







































Ecology, Economy and Society–the INSEE Journal 5 (1): 63-87 January 2022 

THEMATIC ESSAY 

Technological Innovations, Behavioural Interventions, 
and Household Energy Conservation: Policy Insights 
and Lessons 

Chandra Sekhar Bahinipati, Rahul A Sirohi, Sagarika S Rao 

Abstract: The threats associated with climate change and the worsening ecological 
crisis have led to a growing interest in energy-conservation policies. Policymakers 
across the globe have been scrambling to find cost-effective and sustainable 
methods to meet the world’s growing energy demands. Traditional policies have so 
far largely focused on supply-side interventions aimed at encouraging energy 
efficiency via green innovation and new technologies. However, as several studies 
have indicated, supply-side policies alone are unlikely to be adequate to achieve the 
ambitious changes required to make our future sustainable. This review article 
draws on recent studies in behavioural economics to emphasize the need to 
reorient public policy towards altering consumer end-uses through behavioural 
interventions. In an attempt to draw out important lessons for public policy, the 
article reviews this emerging strand of literature and underlines the complex factors 
that influence energy consumption in a household. Although preceding studies 
have primarily focused on developed nations, the output of these studies could 
guide policymakers in developing and emerging market economies as well.  

Keywords: Technological Innovations; Behavioural Interventions; Energy 
Conservation; Household Decision-Making; Public Policy  

 
 Department of Humanities and Social Sciences, Indian Institute of Technology Tirupati, 
Yerpedu, 517619, India, email: csbahinipati@iittp.ac.in.  

 Department of Humanities and Social Sciences, Indian Institute of Technology Tirupati, 
Yerpedu, 517619, India. rahul.sirohi@iittp.ac.in  

Taru Development Initiative Foundation (TDIF), New Delhi, 110020, India. 
sagarika.srinivas.18@gmail.com  

Copyright © Bahinipati, Sirohi, and Rao 2022. Released under Creative Commons 
Attribution © NonCommercial 4.0 International licence (CC BY-NC 4.0) by the author.  

Published by Indian Society for Ecological Economics (INSEE), c/o Institute of Economic 
Growth, University Enclave, North Campus, Delhi 110007.  

ISSN: 2581-6152 (print); 2581-6101 (web).  

DOI: https://doi.org/10.37773/ees.v5i1.530  

mailto:csbahinipati@iittp.ac.in
mailto:rahul.sirohi@iittp.ac.in
mailto:sagarika.srinivas.18@gmail.com
https://doi.org/10.37773/ees.v5i1.530


Ecology, Economy and Society–the INSEE Journal [64] 

 

1. INTRODUCTION 

The evolution and progress of human civilization have been predicated on 
its ability to manipulate and use energy. The discovery of fire almost two 
million years ago gave humans leverage over their environment; it allowed 
them to not only have better nutrition, a greater variety of food, and longer 
life spans, but it also enabled them to migrate and adapt to a variety of 
climatic conditions and perform social tasks, which was crucial for their 
biological and social evolution (Pausas and Keeley 2009). Similarly, the 
large-scale use of fossil fuels in the nineteenth century allowed pre-
industrial economies in Europe and the United Kingdom to evolve into 
economic superpowers (Wrigley 2013). 

The significance of energy for economics was recognized early on by 
classical political economists who emphasized the constraints imposed on 
economies by natural conditions. Notably, Ricardo (1817) highlighted the 
constraining effect of land supply on growth when he suggested that 
growth cannot be sustained infinitely in the long run due to the diminishing 
returns of capital and labour on a fixed supply of land. Similarly, his 
contemporary, Malthus, famously suggested that there exists a fundamental 
mismatch between the rate of human reproduction and the world’s natural 
resource base (Wrigley 2013). However, the relationship between economic 
development and energy use turned out to be far more complex than what 
classical economists initially predicted. It was the “discovery” of fossil fuels 
that dramatically changed the nature of the growth–energy nexus (Malm 
2016). While “organic economies” that predated the Industrial Revolution 
primarily derived their energy requirements from “plant photosynthesis”—
which was too slow and inefficient to really ignite long-term growth—the 
Industrial Revolution in Europe emerged in parallel with the radical shift to 
fossil fuels (Wrigley 2013, 1–10).  

