Behavioural evidence identifies barriers such as procrastination to be potential determinants for an environmentally-friendly behaviour (Grubb et al., 2009). In this line, McNamara & Grubb (2011) pinpoint that certain determinants can be influenced by the fact that environmental agents such as energy or recycling are abstract, invisible and intangible, which implies to be difficult to quantify them.
Nowadays, behavioural researchers centre efforts on uncovering the determinant factors of an environmentally-friendly behaviour, with the aim of implementing environmental policies from public institutions. But, in world where most of its citizens are not even aware of the dangers that energy-saving could avoid, how those policies could efficiently impact upon population? An interesting insight in search of those determinants is given by Lillemo (2013), who conducts a quantitative inquiry to capture householders’ utility function on energy-saving—a sector which implies a 1/3 of the total energy use in Europe and a saving potential of 27% by 2020 (EEA, 2008; European Commission, 2006).
Lillemo (2013) regresses a vector, x, of factors such as the degree of procrastination and the level of environmental awareness on the utility function of energy-saving, u, of a certain population. Empirical results reveal a statistically-significant relationship between energy-saving’s utility function and (1) procrastination (negative); (2) environmental awareness (positive); (3) the fact of owing a house (positive); (4) other factors such as being young or living alone (negative). Positive (negative) relationships would imply favourable (detrimental) factors for achieving energy-saving from the policy-implementation lens.
The evidence drawn from Lillemo’s (2013) empirical findings implies an interesting linkage between individuals’ temporal motivation function for procrastination and the role of external, affective states which may drive behaviour. Let us reflect on how to address negative effects drawn from those factors of an environmentally-friendly behaviour.
First, following Temporal Motivation Theory, individuals’ utility function of a task is determined by their confidence in succeeding on it, their subjective value of pleasure on this task, their sensitivity to procrastinate and by how fast the reward is received. Following Lillemo (2013), procrastination is negatively correlated with individuals’ motivation for saving energy, which, in the context of Temporal Motivation Theory, would lead to systematically delay the task of energy-saving. Bearing in mind that some consequences of energy-saving such as pollution or environmental disasters are often concerned with future costs/benefits, the fact that people is highly influenced by the immediateness of outcomes makes those benefits to look small from a today’s perspective, implying a considerable factor to accentuate procrastination. Because of those reasons, a feasible proposition for policy-makers might be to bring closer future benefits—for instance, by providing information on their potential saving benefits—and so the immediateness of benefits. This may lead to decrease the time of perceiving energy-saving benefits by individuals and hence achieve to increase the temporal motivational utility of saving energy. Furthermore, let us consider whether this effect could be potentially maximised if some of the information provided also reports fatal consequences that are happening in our world because of connoted effects of excessive energy-spending, such as pollution, greenhouse gas emissions, etc. Framing cost/benefits in a negative, immediate set of events may also influence individuals’ affective states by accentuating individuals’ loss-aversion.
Second, the positive relationship of energy-saving with the environmental awareness drawn from Lillemo’s (2013) findings may imply a tool to fight the abstract, invisible and, hence, difficult-to-quantify nature of the energy. Incidentally, Gaker et al. (2010) find that they can nudge individuals on a research experiment to a sustainable transport behaviour by providing context-specific, personalised information on annual costs and greenhouse emissions of owning a car. As a consequence, information regarding to consume statements influences individuals by making energy and emissions more visible and quantifiable, leading them to be more sensitive to engage them in an environmentally-friendly behaviour.
Third, it can be noticed that other factors such as being young are also significant determinants for energy-saving. In light of such insights, it could be suggested to centre efforts on some of their causal factors such as improving environmental education in schools to increase youngsters’ environmental awareness.
Using the experimental method is insightful. Accurate, scientific information to fight against environmental problems can be an added-value for policy implementations that can make them to succeed. However, is the magnitude of the sample size of experiments such as that of Lillemo (2013) representative enough for making this generalisation efficient? Why are policy-makers constantly in search of improving implementations? Why do many policies seem to not work completely? Is it that we are really failing in energy-failing or is just that we must try to make policies by studying each target environment?