Estimating home energy decision parameters for a hybrid energy—economy policy model

Hybrid energy–economy models combine the advantages of a technologically explicit bottom–up model with the behavioral realism sought after by top–down models in order to help policymakers assess the likely technology-specific response and economy-wide impact of policies to induce technological change. We use a discrete choice survey to estimate key technology choice parameters for a hybrid model. Two choice experiments are conducted for household energy-related decisions about retrofitting home building structures and choosing a space heating and conditioning system. Based on a discrete choice survey of 625 householders, we estimate a discrete choice model and then demonstrate how its parameters translate into the behavioral parameters of a hybrid model. We then simulate household energy policies, including, individual subsidies and increased regulations.     

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.     

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.     

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.     

Human Capital,Innovation, and Climate Policy: an Integrated Assessment

This paper looks at the interplay between human capital and innovation when climate and educational policies are implemented. Following recent empirical studies, human capital and general purpose research and development (R&D) are introduced in an integrated assessment model used to study the dynamics of climate change mitigation. Our results suggest that climate policy stimulates general purpose as well as clean R&D but reduces the incentive to invest in human capital formation. Both innovation and human capital have a scale effect, which increases pollution, as well as a technique effect, which saves emissions for each unit of output produced. While the energy-saving effect prevails when innovation increases, human capital is pollution-using, also because of the gross complementarity between the labor and energy input. When the role of human capital is the key input in the production of general purpose and energy knowledge is accounted for, the crowding-out of education induced by climate policy is mitigated, though not completely offset. By contrast, a policy mix that combines educational as well as climate objectives offsets the human capital crowding-out, at moderate and short-term costs. Over the long run, the policy mix leads to global welfare gains.     

Assessing Spatial, Temporal, and Analytical Variation of Groundwater Chemistry in a Large Nuclear Complex, USA

Statistical analyses were applied at the Hanford Site, USA, to assess groundwater contamination problems that included (1) determining local backgrounds to ascertain whether a facility is affecting the groundwater quality and (2) determining a ‘pre-Hanford' groundwater background to allow formulation of background-based cleanup standards. The primary purpose of this paper is to extend the random effects models for (1) assessing the spatial, temporal, and analytical variability of groundwater background measurements; (2) demonstrating that the usual variance estimate s ², which ignores the variance components, is a biased estimator; (3) providing formulas for calculating the amount of bias; and (4) recommending monitoring strategies to reduce the uncertainty in estimating the average background concentrations. A case study is provided. Results indicate that (1) without considering spatial and temporal variability, there is a high probability of false positives, resulting in unnecessary remediation and/or monitoring expenses; (2) the most effective way to reduce the uncertainty in estimating the average background, and enhance the power of the statistical tests in general, is to increase the number of background wells; and (3) background for a specific constituent should be considered as a statistical distribution, not as a single value or threshold. The methods and the related analysis of variance tables discussed in this paper can be used as diagnostic tools in documenting the extent of inherent spatial and/or temporal variation and to help select an appropriate statistical method for testing purposes.     

Natural capital in ecology and economics: an overview

The Brundtland Commission report, Our Common Future, defined sustainable development as development that meets the needs of the present without compromising the ability of futuregenerations to meet their own needs. Although the idea of sustainable development has been widely accepted, it has proveddifficult to identify and implement policies and practices thatpromote sustainable economic growth. Some economists, environmental scientists and policy analysts believe that they can transform the consensus about sustainability into manageablepractices. They propose to accomplish this feat with a set of new ideas about the relationships between the economy and theenvironment offered under the banner of 'natural capital'. An ideal account of natural capital would be one or more standard measures or models that would allow the direct comparison of environmental goods, like forests, fresh water and clean air, with economic goods, like money, capital and productivity. By bringing economic science and environmental science to an objective common ground, a natural capital model has the potentialto provide a concrete means of comparing the economic and ecological costs and benefits of particular policies and programmes. This paper offers a survey and analysis of several new contributions to the formation of the natural capital concept from economists, ecologists, policy analysts, biometricians, foresters and a philosopher. The paper concludes that existingmicroeconomic theory may be 'ungreenable', if it is not reformulated. While macroeconomic approaches to natural capitalhave been more successful, they share the limitation that ecosystems and species are valued solely in monetary terms. These problems are taken to suggest that the development of a successful natural capital model may require economic theory tobe recast to include non-monetary social preferences and values.     

