metadata: authors: 0: author: Commander,Simon John
1: author: Nikoloski,Zlatko Slobodan
2: author: Vagliasindi,Maria
count: Egypt, Arab Republic of,Yemen, Republic of,Middle East and North Africa
docna: 0: docna: Estimating the size of external effects of
            energy subsidies in transport and agriculture
docty: Policy Research Working Paper
subtopic: Health Care Services Industry,Disability,Services & Transfers to Poor,Energy Policies & Economics,Hydrology,Economic Assistance,Access of Poor to Social Services,Energy Demand,Energy and Environment,Energy and Mining,Transport Services
teratopic: Transport,Water Resources
abstracts: cdata!: It is widely accepted that the costs of
            underpricing energy are large, whether in advanced or
            developing countries. This paper explores how large these
            costs can be by focussing on the size of the external
            effects that energy subsidies in particular generate in two
            important sectors—transport and agriculture—in two countries
            in the Middle East and North Africa, the Arab Republic of
            Egypt (transport) and the Republic of Yemen (agriculture).
            The focus is mainly on the costs associated with congestion
            and pollution, as well as the impact of underpriced energy
            for depletion of scarce water resources, including through
            crop selection. Quantifying the size of external effects in
            developing countries has received relatively little
            analytical attention, although there is a significant body
            of literature for developed countries. By building on
            earlier research, as well as employing the United Nations
            ForFITS model, the paper provides indicative estimates of
            the external costs of energy subsidies, as manifested in
            congestion and pollution. The estimates using simulations
            indicate that these costs could be materially reduced by
            elimination or reduction of energy subsidies. The paper also
            describes the impact of energy subsidies on water
            consumption in a region where water resources are
            particularly limited.  The findings provide further evidence
            of the adverse and significant consequences of subsidizing energy.
repnme: repnme: Estimating the size of external effects of
            energy subsidies in transport and agriculture
display_title: Estimating the size of external
            effects of energy subsidies in transport and agriculture
listing_relative_url: /research/2015/04/24249970/estimating-size-external-effects-energy-subsidies-transport-agriculture
topicv3: Energy Subsidies m1326386 1627,Water m1326522 137,Energy and Extractives m1326595 507,Transport m1326661 135,Health m1504661 3480
publishtoextweb_dt: 2015-04-01T00:00:00Z
historic_topic: Transport,Water Resources
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
no_of_pages: 45
author_keys: 0: author_key: 93418
1: author_key: 686518
2: author_key: 619680
model_extractions: WPS7227 Policy Research Working Paper 7227 Estimating the Size of External Effects of Energy Subsidies in Transport and Agriculture _Simon Commander Zlatko Nikoloski Maria Vagliasindi_ Energy and Extractives Global Practice Group April 2015
Policy Research Working Paper 7227

# **Abstract**

It is widely accepted that the costs of underpricing energy are large, whether in advanced or developing countries. This paper explores how large these costs can be by focussing on the size of the external effects that energy subsidies in particular generate in two important sectors—transport and agriculture—in two countries in the Middle East and North Africa, the Arab Republic of Egypt (transport) and the Republic of Yemen (agriculture). The focus is mainly on the costs associated with congestion and pollution, as well as the impact of underpriced energy for depletion of scarce water resources, including through crop selection. Quantifying the size of external effects in developing countries has received relatively little analytical attention, although there is a significant body of literature for developed countries. By building on earlier research, as well as employing the United Nations ForFITS model, the paper provides indicative estimates of the external costs of energy subsidies, as manifested in congestion and pollution. The estimates using simulations indicate that these costs could be materially reduced by elimination or reduction of energy subsidies. The paper also describes the impact of energy subsidies on water consumption in a region where water resources are particularly limited. The findings provide further evidence of the adverse and significant consequences of subsidizing energy.

This paper is a product of the Energy and Extractives Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at mvagliasindi@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Estimating the Size of External Effects of Energy Subsidies in Transport and Agriculture**

**Simon Commander, Zlatko Nikoloski and Maria Vagliasindi**<sup>**1**</sup>

**Keywords: energy subsidies, pollution, congestion, health effects of energy subsidies JEL classification codes: O13, R41, Q41, Q53, I15**

> 1 Author affiliations: Simon Commander, Altura Partners and IE Business School, scommander@alturapartners.org; Zlatko Nikoloski, London School of Economics, z.nikoloski@lse.ac.uk and Maria Vagliasindi, World Bank, <u>mvagliasindi@worldbank.org. Our special thanks go to Shanta Devarajan and Junaid Ahmad for their excellent</u> advice and support throughout the preparation of this paper and to Hanane Ahmed, Pierpaolo Cazzola, Ziad Nakat, Maurice Saade, Andreas Schliessler, Steven Schonberger, Caroline van den Berg, and Patricia Veevers-Carter for their helpful suggestions.
# **1. Introduction**

The underpricing of energy – notably fuel products – has a predictable, and sometimes significant, impact on demand. Energy subsidies – if persistent – can also affect the dynamic factor mix, creating a bias for energy-intensive production and usage. Indeed, existing evidence from MENA indicates that pervasive energy subsidies have created large distortions in markets and investment choices.

One consequence of the underpricing of energy that is relatively understudied and quantified concerns the external costs that arise through the pollution and congestion that result from excess use of fossil-fuel-powered vehicles, as well as the associated transport modal choices. For example, evidence from the Arab Republic of Egypt, notably Cairo, indicates massive – and growing - congestion with high levels of associated pollution, part of which can be attributed to the excess demand for private vehicles and fuel consumption that results from the large subsidies that fuels attract. While transport – particularly road transport – may offer a particularly stark illustration of the external costs, significant costs may also arise in other sectors. In agriculture, fuel subsidies – depending on the institutional and pricing arrangements for water supply – can lead to over-rapid depletion of water reserves and to crop selection that may principally reflect the underpricing of water rather than the comparative advantage of the country or region. Similarly in manufacturing, the sectoral mix of output and employment may be directly affected by the price of energy that may also influence the choice of technology. When energy prices are low, production will tend to be energy intensive and wasteful and this is often associated with relatively high rates of pollution and other external effects.

The magnitude of external effects associated with the underpricing of energy has not been consistently measured. However, the IMF (2013) has attempted to estimate the difference between pre- and post-tax subsidies where the latter include a tax aimed at charging for external effects linked to pollution, CO2 emissions, congestion and so on, as well as an additional tax on energy consistent with the standard indirect tax rates applying in the country. Although their calculation is clearly very approximate, it provides an initial reference point. For Egypt the gap between pre-and post-tax subsidies expressed as a share of government revenues was over 8 percentage points (30.6 versus 39.1) in 2011. This discrepancy is far from trivial and for reasons that will become clear later, is likely to be an underestimate.