The relationship between development and energy consumption has always 
been complex. Ever since the Industrial Revolution, the world has 
witnessed ever increasing rates of energy use (Vance et al. 2015; Pablo–
Romero and De Jesús 2016; Malm 2016). Between 1965 and 2012, it is 
estimated that global energy consumption grew by well over 200% (IEA 
2013). The share of electricity in this estimate, which was recorded to be 
19% in 2017, is projected to increase from 20.3% in 2025 to 23.7% in 2040 
(IEA 2018). Indeed, even among contemporary developing economies like 
China and India, which have grown at a tremendous pace over the last few 
decades—a ravaging hunger for energy has characterized their respective 
development trajectories (IEA 2020). Given the high estimated demand, it 
is clear that not only does future growth depend on the ready supply of 
cheap energy but that the world will face dire environmental consequences 



[65] Chandra Sekhar Bahinipati, Rahul A Sirohi, Sagarika S Rao 
 

from the accelerating use and demand for energy. There is considerable 
evidence linking growing energy consumption to dangerously high 
greenhouse gas (GHGs) emissions and the ongoing challenges posed by 
climate change (Malm 2016; Pablo–Romero and De Jesús 2016). The 
energy sector is a major contributor of GHGs as more than half of the 
demand is met through fossil fuels. This renders its total share of 
anthropogenic GHG emissions 35% as of 2010 (Bruckner et al. 2014).  

In consideration of this, managing energy consumption and supply has 
garnered notable importance within national and international 
policymaking. It is being stressed that nothing less than a paradigm shift in 
terms of production and consumption will be adequate to reduce the global 
energy footprint (Fanghella et al. 2019). In this regard, growing importance 
has been placed on increasing energy efficiency in extraction and 
conversion, shifting towards non-fossil-fuel-based energy (e.g., renewable, 
nuclear, etc.), and so forth. From the supply angle, the challenge has been 
to find contemporary and economical methods to meet the current and 
foreseeable growth in energy demand. The Paris Agreement (2015) was 
signed by several developed and developing countries, indicating that they 
are finally taking climate change seriously. There has also been a heightened 
emphasis on finding alternative, clean technologies for meeting energy 
requirements. Goal 7 (Affordable and Clean Energy) of the Sustainable 
Development Goals (SDGs) is committed solely to issues related to energy. 
It is worth noting that this goal mainly focuses on access to renewable 
energy and energy efficiency (UNEP n.d.). 

Technology is given utmost importance in the context of energy 
efficiency—this stems from the assumption that technological interventions 
can mitigate environmental damage by reducing the quantity of energy 
required for output production and by increasing the supply of cleaner 
sources of energy (Foster 2001). However, what is often overlooked is that 
the availability of cheap and efficient technologies does not automatically 
result in their adoption by end-users. There is no apparent reason for 
entrepreneurs and consumers to choose energy-efficient technologies and 
appliances just because they are available. In fact, actual adoption patterns 
and consumer choices are governed by complex social, cultural, and 
psychological processes. Moreover, as numerous studies have shown, the 
availability of efficient technologies can generate complex feedback loops, 
which may inversely encourage rather than reduce energy consumption 
(Polimeni and Polimeni 2006; Sorrell 2009). From the perspective of public 
policy, this implies that apart from supply-side interventions, influencing 
demand-side determinants of energy efficiency is crucial for managing 
energy use (Parrish et al. 2020). Typically, demand can either be influenced 



Ecology, Economy and Society–the INSEE Journal [66] 

 

by non-price mechanisms or by various price-based incentives such as 
taxes, subsidies, etc. (Allcott 2011a; 2011b). Previous studies on the effect 
of monetary incentives on consumption have proved to be inconclusive 
(Fishman et al. 2016) or even negative (Sudarshan 2017; Frederiks et al. 
2015). Price-based incentives are also much costlier to implement than 
behavioural nudges (Allcott 2011a; 2011b). More importantly, the adoption 
behaviours of end-users may not always be driven by the calculus of gains 
and losses but are instead likely to be shaped by informal institutions, 
traditions, and past habits and are often shaped by boundedly rational 
conditions. As a result, standard price-based incentives may not always 
work as planned or may require complementary non-price interventions 
(Thaler and Sunstein 2009). 

Recent literature on behavioural economics suggests that “nudging” 
producers and consumers by providing them with information about energy 
costs, environmental externalities, and other relevant information could 
have significant implications on their choices. It is in this context that this 
review paper seeks to centre consumer end-use as an important locus of 
public action. In contrast to the conventional one-sided emphasis on 
supply-side interventions, it highlights the growing significance and 
effectiveness of behavioural interventions in altering the energy choices of 
households. Although a vast majority of the existing literature focuses on 
developed nations (Abrahamse et al. 2005; Andor and Fels 2018; Brandon et 
al. 2017), the outcomes of these studies are likely to provide policy lessons 
for developing nations and emerging markets as well, especially considering 
that policymakers have been making concerted efforts at tackling their 
growing energy footprints. 

