Energy sector responsible for two-thirds of GHG emissions

About two-third of the world’s anthropogenic greenhouse-gas (GHG) emissions is accounted for by the energy production and consumption activities (IEA, 2015). European Commission in its “Energy 2020” strategy document mentions, “Energy efficiency is the most cost-effective way to reduce emissions, improve energy security and competitiveness, make energy consumption more affordable for consumers as well as create employment, including in export industries.” In this context, Energy Efficiency (EE) interventions are not only considered as one of the lowest cost options for saving energy and money but also important instrument in containing GHG emissions (EESI, 2017).

Despite the importance, it is commonly believed that energy consumers do not utilize efficient products and techniques to their full potential in their daily lives. This disconnect between the theoretically available cost-effective EE potential and the actual realized savings, is commonly known as “energy efficiency gap” (Jaffe, 1994). EE gap has been the subject matter of continued debate among scholars, practitioners, and policy-makers since the very beginning of the energy conservation measures initially adopted following the oil crisis in 1970s.

However, the extent, magnitude, and causes of this gap have eluded finality.

On the one side of this debate, technical experts and environmentalists cite market barriers as plausible reasons of EE gaps claiming significant potential for profitable savings using efficient equipment based on technical innovations. In comparison, some of the neoclassical economists and energy practitioners doubt the extent and magnitude of technical gap and explain it mainly on the basis of market failures (Jaccard, 2011). Recently, another group of energy experts and behavioral economists have tried to explain this gap in terms of human behavioral anomalies arising out of heuristic behavior rather than traditional economics principles guided by rational choice theories.

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In doing so, they also bring out modelling errors in the estimation of baselines (Richard G. Newell, 2013)

In what follows, I have tried to explain the apparent contradictions in the nature, causes, and extent of EE gap from different theoretical approaches highlighting the relative strengths and weaknesses. I argue that none of the frameworks in isolation is capable of explaining the energy efficiency gap completely. Instead, there is a need for choosing policy options depending on the situational context with focus on human factors.

Energy efficiency has been described by the World Energy Council as “reduction in the energy used for a given service (heating, lighting etc.) or level of activity” (WEC, 2004). However, this concept is not limited to technical improvements only but also includes the cost of the interventions and overall economic efficiency. (Jaccard, 2011) has explained the theoretical lifetime energy saving potential of different technologies by plotting life-cycle cost per unit of electricity saved against the amount of electricity consumed (Figure-1). It is observed from the figure that the curve rises upwards in steps as more

efficient equipment is brought into consumption. An interesting point to note is that for some of the technologies lying below the horizontal axis, the total lifetime cost appears to be negative suggesting overall gain from purchase and use of that equipment.

Technologists generally explain this gap due to existence of market barriers such as lack of awareness about efficient products and services (information asymmetry), segregation of energy use and equipment costs between the landlord and tenants (split incentives), and lack of enabling institutional and financial resources causing liquidity constraints (capacity constraints). To bridge this efficiency gap, one group of scholars and energy practitioners advocate active government intervention in the market through subsidies, tax-incentives, standards, regulations, and informational campaigns for specific energy systems characterized by a system boundary. In fact, most of the states in the US administer such EE programs in one form or the other and the annual ratepayer funded planned expenditure spending was estimated to be about $6.6 billion in 2010(ACEEE,2016).Further, New Jersey has more than ten such active programs with an annual budget ranging from 0.4 to 0.5 billion(NJCEP,2016).In 2017, New Jersey also enacted a law which requires installation of smart thermostats in all new residential construction(Senate,No.3066).In transportation sector, the Corporate Average Fuel Economy (CAFÉ) regulations enacted by EPA in 1975 are the earliest examples of EE standards. Based on these measures, the program administrator cost of saved electricity for the national portfolio of all programs and related activities in the US between 2009 and 2015 has been estimated at $0.025/kWh in constant 2016 dollars (Ian Hoffman, 2018) which is much lower than the average per unit price of electricity in the US at $10.86/kWh(EIA, 2017) indicating a net societal savings as shown in Figure-1.

