Historical Transitions in Transport Systems iTEAM4 Workshop, IIASA - - PowerPoint PPT Presentation

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Historical Transitions in Transport Systems iTEAM4 Workshop, IIASA - - PowerPoint PPT Presentation

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Historical Transitions in Transport Systems iTEAM4 Workshop, IIASA October 30, 2018 Arnulf Grubler Technological/Infrastructural Transitions: Main Messages End-use innovation driven (people!) Involves Hardware + Software + Orgware

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Historical Transitions in Transport Systems

iTEAM4 Workshop, IIASA October 30, 2018 Arnulf Grubler

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Technological/Infrastructural Transitions: Main Messages

  • End-use innovation driven (people!)
  • Involves Hardware + Software + Orgware
  • Important complementarities (infrastructures, society)
  • Fractal nature of transition speeds from fast (end-use)

to slow (infrastructures)

  • Implications for modeling: Explicit representation of:
  • - actors and interactions
  • - technologies (“which”) and techniques (“how used”)
  • - time and space (“when, where”)
  • - interdependencies
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1800 1850 1900 1950 2000 Km/day-cap 10-1 10-2 100 101 102 Meter/day-cap 100,000 10,000 1,000 100 10

All modes Buses + cars Rail 2-Wheelers Horses Air TGV Railways Waterways

France – Growth in Motorized Mobility

Source: A. Grubler, The Rise and Fall of Infrastructures, Physica, 1990

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Darum geht zu allen Völkern […] und lehrt sie alles zu befolgen was ich Euch geboten habe. Seid gewiss: Ich bin bei euch alle Tage bis ans Ende der Welt. Disruptive, Rapid Change

Source: Campanale, Carbontracker

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USA – Substitution of Horses by Automobiles

Saturation density: 1 vehicle/person ?

Source: N. Nakicenovic, 1986, The Automobile Road to Technological Change, Technological Forecasting & Social Change 29(4):309-340.

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Ford Model T

Learning by Doing-1: Fordism Learning by Doing-2: Users aka interpretative flexibility

  • R. Kline and T. Pinch, 1996,

Users as Agents of Technological Change: The Social Construction of the Automobile in the Rural Unites States, Technology and Culture 4(October):763-795.

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Social Change: Change in Car Driving Licenses Held by Young Trends: near-term: <50%, long-term: ~0?

Note in particular much larger prevalence of declining driving license ownership and shift from growth to decline trends in Austria and Israel around 2008/2010 (for Finland, Netherlands, Spain no more recent data are available to uncover similar trend breaks)

Location year a year b age group % of age group with drivers license change year a year b %-points Austria 2 2010 2015 17-18 39 28

  • 11

Germany 2008 2017 18-24 71 66

  • 5

Great Britain 1995 2008 17-20 43 36

  • 7

Great Britain 1995 2008 21-29 74 63

  • 11

Israel 2 2005 2015 17-18 34 30

  • 4

Israel 2 2009 2016 19-24 65 64

  • 1

Japan 2001 2009 16-19 19 17

  • 2

Japan 2001 2009 20-24 79 75

  • 4

Norway 1991 2009 19 74 55

  • 19

Norway 1991 2009 20-24 85 67

  • 18

Sweden 1983 2008 19 70 49

  • 21

Sweden 1983 2008 20-24 78 63

  • 15

Switzerland 1994 2015 18-24 71 61

  • 10

USA 1983 2014 18 80 60

  • 20

USA 1983 2014 19 86 69

  • 17

USA 1983 2014 20-24 91 77

  • 14

Location year a year b age group % of age group with drivers license change year a year b %-points Austria 1 2006 2010 17-18 32 39 7 Finland 1983 2008 18-19 37 68 31 Finland 1983 2008 20-29 51 82 31 Israel 1 1983 2008 19-24 42 64 22 Israel 1 1983 2008 25-34 62 78 16 Netherlands 1985 2008 18-19 25 45 20 Netherlands 1985 2008 20-24 64 64 Spain 1999 2009 15-24 37 50 13

Data sources: Sivak & Schottle, 2011; Delbosc & Currie, 2013; Nat.Stats, 2017 for Austria, Germany, Israel, Switzerland

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Updated (Malmodin & Lundén, 2018; Bento, 2016) from Grubler et al, 2018. Pictorial representation based on Tupy, 2012.

