Editing IPCC:AR6/WGIII/Chapter-10 (section)

=== 10.2.2 Behaviour and Mode Choice ===

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Behaviour continues to be a major source of interest in the decarbonisation of transport as it directly addresses demand. Behaviour is about people’s actions based on their preferences. [[IPCC:Wg3:Chapter:Chapter-5|Chapter 5]] described an ‘Avoid, Shift, Improve’ process for demand-side changes that affect sectoral emissions. This section discusses some of the drivers of behaviour related to the transport sector and how they link to this ‘Avoid, Shift, Improve’ process.

'''Avoid: the effect of prices and income on demand.''' Research has shown that household income and price have a strong influence on people’s preferences for transport services ( [[#Bakhat--2017|Bakhat et al. 2017]] ; [[#Palmer--2018|Palmer et al. 2018]] ). The relationship between income and demand is defined by the income elasticity of demand. For example, research suggests that in China, older and wealthier populations continued to show a preference for car travel ( [[#Yang--2019|Yang et al. 2019]] ) while younger and low-income travellers sought variety in transport modes ( [[#Song--2018|Song et al. 2018]] ). Similarly, [[#Bergantino--2018b|Bergantino et al. (2018b)]] evaluated the income elasticity of transport by mode in the UK. They found that the income elasticity for private cars is 0.714, while the income elasticities of rail and bus use are 3.253 (the greater elasticity, the more the demand will grow or decline, depending on income). Research has also shown a positive relationship between income and demand for air travel, with income elasticities of air travel demand being positive and as large as 2 ( [[#Gallet--2014|Gallet and Doucouliagos 2014]] ; [[#Valdes--2015|Valdes 2015]] ; [[#Hakim--2016|Hakim and Merkert 2016]] ; [[#Hakim--2019|Hakim and Merkert 2019]] ; [[#Hanson--2022|Hanson et al. 2022]] ). A survey in 98 Indian cities also showed income as the main factor influencing travel demand (Ahmad and de Oliveira 2016). Thus, as incomes and wealth across the globe rise, demand for travel is likely to increase as well.

The price elasticity of demand measures changes in demand as a result of changes in the prices of the services. In a meta-analysis of the price elasticity of energy demand, [[#Labandeira--2017|Labandeira et al. (2017)]] report the average long-term price elasticity of demand for gasoline and diesel to be –0.773 and –0.443, respectively. That is, demand will decline with increasing prices. A similar analysis of long-term data in the United States (US), the United Kingdom (UK), Sweden, Australia, and Germany reports the gasoline price elasticity of demand for car travel (as measured through vehicle-kilometre – vkm – per capita) ranges between –0.1 and –0.4 ( [[#Bastian--2016|Bastian et al. 2016]] ). For rail travel, the price elasticity of demand has been found to range between –1.05 and –1.1 ( [[#Zeng--2021|Zeng et al. 2021]] ). Similarly, price elasticities for air travel range from –0.53 to –1.91 depending on various factors such as purpose of travel (business or leisure), season, and month and day of departure ( [[#Morlotti--2017|Morlotti et al. 2017]] ). The price elasticities of demand suggest that car use is inelastic to prices, while train use is relatively inelastic to the cost of using rail. Conversely, consumers seem to be more responsive to the cost of flying, so that strategies that increase the cost of flying are likely to contribute to some avoidance of aviation-related GHG emissions.

While the literature continues to show that time, cost, and income dominate people’s travel choices (Ahmad and de Oliveira 2016; [[#Capurso--2019|Capurso et al. 2019]] ; [[#He--2020|He et al. 2020]] ), there is also evidence of a role for personal values, and environmental values in particular, shaping choices within these structural limitations ( [[#Bouman--2019|Bouman and Steg 2019]] ). For example, individuals are more likely to drive less when they care about the environment ( [[#De%20Groot--2008|De Groot et al. 2008]] ; [[#Abrahamse--2009|Abrahamse et al. 2009]] ; [[#Jakovcevic--2013|Jakovcevic and Steg 2013]] ; [[#Hiratsuka--2018|Hiratsuka et al. 2018]] ; [[#Ünal--2019|Ünal et al. 2019]] ). Moreover, emotional and symbolic factors affect the level of car use ( [[#Steg--2005|Steg 2005]] ). Differences in behaviour may also result due to differences in gender, age, norms, values, and social status. For example, women have been shown to be more sensitive to parking pricing than men ( [[#Simićević--2020|Simićević et al. 2020]] ).

Finally, structural shocks, such as a financial crisis, a pandemic, or the impacts of climate change could affect the price and income elasticities of demand for transport services ( [[#van%20Ruijven--2019|van Ruijven et al. 2019]] ). COVID-19 lockdowns reduced travel demand by 19% (aviation by 32%) and some of the patterns that have emerged from the lockdowns could permanently change the elasticity of demand for transport ( [[#Tirachini--2020|Tirachini and Cats 2020]] ; [[#Hendrickson--2020|Hendrickson and Rilett 2020]] ; [[#Newman--2020a|Newman 2020a]] ; [[#SLoCaT--2021|SLoCaT 2021]] ; [[#Hanson--2022|Hanson et al. 2022]] ). In particular, the COVID-19 lockdowns have spurred two major trends: electronic communications replacing many work and personal travel requirements; and revitalised local active transport and e-micromobility ( [[#Newman--2020a|Newman 2020a]] ; [[#SLoCaT--2021|SLoCaT 2021]] ). The permanence of these changes post-COVID-19 is uncertain but possible ( [[#Earley--2021|Earley and Newman 2021]] ) (Cross-Chapter Box 1 in Chapter 1). However, these changes will require growth of infrastructure for better ICT bandwidths in developing countries, and better provision for micromobility in all cities.

