Analysis of Technological Portfolios for C02 Stabilizations and Effects of Technological Changes

Abstract

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. The broad aim of the collaboration is to advance understanding of the economic issues surrounding atmospheric stabilisation, and the specific aims of the IMCP are to provide insights into the "state of the art" and implications of endogenous modeling of technical change in global energy-environment models when applied to various levels of atmospheric stabilisation. Terms of use: Documents inMembers of the Steering Committee provided review comments on earlier drafts and the paper has been forwarded to external review, the final results will be published as a Special Issue of the Energy Journal. The papers have all been encouraged to draw on a common baseline (the "Common Poles-Image baseline") and to report results in comparable formats, so as to facilitate intercomparison of the different modeling results. All the results and judgements expressed here remain the responsibility of the authors.The work presented in this paper was partly supported by the New Energy and Industry Technology Development Organization (NEDO), Japan. FEEM fund the working papers series, and seed money for the coordination work of the Innovation Modeling Comparison Project was provided by UK Department of Environment, Food and Rural Affairs and the German Ministry of Environment. Analysis of Technological Portfolios for CO 2 Stabilizations and Effects of Technological Changes SummaryIn this study, cost-effective technological options to stabilize CO 2 concentrations at 550, 500, and 450 ppmv are evaluated using a world energy systems model of linear programming with a high regional resolution. This model treats technological change endogenously for wind power, photovoltaics, and fuel-cell vehicles, which are technologies of mass production and are considered to follow the "learning by doing" process. Technological changes induced by climate policies are evaluated by maintaining the technological changes at the levels of the base case wherein there is no climate policy. The results achieved through model analyses include 1) cost-effective technological portfolios, including carbon capture and storage, marginal CO 2 reduction costs, and increases in energy system cost for three levels of stabilization and 2) the effect of the induced technological change on the above mentioned factors. A sensitivity analysis is conducted with respect to the learning rate.

Uncertainty in the learning rates of energy technologies: An experiment in a global multi-regional energy system model

“…This work outlines the specific costs of the energy technologies on the basis of time scales (Sano et al, 2006) by conversion from the specific costs on a cumulative-capacity basis, i.e., the learning curve, (Barreto, 2001) within an energy system model using the endogenous global learning approach that is guided by learning investments. The study has used a multi-regional energy system model based on the TIMES framework.…”

“…Regional learning in a multi-regional energy system model has limitations, such as the binary variable and the enlargement of the binary tree that uses the branch and bound algorithm to find integer solutions (Barreto and Kypreos, 2002;Williams, 1990); this is reminiscent of deficiencies in the optimization algorithm and the restrictions on model size (Sano et al, 2006). In addition, availability of correct data on a regional scale is very difficult to acquire, especially for the immature technologies.…”

“…This indicates that the cost of the technology has reached the floor level (minimum level) Nevertheless, in the case of emerging technologies, the floor cost of the future cannot be anticipated at present. Therefore, the floorcost values are assumed to control the MIP approach in a linear programming energy system model; moreover, the cumulative capacity-controlled endogenous learning may not reach a specific cost of approximately zero (Barreto, 2001;Sano et al, 2006;Feber et al, 2003). Nine technologies-biogasification (BIOG), integrated gasification combined cycle (IGCC), combined-cycle gas turbine (CCGT), molten carbonate-fuel cell (MCFC), solid oxide fuel cell (SOFC), solar PV (SOLPV), wind onshore (WON), wind offshore (WOF), and geothermal heat pump (GEO)-are considered for learning (Table 3).…”

Introduction of subsidisation in nascent climate-friendly learning technologies and evaluation of its effectiveness

“…To accelerate the technological learning track, subsidies are introduced in the path of endogenous learning. To deal with the endogenous learning methodologies, this study follows Sano et al (2006) and Seebregts et al (2000):…”

“…Further, memory deficiency in the optimization algorithm as well as the model size restricts its implementation (Sano et al, 2006), and at the same time, the technologies are becoming global (Kydes, 1999 terms, global learning is the regional cluster of a common learning process. Further, international spillover underpins the technological proximity of the learning clusters, and the dissemination of technologies across regions strengthens the technological spillover by reducing the technology gap and produces a positive economy in both the host and follower regions (Frisvold, 1997;Irwin and Klenow, 1994;Rout et al, 2009a).…”