An Integrated Assessment by Models for Energy Systems Analysis and Life-Cycle Assessment with a Case Study of Advanced Fossil-Fired Power Plants in China

We developed an integrated assessment (IA) using models for energy systems analysis and life-cycle assessment (LCA). Based on this assessment framework, we developed cost-benefit analysis (CBA) case studies for a hypothetical project designed to introduce advanced fossil-fired power generation technologies in China. Our MARKAL model for Japan confirmed that radical reductions (i.e., 80 % by 2050) of carbon dioxide (CO₂) could be attained from energy systems alone and that credit for emission allowances was required. We evaluated life-cycle costs and emissions of carbon dioxide, sulfur oxide, and nitrogen oxide gases for the energy technologies using an LCA model. Further, we applied a power generation planning model for six Chinese grids to provide a power mix structure, potentially producing credit by installing fossil-fired power generation technology and by using baseline grid emission factors with an average cost of electricity. Finally, by using dynamic emission reductions and additional costs from the two models, we conducted case studies of CBA for a hypothetical project to install the technologies in China. This was accomplished by evaluating emission reductions in monetary terms and by applying a life-cycle impact assessment model. A unique feature of our IA is its dynamic (time-varying) assessment of costs and benefits.     

Meta-Modeling to Assess the Possible Future of Paris Agreement

In the meta-modeling approach, one builds a numerically tractable dynamic optimization or game model in which the parameters are identified through statistical emulation of a detailed large scale numerical simulation model. In this paper, we show how this approach can be used to assess the economic impacts of possible climate policies compatible with the Paris Agreement. One indicates why it is appropriate to assume that an international carbon market, with emission rights given to different groups of countries will exist. One discusses the approach to evaluate correctly abatement costs and welfare losses incurred by different groups of countries when implementing climate policies. Finally, using a recently proposed meta-model of game with a coupled constraint on a cumulative CO₂ emissions budget, we assess several new scenarios for possible fair burden sharing in climate policies compatible with the Paris Agreement.     

Factor-Augmenting Technical Change: An Empirical Assessment

This paper estimates factor-specific technical change and input substitution using a structural approach. It contributes to the existing literature by introducing various technology drivers for factor productivities and by assessing the impact of endogenous technical change on the elasticity of substitution. The empirical results suggest that factor productivities are indeed endogenous. In addition, technology drivers are factor-specific. Whereas the R&D stock and machinery imports are important determinants of energy and capital productivity, the education stock is statistically related to labour productivity. The rate of energy-augmenting technical change is larger than that of either labour or capital. By contrast, the productivity of these two factors grows at similar rates. Estimates of the elasticity of substitution are within the range identified by previous literature. In addition, we show that endogenous technical change reduces substitution. Because the elasticity of substitution is lower than one, knowledge and human capital can ultimately have an energy-using effect. The estimated structure of endogenous technical change suggests that Integrated Assessment models focusing on energy-saving technical change might underestimate climate policy costs.     

Improving Efficiency Of Uncertainty Analysis In Complex Integrated Assessment Models: The Case Of The Rains Emission Module

Abstact Ever since the Regional Acidification Information and Simulation Model (RAINS) has been constructed, the treatment of uncertainty has remained an issue of major interest. In a recent review of the model performed for the Clean Air for Europe (CAFE) programme of the European Commission, a more systematic and structured uncertainty analysis has been recommended. This paper aims at contributing to the scientific debate how this can be achieved. Because of its complex structure on the one hand and limited research resources (time, computational capacities) on the other hand a full-blown uncertainty analysis in RAINS is hardly feasible. Therefore, all types of uncertainty require more efficient ways for uncertainty analysis. With respect to parameter uncertainty, we propose to focus research efforts for uncertainty analysis on key parameters. Among different approaches to select key parameters that have been discussed in the literature screening methods seem to be particularly appropriate for complex, deterministic Integrated Assessment models such as RAINS. Surprisingly, in Integrated Assessment modelling for air pollution problems of screening design have not been taken up so far. As a case study we consider the emission module of RAINS. We show that its structure allows for a straightforward and effective screening procedure     

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.     