This paper is exploratory – not least because of serious data limitations - and as a consequence we restrict our attention to understanding the size of external effects of energy subsidies in two sectors – transport and agriculture – focusing, in the first instance, on the congestion and pollution costs and, in the second instance, focusing on resource (water) depletion and crop selection. At this point, two locations are selected – Cairo in Egypt for the transport dimension and the Republic of Yemen for the agriculture dimension.

The paper is organized as follows. Section 2 identifies the external effects that we are interested in and the main channels or processes through which energy pricing affects the main variables of interest. It then outlines how they affect the performance of economic actors and the economy as a whole. In discussing the main methodological and empirical issues it also reviews the results of earlier, relevant studies. Section 3 then provides evidence for transport from the MENA 2
region concerning both context and selected outcomes. Section 4 then looks in more detail at Egypt starting with an overview of fuel pricing before turning to estimates of the costs of congestion and pollution. Section 5 is concerned with the water-energy nexus and the ways in which mispricing of energy affects water use and depletion with a specific emphasis on the Republic of Yemen. Section 6 provides a first, tentative estimate of the costs of pollution and congestion drawing on data contained in a recent World Bank study of Cairo. These measures are linked to a wider health/productivity indicator initially assembled by the World Health Organization, termed DALY. We provide a simple simulation of the impact of energy price increases (viz., reduction in subsidy) on these variables. Section 7 lays out a more extended modeling framework (relying on the UN ForFITS model) that we apply to measure the impact of energy pricing on pollution, congestion and CO2 emissions, as well as capturing the interactive effects relating to modes of transport and substitutions across modes. More specifically, this section represents a simulation exercise that links various scenarios (changes in international oil prices and reduction in domestic subsidies, both in medium and short term) to the overall CO2 emissions. The results from this exercise are then used to quantify the cumulative health effect of energy subsidies reduction.

# **2. Transport: External effects and the main channels**

Vehicular transport reliant on fossil fuels generates a range of effects that have economic costs. These can be grouped under a number of rubrics. The first relates to the direct effect of emissions. There is a substantial body of evidence that vehicle emissions can affect not only individuals’ health but also have a wider effect on climate change variables, the consequences of which may be both local and global, although these may not be easily quantifiable. Regarding the first channel, the usual way of thinking about this is to try to estimate what the emissions associated with vehicle usage do to a set of health indicators and, by implication, to productivity. A negative shock to health – whether through a fall in life expectancy or an increase in morbidity – will have a direct impact on productivity and potentially on growth. For simplicity, this can be termed the productivity channel where the link is from use of a particular set of transport technologies to emissions to health outcomes. In these instances, there may be a complex lag structure and some important non-linearities. For example, emissions may reach certain thresholds beyond which health outcomes deteriorate at an accelerated pace. But fossil-fuel transport use will also tend to affect productivity through further related channels. Notably, congestion – resulting from an excess of vehicles for a given stock of transport infrastructure – will affect the amount of time that people require for, inter alia, getting to and from work and hence act as an effective subtraction from working time. There may in addition be a host of other more intangible effects of a psychological nature that can have an impact on individuals’ behavior and productivity. Anecdotal data from a wide range of locations – not just in MENA – suggests that these effects can be large and highly deleterious.

To this point, the broad problem has been framed in terms of an impact of a broad technology on outcomes at the level of individuals with that impact then aggregating to impose some economy-wide impact. However, our problem in this paper is narrower. Specifically, we are interested in the marginal impact of an energy price subsidy – defined as the deviation from a 3
market price – on demand and ultimately through consumption on the external indicators, congestion and pollution in the case of transport, and through these the wider economic and social costs that are imposed. As such, we are interested in measuring the incremental impact on pollution and congestion that result from the underpricing of energy.

Our aim at this stage is to arrive at some initial benchmark estimates of the scale of these costs using a variety of measures. An important caveat is in order at this point. Such calculations tend to require highly disaggregated information, whether on types of vehicle use and occupancy, as well as on outcome variables, whether it be congestion and/or pollution themselves or associated indicators, such as accidents. There is also the link to be made between the variables of interest – congestion and pollution – and individuals’ productivity. Each of these steps is relatively data-intensive. Yet, the reality is that for most developing countries, including in MENA, such data are not available or are limited in coverage and quality. This necessarily makes precision in calculation difficult, if not impossible. To surmount these major limitations, we have, in the initial part of the paper, recourse to values or parameter estimates that may be drawn from more data-rich contexts, either from advanced economies or other MENA countries.

# **2.1 Agriculture**

Our main focus in this paper is on how energy subsidies might have an impact upon: (i) water usage (primarily through affecting the cost of pumping) and, (ii) crop selection. Much of the available literature has focused on India where two main channels through which energy subsidies impact upon the economy have been identified. First, they have encouraged farmers to withdraw groundwater at high, probably unsustainable, rates. The rapid rates of groundwater extraction lowers groundwater tables, which in turn requires more energy to pump water to the surface: this process creates a trap in which eliminating or lowering the subsidy leads to groundwater extraction costs that would make agricultural production unprofitable for many farmers. Second, the excessive use of electricity in water extraction makes electricity more expensive for the non-farm economy, inhibiting thus the non-farm economy’s ability to absorb labor from the farm economy, and hence, serves as a drag on the county’s economic growth potential (IFPRI (2011)).

Based upon this, Nelson _et al._ (2013) use a computable general equilibrium model to evaluate the economic impact of groundwater depletion on the agricultural and non-agricultural sectors of Punjab and the rest of India. Their findings suggest that eliminating electricity subsidies for irrigation could lead to less groundwater consumption and lower agricultural production levels and agricultural income, while increasing the productivity and income of the non-farm sectors (via decreased energy prices). When cutting the electricity subsidy, the results suggest that farmers could decrease water use by 30%, while minimally hurting agricultural value-added economy (a decrease of approximately 5% of income in 2007 dollars). Perhaps more surprising is the finding that this could be associated with a very large increase in manufacturing output.

Additional methodologies for assessing the link between energy and agriculture include inputoutput models. In the Malaysian context, Bekhet (2010) uses an input-output model to estimate the link between three energy sectors and agriculture for the period 1991-2000. The analysis shows that the agriculture sector is heavily based upon inputs from petrol and coal industries, 4
most notably because of subsidized energy products. Given the weak linkages between agriculture and the electricity and gas sectors, the paper advocates switching to these energy products whenever possible.

