

DOI:10.1038/s41558-018-0141-x
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Manfred Lenzen at The University of Sydney
Manfred Lenzen
Ya-Yen Sun
Ya-Yen Sun
Ya-Yen Sun
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Futu Faturay at The University of Queensland
Futu Faturay
Yuan-Peng Ting
Yuan-Peng Ting
Yuan-Peng Ting
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Abstract and Figures

Tourism contributes significantly to global gross domestic product, and is forecast to grow at an annual 4%, thus outpacing many other economic sectors. However, global carbon emissions related to tourism are currently not well quantified. Here, we quantify tourism-related global carbon flows between 160 countries, and their carbon footprints under origin and destination accounting perspectives. We find that, between 2009 and 2013, tourism’s global carbon footprint has increased from 3.9 to 4.5 GtCO2e, four times more than previously estimated, accounting for about 8% of global greenhouse gas emissions. Transport, shopping and food are significant contributors. The majority of this footprint is exerted by and in high-income countries. The rapid increase in tourism demand is effectively outstripping the decarbonization of tourism-related technology. We project that, due to its high carbon intensity and continuing growth, tourism will constitute a growing part of the world’s greenhouse gas emissions.
| Top bilateral embodied carbon movements. In 2013, international travel caused a carbon footprint of about 1 GtCO 2 e, or 23% of the global carbon footprint of tourism. Arrows point in the direction of embodied carbon flow, which-in accordance with the literature-is the direction of commodity trade, and is opposite to the movement of people. Red arrows: bilateral international movements belonging to the top 10% of the total 1 GtCO 2 e. Yellow arrows: top 10-30%. Orange arrows: 30-50%. Blue arrows: the remainder.
| Top bilateral embodied carbon movements. In 2013, international travel caused a carbon footprint of about 1 GtCO 2 e, or 23% of the global carbon footprint of tourism. Arrows point in the direction of embodied carbon flow, which-in accordance with the literature-is the direction of commodity trade, and is opposite to the movement of people. Red arrows: bilateral international movements belonging to the top 10% of the total 1 GtCO 2 e. Yellow arrows: top 10-30%. Orange arrows: 30-50%. Blue arrows: the remainder.
… 
Carbon footprint measures of selected top-ranking countries for 2013 Top left, RBA carbon footprint by nationality of visitor. Blue, international travel; yellow, domestic travel. Bottom left, Net RBA–DBA balance. Positive for net origins; negative for net destinations. Top right, Per capita DBA carbon footprint by destination. Blue, international travel; yellow, domestic travel. Bottom right, Per capita net RBA–DBA balance. Positive for net travellers; negative for net hosts.
Carbon footprint measures of selected top-ranking countries for 2013 Top left, RBA carbon footprint by nationality of visitor. Blue, international travel; yellow, domestic travel. Bottom left, Net RBA–DBA balance. Positive for net origins; negative for net destinations. Top right, Per capita DBA carbon footprint by destination. Blue, international travel; yellow, domestic travel. Bottom right, Per capita net RBA–DBA balance. Positive for net travellers; negative for net hosts.
… 
Top bilateral embodied carbon movements to and/or from Europe Arrows point in the direction of embodied carbon flow, which—in accordance with the literature—is the direction of commodity trade, and is opposite to the movement of people. Top flows to and/or from Europe that constitute 30% of the total 1 GtCO2e are coloured red on the map.
Top bilateral embodied carbon movements to and/or from Europe Arrows point in the direction of embodied carbon flow, which—in accordance with the literature—is the direction of commodity trade, and is opposite to the movement of people. Top flows to and/or from Europe that constitute 30% of the total 1 GtCO2e are coloured red on the map.
… 
Breakdown of the tourism carbon footprint into purchased commodities and emitting industries, and into high-, middle- and low-income countries ‘Purchased commodities’ represent the consumers’ end-of-the-supply-chain network, ‘emitting industries’ the producers’ end. Due to the many input–output tables of low-income countries not distinguishing modes, ‘Trans’ represents unspecified transport, which includes air transport. The three per capita GDP brackets are L (<US$3,000), M (US$3,000–US$10,000) and H (>US$10,000), and N represents the number of countries in the income group. 2013 tourist volumes from the three groups are 53.9 million (L), 281.5 million (M) and 656.7 million (H). For further details and an explanation of sector abbreviations see Supplementary Section 3.3.
Breakdown of the tourism carbon footprint into purchased commodities and emitting industries, and into high-, middle- and low-income countries ‘Purchased commodities’ represent the consumers’ end-of-the-supply-chain network, ‘emitting industries’ the producers’ end. Due to the many input–output tables of low-income countries not distinguishing modes, ‘Trans’ represents unspecified transport, which includes air transport. The three per capita GDP brackets are L (<US$3,000), M (US$3,000–US$10,000) and H (>US$10,000), and N represents the number of countries in the income group. 2013 tourist volumes from the three groups are 53.9 million (L), 281.5 million (M) and 656.7 million (H). For further details and an explanation of sector abbreviations see Supplementary Section 3.3.