Choices of ecosystem capital without discounting and prices

In the last decade, neo-classical economists have used the Consumption-Growth Based Utility (CGBU) framework to suggestan appropriate discount rate for eco-system capital. The CGBU framework, being based on the simplicity and single-valued nature of man-made capital, is subject to many limitations. Three general limitations of the framework, irrespective of its use for man-made capital or ecosystem capital, paradox of consumption growth, expectations' insensitive utility specification, and insensitivity to the nature of returns, arediscussed. With respect to its use for ecosystem capital, the framework has two limitations: ecosystems cannot be commoditizedand ecosystem capital satisfies differentiated needs, and, hence, gross substitution between different components of ecosystem capital or between ecosystem capital and man-made capital is not possible. Therefore, the rate of discount for ecosystem capital suggested on the basis of the framework is arbitrary. In the context of international security, choices ofecosystem capital are similar to the choices of military decisions. Similar to military decisions, ecosystem choices should be made without discounting and prices. The neo-classicalframework does not have appropriate tools for such decision analyses, and other streams of economics, specifically post-Keynesian economics, may assist decision- makers in analysing ecosystem choices without prices and discounting.     

Predicting copper concentrations in acid mine drainage: a comparative analysis of five machine learning techniques

Acid mine drainage (AMD) is a global problem that may have serious human health and environmental implications. Laboratory and field tests are commonly used for predicting AMD, however, this is challenging since its formation varies from site-to-site for a number of reasons. Furthermore, these tests are often conducted at small-scale over a short period of time. Subsequently, extrapolation of these results into large-scale setting of mine sites introduce huge uncertainties for decision-makers. This study presents machine learning techniques to develop models to predict AMD quality using historical monitoring data of a mine site. The machine learning techniques explored in this study include artificial neural networks (ANN), support vector machine with polynomial (SVM-Poly) and radial base function (SVM-RBF) kernels, model tree (M5P), and K-nearest neighbors (K-NN). Input variables (physico-chemical parameters) that influence drainage dynamics are identified and used to develop models to predict copper concentrations. For these selected techniques, the predictive accuracy and uncertainty were evaluated based on different statistical measures. The results showed that SVM-Poly performed best, followed by the SVM-RBF, ANN, M5P, and KNN techniques. Overall, this study demonstrates that the machine learning techniques are promising tools for predicting AMD quality.     

Marginal Abatement Costs and Marginal Welfare Costs for Greenhouse Gas Emissions Reductions: Results from the EPPA Model

Marginal abatement cost (MAC) curves, relationships between tonnes of emissions abated and the CO₂ (or greenhouse gas (GHG)) price, have been widely used as pedagogic devices to illustrate simple economic concepts such as the benefits of emissions trading. They have also been used to produce reduced-form models to examine situations where solving the more complex model underlying the MAC is difficult. Some important issues arise in such applications: (1) Are MAC relationships independent of what happens in other regions?, (2) are MACs stable through time regardless of what policies have been implemented in the past?, and (3) can one approximate welfare costs from MACs? This paper explores the basic characteristics of MAC and marginal welfare cost (MWC) curves, deriving them using the MIT Emissions Prediction and Policy Analysis model. We find that, depending on the method used to construct them, MACs are affected by policies abroad. They are also dependent on policies in place in the past and depend on whether they are CO₂-only or include all GHGs. Further, we find that MACs are, in general, not closely related to MWCs and therefore should not be used to derive estimates of welfare change. We also show that, as commonly constructed, MACs may be unreliable in replicating results of the parent model when used to simulate GHG policies. This is especially true if the policy simulations differ from the conditions under which the MACs were simulated.     

POLICIES FOR RESOLVING THE CLIMATE CHANGE AND THE STRATOSPHERIC OZONE DEPLETION ISSUES: ANTAGONISTIC OR RE-ENFORCING?

Unlike the past, anthropogenic activities have begun to have greaterand greater impact on the environment. Policy formulation is no longerconfined to just responding to an existing set of environmentalconditions, but involves taking into account how the decision mightaffect the original set of environmental conditions to which the originalpolicy was formulated as a response.We discuss how certain policies could be developed, using the resultsfrom two integrated scientific models which incorporate parametersreflecting two air issues: (1) climate change, and (2) stratospheric ozonedepletion. Model results suggest very clearly that these two air issuesare not scientifically independent from each other. Following asingle-issue policy would lead to inconsistencies and undesirableconsequences in other related issues. Thus, the policy decision processhas now become complex.One of the sources of this complexity is the uncertainty in the responseof the environmental system to certain policy decisions, complexityarising from the fact that the environmental system is highly nonlinearand evolving. Model results indicate that it is essential for the policyformulation process to be flexible enough to take into account thenonlinear evolutionary nature of the environmental system. A policymust reflect, and be tailored to, the dynamical history and present stateof the system, if it is to be effective in influencing the future evolution of the system.     

