01.07.2020 • 11/2020
New Horizon 2020 project: The Challenge of the Social Impact of Energy Transitions
Funded by the European Commission’s Framework Programme Horizon 2020, the ENTRANCES project recently closed its kick-off meeting with a high scientific and institutional participation, and taking on the challenge of modeling the social impact of the energy transition.
Oliver Holtemöller
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Transition to Clean Technology
Daron Acemoglu, Ufuk Akcigit, Douglas Hanley, William R. Kerr
Journal of Political Economy,
No. 1,
2016
Abstract
We develop an endogenous growth model in which clean and dirty technologies compete in production. Research can be directed to either technology. If dirty technologies are more advanced, the transition to clean technology can be difficult. Carbon taxes and research subsidies may encourage production and innovation in clean technologies, though the transition will typically be slow. We estimate the model using microdata from the US energy sector. We then characterize the optimal policy path that heavily relies on both subsidies and taxes. Finally, we evaluate various alternative policies. Relying only on carbon taxes or delaying intervention has significant welfare costs.
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A Model for the Valuation of Carbon Price Risk
Henry Dannenberg, Wilfried Ehrenfeld
Antes, R.; Hansjürgen, B.; Letmathe, P.; Pickl, S. (Hrsg.), Emissions Trading - Institutional Design, Decision Making and Corporate Strategies (Second Edition),
2011
Abstract
Modeling the price risk of CO2 emission allowances is an important aspect of integral corporate risk management related to emissions trading. In this paper, a pricing model is developed which may be the basis for evaluating the risk of emission certificate prices. We assume that the certificate price is determined by the expected marginal CO2 abatement costs in the current trade period as well as by the long-term marginal abatement costs. The price risk is modeled on the basis of a mean reversion process. Due to uncertainties about the future state of the environment, we suppose that within one trade period erratic changes in the expected marginal abatement costs may occur leading to shifts in the price level. In addition to the parameter estimation, it is also an objective of this work to modify the mean reversion process so that such abrupt changes in the expected reversion level can be displayed. Because of the possibility of transferring spare allowances to a subsequent period we take into account the fact that the expected long run marginal abatement costs act as a lower limit for the price in the trading period.
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Stochastic Income Statement Planning as a Basis for Risk Assessment in the Context of Emissions Trading
Henry Dannenberg, Wilfried Ehrenfeld
Greenhouse Gas Measurement and Management,
No. 1,
2011
Abstract
The introduction of the European emissions trading system means that those enterprises taking part have a new planning risk factor to consider – emissions allowance prices. In this article, we analyse how risk emerging from emissions trading can be considered in the stochastic income statement planning of corporations. Therefore, we explore which planned figures are affected by emissions trading. Moreover, we show an approach that models these positions in a planned profit and loss account, taking into account uncertainties and dependencies. Consequently, this model provides a basis for risk assessment and investment decisions in the uncertain environment of emissions trading.
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Will Oil Prices Decline Over the Long Run?
Filippo di Mauro, Robert K. Kaufmann, Pavlos Karadeloglou
ECB Occasional Paper Series,
No. 98,
2008
Abstract
At present, oil markets appear to be behaving in a fashion similar to that in the late 1970s and early 1980s when oil prices rose sharply over an extended period. Furthermore, like at that time, analysts are split on whether such increases will persist or reverse, and if so by how much. The present paper argues that the similarities between the two episodes are not as strong as they might appear at first sight, and that the likelihood of sharp reversals in prices is not particularly great. There are a number of reasons in support of the view that it is unlikely that the first two decades of this century will mimic the last two decades of the previous century. First, oil demand is likely to grow significantly in line with strong economic growth in non-OECD countries. Second, on the supply side, OPEC is likely to enhance its control over markets over the next two decades, as supply increases in newly opened areas will only partially offset declining rates of production in other geologically mature non-OPEC oil regions. Moreover, while concerns about climate change will spur global efforts to reduce carbon emissions, these efforts are not expected to reduce oil demand. Finally, although there is much talk about alternative fuels, few of these are economically viable at the prices currently envisioned, and given the structural impediments, there is a reduced likelihood that the market will be able to generate sufficient quantities of these alternative fuels over the forecast horizon. The above factors imply that oil prices are likely to continue to exceed the USD 70 to USD 90 range over the long term.
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Forecasting the CO2 certificate price risk
Henry Dannenberg, Wilfried Ehrenfeld
IWH Discussion Papers,
No. 5,
2008
Abstract
Modeling the price risk of CO2 certificates is one important aspect of integral corporate risk management related to emissions trading. The paper presents a risk model which may be the basis for evaluating the risk of emission certificate prices. We assume that the certificate price is determined by the expected marginal CO2 abatement costs prevailing at the current trade period and stochastically fluctuates around the respective level as returned from the mean reversion process. Due to uncertainties about future environmental states we suppose that within one trade period, erratic changes in the expected marginal abatement costs may occur leading to shifts in the price level. The aim of the work is to model the erratic changes of the expected reversion level and to estimate the parameters of the mean reversion process.
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