Flexible resilience indicators

Talk about the emergence of resilience as a concept around which to base urban indicators. The talk touches on the opportunities, risks and new methods associated with the term. Presented at the Royal Melbourne Institute of Technology (RMIT), 18th December, 2014.

Transcript

1. Royal Melbourne Institute of Technology, 18th December 2014 Flexible resilience indicators Robin Lovelace, University of Leeds @robinlovelace
2. Structure • Context • Resilience – What is resilience? – Previous work – Ideal datasets • Data – Local -> Regional -> National -> Global • Discussion
3. Part 1: Background
4. Where I'm coming from
5. Where I've been (UK)
6. Erasmus year in Salamanca Above: view from my 'piso' and where I learned Castilian Below: a book that heavily influenced my thinking
7. 1 Yr MSc in Environmental Science (York), PhD in 'E-futures' (Sheffield) • Growing interest in behaviour + environment • Energy: root of many problems http://campfire.theoildrum.com/node/6
8. Energy costs of modal shift
9. Conceptualising energy costs of transport After Smil (1993)
10. Time-series analysis
11. The big picture!
12. Thesis: Geography of transport energy use
13. The method: spatial microsimulation
14. Key finding: energy use varies!
17. Individual-level variability
18. Inequalities within areas
20. Going Dutch: aggregate-level results (Yorkshire and the Humber)
21. National-level comparisons Average energy costs per one way trip to work in English regions (2001) and Dutch provinces (2010)
22. Going Finnish
23. Application to the energy crisis Trips vs distance vs energy use measures of transport system performance. See Lovelace and Philips (2014).
24. Part 2: Resilience Source: The energy costs of commuting: a spatial microsimulation approach http://etheses.whiterose.ac. uk/5027/ Pilbara, western Australia http://tinyurl.com/bde9y56
25. Oil prices - 92 to 2012
26. Oil prices: 2009 until present
27. The threat of LOW oil prices! http://www.theguardian.com/world/2014/dec/02/russia-warns-fall-into-recession-2015-sanctions-oil-price
28. Local natural disasters
29. The climate risk
30. Energy use for air con... http://www.pir.sa.gov.au/pirsa/ Southern Australia
31. Sea level rise http://www.ipcc.ch/report/ar5/wg1/
32. Coastlines in 3000 AD? http://ngm.nationalgeographic.com/2013/09/rising-seas/if-ice-melted-map
33. Human-caused shocks
34. What is resilience anyway? "the ability of a set of mutually reinforcing structures and processes to persist in the presence of disturbance and stresses (Holling, 1973; Gunderson, 2000)" Why has it replaced sustainability? Luers, A. et al. (2003). A method for quantifying vulnerability, applied to the agricultural system of the Yaqui Valley, Mexico. Global Environmental Change, 13(4), 255–267. doi:10.1016/S0959-3780(03)00054-2
35. Resilience to what? • Resilience to coastal flooding • Resilience to disease • Resilience to oil price shocks • Resilience to austerity 2013 poll "according to almost half of US national security leaders" (!) http://www.defensenews.com/section/STATIC26/Leadership-Pollll
36. The ideal resilience indicator "Ultimately, we conclude that the social and spatial distribution of oil vulnerability depends on how an energy-constrained future is envisioned" (Lovelace and Philips, 2014). • Objective and quantifiable (like GDP) • Flexible enough for local contexts • Not so flexible as to become meaningless • Compatible with change
37. Published resilience indicators • Dodson and Sipe (2005) developed VIPER and described underlying motivations • Subsequent indicators have refined methods – VAMPIRE (Dodson and Sipe, 2008) – 4 metrics for commuting (Lovelace and Philips, 2014) – % who can walk/cycle to work (Philips, forethcoming) • Limited uptake by policy makers • Influencing policy debate
38. Data
39. The ideal dataset
40. Data considerations • Already hundreds of detailed datasets • With 'big data' revolution, ever increasing • 'Smart cities' projects • Variability • Order • Continuity • Scale
41. Example datasets • Individual • Local • Regional • National • Global