Taking into consideration the increasing importance of energy end-use, and 
the consequent pledge made by several governments during the Paris 
Climate Convention to reduce their energy footprints, a careful analysis of 
non–price interventions is extremely important. It is with this intention that 
the review article is structured as follows. The section following the 
introduction examines the limits of supply-side interventions aimed at 
efficiency enhancement through technological upgradation, and the third 
summarizes the findings from different empirical papers that adopted non-
price-based behavioural interventions to influence the adoption of energy-
efficient technologies. The fourth section provides the discussion and 
policy lessons, while the final segment concludes the review.  

 

 



[67] Chandra Sekhar Bahinipati, Rahul A Sirohi, Sagarika S Rao 
 

2. LIMITS OF SUPPLY-SIDE INTERVENTIONS: BEYOND 
TECHNOLOGICAL FIXES 

The risks associated with rising energy consumption have been widely 
noted in the scientific community; however, there has also been a tendency 
to consider the related issues—climate change, GHG emissions, etc.—as 
primarily engineering tasks, i.e., a set of problems that can be solved 
through technological improvements alone (Foster 2001; Brookes 1990; 
Geels et al. 2017). While it is true that by reducing energy input per unit 
output and generating energy through clean, renewable sources, 
technological innovations can play a key role in meeting the growing energy 
demands of the world economy sustainably, we argue that an exclusive 
focus on technological fixes is inadequate. For one, despite the consistent 
improvement in technology over the last several decades, energy demands 
have shown no signs of decline. Based on this, Vance et al. (2015) estimate 
that efficiency increases alone may not effectively influence the energy 
demand–supply matrix, since energy efficiency would have to grow at 
historically unprecedented proportions in the coming years to meet the 
growing needs of the global population. Further, even if efficiency 
successfully improved to that magnitude, there is no guarantee that it would 
result in a reduction in the rate of energy consumption. For although 
technological improvements may result in the use of fewer inputs for a 
given level of output, this change may also generate complex backward and 
forward reactions that may spur producers to use more energy or 
consumers to increase their demand of energy-intensive goods (Foster 
2001). In the long run, the incentives of economies of scale may push 
producers to increase production of the said good, and, consequently, 
demand greater amounts of energy (Brookes 1990). If this increase in 
production is accompanied by declining prices, it may give rise to additional 
pressures on energy use as a direct result of rising consumer demand as 
well.  

The paradoxical effects of efficiency improvements were noted in the late 
nineteenth century itself, when a pioneer of neoclassical economics, William 
Stanley Jevons, suggested that technological improvements may increase 
energy consumption rather than decrease it. He asserted that the 
cheapening effect these technologies have on the final product could 
unintentionally lead to higher consumer and producer demand than before 
(Jevons 1906 [1865]). Though originally propounded by Jevons to explain 
how steam technology led to increased consumption of coal, this paradox 
has been the subject of a large body of contemporary literature that 
underlines the very same unintended consequences of efficiency on energy 
demand. Several studies have emphasized these rebound effects in the 



Ecology, Economy and Society–the INSEE Journal [68] 

 

context of both micro-level and macro-level data (Polimeni and Polimeni 
2006; Aydin et al. 2017; Fowlie et al. 2018). To expound, Davis et al. (2014) 
conducted a study on the cash-for-coolers subsidy that was rolled out by 
the Mexican government to enable households to replace old air 
conditioners and refrigerators with newer, more efficient models. They 
reported that in some cases, energy usage increased as a result of the 
subsidy. The authors also found that the additional features included in the 
new appliance models may have driven consumers to use them more “than 
one would have expected based on the pure price response” (Davis et al. 
2014, 229). In the Netherlands, Aydin et al. (2017) found that the rebound 
effect for energy efficiency is around a third for homeowners and around 
two-fifths for tenants and is reported to be higher for lower wealth 
quantiles; further, they note a positive relation between the rebound effect 
and wealth. Newman and Kenworthy (2000), similarly, point out how the 
expansion of roads and freeways, often justified as a means of reducing 
traffic congestion, may, in reality, incentivize more automobile use than 
predicted, thus paradoxically contributing to more traffic congestion.  

In a slightly different setting, studies have noted how the increasing share of 
renewable sources may have complex effects on the price of electricity, 
which could in turn increase the overall household demand for energy, 
leading to a reduction in potential environmental savings. Velez–Henao et 
al. (2020) refer to this as the environmental rebound effect. More precisely, 
in an attempt to diversify the energy system, the Colombian government 
adopted a policy promoting the use of wind, solar, and other sources of 
renewable power. The empirical analysis by Velez–Henao et al. (2020) 
shows the environmental rebound effect offsetting the potential 
environmental saving. In a similar context, Bahinipati and Viswanathan 
(2019a) observe that large-scale adoption of irrigation-efficient technologies 
may not reduce groundwater extraction in water-stressed regions in Gujarat, 
India. 

