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INDEX OF SUSTAINABLE METROPLITAN AREAS 2018

SDGS CHALLENGES FOR MEXICAN METROPOLITAN AREAS TOWARDS 2030 OBJECTIVE

The objective of this Project is to create an index and a dashboard that measure SDGs progress of the Mexican Metropolitan Areas towards 2030, using available subnational data.

*There is a lack of official information for 3 of the 59 Mexican Met Areas. CONTEXT

17 231 169 Goals Objectives Indicators

57% of the total Population population in 2017

Metropolitan Areas

76.4% of total Mexican GDP GDP in 2015 CHARACTERISTICS OF THE PROJECT:

Descriptive study with quantitative data.

107 environmental, social and economic indicators.

Data gathered at the municipal level and aggregated at the metropolitan level.

Indicators are associated with one of the goals and with thresholds established in the 2030 Agenda.

Scores for 56 of 59 metropolitans areas until 2017 for 16 of 17 Goals.

“Optimal” values for each indicator are established according to the goals established by the UN. METHODOLOGY: STANDARDIZED VALUE &'() $ − +,-$ !"#$ = ×233 ; ./",0() − +,-$

Where: StdV: standardized value in a 0 to 100 scale; Real Value: value shown by an indicator for a certain year and metropolitan area; Optimal: value that should be reached 2030 by the indicator; and MinV: value obtained by the Metropolitan Area with the least favorable performance regarding an indicator in a specific year. METHODOLOGY: TRAFFIC LIGHT

Traffic light for each indicator:

Promedio 3 resultados menos favorables Optimal

1/3 rango 2/3 rango Rango

Traffic Light by SDG: The Mode (most repeated color from indicators within an SDG) RESULTS FROM THE GENERAL INDEX RESULTS FOM THE GENERAL INDEX RESULTS BY GOAL Goal Best Results Least Favorable Results

Monclova-Frontera (90.19) Tehuacán (20.27) (87.93) Poza Rica (22.22) 1. No poverty Rioverde-Ciudad Fernández (83.27) (31.85) (72.76) Acapulco (2.58) 2. Zero Hunger Monclova-Frontera (72.73) Minatitlán (14.22) San Luis Potosí (70.59) Tehuacán (17.67) 3. Good Health and Tula (75.00) Chihuahua (41.70) Reynosa-Río Bravo (73.50) San Francisco del Rincón (45.23) Well-Being Mexicali (72.13) Tuxtla Gutiérrez (48.21) -Villa de Álvarez (73.38) Zamora-Jacona (7.68) 4. Quality Education -Guadalupe (63.67) Tehuantepec (19.41) (58.82) Tuxtla Gutiérrez (21.37) Colima-Villa de Álvarez (77.36) Tula (37.10) 5. Gender Equality Valle de México (74.48) Córdoba (40.77) (72.31) Piedras Negras (40.95) 6. Clean Water and (90.07) Ocotlán (22.94) Colima-Villa de Álvarez (84.65) Valle de México (28.95) Sanitation Mexicali (81.71) (30.42) 7. Affordable and Clean (72.39) Guaymas (20.91) (68.64) Cancún (27.03) Energy Ocotlán (67.21) Minatitlán (27.16) Monterrey (73.14) Moroleón-Uriangato (13.13) 8. Decent work and Rioverde-Ciudad Fernández Saltillo (62.48) Economic Growth (23.82) Querétaro (59.32) Tulancingo (25.36) Goal Best Results Least Favorable Results

