Download Cost-benefit analysis of climate action in adaptive economic systems

Cost-benefit analysis of climate action in adaptive economic systems:
a technology production and supply chain perspective
P. Salas1, J.-F. Mercure1, R.-J. Lange2 and M. J. Grubb1,2
1
Cambridge Centre for Climate Change Mitigation Research (4CMR), Department of Land Economy,
University of Cambridge
2
Faculty of Economics, University of Cambridge
1. Overview
Strategies for finding optimal policies for reducing greenhouse gas emissions are often understood and derived
from cost-benefit analysis (CBA), using marginal abatement cost curves (MACCs, e.g. Nordhaus, 2010; Stern,
2006). This approach however has been criticised for its lack of representation of industrial dynamics, i.e.
changing costs as technologies evolve (e.g. Ekins, Kesicki, & Smith, 2011; Vogt-Schilb & Hallegatte, 2011).
Cost calculations that involve learning-by-doing have also recently been scrutinised with regard to their inherent
uncertainty (W. D. Nordhaus, 2014). Few or none these analyses include the adaptive capacity of industry and
technology systems, i.e. inertia and the development of alternate supply chains for the deployment of new
technologies. The adaptive capacity is an intrinsic property of the economy and including it in CBA
fundamentally changes the outcome for optimal climate policy (Grubb, Hourcade, & Neuhoff, 2014; Ha-Duong,
Grubb, & Hourcade, 1997), since it further introduces the rate of transformation as a key component.
2. Method
Following the seminal work by Grubb et al., 2014; Ha-Duong et al., (1997), here we present a microeconomic
theoretical representation of both learning-by-doing, early scrapping as well as adaptive costs in a simple CBA
framework consistent with Nordhaus, (2010). Technologies used to generate societal services (electricity,
transport, etc), as well as producing industries, have a natural rate of turnover (J.-F Mercure, 2013). The costs of
emissions abatement do not only stem from technology costs, but also from those of possible stranded assets and
scaling up production capacity. Starting from a static MACC framework for technology costs, we explore
technology dynamics based on a microeconomic technology diffusion-based approach. We first introduce
learning in the MACC. We then add the costs of stranded assets as well as the adaptive component which have
functional dependencies on abatement that include its rate of change. As in the work of Grubb, this results in
different dynamics to the classical CBA model in which the functional dependence refers solely to abatement
technology costs.
The MACC curve is a present day picture of anticipated costs of mitigation of various options explored by
expert engineers, in order of increasing cost. However, it has been extensively discussed that such costs are
dynamic and change with the level of involvement in each available measure, as economies of scale and
production capacity grows. Therefore, we expect the cost curve, as it turns out to be in an emissions reduction
scenario, to be different than initially measured. We show here in a simplified model that if costs in the MACC,
as initially measured, scale approximately linearly with the level of abatement, then the total cost as measured
post fact likely scales with an exponent of 2-b, where b is an average learning exponent across the options,
related to a learning rate of about 15%. A similar process is likely to take place in the production of factories,
supply chains and production capacity, which yields costs that scale this time with the rate of abatement with an
exponent of 2-bN, where bN is a learning rate applying to such new infrastructure. Effectively, whenever costs
are associated with the construction of means of producing abatement technologies, costs involve a time
derivative of the abatement level. Furthermore, each time we move up one tier of the supply chain, an additional
time derivative is involved, i.e. a higher order rate of abatement.
These results are used to derive an optimal pathway of emissions reductions in this simple model, using costbenefit analysis in terms of mitigation costs versus climate damage costs. We used a simple inter-temporal costoptimisation where climate damages depend on cumulative emissions, and mitigation costs to the level of
abatement.
3. Results
We show that consistent with earlier work of Grubb, the introduction of the adaptive component, which depends
on the rate of abatement, leads to optimal climate policies to take place much earlier in time than would result
from a classical CBA, such as the DICE model. In classical CBA, mitigation costs depend only on the level of
abatement, and the natural optimal solution emerging is always one where waiting for climate damages to mount
to a level similar to mitigation costs takes place before any mitigation measures are justified. Including to this
optimisation costs that relate to the rate of abatement (or higher order rates) introduces a further force that seeks
to transform the system as slowly as possible, counterbalancing this waiting effect. Effectively, starting to
decarbonise the economy early gives more time to less production capacity and infrastructure to achieve the
same level of abatement, with less investment in total diverted from other economic uses. In traditional CBA
where waiting takes place, very large numbers of factories (e.g. for wind turbines, electric cars, solar panels etc)
would need to be built in a very short time, most likely leading to higher total costs.
