Bias in IPCC WGIII? A Guest Post by Richard Tol, Part II

This post is Part II of Richard Tol's look at Chapter 11 of the IPCC AR4 WGIII. Part I is here.

Richard Tol is a research professor at ESRI in Ireland, one of the top 175 economists in the world and a contributor to the work of the Intergovernmental Panel on Climate Change (IPCC), where his work is widely cited. In this guest post, the second of a series, Richard takes a look at parts of the IPCC AR4 Working Group III, which has largely escaped scrutiny in recent months. In this Part II he concludes:

Chapter 11 claims a certainty that does not exist.

Please have a look at Richard's full discussion below. If you have questions or criticisms of Richard's analysis please submit them in the comments, I am sure that Richard will be happy to engage.

Part II, Technological change

To a first approximation, the costs of emission reduction are driven by the difference in the costs of fossil energy and its carbon-neutral alternatives. It costs about 4 cents per kilowatthour to make electricity with coal, about 8 cents with wind, and about 24 cents with solar. That is today. The bulk of emission reduction will take place in the future. Estimates of the costs of emission reduction are therefore largely driven the assumed evolution of the prices of carbon-neutral energy sources, relative to the prices of fossil fuels.

It is hard to predict future price changes. This implies that the estimates of the costs of emission reduction are very uncertain. If you assume rapid technological progress in renewable energy and scarce oil and gas, emission reduction will be cheap. If you assume slow technological progress in renewables and rapid progress in unconventional oil and gas, emission reduction will be expensive.

Models used to assume that technological change in energy is independent of climate policy. This assumption has been challenged, and rightly so. There is ample evidence than inventors and innovators respond to policy and price signals. There are now a number of models in which technological progress is partly driven by climate policy. The Summary for Policy Makers (SPM) of AR4 WG3 states

In the models that adopt these approaches, projected costs for a given stabilization level are reduced; the reductions are greater at lower stabilisation levels.

Studies that assume the possibility that climate change policy induces enhanced technological change also give lower costs.

Although most models show GDP losses, some show GDP gains because they assume […] that more technological change may be induced by mitigation policies.

Modelling studies […] show carbon prices rising to 20 to 80 US$/tCO2-eq by 2030 and 30 to 155 US$/tCO2-eq by 2050. For the same stabilization level, studies […] that take into account induced technological change lower these price ranges to 5 to 65 US$/tCO2-eq in 2030 and 15 to 130 US$/tCO2-eq in 2050

The SPM asserts three times that induced technical change reduces the costs of abatement, and once that it may even revert the sign. Chapter 11 is the source of these claims. What evidence does it offer?

The main source of information is the Innovation Modelling Comparison Project (IMCP), which was led by Barker, Edenhofer and Grubb who were all lead authors of Chapter 11. Most of the models surveyed indeed show a drop in emission reduction costs if innovation responds to policy. The extent to which costs fall depends, among other things, on the assumed “crowding-out” – that is, if economies invest more in research and development (R&D) of clean energy, do they then invest less in other R&D? Chapter 11 identifies Nordhaus (2002) as the study that assume the greatest crowding-out: Energy R&D comes at the expense of other R&D. Chapter 11 (p. 653) writes

While some models find a large reduction in mitigation costs (e.g. Popp, 2006a), some find small impacts (e.g. Nordhaus, 2002).

Nordhaus (2002) writes

the introduction of induced innovation increases the discounted value of world consumption by US$238 billion. This is about 40 percent of the welfare gain from substitution policies, which is $585 billion

That is, Nordhaus reports a small gain in welfare if the model includes induced technological change; Nordhaus finds a welfare gain because the benefits of avoided climate change are larger than the costs of emission reduction. In Nordhaus’ results, welfare falls by $585-$238=$248 billion. While Chapter 11 claims that Nordhaus finds a small but positive impact, Nordhaus in fact finds a negative impact.

Nordhaus explains

The primary reason for the small impact of induced innovation on the overall path of climate change is that the investments in inventive activity are too small to make a major difference unless the social returns to R&D are much larger than the already-supernormal returns. R&D is about 2 percent of output in the energy sector, while conventional investment is close to 30 percent of output. Even with supernormal returns, the small fraction devoted to research is unlikely to outweigh other investments.

That is, energy is a small factor in the economy; focusing R&D on energy has a large opportunity cost as energy R&D detracts from other R&D.

Nordhaus’ result is well in line with the more theoretical work by Lans Bovenberg, Larry Goulder, Adriaan van Zon, Sjak Smulders and others. In fact, Smulders shows that an incomplete specification of R&D tends to lead to cost reductions, while a complete specification tends to lead to cost increases. This issue was raised by two referees of the First Order Draft. The authors respond thus:

A very few authors (e.g. Smulders) have found that allowing for ETC in top-down models increases costs, and many have found that it reduces them. This is not a consensus, but it does suggest that the balance of findings is that inclusion of ETC in the modelling reduces the cost estimates.

That is, the existence of Smulders’ work is acknowledged, but its theoretical superiority is not.

The issue was again raised by a referee of the Second Order Draft. The authors respond thus:

The text is describing the literature. ITC through LBD reduces the costs in the model applications reviewed.

That is, Smulders’ work is no longer deemed relevant.

In the published version of the chapter, Smulders appears as follows:

There have been many reviews (see Clarke and Weyant, 2002; Grubb et al., 2002b; Löschel, 2002; Jaffe et al., 2003; Goulder, 2004; Weyant, 2004; Smulders, 2005; Grübler et al. 2002; Vollebergh and Kemfert, 2005; Clarke et al., 2006; Edenhofer et al., 2006b; Köhler et al., 2006; Newell et al., 2006; Popp, 2006b; Sue Wing, 2006; Sue Wing and Popp, 2006).

A paper that was known to give a contradictory result in the First Order Draft, was hidden in the chapter.

The Executive Summary of Chapter 11 reads:

Using different approaches, modelling studies suggest that allowing for endogenous technological change reduces carbon prices as well as GDP costs, this in comparison with those studies that largely assumed that technological change was independent of mitigation policies and action.

I would argue that the higher quality studies show the opposite of this conclusion. Others may disagree with me, but one cannot deny that the literature is ambiguous. Chapter 11 claims a certainty that does not exist.

Chapter 11 (p. 650) writes

The TAR […] reported that endogenizing technological change could shift the optimal timing of mitigation forward or backward (8.4.5). The direction depends on whether technological change is driven by R&D investments (suggesting less mitigation now and more mitigation later, when costs decline) or by accumulation of experience induced by the policies (suggesting an acceleration in mitigation to gain that experience, and lower costs, earlier).

This is an accurate summary of the TAR and indeed the literature. However, on p. 651, we read

Learning-by-doing implies that larger (and more costly) efforts are justified earlier as a way to lower future costs.

That is a remarkable turnaround. An ambiguous finding (up or down) is turned into a clear result (up). What is more remarkable is that there is no discussion of this at all in Chapter 11: No new studies are cited that support the claim on p. 651. Chapter 11 could have cited Schwoon and Tol (2006, Energy Journal, 27 (4) 25-60; working paper available since 2004), who show that, if anything, the literature has shifted in the opposite direction.