EP06: Energy and Climate Change Dr Jean-Francois Mercure, [email protected]@cam.ac.uk Pablo...

EP06: Energy and Climate Change

Dr Jean-Francois Mercure, [email protected] Salas, [email protected]

mailto:[email protected]



Lecture 5 – Innovation and technological change

Lecture 5-H1: Mechanisms of technological change

- Historical trends of technology diffusion

- Classic examples: Marchetti & Nakicenovic

- Logistic transformation: how it works

- Classic examples: vehicles, infrastructure, others

- Historical trends of learning-by-doing

- The diffusion of innovations as seen by Rogers

Lecture 5-H2: The diffusion of innovations

- Path dependence:

- As a result of learning-by-doing

- Brian Arthur: Increasing returns to technology adoption

- Brian Arthur: lock-ins by historical events

- Transitions in socio-technical regimes: the multi-level perspective

- Technology transitions: the historical perspective

- Defining the levels: niches, regimes and the landscape

- The multi-level perspective and socio-technical regime transitions

- An analogy with ecosystems and evolutionary economics

- Mutation, selection, diffusion

- Co-evolution and the Red Queen hypothesis



- Classic examples: Marchetti & Nakicenovic

Marchetti & Nakicenovic, IIASA Working Paper, (1978)

- World went historically from: Biomass burning (up to 1850s)Coal use (industrial revolution)Oil/electricity use (internal combustion engine)Gas/nuclear/other (after oil shocks 1970s)

- Illustrating as F/(1-F), we observe a very particular pattern




TechnologyDiffusion

TechnologyDiffusion and decline



- Classic examples: Vehicles

Nakicenovic, Technology Forecasting and Social Change (1986)See also Grubler, Nakicenovic and Victor, Energy Policy (1999)



- Classic examples: Infrastructure, industry, consumption goods

Grubler, Nakicenovic and Victor, Energy Policy (1999)Sharif & Kabir, Technology Forecasting and Social Change (1976)

1- Infrastructure diffusion2- Building materials3- Competition and substitution in

steelmaking



- Classic examples: Fisher & Pry

Fisher & Pry, Technology Forecasting and Social Change (1971)

1st Ordering principle:The diffusion curve




TechnologyDiffusion

TechnologyDiffusion and decline



- Multi-technology competition (not in exam!)

J.-F. Mercure, Energy Policy, 48 799-811 (2012)J.-F. Mercure, ArXiv http://arxiv.org/abs/1304.3602 (2014)

To expand theoretically, see Sigmund & Hofbauer, Evolutionary Games and Population Dynamics, CUP (1998)

- What happens if competition happens between several technologies simultaneously?

- How do we write down the interaction?

http://arxiv.org/abs/1304.3602







- What happens if competition happens between several technologies simultaneously?

- How do we write down the interaction?








- Are such dynamics reminiscent of other systems?- Yes! This is the same as the competition of species in ecosystem:

The process of natural selection in evolutionary biology- i.e. this is the technology selection process

- The constant αij contains the information concerning investor/consumer choice

Replicator dynamics

Lotka-Volterra equation





- Learning-by-doing in technology production

IEA Experience Curves for Energy Technology Policy (2000)Grubler, Nakicenovic and Victor, Energy Policy (1999)

It was early observed by aeronautical engineers, particularly T. P. Wright, that the number of labor-hours expended in the production of an airframe (airplane body without engines) is a decreasing function of the total number of airframes of the same type previously produced. Indeed, the relation is remarkably precise; to produce the Nth airframe of a given type, counting from the inception of production, the amount of labor required is proportional to N-1/3. This relation has become basic in the production and cost planning of the United States Air Force.

Arrow K., Review of Economic Studies (1962)

Learning rate: % cost decline for doubling of sales

2nd Ordering principle:The learning curve



- Energy technologies

IEA Experience Curves for Energy Technology Policy (2000)Grubler, Nakicenovic and Victor, Energy Policy (1999)

Gas Turbines



- Energy technologies

Weiss M. Technological Forecasting & Social Change 77 (2010) 411–428

Lecture 5-H1:

Van Bursik et al Environ. Res. Lett. 9 (2014) 114010



Time

Ado

ptio

n ra

te

E.M. Rogers, Diffusion of Innovations, Fifth Edition p281 (2010)

Adoption rates depend on:- Relative advantage- Compatibility- Complexity- Trialability- Observability

Adopters are diverse by nature, diverse profiles- Innovators- Early adopters- Early majority- Late majority- LaggardsThis can include income distributions



Time

Ado

ptio

n ra

teC

ost

Time

Diffusion and learning interact

Lecture 5 – Innovation and technological change


- Path dependence:



- Brian Arthur: lock-ins by historical events




- The multi-level perspective and socio-technical regime transitions

- An analogy with ecosystems and evolutionary economics




- Path dependence:


Learning-by-doing, if interacting with the diffusion, leads to a self-reinforcing cycle

Cost-reductions Faster diffusion more cost reductions Faster diffusion …

This can lead to technology avalanches!

