We have compiled historical greenhouse gas emissions and their uncertainties on country and sector level and assessed their contribution to cumulative emissions and to global average temperature increase in the past and for a the future emission scenario. We find that uncertainty in historical contribution estimates differs between countries due to different shares of greenhouse gases and time development of emissions. Although historical emissions in the distant past are very uncertain, their influence on countries' or sectors' contributions to temperature increase is relatively small in most cases, because these results are dominated by recent (high) emissions. For relative contributions to cumulative emissions and temperature Climatic Change (2011) 106:359-391 rise, the uncertainty introduced by unknown historical emissions is larger than the uncertainty introduced by the use of different climate models. The choice of different parameters in the calculation of relative contributions is most relevant for countries that are different from the world average in greenhouse gas mix and timing of emissions. The choice of the indicator (cumulative GWP weighted emissions or temperature increase) is very important for a few countries (altering contributions up to a factor of 2) and could be considered small for most countries (in the order of 10%). The choice of the year, from which to start accounting for emissions (e.g. 1750 or 1990), is important for many countries, up to a factor of 2.2 and on average of around 1.3. Including or excluding land-use change and forestry or non-CO 2 gases changes relative contributions dramatically for a third of the countries (by a factor of 5 to a factor of 90). Industrialised countries started to increase CO 2 emissions from energy use much earlier. Developing countries' emissions from land-use change and forestry as well as of CH 4 and N 2 O were substantial before their emissions from energy use.

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The market for an energy-consuming device offers a range of models that will meet consumers' needs for an energy service with different levels of energy efficiency. A more efficient model is likely to have greater up-front costs, but the increased efficiency will eventually translate into energy cost savings over the device's lifespan. Cost-effectiveness indicators (namely, net benefit and benefit-cost ratio) can be used to assess whether a more efficient model can be a better alternative for consumers. However, whereas these indicators express to what extent the additional benefits outweigh the additional costs, they do not indicate how efficiently each model allocates capital and energy to provide the energy service. They, therefore, lack the economic efficiency dimension of the problem. This paper introduces a data-oriented, non-parametric approach to evaluate such efficiency for a set of alternative models of an energy-consuming device. It relies on data envelopment analysis (DEA) to calculate relative efficiency coefficients. The coefficients establish an input efficient frontier for the energy service provided and indicate the models that provide the energy service at the least cost. DEA is further extended to calculate the highest cost-effectiveness achievable and indicate the most cost-effective alternatives. The approach proves useful to support consumers' decision-making when shopping for energy-consuming equipment, to guide manufacturers when benchmarking the models they produce, and to inform energy efficiency policymaking and program designing.

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