It is worth noting that although rebound effects are crucial determinants of 
energy savings, some studies have documented relatively modest energy 
savings in contexts where there are no rebound effects as well. For 
example, evaluating a nationwide energy-efficiency programme, Fowlie et al. 
(2018) studied a sample of 30,000 households in the US and reported that 
energy savings tend to be much lesser than the investment costs associated 
with energy efficiency. Estimates also suggest that actual energy savings are 
significantly lesser than the predicted values. Although indicative of a 
rebound effect, the authors find no strong evidence for the hypothesis. 
However, it must be noted that even in such cases, the central distinction 
between technological innovation and ultimate adoption remains crucial. 



[69] Chandra Sekhar Bahinipati, Rahul A Sirohi, Sagarika S Rao 
 

Even if technological improvements are made at the desired rates—and we 
assume away the complex rebound effects that they may give rise to—an 
equally important issue is the challenge of getting producers and consumers 
to adopt green and efficient technologies. The mere existence of efficient 
technologies does not mean that they are diffused and put into use by 
default, especially in decentralized market economies.  

To put things into perspective, technology adoption is usually described in 
rather simple terms in the standard economic literature. One of the central 
tenets of traditional neoclassical economics is that monetary incentives are 
crucial determinants of consumer and producer behaviour. Based on the 
assumption that the typical economic agent is a “lightning calculator of 
pleasures and pains”, the traditional, textbook model of choice tells us that 
economic agents, be it consumers or entrepreneurs, make choices by 
balancing their costs and benefits and ultimately only use those alternatives 
that maximize their net rewards (Veblen 1898, 389). Given that prices are 
crucial to these calculations, changes in price can have powerful effects on 
what actions people take. For example, as natural resources diminish and 
their prices increase, standard models tell us that economic agents are likely 
to shift towards technologies that use these inputs less intensively 
(Velthuijsen and Worrell 2002). Thus, it has been argued that the 
introduction and diffusion of the steam engine in eighteenth-century Britain 
were largely driven by the fact that not only were labour costs high, but coal 
and capital costs were low, incentivizing firms to switch towards labour-
saving, energy-intensive technologies (Allen 2012). Or as put by Allen 
(2012, 23), “People respond to price incentives. The timing of the shift to 
coal, and the invention of technologies to expand its use, reflected the 
prices of coal, labour, and capital.”  

In a more contemporary example, Davis et al. (2014) have noted how 
incentives for large-scale appliance replacement motivated around 1.9 
million households in Mexico to exchange old refrigerators and air 
conditioners between 2009 and 2012. Observing incentivizing programmes 
in India, Bahinipati and Viswanathan (2019b) observe that a pecuniary 
benefit, along with the removal of the ban on groundwater extraction, has 
played a major role in the diffusion of micro-irrigation technologies in 
water-scarce regions in Gujarat. Studies have also noted the importance of 
Pigouvian taxes to correct the market distortion caused by environmental 
externalities. Previous studies have suggested imposing a carbon tax based 
on the social cost of carbon to generate revenue and reduce GHG 
emissions (Paul et al. 2013). Obviously, following the demand law, raising 
electricity prices could reduce household demand, with the size effect 



Ecology, Economy and Society–the INSEE Journal [70] 

 

varying according to elasticity. Paul et al. (2013) present evidence of 
changes in the electricity demand and the technology mix due to the carbon 
tax in the US. In the context of the European Union and the United 
Kingdom, Platchkov and Pollitt (2011) observe the role of price in driving 
energy demand, i.e., lesser demand is associated with a high price, including 
tax. Regarding India, Filippini and Pachauri (2004) calculated that price 
elasticity varies between –0.29 to –0.51, indicating a decline in the energy 
use of urban households, approximately 0.3–0.5%, with a 1% increase in 
the price index.   

Given the widespread prevalence of the view that choice is determined by 
efficiency concerns alone, which underplays the distinction between 
technological innovation and diffusion, it is not surprising that much of the 
policymaking discussions on mitigating climate change and conserving 
energy use focus on technology while missing broader, psychological, 
cultural, and social structures that determine the use and adoption of such 
technologies in the first place (Foster 2001; Geels et al. 2017). However, as 
several studies indicate, the issue of adoption is far more complex than it is 
made out to be, and decentralized decision-making does not always lead to 
superior, more efficient technologies being adopted, even if they are 
available in the agents’ choice set.  