This has increasing returns to adoption (increasing returns to scale):- If adoption at time t depends on the level of diffusion at time t-1,

The system is called path dependent. Means decisions taken at time t influence events at all subsequent times Can lead to technology lock-ins

E.g. lock-in to an inferior technology


- Path dependence:

- Hypothesising the technology ladder

a. Technologies evolve endogenously with a possible transition

b. With a driver (e.g. a carbon price), technologies (i) gradually and successively get replaced by a series (i+1, i+2…) of ever slightly more efficient/productive (e.g. low carbon) systems. The system does not jump directly to a final solution (e.g. zero carbon) but uses intermediates that already exist. The highest efficiency technologies (e.g. zero carbon) remain in niches until they have cumulated enough cost reductions (due to early adopters) to be brought into the mainstream.

The result is a gradually changing aggregate efficiency as technologies come in and go.

c. With a stronger driver (that evolves faster), the succession of technologies takes place faster and reaches a deeper level of efficiency increase (e.g. rate of reduction in carbon emissions). A maximum to the rate exists which is linked to the average lifetime of technologies

d. Real technology evolution is not so well ordered as in a. to c., since technology costs are not uniformly distributed, learning rates and lifetimes can differ. Moreover, there can be very similar competing designs. But the picture remains the same

J.-F. Mercure, Energy Policy (2012)


- Path dependence:

- From increasing returns to technology lock-ins

M. Grubb Planetary Economics Ch10 p.385 (2014)See also Brian Arthur, Increasing Returns and Path Dependence in the Economy, Michigan press (1994)

What is a technology lock-in? Why do we see technology lock-ins?

- A perfectly efficient market would deliver optimal technological change(e.g. cost-optimal technology for every value of the carbon price)

- Do market imperfections lead to lock-ins?- Actually, increasing returns to adoption and technology lifetimes lead to lock-ins

- Whenever we have adoptions that lead to higher likelihood of further adoptions- This can lead the whole system towards

sub-optimal configurations(Configurations that do not optimise the system)

- Many lock-ins exist:- The qwerty keyboard- AC power lines- The VHS video player- The driving side of the road- The fossil fuel infrastructure

- Increasing returns to adoption originate from4 classes of causes

- Large fixed costs- Learning effects- Coordination effects- Expectations


- Path dependence:


Brian Arthur, The Economic Journal 99 394 (1989)See also Brian Arthur, Increasing Returns and Path Dependence in the Economy, Michigan press (1994)

2 equivalent technologies, competing, random walk, increasing returns to adoption:- Initial outcome is indeterminate- There is not a single equilibrium: instead there are 2 (either technologies dominating)

- This is due to the higher the adoption of A (or B), the higher the adoption rate of A (or B)- Small events become important: they cumulate- Different scenarios have radically different outcomes if they have different historical events- Lock-ins eventually occur - E.g. VHS against BETA tape videos, the QWERTY keyboard,


- Path dependence:

- Brian Arthur: lock-ins by historical events (not in exam)

Brian Arthur, Journal of Operational Research 30 294-303 (1987)See also Brian Arthur, Increasing Returns and Path Dependence in the Economy, Michigan press (1994)

Example of path-dependent process:Standard Polya urn process: large urn, start with two balls, 1 red 1 white.(1) Pick a ball, put it back, (2) if it is red, add 1 red, if it is white, add 1 white- Outcome is indeterminate- Initial fluctuations, but converge eventually, to a fixed value uniformly distributed- Each run gives a different ‘storyline’


- Path dependence:

- Brian Arthur: lock-ins by historical events (not in exam)

See Brian Arthur, Increasing Returns and Path Dependence in the Economy, Michigan press (1994)

Example of path-dependent process:Non linear Polya urn process: give a probability weighting to the responsee.g. increasing returns: if more red balls are in the urn, add red more often- Outcome is still indeterminate- Initial fluctuations, but still converges eventually, to a value not uniformly distributed- Increasing returns: storylines bunch up nearer to 1 or 0 concentration of red balls- Decreasing returns: storylines bunch up to a single equilibrium near to .5- Applied to technology:

- Increasing returns to adoption lead to path-dependence- The path eventually gets locked-in to a particular state




- Technology transitions should be seen through its societal context:Technology only takes meaning through its use by people

- Frank Geels lays out a framework for studying technology transitions in their context- At the heart of Transitions Theory- Please read his paper, which tells the story of the substitution of the steamship for

the sailing ship

See Frank Geels, Research Policy, 31 1257-1274 (2002)




- Technology and the social system form a unique socio-technical regime, which can undergo transitions.