5 Watts

2.5 Watts

449 Watts

72 Watts Power Stand-by energy use

75 kWh

0.1 kg

1706 kWh

26 kg Embodied energy Weight

Resource Impacts of Digital Convergence

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France - Transport and Communication Volume

1850 2000 Index 1985 = 100 10-2 10-1 100 101 102

Transport

1975 1925 1900 1875 1950 1800 1825 100 10 1 0.1 0.01

Communication

Source: A. Grubler, The Rise and Fall of Infrastructures, Physica, 1990

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The Pitfalls of Supply-side Only:

Failed Innovations, ex. Palmer 1828 monorails using sails Example today: Hyperloop with integrated PV ??

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slow transitions fast transitions

co-ordination problems, vested interests strong co-ordination and policy direction systemic discrete technologies novel concepts, formative phases market-ready substitutes weak adopter benefits (mainly less externalities) strong adopter benefits, high relative advantage early adopting markets late adopting markets

Explaining Fast vs. Slow Transitions

Grubler, A., Wilson, C., Nemet, G., 2016, Apples, oranges and consistent comparison of the temporal dynamics of energy transitions, Energy Research and Social Science 22:18-25.

lumpy technologies & infrastructures granular technologies & social networks

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2 Modeled Transitions – World Transport Energy Use (EJ) GEA Supply vs. LED

50 100 150 50 100 150

2010 2020 2030 2040 2050 2010 2020 2030 2040 2050

  • il products

synliquids electricity

GEA Supply (2012) Conventional Transport Low asset utilization (efficiency of use) IC with oil+substitutes LED (2018) Shared urban mobility High asset utilization (efficiency of use) Electrification+H2

Source: Riahi et al., 2012, GEA Chapter 17. Source: Grubler et al., 2018, Nature Energy.

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Implications for Modeling

  • Represent actors and their interactions

(e.g. ABM)

  • Move from technology choice only

to include also technology use

  • Time and space: consider interactions (e.g.

Haegerstrand) and constraints (beyond Zahavi, e.g. urban size and densities)

  • Model systems rather than sectors/activities

(takebacks, interdependencies, synergies)

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20 40 60 80 100 20 40 60 80 100 Cumulative percent of global access/owenership Cumulative percent of global population/households

Access to Technologies & Services

(Lorenz Curves)

Technologies & Infrastructures

cell phones 2014 radios 2000 bicycles 2014 automobiles 2013 piped water 2012 electricity 2005 broadband 2014

Granularity Equity

0.11 0.21

2014

0.17 0.43 0.77 0.89 0.88

2000

0.58 GDP

PPP & MER

piped water 2012 electricity 2005 broadband 2014

Source: Zimm & Grubler (in preparation)

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Global Access to Technologies (Lorenz Curves) Development & Technology Gaps

20 40 60 80 100 20 40 60 80 100 Cumulative percent of global consumption Cumulative percent of global population

equity line GDP MER GDP PPP radios 2000 bicycles 2014 mobile phones 2000 mobile phones 2014 automobiles 2013 internet

Data source: World Bank WDI 2016 and PEW Survey 2016

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Global Access to Technologies (Lorenz Curves) Development & Technology Gaps

equity line GDP MER GDP PPP radios 2000 bicycles 2014 mobile phones 2000 mobile phones 2014 automobiles 2013 internet

20 40 60 80 100 20 40 60 80 100 Cumulative percent of global consumption Cumulative percent of global population

Data source: World Bank WDI 2016 and PEW Survey 2016

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lumpy large unit size high unit cost Indivisible high risk granular small unit size low unit cost modular low risk

Technology Unit Size

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Healey, S. (2015). Separating Economies of Scale and Learning Effects in Technology Cost Improvements. IR-15-009. International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria.

smaller units

  • > more units
  • > more
  • pportunities to

experiment

  • > more learning

heat pumps nuclear

Granularity Benefits 2: Higher Learning

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Source: A. Grubler, 1998. Technology and Global Change, Cambridge University Press.

