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.     

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.     

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.     

Potential Impact of Climate Change on Residential Energy Consumption in Dhaka City

This study aimed to assess the impacts of climate change on residential energy consumption in Dhaka city of Bangladesh. The monthly electricity consumption data for the period 2011–2014 and long-term climate variables namely monthly rainfall and temperature records (1961–2010) were used in the study. An ensemble of six global circulation models (GCMs) of coupled model intercomparison project phase 5 (CMIP5) namely, BCCCSM1-1, CanESM2, MIROC5, MIROC-ESM, MIROC-ESM-CHEM, and NorESM1-M under four representative concentration pathway (RCP) scenarios were used to project future changes in rainfall and temperature. The regression models describing the relationship between historical energy consumption and climate variables were developed to project future changes in energy consumptions. The results revealed that daily energy consumption in Dhaka city increases in the range of 6.46–11.97 and 2.37–6.25 MkWh at 95% level of confidence for every increase of temperature by 1 °C and daily average rainfall by 1 mm, respectively. This study concluded that daily total residential energy demand and peak demand in Dhaka city can increase up to 5.9–15.6 and 5.1–16.7%, respectively, by the end of this century under different climate change scenarios.     

Advancing Energy Access Modelling with Geographic Information System Data

One of the main goals in pursuing sustainable development is to provide universal access to modern energy services, notably through the use of off-grid renewable energy technologies. To date, integrated assessment models (IAMs) poorly address energy access targets. In the context of research dedicated to energy scenarios and climate change mitigation in Africa, we attempt to advance the representation of energy access in one such IAM by using GIS data. In a case study for Ethiopia with the TIAM-ECN model, we demonstrate that by enriching an IAM with information derived from GIS databases, insights are obtained that better capture the dynamics of energy access developments, in comparison to conventional IAM analysis of energy technology deployment pathways. When duly accounting for the geographical spread in demography and technology costs in a developing country, we find that many people may gain access to electricity in remote areas thanks to the availability of affordable off-grid power production options that render expensive grid extensions unnecessary. This effect is not explicitly accounted for in most traditional IAMs. By the middle of the century, off-grid technologies could provide affordable electricity to 70% of the Ethiopian population, based almost entirely on renewable sources such as wind, solar and hydropower.     

The Effect of Green Investments in an Agent-Based Climate-Economic Model

Climate-economic modeling often relies on macroeconomic integrated assessment models (IAMs) that in general try to capture how the combined system reacts to different policies. Irrespective of the specific modeling approach, IAMs suffer from two notable problems. First, although policies and emissions are dependent on individual or institutional behavior, the models are not able to account for the heterogeneity and adaptive behavior of relevant actors. Second, the models unanimously consider mitigation actions as costs instead of investments: an arguable definition, given that all other expenditures are classified as investments. Both are challenging if the long-term development of climate change and the economy shall be analyzed. This paper therefore proposes a dynamic agent-based model, based on the battle of perspectives approach (Janssen [1]; Janssen and de Vries [2]; Geisendorf [3, 4]) that details the consequences of various behavioral assumptions. Furthermore, expenditures for climate protection, e.g., the transition of the energy system to renewables, are regarded as investments in future technologies with promising growth rates and the potential to incite further growth in adjoining sectors (Jaeger et al. [5]). The paper analyzes how a different understanding of climate protection expenditures changes the system’s dynamic and, thus, the basis for climate policy decisions. The paper also demonstrates how erroneous perceptions impact on economic and climate development, underlining the importance to acknowledge heterogeneous beliefs and behavior for the success of climate policy.     

