Induced technical change in the transportation sector and induced mobility.

Transcription

1 Induced technical change in the transportation sector and induced mobility. Sassi O., Crassous R., Hourcade J.-C. Centre International de Recherche sur l Environnement et le Développement, Paris. International Energy Workshop, Kyoto. July 7 th 2005.

2 Why do emissions from transportation raise a specific long term policy challenge? Recent historical emissions show vigorous upward trends. After oil shocks, reactions show low price elasticity of final demand Gasoline from fossil fuels has a large competitiveness margin. How to bend transportation emissions implies to understand complex dynamics behind mobility needs.

3 Towards a broader view of the Induced Technical Change vs Autonomous Technical Change debate. Modeling Technical Change as induced by R&D, learning by doing and incentive systems is a way out of the representation of TC as a manna from heaven But TC is not only a matter of production function unless it is assumed to be with no impact on structural change (lifestyles, dematerialization, land use patterns). This cannot be the case for transportation : mobility demand result from a non trivial interplay between Consumption patterns, Available technologies Localization patterns

4 Contents Including transportation demand dynamics in the IMACLIM-R model. Long term transportation scenario : baseline results. Traffic volumes. Modal shares. Long term transportation scenario : policy results. Induced technical change and transportation emissions. Induced mobility and transportation emissions.

5 Contents Including transportation demand dynamics in the IMACLIM-R model. Long term transportation scenario : baseline results. Traffic volumes. Modal shares. Long term transportation scenario : policy results. Induced technical change and transportation emissions. Induced mobility and transportation emissions.

6 Stylised facts in transportation demand: when technical change impacts the level and structure of final demand Rebound effect due to energy efficiency improvement, Demand induction by transportation and urban infrastructures, Drivers of demand evolve over different time scales: Infrastructures (decades) Localization of production/consumption/housing/ (decades and sometimes one century) Private equipments (a few years) Energy prices (volatile), Real Estate prices Inertia, lock-in, risks of maladjustments

7 Integrating transportation demand specifics in a long-term prospective exercise : principles Aim to disentangle specific mechanisms at play behind emissions dynamics and especially non-trivial interactions driving transportation patterns. Considering a comprehensive architecture, including a growth engine, general equilibrium consistency and technical information. Testing various trajectories due to different kind of policies. Recent ongoing works on this topic (Schäfer et al., Edmonds et al., WBCSD Mobility 2030 )

8 Salient features of the hybrid model IMACLIM-R IMACLIM-R is a LT growth model based on : Succession of static equilibriums under short-term constraints Allows for macroeconomic feedbacks on household consumption Moving constraints informed by reduced forms from BU models Includes technological asymptotes, technological competition Physical account of energy (Mtoe) and transportation (PKT) consumption. Fossil fuel reserves and endogenous price of oil. Investment in infrastructures are governed by profit maximisation or by routine behaviours capturing various public decision styles.

9 Recursive structure of IMACLIM-R Time path Static Equilibrium t Static equilibrium t+1 Static equilibrium under short-term constraints Updated parameters (tech. coef., stocks, etc.) Bottom-up submodels or reduced forms Demography, Capital and technology dynamics IEW July, 4th, 2005 Price-signals, rate of return Physical flows Moving constraints

10 Salient features of the hybrid model IMACLIM-R IMACLIM-R is a LT growth model based on : Succession of static equilibriums under short-term constraints Allows for macroeconomic feedbacks on household consumption Moving constraints informed by reduced forms from BU models Includes technological asymptotes, technological competition Physical account of energy (Mtoe) and transportation (PKT) consumption. Fossil fuel reserves and endogenous price of oil. Investment in infrastructures are governed by profit maximisation or by routine behaviours capturing various public decision styles.

11 Modeling short-term modal choice and mobility demand Utility maximization: U With Under two constraints: i ξi Π ( C i i) ( j j) j = bn S bn ξ cars ( ) Income = p C + p PKT 0,15+ p PKT + p PKT α Tdisp Modes Tj 0 i i public public air air Ei cars Ei Energies Ei PKT T j = τ j ( ) u du (,,, ) S = CES PKT PKT PKT PKT Mobility air public cars non motorized Time per PKT (h/km). 0,25 Capacity=function ( infrastructures, equipments ) 0,2 0,1 0, Modes Time per PKT (h/km). 0,25 0,2 0,15 0,1 0,05 0 PKT PKT Capacity

12 Contents Including transportation demand dynamics in the IMACLIM-R model. Long term transportation scenario : baseline results. Traffic volumes. Modal shares. Long term transportation scenario : policy results. Induced technical change and transportation emissions. Induced mobility and transportation emissions.

13 Results: baseline scenario 5 regions (OECD, ASIA, REF, ALM, OPEC) 10 sectors from 2000 to 2100 Aggregate dynamics close to SRES A2 (medium growth - high emissions) Pessimistic view of future carbon-free technology (no backstop) Technical asymptote of energy efficiency gains in personal vehicles (x4) Transportation dynamics: Traffic volume. Modal share. As demand is completely endogenous, transportation growth can be constraint by fossil fuels scarcity.

14 100% 90% 80% 70% Cars Modal shares 60% 50% 40% 30% 20% 10% 0% Years 100% 90% 80% 70% Air Bus+ railways Bus+ railways Motorized mobility bill. pkm ASIA OECD Modal shares 60% 50% 40% 30% 20% Cars Years 10% Air 0% Years

15 Results: baseline scenario 5 regions (OECD, ASIA, REF, ALM, OPEC) 10 sectors from 2000 to 2100 Aggregate dynamics close to SRES A2 (medium growth - high emissions) Pessimistic view of future carbon-free technology (no backstop) Technical asymptote of energy efficiency gains in personal vehicles (x4) Transportation dynamics: Traffic volume. Modal share. As demand is completely endogenous, transportation growth can be constraint by fossil fuels scarcity.

16 Contents Including transportation demand dynamics in the IMACLIM-R model. Long term transportation scenario : baseline results. Traffic volumes. Modal shares. Long term transportation scenario : policy results. Induced technical change and transportation emissions. Induced mobility and transportation emissions.

17 Results: Policy scenarios. Run under Autonomous or Endogenous technical change assumptions (ATC/ETC). Two stabilizations targets (450 ppm and 550 ppm) with tax only policies. Alternative policy scenarios combining tax and infrastructures policies.

18 Rebound effect on transportation : sectoral emissions. World mobility and transportation emissions index RED = mobility BLUE = emissions ETC Years ATC ETC ATC

19 Infrastructure policy lightens the required tax burden. Carbon tax only Combined: Carbon tax + transportation infrastructure policy ppm 550 ppm Annual tax increment from 2005 to Carbon budget (GtC)

20 Conclusions and further works Conclusions: Energy efficiency improvements in the transportation sector are partially off-set by an increase of total mobility. Transportation emission mitigation needs for clear and consistent policy signals. Further works: Behind our hypothesis on infrastructure investment: Real estate price dynamics. Households localization choices. Land use policies. Toward a more explicit spatial representation?

21 Induced technical change in the transportation sector and induced mobility. Sassi O., Crassous R., Hourcade J.-C. Centre International de Recherche sur l Environnement et le Développement, Paris. International Energy Workshop, Kyoto. July 7 th 2005.