This post deconstructs the following statement:
If reducing greenhouse emissions had economic benefits then we would do it anyway without new policy.
The statement above is used by economists to argue against the introduction of policies to reduce greenhouse gas emissions on the basis that the costs would outweigh the benefits of reducing climate change. It is part of a wider narrative that regulatory policy can only lead to economic costs. However, the statement is perhaps one of the most perverse conclusions from neoclassical economics. It depends on a raft of assumptions that run contrary to real-world experience. Further, as discussed below, if just one assumption is taken out, the conclusion changes.
Sadly, economists and (in particular) economic modellers, have played a key role in turning this fallacy into accepted reality. They have done this by using simple optimisation-based approaches that make strong assumptions about human behaviour. Often the modellers do not critically question or even fully understand these assumptions.
It should be noted that there is nothing wrong with optimisation per se. It is useful to know how to minimise costs in a system. However, once assumptions of optimisation extend into human behaviour, it is easy to draw false conclusions because the models portray an idealised version of a complex reality. As I mentioned in Optimisation and simulation models – how, and when, to use them, “when an optimisation tool is used to estimate the effects of a policy. All the assumptions that were previously used to solve an optimisation problem suddenly become predictions of real-world behaviour”. Unfortunately, most climate-economy models operate in this way.
William Nordhaus, using the Dynamic Integrated Climate Economy model (DICE), was a front runner in climate-economy modelling and also an early adopter of the optimisation-based approach. His work earned him the Nobel Memorial Prize in Economic Sciences in 2018. Since then, a new generation of ‘Integrated Assessment Models’ have been developed; with more detail about different technologies, but still based on the same neoclassical assumptions and with the same potential to produce misleading results.
The figure below provides an example of some results. It is taken from the Intergovernmental Panel on Climate Change’s (IPCC’s) Fifth Assessment Report (AR5) and provides estimates of the impacts of reducing emissions to meet a 2°C climate target. Note that the axis is expressed in costs, so virtually all the models show negative impacts.
Source: Clarke et al (2014)
However, the way the models get these results can be derived easily from the underlying assumptions. The baseline case is obtained through the optimisation process. Adding policy to meet the temperature target turns it into a constrained optimisation. As the statement at the top of this post suggests, with new policy, things can only get worse. The results are therefore little more than a restatement of the assumptions. Here we break down those assumptions.
First, the models assume that individuals are ‘rational’ and behave in an optimal manner. This ignores findings by behavioral economists (and others) that indicate the contrary. Secondly, the models assume that markets function perfectly, ignoring the role of institutions in shaping the functioning of markets.
Second, the models also assume ‘perfect’ knowledge. As Keynes noted, people can only be sure to select the best option if they know all the possible options. Perfect knowledge is therefore required to make it work. In some models, the assumption of perfect knowledge is extended to knowledge of the future (i.e. perfect foresight). But in the context of the modern economy, it is not realistic for agents to know all the available products out there (let alone what might be there in the future).
Finally, we also need to think carefully about technology. The low-carbon transition is primarily one of technology development and adoption, but optimisation-based models have little to say about either. Most notably, the paths of gradual diffusion that we get from innovation theory are not compatible with a system where everyone instantly switches to the cheapest (i.e. optimal) technology.
There are further assumptions, including the use of representative agents and an unrealistic treatment of finance (see “The role of money and the financial sector in energy-economy models used for assessing climate and energy policy”. None of these assumptions are realistic – and, as shown below, they are not necessary either. Unfortunately, for optimisation modellers though, changing the assumptions would mean a fundamental rethink of how their models work.
In the meantime, there are some alternative modelling approaches available. For example, Cambridge Econometrics’ E3ME model is based on post-Keynesian foundations. It allows for conditions of uncertainty and does not assume fully rational behaviour. The model’s results show that rates of technology uptake and debt dynamics are important in determining policy impacts. Policy that impacts on technology will affect both the composition and rate of economic growth, while additional investment will create short-term stimulus effects and higher debt levels that may reduce long-run growth.
Contrary to the results published in the IPCC report, E3ME shows that some climate policies can lead to positive impacts on GDP and employment levels (see “Unlocking the inclusive growth story of the 21st century” and “Renewable energy benefits: measuring the economics”). Other policies lead to costs; the important point, however, is that the direction of results is not pre-determined by unrealistic assumptions.
E3ME is not the only model to replace assumptions about perfect knowledge and optimal behaviour with a more empirical approach (see e.g. DEFINE (Dynamic Ecosystem-FINance-Economy) model and Energie und Klima Modelle). But these models are very much in a minority against the ‘consensus’ that is presented elsewhere, meaning that it can be hard for results to gain traction in the wider scientific community. More models are needed to fill this wide gap in scientific knowledge.
The way forward
Using their optimisation models, economists have been largely responsible for creating the illusion that addressing climate change is necessarily an economic cost. As more climate policy is introduced, they will again be called upon for policy assessment and evaluation. They have a chance to make up for previous mistakes but, with the next IPCC assessment report (AR6) due soon, it is not clear that this chance will be taken.
It is therefore up to all of us to question the interpretation of these models’ results. And the next time someone says that there must be costs, point to the example of solar power. Solar is now the cheapest source of electricity in many parts of the world, benefitting rich and poor regions alike (regardless of their current climate policies). However, solar costs are only so low because of previous government interventions in Germany and other European countries. In summary, these interventions have led to tangible economic benefits.
In the real world there are many more examples. In models often not. But we must be clear that the statement at the top of this post refers to the results from the models and not the real world.