Using co-integration analysis, Jha _et al._ (2012) analyze the relationship between agriculture and energy use. Their conclusion is that agriculture in India has become very energy intensive, thus requiring further technological change involving more energy-efficient farm machinery and irrigation systems. However, concentrating solely on technically improving the efficiency of pumps might further aggravate the speed at which water tables are depleted. There is thus a need to first optimize water demand in agriculture through a broader approach to the water-energy nexus. This would include massive state investments to improve surface irrigation, groundwater table management, irrigation technologies, agricultural practices (including organic agriculture and crop diversification) as well as food procurement policies.

The available literature suggests that the problem of water depletion is both technical (for example, imposing metered tariffing for water), and political. IFPRI sums up the policy recommendations for addressing groundwater depletion due to excessive energy use as, (1) options linked to electricity supply: (a) meter use and increase in agricultural tariff; (b) restrictions on timing of electricity supply; (c) possible restrictions on choice of crops by withdrawing free electricity or subsidy; and (2) options not directly linked to electricity supply, including, (a) increased regulation; (b) community-based groundwater management; (c) state ownership and management of bore wells; (d) promotion of less water-intensive crops and cultivation practices, and (e) groundwater-recharge measures (IFPRI (2011)).

# **2.2 Measuring productivity-affecting outcomes**

# **Pollution**

While the measures that are widely used to quantify pollution (either pollution in general or air pollution due to traffic) are largely uncontroversial, what we are primarily interested in is the impact of those pollution outcomes on persons. Here, a widely used measure is the WHO metric of DALY or Disability-Adjusted Life Year. The basic idea is that a combination of increased mortality/morbidity (i.e. increase of deaths and increase of years lived in sub-optimal health) will result from higher levels of emissions, as measured, for example, by particles (PM10, PM2.5, Pb). The calculations are based on epidemiological functions, whose coefficients capture the increased level of mortality and morbidity beyond the particles’ threshold point (20 mg per cubic meter for PM10, 10 mg per cubic meter for PM2.5, etc.). DALYs for a disease or health condition are calculated as the sum of the Years of Life Lost (YLL) due to premature mortality in the population and the Years Lost due to Disability (YLD) for people living with the health condition or its consequences. For our purposes, DALY would be a sum of deaths due to vehicle-specific air pollution (multiplied by the average life expectancy in the city/country) and YLD of the (vehicle specific) air pollution. Calculating YLD is a bit more complex but in essence it is a product of the number of incident cases of disease caused by air pollution (respiratory, cardio-vascular), disability weight and average duration of the case until remission to death. The disability weight (also called the dose function) depicts the relationship between the pollutants 5
(lead (Pb), CO, CO2 and particulates such as PM10, PM2.5) and specific health variables (incidence of bronchitis, hospital admissions, emergency room visits etc.). The specific parameters of the dose function for air pollution have been reasonably well studied (for example, Mayeres et al (1996), WHO (1999), Ostro et al (2004), Pope et al (1995, 2002 and 2009), as well as the EU project ExternE). The final estimate of the social cost of pollution would be a product of the DALY and average GDP per capita (for a given period).<sup>2</sup>

# **Congestion**

There is also a large literature on congestion. Recent studies include Roobuste et al (2001), Mizutani et al (2011) with their common focus on estimating the social costs of traffic congestion. Measuring these costs is often done in in three steps. The first is to estimate the daily time lost due to traffic congestion. Time loss is specified as a function of traffic volume and road length and speed. The second is to calculate the annual time loss – or opportunity cost - due to traffic congestion. Finally, the monetary value of traffic congestion is calculated. Time loss caused by congestion relies on a measure of the value of time. For example, detailed studies have calculated the value of time for each vehicle type, as vehicle types serve as proxies for different groups of people and hence different opportunity costs (see INFRAS/IWW 2004). Additional costs of vehicular use can include accidents and noise. Again, for simplicity, studies tend to rely on unit costs for specific modes of transport. In short, measurement of congestion costs boils down to: (a) loss of time and its value; and (b) excess energy use due to recurrent and nonrecurrent delays.

# **Intermodal choice**

Fuel subsidies can affect not only aggregate demand but can also have implications for the modal mix, often with complex feedbacks. For example, Forkenbrock (1999, 2001) has estimated the external costs of intercity truck freight and railway transportation looking at pollution, traffic accidents and noise, as well as climate change.<sup>3</sup> Another recent study done for Barcelona, finds that the external costs of cars and motorcycles are 9 euro cents per km trip; almost seven times larger than the external costs of public transport, estimated at 1.3 euro cents per km trip. Although that study does not estimate the indirect cost of freight (railway vs. road), the same logic could be applied in calculating those numbers as well (Robuste _et al.,_ 2001).

A significant proportion of the literature is devoted to studying the determinants of modal choice (mainly for passenger transport). Among the main factors are socio-demographic commonly measured by age, gender, occupation, education, income, household composition and car availability (De Witt _et al._ , 2013). The most robust relationship is between income and car ownership. More rudimentary models only consider income as the only determinant of car ownership (Schaefer, 2000, for instance etc.). Similarly, a paper on Jordan (Al-Ghandor _et al._ ,

> 2 This seems to be the dominant method. See, for instance, Doumani (2011) in the case of Cairo, Mizutani _et al._ (2011) in the case of Japan. Monzon and Guerrero (2004) for the case of Madrid use a simpler method of valuing the cost of pollution as the sum of deaths due to pollution (times GDP per capita) and total excess hospital costs due to increased hospital admissions as a result of increased pollution.

> 3 The unit costs for air pollution, climate change and noise are all taken from INFRAS/IWW study (1994) and its subsequent updates.

6
2013) using historical data and multinomial regression analysis finds that an increase in income leads to increased vehicle ownership, which, in turn, increases gasoline consumption. The literature also looks at spatial indicators, such as density, diversity, proximity to infrastructure and services, frequency of public transport and parking. Most of the literature here focuses on the urbanization/public transport nexus and finds a positive link between urbanization and reliance on public transport (Camagni _et al._ , 2002; Limtanakool _et al._ , 2006), although with the caveat that this research is based on findings from advanced economies. A further strand concentrates on journey characteristics including travel motives, distance and time, travel costs and so on. A paper on Spain (Rojo _et al._ , 2012) focusing on inter-city travel, uses discrete choice analysis to suggest that passengers tend to value time the most. Travel cost is also well documented as an important determinant of modal choice (De Witt _et al._ , 2013), with consumers being sensitive to price changes, but the extent to which depending on several factors, including the purpose of the trip etc. (Annema, 2002; Litman, 2004). In addition, socio-psychological factors have also been considered. Some of these (attitudes, lifestyle, experience) are largely a function of income, so the literature suggest a somewhat similar relationship between them and decisions on a modal choice (De Witt _et al._ , 2013). Needless to say, most of this research has been conducted in advanced economies.

