Challenges of combined indicators for cross-sectoral urban infrastructure assessments and urban social-ecological- technological sustainability Rae Zimmerman, PhD Research Professor and Professor Emerita of Planning and Public Administration Wagner Graduate School of Public Service New York University [email protected]

Presentation at the National Adaptation Forum Symposium 23-25 April 2019, Madison, WI Session Title: Advancing Practical Application of Indicators for Urban Resilience and Adaptation Planning April 23, 2019 Distribution for Session members only I. Introduction What Are Indicators and Why Do We Need Them? Indicators systematize knowledge and support consistent comparisons Indicator structure: • Indicators consist of a numerator and a denominator and defining and quantifying these components is critical to their goals. Examples (Stern and Feinberg, eds. 1996: 50): • Numerators: death, injury, loss of life expectancy, physical destruction, economic and social damages • Denominators: per capita, per GDP, per facility, per level of exposure, per level of concentration of a harmful substance • Indicator outcomes can vary depending on what is measured and how, e.g., • Parameter selection • Numerical scales used for parameters • Spatial scale of the data • Quantitative formulations used to combine indicators • Assumptions behind each of these data elements Sensitivity of Outcomes to Indicator Structure: Numerator/Denominator Issues Numerator/denominator selection • Indicators must have both numerators and denominators, that is, a specific measure, metric or value should be expressed relative to something else. • Example of how denominator selection can affect results: Evaluating and Interpreting Trends in Accidental Deaths from Coal Mining, U.S. 1950- 1970 • Per ton of coal mined (million tons) shows a declining trend • Per employee in coal mining (thousand employees) shows a generally increasing trend

Source: Paul C. Stern and Harvey V. Fineberg editors. 1996. Understanding Risk. Informing Decisions in a Democratic Society. Washington DC: National Academy of Sciences, p. 51. Used by permission of the authors from E. Crouch and R. Wilson (1982) Risk Benefit Analysis. Cambridge, MA: Ballinger Publishing Co. Sensitivity of Outcomes to Indicator Structure: Other Structural Characteristics • “Green” calculators provide instructive lessons for climate- related infrastructure indicators, e.g., Walkscore, Global Footprint Network, U.S. EPA “Household Emissions”, NYS MTA “Transit Effect”) • Results are sensitive to factors included in the calculator – they are pre-selected, i.e., the user doesn’t choose • Results can be sensitive to the scale structure (ordinal, interval) and number of divisions, and such metric structures may not reflect the degree of precision of the measurements • Weighting systems provide some adjustment for linearity but introduce some of their own biases • Results usually reflect “expressed preferences” which may not correspond to actual behavior • Calculators vary in the degree of detail about climate and its impacts

Source: R. Zimmerman (2012) Transport, the Environment and Security. Making the Connection, Cheltenham, UK and Northampton, MA: Edward Elgar Publishing, Ltd. ISBN 978 1 84980 020 4. Pages 105-111. Indicators versus Indexes • Indexes are distinct from indicators, in that they are aggregates of some property, often expressed as aggregates of indicators. As in the case of indicators, they are expressed as a scale or set of ordered values, but not usually possessing numerators or denominators. • Different indexes can produce vastly different results depending upon how they are aggregated and use weighting factors. Bakkensen et al (2017) compared five indexes and identified variations in the conclusions, since they used different data, scales and geographic units for similar measures.

Source: Bakkensen, Laura A., Cate Fox-Lent, Laura K. Read, and Igor Linkov 2017 Validating Resilience and Vulnerability Indices in the Context of Natural Disasters, Risk Analysis, Vol. 37, No. 5, 2017, pp. 982-1005. II. Application of Indicators to Infrastructure: Single Infrastructures

• Traditional infrastructure indicators generally do not take into account interdependencies and or climate and extreme event related factors directly, but could • Infrastructure indicators rarely explicitly connect infrastructure physical characteristics to social preferences and needs including environmental justice 1. Indicator of Impervious Surfaces (Roads) Land Consumption by Roads in Selected Countries, per capita, 2010 Road Kilometers per 1,000 Persons

