new approaches to environmental challenges · energy portfolio •technologies follow uncertain...
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New Approaches to Environmental Challenges
What do we learn from the long-run evolution of technological systems?
François Lafond
Key points
• There are increasing returns in technology investmentè Do not wait to invest in climate mitigation technologies
• Technologies are interdependentè Support the knowledge base and technologies that fit well the
current era
• Technologies are disruptiveè Anticipate and mitigate negative consequences
There are increasing returns in technology investment
è Do not wait to invest in climate mitigation technologies
Trends are persistent
1900 1950 2000 2050
pric
e in
$/k
Wh
0.01
0.1
1
10
100
0.17$/kWh= 2013 price in $/Wp5c/kWh (~ coal electricity)
coal fuel price for electricity generation ~ 40% of cost
Hinkley Point
US nuclearelectricity
solar photovoltaic modules
0.1
0.51
510
50100
pric
e in
$/W
p
Updated from Farmer and Lafond (2016), Research Policy.
Experience curves for conditional prediction
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MWp of PV modules
PV m
odul
e pr
ice
in 2
016
$/W
p0.05
0.2
0.512
51020
50
1 102 104 106
●●●●●●●●●●± 1 std. dev.
± 1.5 std. dev.± 2 std. dev.
Lafond, François, Aimee Gotway Bailey, Jan David Bakker, Dylan Rebois, Rubina Zadourian, Patrick McSharry, and J. Doyne Farmer. "How well do experience curves predict technological progress? A method for making distributional forecasts." Technological Forecasting and Social Change 128 (2018): 104-117.
What will be the price of solar energy if we decide to produce x more?
But is this really a causal relationship?
“Experience causes cost improvement”
“Lower prices causeincreased demand”
World War II as a natural experiment
Cheaper solar panels
More demand for solar panels
5 50 500 5000
number of planes
man
hour
s pe
r pou
nd o
f airf
ram
e
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monthlyproduction
cumulativeproduction
Willow Run plant, Detroit
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MWp of PV modules
PV m
odul
e pr
ice
in 2
016
$/W
p
0.5
1
2
5
10
20
50
1 102 104
annualproduction
cumulativeproduction
Cheaper bombers
More demand for bombers
Lafond, Greenwald and Farmer, Does stimulating demand drive costs down? WWII as a natural experiment, in progress
Energy portfolio
• Technologies follow uncertain experience curves • Applied to energy
transition: costs may be lower than business as usual.
Way, Lafond, Lillo, Panchenko & Farmer (2019), Wright meets Markowicz, J. of Econ. Dynamics and Control. Way, Mealy & Farmer, The net cost of green energy transition, in progress. Farmer et al. (2019), Sensitive Intervention points in the post-carbon transition, Science.
solarwind hydro
gas
coalbiomass
nuclear
slow transition scenario in Way et al. (2019, in progress)
Technologies are interdependent
è Support the knowledge base and technologies that fit well the current era
Automobile and
ChemistryTools andinstruments
Machines and Agriculture
Data and networks
1850 1900 1950 2000
• Some groups of technologies reinforce each other
• Groups of technologies have their golden age where they concentrate most of the activity
• Technological eras: groups of technologies are central at the same time
Technological eras
Asano, Wary, Lafond, Farmer and Beguerrise Díaz, Uncovering technological eras, in progress
Knowledge base of green technologies
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●geo
hydrowind
tidal
solarthermal
pv
nuclear
nuclear engineering
wind motors
hydro turbines
diagnosticdevices
electric digitalprocessing
mechanical powerfrom heat
heatcollectors
heat exchangeapparatus
semi−conductors
• There are clusters of interrelated technologies
• Some technology clusters are overall more isolated than others
• PV, and to some extend wind, rely on digital and semiconductor technologies.
