Transactions on Ecology and the Environment vol 1, 1993 WIT Press, ISSN

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1 Multi-attribute decision theory methodology for pollution control measure analysis A.S. Barrera Roldan," A. Corona Juarez," R.W. Hardie/ G.R. Thayer* "Gerencia de Energeticos Alternos y Quimica Ambiental, Instituto Mexicano del Petroleo, Apdo. Postal , Mexico D.F., 07730, Mexico *PO Box 1663, Mail Stop F611, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA ABSTRACT A methodology based in Multi-Attribute Decision Theory was developed to prioritize air pollution control measures and strategies (a set of measures) for Mexico City Metropolitan Area (MCMA). We have developed a framework that takes into account economic, technical feasibility, environmental, social, political, and institutional factors to evaluate pollution mitigation measures and strategies utilizing a decision analysis process. In a series of meetings with a panel of experts in air pollution from dferent offices of the mexican government we have developed General and Specic Criteria for a decision analysis tree. With these tools the measures or strategies can be graded and a figure of merit can be assigned to each of them, so they can be ranked. Two pollution mitigation measures were analyzed to test the methodology, the results are presented. This methodology was developed specically for Mexico City, though the experience gained in this work can be used to develop similar methodologies for other metropolitan areas throughout the world. INTRODUCTION Air pollution in the MCMA is representative of critical air quality problems which exist in many major metropolitan areas worldwide and has became one of the

2 612 Air Pollution biggest concerns of the mexican government. We have developed a methodology based in Multi-Attribute Decision Theory*' 2, 3 to help the mexican decision makers to prioritize air pollution control measures and strategies (a set of measures) taking into account economic, technical feasibility, environmental, social, political, and institutional factors so they can count with a tool that help them choose measures and strategies to obtain the best results with the least cost. When comparing dferent strategies to reduce air pollution the two common elements considered are, the amount the air quality is improved (or alternatively the amount the emissions of pollutants are reduced) and the cost of obtaining this improvement. However there are many more factors that need to be considered when choosing a strategy to reduce air pollution. Factors such as the increase in imports required, the ability of the administrative structure to administer the new rules and regulations, the popularity of the measures being proposed, who pays,etc., are also an important part of the decision. One method of considering these factors in a structured and transparent way is to use decision analysis techniques. The procedure for the Multi-Attribute Decision analysis methodology starts with choosing the important criteria in the decision and weighting them relative to each other. Next an evaluation is determined for each criterion. These evaluations are then normalized on a scale of 0 to 1 using an utility function. This utility function provides the relationship between the criteria evaluation and its utility. The importance of the output of the decision analysis method is not only the resulting scores for the strategies, but also the process of going through the methodology. The formal process insures that important factors in the decision have been considered. The weights for the criteria and the utility values assigned for each strategy, provide a record of how the decision was made. This record is useful when the decision needs to be explained or defended. Two pollution mitigation measures were analyzed^ to test the decision analysis framework: "The installation of catalytic converters on new vehicles sold in Mexico from October 1990 onward" and "The substitution of natural gas instead of fuel oil in the two major electric power plants in the MCMA". The analysis results show the capabilities of the methodology.

3 Air Pollution 613 This methodology was developed as a part of the Mexico City Air Quality Research Initiative (MARI)*, an ongoing project between the Institute Mexicano del Petroleo and Los Alamos National Laboratory. The MAR I objective is to create or develop tools to evaluate air pollution control measures in the MCMA. METHODOLOGY Based on Multi-Attribute Decision Theory*' %' ^ we have developed a decision tree. The decision tree was developed in a series of meetings with a panel of experts in air pollution formed with representatives from: Departamento del Distrito Federal, DDF (Mexico City Government), Comision Estatal de Ecologia, Edo. de Mexico (State of Mexico Government), Secretaria de Desarrollo Social, SEDESOL (Secretary of Social Development, Federal Government), Secretaria de Salud (Secretary of Health, Federal Government), Petroleos Mexicanos, PEMEX (The Mexican Petroleum Company), Institute Mexicano del Petroleo, IMP (The Mexican Institute of Petroleum), and Los Alamos National Laboratory, LANL. In these meetings we used the Delphi technique to divide the problem in General and Specic Criteria, assign weighting factors to each of them according to their estimated importance in Mexico, and define utility functions for the Specic Criteria or Attributes. The main branches of the decision analysis tree or General Criteria were chosen to be: Technical. Economic. Environmental. Social, Political and Institutional. Each of these General Criteria were subdivided in Specic Criteria and some of them were also subdivided in more Specic Criteria. The whole decision analysis tree is shown in figure 1. The utility functions for each of the Attributes and the weighting factors for the General Criteria and Attributes are shown below: Technical Criterion Technological Evaluation Availability Available and Applicable Available but needs some adaptation Available but needs large adaptation Exists but not available It does not exist Technological Level

4 614 Air Pollution INPUT MATERIAL AND ENERGETICS AVAILABILITY AND CONSUMPTION AVAILABILITY TECHNOLOGICA LEVEL [- I L_ INITIAL INVESTMENT FINANCING IMPLEMENTATION CAPABILITIES AND TECHNOLOGICAL INNOVATION TECHNOLOGY EFFICIENCY Figure 1.- Customized Multi-Attribute Decisio Analysis tree to rank air pollution control measures and strategies for Mexico City.