45.00

40.00

35.00

30.00

25.00

20.00

15.00

10.00

5.00

0.00 China Japan France Germany United Canada Mexico United States Kingdom

Source: U.S. Department of Transportation, Federal Highway Administration (FHWA) (October 2010) Highway Statistics 2009 Road System Measures for Selected Countries (Metrics), Table IN-3 http://www.fhwa.dot.gov/policyinformation/statistics/2009/in3.cfm 2. Indicators of Road Congestion: Level of Service

Source: State of Florida DOT (2009) 2009 Quality/Level of Service Handbook, Tallahassee, FL p. 15 http://www.dot.state.fl.us/planning/systems/sm/los/pdfs/2009FDOTQLOS_Handbook.pdf 3. Indicators of Transit Performance (Used by Provider) User delay-related measures: • Mean distance between breakdowns • Number of miles between emergency calls • Vehicle age (years) over expected lifetime • Maintenance expenditures • Monthly Ridership • Weekday 24-hour On-Time Performance • Wait Assessment • Enroute Schedule Adherence

User safety-related measures: • Customer Accident Injury Rate • Miscellaneous services, e.g., elevators

Source: MTA Web Site http://web.mta.info/developers/performance.html Example of Trends for a Traditional Indicator of Transit Performance: Mean Distance Between Failures, NYC Transit, 2006 and 2007

Interpretation: the smaller the distance, the worse the performance From August 2013 to August 2014, MDBF increased from 129,081 to 143,592 Source:http://web.mta.info/mta/news/books/pdf/141027_1030_Transit.pdf. P. 28. “Subway Mean Distance Between Failure (MDBF) is the measure of subway car fleet reliability and is calculated as revenue car miles divided by the number of delay incidents attributed to car related causes.”

Source: http://www.mta.info/mta/ind-perform/month/nyct-s-mdbf.htm. MDBF: “Average number of miles a subway car travels in service before a mechanical failure that makes the train arrive at its final destination later than 5 minutes” (http://web.mta.info/persdashboard/agencies/nyctsubway/ip/67816_chartmth.htm) “The higher the mileage for the MDBF, the more reliable the subway car and the service.” 4. Challenges of Indicator-Based Measures of Equity: Social Benefits of Transit in terms of Transit Accessibility and Employment Brookings Institution: • Access of workers to transit to commute to work and the quality of the commute (time and cost) varies geographically • In the 100 largest metropolitan areas, 70 percent of working age residents have access to transit • Access is much greater in the large cities of the far west and northeast

Source: A. Tomer, E. Kneebone, R. Puentes, and A. Berube (May 2011) Missed Opportunity: Transit and Jobs in Metropolitan America, Washington, DC: The Brookings Institution. http://www.brookings.edu/research/reports/2011/05/12- jobs-and-transit Example of Equity Considerations: Subway and Bus Connectivity by Income (Low- Income Areas), Queens and Brooklyn, NY Source: R. Zimmerman, C.E. Restrepo, J. Sellers, A. Amirapu, and T. R. Pearson, “Promoting Transportation Flexibility in Extreme Events through Multi-Modal Connectivity,” U.S. Department of Transportation Region II Urban Transportation Research Center, New York, NY: NYU-Wagner, June 2014, p. 38 (Brooklyn), 37 (Queens) . http://www.utrc2.org/sites/default/files/pubs/Final-NYU-Extreme-Events-Research-Report.pdf III. Introducing Infrastructure Dependencies and Interdependencies: Definitions

Definitions [1] • A dependency is activity in one direction: a flow of people, information, commodities from one point to another • An interdependency is a flow of at least two ways Types of interdependencies Interdependencies can occur in a number of different forms [1]: • Functional • Spatial (proximity) • Cyber • Logical

Source: [1] Rinaldi, S., Peerenboom, J., and Kelly, T. (2001). “Identifying, understanding, and analyzing critical infrastructure interdependencies.” IEEE Control Systems Magazine, 21(6), 11-25. Importance of Interconnections The broadest construction of interconnections: Social- Environmental-Technological/Infrastructural (SETs)

Source: Arizona State University lead—Urban Resilience to Extreme Weather Related Events Sustainability Research Network (UREx SRN) Importance of Interconnections Among Infrastructure Systems for Indicator Construction

• Infrastructure interconnections provide an important foundation and challenge for indicators.

