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Given that electricity generation investments are expected to operate for 40 or more years, the decisions we make today can have long-term impacts on the electricity system and the ability and cost of meeting long-term environmental goals. This research investigates socially optimal near-term electricity investment decisions under uncertainty in future technology costs and policy by formulating a computable general equilibrium (CGE) model of the U.S. as a two-stage stochastic dynamic program. The unique feature of the study is a stochastic formulation of technological learning. Most studies that include technological learning utilize deterministic learning curves in which a given amount of investment, production or capacity leads to a given cost reduction. In a stochastic framework, investment in a technology in the current period depends on uncertain learning that will result and lower future costs of the technology. Results under stochastic technological learning suggest that additional near-term investment relative to what is optimal under no learning can be justified at technological learning rates as low as 10–15%, and at the 20–25% rates commonly found in literature for advanced non-carbon technologies, significant additional near-term investment can be justified. We also find it can be socially optimal to invest more in non-carbon technology when the rate of learning is uncertain compared to the case where the learning rate is certain. Increasing marginal costs produce an asymmetric loss function that under uncertainty leads to more near-term non-carbon investment in attempt to avoid the situation of high non-carbon costs and an external economic environment that creates high demand for non-carbon technology., The authors gratefully acknowledge the financial support for this work provided by the U.S. Department of Energy, Office of Science under grants DE-SC0003906 and DE-FG02-94ER61937; the U.S. Environmental Protection Agency under grant XA-83600001-1; and other government, industry, and foundation sponsors of the Joint Program on the Science and Policy of Global Change.

R&D is an uncertain activity with highly skewed outcomes. Nonetheless, most recent empirical studies and modeling estimates of the potential of technological change focus on the average returns to research and development (R&D) for a composite technology and contain little or no information about the distribution of returns to R&D – which could be important for capturing the range of costs associated with climate change mitigation policies – by individual technologies. Through an empirical study of patent citation data, this paper adds to the literature on the outcomes of energy R&D by focusing on the behavior of the most successful innovations for six energy technologies, allowing us to determine whether uncertainty or differences in technologies matter most for success. We highlight two key results. First, we compare the results from an aggregate analysis of six energy technologies to technology-by-technology results. Our results show that existing work that assumes diminishing returns but assumes one generic technology is too simplistic and misses important differences between more successful and less successful technologies. Second, we use quantile regression techniques to learn more about patents that have a high positive error term in our regressions – that is, patents that receive many more citations than predicted based on observable characteristics. We find that differences across technologies, rather than differences across quantiles within technologies, are more important. The value of successful technologies persists longer than those of less successful technologies, providing evidence that success is the culmination of several advances building upon one another, rather than resulting from one single breakthrough. Diminishing returns to research activities appear most problematic during rapid increases of research investment, such as experienced by solar energy in the 1970s., National Science Foundation (U.S.) (Grant 0825915)

Long-term planning for electric power systems, or capacity expansion, has traditionally been modeled using simplified models or heuristics to approximate the short-term dynamics. However, current trends such as increasing penetration of intermittent renewable generation and increased demand response requires a coupling of both the long and short term dynamics. We present an efficient method for coupling multiple temporal scales using the framework of singular perturbation theory for the control of Markov processes in continuous time. We show that the uncertainties that exist in many energy planning problems, in particular load demand uncertainty and uncertainties in generation availability, can be captured with a multiscale model. We then use a dimensionality reduction technique, which is valid if the scale separation present in the model is large enough, to derive a computationally tractable model. We show that both wind data and electricity demand data do exhibit sufficient scale separation. A numerical example using real data and a finite difference approximation of the Hamilton–Jacobi–Bellman equation is used to illustrate the proposed method. We compare the results of our approximate model with those of the exact model. We also show that the proposed approximation outperforms a commonly used heuristic used in capacity expansion models.

