French data Empirical strategy Preliminary results Main results CO2 predictions The carbon footprint of suburbanization: Evidence from French household data e 1 and Miren Lafourcade 2 Camille Blaudin de Th´ 1 Systra 2 University Paris-Sud (RITM) / University Paris-Saclay & Paris School of Economics (PSE) November 30, 2016 PSE-TSE-MEEM Seminar Miren Lafourcade (University Paris-Sud / Saclay & PSE) PSE-TSE-MEEM Seminar
French data Empirical strategy Preliminary results Main results CO2 predictions Motivation Suburbanization is a long-standing trend (1950-...) ⇒ Accelerated growth in real per capita income since WWII ⇒ Decades of low energy prices, steep decline in transport costs ⇒ Preference for living in detached single-family homes... But urban sprawl has come at many costs (market failures) : ⇒ Consumption of undeveloped land & reduction of bio-diversity ⇒ Growth of GHG emissions due to commuting patterns... �→ Road transport : ∼ = 35% of anthropogenic CO 2 emissions �→ Personal driving : ∼ = 60% of road emissions in France �→ Rising share ( � = other sources), despite improved vehicle fleet ⇒ But strong resistance to carbon taxes related to the use of fuel... ⇒ Spatial policies as complements (substitutes ?) to other CO 2 mitigating macro-policies such as carbon taxes ? Miren Lafourcade (University Paris-Sud / Saclay & PSE) PSE-TSE-MEEM Seminar
French data Empirical strategy Preliminary results Main results CO2 predictions Motivation (cont’d) Vast body of literature since Newman & Kenworthy (1989) ⇒ Negative relation between density and transport-related energy ⇒ Key contributions include : Bento et al. (2005), Browstone & Golob (2009), Glaeser & Kahn (2010), Zheng et al. (2011)... ⇒ But causal impact of urban form or sorting / endogeneity issues ? �→ People who like driving sort into areas of particular density �→ Density correlated with unobserved variables affecting driving What this paper adds to the literature ⇒ First attempt to correct for both sorting and endogeneity issues �→ Not only density : job-housing distance, transport access... ⇒ Europe is seldom investigated, whereas sprawl increases rapidly �→ French MAs have expanded by 20% in the last decade... �→ SEEID studies (Lemaˆ ıtre and Kleinpeter or Perrissin, 2009...) ⇒ Policy prospects : is there an optimal or a sub-optimal city ? �→ Ranking of cities wrt CO 2 emissions of a “marginal” household �→ Relationship between MA-size and driving emissions : ∩ -curve Miren Lafourcade (University Paris-Sud / Saclay & PSE) PSE-TSE-MEEM Seminar
French data Empirical strategy Preliminary results Main results CO2 predictions Household data The fuel consumption associated with personal driving ⇒ French survey on “Family expenditures” (INSEE, 2006) : �→ 10,215 households (25,364 individuals), 7,800 urbanites ⇒ Exhaustive coverage of consumption expenditures : �→ 2 weeks purchases, 900 budgetary items �→ Gasoline, diesel and LPG expenditure... �→ Converted into volumes with related fuel prices in 2006 ⇒ Regular or extraordinary resources (gifts, lottery, inheritance,...) ⇒ Socioeconomic characteristics of all household members : �→ Nb of children, workers, job seekers, retired... �→ Age, gender, diploma, occupation (head of the household) ⇒ Dwelling characteristics : �→ Municipality code (statistical disclosure) Miren Lafourcade (University Paris-Sud / Saclay & PSE) PSE-TSE-MEEM Seminar
French data Empirical strategy Preliminary results Main results CO2 predictions Descriptive statistics Miren Lafourcade (University Paris-Sud / Saclay & PSE) PSE-TSE-MEEM Seminar
French data Empirical strategy Preliminary results Main results CO2 predictions Core-base geographical unit of observation ⇒ 352 French Metropolitan Areas (MAs) Miren Lafourcade (University Paris-Sud / Saclay & PSE) PSE-TSE-MEEM Seminar