• Component level indicators, e.g. at the level of infrastructure material and design impairments from non-weather and climate incidents can be scaled to climate-related effects. Lifeline Interdependencies: Electricity Focus

Source: U.S. Department of Energy (DOE). January 2017. Quadrennial Energy Review: Transforming the Nation’s Electricity System: The Second Installment of the QER, p. 1-9, figure 1-2. https://energy. gov/sites/prod/files/2017/02/f34/Quadrennial%20Energy%20Review- Second%20Installment%20%28Full%20Report%29.pdf Lifeline Interdependencies: Water Focus

Source: U.S. DHS (2010) Water and Wastewater Systems Sector-Specific Plan An Annex to the National Infrastructure Protection Plan 2010, Washington, DC: U.S. DHS, p. 10. http://www.dhs.gov/xlibrary/assets/nipp-ssp-water-2010.pdf Sector Receiving the Service Sector Energy: Oil & Energy: Transportation Water Communication Gas Electricity Generating or Providing Generic the Service to Another Infrastructure (Receiving) Interdependencies Sector Energy: Oil & Gas Fuel to Fuel to operate Fuel to Fuel to maintain operate transport vehicles operate temperatures for power plant pumps and equipment; fuel motors and treatment for backup power generators Energy: Electricity Electricity for Power for Electric Energy to run cell extraction and overhead transit power to towers and other transport lines operate transmission (pumps, pumps and equipment generators) treatment

Transportation Delivery of Delivery of Delivery of Delivery of Sources: From R. Zimmerman and C.E. supplies and supplies and supplies and supplies and Restrepo. 2009. Analyzing Cascading workers workers workers workers Effects within Infrastructure Sectors for Consequence Reduction. Proceedings of Water Production Cooling and Water for Water for the HST 2009 IEEE Conference on water production vehicular equipment and Technologies for Homeland Security, water operation; cleaning Waltham, MA, Table 1, p. 166;pp. 165-170. cleaning DOI: 10.1109/THS.2009.5168029. Note: Communication Breakage and Detection and Identification and Detection and exchanges or interconnections within each leak detection maintenance location of control of sector (box) also occur, but are not shown and remote of operations disabled vehicles, water supply here. control of and electric rails, roads; the and quality Note: These examples are illustrative and operations transmission provision of user service not intended to be comprehensive. information Electric Power and Transit Interconnections: Illustrative Cases • 2003 northeast U.S. and Canada blackout: Transit rail (electrified) took about 1.3 times and traffic signals 2.6 times as long to be restored relative to electric power restoration. Data also available for water systems.* • The Metro-North railway outage: Large power line impairment affected transit > a week.** • September 29, 2011 Lightening Strike on a LIRR Computer:*** Highly centralized network (most trains go through Jamaica station), high volume of traffic (81 million annually), few rail alternative; Multiple failures at the same time increased consequences dramatically - lightening strike disables trains west of Jamaica, programming error, third rail shut, 17 stranded trains, 9 standing trains. Opportunities for Intervention: More communication, computer training, securing facilities from natural hazards, travel alternatives • Transformer explosions impairing transit: July 29, 2001 (NYC); power outages caused closures of San Francisco Bay Area and Chicago transit lines.**** • Blackouts and intermittent power disruptions affect transit operations dependent upon power through third rail and catenary structures. Over a dozen occurred in NYC in 2017-2018

Sources: *R. Zimmerman and C. E. Restrepo (2006) “The Next Step: Quantifying Infrastructure Interdependencies to Improve Security,” International Journal of Critical Infrastructures, Vol. 2, Nos. 2/3, pp. 215-230; p. 223. **Matt Flegenheimer (September 25, 2013) Power Failure Disrupts Metro North’s New Haven Line; May Last Days, New York Times http://www.nytimes.com/2013/09/26/nyregion/metro-norths-new-haven-line-suspended-after-power-loss.html ***Metropolitan Transportation Authority (MTA) (October 2011), ‘Preliminary review September 29, 2011 lightning strike at Jamaica’, New York, NY, USA: MTA. ****Summarized and references contained in R. Zimmerman (2005) “Mass Transit Infrastructure and Urban Health,” J. of Urban Health, 82(1), pp. 21-32; pp.27-28. Bridge Collapses • About a couple of dozen bridges have collapsed in the U.S. since the mid-1960s • Age alone does not explain why bridges collapsed • Bridges that have collapsed have generally been younger in age than the overall bridges in the U.S. even though bridge condition generally declines with age (National Bridge Inventory) Why? • A combination of causes related to interdependencies usually contributes to bridge collapses, including drainage failures (water management interconnection) • Non-redundant design, common in the mid-20th century can contribute to the severity of consequences of bridge collapses from susceptibility to water interdependencies • Environmental conditions, including weather, can also be contributing factors

