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Estimates of world regional potentials of the sustainable use of biomass for energy uses through the year 2050 are presented. The estimated potentials are consistent with scenarios of agricultural production and land use developed at the International Institute for Applied Systems Analysis, Austria. They thus avoid inconsistent land use, in particular conflicts between the agricultural and bioenergy land use. As an illustration of the circumstances under which a large part of this potential could be used in practice, a global energy scenario with high economic growth and low greenhouse gas emissions, developed by IIASA and the World Energy Council is summarised. In that scenario, bioenergy supplies 15% of global primary energy by 2050. Our estimation method is transparent and reproducible. A computer program to repeat the calculation of the estimates with possibly changed assumptions is available on request.

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MESSAGE-MACRO is the result of linking a macroeconomic model with a detailed energy supply model. The purpose of the linkage is to consistently reflect the influence of energy supply costs as calculated by the energy supply model in the optimal mix of production factors included in the macroeconomic model. In this article, we describe an automated link of two independently running models. The advantages of this setup over a single, fully integrated model are twofold: First, it is more flexible, leaving the constituent models intact for independent runs , thus making further model development an easier task. Second, the decomposed model solution benefits numerically from having the most non-linearities concentrated in the smaller of the two modules. The emphasis of the paper is on methodology, but we also include an example demonstrating the feedback mechanisms of MESSAGE-MACRO by applying it to two global economicenergy-environment scenarios. The two scenarios are a reference scenario and a scenario that limits the global atmospheric carbon concentration to 550 ppmv. The scenarios are compared in terms of GDP, energy su pply and demand. and energy prices.

This paper analyzes potentials of carbon capture and sequestration technologies (CCT) in a set of long-term energy-economic-environmental scenarios based on alternative assumptions for technological progress of CCT. In order to get a reasonable guide to future technological progress in managing CO 2 emissions, we review past experience in controlling sulfur dioxide (SO 2 ) emissions from power plants. By doing so, we quantify a ''learning curve'' for CCT, which describes the relationship between the improvement of costs due to accumulation of experience in CCT construction. We incorporate the learning curve into the energy-modeling framework MESSAGE-MACRO and develop greenhouse gas emissions scenarios of economic, demographic, and energy demand development, where alternative policy cases lead to the stabilization of atmospheric CO 2 concentrations at 550 parts per million by volume (ppmv) by the end of the 21st century. We quantify three types of contributors to the carbon emissions mitigation: (1) demand reductions due to the increased price of energy, (2) fuel switching primarily away from coal, and (3) carbon capture and sequestration from fossil fuels. Due to the assumed technological learning, costs of the emissions reduction for CCT drop rapidly and in parallel with the massive introduction of CCT on the global scale. Compared to scenarios based on static cost assumptions for CCT, the contribution of carbon sequestration is about 50% higher in the case of learning, resulting in cumulative sequestration of CO 2 ranging from 150 to 250 billion (10 9 ) tons with carbon during the 21st century. Also, carbon values (tax) across scenarios (to meet the 550 ppmv carbon concentration constraint) are between 2% and 10% lower in the case of learning for CCT by 2100. The results illustrate that assumptions on