In the context of eighteenth- and nineteenth-century Britain for instance, 
Malm (2012; 2016) notes that the adoption of the coal-powered steam 
engine over alternative energy sources like water was not driven by a loss–
benefit calculus alone, but also by the desire to bring workers under their 
control. Or as Malm (2012, 113) puts it, “As a prime mover based on a 
fossil fuel, rather than on water flowing through particular spots determined 
by the contours of landscapes, it granted capital the power to search out the 
cheapest and most disciplined labour.” To take another classic example—it 
is well recognized that mass transit in the US is far weaker than private 
transport, partly due to the influence of powerful “automobile–oil–rubber” 
lobbies (Whitt and Yago 1985, 37–65; Newman and Kenworthy 2000, 15–
25). More than an individual choice based on cost and benefit alone, the 
diffusion of the automobile was a result of hidden state subsidies, 
enormous volumes of cheap credit, provision of roadways, and an entire 
cultural industry built around automobile consumption, all of which played 
a pivotal role in building up mass demand for automobiles instead of public 

 
 The results of these studies must be considered in light of their contexts, because, as Chen 
(2017) notes in a study based in Taiwan, the demand curve for energy consumption could be 
inelastic. Thus, in the complete absence of substitution options, electricity prices play an 
insignificant role in household decision-making. 



[71] Chandra Sekhar Bahinipati, Rahul A Sirohi, Sagarika S Rao 
 

transport (Mattioli et al. 2020). Similarly, historical institutionalists and new 
institutionalists who emphasize the importance of path dependence suggest 
that technological diffusion is often dictated by past historical events. Once 
technologies are chosen, even when feasible and more efficient alternatives 
exist, the choice tends to be self-perpetuating, creating inertia towards the 
status quo (Puffert 2008). More recently, randomized experiments have 
been used to analyze technology adoption practices, and studies have found 
evidence of organizational and cognitive barriers that may prevent optimal 
technology adoption practices in the context of developing countries 
(Hanna et al. 2014). Hanna et al. (2014), for example, studied the technology 
choices of Indonesian seaweed farmers. The study found that while farmers 
are generally aware of important aspects of the farming process, a large 
proportion failed to notice the importance of pod size in determining 
output, despite high rates of literacy and long years of farming experience.  

Inversely put, efficiency need not always dictate the choices that agents 
make, especially in uncertain and complex systems. This is as true of 
consumer choices as it is of those made by producers. A growing literature 
has emerged on the so-called “energy paradox” or “efficiency gap”, which 
points to the non-adoption of feasible and affordable energy-conserving 
technologies and appliances by consumers (Sallee 2014; Allcott et al. 2014, 
72–88; Allcott and Greenstone 2012, 3–28). Studies suggest that consumers’ 
reluctance to choose energy-saving options, despite the massive savings that 
could accrue in the long run, stems from complex behavioural factors that 
underlie how people make decisions when faced with informational 
asymmetries of various kinds and fundamental uncertainties (Alcott and 
Mullainathan 2010). While the hard sciences are useful to develop 
technologies, it is observed that cost-effective behavioural interventions 
drive households to save energy (Alcott and Mullainathan 2010). Given that 
technological fixes alone may not provide a way out of the current 
conundrum facing the global economy—and given that a large proportion 
of the energy demand emanates from the residential sector—this line of 
analysis stresses the need to find ways to reduce energy consumption 
directly by influencing the demand-side determinants of technology 
adoption (Parrish et al. 2020). It emphasizes the need to understand the 
non-monetary determinants of end-use, which it argues can be as powerful 
a tool to alter energy consumption as any technological fix; it is to these 
studies that we turn in the next section.   



Ecology, Economy and Society–the INSEE Journal [72] 

 

3. NON-PRICE BASED INTERVENTIONS: INSIGHTS FROM 
BEHAVIOURAL ECONOMICS  

Behavioural economics incorporates insights from psychology into the 
study of economic decision-making. It is rooted in standard neoclassical 
economics in the sense that the analysis begins with the individual, but it 
seeks to make these models more realistic by relaxing their stringent 
assumptions about human behaviour (Kahneman 2003; Thaler and 
Sunstein 2009). In particular, this strand of literature shows that in the 
context of limited information, uncertainties, and risks, people take actions 
that are not always optimal. More importantly, their behaviour is not merely 
a pathology that is to be assigned to the margins of theory but is a 
persistent and repeated response to the highly complex situations they are 
faced with. Despite generally being brushed off by standard models, this 
behaviour can constitute the predictable, albeit irrational, response of 
human beings to the uncertainties that they face. These modes of behaviour 
can be explained by applying psychological theories on human behaviour to 
the economics of decision-making. In the following paragraphs, we outline 
the findings of papers that aim to investigate the effectiveness of nudge-like 
interventions in bringing down energy use at the household level. The paper 
surveys studies that investigate how households can be induced to reduce 
their energy use, either by encouraging them to cut down their energy use in 
absolute terms or by persuading them to make investments in energy-
efficient technologies. Inducing households to do either requires targeted 
policies, and the following studies note the role of behavioural interventions 
in promoting these measures (Andor and Fels 2018). 