French data Empirical strategy Preliminary results Main results CO2 predictions Urban form The metrics of urban sprawl : the three D’s ⇒ Density : Population increases faster at the urban fringe �→ Growth of city centers : +0,3%/year (1999-2006) �→ Growth of inner (+0,6%) & outer (+1,3%) suburbs �→ Density gradients fall with distance to city-centers... �→ Census data : number of inhabitants/jobs per km 2 ⇒ Design : The centrality of the urban space decreases as... �→ Workplaces and residence places develop further away �→ Commuting distances between residence and CBD increase... �→ “Great-circle” distance between the residence and the CBD ⇒ Diversity : Transport networks shape cities �→ Tentacular cities and “leap frog” sprawl �→ Possibly countered by transport networks �→ Public transit substitutes to personal driving �→ Public transport increases both local/global connectedness �→ GIS BD-TOPO (NGI) : all transports but buses Miren Lafourcade (University Paris-Sud / Saclay & PSE) PSE-TSE-MEEM Seminar
French data Empirical strategy Preliminary results Main results CO2 predictions Econometric specifications Fuel i ( k ) = α 0 + α 1 Paris k + α 2 IS k + α 3 OS k + α 4 M k + α 5 R k + X i θ + ǫ i ( k ) Fuel i ( k ) = α + β ln dens P k + δ ln dist CBD + γ ln dens T k + � x λ x ln TP k ( x )+ X i θ + ε i ( k ) k ⇒ Fuel i ( k ) : (Log)Fuel volume of household i living in municipality k ⇒ dens P k : population density in the municipality of residence k ⇒ dist CBD : distance between k and the CBD of the MA k ⇒ dens T k : density of train/metro stations in municipality k dens k ′ ( x ) ⇒ TP k ( x ) = � : connection of k within the MA dist kk ′ k ′ � = k , ∈ MA �→ Number of train/metro stations (as many as transit lines) �→ Length of the road network (weighted by traffic levels) ⇒ X i : Socioeconomic characteristics of household i : �→ Family composition : nb of (non-)working adults and children �→ Age, gender, diploma, occupation of the household-head ⇒ If distance to CBD doubles, fuel cons. varies by ln 2 × δ = 0 , 7 × δ Miren Lafourcade (University Paris-Sud / Saclay & PSE) PSE-TSE-MEEM Seminar
French data Empirical strategy Preliminary results Main results CO2 predictions Miren Lafourcade (University Paris-Sud / Saclay & PSE) PSE-TSE-MEEM Seminar in log
French data Empirical strategy Preliminary results Main results CO2 predictions Preliminary results : to sum up Fuel consumption and individual characteristics ⇒ Affluent households and (working) families consume more fuel... ⇒ The impact of a working-adult is 1.5-fold that of a non-working adult, and 15-fold that of an underage child ⇒ Households headed by elderly and women consume less fuel The impact of suburbanization on fuel consumption ⇒ Moving a household living in a non-Parisian city-center to : �→ The Parisian pole would save 60 gallons/year �→ Paris “intra-muros” would save 135 gallons/year �→ The inner suburb of Paris would save 40 gallons/year �→ The inner suburb of another city would add 40 gallons/year �→ An outer / multipolar suburb would add 90 gallons/year �→ A rural area would add 80 gallons/year ⇒ A household living at the fringe of a MA instead of a city-center would bear an extra-consumption of about 6 fuel tanks per year Miren Lafourcade (University Paris-Sud / Saclay & PSE) PSE-TSE-MEEM Seminar
French data Empirical strategy Preliminary results Main results CO2 predictions Sorting and endogeneity issues Econometric issues ⇒ Omitted variables (bus...) / Reverse causality (density...) ⇒ Instrumental Variables econometric techniques : �→ Length of the royal road departmental network in 1837 pro-rated on the basis of the surface area of each municipality (Source : Statistique g´ en´ erale de la France, Tome 2, 1837) �→ Lagged market potentials (pop. dens.) (Sources : INED, 1936) �→ Lagged density of deaths by municip. (Source : Census, 1962) ⇒ Sorting of (non-)motorized households ⇒ OLS / IV restricted to the sample of motorized households ⇒ Heckman two-step estimation : �→ 15% of households do not have a car... �→ Selection equation : probability to own (at least) a car = α + β log dens k + δ log dist CBD + γ log dens T � Car i ( k ) ≥ 1 � k + � x λ x log TP k ( x )+ Y i θ + µ i ( k ) P k �→ Outcome equation : Same as before - underage children + 1/Mill’s ratio Miren Lafourcade (University Paris-Sud / Saclay & PSE) PSE-TSE-MEEM Seminar
French data Empirical strategy Preliminary results Main results CO2 predictions Miren Lafourcade (University Paris-Sud / Saclay & PSE) PSE-TSE-MEEM Seminar
Recommend
More recommend
