Healthy climate, healthy bodies: Optimal fuel taxation and physical activity [original]

Accepted: 15 September 2023

DOI: 10.1111/ecca.12497

ORIGINAL ARTICLE

Healthy climate, healthy bodies: Optimal fuel

taxation and physical activity

Inge van den Bijgaart1David Klenert2Linus Mattauch3,4,5

Simona Sulikova5

1Utrecht University, Utrecht, Netherlands

2Joint Research Centre, European Commission,

Seville, Spain

3Technical University of Berlin, Berlin,

Germany

4Potsdam Institute for Climate Impact

Research - member of the Leibniz Association,

Potsdam, Germany

5University of Oxford, Oxford, United

Kingdom

Correspondence

Linus Mattauch Faculty of Economics and

Management, Technical University of Berlin

Email: linus.ma[email protected]

Passenger transport has significant externalities, including

carbon emissions and air pollution. Public health research

has identified additional social gains from active travel,

due to the health benefits of physical exercise. Per mile,

these benefits greatly exceed the external costs from car

use. We introduce active travel into an optimal fuel taxa-

tion model and characterize analytically the second-best

optimal fuel tax. We find that accounting for active travel

benefits increases the optimal fuel tax by 44% in the USA

and 38% in the UK. Fuel taxes should be implemented

jointly with other policies aimed at increasing the uptake

of active travel.

1INTRODUCTION

Transport policies need to balance the economic gains from passenger vehicle use with a large

number of significant externalities, including air pollution, accidents, congestion and climate

change. For example, in the USA and the UK, the transport sector is the largest contributor of

greenhouse-gas emissions (Hockstad and Hanel 2018;Gabbatiss2018). Increased active travel

such as cycling and walking—even to the nearest public transport stop—can reduce these exter-

nalities, especially in urban areas. In addition, the physical exercise involved in active travel is

highly beneficial for public health, especially given high rates of inactivity and obesity in many

societies. Previous scenario-based modelling in public health research has indicated that the

health benefits of active travel exceed those from abating emissions and air pollution of private

vehicles (Woodcock et al. 2009; De Hartog et al. 2010). For example, Woodcock et al. (2009) find

that an increased active travel scenario would avoid 530 premature deaths per million population

in London annually, while a low-carbon motor vehicles scenario would save only 17.

Surprisingly, economists have yet to examine the significance of the health benefits from

active travel for optimal regulation of urban transport. While they can be assumed to know that

exercise is good for health in general, most citizens are not aware of the full extent to which it

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94 ECONOMICA

provides health benefits (Fredriksson et al. 2018). Furthermore, the effectiveness of simple inter-

ventions such as reminders or initial payments to attend a gym (Calzolari and Nardotto 2017;

Charness and Gneezy 2009) and evidence of overspending on gym contracts (DellaVigna

and Malmendier 2006) point to self-control problems and an underappreciation of the health

benefits of exercise. One might think that health benefits of active travel are a local phenomenon,

which has limited relevance for transport policy at the macro level. It is, however, yet to be

determined how fuel taxes, or more targeted instruments, should be set to reap these health

benefits in addition to mitigating the externalities of car use.

In this paper, we examine a novel economic effect by adding an active travel mode to a

model of transport externalities from car use. Households respond to higher fuel taxes by buying

more fuel-efficient cars and reducing car travel, but do not fully internalize the health benefits of

switching to active travel modes. We confirm that on a per mile basis, the monetary value of the

health benefits from active travel exceeds the social costs of unregulated externalities of carbon

emissions, air pollution, congestion and accidents by up to two orders of magnitude. First-best

policy would thus involve a large subsidy to promote active travel (at least absent health policy

to increase activity levels in general). Without such subsidies, we derive the second-best optimal

fuel tax that corrects for the externalities and the unrealized health benefits. We examine the dif-

ference for the tax rule, and quantify the appropriate tax rate when including or excluding health

benefits from active travel.

