Jana Szolgayova

Effects of low-cost offsets on energy investment: new perspectives on REDD.

Tropical deforestation is one of the major sources of carbon emissions, but the Kyoto Protocol presently excludes avoiding these specific emissions to fulfill stabilization targets. Since the 13th Conference of the Parties (COP) to the UNFCCC in 2007, where the need for policy incentives for the reduction of emissions from deforestation and degradation (REDD) was first officially recognized, the focus of this debate has shifted to issues of implementation and methodology. One question is how REDD would be financed, which could be solved by integrating REDD credits into existing carbon markets. However, concern has been voiced regarding the effects that the availability of cheap REDD credits might have on energy investments and the development of clean technology. On the other hand, investors and producers are also worried that emissions trading schemes like the one installed in Europe might deter investment into new technologies and harm profits of existing plants due to fluctuations in the price of emissions permits. This paper seeks to contribute to this discussion by developing a real options model, where there is an option to invest in less carbon-intensive energy technology and an option to purchase credits on REDD, which you will exercise or not depending on the future evolution of CO2 prices. In this way, unresolved questions can still be addressed at a later stage, while producers and investors hold REDD options to maintain flexibility for later decisions. We find that investment in cleaner technology is not significantly affected if REDD options are priced as a derivative of CO2 permits. Indeed, the availability of REDD options helps to smooth out price fluctuations that might arise from permit trading and thus decreases risk for the producer - thereby being a complement to permit trading rather than an obstacle undermining cap-and-trade.

An integrated CVaR and real options approach to investments in the energy sector

The objective of this paper is to combine a real options framework with portfolio optimization techniques and to apply this new framework to investments in the electricity sector. In particular, a real options model is used to assess the adoption decision of particular technologies under uncertainty. These technologies are coal-fired power plants, biomassfired power plants and onshore wind mills, and they are representative of technologies based on fossil fuels, biomass and renewables, respectively. The return distributions resulting from this analysis are then used as an input to a portfolio optimization, where the measure of risk is the Conditional Value-at-Risk (CVaR).

Investment in irrigation systems under precipitation uncertainty

Efficient agricultural water management is indispensable in meeting future food demands. The European Water Framework Directive promotes several measures such as the adoption of adequate water pricing mechanisms or the promotion of water-saving irrigation technologies. We apply a stochastic dynamic programming model (SDPM) to analyze a farmer’s optimal investment strategy to adopt a water-efficient drip irrigation system or a sprinkler irrigation system under uncertainty about future production conditions, i.e. about future precipitation patterns. We assess the optimal timing to invest into either irrigation system in the planning period 2010 to 2040. We then investigate how alternative policies, (a) irrigation water pricing, and (b) equipment subsidies for drip irrigation, affect the investment strategy. We perform the analysis for the semi-arid agricultural production region Marchfeld in Austria, and use data from the bio-physical process simulation model EPIC (Environmental Policy Integrated Climate) which takes into account site and management related characteristics as well as weather parameters from a statistical climate change model. We find that investment in drip irrigation is unlikely unless subsidies for equipment cost are granted. Also water prices do not increase the probability to adopt a drip irrigation system, but rather delay the timing to invest into either irrigation system.

The Influence of Negative Emission Technologies and Technology Policies on the Optimal Climate Mitigation Portfolio

Combining policies to remove carbon dioxide (CO2) from the atmosphere with policies to reduce emissions could decrease CO2 concentrations faster than possible via natural processes. We model the optimal selection of a dynamic portfolio of abatement, research and development (R&D), and negative emission policies under an exogenous CO2 constraint and with stochastic technological change. We find that near-term abatement is not sensitive to the availability of R&D policies, but the anticipated availability of negative emission strategies can reduce the near-term abatement optimally undertaken to meet 2°C temperature limits. Further, planning to deploy negative emission technologies shifts optimal R&D funding from “carbon-free” technologies into “emission intensity” technologies. Making negative emission strategies available enables an 80% reduction in the cost of keeping year 2100 CO2 concentrations near their current level. However, negative emission strategies are less important if the possibility of tipping points rules out using late-century net negative emissions to temporarily overshoot the CO2 constraint earlier in the century.

Optimal mitigation strategies with negative emission technologies and carbon sinks under uncertainty

In recent years a body of literature has arisen on the topic of how to compose the optimal portfolio of mitigation options. The focus has been mainly on options involving shifts from high- to low- or even negative-carbon technologies. Natural sinks play an important role in any attempt to stabilize atmospheric CO2 and usually enter as a constant term in the overall carbon budget. In this paper, we introduce natural sinks to the carbon management problem and analyze the implications for negative emission technology deployment and the overall mitigation strategy. Amongst other sensitivity analyses, we also investigate the impact of uncertainty in the carbon sink, which we find to raise the importance of negative emissions in the mitigation portfolio significantly lowering the cost of the policy mix.