42. Individual level data Sleep deprivation: "percentage of those who report sleeping less than 7 hours on average on a typical weekday" (VicHealth Indicators Survey, 2011 Sedentary behaviour: "The proportion of people who sit for 7 hours or more per day." Method of Travel: http://www.abs.gov.au/censusContact Other variables (Travel surveys)
43. Geographic aggregation
45. Data: Read the small print "Data was collected via telephone interviews. The survey was conducted in each of Victoria’s 79 Local Government Areas (LGAs), with a total sample of 25,075 participants aged 18 years and over." Population of Victoria: Almost 6 million Number of participants per zone: 300 Sample methodology and size issues with this one!
46. Local variables • Proximity to... • Water source • Shops • Schools/hospitals • Level of community cohesion http://commons.wikimedia.org/
48. Regional variables • Renewable energy • Infrastructure • Urban morphology • Aggregate low- level data • Lack of compatibility • between states
49. National-level variables • Gross domestic product • Wellbeing indeces • Natural resources • Millitary defences • Conclusion: focus is more on 'energy security', over the head of most planners
50. Categories of metrics Need to impose order on the metrics • Social: how well people can 'bounce back' - linked to social capital, education • Economic: the dominant force in global system • Technological: e.g. number of electric cars • Infrastructure: e.g. renewable energy installations, bicycle paths • Environmental
51. Discussion
52. Individual-level variability
53. Links to agent-based models Spatial microsimulation applied to transport modelling problems. See Lovelace et al. (2014)
54. Flexibility "Ultimately, we conclude that the social and spatial distribution of oil vulnerability depends on how an energy-constrained future is envisioned" (Lovelace and Philips, 2014).
55. Datasets ready for deployment • Mode of travel to work • Australian travel dataset • Long-distance commute (Victoria) • Social variables (Victoria) • Likelihood of flooding (altitude) Variables to be calculated • Proximity to key services (OSM + GIS) • Distance of commute (ABS statistics)
56. Conclusion: Policy impact • Ultimately it's about 'evidence-based policy' • Because approach is not prescriptive, should be more attractive politically • But most use probably local urban planners and decision makers • Make discussion of future more realistic • Whilst avoiding regressive interpretations
57. Method: online visualisation
58. Is resilience conservative?
59. The wider picture: reducing the need for resilience "We’re not going to be able to burn it all. Over the course of the next several decades, we’re going to have to build a ramp from how we currently use energy to where we need to use energy. And we’re not going to suddenly turn off a switch and suddenly we’re no longer using fossil fuels, but we have to use this time wisely, so that you have a tapering off of fossil fuels replaced by clean energy sources" (Obama, 2014)
60. References Dodson, J., & Sipe, N. (2005). Oil Vulnerability in the Australian City. Lovelace, R. et al. (2011). Assessing the energy implications of replacing car trips with bicycle trips in Sheffield, UK. Energy Policy, 39(4). Lovelace, R., & Ballas, D. (2013). “Truncate, replicate, sample”: A method for creating integer weights for spatial microsimulation. Computers, Environment and Urban Systems, 41, 1–11. Lovelace, R., Ballas, D., & Watson, M. (2014). A spatial microsimulation approach for the analysis of commuter patterns: from individual to regional levels. Journal of Transport Geography, 34(0), 282–296. Lovelace, R., & Philips, I. (2014). The “oil vulnerability” of commuter patterns: A case study from Yorkshire and the Humber, UK. Geoforum, 51(0), 169–182. Obama, B. (2014). Quoted in 'Obama on Obama on Climate', NY Times. Rockstrom, J et al. (2009). A safe operating space for humanity. Nature, 461(7263). Wickham, H. (2014). Tidy data. The Journal of Statistical Software, 14(5). Retrieved from http://www.jstatsoft.org/v59/i10 .