In this context, recent literature on household decision-making suggests 
that people repeatedly underestimate the energy savings that they can make 
from adopting efficient or clean technologies, which results in “the low 
adoption of energy-efficient technologies despite potentially large saving” 
(Alcott and Taubinsky 2013, 2). Economists have started to stress on how 
behavioural factors can act as barriers to optimal decision-making. It has 
been pointed out that to calculate the costs and benefits of alternatives, a 
consumer requires a certain level of literacy and a familiarity with numeracy 
(Blasch et al. 2019). Energy labelling and star ratings are two mechanisms 
that help increase energy literacy levels, apart from conducting programmes 
and implementing non-price-based policies (Jain et al. 2018a; 2018b). 
Further, even in cases where cognitive barriers are not significant, it has 
been argued that consumers and producers simply do not make use of the 
available information and thus underestimate the hidden costs of their 
actions (Kahneman 2003; Allcott and Greenstone 2012). This is what Sallee 
(2014) refers to as “inattention bias”, which she defines as the unwillingness 



[73] Chandra Sekhar Bahinipati, Rahul A Sirohi, Sagarika S Rao 
 

of decision-makers to find and incorporate relevant information while 
making choices due to the perception that the cost of such an exercise far 
outweighs its benefits. This is particularly true in the case of inter-temporal 
choices, where agents are required to make decisions that not only impact 
them in the present but across several other periods as well.  

In a slightly different context, Leard (2018) uses a household-level survey to 
show how inattention among automobile buyers leads them to undervalue 
fuel costs. His results demonstrated how estimates of savings vary between 
attentive and inattentive buyers. Even when choices are less complex in 
terms of temporality, the sheer abundance of choices often pushes 
consumers to make decisions based on rules of thumb, heuristics, and past 
habits, all of which make them less likely to change their consumption 
patterns even when such shifts may be beneficial (Abadie and Gay 2006). 
Such inertia, or what has been called a “status quo bias”, may arise when 
consumers deem search costs to be high when their choices are shaped not 
only by economic factors such as prices and income but also by existing 
endowments or when faced with considerable uncertainty with regards to 
future demand/price (Kahneman et al. 1991, 193–206). Thus, previous 
studies have uncovered a persistent tendency on the part of residential 
consumers to stick to incumbent electricity providers, leading to greater 
concentration in markets that could potentially hurt consumer welfare 
(Brennan 2007). In a study of Swiss households, Alberini et al. (2013) find 
that price-related uncertainty tends to make families stick to the status quo 
when it comes to decisions regarding investments in energy-saving home 
renovations.  

In summation, due to certain psychological biases and limitations to 
cognitive resources, agents often fail to make choices predicted by utility 
maximization. These behavioural factors also affect energy consumption 
patterns either directly or indirectly. To put things in perspective, traditional 
literature has a lot to say on market failures and their effects on consumer 
welfare, but it has been relatively silent on failures stemming from 
consumer behaviour itself. Behavioural economics, instead, stresses how 
individuals—driven by complex psychological dynamics—fail to take 
optimal actions. This issue can be explicated by comparing it to the 
established idea of externalities. Traditionally, externalities lead to a non-
coincidence between privately optimal actions and socially optimal ones, 
arising from the external costs/benefits that agents impose on one another. 
Behavioural economics, however, points us towards the possibility that 
agents may impose costs upon themselves by acting in ways that do not 
correspond with their long-term interests. All of this has crucial 
implications for public policy. If psychological factors affect consumer 



Ecology, Economy and Society–the INSEE Journal [74] 

 

choices and behaviours, standard monetary incentives and punishments 
may not be enough to control under-investment in energy-saving 
appliances. If at all, when such incentives are applied without heeding 
underlying consumer preferences, there is a chance that it will backfire. As 
has been argued by Frey and Jegen (2001, 589–611) and others, external 
motivations of this sort may “crowd out” intrinsic motivations, including 
pro-environmental ones (Frederiks et al. 2015, 1383–1394). Under these 
conditions, it becomes incumbent for policymakers to consider the 
psychological dimensions of consumer behaviour while developing energy-
related policies.  

