Hadi Dowlatabadi

Sensitivity and Uncertainty Analysis of Complex Models of Disease Transmission: an HIV Model, as an Example

Summary HIV transmission models have become very complex. The behavior of some of these models may only be explored by uncertainty and sensitivity analyses, because the structural complexity of the model are coupled with a high degree of uncertainty in estimating the values of the input parameters. Uncertainty analysis may be used to assess the variability (prediction imprecision) in the outcome variable that is due to the uncertainty in estimating the input values. A sensitivity analysis can extend an uncertainty analysis by identifying which parameters are important in contributing to the prediction imprecision (i.e., how do changes in the values of the input parameters alter the value of the outcome variable). In this paper an uncertainty and a sensitivity analysis are described and applied; both analyses are based upon the Latin Hypercube Sampling (LHS) scheme, which is an extremely efficient sampling design proposed by McKay, Conover & Beckman (1979). The methods described in this paper have not previously been applied to deterministic models of disease transmission, although these models have many characteristics in common with the risk assessment models that the strategies were designed to investigate. The utility of the LHS uncertainty and the LHS/PRC (Latin Hypercube Sampling/Partial Rank Correlation) sensitivity analysis techniques are illustrated by analyzing a complex deterministic model of HIV transmission.

Consumer lifestyle approach to US energy use and the related CO2 emissions

Abstract Historically, a sectoral approach (based on the industrial, transportation, commercial, and residential sectors) has shaped the way we frame and analyze issues of energy conservation and CO 2 mitigation. This sectoral categorization, however, is limited in its capacity to reveal the total impacts of consumer activities on energy use and its related environmental impacts. In this paper, we propose an alternative paradigm, called the Consumer Lifestyle Approach (CLA), to explore the relationship between consumer activities and environmental impacts in the US. Estimates based on our methodology reveal that more than 80% of the energy used and the CO 2 emitted in the US are a consequence of consumer demands and the economic activities to support these demands. Direct influences due to consumer activities (home energy use and personal travel) are 4% of the US GDP, but account for 28% and 41% of US energy use and CO 2 emissions, respectively. Indirect influences (such as housing operations, transportation operations, food, and apparel) involve more than twice the direct energy use and CO 2 emissions. Characterization of both direct and indirect energy use and emissions is critical to the design of more effective energy and CO 2 emission policies. It may also help erode the false dichotomy of “them versus us” (industrial polluters versus consumers) references to the locus of responsibility for control of energy use and CO 2 emissions.

Learning from integrated assessment of climate change

The objective of integrated assessment of climate change is to put available knowledge together in order to evaluate what has been learned, policy implications, and research needs. This paper summarizes insights gained from five years of integrated assessment activity at Carnegie Mellon. After an introduction, in Section 2 we ask: who are the climate decision makers? We conclude that they are a diffuse and often divergent group spread all over the world whose decisions are primarily driven by local non-climate considerations. Insights are illustrated with results from the ICAM-2 model. In Section 3 we ask: what is the climate problem? In addition to the conventional answer, we note that in a democracy the problem is whatever voters and their elected representatives think it is. Results from studies of public understanding are reported. Several other specific issues that define the problem, including the treatment of aerosols and alternative indices for comparing greenhouse gases, are discussed. In Section 4 we discuss studies of climate impacts, focusing on coastal zones, the terrestrial biosphere and human health. Particular attention is placed on the roles of adaptation, value change, and technological innovation. In Section 5 selected policy issues are discussed. We conclude by noting that equity has received too little attention in past work. We argue that many conventional tools for policy analysis are not adequate to deal with climate problems. Values that change, and mixed levels of uncertainty, pose particularly important challenges for the future.