… 
Affluence and technology as drivers of the carbon footprint of global tourism for the RBA perspective Left, Affluence is measured as per capita GDP (including regression curve from Supplementary Section 4.8.3). Right, Technology is measured as carbon intensity. Circle size represents population, and N represents the number of countries in the sample.
Affluence and technology as drivers of the carbon footprint of global tourism for the RBA perspective Left, Affluence is measured as per capita GDP (including regression curve from Supplementary Section 4.8.3). Right, Technology is measured as carbon intensity. Circle size represents population, and N represents the number of countries in the sample.
… 
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Articles
https://doi.org/10.1038/s41558-018-0141-x
1ISA, School of Physics A28, The University of Sydney, Sydney, New South Wales, Australia. 2Department of Transportation & Communication
Management Science, National Cheng Kung University, Tainan City, Taiwan, Republic of China. 3UQ Business School, The University of Queensland,
Brisbane, Queensland, Australia. 4Fiscal Policy Agency, Ministry of Finance of the Republic of Indonesia, Jakarta, Indonesia. 5Sydney Business School,
The University of Sydney, Sydney, New South Wales, Australia. *e-mail: arunima.malik@sydney.edu.au
Global tourism is a trillion-dollar industry, representing in the
order of 7% of global exports and contributing significantly
to global gross domestic product (GDP)1. International arriv-
als and tourism receipts have been growing at an annual 3–5%, out-
pacing the growth of international trade, and in 2016 exceeded 1
billion and US$1.2 trillion, respectively1,2. Clearly, economic activity
at this scale has a significant impact on the environment3. In par-
ticular transport, a key ingredient of travel, is an energy- and car-
bon-intensive commodity, rendering tourism a potentially potent
contributor to climate change. The sensitivity and vulnerability of
destinations (such as winter- and coastal-recreation locations) to
weather and climate change also suggest that, as a result of climate
change, the tourism industry will in turn undergo drastic future
change and will need to adapt to increasing risk4. Given future pro-
jections of an unabated 4% growth beyond 20251,2, the continuous
monitoring and analysis of carbon emissions associated with tour-
ism is becoming more pressing.
By definition, the carbon footprint of tourism should include
the carbon emitted directly during tourism activities (for example,
combustion of petrol in vehicles) as well as the carbon embodied in
the commodities purchased by tourists (for example, food, accom-
modation, transport, fuel and shopping; Supplementary Section 1).
Tourism carbon footprints therefore need to be evaluated using
methods that cover the life cycle or supply chain emissions of
tourism-related goods and services (Supplementary Section 1).
Life-cycle assessment5–7 and input–output analysis8–14 have been
used to quantify the carbon footprint of specific aspects of tour-
ism operations such as hotels5, events6 and transportation infra-
structure7,15, and in particular countries (or regions thereof) such
as Spain5,10,11, the UK8, Taiwan9, China15, Saudi Arabia6, Brazil7,
Iceland14, Australia13 and New Zealand12.
Previous estimates of global CO2 emissions from selected tour-
ism sectors give values of 1.3 and 1.17 GtCO2 for 200516,17 and 1.12 Gt
for 201018, amounting to about 2.5–3% of global CO2-equivalent
(CO2e) emissions. However, these analyses do not cover the sup-
ply chains underpinning tourism, and do not therefore represent
true carbon footprints. A WTO–UNEP–WMO report16 states that
(p. 134) ‘[t]aking into account all lifecycle and indirect energy needs
related to tourism, it is expected that the sum of emissions would be
higher, although there are no specific data for global tourism avail-
able’. Similarly, Gössling and Peeters18 state that (p. 642) “… a more
complete analysis of the energy needed to maintain the tourism
system would also have to include food and beverages, infrastruc-
ture construction and maintenance, as well as retail and services, all
of these on the basis of a life cycle perspective accounting for the
energy embodied in the goods and services consumed in tourism.
However, no database exists for these and the estimate thus must be
considered conservative.”
This work fills an important knowledge gap by offering a com-
prehensive calculation of the carbon footprint of global tourism.
We source the most detailed compendium of tourism satellite
accounts (TSAs) available so far (55 countries with individual
TSAs and 105 countries with United Nations World Tourism
Organization (UNWTO) data; Supplementary Sections 2.2 and
3.1.2), integrate this into a comprehensive global multi-region
input–output (MRIO) database (Supplementary Section 2.5), and
use Leontief’s standard model (Section ‘Input-output analysis’) to
establish carbon footprint estimates that cover both the direct and
indirect, supply chain contributions of tourist activities. In addi-
tion, we advance current knowledge by (1) including not only
emissions of CO2 but also those of CH4, N2O, hydrofluorocarbons
(HFCs), chlorofluorocarbons (CFCs), SF6 and NF3 (Supplementary
Section 3.2), (2) presenting an annual carbon footprint time series
from 2009 to 2013, (3) analysing drivers of change, (4) providing
details about carbon-intensive supply chains, and (5) comparing
two accounting perspectives.