Generally speaking, behavioural policy interventions can take on myriad 
forms. Unlike price-based instruments, however, these interventions seek to 
influence consumer preferences directly or indirectly through what are 
called “nudges”. These usually involve a comprehensive set of interventions 
ranging from providing energy-saving information, goal-setting, and a peer 
comparison model of energy consumption, which allows households to 
compare their consumption with their neighbours (Sudarshan 2017, 320–
335). While the distinction between nudges and traditional price-based 
instruments is often blurred, the idea behind nudges is derived from the 
notion of “libertarian paternalism”, i.e., the idea that interventions be 
designed to not overtly constrain consumer sovereignty and gently push 
them to adopt privately optimal alternatives (Hansen 2016, 155–174). They 
are, in Galle’s (2014, 839) words, “innocuous little speed bumps” that 
decision-makers can opt out of at minimal cost. More generally speaking, 
Hansen (2016, 158) states that  

A nudge is a function of (I) any attempt at influencing people’s judgement, 
choice or behaviour in a predictable way that is motivated because of 
cognitive boundaries, biases, routines, and habits in individual and social 
decision-making posing barriers for people to perform rationally in their 
own declared self-interests and which (2) works by making use of those 
boundaries, biases, routines, and habits as integral parts of such attempts. 

Nudging, like behavioural interventions, is considered to be an inexpensive 
approach to altering energy use (Allcott and Mullainathan 2010; Allcott et al. 
2014; Brandon et al. 2017). Based on 38 natural field experiment studies, 
Brandon et al. (2017) concluded that behavioural interventions reduce 
energy consumption by 35–55% even after the end of treatment. Another 
study by the European Environment Agency (2013) summarized that 
various behavioural interventions (e.g., feedback, target-setting, etc.) could 
save energy up to 20%. 



[75] Chandra Sekhar Bahinipati, Rahul A Sirohi, Sagarika S Rao 
 

Often, providing consumers with relevant information regarding energy use 
through easy and relatively cost-effective means is an effective method of 
nudging them towards energy conservation (Andor et al. 2020; Brülisauer et 
al. 2020; McCalley and Midden 2002; Tyagi et al. 2020). In a field 
experiment involving graduate students at the National University of 
Singapore, Brülisauer et al. (2020, 111742) tested three treatments: (i) 
sharing information on energy use by air conditioners relative to total use, 
(ii) sharing information on the neighbour’s electricity consumption, (iii) 
sharing information on three unknown residents’ electricity consumption. 
The study reported that providing appliance-specific feedback reduces the 
level of consumption—i.e., the electricity consumption by air conditioners 
reduced by 17%. Similarly, Andor et al. (2020) conducted a field experiment 
in Germany, wherein the sample households were asked to install 
appliance-specific smart meters. The results suggest that electricity 
consumption was reduced by 5%, and a further reduction was observed 
during peak hours, of approximately 10–15%. Such a reduction could be 
augmented if appliance-level feedback is provided—the study estimates that 
such an intervention could potentially generate around €570–600 million 
per annum in consumer surplus for households in Germany (Andor 2020). 
In a similar context, several studies have shown how energy labels allow 
consumers to easily process information and make informed choices by 
taking into consideration the energy efficiency of household appliance 
models (Sammer and Wüstenhagen 2006; Shen and Saijo 2009; Jain et al. 
2018a; 2018b). 

Altering intrinsic preferences can, as studies have shown, serve as a 
powerful mechanism to reduce energy demand. However, individual 
choices are made in social contexts. Institutional scaffolding, social 
practices and mores, kinship and familial ties, broader class conflicts, and 
state interventions can shape the dynamics of how individuals make 
choices. Recognizing the social contexts within which individuals act is 
important as it widens our understanding of the forces that drive energy 
transitions and thus broadens the scope of policy interventions aimed at 
energy transitions (Stephenson et al. 2015; Klaniecki et al. 2020; Geels et al. 
2017). Behavioural economists, while being rooted in methodological 
individualism, partially recognize the social nature of decision-making and 
emphasize how “behavioural choices by individual agents (as well as their 
objective functions) can (either positively or negatively) be affected by other 
players’ preferences, material well-being, intentions and/or behavioural 
choices” (Zarri 2010, 563). In this context, a widely commented upon 
nudge involves the use of social norms in the form of peer comparisons. 
OPOWER’s experiment with home energy reports (HERs) is one example 



Ecology, Economy and Society–the INSEE Journal [76] 