Scale, context, and decision making in agricultural adaptation to climate variability and change

This work presents a framework for viewing agricultural adaptation, emphasizing the multiple spatial and temporal scales on which individuals and institutions process information on changes in their environment. The framework is offered as a means to gain perspective on the role of climate variability and change in agricultural adaptation, and developed for a case study of Australian agriculture. To study adaptation issues at the scale of individual farms we developed a simple modelling framework. The model highlights the decision making element of adaptation in light of uncertainty, and underscores the importance of decision information related to climate variability. Model results show that the assumption of perfect information for farmers systematically overpredicts adaptive performance. The results also suggest that farmers who make tactical planting decisions on the basis of historical climate information are outperformed by those who use even moderately successful seasonal forecast information. Analysis at continental scales highlights the prominent role of the decline in economic operating conditions on Australian agriculture. Examples from segments of the agricultural industry in Australia are given to illustrate the importance of appropriate scale attribution in adapting to environmental changes. In particular, adaptations oriented toward short time scale changes in the farming environment (droughts, market fluctuations) can be limited in their efficacy by constraints imposed by broad changes in the soil/water base and economic environment occuring over longer time scales. The case study also makes the point that adaptation must be defined in reference to some goal, which is ultimately a social and political exercise. Overall, this study highlights the importance of allowing more complexity (limited information, risk aversion, cross-scale interactions, mis-attribution of cause and effect, background context, identification of goals) in representing adaptation processes in climate change studies.

Integrated assessment models of climate change: An incomplete overview

Integrated assessment is a trendy phrase that has recently entered the vocabulary of folks in Washington, DC and elsewhere. The novelty of the term in policy analysis and policy making circles belies the longevity of this approach in the sciences and past attempts at their application to policy issues. This paper is an attempt at providing an overview of integrated assessment with a special focus on policy motivated integrated assessments of climate change. The first section provides an introduction to integrated assessments in general, followed by a discussion of the bounds to the climate change issue. The next section is devoted to a taxonomy of the policy motivated models. Then the integrated assessment effort at Carnegie Mellon is described briefly. A perspective on the challenges ahead in successful representation of natural and social dynamics in integrated assessments of global climate change is presented in the final section.

A model framework for integrated studies of the climate problem

Abstract Establishing research priorities and developing and evaluating alternative policy options in the domain of climate change are activities that require a broad look across all the elements of the climate problem. Such broad integrated assessment should involve different analytical approaches in different parts of the problem. However, appropriate computer model frameworks, into which a variety of specific alternative results and submodels can be inserted, can provide convenient vehicles for putting the pieces together. Such a framework is described. A simple version is used to illustrate how such frameworks can be used to perform uncertainty analysis, and how, together with expert judgment, they might be used in setting research priorities.

Sensitivity of climate change mitigation estimates to assumptions about technical change

With greater certainty in anthropogenic influence on observed changes in climate there is increasing pressure for agreements to control emissions of greenhouse gases (Houghton et al., 1996). While it is difficult to assess the appropriate level of mitigation, it has been argued that flexibility in meeting emission targets offers significant economic savings. Such flexibility can be exercised in terms of timing of mitigation (i.e. delay) or geographic location of the intervention (e.g. permit trading and Joint-Implementation). Much of this insight is based on standard models of technical change in energy supply and demand. However, standard model formulations rarely consider: (i) a link between the pattern of technical change and policy interventions; (ii) economies of learning; and (iii) technical progress in discovery and recovery of oil and gas. While there is evidence to support the importance of these factors in historic patterns of technical progress, the data necessary to calibrate internally consistent economic models of these phenomena have not been available. In this paper simple representations of endogenous and induced technical change have been used to explore the sensitivity of mitigation cost estimates to how technical change is represented in energy economics models. The scenarios involve control of CO2 emissions to limit its concentration to no more than 550 ppm(v), starting in the year 2000, and delayed to 2025. This sensitivity analysis has revealed four robust insights: (i) If endogenous technical change is assumed, expected business as usual emissions are higher than otherwise estimated — nevertheless, while 25% greater CO2 control is required for meeting the CO2 concentration target, the cost of mitigation is 40% lower; (ii) If technical progress in oil and gas discovery and recovery is assumed, energy use and CO2 emissions increase by 75% and 65%, respectively above the standard estimates; (iii) If the economies of learning exhibited in various manufacturing sectors are repeated in development of non-fossil technologies and abatement of CO2 emissions, the costs of abatement can be 50% lower than those assessed using standard models; and (iv) In this sequential learning framework, delay in abatement towards a 550 ppm(v) CO2 concentration target leads to expected net economic loss in seven of nine model structures studied. Only when the model structure permits new oil and gas discoveries while keeping other features of standard models does delay offer economic gain with greater than 60% confidence.

