Investments in Power Generation Under Uncertainty—a MIP Specification and Large-Scale Application for EU

Investments in power generation constitute a typical budget allocation problem in the context of multiple objectives, while all factors influencing investor’s decisions for power plants are subject to considerable uncertainties. The paper introduces a multi-objective stochastic model designed to optimize budget allocation decisions for power generation in the context of risk aversion taking into account several sources of uncertainty, especially with regard to volatility of fossil fuel and electricity prices, technological costs, and climate policy variability. Probability distributions for uncertain factors influencing investment decisions are directly derived from the stochastic global energy model PROMETHEUS and thus they take into account complex interactions between variables in the systemic context. In order to fully incorporate stochastic characteristics of the problem, the model is specified as an optimization problem in which the probability that an objective exceeds a given threshold is maximized (risk aversion) subject to a set of deterministic and probabilistic constraints. The model is formulated as a mixed integer program providing complete flexibility on the joint distributions of rates of return of technologies competing for investments, as it can handle non-symmetric distributions and take automatically into account complex covariance patterns as emerging from comprehensive PROMETHEUS stochastic results. The analysis shows that risk is a crucial factor for power generation investments with investors not opting for technologies subject to uncertainty related to climate policies and fossil fuel prices. On the other hand, combination of options with negative covariance tends to benefit in the context of risk-hedging behavior.     

Uncertainty in Integrated Assessment Models of Climate Change: Alternative Analytical Approaches

Uncertainty plays a key role in the economics of climate change, and research on this topic has led to a substantial body of literature. However, the discussion on the policy implications of uncertainty is still far from being settled, partly because the uncertainty of climate change comes from a variety of sources and takes diverse forms. To reflect the multifaceted nature of climate change uncertainty better, an increasing number of analytical approaches have been used in the studies of integrated assessment models of climate change. The employed approaches could be seen as complements rather than as substitutes, each of which possesses distinctive strength for addressing a particular type of problems. We review these approaches—specifically, the non-recursive stochastic programming, the real option analysis, and the stochastic dynamic programming—their corresponding literatures and their respective policy implications. We also identify the current research gaps associated with the need for further developments of new analytical approaches.     

Combining Stochastic Optimization and Monte Carlo Simulation to Deal with Uncertainties in Climate Policy Assessment

In this paper, we explore the impact of several sources of uncertainties on the assessment of energy and climate policies when one uses in a harmonized way stochastic programming in a large-scale bottom-up (BU) model and Monte Carlo simulation in a large-scale top-down (TD) model. The BU model we use is the TIMES Integrated Assessment Model, which is run in a stochastic programming version to provide a hedging emission policy to cope with the uncertainty characterizing climate sensitivity. The TD model we use is the computable general equilibrium model GEMINI-E3. Through Monte Carlo simulations of randomly generated uncertain parameter values, one provides a stochastic micro- and macro-economic analysis. Through statistical analysis of the simulation results, we analyse the impact of the uncertainties on the policy assessment.     

Integrated Assessment Modeling for Global Climate Change: An Infinite Horizon Optimization Viewpoint

In this paper one uses an infinite time horizon optimal control paradigm to deal with three important issues in global climate change integrated assessment, namely the equitable treatment of all the generations involved, the representation of technical progress, and the uncertainty concerning technological progress and climate change processes. The notion of economic sustainability is associated with the concept of turnpike in infinite horizon optimization models. The issue of deciding on the proper discount rate is addressed in this context and a method is proposed to reconcile long term sustainability and short term time preference for current generations. One also formulates a model where environmental damage and/or technical progress are represented as stochastic jump processes. One calls this random evolution a mode switching process. In this context, sustainability is represented by a family of turnpikes, and the economy will be driven from one steady state to the other as the modes switch. These concepts are illustrated on the DICE model and their possible implementation in other types of integrated assessment models is discussed.     

Mitigation Portfolio and Policy Instruments When Hedging Against Climate Policy and Technology Uncertainty

In this paper, we use a stochastic integrated assessment model to evaluate the effects of uncertainty about future carbon taxes and the costs of low-carbon power technologies. We assess the implications of such ambiguity on the mitigation portfolio under a variety of assumptions and evaluate the role of emission performance standards and renewable portfolios in accompanying a market-based climate policy. Results suggest that climate policy and technology uncertainties are important with varying effects on all abatement options. The effect varies with the technology, the type of uncertainty, and the level of risk. We show that carbon price uncertainty does not substantially change the level of abatement, but it does have an influence on the mitigation portfolio, reducing in particular energy R&D investments in advanced technologies. When investment costs are uncertain, investments are discouraged, especially during the early stages, but the effect is mitigated for the technologies with technological learning prospects. Overall, these insights support some level of regulation to encourage investments in coal equipped with carbon capture and storage and clean energy R&D.     