An effective policy response to climate change will include, among other things, investments in lowering greenhouse gas emissions (mitigation), as well as short-term temporary (flow) and long-lived capital-intensive (stock) adaptation to climate change. A critical near-term question is how investments in reducing climate damages should be allocated across these elements of a climate policy portfolio, especially in the face of uncertainty in both future climate damages and also the effectiveness of yet-untested adaptation efforts. We build on recent efforts in DICE-based integrated assessment modeling approaches that include two types of adaptation—short-lived flow spending and long-lived depreciable adaptation stock investments—along with mitigation, and we identify and explore the uncertainties that impact the relative proportions of policies within a response portfolio. We demonstrate that the relative ratio of flow adaptation, stock adaptation, and mitigation depend critically on interactions among: 1) the relative effectiveness in the baseline of stock versus flow adaptation, 2) the degree of substitutability between stock and flow adaptation types, and 3) whether there exist physical limits on the amount of damages that can be reduced by flow-type adaptation investments. The results indicate where more empirical research on adaptation could focus to best inform near-term policy decisions, and provide a first step towards considering near-term policies that are flexible in the face of uncertainty.

We present a numerical method for finite-horizon stochastic optimal control models. We derive a stochastic minimum principle (SMP) and then develop a numerical method based on the direct solution of the SMP. The method combines Monte Carlo pathwise simulation and non-parametric interpolation methods. We present results from a standard linear quadratic control model, and a realistic case study that captures the stochastic dynamics of intermittent power generation in the context of optimal economic dispatch models., National Science Foundation (U.S.) (Grant 1128147), United States. Dept. of Energy. Office of Science (Biological and Environmental Research Program Grant DE-SC0005171), United States. Dept. of Energy. Office of Science (Biological and Environmental Research Program Grant DE-SC0003906)

Current proposals for greenhouse gas emissions regulations in the United States mainly take the form of emissions caps with tradable permits. Since Weitzman's (1974) [3] study of prices vs. quantities, economic theory predicts that a price instrument is superior under uncertainty in the case of stock pollutants. Given the general belief in the political infeasibility of a carbon tax in the US, there has been recent interest in two other policy instrument designs: hybrid policies and intensity targets. We extend the Weitzman model to derive an analytical expression for the expected net benefits of a hybrid instrument under uncertainty. We compare this expression to one developed by Newell and Pizer (2006) [6] for an intensity target, and show the theoretical minimum correlation between GDP and emissions required for an intensity target to be preferred over a hybrid. In general, we show that unrealistically high correlations are required for the intensity target to be preferred to a hybrid, making a hybrid a more practical instrument in practice. We test the predictions by performing Monte Carlo simulation on a computable general equilibrium model of the US economy. The results are similar, and we show with the numerical model that when marginal abatement costs are non-linear, an even higher correlation is required for an intensity target to be preferred over a safety valve., Doris Duke Charitable Foundation

The feasibility, cost, and air quality impacts of using electrical grids to shift water use from drought-stricken regions to areas with more water availability were examined. Power plant cooling represents a large portion of freshwater withdrawals in the United States, and shifting where electricity generation occurs can allow the grid to act as a virtual water pipeline, increasing water availability in regions with drought by reducing water consumption and withdrawals for power generation. During a 2006 drought, shifting electricity generation out of the most impacted areas of South Texas (~10% of base case generation) to other parts of the grid would have been feasible using transmission and power generation available at the time, and some areas would experience changes in air quality. Although expensive, drought-based electricity dispatch is a potential parallel strategy that can be faster to implement than other infrastructure changes, such as air cooling or water pipelines., National Science Foundation (U.S.). Office of Emerging Frontiers in Research and Innovation (Grant 0835414), United States. Dept. of Energy

