Co-Control of Urban Air Pollutants and Greenhouse Gases in Mexico City

1. Co-Control of Urban Air Pollutants and Greenhouse Gases in Mexico City J. Jason West, Patricia Osnaya, Israel Laguna, and Julia Martínez Instituto Nacional de Ecología, México with support from: Integrated Environmental Strategies Program US Environmental Protection Agency National Renewable Energy Laboratory

2. Joint Urban Global • Low-sulfur coal • Smokestack • controls • Catalytic • converters • Inspection and • maintenance • Diesel particle • traps • Evaporative • controls • - Clean fuels: • wood > coal > oil > • gas > renewables • Energy • efficiency • Carbon and energy • taxes • Public transport and • land use • Retirement of old • vehicles • Efficiency standards • for new vehicles • Carbon • sequestering • Forest • management • Control of other • GHGs (CH4, • N2O, CFCs, SF6) • - Geoengineering

3. Co-benefits and Co-control Studies Control measures GHG emissions Local emissions Co - benefits Exposure and Concentrations • What is the “co-benefit” for local air quality and for health from actions to control GHG emissions? Health effects and Economic benefits

4. Co-benefits and Co-control Studies Control measures GHG emissions Local emissions Co - control Exposure and Concentrations • How can we plan to achieve local and GHG objectives simultaneousely? Health effects and Economic benefits

5. Goals of Co-control Study “To support the capacity in Mexico to analyze and develop policies addressing local air pollution and climate change in an integrated manner.” • Unify diverse studies of measures for the control of local air pollution and of GHGs, into a harmonized database of options, which is consistent among measures. • Develop and apply quantitative methods of analysis of policies, based on linear programming (LP) and goal programming (GP), to analyze minimum cost programs that achieve objectives for multiple pollutants.

6. Mexico City • Local air pollution dominated by transport emissions • (80% NOX, 40% HC, 36% PM10). • Overlapping environment/development goals: • mobility, energy, poverty, air quality, climate.

7. Summary of costs and emissions reductions Measures applied locally Percents are with respect to total projected emissions in 2010.

8. Cost-effectiveness of CO2 and NOX

9. Linear Programming Formulation Minimize: Cost = Σ AiCi Changing: Activity levels of meausres (Ai) Subject to restrictions: 1) Maximum levels, each measure: Ai  (Ai)max 2) Minimum levels, each measure : Ai  0 3) Emissions reductions: Σ(AiEi,k)  Tk • This can be a good tool when considering multiple pollutants simultaneously. • We developed this in Excel for easy application.

10. Min. NPV (fuel) Minimize NPV (fuel), using PROAIRE Measures PROAIRE

11. Local PROAIRE Objectives,including other local measures

12. Local control with CO2 objectives Minimize NPV (fuel) for PROAIRE objectives, and vary the restrictions for CO2 emissions. For all the local measures.

13. Local control with CO2 objectives Minimize NPV (fuel) and total investment for PROAIRE objectives, and vary the restrictions for CO2 emissions. For all the local measures.

14. Local control with CO2 objectives

15. Local and CO2 control - including national measures Minimize NPV (fuel) and total investment for PROAIRE objectives, and vary the restrictions for CO2 emissions. Including national measures.

16. Local and CO2 control -including national measures

17. Conclusions For Mexico City – • PROAIRE has a significant global “co-benefit” (3.1% of CO2). • Efficiency measures can reduce CO2 at a net cost-savings, with high investment costs, and modest local emissions benefits. • The benefits of simultaneously planning for local and global pollution are often small (but not zero). For air quality / climate management – • A measure with good “co-benefits” may not be the best way to solve problems simultaneously. It is important to include all possible measures in the analysis.