A separate strand of the literature has focused on simulating/studying the impact of various policy actions on the modal choice (and in addition on car ownership/gasoline consumption and CO2 emissions). For Jordan, Al-Ghandor _et al._ , (2013) do some projections of gasoline consumption with the current level of subsidies and with an alternative policy scenario of removing fuel/energy subsidies. The findings suggest that with current pricing policies gasoline consumption is expected to rise by 1.8% a year (in line with the increase in per capita income/car ownership). Reduction of energy subsidies could reduce the increase of gasoline consumption to 0.5% a year. A similar study on China (He _et al._ , 2013) also simulates fuel consumption/CO2 emissions in China under various scenarios. A paper on Australia (Stanley _et al._ , 2013) considers various policies that have a direct impact on the modal choice and therefore on greenhouse gas emissions (GHG): (i) reduce urban car kilometers traveled; (ii) increase the share of urban trips performed by walking and cycling; (iii) increase public transport’s mode share of urban motorized trips; (iv) increase urban car occupancy rates; (v) reduce forecast fuel use for road freight; (vi) improve vehicle efficiency. Of all of the factors, the paper suggests that increasing fuel efficiency (along with behavioral changes in buying more fuel efficient cars) could significantly reduce (GHG). The paper also suggests comprehensive congestion charging as a possible way forward. Other studies of advanced economies report mixed findings on the likely efficacy of policy. For example, Small (2012) simulates the impact of various energy policies for passenger motor vehicles in the USA. He finds that the impact of various policies suggested/debated would be modest. His conclusion is that higher fuel taxes seem to be the best policy option as their effects set in quickly and are not counteracted by the ‘‘rebound effect’’. Furthermore, a high fuel tax can be combined effectively with a policy mandating new-vehicle fuel efficiency, providing greater effectiveness at a unit cost comparable to that of the fuel tax alone. The paper (even though it does not include any external costs of driving in the model (accidents, congestion etc.) suggests that a fuel tax discourages vehicle travel. A paper by Rentziou _et al._ (2012) offers similar findings. The wider literature looking at fuel price elasticities unambiguously finds that fuel price increases lead fuel consumption to decline, in the short-term 7
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<!-- Start of picture text --> 3500000<br>3000000<br>=e Private; car<br>2500000 === Motorcycle<br>2000000 @@ Lor<br>ete TruckY<br>1500000 Taxi<br>1000000 === Bus<br>500000 e=se=Tractor=== Other<br>fe)<br>2006 2007 2008 2009 2010 2011 2012<br><!-- End of picture text --> <!-- Start of picture text --> 100%<br>90%<br>30% Other<br>70% Tractor<br>@ Bus<br>56 0% Taxi<br>40% i Truck<br>30% @ Lorry<br>21 0% @4 MotorcyPrivate c arle<br>0%<br>2006 2007 2008 2009 2010 2011 2012<br><!-- End of picture text --> <!-- Start of picture text --> 60000 — Railway passengers —e Railway freight ; 5000<br>| - aso0 &<br>7mm - 4000 §a<br>= g<br>22< 40000 - j- 35003000 3a§<br>=Zz 30000 1 - 2500 =€<br>rj , 2000 &<br>=z 20000 - - 1500 g3<br>z 2<br>cA= 10000 ~ 1000 =<br>500 %<br>«<br>0 Soe Fe + 0<br>GaRRSRaREASSSSSSSSSISSAaaagaanagngnagaangnggegoscssesnscsedasgson7osnd<br><!-- End of picture text -->

<!-- Start of picture text --> <=—=Poor quality «=m=High charges<br>me<br>\|<br>60% }<br>40%<br>20%<br>Rail < —~ |, 7 Airports<br>\/<br>/<br>J<br><!-- End of picture text -->
<!-- Start of picture text --> 8000 . .<br>—¢*—Road sector diesel consumption<br>7000 Road sector gasoline consumption<br>6000<br>5000<br>4000<br>3000<br>2000<br>1000<br>0<br>SERRPRRRRRSSSSSESRESEARSRSRRSARASSSBSSESSESBSBSSant tat tet AH TH TH TH TH TN NNN NNN NN NS<br><!-- End of picture text --> <!-- Start of picture text --> 90 . : . we .<br>——Road sector diesel intensity —llRoad sector gasoline intensity<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>RERRERRAERRSSSSEESREBEDAAADAADAAARDADSCSSOSSSSOSSSESAnt nt te Kt Kt TRH TR TH TH NNN NNN NNN NS<br><!-- End of picture text --> <!-- Start of picture text --> MENA<br>ECA<br>SSA<br>SAR<br>non EU OECD<br>EAP.<br>EU<br>LAC<br>0 5000 10000 15000 20000 25000 30000 35000 40000<br>RE water perte capitaita (m3/inh/yr)3 /inh,<br><!-- End of picture text --> <!-- Start of picture text --> Kuwait |<br>UAE<br>Qatar @<br>Yemen<br>Saudi Arabia<br>Bahrainiba =~<br>West Bankia”aJordan zz<br>ae’!<br>Ageia ml<br>jibhit<br>OO ——<br>COC<br>CT<br>OOWW ae__ ae<br>=e<br>T_T<br>0 500 1000se water per capita1500 (m3/inh/e) 2000 2500 3000<br><!-- End of picture text -->
<!-- Start of picture text --> UAE ——————————————————————————————<br>BahrainQatar el—————<br>West Bank &YemenJordanGazatraq ———————————es<br>saudi Arabia —————<br>Syria ee<br>DjiboutiIsrael ee e<br>kuwait ———|<br>Libya |<br>‘Oman —<br>Egypt ————<br>Algeria —<br>Lebanotra n —————<br>MoroccoTunisia wy——l<br>80% 10% 00% “50% 40% 30% 20% “10% o%<br>RE water depletion (% decrease of water resources)<br><!-- End of picture text -->