Our main finding is that the optimal tax increases by 44% in the USA, and 38% in the UK,

when health benefits from physical exercise are included. The second-best optimal fuel tax for

the USA is $12.92 per gallon, and $8.99 per gallon without physical inactivity costs, while the

current1rate in the USA is $0.50 per gallon (US Energy Information Administration 2023). The

optimal fuel tax for the UK is $6.31 per gallon, which is higher than the current rate of $3.82 per

gallon (RAC 2023). Without physical activity costs, the second-best optimal tax would be $4.56

per gallon.2

Our main contribution is to show that the health benefits from active travel are so significant

that they should change the second-best fuel tax—the most archetypal, widely used, and very

effective instrument of transport regulation. To be clear, other transport policies such as conges-

tion charges, adequate walking and cycling pathways, and parking fees will be better suited to

promoting active travel than fuel taxes (see the final subsection of Section 3, on first-best poli-

cies). Furthermore, they can better address concerns about the negative impacts that increasing

fuel prices have on low-income households, as they can be differentiated between locations.

Analysing which mix of fuel- or mileage-based taxes and more local instruments, such as urban

infrastructure overhaul, are feasible to best achieve sustainable mobility (Banister 2008) requires

geographical context and is beyond scope of our approach.

This paper builds on three distinct strands of the literature.

First, a large body studies optimal levels of fuel taxes, and which externalities should be

addressed by them (van Essen et al. 2019). In addition to generating government revenue,

fuel taxes are typically used for the purpose of reducing most forms of non-priced costs of

transport—for example, the externalities of carbon dioxide and particulate matter, or reducing

congestion by raising the cost of driving. Parry and Small (2005) derive the optimal gasoline

taxes for the USA and the UK, accounting for congestion, accidents, carbon emissions and air

pollution, and Antón-Sarabia and Hernández-Trillo (2014) apply this framework to Mexico.

Sterner (2012) compares the optimality of fuel taxes in Europe and the USA, concluding that fuel

taxes vary considerably between countries. They are inefficiently low in most European countries

(Santos 2017). Yet the optimal fuel tax literature has so far not considered the health benefits

from active travel.

Second, the field of public health, starting with Woodcock et al. (2009), has identified high

social benefits from active travel over and above the benefits from abating emissions and air pol-

lution of private vehicles (De Hartog et al. 2010; Wolkinger et al. 2018). To the majority of the

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HEALTHY CLIMATE, HEALTHY BODIES 95

population, increasing physical activity outweighs the negative impacts of increased exposure to

air pollution (Tainio et al. 2016). This is due to the overwhelmingly sedentary lifestyles in both

the UK and the USA, making physical inactivity a leading risk factor for 6 of the 10 largest

causes of death worldwide (World Health Organization 2019). Most UK adults do not exercise

regularly (37% never, 16% less than once a week, 57% admit they never do activity strenuous

enough to be out of breath; European Commission 2018). This leads to significant costs, includ-

ing higher rates of disease incidence, lower quality of life, loss of income, excess healthcare costs,

and productivity losses in the workplace. Meeting the minimum recommendations of 150 minutes

of moderate-intensity physical activity per week can reduce the risk of cognitive impairment and

dementia, depression, hypertension, several kinds of cancer, type 2 diabetes, and cardiovascular

disease (Davies et al. 2019). We build on the valuation methods in public health to quantify the

welfare cost of travel that is inactive.

Third, behavioural public economics research has elaborated on the important role of

‘internalities’ in various domains of public policy (Allcott and Sunstein 2015). An ‘inter-

nality’ occurs when an individual imposes a significant cost on herself due to behavioural

failures, broadly understood as being prevented from acting according to her true motiva-

tion. As these private costs are imposed only or mainly on oneself—which is true for lack

of physical activity—they fall outside the definition of an externality. Nonetheless, govern-

ments regulate internalities when scientific evidence substantiates them. Internality taxes have

been applied to the market for smoking (Gruber and K˝

oszegi 2004), gym memberships and

exercise (in the form of subsidies; DellaVigna and Malmendier 2006), sugary drinks (Allcott

et al. 2019a,b) and the energy and automobile market (Allcott and Wozny 2014; Allcott

and Sunstein 2015), where they also interact with environmental externalities. In the lat-

ter case, the interaction leads to a behavioural–environmental second-best problem (Shogren

and Taylor 2008).3‘Sin taxes’—surcharges on prices of goods of which people consume

too much because of internalities—have been modelled as either simple extensions of a

Pigouvian tax (O’Donoghue and Rabin 2006) or complex interactions between taxes and

individuals’ heuristics and decisions, to achieve an optimal outcome in second-best settings

(Allcott et al. 2014).