Source: R. Zimmerman (2012) Transport, the Environment and Security, Cheltenham, UK and Northampton, MA: Edward Elgar Publishing, p. 199. Interdependent Infrastructure Indicators: Transportation and Water Infrastructure Schoharie Bridge, NYS Thruway I-90 Collapse, near Amsterdam, NY • Background • April 5, 1987: One of the piers of New York Thruway (I-90) Bridge over the , near Amsterdam, NY gave way followed by the collapse of another pier 90 minutes later, and the resulting collapse of the spans fatally injuring 10 people • Initiated and Connected Causes and Conditions • Design elements did not capture population growth rates and their flooding effects • Supporting elements were non-redundant • Modifications of the distance between bridge piers during construction placed piers in the water and increased water flows between them • Repairs only focused on concrete pitting but not pier stability • Inspection and oversight procedures did not incorporate bridge scour • Heavy uncontrolled storm-related floodwaters stressed piers and causes collapse • Weather communication was insufficient to warn of dangers • Lessons Learned: How to Intervene • Design to accommodate population growth estimates and their flooding impacts • Review and control modifications made during and after construction • Change inspection procedures to incorporate bridge scour (done)

National Transportation Safety Board (NTSB) (1988), Highway Accident Reports: Collapse of New York Thruway (I-90) Bridge over the Schoharie Creek, near Amsterdam New York, April 5, 1987, http://www.ntsb.gov/investigations/summary/HAR8802.htm. Interdependent Infrastructure Indicators: Transportation and Water Infrastructure Mianus Bridge (I-95) Collapse near Greenwich, CT • Background • June 28, 1983. A span of the I-95 bridge collapsed over the Mianus River near Greenwich CT killing 3 people, seriously injuring 3 others. • Initial conditions and causes • Non-redundant pin and hanger structure • Pin thickness not subject to professional standards • Maintenance deficiencies affecting the condition of steel components (e.g., painting), bridge decking, and drainage to channel water away from steel to avoid rusting • Examination of the condition of the pins not incorporated into inspection procedures • Lessons learned • Design: Bigger pins, redundant support elements • Maintenance: Routine painting of pins to prevent rusting; ensure drainage to avoid water in contact with steel elements • Inspection: Inspect sub-deck elements for rusting Source: Summarized from National Transportation Safety Board (1984), Highway Accident Report: Collapse of a Suspended Span of Interstate Rte 95 Highway Bridge over the Mianus R.Greenwich, CT. 6/28/83, Washington, DC, USA: NTSB. IV. Introducing Climate-Related Factors Electric Power and Transportation Interconnections: Effects of Heat • Trains that rely upon overhead power lines through pantograph /catenary connections, are affected when the lines sag from overheating and the connections become tangled in the lines.

• Hodges (2011: 23) Example of catenary and pantagraph noted incidents of interconnections with electric power lines this for NJ Transit, LA Source: Northeast Corridor Commissions (NEC) Metro, and TriMet. http://www.nec- commission.com/cin_projects/catenary-power- supply-systems/ Infrastructure Disruptions from Extreme Weather: Electric Power

• Electric power outages: Simonoff, Restrepo and Zimmerman (2007)* analyzed electric power outages in the U.S. and Canada from 1994-2004 (a total of 396 and 97 in each country respectively) and found that: – Weather accounted for almost half of the outages in the U.S. and a third in Canada (p. 549) – Weather-related outages last longer than those caused by equipment failure (p. 558, 560, 562) – Weather is increasing over time since mid-1990s as a factor contributing to outages (p. 562) • Similar analyses have been conducted for the effects of weather on other types of infrastructure, e.g. pipelines, where individual events are aggregated into large datasets