<!-- Start of picture text --> SSA<br>SAR<br>MENA<br>EAP<br>LAC<br>non-EU OECD<br>EU<br>ECA<br>-50% -40% -30% -20% -10% 0% 10%<br>RE water depletion (% decrease of water resources)<br><!-- End of picture text --> <!-- Start of picture text --> MENA<br>ECA<br>SSA<br>SAR<br>non EU OECD<br>EAP<br>EU<br>Lac<br>0% 100% 200% 300% 400% 500%<br>Water withdrawal (% RE water resource)<br><!-- End of picture text --> <!-- Start of picture text --> 0<br>OTST<br>GCjit {qT<br>Was<br>aha —<br>YemenJordan jamalaa<br>Egypt jm<br>Oman la<br>Syrian Arab Republic jamal<br>israel<br>nq<br>Iran a<br>Tunisia a<br>West Bank &Algeria Gaza katha<br>Morocco<br>lebanon 7<br>a 500% Water100% withdrawal (% RE water100% resource) 200% 250%<br><!-- End of picture text -->
<!-- Start of picture text --> @ Low diesel price High diesel price<br>250<br>200<br>150<br>100 -<br>so<br>oO<br>RE Water withdrawal MENA RE Water withdrawal<br>(% RE resource)*** (% RE resource)***<br><!-- End of picture text --> <!-- Start of picture text --> Fresh groundwater la Fresh surface water<br>Agricultural drainage water @ Desalinated water<br>MENA "<br>SAR " :<br>LAC :<br>fu<br>EAP ; .<br>J<br>HIC . \<br>ECA : :<br>SSA<br>0% 20% 40% 60% 80% 100%<br><!-- End of picture text --> <!-- Start of picture text --> Fresh groundwater Fresh surface water wAgricultural drainage water Desalinated water<br>VOas<br>00<br>Vin) ail____—_——<br>ECL<br>ON<br>|<br>El<br>i saa,<br>| aaa,_——<br>(aaa,<br>la<br>Egypt<br>OOel<br>OCEl<br>O% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%<br><!-- End of picture text -->
<!-- Start of picture text --> Agricultural tm Municipal ta lndustrial<br>SAR<br>EAP<br>non-EU OECD<br>LAC<br>SSA<br>ECA<br>EU<br>0% 20% 40% 60% 80% 100%<br><!-- End of picture text --> <!-- Start of picture text --> w@AgricuRural Municipal windustriat<br>_<br>a_i— aa<br>Td<br> i—evwwgwgwuwumrrr__<<<br>OO Tl<br>CTT<br>a<br>laa<br>i _<br>iaa<br> ———— Ts<br>atT<br>WC_ Ti,<br>|GC<br>0% = 10% 20%@ 80K AK SOK OK BOK HK 100%<br><!-- End of picture text -->

<!-- Start of picture text --> @Lowdiesel price \.sHigh diesel price<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Agriculture water withdrawal*** = MENA agriculture water withdrawal**<br><!-- End of picture text -->
<!-- Start of picture text --> MENA<br>EU<br>SAR<br>Lac<br>HIC<br>SSA<br>ECA<br>EAP<br>0% 20% 40% 60% 80% 100%<br>% irrigated area (by groundwater)<br><!-- End of picture text --> <!-- Start of picture text --> Kuwaiteeee<br>Ce<br>CO T_______—_<br>win?_<br>OT<br>Ce<br>EC<br>Oe<br>STOC<br>DjiboutiWwW ——————i<br>0<br>oT<br>oOtho ————s<br>tranWag | ; ;<br>% 20% 40% 60% 80% 100% 120%<br>% irrigated area (by groundwater)<br><!-- End of picture text -->

<!-- Start of picture text --> SAR<br>EAP<br>MENA<br>SSA<br>LAC<br>ECA<br>non-EU OECD<br>i<br>-10% 10% 30% 50% 70% 90% 110% 130% 150%<br>% harvested irrigated crop area<br><!-- End of picture text --> <!-- Start of picture text --> Egypt<br>Jordan<br>Yemen<br>Oman<br>syria<br>tran<br>UAE<br>Tunisia<br>Lebanon<br>Bahrain<br>MoroccoAlgeria<br>israelLibya<br>Kuwait<br>Saudi Arabia<br>Qatar<br>Djibouti<br>0% «20% ~—=—O:«S« GOKU: NUK: «10K 1HOK «NOK «18K 200%<br>% harvested irrigated area<br><!-- End of picture text -->
<!-- Start of picture text --> Low diesel price LiHigh diesel price<br>300<br>250<br>200<br>150<br>100<br>50<br>0<br>Irrigation water requirement pc*** = MENA irrigation water requirement<br>pe**<br><!-- End of picture text --> <!-- Start of picture text --> @Lowdiesel price LiHigh diesel price<br>120<br>100<br>80<br>60<br>40<br>20<br>fo)<br>Harvested Irrigated Crop area*** MENA Harvested Irrigated Crop<br>area***<br><!-- End of picture text -->
the most water scarce countries in the world with only about 120 cubic meters of renewable internal freshwater resources available per capita. Food imports now account for around 80% of cereal consumption. In 2000, the Republic of Yemen used 10% of its export earnings to import food; by 2012 this had risen to 35%.

Agriculture remains a key sector in the Yemeni economy and the main source of income – direct and indirect - for nearly three-quarters of the population while employing more than half of the labor force. Yet agricultural productivity is very low and faces severe resource constraints that limit the potential for productivity growth. The ground waters on which more than half of agricultural output now depends are almost fully exploited and reserves are being rapidly depleted.

Behind this depletion, lies specific crop selection. In particular, _qat_ - a stimulant widely chewed by Yemenis – accounts for around 40% of total water resource use. While profitable at current relative prices, _qat_ crowds out production of food crops or export crops, and its consumption creates both social and health problems. However, a recent World Bank study (2010) concluded that reducing the subsidy to diesel fuel was unlikely to reduce water extraction or extend the life of the aquifers significantly. It is possible that technological changes combined with changes in institutional arrangements and energy prices may be able to achieve more efficient and sustainable resource use.

