Public Acceptance Clusters of Energy Technologies
Public acceptance (PA) clusters are defined by a number of rather intangible commonalties dealing with social values and public preferences. Societal values influencing the public acceptance of energy technologies include the following:
The internalization of environmental external costs. In this respect, all energy technologies with high SO2 emission intensities form the acidification cluster;
The internalization of social external costs. Social externalities such as risks are involved in deep-coal mining, for instance. Social externalities appear to increase with the increasing general wealth of a society;
Decentralized vs. Centralized energy conversion. Societal preferences of the kind ‘small is beautiful’ give rise to a cluster that describes low energy technology interconnectedness by grids or pipeline networks. Another important aspect of this PA cluster is that it ranks high in terms of vulnerability of energy supply, for example, to natural catastrophes. As rising shares of the residential sector in total GDP go along with more flexible, decentralized technologies, the prevalence of this PA cluster also reflects structural changes in an economy.
Security of energy supply: many countries pay considerable attention to ensuring a minimum degree of national dependency on foreign imports of energy technologies and fuels. The risk associated with a politically motivated disruption of such sources can be valued highly by society;
Public acceptance of nuclear power. Looking back in time, the most striking historic example of the effect of public acceptance on a technology’s success is the development of nuclear power. In the late 1970s and early 1980s, nuclear power was seen as the most promising technology in the energy sector. It was widely accepted by the public and among energy experts alike.As a consequence, the nuclear industry boomed, and the shares of nuclear electricity generation grew rapidly. Following accidents in nuclear reactors, however, public acceptance decreased in most countries to very low levels, especially in OECD countries;
Quality of fuels: another value influencing consumers’ choices is the flexibility and convenience of final-energy use. Such criteria explain the use of electricity instead of fuel wood for cooking despite the higher costs of electricity.
Varying assumptions on the future evolvement of societal values can lead to a wide variety of possible future energy technology mixes. PA clusters are therefore an important determinant of long-term energy scenarios, in particular SD scenarios.
Including societal values in the MESSAGE model is accomplished in different ways. High acceptance of environmentally energy sources technologies, for example, is modeled by constraints on emissions reduction or the internalization of environmental costs; the shift towards more widely accepted and more flexible energy forms is modeled by so-called ‘inconvenience costs’. Inconvenience costs reflect the assumption that consumers are prepared to pay a higher price for more convenient energy services (for example, the preference to heat with electricity instead of using cheaper coal). Finally, societal costs, such as externalities from coal mining, are modeled by cost premiums in addition to the real (physical) costs of the technology.
Five technologies have been identified as meeting low public acceptance in SD scenarios. These are mainly various types of coal and oil technologies, but also include conventional gas technologies with low efficiencies. The cluster labeled ‘medium acceptance’ can also be interpreted as a ‘grey zone’ between the clusters with high and low acceptance. This ambiguity expresses the fact that a clearcut distinction between the PA clusters appears hardly possible. Moreover, the technologies in To reflect this diversity with respect to designs for some technologies, ranges of future public acceptance are reported. For example, ‘oil technology’ in the PA cluster with low acceptance might be a peak-load diesel engine with medium flexibility, but also a conventional oil power plant for base-load application with comparatively low flexibility (of utilization).
PA clusters are useful in identifying key technology groups that are compatible with the major societal future concerns assumed to prevail in a scenario. Moreover, they are most helpful in translating the storyline of a scenario into modeling assumptions. However, PA clusters do not give information on which technology group will eventually be the most successful, that is, those technologies that are the key contributors to a given future development. There is a need, therefore, to analyse the success of technologies separately and this is done in the following section, especially emphasizing SD scenarios.