Predicting coral bleaching in response to environmental stressors using 8 years of global-scale data

Coral reefs have experienced extensive mortality over the past few decades as a result of temperature-induced mass bleaching events. There is an increasing realization that other environmental factors, including water mixing, solar radiation, water depth, and water clarity, interact with temperature to either exacerbate bleaching or protect coral from mass bleaching. The relative contribution of these factors to variability in mass bleaching at a global scale has not been quantified, but can provide insights when making large-scale predictions of mass bleaching events. Using data from 708 bleaching surveys across the globe, a framework was developed to predict the probability of moderate or severe bleaching as a function of key environmental variables derived from global-scale remote-sensing data. The ability of models to explain spatial and temporal variability in mass bleaching events was quantified. Results indicated approximately 20% improved accuracy of predictions of bleaching when solar radiation and water mixing, in addition to elevated temperature, were incorporated into models, but predictive accuracy was variable among regions. Results provide insights into the effects of environmental parameters on bleaching at a global scale.     

Gaseous and particulate emissions from thermal power plants operating on different technologies

This paper presents the assessment of gaseous and particulate emissions from thermal power plants operating on different combustion technologies. Four thermal power plants operating on heavy furnace oil were selected for the study, among which three were based on diesel engine technology, while the fourth plant was based on oil-fired steam turbine technology. The stack emissions were monitored for critical air pollutants carbon monoxide, carbon dioxide, oxides of nitrogen, sulfur dioxide, particulate matter, lead, and mercury. The pollutant emissions were measured at optimum load conditions for a period of 6 months with an interval of 1 month. The results of stack emissions were compared with National Environmental Quality Standards of Pakistan and World Bank guidelines for thermal power plants, and few parameters were found higher than the permissible limits of emissions. It was observed that the emissions carbon monoxide, oxides of nitrogen, and particulate matters from diesel engine-based power plants were comparatively higher than the turbine-based power plants. The emissions of sulfur dioxide were high in all the plants, even the plants with different technologies, which was mainly due to high sulfur contents in fuel.     

Impacts of Decentralized Power Generation on Distribution Networks: a Statistical Typology of European Countries

The development of decentralized sources of power out of renewable sources of energies has been triggering far-reaching consequences for Distribution System Operators over the past decade in Europe. Our paper benchmarks across 23 European countries the impact of the development of renewables on the physical characteristics of power distribution networks and on their investments. It builds on a large spectrum of databases of quantitative indicators about the dynamics of installed capacity of renewable energy resources and the power generation out of them, electricity independence, quality of electricity distribution, smart grids investments, Network System Operators capital expenditures, length of the distribution networks, overall costs of power networks paid by private agents, and electricity losses, all in relation with the development of decentralized generation. The heterogeneity of these indicators across Europe appears to be wide notably because of physical constraints, historic legacies, or policy and regulatory choices. A cluster analysis allows for deriving six groups of countries that display statistically homogenous characteristics. Our results may provide decision makers and regulators with a tool helping them to concentrate on the main issues specific to their countries as compared to the European median, and to look for possible solutions in the experience of other clusters which are shown to perform better for some indicators.     

Permits and effluent charges in the water pollution control policies of France,West Germany,and the Netherlands

In this article the water pollution control policies of these countries and their effects on emitters are analyzed. In the Netherlands, local water control boards levy pollution charges on both direct and indirect emitters. The charges are based upon measured emissions and actual treatment costs and they vary among the boards. Discharges into surface waters are by permission only. West German law sets nationally uniform rates only for direct emitters and some pollutants, irrespective of treatment costs. The States (Länder), however, may make indirect emitters liable to pay as well. In France, river basin agencies charge emitters and grant discounts where abatement facilities have been installed. Further policy instruments are tax cuts, subsidies, and standards set on local and national levels. France, in this complex policy, also uses contrats de branche where government and industries agree by contract on pollution abatement. Evidence shows that all these policies have reduced water pollution. As emissions decrease, problems of overcapacity might occur where collective water treatment plants have been installed already. Moreover, investment in additive abatement technology may inhibit the introduction of low-waste, integrated technologies. Yet the development of the latter, though expensive in the short run, should enable industry to meet more stringent standards in the future.