# **6. Simulating the impact of fuel price changes on congestion in Cairo**

Preliminary to a more in-depth exploration of the external costs of fuel subsidies and as a simplified benchmark for quantifying the impact, we now apply a simple simulation exercise to look at the impact of a fuel price increase (i.e., a fall in subsidy) on the social cost of congestion in Cairo. The data for the baseline case are taken from the World Bank Congestion Study (2010) that estimated the social cost of congestion in 11 major corridors in Cairo.<sup>5</sup> The social costs of congestion in the study were divided into four major categories. These were; (i) the cost of travel time delay imposed on users; (ii) the cost of travel time unreliability (both for passenger and freight transport); (iii) the cost of excess fuel consumption and, (iv) CO2 emissions. The estimation of costs was done using two approaches – speed plots and volume-to-capacity. They yielded the following magnitudes;

- Speed plots approach: (a) cost of travel time delay– 2.6 billion Egyptian pounds (LE); (b) cost of travel time unreliability – 1.71 billion LE; (c) cost of excess fuel consumption – 2.85 billion LE; and (d) cost of CO2 emissions – 0.097 billion LE;

- Volume-to-capacity ratio approach:  (a) cost of travel time delay– 2.4 billion LE; (b) cost of travel time unreliability – 1.71 billion LE; (c) cost of excess fuel consumption – 2.38 billion LE; and (d) cost of CO2 emissions – 0.086 billion LE;

The total social cost of congestion was estimated at roughly 7 billion LE, amounting to 0.6% of 2010 GDP.

> 5 Note that the magnitudes for the Greater Cairo region are roughly twice as large.

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In order to simulate the impact of a price change, we need to have some estimates of both short and long term elasticities of vehicle travel to changes in fuel prices. Table 1 summarizes the findings of the recent literature. It suggests that the average short run elasticity of vehicle travel is -0.1, whilst the long run is -0.3 – parameters that will also be applied in the simulation exercise. However, it should be pointed out that these estimates are drawn from analysis of advanced economies and may not reflect well developing country values. In addition, we are making two strong assumptions. The first is that we assume that the elasticity of social cost of congestion is equivalent to the elasticity of vehicle travel. The second is that we assume that there is no switching across modes of transport. We report the result of 10%, 20% and 50% increases in price for both the short and long run.

Figures 26 and 27 illustrate the short run impact of a 10%, 20% and 50% increase in fuel prices. As suggested by the elasticities mentioned above, a 50% increase in the price of fuel will lead to a roughly 5% decrease in the overall social cost of congestion, which translates into a monetary value of approximately 360 million LE.

Figures 28 and 29 illustrate the long run impact of a 10%, 20% and 50% increase in fuel prices. The long-run elasticities suggest that a 50% increase in the price of fuel will lead to a roughly 15% decrease in the overall social cost of congestion, translating into 1.089 billion LE which is approaching 0.09% of GDP.

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**Table 1. Summary of vehicle travel price sensitivity studies**

|**Study**|**study type**|**study scope**|**Major results**|
|---|---|---|---|
|Johansson and Schipper (1997)|summary of previous studies|International|-0.2 long run|
|Goodwin, Dargay and Hanly (2004)|Summary of various fuel price<br>and income elasticity studies|1929 to 1991, mostly North<br>America and Europe|-0.1 short run; -0.3<br>long run|
||Elasticity of vehicle travel with|<br>1950 to 1994 time series and||
|Schimek (1997)|respect to fuel price|1988 to 1992 pooled data, US|-0.26|
||||1966 to 2001 ( -<br>0.047 short run and -|
|Small and Van Dender (2010)|||0.22 long run); 1997|
||Comprehensive model using||to 2001 ( -0.026|
||vehicle travel elasticity with||short run and -0.121|
||respect to fuel price<br>Comprehensive model using<br>|1966-2001, US|long run)|
|Hymel, Small and Van Dender (2010)|state-level cross-sectional<br>time series gasoline price||-0.026 short run, -|
||elasticity<br>Comprehensive model of|1966 to 2004, US|0.131 long run|
|Li, Linn and Muechlegger (2011)|vehicle travel with respect to|||
||fuel price|1968-2008, US|-0.24 to -0.34|
||||-0.12 to -0.17 short|
|Brand (2009)|||run; -0.21 to -0.3|
||Gasoline price elasticity|2007-2008,US|long run|
||||-0.15 to -0.20|
|Gillingham (2010)|comprehensive model using<br>odometer and fuel||medium run, with<br>variation by vehicle|
||consumption data|2005-2008, California|type and location|
||||-0.67 short run, with|
|Spiller and Stephens (2012)|Comprehensive model of||variations by|
||monthly state-level fuel price||household income|
||and VMT data|2009 US travel survey data|and location|

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**Figure 26: Speed plots approach – impact of short term price change on social costs of congestion (in billion LE)**

_Source:_ Authors’ elaboration based on World Bank (2010) and Litman (2012)

**Figure 27: Volume to capacity ratio approach – impact of short term price change on social cost of congestion (in billion LE)**

_Source:_ Authors’ elaboration based on World Bank (2010) and Litman (2012) 26
**Figure 28: Speed plots approach – impact of long term price change on social costs of congestion (in billion LE)**

_Source:_ Authors’ elaboration based on World Bank (2010) and Litman (2012)

**Figure 29: Volume to capacity ratio approach – impact of long term price change on social costs of congestion (in billion LE)**

_Source:_ Authors’ elaboration based on World Bank (2010) and Litman (2012) 27
# **6.1 Linking emissions to health outcomes (DALYs)**

The simulations reported above give some sense of what happens to CO2 emissions and their cost. While there tends to be a relationship between CO2 and other pollution measures of relevance in a health or productivity context (such as PM10 and PM2.5), this is not mechanical. And it is these latter indicators that are used to make the link to health status.

At this point, we can retrieve some baseline data are from the World Bank Cairo Air Pollution study (2009). This estimates the total DALY (Disability Adjusted Life Years) due to pollution at 164,124. Dhont et al (2013) estimate the impact of a 20% increase in the price of fuel on the changes of DALY using data from Belgium. They find that a 20% increase in the price of fuel would be associated with a rough improvement of 1650 DALYs. If applied to the Egypt base estimate from WB (2009) this would correspond to a decrease in DALY of around 1 percentage point.

However, an important caveat is in order. The pollution particulates responsible for increased mortality/morbidity in Belgium are very much lower than in Cairo. For instance, PM10 and PM2.5 concentrations in Brussels are 26 and 18 milligrams per cubic meter – significantly lower than the PM10 and PM2.5 concentration in Cairo of 144 and 83 milligrams per cubic meter respectively. This suggests that the calculated 1% decrease in DALYs as a result of a price increase is a significant underestimate.