19. Acknowledgments • CAM • V. H. Páramo, J. Sarmiento, R. Perrusquía, B. Valdez, M. Flores • O. Vázquez, B. Gutiérrez, J. Escandón, O. Higuera • C. Reyna, R. Reyes, S. Victoria • INE • A. Fernández, V. Garibay, P. Franco, H. Martínez, A. García, A. Guzmán, H. Wornschimmel • US EPA and NREL • J. Renné, C. Green, D. Kline, J. Leggett, S. Laitner, S. Brant, K. Sibold, L. Sperling, B. Hemming • Others • M. Hojer, O. Masera, W. Vergara, R. Favela, J. Gasca, J. Quintanilla, F. Manzini, A. Sierra, S. Connors, P. Amar

20. Contexts of Study • Local air quality management – PROAIRE and its reviews every two years. • Climate change – there is domestic and international interest in reducing GHG emissions in Mexico. • International Co-benefits research

21. Goals of Co-control Study “To support the capacity in Mexico to analyze and develop policies addressing local air pollution and climate change in an integrated manner.” • Unify diverse studies of measures for the control of local air pollution and of GHGs, into a harmonized database of options, which is consistent among measures. • Develop and apply quantitative methods of analysis of policies, based on linear programming (LP) and goal programming (GP), to analyze minimum cost programs that achieve objectives for multiple pollutants: • as a tool that CAM can use for informing decisions. • to explore the relationships between controls on local pollutants and GHGs. • develop methods of analysis which are complementary to Co-benefits methods.

22. Construction of a harmonized database of measures ** We conducted an open process, in which all of the offices of CAM participated. Sources of data about the measures: -PROAIRE (2002-2010), and COMETRAVI (1999). - Studies of GHG measures at a national level (Sheinbaum, 1997; Sheinbaum y Masera, 2000). - Studies of other technologies (funded by World Bank): - solar water heaters (Quintanilla et al., 2000). - reducing leaks of residential LPG (TUV Rheinland, 2000). - hybrid electric buses (World Bank report, 2000).

23. Construction of a harmonized database: Emissions and Costs EMISSIONS –emissions reductions, with respect to the baseline, in 2010 (ton/yr), consistent with PROAIRE. • NOTE: it is not possible to compare our $/ton calculations with those in the literature, because we use emissions in 2010 only. COSTS – PROAIRE reports undiscounted investment costs (public and private), while GHG studies present the discounted NPV (9% discount rate). • It was not possible to estimate the NPV for all of the PROAIRE measures, with all of the changes in operation and maintenance expenditures. • We use investment costs and the NPV (fuel) as indicators.

24. Guide to the Harmonized Database 1&2) Public & private investment = sum of investments in capital from 2002 to 2010, without discounting. 3) Total investment = private + public. 4) NPV (fuel) = costs of investment and expenditures for fuel and electricity (2002-2010), and the salvage value in 2010. This is with respect to the baseline. Discounted to a NPV using a 9% discount rate. Does not include other social or environmental benefits. 5) NPV (all) = costs of investment and all of the operation and maintenance costs, with respect to the baseline, discounted to NPV. 6) Emissions reductions – in ton /yr in 2010. 7) Maximum level – The maximum level of application of each measure (the maximum feasible technically and practically), divided by the level in the Table. • The level of application in PROAIRE is 1.0.

25. PROAIREDATABASE PROAIRE: 24 vehicle measures; 14 transport; 7 industry; 9 services; 15 conservation of natural resource; 8 health; 4 environmental education; 8 institutional strengthening. THIS STUDY: 8 vehicle measures; 8 transport; 4 industry; 2 services

26. Mitigation measures for GHGs • Information obtained in the document: • National Potential for the years 2000, 2005, 2010 in ton CO2 / yr • Implemented in 1997 -2010 • Costs of reduction in US$ / ton CO2 (annualized) (includes Investment, Operation and Maintenance) • Information required for the database: • Local fraction of application for CO2 • Emissions of local pollutants (PM10, SO2, CO, NOx, HC) • Investment costs (million dollars) • NPV of each measure (US$/ton)

27. a) Mexico City Metropolitan Area (MCMA) • G2 Residential efficient lighting • G3 Commercial efficient lighting • G4 Pumping of potable water • G5 Electric motors in industry • G7 Industrial cogeneration • G11 Forest restoration • G12 Agroforestry options • b) Rest of the nation • GN2 Residential efficient lighting • GN3 Commercial efficient lighting • GN4 Pumping of potable water • GN5 Electric motors in industry • GN7 Industrial cogeneration • GN8 Wind electricity generation • GN9 Temperate forest management • GN10 Tropical forest management • GN11 Forest restoration • GN12 Agroforestry options Electrical Forestry

28. % Assumptions of the effect of changes in electricity consumption on the generation within the MCMA 1 0 Completely outside of the MCMA 2 100 Entirely from plants within the MCMA 3 3.1 Considering the interconnected system 4 20 Function of the relation consumption MCMA / generation MCMA Electricity measures  We consider scenario #3 to be most realistic.