Influences of Climatic Change on Some Ecological Processes of an Insect Outbreak System in Canada's Boreal Forests and the Implications for Biodiversity

Insect outbreaks are a major disturbance factor in Canadian forests. If global warming occurs, the disturbance patterns caused by insects may change substantially, especially for those insects whose distributions depend largely on climate. In addition, the likelihood of wildfire often increases after insect attack, so the unpredictability of future insect disturbance patterns adds to the general uncertainty of fire regimes. The rates of processes fundamental to energy, nutrient, and biogeochemical cycling are also affected by insect disturbance, and through these effects, potential changes in disturbance patterns indirectly influence biodiversity. A process-level perspective is advanced to describe how the major insect outbreak system in Canadian forests, that of the spruce budworm (Choristoneura fumiferana Clem. [Lepidoptera: Tortricidae]), might react to global warming. The resulting scenarios highlight the possible importance of natural selection, extreme weather, phenological relationships, complex feedbacks, historical conditions, and threshold behavior. That global warming already seems to be affecting the lifecycles of some insects points to the timeliness of this discussion. Some implications of this process-level perspective for managing the effects of global warming on biodiversity are discussed. The value of process-level understanding and high-resolution, long-term monitoring in attacking such problems is emphasized. It is argued that a species-level, preservationist approach may have unwanted side-effects, be cost-ineffective, and ecologically unsustainable.     

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.     

Industrial transformation towards sustainability of the energy system

Human induced climate change is one of the single most significant indicators that human society is not pursuing a sustainable trajectory. Managing the risks requires a major transformation of the way energy needs are met. Such a transformation includes changes in the production and consumption system and the incentive structure that shapes this system. The major driving force for transformation is the public concern about the environmental impact of the present fossil fuel based energy system. We may expect that energy producers, encouraged by governments, NGOs and consumer preferences will be responding to these concerns and expectations sooner or later. In fact a number of major international energy companies are presently adjusting their strategies to the needs and concerns of the public. A mix of measures including energy efficiency, a switch to natural gas, major investments in low carbon and renewable energy technologies and underground storage of carbon are elements of such new strategies. Consumers in a number of OECD countries have expressed their willingness to pay more for energy, provided it is green and clean. NGOs continue to put pressure on governments to deal with the climate problem. The challenge for governments is to develop an institutional framework that helps the producers and consumers to go through a transformation of the energy system. As different groups in society are likely to support different strategies, this paper suggests that a pluralistic policy approach including efficiency standards, renewable energy portfolio standards, carbon taxes, and the introduction of a system of tradable emission permits is the most promising approach for a transformation towards a low carbon energy economy. Research can support a transformation of the energy system by exploring the various transformation scenarios. Such research should take a multi-disciplinary approach, it should focus on the energy system as a whole, including production, consumption and the incentive structure that shapes the interaction between the two and it should be international in scope. This revised version was published online in July 2006 with corrections to the Cover Date.     

Carrots and sticks for new technology: Abating greenhouse gas emissions in a heterogeneous and uncertain world

Many governments use technology incentives as an important component of their greenhouse gas abatement strategies. These carrots are intended to encourage the initial diffusion of new, greenhouse-gas-emissions-reducing technologies, in contrast to carbon taxes and emissions trading which provide a stick designed to reduce emissions by increasing the price of high-emitting technologies for all users. Technology incentives appear attractive, but their record in practice is mixed and economic theory suggests that in the absence of market failures, they are inefficient compared to taxes and trading. This study uses an agent-based model of technology diffusion and exploratory modeling, a new technique for decision-making under conditions of extreme uncertainty, to examine the conditions under which technology incentives should be a key building block of robust climate change policies. We find that a combined strategy of carbon taxes and technology incentives, as opposed to carbon taxes alone, is the best approach to greenhouse gas emissions reductions if the social benefits of early adoption sufficiently exceed the private benefits. Such social benefits can occur when economic actors have a wide variety of cost/performance preferences for new technologies and either new technologies have increasing returns to scale or potential adopters can reduce their uncertainty about the performance of new technologies by querying the experience of other adopters. We find that if decision-makers hold even modest expectations that such social benefits are significant or that the impacts of climate change will turn out to be serious then technology incentive programs may be a promising hedge against the threat of climate change.     