However, this body of literature has not considered the internality of physical inactivity in

urban transport. Walking, cycling and switching to public transport are considered ways in

which people can achieve ‘appropriate’ levels of physical activity as prescribed by public health

guidelines (Gibson-Moore 2019; Office of the Surgeon General 2015). People generally under-

value the contribution of physical exercise to their long-term health (Zamir and Teichman 2014).

There are two behavioural biases behind this undervaluation, which lead to insufficient levels of

exercise and further health impacts: imperfect information and insufficient self-control (Allcott

et al. 2019b). This reinforces the case for building active travel into commuting routines.

Our contribution is threefold. First, we introduce a physical-activity-related health internal-

ity into an established framework of transport decisions (Parry and Small 2005), and use this

behavioural–environmental framework to provide an analytical solution for the second-best opti-

mal fuel tax. Second, we provide an updated quantification of the external costs of travel provided

by Parry and Small (2005), considering recent research and global climate policy goals, and com-

plement this with a quantification of the health benefits of active travel. For example, updating the

carbon price estimates increases the contribution of fuel pollution to the optimal fuel tax by two

orders of magnitude. Congestion costs have also risen more significantly than accident costs since

the year 2000. In the USA, though not in the UK, the Ramsey tax component contributes more

than the individual externalities to the optimal fuel tax. Third, in terms of policy implications,

we contribute to evaluating fuel taxes as opposed to other policies. We confirm that raising the

propensity of consumers to switch to active travel modes can impact greatly the appropriate fuel

tax: the demand for vehicle miles travelled (VMT) is so inelastic that increasing the appropriate

96 ECONOMICA

elasticities to their upper bound found in the literature raises the fuel tax by up to 58% for the

UK and 78% for the USA.

To be clear, our paper does not aim for a complete characterization of first-best policy

instruments to address the underappreciation of physical activity, including information

provision, commitment devices and direct monetary incentives by health providers. Rather,

our contribution is more modest: insofar as there remains uninternalized valuation of physical

activity from direct attempts to reduce it, we show how it affects optimal transport policy through

fuel taxation.4

While local policies are attractive for addressing specific transport externalities, we focus on

fuel taxes to show that the health benefits of active travel matter even for getting the macro level of

optimal transport regulation right. Fuel taxes have some advantages over more specific transport

policies. First, they can be implemented with relatively small administrative costs compared to

other policies, since most countries already have fuel taxes in place and levels would only have

to be adjusted accordingly. Second, fuel taxes have a proven track record of reducing carbon

emissions (Bento et al. 2009;Sterner2012;OECD2019; Bretschger and Grieg 2020). Third,

they generate government revenue—which remains true if more electric vehicles in the future

will prompt a switch from fuel- to mileage-based road taxes. This revenue could be used either

for green spending, for instance on low-carbon transport infrastructure, or for compensating

households that are especially affected by the tax (Bento et al. 2009). Both measures could make

the public more supportive of fuel- or mileage-based taxation (Klenert et al. 2018).

The analysis is organized as follows. Section 2describes the analytical model, which includes

the standard externalities of road transport and the behavioural failure of ignoring health ben-

efits. We derive an analytical result for the optimal fuel tax. Section 3explains our choice of

parametrisation for quantifying second-best fuel taxes and drawing implications for first-best

options. Section 4presents the quantitative results on fuel tax levels, and traces sensitivity

and welfare effects. Section 5discusses limitations of our analysis and explains the context of

our result on fuel taxes in transport economics and policy. Section 6concludes with policy

implications.