- The regime includes all aspects tied between agents and technologies: culture, uses, costs, meaning, infrastructure, access to finance, markets, brands, etc

- The regime can undergo transitions, but has significant inertia





- The multi-level perspective involves a paradigm of understanding that requires examining technology transitions by simultaneously looking at the 3 levels:niches, socio-technical regimes and the environment


- New technologies lie in niches. These are protective areas of use (e.g. protecting from mainstream markets) that justify high initial costsE.g.: Remote location use of solar panels, luxury electric cars, etc

- The diffusion of new technologies from niches is governed by socio-technical regimes, and regime shifts

- The evolution of regimes is influenced by the environment

- Shifts in the environment can result in massive diffusion of innovations lying dormant in niches



The process of a transition of socio-technical regime is described using the Multi-level Perspective:- Technologies initially lie in several niches with erratic innovations and diversity

- They are ready to diffuse given the right conditions- A dominant design emerges- Its diffusion is influenced by the socio-technical context, and gains momentum- It is ultimately determined by the broader landscape



- An analogy with ecosystems


Technology undergoes competition (selection), innovation (mutation) and replicationThese are the required 3 ingredients for evolutionary dynamicsThis is the basis for the huge field called evolutionary economics:- Innovators continuously improve products to increase market success (fitness):

- Products undergo increasing differentiation in the market (increasing diversity)- Consumers operate a continuous selection process (natural selection)- If the conditions change, products adapt accordingly

- E.g. if the renewables policy landscape changes, some technologies may emerge as ‘better suited’ than others and begin more successful diffusion

E.g. see Metcalfe, Journal of Technology Transfer, 30 1/2 171-181 (2005)


- An analogy with ecosystems


In the evolutionary perspective, technologies and socio-technical systems mutually interact and co-evolve- System are co-dependent

- Parasitism or mutualism- Systems co-evolve through competition

- E.g. the sailing ship effect: systems threatened fight for survival- Socio-technical regimes co-evolve with other regimes

- E.g. telecom with computing- Red Queen hypothesis: organisms must constantly adapt, evolve, and proliferate not

merely to gain reproductive advantage, but also simply to survive while pitted against ever-evolving opposing organisms in an ever-changing environment

From a telephone towards a computer Species co-evolution

Lecture 5: Further reading

- Following this lecture, please read:

- Frank Geels, Technological transitions as evolutionary reconfiguration processes: a multi-level perspective and a case-study, Research Policy, 31 1257-1274 (2002)

- Grubler, Nakicenovic and Victor, Dynamics of energy technologies and global change, Energy Policy 27 (1999) 247-280

Lecture 5: References

Brian Arthur, Increasing Returns and Path Dependence in the Economy, Michigan press (1994)

Fisher & Pry, Technology Forecasting and Social Change (1971)

Frank Geels, Technological transitions as evolutionary reconfiguration processes: a multi-level perspective and a case-study, Research Policy, 31 1257-1274 (2002)

Grubb Planetary Economics Ch10 (2014)

Grubler, Nakicenovic and Victor, Dynamics of energy technologies and global change, Energy Policy 27 (1999) 247-280

IEA Experience Curves for Energy Technology Policy (2000)

Marchetti & Nakicenovic, IIASA Working Paper, (1978)

Mercure, ArXiv http://arxiv.org/abs/1304.3602 (2014)

Metcalfe, Journal of Technology Transfer, 30 1/2 171-181 (2005)

Nakicenovic, Technology Forecasting and Social Change (1986)

Rogers, Diffusion of Innovations, Fifth Edition (2010)

Sharif & Kabir, Technology Forecasting and Social Change (1976)

Sigmund & Hofbauer, Evolutionary Games and Population Dynamics, CUP (1998)

Van Bursik et al Environ. Res. Lett. 9 (2014) 114010

Weiss M. Technological Forecasting & Social Change 77 (2010) 411–428


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EP06: Energy and Climate Change Dr Jean-Francois Mercure, [email protected]@cam.ac.uk Pablo...

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