Select Committee on Economic Affairs Minutes of Evidence

Memorandum by Professor Richard S J Tol, Hamburg, Vrije and Carnegie Mellon Universities


  Probabilistic scenarios cannot be avoided. If one is mainly interested in quantitative results, this is obvious. A number is meaningless without a confidence interval. If one is mainly interested in qualitative results, probabilistic analysis is not necessarily called for, or so it seems. However, an insight is meaningless if it is not robust. Alternative scenarios are needed to test robustness. Alternative scenarios should span the range of not implausible futures. That range can only be derived from probabilistic scenarios. Besides the question in its title, this paper investigates whether the SRES scenarios span the range of not implausible futures.

  The SRES scenarios were severely criticised by Castles and Henderson. That critique focused on the use of exchange rates. The choice of exchange rates, however, does not matter much (relative to the other uncertainties) for carbon dioxide concentrations and hence for climate change. On the other hand, the choice of exchange rates does matter for assumed development pathways, and hence vulnerability to climate change, and for the distribution of carbon dioxide emissions, and hence the distribution of mitigation costs and responsibilities. The choice of the discount rate matters so much, primarily because of the convergence of per capita incomes and emission intensities assumed in the SRES scenarios.

  The SRES scenarios were built with models that were originally designed for the analysis of energy policies. Such models use scenarios, but here they were used to build scenarios. Using and building scenarios are different things. Also, for building emissions scenarios, more knowledge is required than knowledge of energy systems. Furthermore, the models used were calibrated to data sets with a relatively short time span. Because of funding constraints, validating the models against longer time series was never a priority.

  I collected long term data on population, per capita income, energy use, and carbon dioxide emissions from energy use. I plotted these data together with the four alternative projections according to the IMAGE 2.2 model. I used the data to estimate the Kaya identity in differential form. I extrapolated the model, and used the forecast error to calculate the relative probabilities of the four SRES scenarios. The following results emerge.

  The population scenarios are largely in accordance with history. It is peculiar that the A1 and B1 scenarios have the same populations, even though their economies are very different.

  The per capita income scenarios for developed countries are largely in accordance with history. For developing countries, this is not the case. China's economy, for instance, has been stagnant if not declining for most of the last five centuries. Only the last two decades saw rapid economic growth in China. All four scenarios continue the pattern of most recent times. Rapid economic growth is also foreseen, in all scenarios, for other developing countries. For Africa, this is a clear break with the past. The four scenarios foresee rapid convergence of incomes across the world in the current century, even though the past two centuries witnesses income divergence.

  The projections of energy intensities only partly conform with history. The fastest decreases of energy intensities in the scenarios are not faster than was observed in the past. However, the scenario foresee decreases only, even though energy intensities have increased as well in the past. Energy intensities across the world converge in all scenarios, not faster than the maximum observed rate, but always faster than the minimum observed rate.

  The projections of emission intensities for individual regions span the range of observed past behaviour. All scenarios foresee further convergence of emission intensities first—in line with past observations—but divergence later. The scenarios all show the same qualitative pattern of convergence, and diverge only minimally quantitatively.

  The above pattern suggests that the SRES modellers know a lot about the supply side of the energy system, but less about the demand for energy. Their knowledge of economic development is lacking. Their demographic expertise is sound, but strangely separated. My personal knowledge of the SRES modellers confirms this assessment.

  The relative probabilities of the four alternative SRES scenarios confirms this picture. The scenarios for the period 2000-50 for populations each have probabilities of over 10 per cent; for emission intensities, the A2 and B1 scenario are most likely, but the other two scenario have more than a 0.1 per cent chance. For the scenarios for per capita incomes and energy intensities, the situation is different. Here, the A2 scenario is by far the most likely, and the other three scenarios are extremely unlikely.

  For the period 2050-2100, a similar picture emerges, albeit less extreme. The Kaya trend projection method used here is, however, less suitable for projecting this far into the future.

  If one applies the same trend projection method directly on emissions, the relative probabilities SRES scenarios are all acceptable (that is, not lower than 10 per cent chance) for the period 2000-50. This suggests that the errors in the underlying scenarios largely cancel each other out.

  The following conclusions can be drawn. The SRES scenarios are not equally likely. The A2 is by far the most realistic. The SRES scenarios do not span the range of plausible futures. The range of emissions can be somewhat wider, and the range of underlying development can be much wider. The SRES scenarios do not accord with past trends. On the one hand, this makes for interesting scenarios. On the other hand, it is odd that all SRES scenarios break with past trends at the same time, and that this trend break is sometimes at the point where data end and scenarios start.

  The SRES scenarios are therefore useful as emissions scenarios. When used in climate models, they more or less span the range of not implausible futures. The SRES scenarios are less useful for climate change impact analysis, at least for those studies in which impacts do not only depend on climate change but also on vulnerability and hence development. The SRES scenarios are less useful for emission abatement studies, at least for those studies that use regional models and are interested in the distribution of mitigation costs and responsibilities.

15 January 2005

previous page contents next page

House of Lords home page Parliament home page House of Commons home page search page enquiries index

© Parliamentary copyright 2005