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

Abstract

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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