Spatial Distribution of Forest Fires and Controlling Factors in Andhra Pradesh, India Using Spot Satellite Datasets

Fires are one of the major causes of forest disturbance and destruction in several dry deciduous forests of southern India. In this study, we use remote sensing data sets in conjunction with topographic, vegetation, climate and socioeconomic factors for determining the potential causes of forest fires in Andhra Pradesh, India. Spatial patterns in fire characteristics were analyzed using SPOT satellite remote sensing datasets. We then used nineteen different metrics in concurrence with fire count datasets in a robust statistical framework to arrive at a predictive model that best explained the variation in fire counts across diverse geographical and climatic gradients. Results suggested that, of all the states in India, fires in Andhra Pradesh constituted nearly 13.53% of total fires. District wise estimates of fire counts for Andhra Pradesh suggested that, Adilabad, Cuddapah, Kurnool, Prakasham and Mehbubnagar had relatively highest number of fires compared to others. Results from statistical analysis suggested that of the nineteen parameters, population density, demand of metabolic energy (DME), compound topographic index, slope, aspect, average temperature of the warmest quarter (ATWQ) along with literacy rate explained 61.1% of total variation in fire datasets. Among these, DME and literacy rate were found to be negative predictors of forest fires. In overall, this study represents the first statewide effort that evaluated the causative factors of fire at district level using biophysical and socioeconomic datasets. Results from this study identify important biophysical and socioeconomic factors for assessing ‘forest fire danger’ in the study area. Our results also identify potential ‘hotspots’ of fire risk, where fire protection measures can be taken in advance. Further this study also demonstrate the usefulness of best-subset regression approach integrated with GIS, as an effective method to assess ‘where and when’ forest fires will most likely occur.     

How to Take into Account Uncertainty in Species Extinction Probabilities for Phylogenetic Conservation Prioritization

In this article, we are concerned with the general problem of choosing from a set of taxa T a subset S to protect in order to try to contribute to halting biodiversity loss as efficiently as possible given limited resources. The protection of a taxon decreases its extinction probability, and the impact of protecting the taxa of S is measured by the resulting expected phylogenetic diversity (ePD) of the set T. The primary challenge posed by this approach lies in determining the extinction probability of a taxon (protected or unprotected). To deal with this difficulty, the uncertainty about the extinction probabilities can be described through a set of possible scenarios, each corresponding to different extinction probability values for each taxon. We show how to determine an “optimal robust set” of taxa to protect, defined as the set of taxa that minimizes the maximum “regret,” i.e., the maximum relative gap, over all the scenarios, between (1) the ePD of T obtained by protecting the taxa of this set and (2) the ePD of T which would be produced by protecting the subset of taxa optimal for the considered scenario. In our experimental conditions covering 100 cases, this gap is almost always less than 1%. Consequently, the ePD of T obtained by protecting the taxa of the optimal robust set is not far from the maximum ePD of T that could have been obtained if we had known the true scenario. In other words, a way of escaping (in large part, at least) from the uncertainty related to the extinction probabilities of the taxa consists of choosing to protect those belonging to the optimal robust set. We also compare the optimal robust set to other relevant subsets of T.     

Regional impact analysis of climate change on natural and managed forests in the Federal State of Brandenburg, Germany

A methodology for regional application of forest simulation models has been developed as part of an assessment of possible climate change impacts in the Federal state of Brandenburg (Germany). Here we report on the application of a forest gap model to analyse the impacts of climate change on species composition and productivity of natural and managed forests in Brandenburg using a statistical method for the development of climate scenarios. The forest model was linked to a GIS that includes soil and groundwater table maps, as well as gridded climate data with a resolution of 10 × 10 km and simulated a steady-state species composition which was classified into forest types based on the biomass distribution between species. Different climate scenarios were used to assess the sensitivity of species composition to climate change. The simulated forest distribution patterns for current climate were compared with a map of Potential Natural Vegetation (PNV) of Brandenburg.In order to analyse the possible consequences of climate change on forest management, we used forest inventory data to initialize the model with representative forest stands. Simulation experiments with two different management strategies indicated how forest management could respond to the projected impacts of climate change. The combination of regional analysis of natural forest dynamics under climate change with simulation experiments for managed forests outlines possible trends for the forest resources. The implications of the results are discussed, emphasizing the regional differences in environmental risks and the adaptation potentials of forestry in Brandenburg.     