Sources: *J.S. Simonoff, C.E. Restrepo, and R. Zimmerman, “Risk Management and Risk Analysis-Based Decision Tools for Attacks on Electric Power,” Risk Analysis, Vol. 27, No. 3, 2007, pp. 547-570. **C. E. Restrepo, J. S. Simonoff and R. Zimmerman, “Causes, Cost Consequences, and Risk Implications of Accidents in U.S. Hazardous Liquid Pipeline Infrastructure,” International Journal of Critical Infrastructure Protection, Vol. 2, 2009, pp. 38-50. DOI:10.1016/j.ijcip.2008.09.001. Case-based Recovery Rates Reflecting Interconnections: Illustrations for Hurricane Sandy • Transportation (Transit): Full and Partial Restoration of Subway Lines due to both electric power outages and failures of water infrastructure for drainage, 10/28/12-11/12/12 – October 28, 2012 - impact: Pre-emptive shut down of transit – November 9, 2012 – partial restoration: Approximately 80% of subway lines were operating normally and 20% were partially operating (segments) • Electric Power: Customers Lost as a Percentage of Customers Served and Customers Out (Cumulative) NYC Total and by Borough, October 29- November 7, 2012 – October 29-31, 2012 - impact: Citywide, 20% of total customers served were without power, ranging from 8% in Brooklyn to 63% in – November 3, 2012 – partial recovery: Power was restored to almost half of customers who lost power citywide, ranging from 88% in to a third in the Bronx

Source: R. Zimmerman, RAPID/Collaborative Research: Collection of Perishable Hurricane Sandy Data on Weather-Related Damage to Urban Power and Transit Infrastructure,” National Science Foundation, the U. of Washington (lead), Louisiana State University, and New York University. Electric power findings calculated from Con Edison news releases.; Transit findings are computed from selected NYC Transit data. R. Zimmerman, “Planning Restoration of Vital Infrastructure Services Following Hurricane Sandy: Lessons Learned for Energy and Transportation,” J. Extreme Events, Vol. 1, No. 2, August 2014. Recovery of Transit and Electric Power (Comparison) Post- Hurricane Sandy (Interdependent Infrastructures) • Two weeks after Hurricane Sandy, about 80% of the transit lines were fully recovered and others were partially recovered along their routes. • In 2012 after Hurricane Sandy compared to a similar period in 2011, system ridership declined on average about 14 %.

% Customers served (cumulative) Source: R. Zimmerman, RAPID/Collaborative Electric power recovery followed Research: Collection of Perishable Hurricane Sandy Data on Weather-Related Damage to Urban Power patterns similar to transit. and Transit Infrastructure,” NSF; R. Zimmerman (2014), “Planning Restoration of Vital Infrastructure U.S. Department of Energy (2013) Comparing the Impacts of Northeast Hurricanes on Services Following Hurricane Sandy: Lessons Energy Infrastructure. [Online], p. 11, 12 Available from: Learned for Energy and Transportation,” J. of http://energy.gov/sites/prod/files/2013/04/f0/Northeast%20Storm%20Comparison Extreme Events, Vol. 1, No. 1. _FINAL_041513c.pdf

Picture: MTA. Fixing A Train Tracks on the “flats” near Jamaica Bay Transit required electric power to function for third rails, signals, switches, ventilation, lighting, and to pump out excess water; energy required robust transit infrastructure for physical support and to transport workers to repair sites. Hurricane Recovery Rate Comparisons, 2005 and 2008, 2011 and 2012 for Energy Infrastructure

GUSTAV, IKE, KATRINA, RITA, WILMA IRENE, SANDY Source: U.S. DOE (February 2009) Comparing the Impacts of the 2005 and Source: U. S. Department of Energy. (2013). Comparing the Impacts 2008 Hurricanes on U.S. Energy Infrastructure, p. 22, p. 5. of Northeast Hurricanes on Energy Infrastructure, p. 11, 12. http://www.oe.netl.doe.gov/docs/HurricaneComp0508r2.pdf http://energy.gov/sites/prod/files/2013/04/f0/Northeast%20Storm%20 Comparison_FINAL_041513c.pdf Resiliency as the Ability to Recover Electric Power Connections to Transportation and Water, U.S. Canada 2003 Blackout

Electric Power Outages: Outage Durations, August 2003, approximately 42- 72 hours

