ICT for Sustainability: Wisdom from Models and Data?

Introductory talk given at the 1st International Summer School on ICT for Sustainability (ICT4S), Leiden, NL, July-August 2017.

Transcript

1. ICT for Sustainability: Wisdom from models and data? Steve Easterbrook, University of Toronto Email: sme@cs.toronto.edu Blog: www.easterbrook.ca/steve Twitter: @SMEasterbrook
2. 2 Computing and Sustainability? 1. How can we support more responsible disposal of electronic waste? 2. How can we reduce CO2 emissions? 3. How can we better monitor the state of the natural environment? 4. How can we use technology to foster environmentally responsible behaviour? 5. How can we make better use of renewable resources? 6. How can we make more efficient use of resources? 7. How can we improve operational and process efficiency? 8. How can we use technology to make society more efficient? 9. What is the role of technology? 10. How can we promote less destructive and more satisfying patterns of consumption? Knowles, B., Blair, L., Hazas, M., & Walker, S. (2013). Exploring sustainability research in computing. Proceedings of the 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing - UbiComp’13, 305
3. 3 The Idea of Progress •3 Image Source: http://trolldens.blogspot.ca/2013/02/visions-of-future.html
4. 4 Outline ❍ Climate Change • Brief history of the science • Role of Models • Implications ❍ Energy ❍ Sustainability • Sustainability as a systems condition • Footprints and Overshoot • The Progress Myth and Limits to Growth ❍ The Role of Technology • Progress Traps (technology that is too successful) • Designing Resilient Communities
5. 5 Discovery of the Greenhouse Effect 1850s: John Tyndall discovers through a series of experiments that certain gases absorb infrared radiation; Demonstrated existence of the “greenhouse effect”
6. 6 What are models? ❍ Models of Phenomena ❍ Models of Data ❍ Idealized; Scaled; Simplified ❍ Typically represented as mathematical equations •Incoming shortwave •energy from the sun •Infra-red is radiated in all directions •some sunlight •Is reflected •Atmosphere •(not to scale!)
7. 7 The First Computational Climate Model 1895: Svante Arrhenius constructs an energy balance model to test his hypothesis that the ice ages were caused by a drop in CO2; (Predicts global temperature rise of 5.7°C if we double CO2) •Stockholm
8. 8 Schematic of the model equations Image source: Easterbrook, S. M. (2017) Computing the Climate. Cambridge University Press. Forthcoming
9. 9 Arrhenius’s Model Outputs Arrhenius, S. (1896). On the Influence of Carbonic Acid in the Air upon the Temperature of the Ground.
10. 10 Source: http://www3.epa.gov/climatechange/science/causes.html Exponential rise of GHGs
11. 11 First Computer Model of Weather 1950s: John Von Neumann develops a killer app for ENIAC, the first programmable electronic computer: weather forecasting Imagines uses in weather control, geo-engineering, etc.
12. 12 Basic physical equations •Zonal (East-West) Wind: •Meridional (North-South) Wind: •Temperature: •Precipitable Water: •Air pressure: 1904: Vilhelm Bjerknes identified the “primitive equations” These capture the flow of mass and energy in the atmosphere; Sets out a manifesto for practical forecasting
13. 13 Lynch, P. (2008). The ENIAC Forecasts: A Recreation. Bulletin of the American Meteorological Society
14. 14 •? •Model •Weakness •Develop •Hypothesis •Run •Experiment •Interpret •Results •Peer Review •Try another hypothesis •OK? •New Model Version Model building is “doing science”
15. 15 Observations 1. Causal Models need Data Models and vice versa • When the model and the data disagree, it’s often the data that are wrong 2. A model is never complete, but is sometimes good enough • Models are for improving our understanding and asking “what-if” questions. • Models enable close cross-disciplinary collaboration. 3. Model integration is difficult and inevitable. • A solitary model has very little value • A model won’t make sense out of context 4. Complex models have emergent phenomena… …and a model is most valuable when it surprises you
16. 16 A Climate Model Configuration ? Scientific Question Model Development, Selection & Configuration Running Model Interpretation of results Papers & Reports Scope of typical model evaluations Scope of fitness-for-purpose validation of a modeling system Is this model configuration appropriate to the question? Are the model outputs used appropriately? From models to modeling systems
17. 17 Understanding What-if Experiments •E.g. How do volcanoes •affect climate? Sources: (a) http://www.imk-ifu.kit.edu/829.php (b) IPCC Fourth Assessment Report, 2007. Working Group 1, Fig 9.5.