We thus find that costs of climate policy have as much to do with the magnitude as with the rate of emissions
abatement, in other words as much with abatement technologies as with the permanent transformation of the
global energy system. We find, using a simple optimisation model, that in optimal climate policy where the cost
of climate change damages is internalised, mitigation starts earlier than in DICE (it effectively starts now). We
show however that such pathways do depend on the shape of the MACC and the value of the learning exponent
both in the mitigation technologies and the production infrastructure. This is consistent, and extends the work of,
Grubb et al., 2014; Ha-Duong et al., (1997).
This relates to why using single sector economic models likely leads us in error: if the production of capital used
to produce low-carbon final goods is omitted from the calculation, we are effectively missing a large part of the
investments required for mitigation. Meanwhile, in a multi-sectoral model of the global economy, input-output
tables inevitably come into play, requiring investment at many levels of the global supply chains. The primary
novelty of the work is to provide a microeconomic technology and industry based understanding of this picture.
We thus show how including the deployment of the industrial capacity necessary to produce abatement
technologies, requiring the inclusion of intermediate production represented with input-output tables,
fundamentally changes the dynamics of the model. We show that adding higher order supply chain tiers of
production, generates additional dynamics, involving higher order rates of emissions abatement. This further
description enables a simple understanding of the transformation required for climate change mitigation beyond
the simple purchase of abatement technologies.
4. Conclusion
We conclude this work with a note on the perils of neglecting abatement technology production and supply
chain dynamics in CBA analyses of climate policy. We discuss a parallel with detailed highly disaggregated
global hybrid (macroeconometric and bottom-up energy) modelling of climate change policy (J.-F. Mercure et
al., 2014a, 2014b). This questions the relevance of simple CBA models with limited representations of complex
interactions between technology systems and the economy for informing climate policy, which is necessarily
closely tied to R&D, innovation and technology policy. We indicate that further research on climate policy
should focus on including dynamic cross-sectoral and input-output interactions in order to determine more
realistic agendas and timing for optimal climate action.
5. References
Ekins, P., Kesicki, F., & Smith, A. Z. P. (2011). Marginal Abatement Cost Curves: A call for caution. UCL
Energy Institute Working Paper
Grubb, M., Hourcade, J.-C., & Neuhoff, K. (2014). Appendix: The importance of inertia and adaptability. In
Planetary economics (pp. 495–505). London, UK: Taylor Francis / Routledge.
Ha-Duong, M., Grubb, M. J., & Hourcade, J.-C. (1997). Influence of socioeconomic inertia and uncertainty on
optimal CO2-emission abatement. Nature, 390, 270–273.
Mercure, J.-F. (2013). An age structured demographic theory of technological change. 4th International
Conference on Sustainability Transitions, Zurich, 2013. http://arxiv.org/abs/1304.3602
Mercure, J.-F., Salas, P., Foley, A., Chewpreecha, U., Pollitt, H., Holden, P. B., & Edwards, N. R. (2014a).
Economic benefits of decarbonising the global electricity sector. Preprint Available on ArXiv.
http://arxiv.org/abs/1310.4403
Mercure, J.-F., Salas, P., Foley, A., Chewpreecha, U., Pollitt, H., Holden, P. B., & Edwards, N. R. (2014b). The
dynamics of technology diffusion and the impacts of climate policy instruments in the decarbonisation of
the global electricity sector. To appear in Energy Policy, preprint available on ArXiv.
http://arxiv.org/abs/1309.7626
Nordhaus, W. D. (2010). Economic aspects of global warming in a post-Copenhagen environment. Proceedings
of the National Academy of Sciences.
Nordhaus, W. D. (2014). The Perils of the Learning Model for Modeling Endogenous Technological Change.
Energy Journal, 35(1), 1.
Stern, N. (2006). The Economics of Climate Change. Cambridge, UK: Cambridge University Press.
Vogt-Schilb, A., & Hallegatte, S. (2011). When Starting with the Most Expensive Option Makes Sense: Use and
Misuse of Marginal Abatement Cost Curves.