29. Principal assumptions • Our costs and emissions are correct – we are subject to the limitations of our data sources. • We use the NPV (fuel) instead of the NPV (all), and our horizon is limited to 2010. • It is possible to implement more or less of a measure (with respect to PROAIRE), with proportional costs and changes in emissions, until the maximum level. • The measures are independent, and the costs and emissions are additive. • The tons of each pollutant are equivalent. • These measures are all of the possible measures. • The analysis is static – it reflects decisions made today, and do not reflect the ability to change decisions in time.

30. Minimize NPV (fuel), using PROAIRE Measures Costs are in US$ million, emissions in ton/yr in 2010, and shadow prices are in US$/(ton/yr).

31. What should be the local objectives? Results from min. NPV (fuel) using all of the local measures. PM10 HCs

32. What should be the local objectives? Results from min. NPV (fuel) using all of the local measures. CO NOX

33. Variation of costs with CO Costs can be reduced with a smaller reduction in CO, and with less investment in private auto measures.

34. Testable Hypothesis • Emissions reductions targets for local air quality and global climate can be achieved less expensively if planned simultaneously, than if they were planned separately. • Cost (Urban + Global) < Cost (Urban) + Cost (Global)

35. Testing the Testable Hypothesis Solutions when minimizing the total investment cost, using only local measures. Emissions reductions are tonnes per year in 2010. Costs are US$million.

36. Goal Programming • Alternative to linear programming • There can be many objectives (goals), with penalties if the goals are not met. • Formulation: Minimize sum of weighted deviations from goals: Σ (dj+wj+ + dj-wj-) where dj+ and dj- are deviations from goals, and wj+ and wj- are weights Subject to restrictions: 1) Maximum levels, each measure: Ai  (Ai)max 2) Minimum levels, each measure : Ai  0

37. GP example application Emissions reductions are tonnes per year in 2010. Costs are US$million. Weights are $/$ or US$million / (tonne/yr in 2010).

38. With Metro Min. NPV (fuel) Minimize NPV (fuel), now with the Metro (T25)

39. Minimize NPV (fuel), now with the Metro (T25) Costs are in US$ million, emissions in ton/yr in 2010, and shadow prices are in US$/(ton/yr).

40. Local PROAIRE Objectives, using PROAIRE Measures

41. 75% of Local PROAIRE Objectives

42. Local and CO2 control -including national measures Minimize NPV (fuel) for PROAIRE objectives, and vary the restrictions for CO2 emissions. Including national measures.

43. Conclusions – Harmonized Database 1) PROAIRE measures can reduce emissions of CO2 in the MCMA by 3.1% in 2010. • 50% CO2 from transport measures, and 50% vehicular. • The costs increased and reductions in emissions changed significantly since PROAIRE. 2) The GHG measures can reduce emissions of CO2 by 8.7% in 2010, while their changes in local emissions are less (3.2% HCs, 1.4% NOX). • This reflects that the majority of electricity is produced outside of the Metropolitan Area. • Many of these measures have negative NPVs.

44. Conclusions – Application of the LP for Local Pollution • We develop the LP and GP as tools for planning to achieve multiple pollutant (local-global) co-control. • It is possible to achieve the local emission reduction goals in PROAIRE at less cost, changing the emphasis on measures. • We estimate that the minimum cost can reduce by 20% (total investment and NPV (fuel)). • Lower cost results are not possible because many PROAIRE measures are applied near their maximum levels. • Including other GHG measures, the NPV (fuel) can reduce significantly, with large reductions in CO2 emissions.

45. Conclusions – Management of Local Air Pollutants and GHGs • CO2 emissions can be reduced, with increases in the investment cost and decreases in the NPV (fuel), by applying GHG measures. • The benefits of simultaneously planning for local and global pollution are often small (but not zero). • Although a measure can have significant Co-benefits, other combinations of measures may be better to achieve local and global objectives. • Measures which reduce CO2 outside of the metropolitan area should be considered also.