Building the Capacity of Chile’s Transportation Sector for Sustainable Development through the Clean Development Mechanism “LOCATION EFFICIENCY”: A POTENTIAL CDM OPTION IN SANTIAGO DE CHILE University of Chile (LABTUS)
Reductions: Reductions: Shift demand by transport modes Shift demand by transport modes Shift destination as close as possible Shift destination as close as possible Find an optimal land use pattern that minimizes emissions Methodological Approach Positive approach: Markets opperate with subsidies Realism: Scale of impacts.... city wide
Land Use Scenario Transport Demand Model H,S T(H,S) Urban Impacts and Certified Emission Reductions (CERs) Emission Model E ( T(H,S) ) CERS Subsidies Transport System Co-Benefits Urban Emission Co-Benefits
Empirical Results Scenario:Baseline Observed location pattern in Santiago (2001) O/D travel survey and real state census Scenario: Education supply Spatial distribution of education activities proportional to population Auto Auto Pass. BusWalkMetro Metro comb. TaxiTOTALTrips 14 (3 % ) -3 (-2 % ) -70 (-9 % ) 86 (34 % ) -15 (-24 % ) -12 (-19 % ) 1 (2 % ) 00%00%00%00% CO 2 13 (1 % ) -66 (-16 % ) -7 (-15 % ) -4 (-5 % ) -64 (-3 % ) Changes in Trips and CO 2 Emissions per Peak Period (Daily) Note: Trips in thousands and CO 2 in tonnes
Designing “Optimal” Urban Land Use Optimize E(T(H,S)) vehicle emissions H*,S* E* = E(T(H*,S*)) Land Use Scenario Subsidies CERs
Manager: “City authority” (not individual projects) Tools: Models of urban and transport markets Operational plan: Urban land use and transport plans to comply with emission reduction targets Public and private plan implementation with incentives Management Model CER plan: Estimates emissions reduction tons for a time period Offers and sells emission reduction bonds Certifies emission reductions
Issues Reliability of results Models reproduce observed transport and land use data Models reproduce observed transport and land use data Aggregation of spatial scale and agents according to data availability and policies tested Aggregation of spatial scale and agents according to data availability and policies tested City wide approach: robustness of predictions City wide approach: robustness of predictions Management model: multiple actions to fulfill commitments Management model: multiple actions to fulfill commitments
Issues Timeframe: an issue for further analysis 5 to 7 years allow incentives to operate 5 to 7 years allow incentives to operate Speed of changes: Speed of changes: * Slow (5-10 years): Infraestructure * Medium (3-7 years): Location of activities, work trips * Medium (3-7 years): Location of activities, work trips * Quick (1-3 years): Trips destination and modes * Quick (1-3 years): Trips destination and modes Certification, monitoring and verification Management model Management model City-wide level of responsibility City-wide level of responsibility