However, such information is fraught with assumptions about how to cost and value different actions now and into the future, including those that have non-monetary effects on areas such as ecosystems and health. For example, a local decision maker may worry about how to balance the costs and risks of regulating local industrial emissions with the emerging possible impacts of climate change on local tourism or water supplies and costs of adapting these sectors. But the decision maker is also faced with the problem of competing priorities where they may feel that other urgent issues—such as poverty, housing, and crime—demand the bulk of available financial and human resources, leaving little for responding to climate change. We discuss some of these challenges, and some tools that may help with such complex economic decisions, in Chapter 4.
Decision makers are increasingly aware of the multidisciplinary nature of climate change policy even as they work to make the most of available resources. They need to create policy and stakeholder teams that stretch beyond traditional notions of jurisdiction; they are also seeking ways to leverage resources not only across disciplines but also across physical and temporal scales to maximize strategies and investments. For example, they seek to take advantage of economies of scale to improve purchasing power and the marginal costs of new technologies.
Decision makers also confront the choice of how to best integrate policies and initiatives across multiple geographic and temporal scales. An example might be a local neighborhood development initiative to help a community become more energy efficient, walkable, and environmentally friendly. This initiative would benefit greatly if integrated with a larger regional plan that involves building new or more efficient public transit along a nearby corridor, and an energy plan to construct a connected energy district. Thus, decision makers today need information and support to help them make the major infrastructure investment choices that will be effective across a wide range of possible future conditions.
Decision making about climate change is often conducted not only under conditions of scientific uncertainty but also by people who may be unfamiliar with the details and weight of scientific evidence. Under these conditions, human judgment is greatly influenced by a number of factors, including the “framing” of the problem itself (Ferree et al., 2002; Gamson and Modigliani, 1989; Nisbet and Mooney, 2007; Tversky and