• Green technologies are often found to produce higher spillovers
Schematic depiction of a subset of the technological ecosystem, based on Pichler, Lafond and Farmer, Predicting innovation dynamics in the technological ecosystem, in progressOn spillovers, see Barbieri et al, Dechezlepretre et al
Technologies are disruptive
è Anticipate and mitigate negative consequences
Challenges to labor markets
• Sustainability transition requires the decline of some industries and the rise of others
• But other forces can balance or accentuates this• Automation • Demography Healthcare
ProductionConstruction and Extraction Food
preparation and serving
Office and Administrative
support
Life, Physical & Social Science
Computer, engineering and science
Management
Empirical network of occupational mobility, US 2010-2017. From del Rio-Chanona, R. Maria, Penny Mealy, Mariano Beguerisse-Díaz, Francois Lafond, and J. Doyne Farmer. "Automation and occupational mobility: A data-driven network model." arXiv preprint arXiv:1906.04086 (2019).
Challenges to measurement systems
Intangibles• Computerized
information• Intellectual
property• Economic
Competencies• Etc.e.g. Corrado, Hulten and Sichel (2009)
Natural Capital• Natural assets• Ecosystem services• Pollution• Waste management• Etc.
e.g. https://seea.un.org/
Well-being• Health and
Education• Personal activities• Political voice• Social capital• Security• Inequality• Etc.e.g. Fitoussi, Sen, Stiglitz (2009)
Thanks
Thanks to all my co-authors and the Complexity Economics team (J. Doyne Farmer) at INET Oxfordhttps://www.inet.ox.ac.uk/research/programmes/complexity-economics/
BibliographyAsano, Y., Wary, S., Lafond, F., Farmer J.D. and Beguerrise Díaz, M. (2019), Uncovering technological eras, in
progressBarbieri, N., Marzucchi, A., & Rizzo, U. (2018). Knowledge sources and impacts on subsequent inventions: Do green
technologies differ from non-green ones?. SPRU WP 2018-11.Corrado, C., Hulten, C., and Sichel, D. (2009). Intangible capital and US economic growth. Review of Income and
Wealth 55(3): 661-685.Dechezleprêtre, Antoine, Ralf Martin, and Myra Mohnen. "Knowledge spillovers from clean and dirty
technologies." (2014).
del Rio-Chanona, R. Maria, Penny Mealy, Mariano Beguerisse-Díaz, Francois Lafond, and J. Doyne Farmer (2019), Automation and occupational mobility: A data-driven network model, arXiv preprint arXiv:1906.04086.
Farmer et al. (2019), Sensitive Intervention points in the post-carbon transition, Science.Farmer, J. D., and Lafond, F. (2016), How predictable is technological progress? Research Policy 45, no. 3
(2016): 647-665. Freeman, C., & Soete, L. (1997). The economics of industrial innovation, Routledge.Goldin, I., Koutroumpis, P., Lafond, F. Winkler, J. (2019), Why is productivity slowing down?, Oxford Martin
School working paper.Lafond, F., Bailey, A.G., Bakker, J. D. , Rebois, D., Zadourian, R.. McSharry, P., and Farmer, J. D. (2018) How well
do experience curves predict technological progress? A method for making distributional forecasts. Technological Forecasting and Social Change 128: 104-117.
Lafond, F., Greenwald, D., and Farmer, J.D. (2019) Does stimulating demand drive costs down? WWII as a natural experiment, draft
Pichler, A., Lafond,F. and Farmer, J.D. (2019), Predicting innovation dynamics in the technological ecosystem, in progress
Stearns, P. N. (2018). The industrial revolution in world history. Routledge. Stiglitz, J. E., Sen, A., & Fitoussi, J. P. (2009). Report by the commission on the measurement of economic
performance and social progress.Way, R., Lafond, F., Lillo, F., Panchenko, V., and Farmer, J.D. (2019), Wright meets Markowitz: How standard
portfolio theory changes when assets are technologies following experience curves., Journal of Economic Dynamics and Control 101: 211-238.
Way, R. and Mealy, P. & Farmer, J.D. (2019), The net cost of green energy transition, in progress.
Science Museum refers to the Science Museum in London (visited 2018)Source of undocumented images: Wikimedia commons
Key points
• There are increasing returns in technology investmentè Do not wait to invest in climate mitigation technologies
• Technologies are interdependentè Support the knowledge base and technologies that fit well the
current era
• Technologies are disruptiveè Anticipate and mitigate negative consequences