5 Air Pollution 615 High Medium Low Implementation Capabilities and Technological Innovation It can be implemented and innovated It can be implemented but notinnovated Dficult to implement Impossible to implement Technology efficiency Percentage reduction of pollutants to Input Materials and Energetics Availability and Consumption High availability and low consumption High availability and medium consumption Medium availability and consumption Low availability and medium consumption Low availability and high consumption Service and Repair Available Available with few limitations Available with limitations Available with lots of limitations Not available Economic Criterion Investment and Financing Initial Investment Very Low Low Medium High but manageable High notmanageable Financing (financed by) Users National Enterprises Financial Institutions Government No Existence Operation, Maintenance Costs and Investment Low variable cost and low initial investment Low variable cost and medium initial investment Medium variable cost and high initial investment High variable cost and high initial investment Good or Service Price Market defined Subsidized Free Operation Cost and Implementation Time Low cost and short time Low cost and long time. 0.75

6 616 Air Pollution High cost and short time High cost and long time Balance of Payments No international help required Small international help required Medium international help required Large international help required Total international help required Environmental Criterion Air Quality Ic = Current IMECA. la = After implementation IMECA. N = Impact. F = Utility function = N 1.0 : > N 0.5 : > N 0.2 : > N > 1.0 > 0.5 > Pollutant Emission Reductions (THC, NOx, SOx, Pb, TSP,CO) Tci = Current emission tons of pollutant i. Tai = After implementa. emiss. tons of pollut. i. Tj_ = Toxicity factor of pollutant i. R = Emission reductions factor. F = Utility function. 6 2[T.-T.]*T. L ci ai J i R = Max(j=l,2..,6)[TV*T.] F = < R : R < ( R c R R < < < < Visibility V = Visibility factor. Vc = Current visibility. Va = Visibility after implementation. V# = Visibility without implementation (evaluated at the same time as VQ).

7 V V 2Va - Air Pollution 617 F = < 5 < 3 < 1 < 4. - Time Impact Immeciiately Short: (1-3 years) Long (after 3 yea.rs). R R i R 1 R < R < Km Km Km Km Range Permanent. 1 Medium range. 0.5 Temporary. 0 TT.T5 Imp< act + Range Social, Political and Institutional Criterion Income and Employment Impact Income or employment increase in low income sects Income or employment increase in high income sects No income neither employment impact Income or employ, decrease in high income sects Income or employment decrease in low income sects Public Opinion High Medium Low Citizen Participation No need Government participation required Industrial or commercial associations. participation required Civilian associations participation required All citizens participation required Political Interest Presidential initiative DDF or Edo. de Mex. initiative Institutional initiative Measure without political interest Administration Capabilities There exist administration entities with technical and professional capabilities, normative faculties and established norms There exist administration entities with technical and professional

8 618 Air Pollution capabilities, normative faculties, but there are no norms established There exist administration entities with technical and professional capabilities, but no normative faculties neither established norms No administration entities exist The methodology was designed so the sum of the General Criteria weighting factors would be equal to 100, i. e., A, B, C, and D are the weighting factors for the General Criteria then: A+B+C+D= 100. Similarly the weighting factors for the Attributes under a General Criterion must sum 100, i. e., ai is one of the Attribute weighting factors under one of the General Criterion then: &1 + &2 + + a^ = 100 This is true for Attribute weighting factors of the four General Criteria and is the same for any other subdivision of an Attribute, i. e., an Attribute is subdivided in Sub-Attributes then the Sub- Attribute weighting factors must sum 100 as well. An utility function was assigned to each one of the Attributes. The utility function for a certain Attribute was defined by the experts to evaluate the specic characteristic or characteristics, of a measure or strategy, that was (were) considered within this Attribute, so a measure or a strategy could be graded according to this evaluation in a scale from 0 to 1. For example in constructing a utility function for cost, any costs up to a certain amount would have a utility of 1 (i. e. up to a certain amount the cost would have no effect on the desirability of choosing the strategy), then as costs increase the utility would decrease until at a certain maximum cost after which the utility would be 0 (i. e., any strategy that costs more than this amount is too expensive to be considered). Based on this definition the total grade or Figure of Merit, FOM, of a measure or strategy was defined as follow:

9 A 4 n. J FOM = 2,GCW. /% AG,, AW,, j=l 1=1 where Air Pollution 619 GCWj = Weighting factor of the j-th General Criterion. AGji = Grade obtained by the measure or strategy corresponding to the i-th Attribute under the j-th General Criterion. AWji = Weighting factor of the i-th Attribute under the j-th General Criterion. nj = Number of Attributes under the j-th General Criterion. If an Attribute is subdivided in Sub-Attributes then: n.. AG.. = ji 100 where /, SAG.., SAW.. nji i = Grade obtained by the measure or strategy corresponding to the k-th Sub-Attribute under the i-th Attribute that is under the j-th General Criterion. i = Weighting factor of the k-th Sub-Attribute under the i-th Attribute that is under the j-th General Criterion. = Number of Sub-Attributes under the i-th Attribute that is under the j-th General Criterion. Comparing the FOM of dferent measures or strategies we can rank them in order of importance according to the highest FOM to the lowest. The production of a decision analysis framework is an interactive process. A completed framework must be tested with sample measures or strategies to see it is practical to implement and it produces the desired results. Once this test is completed changes will be made to the framework and another test performed. This process continues until a decision analysis framework is obtained that is appropriate for the particular problem.

