Pathway to zero emissions in global tourism: opportunities, challenges, and implications

Figures & data

Figure 1. The variation in tourism CO₂ emissions for uncertainty in demographic, economic and technological background. The model was run 5000 times following a Monte Carlo distribution for the inputs listed in the text.

Figure 2. The BAU emission projections strongly contrast with the Emissions goal pathway shown by the black line. Notes: (1) The emissions for “other” transport are very small.

Figure 3. The impacts of various off-setting assumptions on aviation emissions. Notes about effects of varying assumptions for offsetting (see Section 1.5 of the Supplementary File for background information):

- Reference (1.5 °C): no offsets.

- CORSIA, ICAO assumptions: CORSIA share of aviation (domestic excluded), 100% effectiveness of off-sets, unlimited off-set resources.

- CORSIA with limited credits: CORSIA share of aviation, 100% effectiveness of off-sets, maximum of 30% share of available off-sets for aviation.

- CORSIA realistic: CORSIA share of aviation, 20% effectiveness and 30% share of available off-sets for aviation.

- Most optimistic off-setting assumptions: 100% aviation emissions, 100% effectiveness of off-sets, 100% share of available off-sets for aviation.

- Technically realistic off-setting assumptions: 100% aviation emissions, 20% offset effectiveness and 30% share of global available off-sets for aviation.

Figure 4. The potential of sustainable aviation fuels (SAF) for various assumptions about types and land-use and renewables constraints. The 1.5 °C refers to a background scenario where the global temperature is limited to 1.5 °C and the carbon cost and amount of renewable energy globally available in such a scenario.

Figure 5. The potential for revolutionary technology in aviation for short-, medium-, and long-haul range aircraft, and all three combined (technology scenario). The fleet replacement case shows the theoretical emissions when zero emissions aircraft would come onto the market for all ranges by 2025. The “Reference (1.5 °C)” refers to the background that limits the global temperature rise to 1.5 °C.

Figure 6. The impact of the isolated policy categories and some combinations. The “Reference (1.5 °C)” refers to a global background scenario that limits the global temperature to 1.5 °C, while the other reference scenario assumes the temperature is allowed to rise to 3.3 °C. The main difference for the tourism system is in the different carbon cost in these two background scenarios.

Table 1. Overview of all assumptions of the ZET scenario that deviate from the Reference Scenario.

Description	Reference Scenario	ZET scenario	Comments
Background Scenario (influences basic carbon cost development)
Temperature anomaly scenario	3.3 °C	1.5 °C	Assume medium GDP and population growth, and default GINI.
Policies sustainable alternative fuels (SAF)
Land-use capacity	Physical	Physical	This choice ensures minimal competition with food and nature for SAF-B. As we apply only SAF-E, the land-use limit is not a constraint.
SAF type	None	Option E: e-fuel	Only rely on e-fuels.
Policy (subsidy/mandate)	None	Mandate
Policy e-fuel production energy conversion efficiency	Not relevant		Rising from 20% in 2025 to 60% in 2036.
Policy SAF maximum renewables (%)	Not relevant	10%	In the ZET scenario, the share peaks in 2038 and reduces to below 7.5% in 2056.
Policy SAF maximum mandate	Not relevant	100%	Zero emissions flight with current jets can only be reached with 100% e-fuel.
SAF S-curve start and end years	–	2025/2050	This governs the steepness of the S-curve mandate.
Policies technology
Car efficiency improvement rate (% per year)	−0.55%	−3.5% per year	Reduces quest for renewables.
Electric car share goal (%)	10%	100%	By 2050.
Air additional efficiency improvement (% per year)	0.00%	−0.27% per year	Based on margins for current jets to additionally improve efficiency by technology.
Other transport efficiency improvement (% per year)	−0.50%	−2.5% per year	Particularly for rail transport.
Other transport electric goal share	50%	100%	Electrification by 2050.
Accommodation efficiency improvement (% per year)	−0.50%	−2.5% per year
Accommodation electric goal	50%	100%	By 2050.
Policy zero emissions aircraft entry into service year	2100, 2100, 2100	2035, 2042, 2048	For respectively short-, medium-, and long-haul aircraft types; if set at 2100, no zero emissions aircraft will be introduced.
Policy fuel cell aircraft emission reduction factor	0.01	0.01	Means 1% emissions per passenger-km remain.
Policy fuel cell aircraft speed reduction	10%	10%	Electric propulsion is likely to reduce the speed of aircraft.
Policies infrastructure
Maximum aircraft scrap age	50 years	40 years	The average final time of service life for aircraft.
High-speed rail investments (billion 1990USD)	10–29 $ per year		In 2000s, some $20–40 billion was invested, rising to $200 billion by 2025 continuing to 2100.
Global airport maximum capacity time curve in the number of slots	500*10^6 flights per year		Unlimited until 2025, then down to 16 million and slowly growing after 2045.
Policies taxes and subsidies
Tourism carbon tax (1990 USD)	$0/ton CO2	$0–450/ton CO2	This is not an imposed tax but the background scenario-induced carbon cost.
Sectors the tourism carbon tax is applied to	All except aviation	All except other transport
Global ticket tax air transport	0%		Maximum of 23% in 2065.
Global kilometre tax car transport	0%		Maximum 50%.
Global ticket subsidy other transport	0%		Maximum 70% subsidy in 2025.
Policies behaviour
Marketing policy that factors the desire to travel (trips per capita per year) into the time curve	1.0		1.17 in 2100, meaning that the number of trips for a certain level of income is roughly 5–10% higher than in the Refence Scenario, depending on several other variables.
Policies off-setting
Off-setting scheme	None	CORSIA	In CORSIA, we assume each ton of offsets reduces may reduce 0.2 tons of emissions and aviation is able to acquire a maximum of 30% of global offset credits.

Figure 7. The final ZET scenario emissions per main tourism subsector.

Figure 8. The impacts of the ZET scenario on the number of trips (upper left), kilometres travelled (upper right), revenues (lower-left and guest nights (lower right).

Figure 9. Market distribution graphs (billion trips; Mton CO₂ (upper-right)) as a function of distance class (lower and upper bounds shown in kms one-way on the horizontal axis).

Figure 10. The impact of policy failure on all tourism emissions for the failure of bringing zero emissions aircraft to the market (EIS delayed, upper left), a failure to implement the SAF-E blending mandate (upper right) and a failure of the slot capacity policy (lower left). The lower right figure shows the impact of all three failures concurrently.