Energy Efficiency (%) and Emissions (g/km) for Horses, and Early and Contemporary Automobiles

Horses Cars (ca. 1920) Cars (1995) Engine efficiency, % 4 10 20 Wastes Solid 400 – – Liquid 200 – – Gaseous, including Carbon (CO2)d 170 120 70 Carbon (CO) – 90 2 Nitrogen (NOx) – 4 0.2 Hydrocarbons 2e 15 0.2

d Total carbon content of fuel e Methane

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Diffusion as Epidemiological Temporal/Spatial Process

Temporal distribution of adopters (countries introducing postage stamps) Source: Pemberton, 1936 Spatial distribution of car adopters in region of Southern Sweden. Source: Haegerstrand, 1968 Distribution of adopters by mean adoption rate (K/2). Rogers, 1962

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ABM Example – Adoption of “green” Products

  • Representation of producers and adopters
  • f technologies (agents) and policy maker (principal)

micro-level interactions yield aggregate macro-level outcomes

  • Heterogeneous products

(performance, price,…,…, environment)

  • Heterogeneous agents

(producers: technological capability, R&D strategy; consumers: preferences and preference weights)

  • Agent interaction 1: producers-consumers
  • Agent interaction 2: consumers-consumers

(“small world network” Watts- Strogatz-1998 model) depending on:

  • - nature and size of social network
  • - peer effect
  • Agent interaction 3:

policy makers – producers – consumers policy options: education, C-tax, R&D subsidy

  • Illustrative results from exploratory (!) modeling
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Results ABM - Network Effects: Network size (critical threshold level) >> peer effect > # of neighbors and their distance

Grubler et al., 2014

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Results ABM Policy Leverages: ∆ consumer preferences >> C-tax > R&D subsidy

Grubler et al., 2014

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Car Diffusion: Catch-up at Lower Levels

Source: A. Grubler, 1998. Technology and Global Change, Cambridge University Press.

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Path Dependent Vehicle Ownership Trends

Source: GEA KM9 (2012) based on IPCC AR4 (2007)

http://www.iiasa.ac.at/web/home/research/Flagship-Projects/Global-Energy-Assessment/GEA_Chapter9_transport_hires.pdf

Country observations: 1990-2004

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Growth of US Infrastructures

Source: A. Grubler, The Rise and Fall of Infrastructures, Physica, 1990

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A Tale of 3 Cities: Public Transport Passengers (Million) and Cars Registered (Thousand)

100 200 300 400 500 600 700 800 900 1000 1945 1955 1965 1975 1985 1995 2005 2015

Vienna

1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 1945 1955 1965 1975 1985 1995 2005 2015

Shanghai

1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 1945 1955 1965 1975 1985 1995 2005 2015

Beijing

BUS&TRAM

  • incl. Metro

CARS

Note 10-times larger volumes in Chinese cities

Data sources: Stat. Wien var. vols., T. Ma, ECUST, priv. comm.

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Source: Adapted from GEA KM9 (2012) and WBCSD (2011) A) Capacity pass./hour

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User Behavior Can be More Powerful than Technological Efficiency: Example Energy End Use in Transport

50 miles/gal 15 miles/gal 8 miles/gal

1 Yalie in Zipcar

Soccer mom + 3 kids Driver + 20 school children Distance traveled (all examples) : 100 km

1.5 1.25 0.50

Energy use: MJ per passenger-km traveled

Toyota Prius Cadillac Escalade

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UK – Replacement within Vehicle Fleets

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USA – Horses vs. Cars for Road Transport (fractional share F in total fleet; linear plot and logit transform)

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1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 1950 1960 1970 1980 1990 2000 2010 2020 2030 2040 2050 Millions

10,000,000 and more 5,000000 to 10,000,00 1,000,000 to 5,000,00 100,000 to 1,000,000 Less than 100,000 Rural

Population by Settlement Type/Size

13 30 340 3192 ??

Number of agglomerations in 2005 growth dominated by small & medium sized cities!

Source: GEA KM18 (2012)

http://www.iiasa.ac.at/web/home/research/Flagship-Projects/Global-Energy-Assessment/Chapte18.en.html