2 ANALYTICAL FRAMEWORK

2.1 Model

To explore how the fuel tax might be adjusted optimally to account for health benefits of public

transport, we extend Parry and Small (2005) to include active travel decisions and associated

health benefits. We take advantage of the fact that in certain settings, internalities can be treated

as extensions of externalities (O’Donoghue and Rabin 2006).

We consider a representative agent with the utility function

U=u(𝜓(C,M,Tin,Tac,G),N)−𝜑(P)−𝛿(A)+𝜉(Q),(1)

where Cis the quantity of numeraire consumption, Mis total distance travelled, Tin and Tac

are total time travelled using active and inactive modes respectively, Gis exogenous government

spending, and Nis leisure, with UC,UM,UG,UN>0andUTin ,UTac <0, where the subscript

denotes a partial derivative. The level of pollution is denoted by P, while Acaptures accidents,

and health is denoted by Q. As in Parry and Small (2005), we assume that u(⋅)and 𝜓(⋅)are

quasi-concave, and 𝜑(⋅)and 𝛿(⋅)are convex. The functions 𝜑(⋅)and 𝛿(⋅)capture the disutility

from pollution and accidents, respectively. We add the concave function 𝜉(⋅), which captures the

positive utility from health Q.5

HEALTHY CLIMATE, HEALTHY BODIES 97

Total travel Mcan be separated into two components, inactive travel Min and active

travel Mac:

M=Min +Mac.(2)

Inactive travel denotes travel using modes that require very little physical activity, most impor-

tantly using the car. Active travel instead captures walking and cycling. We also consider

public transport as an active mode of travel, as it typically requires the individual to walk or

bike to the bus stop, tram stop or train station, in some cases providing up to 30% of daily

exercise recommendations (Besser and Dannenberg 2005). As such, active travel requires spend-

ing S, which will be specified further below. Inactive travel distance Min requires fuel F,and

other travel inputs H:Min =𝜒(F,H). In line with Parry and Small (2005), we assume that

Min is homogeneous of degree one with respect to its inputs. This specification allows for

multiple channels of substitution. For instance, as fuel prices increase, the agent can decide

to: (i) reduce total distance travelled M; (ii) spend more on other inactive travel inputs,

H, such as purchasing a vehicle with higher fuel economy; or (iii) increase active travel

distance Mac.

The agent spends time Tin in inactive travel. For a given distance Min,thistimeisincreasing

in the amount of congestion on roads, which we take as an increasing function of the population

average inactive miles travelled, Min:

Tin =𝜋in(Min)Min,(3)

where 𝜋in

Min >0. Here, 𝜋in is equal to the inverse of the speed of inactive travel, which we assume

the agent takes as exogenous. In equilibrium, Min =Min. For active travel, we abstract from

congestion,6and model time travelled as directly proportional to distance:

Tac =𝜋acMac,(4)

with 𝜋ac the inverse of speed from active mobility. Only inactive travel contributes to pollution, in

the form of both carbon dioxide emissions and local air pollution. Carbon emissions are directly

proportional to fuel use. To capture local air pollution effects, inactive miles travelled offer a

better proxy (Hitchcock et al. 2014).7This allows us to write

P=Pf(F)+Pm(Min),(5)

with Pf

F>0andPm

Min >0. We also assume that the agent will take pollution as given; she will not

internalize the effect of travel decisions on the population averages Fand Min.

Both active and inactive travel are subject to accident risk. We assume that agents internalize

own accident risk and separate accident costs associated with active and inactive travel. For inac-

tive travel, accident costs are increasing with the amount of travel. As travel increases, the agent

also imposes an ‘accident externality’ upon other users: the higher average travel Min,themore

likely a road user will be involved in an accident. For active travel, we similarly assume that higher

travel increases the number of, and thereby costs of, accidents. Yet roads that are busier with cars

tend to be more dangerous to both cyclists and pedestrians. Conversely, there exists a so-called

‘safety in numbers’ effect: more cyclists on the road tend to make cycling safer overall (Elvik and

Bjørnskau 2017; Kahlmeier et al. 2017). Hence we assume that the accident costs associated with

active travel are increasing in the average amount of inactive travel Min, and decreasing in average

active travel Mac.Thisgives

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