Summary: Well-known dynamic characteristics of energy technologies and systems are important for climate policy. A new paper Dynamic determinants of optimal climate policy, just published in the top-decile journal Structural Change and Economic Dynamics[1], demonstrates that including these characteristics in ‘global cost-benefit’ models increases the level of investment economically justified to decarbonise our economies. It also implies a wider diversity of policies, compared to the results from conventional economic models with more traditional ‘static’ assumptions. The need for a dynamic perspective goes beyond just economics and technology, however, to the heart of national decisions in a global context…
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Anyone involved in energy technologies and systems probably knows the following intuitively. Energy technology costs usually come down with scale and investment (typically if imprecisely known as induced innovation). Most major energy developments involve long-lived capital – whether specific investments in production (e.g. mines, wells), conversion (e.g. power plants, refineries), or networks (like electricity grids and fuel distribution); or indeed end-uses (e.g. energy efficiency of buildings). Along with the times taken for new energy technologies to diffuse globally, this creates inertia in global decarbonisation.
For these and other reasons, energy systems are what economists call path-dependent to an unusual degree. What we have today depends on past decisions, what we do today affects what is possible in the future.
To many people that may seem obvious, but it is not how many stylised economic models – and most notably, the ‘canonical’ DICE model used globally in economics classes today – represents the economics of decarbonisation. Pioneered by Prof William Nordhaus in the1990s, that model embodies a simplified world with a focus on the optimal trade-off between the presumed cost of emissions reductions at any given point in time, against the benefit of reduced subsequent climate change. In economic terms, the model calculates an equilibrium balance for each sequential period, based on projected ‘static’ technology costs which aren’t affected by the pace of emission cuts or scale of prior action.
My colleagues and I have now finally published a formal, stylised model on Dynamic determinants of optimal climate policy intended to challenge that thinking. It is the result of a long journey since we started probing ways to overcome the intrinsic limitation in such stylised economic modelling about the ‘abatement cost’ side of the problem, which has received far less attention from mainstream economics.1 It is not by any stretch the only such effort, nor the most sophisticated; indeed, one of our points and motivations is that whilst many models have been developed to start grappling with these complexities (with more technologies, assumed lifetimes, maybe technology learning rates etc), their very complexity risks missing the big messages. None have displaced the general approach of ‘static-equilibrium’ models, as the archetypical approach to economics teaching on climate change, which still frames much economic thinking.
To be sure there has been vast economic debate about DICE and related models, but almost all the focus has been around representation of climate damages and risks (and how to balance that in present-day equivalent, through time-discounting). Yet this implicitly both neglects inertia in emission reduction, and assumes that low-carbon investments in one period do not affect the cost of future emission reductions. Consequently, such models tend defer stronger action until low carbon technologies are assumed – by the modeler – to ‘exogenously’ become cheaper over time, then allowing steep future emission reductions.
The bottom line is that our new analytic model demonstrates the optimal global level of low-carbon investment to be bigger – maybe much bigger – when the dynamics of energy systems are incorporated.
This is obviously true when faced with a fixed target, in which case recognising inertia in the energy system stops models from deferring action and then dropping emissions suddenly as the threshold temperature approaches. We represent aggregate inertia very simply, in terms of a typical transition time required to achieve major system changes.
But it is also true for ‘benefit-cost’ models which seek an explicit ‘optimal balance’ of climate damages against the (presumed) cost of emission reductions, when we include a significant measure of what we term pliability. This represents the overall ability of technologies and systems to respond to economic incentives, scale, and constraints (in early versions of the work, we called this adaptability, but our conference submissions kept getting shunted into sessions on adapting to climate impacts instead, the opposite of our main focus).
In this, readers may note a clear logical link to Bashmakov et al paper that I highlighted in the first of these three summer 24 Commentaries, #1 on Energy Cost Constancy. This showed the extent to which energy systems have adapted to past price shocks, so as to bring overall national energy expenditure back within a limited range. Yet, it takes time – maybe 2-3 decades – to complete the adjustments in technologies and system structures.
Readers would also be right that the underlying approach is not new; not least, it ‘just’ formalises the structural arguments we set out in a 2021 review paper critiquing the conventional modelling approach. But as colleagues observe, data and arguments are not enough: modern economics is a field in which “it takes a model to beat a model”.