The FAIR model: A tool to analyse environmental and costs implications of regimes of future commitments

This article describes the policy decision-support tool, FAIR, to assess the environmental and abatement costs implications of international regimes for differentiation of future commitments. The model links long-term climate targets and global reduction objectives with regional emission allowances and abatement costs, accounting for the Kyoto Mechanisms used. FAIR consists of three sub-models: a simple climate model, an emission-allocation model and a cost model. The article also analyses ten different rule-based emission allocation schemes for two long-term concentration stabilisation targets for greenhouse gases. This analysis shows that evaluating regimes requires not only an assessment of the initial allocation, but also of the distribution of abatement costs and the impacts from emissions trading. The Multi-Stage approach (with a gradual increase of Parties adopting emission intensity or reductions targets) and the Triptych approach (with sectoral targets for all Parties) seem to provide the best prospects for most of the Parties when compared to the other allocation schemes analysed.     

Assessment of Climate Change Effects on Canada's National Park System

To estimate the magnitude of climate change anticipated forCanada's 38 National Parks (NPs) and Park Reserves, seasonaltemperature and precipitation scenarios were constructed for 2050and 2090 using the Canadian Centre for Climate Modelling andAnalysis (CCCma) coupled model (CGCM1). For each park, we assessed impacts on physical systems, species, ecosystems andpeople. Important, widespread changes relate to marine andfreshwater hydrology, glacial balance, waning permafrost, increased natural disturbance, shorter ice season, northern andupward altitudinal species and biome shifts, and changed visitation patterns. Other changes are regional (e.g., combinedEast coast subsidence and sea level rise increase coastal erosionand deposition, whereas, on the Pacific coast, tectonic upliftnegates sea level rise). Further predictions concern individualparks (e.g., Unique fens of Bruce Peninsular NP will migratelakewards with lowered water levels, but structural regulation of Lake Huron for navigation and power generation would destroythe fens). Knowledge gaps are the most important findings. Forexample: we could not form conclusions about glacial massbalance, or its effects on rivers and fjords. Likewise, for theEast Coast Labrador Current we could neither estimate temperature and salinity effects of extra iceberg formation, nor the further effects on marine food chains, and breeding park seabirds. We recommend 1) Research on specific large knowledge gaps; 2) Climate change information exchange with protected area agencies in other northern countries; and 3) incorporating climate uncertainty into park plans and management. We discuss options for a new park management philosophy in the face of massive change and uncertainty.     

Optimal Expansion of Water Quality Monitoring Network by Fuzzy Optimization Approach

River reaches are frequently classified with respect to various mode of water utilization depending on the quantity and quality of water resources available at different location. Monitoring of water quality in a river system must collect both temporal and spatial information for comparison with respect to the preferred situation of a water body based on different scenarios. Designing a technically sound monitoring network, however, needs to identify a suite of significant planning objectives and consider a series of inherent limitations simultaneously. It would rely on applying an advanced systems analysis technique via an integrated simulation-optimization approach to meet the ultimate goal. This article presents an optimal expansion strategy of water quality monitoring stations for fulfilling a long-term monitoring mission under an uncertain environment. The planning objectives considered in this analysis are to increase the protection degree in the proximity of the river system with higher population density, to enhance the detection capability for lower compliance areas, to promote the detection sensitivity by better deployment and installation of monitoring stations, to reflect the levels of utilization potential of water body at different locations, and to monitor the essential water quality in the upper stream areas of all water intakes. The constraint set contains the limitations of budget, equity implication, and the detection sensitivity in the water environment. A fuzzy multi-objective evaluation framework that reflects the uncertainty embedded in decision making is designed for postulating and analyzing the underlying principles of optimal expansion strategy of monitoring network. The case study being organized in South Taiwan demonstrates a set of more robust and flexible expansion alternatives in terms of spatial priority. Such an approach uniquely indicates the preference order of each candidate site to be expanded step-wise whenever the budget limitation is sensitive in the government agencies.     