The possibility of using electricity dispatching strategies to achieve a 50% nitrogen oxide (NOx) emission reduction from electricity generating units was examined using the grid of the Electricity Reliability Council of Texas as a case study. Simulations of a hypothetical policy demonstrate that imposing higher NOx prices induces a switch from some coal-fired generation to natural gas generation, lowering NOx emissions. The simulation is for a day with relatively high electricity demand and accounts for transmission constraints. In addition to the lowering of the NOx emissions, there are co-benefits of the redispatching of generation from coal to natural gas, including reductions in the emissions of sulfur oxides (24%–71%), Hg (16%–82%) and CO2 (8.8%–22%). Water consumption was also decreased, by 4.4%–8.7%. Substantial reductions of NOx emissions can be achieved for an increased generation cost of 4–13%, which is due to the higher fuel price of gas relative to coal (assuming a price of $3.87 per MMBTU (MMBTU: million British thermal units) for natural gas, and $1.89 per MMBTU for coal). However, once the system has reduced NOx emissions by approximately 50%, there is little incremental reduction in emissions due to further increases in NOx prices., National Science Foundation (U.S.) (Grant number EFRI-0835414)

http://globalchange.mit.edu/research/publications/reports/all, We discuss a strategy for investigating the impacts of climate change on Earth’s physical, biological and human resources and links to their socio-economic consequences. The features of the integrated global system framework that allows a comprehensive evaluation of climate change impacts are described with particular examples of effects on agriculture and human health. We argue that progress requires a careful understanding of the chain of physical changes—global and regional temperature, precipitation, ocean acidification and polar ice melting. We relate those changes to other physical and biological variables that help people understand risks to factors relevant to their daily lives—crop yield, food prices, premature death, flooding or drought events, land use change. Finally, we investigate how societies may adapt, or not, to these changes and how the combination of measures to adapt or to live with losses will affect the economy. Valuation and assessment of market impacts can play an important role, but we must recognize the limits of efforts to value impacts where deep uncertainty does not allow a description of the causal chain of effects that can be described, much less assigned a likelihood. A mixed approach of valuing impacts, evaluating physical and biological effects, and working to better describe uncertainties in the earth system can contribute to the social dialogue needed to achieve consensus—where it is needed—on the level and type of mitigation and adaptation actions that are required., The MIT Integrated Global System Model (IGSM) and its economic component used in the analysis, the MIT Emissions Prediction and Policy Analysis (EPPA) model, is supported by a consortium of government, industry, and foundation sponsors of the MIT Joint Program on the Science and Policy of Global Change. (For a complete list of sponsors, see: http://globalchange.mit.edu).

We assess the human health and economic impacts of projected 2000–2050 changes in ozone pollution using the MIT Emissions Prediction and Policy Analysis - Health Effects (EPPA-HE) model, in combination with results from the GEOS-Chem global tropospheric chemistry model of climate and chemistry effects of projected future emissions. We use EPPA-HE to assess the human health damages (including mortality and morbidity) caused by ozone pollution, and quantify their economic impacts in sixteen world regions. We compare the costs of ozone pollution under scenarios with 2000 and 2050 ozone precursor and greenhouse gas emissions (using the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) A1B scenario). We estimate that health costs due to global ozone pollution above pre-industrial levels by 2050 will be $580 billion (year 2000$) and that mortalities from acute exposure will exceed 2 million. We find that previous methodologies underestimate costs of air pollution by more than a third because they do not take into account the long-term, compounding effects of health costs. The economic effects of emissions changes far exceed the influence of climate alone., United States. Dept. of Energy (Office of Science (BER) grant DE-FG02-94ER61937), United States. Dept. of Energy (Office of Science (BER) grant (BER) grants DE-FG02-93ER61677), United States. Environmental Protection Agency (grant EPA-XA- 83344601-0), Massachusetts Institute of Technology. Joint Program on the Science & Policy of Global Change

Growing use of renewables pushes thermal generators against operating constraints - e.g. ramping, minimum output, and operating reserves - that are traditionally ignored in expansion planning models. We show how including such unit-commitment-derived details can significantly change energy production and optimal capacity mix. We introduce a method for efficiently combining unit commitment and generation expansion planning into a single mixed-integer optimization model. Our formulation groups generators into categories allowing integer commitment states from zero to the installed capacity. This formulation scales well, runs much faster (e.g. 5000×) than individual plant binary decisions, and makes the combined model computationally tractable for large systems (hundreds of generators) at hourly time resolutions (8760 hours) using modern solvers on a personal computer. We show that ignoring these constraints during planning can result in a sub-optimal capacity mix with significantly higher operating costs (17%) and carbon emissions (39%) and/or the inability to meet emissions targets.