INASUD project findings on integrated assessment of climate policies

This communication summarizes the main findings of INASUD, an European-wide research project on integrated assessment of climate policies. The project aimed at improving the framing of climate policy analysis through the parallel use of various existing integrated assessment models. It provides a comprehensive examination of the link between uncertainty regarding damages and inertia in economic systems. Results show that the Kyoto targets and timing are consistent with the precautionary principle but offers little insurance for longer-term climate protection. Flexibility mechanisms offer potentials for cooperation with developing countries, and are necessary to tap the environmental and economic benefits of joint carbon and sulfur emissions abatement.     

Optimal Control Models and Elicitation of Attitudes towards Climate Damages

This paper examines the consequences of various attitudes towards climate damages through a family of stochastic optimal control models (RESPONSE): cost-efficiency for a given temperature ceiling; cost-benefit analysis with a pure preference for current climate regime and full cost-benefit analysis. The choice of a given proxy of climate change risks is actually more than a technical option. It is essentially motivated by the degree of distrust regarding the legitimacy of an assessment of climate damages and the possibility of providing in due time reliable and non controversial estimates. Our results demonstrate that (a) for early decades abatement, the difference between various decision-making frameworks appears to matter less than the difference between stochastic and non stochastic approach given the cascade of uncertainty from emissions to damages; (b) in a stochastic approach, the possibility of non-catastrophic singularities in the damage function is sufficient to significantly increase earlier optimal abatements; (c) a window of opportunity for action exists up to 2040: abatements further delayed may induce significant regret in case of bad news about climate response or singularities in damages.     

Review of Mathematical Programming Applications in Water Resource Management Under Uncertainty

Economic development, variation in weather patterns and natural disasters focus attention on the management of water resources. This paper reviews the literature on the development of mathematical programming models for water resource management under uncertainty between 2010 and 2017. A systematic search of the academic literature identified 448 journal articles on water resource management for examination. Bibliometric analysis is employed to investigate the methods that researchers are currently using to address this problem and to identify recent trends in research in the area. The research reveals that stochastic dynamic programming and multistage stochastic programming are the methods most commonly applied. Water resource allocation, climate change, water quality and agricultural irrigation are amongst the most frequently discussed topics in the literature. A more detailed examination of the literature on each of these topics is included. The findings suggest that there is a need for mathematical programming models of large-scale water systems that deal with uncertainty and multiobjectives in an effective and computationally efficient way.     

Uncertainty and economic analysis of climate change: A survey of approaches and findings

The analysis of climate change is confronted with large uncertainties that need to be taken into account to arrive at meaningful policy recommendations. The main contribution of economics to this interdisciplinary task is to provide formal frameworks and techniques for analyzing climate policy in the context of uncertainty. This paper will give an overview of existing approaches and findings to provide a broad picture of what economics can contribute to the debate.     

The Impact of Uncertainty in Climate Targets and CO2 Storage Availability on Long-Term Emissions Abatement

A major characteristic of our global interactive climate-energy system is the large uncertainty that exists with respect to both future environmental requirements and the means available for fulfilling these. Potentially, a key technology for leading the transition from the current fossil fuel-dominated energy system to a more sustainable one is carbon dioxide capture and storage. Uncertainties exist, however, concerning the large-scale implementability of this technology, such as related to the regional availability of storage sites for the captured CO₂. We analyze these uncertainties from an integrated assessment perspective by using the bottom-up model TIAM-ECN and by studying a set of scenarios that cover a range of different climate targets and technology futures. Our study consists of two main approaches: (1) a sensitivity analysis through the investigation of a number of scenarios under perfect foresight decision making and (2) a stochastic programming exercise that allows for simultaneously considering a set of potential future states-of-the-world. We find that, if a stringent climate (forcing) target is a possibility, it dominates the solution: if deep CO₂ emission reductions are not started as soon as possible, the target may become unreachable. Attaining a stringent climate target comes in any case at a disproportionally high price, which indicates that adaptation measures or climate damages might be preferable to the high mitigation costs such a target implies.     