# **7. Modeling external effects in transport**

In this section, we now move to modeling external effects using a multi-year framework developed by the UN named _ForFITS_ .<sup>_6_</sup> This model has two objectives. The first is the estimation of emissions in transport for a given transport stock and its composition and intensity of use. The second is the evaluation of transport policies for, in particular, mitigating CO2 emissions. The model also permits simulation of fuel price changes on transport activity. In an extension, we link CO2 emissions to those for particulates – as measured by PM10 – and then in a further step link to the impact on mortality and morbidity using the measure of DALYs.

The model evaluates transport activity expressed in units such as passenger kilometers (pkm) and vehicle kilometers (vkm), stocks of vehicle types, energy use and CO2 emissions. A range of possible policy contexts can be introduced that include changes in international oil prices as well as the level and structure of domestic fuel taxes/subsidies. The model is essentially sectoral, as it covers both passenger and freight transport services across all transport modes and the focus is mainly on inland transport (especially road and rail). Each mode is further characterized in submodes (when relevant) and vehicle classes. Vehicle classes are further split to take into account different engine or powertrain technologies and vintages. Finally, powertrains are coupled with fuel blends that are consistent with the particular engine technology.

Although in extended form the model requires very detailed information that is not available in the Egyptian context, the core requires a minimum amount of data including information on the

> 6 An acronym: ‘For Future Inland Transport Systems’.

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transport system in the base year (such as the composition of the transport fleet, i.e. numbers of cars, buses, trucks, lorries etc.) and the policy context, such as tax and subsidy rates. Other core variables needed for projections include the level and growth rates of GDP per capita, GDP growth, population, and new vehicle registrations (see _Annex 1_ ).

The evaluation of fuel and energy consumption using information on transport activity and vehicle characteristics is calculated using a decomposition of fuel use into transport activity, energy intensity and structural components, such as the type of transport service (passenger versus freight), mode, vehicle class and powertrain group (generally termed ASIF). This is then extended to measure CO2 emissions. In developing long-term projections, the vehicle-based ASIF approach is supplemented with relationships that link economic parameters with transportrelated ones (such as changes in the cost of travel with variations in travel per vehicle, or changes in income per capita with variations in vehicle ownership), as well as other specifics (i.e. choice models). As such, the model generates estimates of transport activity, vehicle use, fuel consumption and CO2 emissions.

# **7.1 Applying the framework to Cairo**

Our main objective is to study the impact of policy measures – notably the domestic subsidy level for fuels as well as changes in international oil price - on CO2 emissions (as well as total energy use and vehicle stock) and based on that, to infer the level of PM10 emissions / concentrations and the likely impact on human health, using the measure already indicated in the discussion above, DALYs.

Our simulation exercises use 2014 as the base year and consider the impact of policy changes over both the medium term (up to 2024) as well as over the near term (up to 2020). We consider several scenarios in this exercise. For the medium term, we consider four cases in addition to the baseline:

- (i) gradual decrease in fuel subsidies by 50%;

- (ii) gradual elimination of fuel subsidies (100%);

- (iii) gradual drop in fuel subsidies by 50% coupled with an increase in international price of oil by 20%;

- (iv) gradual elimination of subsidies (100%) coupled with an increase in the international price of oil by 20%.

_Table 1_ provides CO2 emissions for all four scenarios and the baseline. ( _Annex Table 1_ also provides the corresponding changes in energy use associated with these emissions). There are a few empirical regularities that emerge. First, when a gradual fall in subsidies in the medium term is simulated, total CO2 emissions decrease by roughly 9 percent. A gradual elimination of subsidies results in CO2 emissions dropping by 16 percent when compared to the baseline scenario. A drop in energy use and CO2 emissions is also in evidence when there is a coupling of policies with external shocks. The third scenario couples a gradual decrease in energy subsidies with an increase in the international price of oil by 20 percent. This policy simulation results in a drop in total CO2 emissions of around 16%. Finally, as expected, the biggest change in energy use and CO2 emissions is observed when a policy of a gradual elimination of fuel subsidies is 29
coupled with an increase in the international price of oil by 20 percent. In this case, CO2 emissions fall by around 25 percent.

The simulations are then repeated but with a shorter time period (up to 2020) for policy changes. The lower part of _Table 1_ gives the results. A more rapid elimination of fuel subsidies by 2020 results in a decrease in CO2 emissions by 15 percent. Additionally, elimination of subsidies by 2020 coupled with an international price of oil increase by 20 percent leads to a decline in energy use of roughly 20 percent and an equivalent drop in CO2 emissions.

# **7.2 Changes in modal composition**

Obviously, one of the main issues in pricing reform is the impact it may have on the relative demand for different types of transport and hence their varying contributions to total energy use and CO2 emissions. Reducing or eliminating subsidies should, for example, affect the propensity to use private vehicles relative to public transport, although the extent of substitution a range of factors including public transport pricing and the size of the public transport network as well as the extent to which provision can be scaled up in response to a shift in relative demand.

When considering the issue of modal competition, it needs to be remembered that the contribution of railways (both passenger and freight) in the overall emission of CO2 is low and does not exceed 1 percent of total emissions. The great bulk of emissions and energy use is attributable to cars and buses in the passenger sector and trucks and lorries for freight.<sup>7</sup> In the scenarios presented in _Table 1_ , this implies that although there are some minor changes in the overall contribution of CO2 emissions of various modes, the shares actually remain fairly stable over the years and with different scenarios. There is a minor increase in the share of CO2 emissions attributable to railways and other public transport modes. But for railways, in particular, to make an impact may require substantial investment in new capacity and infrastructure.

To simulate such an effect – albeit in very stylized form – we consider what might happen to emissions when we mimic the effect of a step-wise change – or positive investment shock – on the transport sector. Embedded in the model is an index of the transport system. It ranges from 0 to 1 where 1 is characteristic of a system with high population densities and very strong focus on public transport and 0 is characteristic of a system with little public transport and a strong focus on use of private vehicles. In setting the initial value of this index, we looked at a number of rankings and comparative scores and assigned Cairo a value of 0.3, characteristic of a location with relatively weak public transport. We then employ two types of shocks aimed at capturing the possible impact of a large investment program aimed at transport infrastructure. The first is a 20% gradual increase in the transport index over 10 years and the second is a 50% increase over the same period. We couple these shocks with changes to subsidies and increases in oil prices as used in _Table 1_ .

