Incorporating Consumer and Business Preferences in Industrial Technology Evolution: a Policy Modelling Application

Industrial production practices are constantly being altered through a dynamic and complex process of technology innovation and consumer adoption. Analytical approaches that have explored the potential and cost of reducing energy throughput in the economy (and associated greenhouse gas emissions), have typically treated these dynamics both simplistically and inadequately. This has taken the form of conceiving of technological change as an abstract, aggregate phenomenon, or alternatively, where models are technologically explicit, of limiting alternative technology adoption to financial costs, while ignoring consumer and business preferences based on issues of reliability, performance, heterogeneity in costs, time preferences, among others. There is a pressing need to gain a much clearer picture of these processes, particularly to aid in the debate and design of newer policy tools that are specifically focused on providing long term signals for market transformation, for instance renewable portfolio standards, sector-specific permit trading and other ‘flexible’ market regulations. The issue of technology transformation has been central to the Energy and Material Research Group’s development of CIMS, a technological-explicit model that allows policy makers to assess the potential effectiveness and costs of environmental policies. CIMS simulates technological evolution based on a decision algorithm that incorporates the other relevant components of consumer and business preferences (beyond anticipated financial cost). By looking at applications of the CIMS model for the National Climate Change Process in Canada, a committee established to assess how Canada might meet its Kyoto commitments, we demonstrate how the potential for industrial and societal transformation vary significantly vary depending on whether the analyst focuses just on the anticipated financial costs of technologies or combines this with other relevant components of consumer and business preferences. We also show how this type of model can allow policy-makers to explore the uncertain relationship between policies and the evolution of technologies and preferences, which are critical factors in the long-run cost dynamics of GHG emission reduction.