Analyses of global climate policy as a sequential decision under uncertainty have been severely restricted by dimensionality and computational burdens. Therefore, they have limited the number of decision stages, discrete actions, or number and type of uncertainties considered. In particular, other formulations have difficulty modeling endogenous or decision-dependent uncertainties, in which the shock at time t+1 depends on the decision made at time t. In this paper, we present a stochastic dynamic programming formulation of the Dynamic Integrated Model of Climate and the Economy (DICE), and the application of approximate dynamic programming techniques to numerically solve for the optimal policy under uncertain and decision-dependent technological change. We compare numerical results using two alternative value function approximation approaches, one parametric and one non-parametric. Using the framework of dynamic programming, we show that an additional benefit to near-term emissions reductions comes from a probabilistic lowering of the costs of emissions reductions in future stages, which increases the optimal level of near-term actions., U.S. National Science Foundation grant num- ber 0825915 and the U.S. Department of Energy grant number DE-SC0003906

The Massachusetts Institute of Technology (MIT) Integrated Global System Model is used to make probabilistic projections of climate change from 1861 to 2100. Since the model’s first projections were published in 2003, substantial improvements have been made to the model, and improved estimates of the probability distributions of uncertain input parameters have become available. The new projections are considerably warmer than the 2003 projections; for example, the median surface warming in 2091–2100 is 5.1°C compared to 2.4°C in the earlier study. Many changes contribute to the stronger warming; among the more important ones are taking into account the cooling in the second half of the twentieth century due to volcanic eruptions for input parameter estimation and a more sophisticated method for projecting gross domestic product (GDP) growth, which eliminated many low-emission scenarios. However, if recently published data, suggesting stronger twentieth-century ocean warming, are used to determine the input climate parameters, the median projected warming at the end of the twenty-first century is only 4.1°C. Nevertheless, all ensembles of the simulations discussed here produce a much smaller probability of warming less than 2.4°C than implied by the lower bound of the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) projected likely range for the A1FI scenario, which has forcing very similar to the median projection in this study. The probability distribution for the surface warming produced by this analysis is more symmetric than the distribution assumed by the IPCC because of a different feedback between the climate and the carbon cycle, resulting from the inclusion in this model of the carbon–nitrogen interaction in the terrestrial ecosystem., United States. Dept. of Energy (Grants DE-FG02-94ER61937 and DE-FG02-93ER61677)

Ambient observations have indicated that high concentrations of ozone observed in the Houston/Galveston area are associated with plumes of highly reactive hydrocarbons, mixed with NOx, from industrial facilities. Ambient observations and industrial process data, such as mass flow rates for industrial flares, indicate that the VOCs associated with these industrial emissions can have significant temporal variability. To characterize the effect of this variability in emissions on ozone formation in Houston, data were collected on the temporal variability of industrial emissions or emission surrogates (e.g., mass flow rates to flares). The observed emissions variability was then used to construct region-wide emission inventories with variable industrial emissions, and the impacts of the variability on ozone formation were examined for two types of meteorological conditions, both of which lead to high ozone concentrations in Houston. The air quality simulations indicate that variability in industrial emissions has the potential to cause increases and decreases of 10-52 ppb (13-316%), or more, in ozone concentration. The largest of these differences are restricted to regions of 10-20 km2, but the variability also has the potential to increase region wide maxima in ozone concentrations by up to 12 ppb. Keywords: Photochemical Grid Model, highly reactive volatile organic compounds (HRVOC), ozone, uncertainty analysis, Monte Carlo simulation.