Determinants of GHG emissions from urban ground transportation:

1. Determinants of GHG emissions from urban ground transportation: review on a sample of European cities Edoardo Croci Sabrina Melandri, Tania Molteni, Olha Zadorozhna 5th International Scientific Conference on Energy and Climate Change 11-12 October 2012, Athens (Hellas)

2. Context More than half of world population lives in cities, and urbanization is growing worldwide. Source: World Bank (2010) “Cities and climate change. An urgent agenda”

3. Context Cities are responsible for a relevant share of global energy demand and related CO2 emissions. Source: World Bank (2010)

4. Context GHG emissions per capita can differ a lot between cities, because several determinants influence local energy demand (climate, urban form, urban economy, infrastructures, relative prices, income, lifestyles….) and because of differences in energy supply. Population and per capita community GHG emissions matrix of cCCR Reporting Cities (Source: ICLEI , 2011)

5. Context Main GHG emissions sources of cities are buildings (residential, commercial) and ground transport. Sectoral breakdown of aggregated annual community GHG emissions of cCCR Reporting Cities (source : ICLEI, 2011)

6. Context • Transportation has a key role in European policies on climate change mitigation and urban environment: urban transport is responsible for about 23% of total CO2 emissions from transport (70% passenger cars, 27% goods transport vehicles) • The sector has interesting CO2 abatement options (fuel efficiency standards, decarbonisation of energy supply, spatial planning and shift to non motorized modes and public transit) • GHG reduction policies in ground transportation have also environmental and non-environmental cobenefits (impact on other pollutants, congestion, noise…) • (source: European Commission, 2011)

7. Aim and structure of paper • Aim: to explore the socio-demographic, economic and spatialdeterminants of GHG emissions from urbanmobility • Structure of paper: • Emissions are decomposed in keyfactors • Determinants of thesefactors are analyzed • A model for emissions from urbantransportistested • Results are commented • Severaldeterminants are analyzedjointly in order to assesstheirrelevance

8. Identities of urban • transport emissions (passengers) Where: Tj = number of passengers’ trips with “j” mode Lj = average length of a single trip with “j” mode (passengers km) lf = load factor of “j” mode (n. passengers/vehicle) EFji = emission factors of “i” fuel with “j” mode (gCO2/vehicle km) i= 1, … 6 1 = gasoline 2 = diesel 3 = LPG 4 = electricity 5= other 6 = no fuel j = 1, … 6 1 = foot 2 = bicycle 3 = subway/rail 4 = bus (and related sub-categories) 5 = passenger car (and sub-categories) 6 = motorcycle (and sub-categories)

9. Identities of urban • transport emissions (freight) Where: VKTzi = kilometres travelled by freight vehicles of “i” fuel and of “z” mode (vehicle km/inhabitants) EFzi = emission factors of “i” fuel with “z” mode (gCO2/vehicle km) z = 1,…3 1 = light duty vehicles (and sub-categories) 2 = heavy duty vehicles (and sub-categories) 3 = rail

10. Determinants of emissions from urban transport • Transport demand and travel modes through which demand is satisfied are influenced by several local features, that can be considered as main determinants of urban emissions of this sector: • demographic and socio-economic features of population • urban economy • city’s location • urban form • urban transport infrastructures & transport modes available in the city • relative price of transport modes

11. Methodology • The paper investigates a set of variables to identify which urban features play the most relevant role as determinants of emissions from urban transport. • Regressions have been performed on data referredto a sample of24 Europeancities. • Data sources: • emissionsreported under the “CovenantofMayors” initiative • socio-demographic, economic, infrastructural and geographical data from Eurostat’s database “UrbanAudit”

12. City sample

13. Methodology • E = f (Density, Incommuters, PTRelativePrice, GDP, Age-Mobility) • Where • “E” are CO2 emissions from ground transport per inhabitant (tCO2) • “Density” is the number of residents per unit of land area (inhab/km2) • “Incommuters” is the amount of commuters the city attracts for its workplaces (proportion of incommuters of persons employed in the city)(%) • “PTRelativePrice” is the relative price of public transportation to private transportation (Cost of a monthly ticket for public transport (for 5-10 km) / price of 1 litre of gasoline + cost of 1 hour parking in the city centre + fee of 1 entrance in the congestion pricing zone, if present) • “GDP” is the Gross Domestic Product of the NUTS3 region per inhabitant in PPS (€) • “Age-Mobility” is the percentage of residents with low mobility on the total population (residents aged > 65 years + residents aged < 14 years / total resident population)(%)

14. Methodology • Main model (Model 1): • E1 = α1 + β1 Density + γ1 Incommuters+ η1 PTRelativePrice+ δ1 GDP + ζ1 Age-mobility + ε1 • Other models have been employed to deal with issues related to the data sample (i.a. non normal distribution of the variable “Density”, correlation between “PTRelativePrice” and “Incommuters”). • Other variables expressing the dimension of public transport network and service could not be included because of high correlation with population density. • All of the models are estimated using OLS.

15. Results • Population density issignificant and negatively related with emissions • PTrelative price is significant and positively related with emissions • Other regressions employed confirm these results. • Incommuters is significant only at certain conditions (i.e. if PTrelative price is not included in the regression: too high correlation between the variables? endogeneity issue?)

16. Results ^ In Model 2. Density is expressed in logarithmic form. Note: ***- 1% significance level; ** - 5% significance level; * - 10% significance level t-statistics in parenthesis

17. Results • Some interpretations: • Population density is used here as a proxy of built-up areas density; the analysis confirms that cities with a compact built environment generate lower levels of emission from transport activity. • High densities are usually associated with a mixed use of space, which is a favourable condition for shorter trips and the use of non-motorized modes; furthermore, high densities are a key requirement for investments in public transport infrastructure, thus they are usually associated with a good public transport supply.

18. Results • Some interpretations: • Relative price of transport modes • The positive sign of its relation with emissions resulting from the analysis can be explained by the modal shift towards motorized modes induced by an increase of the relative price of transit. • Literature already highlights the key role of high fuel prices in discouraging private use of motorized modes (e.g. lower car use in European cities vs. American cities).

19. Thanks! edoardo.croci@unibocconi.it