10 620 Air Pollution RESULTS The main result of this work was the development of the Multi-Attribute Decision Analysis tree shown in figure 1, with its weights and utility functions (shown in the methodology). Also important was the formation of a panel of experts representing the principal government offices of the MCMA. Using this methodology we have analyzed two measures: "The installation of catalytic converters on new vehicles sold in Mexico from October 1990 onward" and "The substitution of natural gas instead of fuel oil in the two major electric power plants in the MCMA". The analysis was also based in a research made by the IMP on these two issues. The General Criteria grades and the FOM obtained by these two measures are shown in table 1. Figure 2 shows the FOM of the two measures. Table 1. General Criteria grades and FOM obtained by the: "Catalytic converter installation on new vehicles" and "Use of natural gas instead of fuel oil in power plants in the MCMA" measures. Measure Criteria ' Technical Economic Environmental Social, Political and Institutional Figure of Merit Installation of C. C. on New Vehicles Natural Gas Instead of Fuel Oil in Power Plants The C. C. measure obtained the highest FOM mainly because of the economic and environmental evaluation. Economically the C. C. measure was graded higher because basically all the costs are charged to the users, whereas with the Gas measure the government is the economic supporter (CFE the Mexican Electric Company is government owned), so in the long run is easy to economically support the C. C. measure; on the other hand the Gas measure could help to increase inflation. Another advantage of the C. C. measure recognized by the experts was that it promotes the polluters pay principle philosophy. Environmentally the main reason why the C. C. measure got the higher grade was that it reduces emissions in 1.9 % from MCMA total emissions, and the Gas measure reduces only 1.1 %&.

11 Air Pollution 621 FIGURE OF MERIT CATALYTIC C. NAT. GAS I TECHNICAL #3 ENVIRONMENTAL CZ] ECONOMIC EE SOCIAL Figure 2. - Figure of Merit comparison between the "Catalytic converter installation on new vehicles" and "Use of natural gas instead of fuel oil in power plants in the MCMA" measures. DISCUSSION AND CONCLUSIONS The importance of this methodology is that it takes into account not only objective factors like cost or air quality improvement but also incorporates in the analysis more subjective factors like public acceptance, political interest, etc., factors that are very important in the decision making process, specially in Mexico. Another important characteristic of this methodology is that it is planned to use modeling results obtained from air quality simulations using the HOTMAC (High Order Turbulence Modeling of Air Circulation) program, a meteorologic simulator, in conjunction with the CIT (Calornia/Carnegie Institute of Technology) airshed model adjusted to the MCMA conditions to evaluate the air quality improvements due to measure or strategy implementations. The modication and adjusting of the models to the MCMA conditions are also part of the MARI project and the models are about ready to be used. Nevertheless we should keep in mind that the methodology we are reporting here can be used without

12 622 Air Pollution modeling results, though the evaluation would be less precise. The methodology can be used cis it is now though as a result of the two measure analysis modications to some attributes are being considered. These modications will be published later on. The decision tree was generated using the Delphi technique with a panel of experts representing the various governmental units responsible for attacking the air pollution problem in MCMA. Thus the decision tree structure represents a consensus from people in the agencies that will be making the decisions on strategies to combat air pollution. This should increase the credibility of the structure that has been generated and improve its usefulness. REFERENCES 1. Book: 1. Saaty, T.L. The Analytic Hierarchy Process McGraw- Hill, Inc., New York, Book: 2. Adelman, Leonard. Evaluating Decision Support and Expert Systems, John Wiley & Sons, Inc., New York, Book: 3. Hwang, Ching-Lai and Yoon, Kwangsun. Multiple Attribute Decision Making - Methods and Applications - A state-of-the-art Survey, Springer-Verlag, New York, Internal Report: 4. Barrera Roldan, A. S., Corona Juarez, A., Hardie, R. W., Lanes Mollinedo, L. R. y Thayer, G. Metodologia de Atributos Multiples para la Evaluacion de Estrategias, report IAIT-92009, Institute Mexicano del Petroleo, may Paper in Conference Proceedings: 5. Guzman, F., Ruiz, M. E. and Sosa, G. 'Mexico City Air Quality Research Initiative', vol. 1.1, pp , Proceedings of the World Energy Council. 15th Congress, Madrid, September Report: 6. Departamento del Distrito Federal. Programa Integral contra la Contaminacion Atmosferica de la Zona Metropolitana de la Ciudad de Mexico, Cd. de Mexico, October 1990.