The distinguishing feature of our model is a highly stylised representation of system-level dynamics – specifically, the logical consequences of induced innovation, inertia and path-dependencies – in emissions abatement. Rather than representing the ‘economically optimal’ approach as one which trades off present costs of emission reductions against the future estimated benefits of avoided climate damages alone, it captures also the potential impact of present-day investments on future energy technologies and systems. Thus it reflects at system level the overwhelming evidence that technologies evolve in response to economic incentives and scale effects, and the reality that energy systems involve large capital investments with long lifetimes, and other factors (e.g. inertia from networks and institutions). Consequently, emission reductions in any given period face transitional costs, but also will reduce the costs of subsequent emission reductions through their dynamic impacts.
The new model shows that for any given set of assumptions about the severity of climate change, the economically optimal level of investment in emission reductions is sensitive to this core assumption. Indeed we show that incorporating these dynamics can more than double the economically optimal level of investment, for the same underlying assumptions about the severity of climate change damages.
The paper is novel in presenting a stylised model with an analytic mathematical solution (‘Theorem 1’), which pinpoints the mathematical significance of these dynamic factors and also highlights analytically the extreme sensitivity of optimal investment to time-discounting in the traditional ‘static-cost’ approach.[2]
An extension of the analysis (‘Theorem 2’) also presents the optimal level of investment when faced with a fixed goal, like the Paris Agreement temperature targets, designed to avoid potentially catastrophic risk thresholds. Whilst the conventional economic models tend to ‘optimise’ a trajectory which involves late, steeply ‘negative emissions’ as the temperature limit is approached, to help clean up earlier emissions that remain in the atmosphere, the dynamic model shows a much smoother pathway of global emission reductions, with more effort to change course immediately.
The discussion in the paper underlines that of course our model, like all models, is a huge simplification. But it doesn’t just imply that more effort is justified, whether in a target-based world or a ‘global cost-benefit’ framing. By highlighting the importance of dynamic factors like induced innovation/scale economies, and inertia, it necessarily implies that policy effort needs to be more nuanced. It doesn’t simply imply ‘a higher carbon price’ is justified. It indicates that policy needs to prioritise actions to decarbonise the systems with the greatest inertia, and in areas with the greatest potential for innovation in technologies, supply chains, and business models in response to investments at scale.
In this, we hope the paper may also stimulate not only greater mainstream economics attention to the climate problem overall, but also contribute to far more sophisticated consideration of the ‘solution’ side of the problem – which seems inadequate in economic analysis – partly because of the over-simplified framing in conventional economic approaches.[3]
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A final Coda, on what may seem like something totally different. As our paper was published, news arrived that a proposed new coal mine (for steel coking coal) in the UK has been ruled illegal. This drew on a UK Supreme Court ruling from July stopping a new oil development on the grounds that emissions from burning the oil should have been considered. Proponents of the mine had argued that, aside from the claimed jobs benefits, the development would have a “broadly neutral effect on the global release of greenhouse gas”, because the coal produced in the UK would simply displace other coal.
It struck me that this is a little microcosm of the economic debate between static and dynamic characterisation. In a static world, a more efficient new coal mine might displace dirtier mining elsewhere (at least if one ignores possible impacts on coal price of additional supply). But building a new coal mine, intended to fuel coal-based steel-making for the next 25 years or more, puts more capital in the wrong direction. It enhances inertia in the coal and blast furnace steel sectors, and has obvious dynamic implications for other technology, investment and policy choices. It has global ramifications – influencing about expectations about what other countries might do and justify. It pays no attention to the wider incentives and alternate investment paths – for example, in clean steel.
Thus it is vital for economic analysis, as well as wider debate, to understand not only what might be the impacts for emissions ‘today’, in a static worldview which justifies something ‘a bit cleaner’ now if it may displace something worse. Economic models and mindsets need to fully embrace the dynamic implications of decisions today, for the global transition over decades.
Notes:
[1] Top-decile economic journal in 2023 Rankings, Journal Impact Factor and Journal Citation Indicator Indices.
[2] The sensitivity of global climate cost-benefit results to the discount rate is well explored numerically in studies with DICE; our model shows that with the traditional static-cost equilibrium framing, the optimal investment includes a term that is inversely proportional to the discount rate to the power 6.
[3] Indeed, a UCL Professor did research on the relatively small coverage of climate change in top economic journals; when I asked why, he said he thinks it is not because economists think the problem is unimportant, but because they think it is really conceptually simple (and therefore boring) – just calculate the damages, and set an equivalent price on emissions. Understanding the dynamics of energy systems makes it far more interesting than that.