Assessing the micro-phytoplankton response to nitrate in Comau Fjord (42°S) in Patagonia (Chile), using a microcosms approach

Anthropogenic (aquaculture) changes in environment nutrient concentrations may affect phytoplankton (biomass and taxa composition) in marine coastal waters off the Chilean Patagonia. The effects of adding nitrate (NO₃ ^?) to natural phytoplankton assemblages were evaluated considering biomass, cell abundance, and taxonomic composition. Microcosm experiments were performed in the spring, summer, and winter in the Comau Fjord located in Subantarctic Patagonia. At the end of the experiments, NO₃ ^? decreased rapidly and was undetectable in treatments, indicating a strong NO₃ ^? deficiency associated with an exponential increase in Chl-a concentrations, particulate organic nitrogen, and carbon in these treatments. Moreover, given the depleted nitrate concentrations of the spring and summer experiments, the micro-phytoplankton taxa structure shifted from mixed diatom and dinoflagellate assemblages (Ceratium spp., Dinophysis spp., Coscinodiscus sp., Rhizosolenia pungens) to assemblages dominated by blooms of the classic chain-forming diatoms found in temperate and cold waters such as Chaetoceros spp., Skeletonema spp., and Thalassiosira spp. Thus, nitrogen sources (i.e., nitrate, ammonia) may influence phytoplankton abundance and biomass accumulation dynamics in the northern section of Patagonia. It also emphasizes the importance of diatom taxa in regards to the short-term response of phytoplankton to changing environmental nutrient conditions due to natural (decreasing freshwater stream flow) and anthropogenic (aquaculture) events. This situation may be one of the future scenarios in the Patagonian fjords, thus stressing the needs for active environmental monitoring and impact assessment.     

A Robust Optimization Model for an Invasive Species Management Problem

Invasive species pose a significant threat to global biodiversity. Managing invasive species often involves modeling the species’ spread pattern, estimating control costs and damage costs due to the invasion, designing control efforts, and accounting for uncertainties in model parameters. Dealing with uncertainty is arguably the most important part of the process, since biological, environmental, and economic factors can cause parameter values to vary greatly. Managers need decision tools that are robust to such limited or variable information. Here, we present a robust spatial optimization model to select treatment sites in a way that maximally reduces the size of an invasive population, given a constraint on financial resources. We develop an integer programming model that includes population dynamics and management costs over space and time. The model incorporates uncertainty in the available budget and the invasive spread rate as sets of discrete scenarios to determine a robust, cost-effective management plan in a novel way.     

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.     

Nuclear Versus Coal plus CCS: a Comparison of Two Competitive Base-Load Climate Control Options

In this paper, we analyze the relative importance and mutual behavior of two competing base-load electricity generation options that each are capable of contributing significantly to the abatement of global CO₂ emissions: nuclear energy and coal-based power production complemented with CO₂ capture and storage (CCS). We also investigate how, in scenarios developed with an integrated assessment model that simulates the economics of a climate-constrained world, the prospects for nuclear energy would change if exogenous limitations on the spread of nuclear technology were relaxed. Using the climate change economics model World Induced Technical Change Hybrid, we find that until 2050 the growth rates of nuclear electricity generation capacity would become comparable to historical rates observed during the 1980s. Given that nuclear energy continues to face serious challenges and contention, we inspect how extensive the improvements of coal-based power equipped with CCS technology would need to be if our economic optimization model is to significantly scale down the construction of new nuclear power plants.