T(i)/T(e) Electric - Transportation Transit-electrified Signals (NYC) 1.3 Traffic Signals (NYC) 2.6

Electric - Water Cleveland water supply system 2.0 Detroit water supply system 3.0

Source: T(i) = duration of the infrastructure outage; T(e) = duration of the electric power outage. Duration expressed in hours. R. Zimmerman and C. Restrepo, “The Next Step: Quantifying Infrastructure Interdependencies to Improve Security,” International Journal of Critical Infrastructures, Vol. 2, Nos. 2/3, 2006, pp. 215-230. Summarized from Table 3. Effect of water distribution lines on other infrastructure or “Effect Ratios”

Source: R. Zimmerman, “Decision- making and the Vulnerability of Critical Infrastructure,” Proceedings of IEEE International Conference on Systems, Man and Cybernetics, SMC 2004, Volume 5, edited by W. Thissen, P. Wieringa, M. Pantic, and M. Ludema. The Hague, The Netherlands: Delft University of Technology, 2004, pp. 4059-4063. Table 1.

NYC Environmental Protection V. Recommendations and Conclusions for Climate-Based Infrastructure Indicators, NYC Panel on Climate Change, 2019 • Indicators were developed in two infrastructure categories – transportation and energy – and also for interdependencies: • Transportation and energy indicators were primarily expressed as the “number, frequency, duration, and geographic extent” of disruptions and ultimate the cost of the delays • Interdependencies were likelihood and severity of the combinations and connections on infrastructure services • It is difficult to separate out climate from non-climate factors affecting infrastructure • Tracking of indicators is critical over time and space • Financial indicators were addressed • Credit ratings were considered useful indicators • Climate seems to have a small effect on New York City’s ratings • Co-generated indicators are an important component of indicator development, to obtain stakeholder input, necessary not only for inclusiveness and equity, but also for the viability of implementation Source: R. Blake, R., K. Jacob, G. Yohe, R. Zimmerman, D. Manley, W. Solecki, and C. Rosenzweig. New York City Panel on Climate Change 2019 Report, “Chapter 8: Indicators and Monitoring.” In: New York City Panel on Climate Change (NPCC), Advancing Tools and Methods for Flexible Adaptation Pathways and Science Policy Integration: NPCC 2019 Report. C. Rosenzweig and W. Solecki, Eds. Annals of the New York Academy of Sciences, 1439, March 15, 2019. Pp. 230-279. https://doi.org/10.1111/nyas.14014. Pages 257-259. Observations and Conclusions • Indicators are critical to systematic knowledge about the state of infrastructure and the impact of climate change. • Many existing indicators and indexes have not taken climate change into account directly with respect to its effects on all of the components and dimensions infrastructure and its services, though individual research is making great strides and suggesting alternatives. • Given the considerable attention to infrastructure indicators at a broad or global level it is now time to do a deep dive into the details of how infrastructure materials and design play out with environmental factors. • Fine-grained information is needed at the level not only of physical materials and design but also how users are affected by them. • Climate change consequences are linked to one another, for example, sea level rise, heat, and indicators need to reflect those connections. Source: U.S. EPA (2012) Climate Change Indicators in the United States, Washington, DC: U.S. EPA 2012. Acknowledgements Portions of the work were supported by:

• The National Science Foundation (#1444755) to Arizona State University lead—Urban Resilience to Extreme Weather Related Events Sustainability Research Network (UREx SRN) to New York University. • Critical Resilient Interdependent Infrastructure Systems and Processes (CRISP) Type 1— “Reductionist and integrative approaches to improve the resiliency of multi-scale interdependent critical infrastructure,” funded by the NSF (#1541164) • Dynamic Resiliency Modeling and Planning for Interdependent Critical Infrastructures,” funded by the Critical Infrastructure Resilience Institute, U. of Illinois, Urbana-Champaign, part of the Homeland Security Center of Excellence funded by the U.S. Department of Homeland Security CIRI Disclaimer: Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author and do not necessarily reflect the views of the sponsors, the National Science Foundation or the U.S. Department of Homeland Security.

NOTE: Portions of this presentation were presented by R. Zimmerman at a variety of conferences, e.g., the National Academies of Sciences, Engineering, and Medicine, Roundtable on Science and Technology for Sustainability and other similar venues.