The two accounting perspectives mentioned in the final point
(5) are residence-based accounting (RBA) and destination-based
accounting (DBA). Both perspectives are variants of the well-
known consumption-based accounting principle19; however, while
RBA allocates consumption-based emissions to the tourist’s coun-
try of residence, DBA allocates them to the tourist’s destination
country13. The two perspectives serve clear and distinct purposes.
RBA can shed light on the determinants of travel choices, such as
The carbon footprint of global tourism
Manfred Lenzen 1, Ya-Yen Sun2,3, Futu Faturay 1,4, Yuan-Peng Ting2, Arne Geschke 1 and
Arunima Malik 1,5*
Tourism contributes significantly to global gross domestic product, and is forecast to grow at an annual 4%, thus outpacing
many other economic sectors. However, global carbon emissions related to tourism are currently not well quantified. Here,
we quantify tourism-related global carbon flows between 160 countries, and their carbon footprints under origin and destina-
tion accounting perspectives. We find that, between 2009 and 2013, tourism’s global carbon footprint has increased from
3.9 to 4.5 GtCO2e, four times more than previously estimated, accounting for about 8% of global greenhouse gas emissions.
Transport, shopping and food are significant contributors. The majority of this footprint is exerted by and in high-income coun-
tries. The rapid increase in tourism demand is effectively outstripping the decarbonization of tourism-related technology.
We project that, due to its high carbon intensity and continuing growth, tourism will constitute a growing part of the world’s
greenhouse gas emissions.
Correction: Author Correction
© 2018 Macmillan Publishers Limited, part of Springer Nature. All rights reserved.
NATURE CLIMATE CHANGE | VOL 8 | JUNE 2018 | 522–528 | www.nature.com/natureclimatechange
522
Content courtesy of Springer Nature, terms of use apply. Rights reserved
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... Several researchers have also explored the intricate relationship between tourism and its consequent environmental ramifications (Scott et al. 2012). The adoption of the carbon footprint as a metric to quantify climate change implications has gained traction in academia (Rosselló-Nadal 2014; Puig et al. 2017;Lenzen et al. 2018), and its application extends to assessing the environmental footprint of hotel swimming pools (Rico et al. 2019;Sun et al. 2020). The tourism sector is constantly improving and the innovation in this sector is correlated with sustainability (Smerecnik and Andersen 2011). ...
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Transboundary health impacts of transported global air pollution and international trade
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Millions of people die every year from diseases caused by exposure to outdoor air pollution. Some studies have estimated premature mortality related to local sources of air pollution but local air quality can also be affected by atmospheric transport of pollution from distant sources. International trade is contributing to the globalization of emission and pollution as a result of the production of goods (and their associated emissions) in one region for consumption in another region. The effects of international trade on air pollutant emissions, air quality and health have been investigated regionally, but a combined, global assessment of the health impacts related to international trade and the transport of atmospheric air pollution is lacking. Here we combine four global models to estimate premature mortality caused by fine particulate matter (PM_(2.5)) pollution as a result of atmospheric transport and the production and consumption of goods and services in different world regions. We find that, of the 3.45 million premature deaths related to PM_(2.5) pollution in 2007 worldwide, about 12 per cent (411,100 deaths) were related to air pollutants emitted in a region of the world other than that in which the death occurred, and about 22 per cent (762,400 deaths) were associated with goods and services produced in one region for consumption in another. For example, PM_(2.5) pollution produced in China in 2007 is linked to more than 64,800 premature deaths in regions other than China, including more than 3,100 premature deaths in western Europe and the USA; on the other hand, consumption in western Europe and the USA is linked to more than 108,600 premature deaths in China. Our results reveal that the transboundary health impacts of PM_(2.5) pollution associated with international trade are greater than those associated with long-distance atmospheric pollutant transport.
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Role of source-destination proximity in international inbound tourist arrival: empirical evidences from India
Article
  • Feb 2017
This paper analyses the influence of source-destination proximity on the relationship between three key determinants of foreign tourist arrival and inbound international tourist volume in India. The data have been collected for top 11 source countries for a period of 1992–2013. By classifying source countries based on the air travel duration to the destination, three different clusters emerge. To analyze the data, panel modeling is used with a dependent variable having negative binomial distribution. The results of the overall panel modeling reveal that while Gross National Income (GNI) and Previous Year Arrival (PYA) are significant influence on inbound tourism demand but Relative Destination Price (RDP) is not. Further, the results show that for cluster 1 (nearby countries), only PYA is a significant influence; for cluster 2, PYA and GNI are significant; and for cluster 3, all three factors are significant. The findings have important implications for International Tourism Policy and Destination Marketing Programs.
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