'''Shift: mode choice for urban and intercity transport.''' Shifting demand patterns (as opposed to avoiding demand) can be particularly important in decarbonising the transport sector. As a result, the cross-elasticity of demand across transport modes is of particular interest for understanding the opportunities for modal shift. The cross-elasticity represents the demand effect on mode i (e.g., bus) when an attribute of mode j (e.g., rail) changes marginally. Studies on the cross-elasticities of mode choice for urban travel suggest that the cross-elasticity for car demand is low, but the cross-elasticities of walking, bus, and rail with respect to cars are relatively large ( [[#Fearnley--2017|Fearnley et al. 2017]] ; [[#Wardman--2018|Wardman et al. 2018]] ). In practice, these cross-elasticities suggest that car drivers are not very responsive to increased prices for public transit, but transit users are responsive to reductions in the cost of driving. When looking at the cross-elasticities of public transit options (bus vs metro vs rail), research suggests that consumers are particularly sensitive to in-vehicle and waiting time when choosing public transit modes ( [[#Fearnley--2018|Fearnley et al. 2018]] ). These general results provide additional evidence that increasing the use of active and public transport requires interventions that make car use more expensive while making public transit more convenient (e.g., smart apps that tell the user the exact time for transit arrival (Box 10.1)).

The literature on mode competition for intercity travel reveals that while cost of travel is a significant factor ( [[#Zhang--2017|Zhang et al. 2017]] ), sensitivity decreases with increasing income as well as when the cost of the trip was paid by someone else ( [[#Capurso--2019|Capurso et al. 2019]] ). Some research suggests little competition between bus and air travel but the cross-elasticity between air and rail suggest strong interactions ( [[#Wardman--2018|Wardman et al. 2018]] ). Price reduction strategies such as discounted rail fares could enhance the switch from air travel to high-speed rail. Both air fares and flight frequency impact high-speed rail (HSR) usage ( [[#Zhang--2019b|Zhang et al. 2019b]] ). Airline companies reduce fares on routes that are directly competing with HSR (Bergantino et al. 2018a) and charge high fares on non-HSR routes ( [[#Xia--2016|Xia and Zhang 2016]] ). On the Rome to Milan route, better frequency and connections, and low costs of HSR resulting from competition between HSR companies have significantly reduced air travel and shares of buses and cars ( [[#Desmaris--2018|Desmaris and Croccolo 2018]] ).

Finally, and as noted in Chapter 5, recent research shows that individual, social, and infrastructure factors also affect people’s mode choices. For example, perceptions about common travel behaviour (what people perceive to be ‘normal’ behaviour) influence their travel mode choice. The research suggests that well-informed individuals whose personal norms match low-carbon objectives, and who believe they have control over their decisions, are most motivated to shift mode. Nonetheless, such individual and social norms can only marginally influence mode choice unless infrastructure factors can enable reasonable time and cost savings ( [[#Convery--2019|Convery and Williams 2019]] ; [[#Javaid--2020|Javaid et al. 2020]] ; [[#Feng--2020|Feng et al. 2020]] ; [[#Wang--2021|Wang et al. 2021]] ).

'''Improve: consumer preferences for improved and alternative vehicles.''' While reductions in demand for travel and changes in mode choice can contribute to reducing GHG emissions from the transport sector, cars are likely to continue to play a prominent role. As a result, improving the performance of cars will be crucial for the decarbonisation of the transport sector. Sections 10.3 and 10.4 describe the technological options available for reduced CO 2 emissions from vehicles. The effectiveness in deploying such technologies will partly depend on consumer preferences and their effect on adoption rates. Given the expanded availability of electric vehicles, there is also a growing body of work on the drivers of vehicle choice. A survey in Nanjing found women had more diverse travel purposes than men, resulting in a greater acceptance of electric bikes ( [[#Lin--2017|Lin et al. 2017]] ). Individuals are more likely to adopt an electric vehicle (EV) when they think this adoption benefits the environment or implies a positive personal attribute ( [[#Noppers--2014|Noppers et al. 2014]] ; [[#Noppers--2015|Noppers et al. 2015]] ; [[#Haustein--2018|Haustein and Jensen 2018]] ). Other work suggests that people’s preference for EVs depends upon vehicle attributes, infrastructure availability, and policies that promote EV adoption, specifically, purchasing and operating costs, driving range, charging duration, vehicle performance, and brand diversity ( [[#Liao--2016|Liao et al. 2016]] ). Behaviour change to enable transport transformations will need to make the most of these factors while also working on the more structural issues of time, space, and cost.

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