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Co-Control of Urban Air Pollutants and Greenhouse Gases in Mexico City J. Jason West, Patricia Osnaya, Israel Laguna, and Julia Martínez Instituto Nacional.

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Presentation on theme: "Co-Control of Urban Air Pollutants and Greenhouse Gases in Mexico City J. Jason West, Patricia Osnaya, Israel Laguna, and Julia Martínez Instituto Nacional."— Presentation transcript:

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 Urban Joint 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 (CH 4, N 2 O, CFCs, SF 6 ) - Geoengineering

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

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

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.” 1)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. 2)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% NO X, 40% HC, 36% PM 10 ). Overlapping environment/development goals: mobility, energy, poverty, air quality, climate.

7 Summary of costs and emissions reductions Measures applied locally Measures Cost (US$ million)Emissions reductions (ton/yr in 2010) Public invest. Private invest. Total invest. NPV (fuel) PM 10 SO 2 CONO X HCCO 2 PROAIRE Total6,5297,74014,2694,9135,180591,206121,09699,907 PROAIRE – 22 measures in this study 6,3307,74014,0704, ,972115,62299,880 This study – 22 measures from PROAIRE 9,93413,02522,9597,6563, % % 1,138, % 90, % 137, % 2,246, % This study – 22 measures from PROAIRE at their maximum levels 13,04118,87131,91210,6455, % % 1,550, % 120, % 184, % 3,267, % This study – GHG measures applied at their maximum levels 1,6311,6953, % 1 0.0% 2, % 3, % 19, % 6,279, % This study – All measures applied at their maximum levels 14,67120,55635,2379,9315, % % 1,553, % 124, % 203, % 9,547, % Percents are with respect to total projected emissions in 2010.

8 Cost-effectiveness of CO 2 and NO X

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

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

11 Local PROAIRE Objectives, including other local measures

12 Local control with CO 2 objectives Minimize NPV (fuel) for PROAIRE objectives, and vary the restrictions for CO 2 emissions. For all the local measures.

13 Local control with CO 2 objectives Minimize NPV (fuel) and total investment for PROAIRE objectives, and vary the restrictions for CO 2 emissions. For all the local measures.

14 Local control with CO 2 objectives

15 Local and CO 2 control - including national measures Minimize NPV (fuel) and total investment for PROAIRE objectives, and vary the restrictions for CO 2 emissions. Including national measures.

16 Local and CO 2 control - including national measures

17 Conclusions For Mexico City – -PROAIRE has a significant global “co-benefit” (3.1% of CO 2 ). -Efficiency measures can reduce CO 2 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.

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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 1)Local air quality management – PROAIRE and its reviews every two years. 2)Climate change – there is domestic and international interest in reducing GHG emissions in Mexico. 3)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.” 1)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. 2)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 ( ), 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 ( ), and the salvage value in 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 )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 PROAIRE DATABASE MeasuresPublic Inv. Private Inv. NPVPM 10 SO 2 CONO X HCCO 2 PROAIRE 89 (17)  THIS STUDY 22  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 Information obtained in the document: National Potential for the years 2000, 2005, 2010 in ton CO 2 / yr  Implemented in  Costs of reduction in US$ / ton CO 2 (annualized) (includes Investment, Operation and Maintenance) Information required for the database:  Local fraction of application for CO 2  Emissions of local pollutants (PM 10, SO 2, CO, NOx, HC)  Investment costs (million dollars)  NPV of each measure (US$/ton) Mitigation measures for GHGs

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 Electricity measures Assumptions of the effect of changes in electricity consumption on the generation within the MCMA 1Completely outside of the MCMA 2Entirely from plants within the MCMA 3Considering the interconnected system 4 Function of the relation consumption MCMA / generation MCMA %  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 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 PROAIREMin. NPV (fuel) Shadow prices Public invest.9,9346,286 Private invest.13,02512,929 Total invest.22,95919,216 NPV (fuel)7,6566,168 PM 10 3,7674,3550 SO ,019,951 CO1,138,167 3,909 NO X 90,698 14,965 HC137,259 20,021 CO 2 2,246,9462,614,201 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? HCs PM 10 Results from min. NPV (fuel) using all of the local measures.

32 What should be the local objectives? CO NO X Results from min. NPV (fuel) using all of the local measures.

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 IndicatorPROAIRE local targets 5 million tonnes/yr CO 2 Local + global Simultaneous local/global Total investment 17,8151,15718,97218,321 NPV (fuel)5,991-1,2484,7435,280 PM 10 4,21274,2194,203 CO1,138,167571,138,2241,138,167 NO X 90, ,18690,698 HC137, ,261137,259 CO 2 2,459,5195,000,0007,459,5195,000,000 Emissions reductions are tonnes per year in Costs are US$million. Solutions when minimizing the total investment cost, using only local measures.

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 (d j + w j + + d j - w j - ) where d j + and d j - are deviations from goals, and w j + and w j - are weights Subject to restrictions: 1) Maximum levels, each measure: A i  (A i ) max 2) Minimum levels, each measure : A i  0

37 GP example application IndicatorLP Solution (min. invest.) GoalWeightGP Solution Total investment 18,58516, ,675 NPV (fuel)6,2335, ,629 PM 10 2,7723, ,192 CO1,115, ,032,456 NO X 86,665103, ,442 HC116,731140, ,028 CO 2 1,991,8452,399,562 Emissions reductions are tonnes per year in Costs are US$million. Weights are $/$ or US$million / (tonne/yr in 2010).

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

39 Minimize NPV (fuel), now with the Metro (T25) PROAIREMin. NPV (fuel) Shadow prices Min. NPV with Metro Public invest.9,9346,2868,837 Private invest.13,02512,92912,949 Total invest.22,95919,21621,786 NPV (fuel)7,6566,1687,039 PM 10 3,7674,35504,365 SO ,019, CO1,138,167 3,9091,138,167 NO X 90,698 14,96590,698 HC137,259 20,021137,259 CO 2 2,246,9462,614,2012,731,086 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 CO 2 control - including national measures Minimize NPV (fuel) for PROAIRE objectives, and vary the restrictions for CO 2 emissions. Including national measures.

43 Conclusions – Harmonized Database 1)PROAIRE measures can reduce emissions of CO 2 in the MCMA by 3.1% in % CO 2 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 CO 2 by 8.7% in 2010, while their changes in local emissions are less (3.2% HCs, 1.4% NO X ). -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 1)We develop the LP and GP as tools for planning to achieve multiple pollutant (local-global) co-control. 2)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. 3)Including other GHG measures, the NPV (fuel) can reduce significantly, with large reductions in CO 2 emissions.

45 Conclusions – Management of Local Air Pollutants and GHGs 1)CO 2 emissions can be reduced, with increases in the investment cost and decreases in the NPV (fuel), by applying GHG measures. 2)The benefits of simultaneously planning for local and global pollution are often small (but not zero). 3)Although a measure can have significant Co-benefits, other combinations of measures may be better to achieve local and global objectives. -Measures which reduce CO 2 outside of the metropolitan area should be considered also.


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