Consideration of climate change impacts and adaptation in EIA practice — Perspectives of actors in Austria and Germany

Current political discussions and developments indicate the importance and urgency of incorporating climate change considerations into EIA processes. The recent revision of the EU Directive 2014/52/EU on Environmental Impact Assessment (EIA) requires changes in the EIA practice of the EU member states. This paper investigates the extent to which the Environmental Impact Assessment (EIA) can contribute to an early consideration of climate change consequences in planning processes. In particular the roles of different actors in order to incorporate climate change impacts and adaptation into project planning subject to EIA at the appropriate levels are a core topic. Semi-structured expert interviews were carried out with representatives of the main infrastructure companies and institutions responsible in these sectors in Austria, which have to carry out EIA regularly. In a second step expert interviews were conducted with EIA assessors and EIA authorities in Austria and Germany, in order to examine the extent to which climate-based changes are already considered in EIA processes. This paper aims to discuss the different perspectives in the current EIA practice with regard to integrating climate change impacts as well as barriers and solutions identified by the groups of actors involved, namely project developers, environmental competent authorities and consultants (EIA assessors/practitioners). The interviews show that different groups of actors consider the topic to different degrees. Downscaling of climate change scenarios is in this context both, a critical issue with regards to availability of data and costs. Furthermore, assistance for the interpretation of relevant impacts, to be deducted from climate change scenarios, on the specific environmental issues in the area is needed. The main barriers identified by the EIA experts therefore include a lack of data as well as general uncertainty as to how far climate change should be considered in the process without reliable data but in the presence of knowledge about possible consequences at an abstract level. A joint strategy on how to cope with uncertain prognoses about main impacts on environmental issues for areas without reliable data requires a discussion and cooperation between EIA consultants and environmental authorities.     

Approaches for performing uncertainty analysis in large-scale energy/economic policy models

A number of key policy insights have emerged from the application of large-scale economic/energy models, such as integrated assessment models for climate change. These insights have been particularly powerful in those instances when they are shared by all or most of the existing models. On the other hand, some results and policy recommendations obtained from integrated assessment models vary widely from model to model. This can limit their usability for policy analysis. The differences between model results are mostly due to different underlying assumptions about exogenous processes, about endogenous processes and the dynamics among them, differences in value judgments, and different approaches for simplifying model structure for computational purposes. Uncertainty analyses should be performed for the dual purpose of clarifying the uncertainties inherent in model results and improving decision making under uncertainty. This paper develops a unifying framework for comparing the different types of uncertainty analyses through their objective functions, categorizes types of uncertainty analyses that can be performed on large models, and compares different approaches to uncertainty analysis by explaining underlying assumptions, suitability for different model types, and advantages and disadvantages. The appendix presents a summary of integrated assessment models for climate change that explicitly account for uncertainty.     

Sequential climate decisions under uncertainty: An integrated framework

We develop an integrated framework for evaluating sequential greenhouse gas abatement policies under uncertainty. The analysis integrates information concerning the magnitude, timing, and impacts of climate change with data on the likely effectiveness and cost of possible response options. Reduced-scale representations of the global climate system, drawn from the MIT Integrated Global System Model, form the empirical basis of the analysis. The method is illustrated in application to emissions control policies of the form considered under the United Nations Framework Convention on Climate Change.     

On the optimal control of carbon dioxide emissions: an application of FUND

This paper presents the Climate Framework for Uncertainty, Negotiation and Distribution (FUND), an integrated assessment model of climate change, and discusses selected results. FUND is a nine‐region model of the world economy and its interactions with climate, running in time steps of one year from 1990 to 2200. The model consists of scenarios for economy and population, which are perturbed by climate change and greenhouse gas emission reduction policy. Each region optimizes its net present welfare. Policy variables are energy and carbon efficiency improvement, and sequestering carbon dioxide in forests. It is found that reducing conventional air pollution is a major reason to abate carbon dioxide emissions. Climate change is an additional reason to abate emissions. Reducing and changing energy use is preferred as an option over sequestering carbon. Under non‐cooperation, free riding as well as assurance behaviour is observed in the model. The scope for joint implementation is limited. Under cooperation, optimal emission abatement is (slightly) higher than under non‐cooperation, but the global coalition is not self‐enforcing while side payments are insufficient. Optimal emission control under non‐cooperation is less than currently discussed under the Framework Convention on Climate Change, but higher than observed in practice. This revised version was published online in July 2006 with corrections to the Cover Date.     

A Stochastic Dynamic Game of Carbon Emissions Trading

This paper proposes a computable stochastic equilibrium model to represent the possible competition between Russia and China on the international market of carbon emissions permits. The model includes a representation of the uncertainty concerning the date of entry of developing countries (e.g., China) on this market in the form of an event tree. Assuming that this date of entry is an uncontrolled event, we model the competition as a dynamic game played on an event tree and we look for a solution called S-adapted equilibrium. We compare the solution obtained from realistic data describing the demand curves for permits and the marginal abatement cost curves in different countries, under different market and information structures: (i) Russia's monopoly, (ii) Russia–China competition in a deterministic framework, (iii) Russia–China competition in a stochastic framework. The results show the possible impact of this competition on the pricing of emissions permits and on the effectiveness of Kyoto and post-Kyoto agreements, without a US participation.     

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.     