However, such claims of energy savings are contested by some economists, who feel that the real “narrow social optimum” potential which can be realized cost-effectively is far lower than the engineering estimates (Figure-2). They argue that estimation of EE savings based on technical potential overstates net benefits (Palmer, 2015). Relying on the concept of economic efficiency, which is estimated using cost- benefit analysis, they argue that engineering estimates of the EE potential tend to overlook hidden administrative and installation costs of efficient products, ignore heterogeneity in the income levels of consumers, and mask the risks associated with future returns. They explain the narrowed EE gap in terms of genuine market failures due to presence of environmental externalities and mismatch between average regulated price and actual marginal cost in the electricity market. As a potential solution, they recommend government intervention limited to correct the market failure by internalizing the environmental costs either in the form of carbon tax or by creating market value to the environmental attributes and setting up market-based framework, such as, cap-and-trade programs which allows for the determination of carbon price by the interplay of demand and supply forces. However, a uniform carbon tax is often not considered politically acceptable. As such, cap-and trade option is often preferred by the policymakers. A recent example of the adoption of such program is the decision of New Jersey state to enter the regulated greenhouse gas initiative (RGGI, pronounced “Reggie”) through executive order no.7, signed in January,2018 which mandates reducing the carbon emissions from fossil fuel based power plants following a specified emission reduction trajectory through levy of an additional surcharge on every ton of carbon emitted from these plants. These measures are considered better in comparison to the carbon tax option but require continuous monitoring and close co-ordination with other clean policies over a fairly large period of time.

Traditionally, EE interventions have focused mainly on technological upgradations and market based interventions, largely overlooking the potential and importance of human factors. However, role of human behavior as a potential instrument in energy conservation programs is being increasingly realized nowadays as “technologies might change rapidly, but people change slowly” (Norman, 2002). Taking the example of programmable thermostats as a technological intervention in conserving energy, research papers have shown no significant savings in households using programmable thermostats when compared with those using non-programmable ones and suggested redesign of these thermostats taking human behavior into consideration (D.Shiller, 2006).

Recently, a group of scholars from diverse backgrounds have come together and looked at the energy efficiency gap from a human behavioral and modelling perspectives. They differ with both the technologists and neo-economists regarding the extent and causes of the gap and argue that human behavior cannot be explained fully in terms of the rational choice theory which assumes that human beings try to maximize their utility function, use all information available, and process the information appropriately to arrive at a conclusion. They explain the gap by using the concept of behavioral anomalies which reasons that deviations from standard assumptions must be systematically biased towards purchase of less energy efficient appliances (Palmer, 2015).While the underlying rationale of this approach appears to be promising, policy options based on these arguments appear to be limited, as of now, due to insufficient data and requires further research.

These experts also differ with others in the calculation and treatment of baselines in the estimation of EE gap citing the concepts of freeriders, spillover, and rebound effects. In a dynamic real life situation, it is difficult to correctly establish the baseline and estimate what would have happened in the absence of specific policy intervention (counterfactual problem). This adds another dimension to the EE gap and being recognized as “energy efficiency measurement gap”. For example, in a ratepayer funded EE program, it is possible that the reductions in energy use are undertaken by participants who would have installed some measures, even in the absence of any program intervention in the form of financial incentives. These participants are called free riders and savings associated with free riders are not truly additional to what would occur otherwise. Closely associated with the concept of freeridership is the concept of spillovers which refers to additional reductions in energy consumption due to program influences beyond program participants (SEEACTION, 2012). Similarly, the rebound effect, which refers to the tendency of the consumers to consume more energy while using higher efficiency equipment can effectively decrease the net EE savings. A common example of rebound effect is associated with extra driving by consumers of efficient cars having higher mileage. These effects are considered important not just for the true estimation of economic efficiency but also from equity implications between the EE program participants and non-participants. These effects are not specific to EE gap but are associated with public funded program in any discipline. However, estimation results of these effects in EE programs have been found to vary significantly defying any logical conclusion. (Khawaja, 2012).

From the analysis of the strengths and weaknesses of different approaches mentioned in the above discussion, it follows that the estimation of energy efficiency gap cannot be done meaningfully by following any one approach. However, the recent recognition and emphasis on human behavioral elements as central point in the overall energy cycle appears to be a welcome and positive step. Incidentally, these three technical, rational, and human behavioral based approaches to estimate the energy efficiency gap remind me of the Aristotelian concept of techne, episteme, and phronesis in the context of their discussion in the relevance of social science study (Flyvbjerg, 2001). While the analogy might not be complete but I find the comparison useful and relevant in likening the phronetic concept of contextual practical wisdom relevant to the current approach focusing on human factors taking measurement errors into account using randomized control trial methods. I am also of the opinion that present day issues and challenges associated with energy policies are difficult and complex. As such, they require innovative and multi-disciplinary approaches which brings together scholars and practitioners from different fields and professions. As such, I suggest that there is a need for taking a holistic approach which accounts for and reconciles the apparently divergent and conflicting relative strengths and weaknesses of the different approaches. Such a paradigm shift might appear complex, impractical, ambitious at the moment, and might not resolve the debate at once, but will definitely be a step in the right direction towards the bridge across the energy efficiency gap.