Why Conventional Tools for Policy Analysis Are Often Inadequate for Problems of Global Change

The past three decades have witnessed an explosive growth in the development and use of tools for quantitative policy analysis. As policy analysts have turned to the consideration of climate and other problems of global change, they have found it natural to employ such now standard tools as utility theory, benefit-cost analysis, statistical decision theory, multi-attribute utility theory, and contingent valuation. However, many issues in global change involve temporal, spatial and socio-political scales that are significantly broader than those encountered in most traditional policy analyses. In such cases, the uncritical application of conventional tools can violate the assumptions on which they are based, produce silly or misleading findings, and occasionally lead to heated controversy, such as the one which erupted over value of life estimates in Chapter 6 of Working Group III in the report of the Intergovernmental Panel on Climate Change (Masood, 1995; Masood and Ochert, 1995; Pearce et al., 1996). The source of difficulty is illustrated in Figure 1. Most tools of modern quantitative policy analysis were developed to address problems that lie near the origin in this space. As one moves outward from the origin, more and more of the underlying assumptions upon which conventional tools are based begin to break down. Because many problems in global change lie far from the origin on all three dimensions, one can expect that the straightforward application of standard ideas and methods will often fail.

The ecological footprint: a non-monetary metric of human consumption applied to North America

Abstract This paper employs ecological footprint analysis as a potential non-monetary metric of human consumption and ecological productivity in a simulation-modeling framework, applied to North America. The ecological footprint provides an indirect basis for considering the long-term ecological risk and sustainability of human settlements, regions or, in this case, a continent. We examine several scenarios for human consumption, ecological productivity and material efficiency, to explore which variables have influence on the ecological budget of North America over the coming century. Only one scenario, which assumes considerable reductions in human consumption, is likely to yield an ecological surplus. Unlike monetary measures of societal well-being, ecological footprint analysis shows that increased economic activity and consumption creates deficits in terms of the balance of ecological productivity and consumption in a region, and may reduce long-term ecological sustainability. Several advantages and disadvantages of this metric are discussed.

Distributed generation and distribution utilities

Distributed (co)generation (DG) represents an alternative paradigm of energy supply and the opportunity for significant CO2 emission reductions. This paper investigates the adoption of the DG technology of internal combustion (IC) engine cogeneration in the Netherlands and UK from 1985–1998. This detailed comparison was motivated to understand why the Netherlands installed 20 times as many units and 40 times as much DG capacity (per capita) compared to the UK. The primary finding of this study emphasizes the win–win partnerships between DG adopters and utilities. While both governments promoted DG as part of their CO2 reduction goals, only distribution utilities in the Netherlands were primed to support greater DG penetration. Crucially, Netherlands utilities offered high electricity buy-back rates which enabled innovative utilization of DG. Flexible operation modes allowed investment in larger units, benefiting from economies of scale due to fixed components in maintenance costs, and extended DG use to the much larger set of sites with limited electricity base-loads. The win–win partnerships between distribution utilities and DG adopters for cost savings also facilitated improved management of the electricity network. A final consequence was a virtuous circle of maintenance cost reductions from geographic concentration of DG units, resulting in improved returns and hence more DG unit sales.