_Table 2_ provides the main results. A positive shock to the transport index of 50% over 10 years, when coupled to either a 50% or 100% reduction of subsidies, results in lowering CO2 emissions by 20 and 29 percent respectively. When considering the cases of a 20 and 50 percent shock to

> 7 This is comparable to international experience, whether in advanced or developing economies

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the transport index, coupled to the elimination of subsidies by 2024 and an oil price increase of 20%, the fall in emissions amounts to around 26 and 32 percent respectively. Compositionally, the share of emissions accounted for by the various types of public transport increases although not by very large magnitudes.

# **7.3 Linking CO2 to PM10 emissions and DALYs**

There is limited information on the exact relationship between CO2 and concentration of particulates or PM10 emissions and hence on the appropriate conversions rates to be applied.<sup>8</sup> What evidence is available suggests that changes in CO2 emissions are associated with roughly proportional changes in PM10 emissions. Using data from the World Bank (Table 2.2; World Bank’s Cairo Pollution study), we now project changes in the concentration of particulates (PM10) in Cairo for the scenarios used above. Our main assumption is that there is a linear relationship in the increase of CO2 and PM10 emissions. However, there are a few additional assumptions that we make in projecting PM10 emissions. First, we assume that an increase in PM10 particulates will be associated with an increase in the overall concentration of particulates in the Greater Cairo region. Second, based on the World Bank Greater Cairo Pollution Study, we assume that transport pollution represents roughly 15 percent of total pollution in the city. Hence, a doubling of PM10 due to transport would be equivalent to 15 percent of the change in overall PM10 emissions. It should be emphasized that this exercise is static as we hold everything else _ceteris paribus_ , i.e. we do not consider changes in emissions/concentration due to industrial pollution and other factors. It is quite feasible that the policy changes we are simulating might also have an impact on those sectors. As such, we are probably underestimating the overall impact of fuel pricing changes.

The results of this exercise are presented in _Table 3_ . Given the assumptions, changes in PM10 concentrations in various scenarios correspond to changes in CO2 emissions from _Table 1_ above. (Clearly, if changes in CO2 and PM10 emissions were not proportional, the estimates would have to be adjusted.) For the four scenarios to 2024, decreases in PM10 concentration range between 8 - 25 percent compared with the base case. The drop in PM10 concentration in the shorter-term scenario (up to 2020) ranges between 15 and 20 percent. Adding the changes in the transport index to simulate an increase in public transport, we can observe at the outer limit that there is a decrease of over 30 percent in the concentration measure; a very substantial decline indeed.

After projecting PM10 emissions/concentration, we again use information from the World Bank Great Cairo Pollution study (Table 5.1: Total DALYs lost to pollution) and the ratios between PM10 concentrations and DALYs lost to project the total DALYs lost to _transport_ pollution in the six scenarios. The results of this exercise are reported in _Table 4_ where the central calculation is of _cumulative_ savings when comparing a particular scenario with the baseline case over the reference period. The table shows that a gradual 50% fall in subsidies by 2024 is associated with

> 8 See Haikun Wang, Lixin Fu and Jun Bi, ‘CO2 and pollutant emissions from passenger cars in China’, _Energy Policy,_ 39, 5, May, 2011, pp3005-3011 and M. Madireddy et al, ‘Micro-Simulation of a Traffic Fleet to Predict the Impact of Traffic Management on Exhaust Emissions’, Ghent, mimeo, 2010

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saving roughly 13,500 DALYs. A gradual elimination of fuel subsidies by 2024 saves nearly 23,000 DALYs. In the scenario where fuel subsidies are eliminated by 2024 and coupled with an increase in the international price of oil of 20 percent, the DALYs saved amount to over 30,000. This corresponds to roughly 0.04 percent of GDP. When we incorporate changes to the transport index in the simulations, at the maximum the cumulative savings of DALYs rise to around 0.07% of GDP. These are clearly not trivial numbers given that the estimated cost of _total_ pollution in the Greater Cairo region amounts to around 0.2 percent of GDP.

# **8. Conclusion**

Our paper covers ground that has been relatively neglected both in analytical and policy terms; namely the externalities associated with energy subsidies. The latter are pervasive in the MENA region and cut across different types of economies, including those without significant natural resources. While the broad consequences of energy mis-pricing are well understood, the magnitudes of the external effects associated with current pricing rules have attracted limited attention, let alone quantification. We attempt to correct this deficiency by focusing on two sectors – transport and agriculture – with a specific emphasis on the congestion and pollution costs associated with the first and the consequences for resource (water) depletion and crop selection in the case of the second. The paper is, however, exploratory both in a methodological and in an empirical sense, not least because of the absence of available data of sufficient quality. Nevertheless, we are able to identify the dominant channels through which energy subsidies create external costs and, particularly in the case of transport, begin to quantify their magnitudes in the context of Cairo. We view this exercise as a prelude to a more detailed study that will require further data assembly and collection.

In the case of transport in Cairo, we are able to quantify the impact of a change in energy pricing involving a reduction in subsidies on energy use and CO2 emissions. We then associate those emissions with that of particulates – as measured by PM10 – and through that channel make a link to health outcomes, as measured in terms of excess mortality and morbidity using the DALY measure. We find that there are non-trivial changes in energy use and CO2 emissions when energy subsidies are reduced. Our simulations indicate at their outer limit that a gradual elimination of fuel subsidies by 2024, when coupled with an increase in the international price of oil by 20 percent, would result in CO2 emissions falling by as much as 25 percent. Eliminating subsidies by 2020 would lead to a decline in energy use of around 20 percent with an equivalent drop in CO2 emissions. As regards the impact on health, subsidy elimination by 2024 would result in large order savings in terms of mortality and morbidity that amount to roughly 0.04 percent of GDP. When factoring in changes in the use of different transport modes linked to a hypothetical increase in public investment in public transport infrastructure, at a maximum the cumulative savings of DALYs rise to around 0.07% of GDP. These are clearly significant given that an earlier World Bank study estimated the cost of total pollution in the Greater Cairo area to be around 0.2 percent of GDP. In sum, our paper provides further evidence on the damaging and corrosive impact of energy subsidies by demonstrating that in one sector alone the external effects are large. Although the paper does not attempt to measure the overall costs associated with energy underpricing on water use and scarcity, it uses available evidence to describe the 32
impact on depletion including through crop selection, notably through the choice of irrigated crops. Using mainly evidence from the Republic of Yemen, as well as data from the MENA region more widely, we show that energy mis-pricing has been associated with accelerated depletion rates. Given the relative scarcity of water resources in this region, the consequences are significantly adverse. Further analysis, and data collection, will allow more precise identification of these costs not only in these two sectors, but also across a broader spectrum of sectors.

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