Improving Long-Range Energy Modeling: A Plea for Historical Retrospectives

Koomey, Jonathan; Craig, Paul; Gadgil, Ashok; Lorenzetti, David M.

doi:10.5547/issn0195-6574-ej-vol24-no4-4

Cited by 31 publications

(9 citation statements)

References 41 publications

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“…Probabilistic exercises may be more appropriate when the system under consideration is smaller, is less complex, and/or when more data are available to perform them. Attempts to provide quantitative estimates of overall uncertainty may neglect the very uncertainties that matter most, or provide a misleading sense of confidence about the results, if applied under conditions of deep uncertainty (Craig et al, 2002;Koomey, 2002;Koomey et al, 2003;Scher and Koomey, 2011).…”

Section: Discussionmentioning

confidence: 99%

Role of future scenarios in understanding deep uncertainty in long-term air quality management

Gamas

Dodder

Loughlin

et al. 2015

Journal of the Air & Waste Management Association

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The environment and its interactions with human systems, whether economic, social, or political, are complex. Relevant drivers may disrupt system dynamics in unforeseen ways, making it difficult to predict future conditions. This kind of "deep uncertainty" presents a challenge to organizations faced with making decisions about the future, including those involved in air quality management. Scenario Planning is a structured process that involves the development of narratives describing alternative future states of the world, designed to differ with respect to the most critical and uncertain drivers. The resulting scenarios are then used to understand the consequences of those futures and to prepare for them with robust management strategies. We demonstrate a novel air quality management application of Scenario Planning. Through a series of workshops, important air quality drivers were identified. The most critical and uncertain drivers were found to be "technological development" and "change in societal paradigms." These drivers were used as a basis to develop four distinct scenario storylines. The energy and emissions implications of each storyline were then modeled using the MARKAL energy system model. NO x emissions were found to decrease for all scenarios, largely a response to existing air quality regulations, whereas SO 2 emissions ranged from 12% greater to 7% lower than 2015 emissions levels. Future-year emissions differed considerably from one scenario to another, however, with key differentiating factors being transition to cleaner fuels and energy demand reductions.Implications: Application of scenarios in air quality management provides a structured means of sifting through and understanding the dynamics of the many complex driving forces affecting future air quality. Further, scenarios provide a means to identify opportunities and challenges for future air quality management, as well as a platform for testing the efficacy and robustness of particular management options across wide-ranging conditions. IntroductionIn the United States, air quality managers at the federal and state levels develop emission reduction strategies to meet air quality standards. Emission inventories are projected into the future, both with and without each candidate strategy. Air quality models are then used to evaluate the resulting changes in air quality. At the federal level, illustrative emission reduction strategies are a key component of Regulatory Impact Analyses (RIAs) (e.g., U.S. Environmental Protection Agency [EPA], 2009[EPA], , 2012a[EPA], 2012b[EPA], , 2014). These strategies demonstrate how an air quality standard can be met and are used in estimating the resulting costs and health and environmental benefits. For states, emission reduction strategies often support State Implementation Plans (SIPs), which specify how the state will comply with air quality standards into the future (e.g., California Air Resources Board [CARB], 2007; Texas Commission on Environmental Quality [TCEQ], 2012). In RIA and SIP applicatio...

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Section: Discussionmentioning

confidence: 99%

Role of future scenarios in understanding deep uncertainty in long-term air quality management

Gamas

Dodder

Loughlin

et al. 2015

Journal of the Air & Waste Management Association

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“…Even short-term energy forecasting, which typically relies on the simple extension of energy-economic trends, has fared somewhat poorly in retrospect (Ascher 1979;Craig et al 2002;DeCanio 2003;Koomey et al 2003;Smil 2000Smil , 2003. As a result, any statement that seeks to extend recent patterns into the future should be cautious of overreaching, especially when looking forward almost a hundred years.…”

Section: What Do the Last 40 Years Mean For The Future?mentioning

confidence: 99%

Psychohistory revisited: fundamental issues in forecasting climate futures

2010

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Uncertainty in the trajectories of the global energy and economic systems vexes the climate science community. While it is tempting to reduce uncertainty by searching for deterministic rules governing the link between energy consumption and economic output, this article discusses some of the problems that follow from such an approach. We argue that the theoretical and empirical evidence supports the view that energy and economic systems are dynamic, and unlikely to be predictable via the application of simple rules. Encouraging more research seeking to reduce uncertainty in forecasting would likely be valuable, but any results should reflect the tentative and exploratory nature of the subject matter.

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“…Lynch (2002) concludes in similar fashion in a comparison between the theory and empirics of oil supply forecasting. Koomey et al (2003) point out that factors like technological innovation and inaccuracy of oil reserve forecasts may also contribute to the forecast errors. Tang and Hammoudeh (2002) show that omission of market participants' expectations account for forecast errors too.…”

Section: Brief Literature Reviewmentioning

confidence: 99%

Forecasting Long‐Run Coal Price in China: A Shifting Trend Time‐Series Approach

Dong

Lin

2010

Review Development Economics

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The paper studies the behavior of mid- to long-run real coal price in the Chinese market. The problem is of great importance because the coal takes a 70% share in China's energy mix, and China is the world's second largest carbon emitter. An accurate forecast in coal price is crucial in predicting China's future energy consumption mix as well as the private sector's energy-type-related investment decisions. In estimation and forecasting, the shifting trend time-series model suggested by Robert Pindyck is used to capture the technological progress that is unobservable to the econometrician. It is found that the shifting trend model with continuous and random changes in price level and trend outperforms plain vanilla ARIMA models. It is argued that the model postulated by Pindyck is robust even in a transition economy where energy prices are subject to relatively rigid regulatory control. Out-of-sample forecasts are provided. Copyright (C) 2010 Blackwell Publishing Ltd.

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Improving Long-Range Energy Modeling: A Plea for Historical Retrospectives

Cited by 31 publications

References 41 publications

Role of future scenarios in understanding deep uncertainty in long-term air quality management

Role of future scenarios in understanding deep uncertainty in long-term air quality management

Psychohistory revisited: fundamental issues in forecasting climate futures

Forecasting Long‐Run Coal Price in China: A Shifting Trend Time‐Series Approach

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