A stochastic version of the dynamic linear programming model MESSAGE III

Messner, S.; Golodnikov, A. N.; Gritsevskii, A.

doi:10.1016/0360-5442(96)00025-4

Cited by 97 publications

(50 citation statements)

References 12 publications

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“…What is therefore required as an addition to this initial experiment is the further expansion of the approach to deal with uncertainties concerning the success of new technologies, as well as the uncertainties concerning the development paths that technology performance takes. An initial step in this direction has been taken by introducing the uncertainties of future technology performance as such into the model formulation [48]. Early experiments with the stochastic version of MESSAGE have shown that, if the uncertainties concerning future technology performance are incorporated, the model tends to spread risk over more technologies.…”

Section: Discussionmentioning

confidence: 99%

Endogenized technological learning in an energy systems model

Messner

1997

Journal of Evolutionary Economics

212

117

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Section: Discussionmentioning

confidence: 99%

Endogenized technological learning in an energy systems model

Messner

1997

Journal of Evolutionary Economics

212

117

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“…The first line of objective function (14) is the same as that of objective function (9), and in the Messner et al 1996). …”

Section: The Modelmentioning

confidence: 99%

Optimizing systematic technology adoption with heterogeneous agents

Chen

2017

European Journal of Operational Research

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Abstract:The traditional operational optimization models of systematic technology adoption commonly assume the existence of a global social planner and ignore the existence of heterogeneous decision makers who interact with each other. This paper develops a stylized (or conceptual) optimization model of systematic technology adoption with heterogeneous agents (i.e., decision makers) and uncertain technological learning. Each agent attempts to identify optimal solutions to adopting technologies for a portion of the entire system. The agents in the model have different foresight and different risk attitudes and interact with one another in terms of technological spillover. This paper first illustrates that although a well recalibrated representative model can perform well enough when the interest is placed on aggregate variables, it could react to a policy (a carbon tax in this paper) differently from the multi-agent model. Then this paper explores how the agents' heterogeneities and interactions affect the optimal solutions of systematic technology adoption. The main findings of the study are that (1) the existence of multiple agents implies a slower adoption of advanced technologies in the entire system than assuming the existence of a global social planner, (2) with homogeneous agents, technological spillover tends to enhance the lock-in effect on previous technologies, and (3) with heterogeneous agents, even a small technological spillover rate can significantly accelerate the adoption of the advanced technology.

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“…Relevant references here are Messner et al (1996), Krey et al (2007), Grubler and Gritsevskyi (2002), and Gritsevskyi and Nakicenovic (2000). These studies not only focus on stochastic energy prices, but also on technological uncertainties, in particular uncertain future technology costs and how these respond to alternative innovation and investment strategies (i.e.…”

Section: Box Real Options Theorymentioning

confidence: 99%

Technology Portfolios: Modelling Technological Uncertainty and Innovation Risks

Grübler¹,

Fuss²,

McCollum³

et al. 2013

Energy Technology Innovation

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AUTHORS' SUMMARYThe case study reviews a range of methodologies and model-based applications that can assist policy decisions under irreducible technology innovation uncertainties. One approach uses scenario analysis. A scenario study that explored a range of salient future uncertainties identified energy efficiency and conservation as the single most important and also most robust technology option for climate mitigation. Another approach uses portfolio theory to quantify the benefits from diversified technology portfolios in the framework of risk-averse decision-making. Such studies suggest risk aversion leads to higher adoption rates of currently higher-cost energy technology options such as modern biomass, renewables, and also carbon capture and storage (CCS). Diversification not only reduces the mean of risk exposure but also drastically lowers the tails of extreme undesirable outcomes. Portfolio theory and scenario analysis can also be combined, as illustrated by a study of three different energy system objectives (energy security, air pollution, climate change) within a multi-criteria optimization framework. Scenarios in which objectives were met individually, and then all together, under a range of uncertainties again demonstrated the benefits of portfolio diversification with greater emphasis on energy efficiency and "general purpose" energy conversion technologies. These examples discussed in this case study show that formal tools and approaches, including scenario analysis, portfolio theory, and multi-criteria optimization, are increasingly available to move technology portfolio decisions onto a more rational ground.

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A stochastic version of the dynamic linear programming model MESSAGE III

Cited by 97 publications

References 12 publications

Endogenized technological learning in an energy systems model

Endogenized technological learning in an energy systems model

Optimizing systematic technology adoption with heterogeneous agents

Technology Portfolios: Modelling Technological Uncertainty and Innovation Risks

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