18. 18 Knutti, R., & Sedláček, J. (2012). Robustness and uncertainties in the new CMIP5 climate model projections. Nature Climate Change, (October), 1–5.
19. 19 Warming is linear with cumulative emissions Source: IPCC AR5 WG1 Fig SPM10
20. 20 Will the Paris Agreement Help? Source: http://www.easterbrook.ca/steve/2016/10/missing-the-target-canadas-deplorable-record-on-carbon-emissions/
21. 21 Slide shamelessly borrowed from Sir David King of Oxford University’s Smith School of Enterprise and Environment
22. 22 Outline ❍ Climate Change • Brief history of the science • Role of Models • Implications ❍ Energy ❍ Sustainability • Sustainability as a systems condition • Footprints and Overshoot • The Progress Myth and Limits to Growth ❍ The Role of Technology • Progress Traps (technology that is too successful) • Designing Resilient Communities
23. 23 The story of cheap energy Source: https://www.e-education.psu.edu/earth104/node/1347
24. 24 The story of cheap energy consumption Source: https://www.e-education.psu.edu/earth104/node/1347
25. 25 Can we live on 100% renewables? Sources: Jacobson, et al (2015). Proceedings of the National Academy of Sciences, 112(49), 15060–15065. Clack, et al (2017). Proceedings of the National Academy of Sciences, 114(26), 6722–6727.
26. 26 Outline ❍ Climate Change • Brief history of the science • Role of Models • Implications ❍ Energy ❍ Sustainability • Sustainability as a systems condition • Footprints and Overshoot • The Progress Myth and Limits to Growth ❍ The Role of Technology • Progress Traps (technology that is too successful) • Designing Resilient Communities
27. 27 What do we mean “sustainable”? ❍ Tainter (2006) suggests we should ask: (i) Sustain what? (ii) For whom? (iii) How long? (iv) At what cost? ❍ There are are often trade-offs: • E.g. we might sustain a profitable company by using up social or environmental capital • E.g. we might sustain a healthy ecosystem by preventing human development in the region J. A. Tainter, “Social complexity and sustainability,” Journal of Ecological Complexity, no. 3, pp. 91–103, 2006.
28. 28 Sterman, J. D. (2012). Sustaining Sustainability: Creating a Systems Science in a Fragmented Academy and Polarized World. In M. P. Weinstein & R. E. Turner (Eds.), Sustainability Science: The Emerging Paradigm and the Urban Environment (pp. 21–58). Springer. Sustainability as a System Condition
29. 29 Environmentally- Sustainable- Use$of$Natural$Resources$ Absorp2on$of$Wastes$ (air,$water,$land,$climate,$…)$ Economically- Sustainable- Financial$prosperity$ Investment$&$Profit$ (jobs,$income,$$ capital,$taxes,$…)$$ Socially-- Sustainable$ Standard$of$Living$ Equity$&$Trust$ (community,$educa2on,$$ opportunity,$$ mobility,$…)$$ Social3Environmental- Environmental$Jus2ce;$ Equitable$access$to$$ resources.$ Environmental3Economic- Energy$Efficiency;$ Subsidies$&$incen2ves;$ Carbon$Pricing.$ Social3Economic- Business$Ethics;$ Social$Responsibility;$ Workplace$democracy;$ - Fully- Sustain3- able-
30. 30 5 dimensions of sustainability Human • sustaining health, well-being, and human dignity Social • sustaining a just and equitable society Economic • sustaining the flow of resources and capital Technical • sustaining physical and digital infrastructures Environmental • sustaining healthy ecosystems and a stable climate
31. 31 Environmental Footprint of Progress •I = P x A x T •Impact = Population x Affluence x Technology •# persons consumption emissions •person consumption •Emissions = Population × Wealth × Energy × Carbon • per capita Intensity Intensity Total emissions = population X GDP /population X Energy /GDP X Emissions /energy •Kaya Identity:
32. 32 Steffen, W., et. al. (2015). Planetary boundaries: Guiding human development on a changing planet. Science, 347(6223). Planetary Boundaries
33. 33 Overshoot http://www.footprintnetwork.org/images/article_uploads/National_Footprint_Accounts_2012_Edition_Report.pdf
34. 34 How is overshoot possible? Images: http://www.pinterest.com/smeasterbrook/big102y-images/ •What happens if you spend more than you earn each year? •What happens if a farmer uses more water than falls in rain each year? •What happens if we produce more CO2 each year than the soils and oceans can absorb?