Options on low-cost abatement and investment in the energy sector: new perspectives on REDD

Deforestation is one of the major sources of carbon emissions, but the Kyoto Protocol presently excludes avoiding these emissions to fulfill stabilization targets. Since the need for policy incentives for the reduction of emissions from deforestation and degradation (REDD) was officially recognized in 2007, the focus of this debate has shifted to issues of implementation. Concerns about the effects that the availability of low-cost REDD credits might have on energy investments, and the development of clean technology constitute the main motivation of this paper. We analyze the production side of the problem with the help of a real options model with an option to invest in less carbon-intensive energy technology and an option to purchase credits on REDD, which will (or will not) be exercised in the future. Unresolved questions can thus still be addressed later, while producers and investors hold REDD options to maintain flexibility for later decisions.

Robust Energy Portfolios Under Climate Policy and Socioeconomic Uncertainty

Concerning the stabilization of greenhouse gases, the UNFCCC prescribes measures to anticipate, prevent, or minimize the causes of climate change and mitigate their adverse effects. Such measures should be cost-effective and scientific uncertainty should not be used as a reason for postponing them. However, in the light of uncertainty about climate sensitivity and other underlying parameters, it is difficult to assess the importance of different technologies in achieving robust long-term climate risk mitigation. One example currently debated in this context is biomass energy, which can be used to produce both carbon-neutral energy carriers, e.g., electricity, and at the same time offer a permanent CO2 sink by capturing carbon from the biomass at the conversion facility and permanently storing it. We use the GGI Scenario Database IIASA [3] as a point of departure for deriving optimal technology portfolios across different socioeconomic scenarios for a range of stabilization targets, focusing, in particular, on new, low-emission scenarios. More precisely, the dynamics underlying technology adoption and operational decisions are analyzed in a real options model, the output of which then informs the portfolio optimization. In this way, we determine the importance of different energy technologies in meeting specific stabilization targets under different circumstances (i.e., under different socioeconomic scenarios), providing valuable insight to policymakers about the incentive mechanisms needed to achieve robust long-term climate risk mitigation.

Development of Transportation Infrastructure in the Context of Economic Growth

Developed road infrastructure is one of the main ingredients to economic growth. At the same time, economic growth enables further expansion of infrastructure. The co-evolutionary aspects of the growth of economic output and road infrastructure are thus apparent and represent the main motivation for the study presented in this chapter. We develop a model analyzing the interdependence between a country’s economic growth and the development of transportation infrastructure in this country, explicitly taking into account the mutual influence of the rate of economic growth and the transportation capacity. Formulating an optimal control problem, the optimal investment rate can be determined. This model forms a comprehensive framework for understanding the underlying dynamics and the patterns of economic growth in relation to transport infrastructure. An analytical solution for the infinite horizon problem is derived and the steady state is shown to depend crucially on the rate of physical decay of roads. Testing the model for the data of two countries illustrates the usefulness of such an approach to real world problems and possibly policy recommendations, even though the model would have to be adapted to the specific characteristics of each country or region to make precise statements.

The benefits of investing into improved carbon flux monitoring

Abstract Operationalizing a Global Carbon Observing and Analysis System (www.geocarbon.net) would provide a sound basis for monitoring actual carbon fluxes and thus getting quantities right when pricing carbon – be it in a cap-and-trade scheme or under a tax regime. However, such monitoring systems are expensive and—especially in times of economic weakness—budgets for science and environmental policy are under particular scrutiny. In this study, we attempt to demonstrate the magnitude of benefits of improved information about actual carbon fluxes. Such information enables better-informed policy-making and thus paves the way for a more secure investment environment when decarbonizing the energy sector. The numerical results provide a robust indication of a positive social value of improving carbon monitoring systems when compared to their cost, especially for the more ambitious climate policies.

The Value of Determining Global Land Cover for Assessing Climate Change Mitigation Options

Land cover maps provide critical input data for global models of land use. Urgent questions exist, such as how much land is available for the expansion of agriculture to combat food insecurity, how much land is available for afforestation projects, and whether reducing emissions from deforestation and forest degradation (REDD) is more cost-effective than carbon capture and sequestration. Such questions can be answered only with reliable maps of land cover. However, global land cover datasets currently differ drastically in terms of the spatial extent of cropland distributions. One of the data layers that differ is cropland area. In this study, we evaluate how models designed to help in policy design can be used to quantify the differences in implementation costs. By examining these cost differences, we are able to quantify the benefits, which equal the loss from making a decision under imperfect information. Taking the specific example of choosing between REDD and carbon capture and storage under uncertainty about the available cropland area, we have developed a methodology on how the value derived from reducing uncertainty can be assessed. By implementing a portfolio optimization model to find the optimal mix of mitigation options under different sets of information, we are able to estimate the benefit of improved land cover data and thus determine the value of land cover validation efforts. We illustrate the methodology by comparing portfolio outputs of the different mitigation options modeled within the GLOBIOM economic land use model using cropland data from different databases.