Accounting for Uncertainty in Value Judgements when Applying Multi-Attribute Value Theory

In environmental decisions, analysts commonly face substantial uncertainties around stakeholders’ values judgments. Multi-Attribute Value Theory (MAVT), a family of multi-criteria decision analysis techniques, is applied in participative settings to articulate stakeholders’ values in decision-making. In MAVT, value judgments represent the intensity of individuals’ preferences in a set of objectives, which are operationalized as scaling factors or weights. Different sets of weights may express variation in people’s preferences or value judgments. Unfortunately, there are still important methodological gaps regarding how to incorporate uncertainty and the substantial variation commonly encountered in stakeholders’ preferences. This article presents a model of uncertainty that encompasses the dispersion of value judgments in MAVT. To achieve this goal, we draw on info-gap theory, which provides a mathematically grounded method for exploring sensitivity to preference weights when there are relatively high levels of uncertainties. We experimentally tested the uncertainty model in an environmental decision problem. We found that MAVT can use info-gap analysis to deal with multiple value judgments, avoiding exclusive reliance on nominal expected values to inform decisions. We explored a mechanism to explicitly consider the trade-offs between the performance of alternatives and the level of uncertainty that in any specified context a decision maker is willing to accept. Findings emphasize the potential of MAVT to support environmental management decisions, particularly in situations where multiple stakeholders and their contested value judgments have to be considered simultaneously to explore uncertainties around value trade-offs.     

Application of a Life Cycle Simulation Model to Evaluate Impacts of Water Management and Conservation Actions on an Endangered Population of Chinook Salmon

Fisheries and water resource managers are challenged to maintain stable or increasing populations of Chinook salmon in the face of increasing demand on the water resources and habitats that salmon depend on to complete their life cycle. Alternative management plans are often selected using professional opinion or piecemeal observations in place of integrated quantitative information that could reduce uncertainty in the effects of management plans on population dynamics. We developed a stochastic life cycle simulation model for an endangered population of winter-run Chinook salmon in the Sacramento River, California, USA with the goal of providing managers a tool for more effective decision making and demonstrating the utility of life cycle models for resource management. Sensitivity analysis revealed that the input parameters that influenced variation in salmon escapement were dependent on which age class was examined and their interactions with other inputs (egg mortality, Delta survival, ocean survival). Certain parameters (river migration survival, harvest) that were hypothesized to be important drivers of population dynamics were not identified in sensitivity analysis; however, there was a large amount of uncertainty in the value of these inputs and their error distributions. Thus, the model also was useful in identifying future research directions. Simulation of variation in environmental inputs indicated that escapement was significantly influenced by a 10% change in temperature whereas larger changes in other inputs would be required to influence escapement. The model presented provides an effective demonstration of the utility of life cycle simulation models for decision making and provides fisheries and water managers in the Sacramento system with a quantitative tool to compare the impact of different resource use scenarios.     

Assessing different sources of uncertainty in hydrological projections of high and low flows: case study for Omerli Basin,Istanbul, Turkey

This study investigates the assessment of uncertainty contribution in projected changes of high and low flows from parameterization of a hydrological model and inputs of ensemble regional climate models (RCM). An ensemble of climate projections including 15 global circulation model (GCM)/RCM combinations and two bias corrections (change factor (CF) and bias correction in mean (BC)) was used to generate streamflow series for a reference and future period using the Hydrologiska Byråns Vattenbalansavdelning (HBV) model with the 25 best-fit parameter sets based on four objective functions. The occurrence time of high flows is also assessed through seasonality index calculation. Results indicated that the inputs of hydrological model from ensemble climate models accounts for greater contribution to the uncertainty related to projected changes in high flows comparing to the contribution from hydrological model parameterization. However, the uncertainty contribution is opposite for low flows, particularly for CF method. Both CF and BC increases the total mean variance of high and low flows. The variability in the occurrence time of high flows through RCMs is greater than the variability resulted from hydrological model parameters with and without statistical downscaling. The CF provides more accurate timing than BC and it shows the most pronounced changes in flood seasonality.     

The Cost of Co-viability in the Australian Northern Prawn Fishery

Fisheries management must address multiple, often conflicting objectives in a highly uncertain context. In particular, while the bio-economic performance of trawl fisheries is subject to high levels of biological and economic uncertainty, the impact of trawling on broader biodiversity is also a major concern for their management. The purpose of this study is to propose an analytical framework to formally assess the trade-offs associated with balancing biological, economic and non-target species conservation objectives. We use the Australian Northern Prawn Fishery (NPF), which is one of the most valuable federally managed commercial fisheries in Australia, as a case study. We develop a stochastic co-viability assessment of the fishery under multiple management objectives. Results show that, due to the variability in the interactions between the fishery and the ecosystem, current management strategies are characterized by biological and economic risks. Results highlight the trade-offs between respecting biological, economic and non-target species conservation constraints at each point in time with a high probability and maximizing the net present value of the fishery.