Moroleón-Uriangato Valle de México (72.78) (11.23) 9. Industry, Innovation and Rioverde-Ciudad (60.03) Infraestructure Fernández (16.58) La Piedad-Pénjamo San Luis Potosí (54.04) (17.60) Nuevo Laredo (74.01) Minatitlán (8.73) Tampico (57.16) Tula (10.91) 10. Reduced Inequalities Colima-Villa de Álvarez (18.82) (54.57) 11. Sustainable Cities and Valle de México (62.86) Poza Rica (21.49) (58.91) Acapulco (28.54) Communities Pachuca (58.31) Minatitlán (28.87) Toluca (59.78) Puerto Vallarta (20.19) 12. Responsible Consumption and Tehuantepec (53.50) (23.86) Rioverde-Ciudad Production Tijuana (24.54) Fernández (53.14) Guadalajara (90.69) Tecomán (5.30) 13. Climate Action León (88.63) Tehuantepec (5.36) Tijuana (88.45) Minatitlán (11.89) (96.37) Reynosa-Río Bravo (0) 15. Life on land Orizaba (81.11) Tijuana (0) Toluca (75) Mexicali (2.33) Moroleón-Uriangato Mexicali (28.34) 16. Peace, Justice and Strong (82.74) Intitutions Ocotlán (75.31) Acapulco (40.22) Minatitlán (73.26) Tecomán (41.31) Querétaro (84.60) Tuxtla Gutiérrez (45.79) 17. Partnership for goals Cancún (82.70) Ocotlán (46.60) Morelia (80.75) Zamora-Jacona (49.19) RESULTS FROM THE TRAFFIC LIGHT RESULTS OF THE TRAFFIC LIGHT GROSS SUMMARY

33 26

51 49 46 42

44 40 33

38 28 26 22

32 29 27 CLUSTER ANALYSIS CLUSTER ANALYSIS COMPARATIVE ADVANTAGES AND DISADVANTAGES

Average Comparative Comparative Cluster Metropolitan Area Index and Advantage Disadvantage ranking (SDG) (SDG) Saltillo, La Laguna, Guadalupe- Zacatecas, Tepic, Pachuca, Piedras A Negras, Querétaro, Mérida, 53.13 (2) 1, 2, 6 y 17 Monterrey, Juárez, Mexicali, San Luis Potosí, Monclova-Frontera y Chihuahua

B Tecomán, Acapulco, Zamora- 39.10 (9) 3 2, 4, 9, 13 y 15 Jacona y Tehuantepec

Tulancingo, La Piedad- C Pénjamo, , Tula, - 45.14 (3) 6, 8, 9, 10 y 15 Apizaco, Rioverde-Ciudad Fernández y Ocotlán Toluca, Cuautla, Cuernavaca, D -Tlaxcala, Orizaba y Valle 51.52 (5) 15 1 y 6 de México CLUSTER ANALYSIS COMPARATIVE ADVANTAGES AND DISADVANTAGES

Average Index Comparative Comparative Cluster Metropolitan Area and ranking Advantage Disadvantage

E Tampico, Colima-Villa de Álvarez 53.82 (1) 1, 3, 5 y 6 7 y 12 y Puerto Vallarta

F Reynosa-Río Bravo, Nuevo 52.9 (3) 3, 5, 6 y 13 4, 12 y 15 Laredo y Tijuana Veracruz, Villahermosa, G Guaymas, Coatzacoalcos, Cancún, 48.18 (6) 17 2, 7, 11 y 12 Córdoba y

H Poza Rica, Minatitlán, Tehuacán 42.66 (8) 15 y 16 1, 2, 9, 10 y 13 y Tuxtla Gutiérrez Celaya, León, San Francisco del I Rincón, Morelia, Moroleón- 52.19 (4) 5, 13 y 17 4 Uriangato, Matamoros, Aguascalientes y Guadalajara DASHBOARD AND FACT SHEET BY METROPOLITAN AREA FACT SHEET FOR METROPOLITAN AREA CONCLUSIONS

1. Mexican Metropolitan Areas have a long way to go to attain the goals set in the SDGs in 2030 (Average of the General Index: 49.8%) 2. There are very important development disparities among the areas. 3. Cluster Analysis: helps to identify common challenges and may be used for the design of common public policies to solve problems. 4. There are important areas of opportunity to generate and process information at the municipal and metropolitan levels: 1. Measurement of aspects and indicators related with SDGs. 2. There is an urgent need to have a timely publication of official information. 3. There is an opportunity to generate georeferenced information which would give more accuracy to the concept of Metropolitan Area. 5. This project is a pioneering effort in to measure the fulfillment of SDGs at the metropolitan level.