35. 35 Threats to Progress 1. Exhaustion of finite non-renewable resources on 'spaceship earth’ 2. Build up of environmental pollutants that threaten human health 3. Global environmental change, e.g. loss of biodiversity and climate change 4. Inability to scale up food production to feed a growing world population 5. Social disintegration arising from our inability to deal with the complexity of modern civilization 6. Highly unequal income levels and a growing gap between rich and poor 7. The rate of change of population and the corresponding high rate of growth in consumption •3 Source: Brooks, H. "Can Technology Assure Unending Material Progress?". In Almond, G.A., Chodorow, M., and Pearce, R. H., Progress and Its Discontents. University of California Press, 1982.
36. 36 Outline ❍ Climate Change • Brief history of the science • Role of Models • Implications ❍ Energy ❍ Sustainability • Sustainability as a systems condition • Footprints and Overshoot • The Progress Myth and Limits to Growth ❍ The Role of Technology • Progress Traps (technology that is too successful) • Designing Resilient Communities
37. 37 Can a Technology Be Too Successful? The Automobile Antibiotics Cheap Energy The Computer
38. 38 Progress Traps •…an idea or a technology that generates splendid results at first but leads to a deadly, impossible end.
39. 39 1st, 2nd and 3rd Order Effects •Smart signals respond to internet devices in cars to improve traffic flow through intersections •People perceive an improvement in traffic flows through the city •More people choose to drive, so congestion gets worse again
40. 40 What if the internet is too successful? Image source: http://www.digitalistmag.com/digital-economy/iot/internet-of-things-is-everywhere-01562372
41. 41 Erinn G. Ryen; Callie W. Babbitt; Eric Williams; 2015. Consumption-weighted life cycle assessment of a consumer electronic product community. Environ. Sci. Technol. 49 (4): 2549-2559.
42. 42 Source: http://www.ericsson.com/res/docs/2015/ericsson-mobility-report-june-2015.pdf
43. 43 Technology Debt? http://www.aljazeera.com/indepth/features/2013/10/inside-ghana-electronic-wasteland-2013103012852580288.html
44. 44 Other Perspectives ❍ The Club of Rome • Limits to Growth ❍ No-Growth Economists • E.g. Tim Jackson ❍ Neo-Luddites • E.g. Chellis Glendinning ❍ Deep Ecologists • E.g. James Lovelock ❍ Transition Town Movement • E.g. Rob Hopkins
45. 45 Key Points ❍ Progress is a dominant modern myth ❍ Need to distinguish ‘progress’ and ‘growth’ ❍ Most measures of innovation are growth measures • E.g. Moore’s Law • E.g. Number of patents • E.g. Number of internet users ❍ Unlimited growth is impossible on a finite planet ❍ We have different cultural expectations about progress (including its absence) ❍ There is no simple measure of progress ❍ Rates of change may be the greatest threat
46. 46 Conclusions? ❍ Sustainability is a systems concept • Need a robust boundary critique • Need trans-disciplinary thinking ❍ Computational modeling enables trans-disciplinarity • Integrated model makes the shared understanding explicit • Resolving model integration questions deepens understanding of feedback loops and emergent behaviour • The hard questions are in the gaps between disciplines ❍ Models need data; data need models • Data analytics reveals correlations • Simulation models explore causality • Each without the other is unwise