Optimal forest management with stochastic prices & endogenous fire risk

Earth observations are one way to reduce the risk to standing forests from damages caused by wild fires, since they enable early warning systems, preventive actions and faster extinguishing of fires, before they spread out. Another channel through which fire hazard can be reduced is the thinning of the forest, so the risk of a fire occurring becomes partially endogenous. In order to shed more light on optimal forest management under such endogenous fire risk, we develop a real options model, where the price of biomass is stochastic and the harvesting decision needs to be timed optimally in the face of these uncertainties. We find that there is a positive value of information. In other words, there is a positive willingness to pay for Earth observations by forest managers

Integrated assessment of crop management portfolios in adapting to climate change in the Marchfeld region

Portfolio optimization is an adequate tool to find optimal crop management options in adapting to climate change. The risk farmers have to face can be caused by different sources. In our study, we focus on the risk arising from unknown weather conditions. Therefore, we developed stochastic climate change scenarios for the Marchfeld region. Two portfolio models have been applied in the time periods 2008-2020, 2021-2030 and 2031-2040: a traditional non-linear mean-variance (E-V) model and a model using the Conditional Value at Risk (CVaR) as risk metric. Investigated crops are corn, winter wheat, sunflower and spring barley with different crop management alternatives. Minimum tillage appears in all portfolios. We found a decreasing share of winter wheat that gets partially substituted by sunflower over the time periods. When including environmental constraints (soil organic carbon content, nitrate leaching) the reverse effect on the resulting portfolio shares is observed with corn being included. The E-V model reveals more diversification with respect to the crops, whereas the CVaR model shows more diversification with respect to crop management options

Valuing climate change uncertainty reductions for robust energy portfolios

Climate policy uncertainty has decisive influence on energy sector strategies. Potential stranded climate-energy investments may be enormous. Remote sensing can improve our understanding of the climate system and thus better inform climate policy and reduce associated uncertainties. We develop an integrated energy-portfolio model to value these uncertainties. The operations of individual power plants are optimized using real options given scenarios of stochastically evolving CO2 prices mimicking observation-induced climate policy uncertainty. The resulting profit distributions are used in a portfolio optimization. The optimization under imperfect information about future CO2 prices leads to substantially lower profits for a given risk level when portfolios are to be robust across all plausible scenarios. A potential uncertainty reduction associated with an improved climate modeling supported by remote sensing will thus not only lead to substantial financial efficiency gains, but will also be conducive to steering investments into the direction of higher shares of renewable energy

Development of Transportation Infrastructure in the Context of Economic Growth

Developed road infrastructure is an essential factor facilitating and accelerating economic growth, which will in turn enable the addition of more roads. At the same time, the marginal benet of adding roads to a large stock of existing capacity might be diminishing. It is thus evident that the co-evolution of economic output and road infrastructure is rather intricate and deserves special attention. The model developed in this paper therefore investigates the interdependency between a country's economic growth and the development of transportation infrastructure in this country. To this end, a co-evolutionary perspective is developed, where the mutual inuence of the rate of economic growth and the capacity of transportation infrastructure are explicitly taken into account. This approach enables us to set up an optimal control problem, where the optimal investment rate is determined considering the co-evolutionary dynamics of GDP growth and capacity expansion. This model forms a comprehensive framework for understanding the underlying dynamics and the patterns of economic growth in relation to transport infrastructure. We nd an analytical solution for the innite horizon problem, where the control turns out to be a constant. The steady state is shown to depend crucially on the rate of physical decay of roads, which we think can be interpreted as an index of quality, and the speed of adjustment, at which the economy moves along a trajectory. Testing the model for the data of two countries, France and Finland, illustrates the usefulness of such an approach to real world problems and possibly policy recommendation, where the model would have to be adapted to the peculiarities of each country or region to make precise statements

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, biomass-fired 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)

Optimal irrigation management strategies under weather uncertainty and risk

Climate change will affect agricultural production through changes in water supply, such that optimal irrigation management strategies gain importance. For the Marchfeld region, we firstly analyze with a stochastic dynamic programming approach the probability of investing into either a water-saving drip or a sprinkler irrigation system until 2040. Secondly, we develop optimal irrigation management portfolios for different degrees of risk aversion using climate data from a statistical model and the simulations for specific crops of the biophysical process model EPIC (Environmental Policy Integrated Climate). Investment in drip irrigation systems is not profitable. Sprinkler irrigation has a positive probability of being adopted for the production of sugar beets and carrots and therefore mostly shows a 100% share in the portfolio optimization

The benefits of investing into improved carbon flux monitoring

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