 

of how non-price-based interventions can positively reinforce energy 
savings by nudging consumers (Allcott 2011a; 2011b). To change 
households’ use of electricity, utility companies in the US recruited 
OPOWER to inform consumers regarding their energy usage. Electricity 
bills along with HERs were sent out to 600,000 households as a 
randomized control trial. Each HER had two major components: a peer-
comparison module and an action-step module. The reports were sent to 
the treatment groups at varying frequencies ranging from monthly to 
quarterly, and their energy usage was compared with control groups. The 
results uncovered by Allcott (2011b) suggest that the effect of HERs was 
“equivalent to an 11–20% short-run price increase or a 5% long-run price 
increase” (Allcott 2011b, 1083). Moreover, Allcott and Rogers (2014) 
emphasized that the OPOWER intervention could result in long-term 
changes in energy-related decisions when treatments are repeatedly applied. 
Specifically, energy consumption declined immediately after HERs were 
received, but it then slid back to pre-treatment levels. However, in the long 
run, when “the intervention is repeated, people gradually develop a new 
‘capital stock’ that generates persistent changes in outcomes” (Allcott and 
Rogers 2014, 3005). This capital stock, Allcott and Rogers (2014) note, 
includes energy-saving appliances that consumers invest in but also new 
habits that consumers imbibe and incorporate into their decision-making 
process.  

Similarly, Brandon et al. (2019) evaluated the effectiveness of two nudges 
implemented on 42,100 households in Southern California by OPOWER—
which involved the provision of peak energy reports (PERs) and HERs to 
households. All households were randomly sorted into four mutually 
exclusive groups and received one out of four options—i.e., only HER, 
only PER, both HER and PER, and no correspondence (control group). 
While the “only PER” group reduced energy consumption by 2–4%, 
consumption plummeted by 7% when both PER and HER were 
implemented together (Brandon et al. 2019, 5293–5298). Corresponding 
observations were reported by papers published in the 1980s. Midden et al. 
(1983), for instance, reported that providing the information alone is 
ineffective and that comparative feedback is more effective in reducing 
electricity consumption than individual feedback. Likewise, Hutton et al. 
(1986) found that households in the US and Canada reduced consumption 
by 4–5% when they received information along with feedback.  



[77] Chandra Sekhar Bahinipati, Rahul A Sirohi, Sagarika S Rao 
 

4. DISCUSSION AND POLICY IMPLICATIONS 

The primary aim of this review is to underscore the importance of 
behavioural interventions as an alternative tool for encouraging household 
energy conservation. It has been established that the residential sector plays 
an important role in meeting energy-related goals; yet, studies show that 
consumers may not make sustainable choices of their own accord even 
when such alternatives are economically beneficial to them. In this regard, 
studies indicate the importance of reshaping how people make decisions. A 
series of studies that have emerged over the last few years suggest that 
public policies aimed at altering energy consumption are likely to be 
effective if they leverage the behavioural dimensions of decision-making. 
That is, if they directly alter how people make choices—“nudging” 
consumers into making sustainable choices. While much of this emerging 
body of literature has been primarily focused on developed nations, these 
studies hold important lessons for developing nations as well. In this regard, 
three issues, in particular, are worth emphasizing.  

First, although energy-conservation goals require new technologies and 
policies to scale up efficiency, several studies have found that technological 
fixes alone may be insufficient. For one, a mere improvement in energy 
efficiency may increase rather than decrease demand due to complex 
rebound effects. Moreover, the mere availability of better technologies is 
not likely to result in their widespread adoption due to behavioural factors 
at the consumers’ end. It follows, therefore, that any policy that aims at 
mitigating the negative effects of growing energy consumption must 
emphasize the importance of directly altering consumer end-use as well.  

Second, if consumer end-use is to be directly influenced, traditional 
monetary incentives such as taxes, subsidies, and so on may be of limited 
use as stand-alone tools. Not only are policies such as Pigouvian taxes likely 
to face implementation difficulties from a political point of view, but the 
carrot–stick approach may fail to leverage important dimensions of human 
behaviour that could be directed towards pro-environmental ends. 
Pecuniary incentives, on the contrary, end up having complex feedback 
effects and may crowd out potential pro-social behaviours of households. 
In such contexts, “nudge”-like interventions such as goal-setting, 
commitment, information, peer-group comparison, appliance-specific 
information, and so on, may prove to be potent measures to alter energy 
use. There is considerable evidence of their effectiveness in recent studies 
(e.g., Allcott 2011b; Brandon et al. 2019).  

Third, while individual behaviours provide an important site of action for 
policymakers seeking to reduce energy use, there are reasons to want to 



Ecology, Economy and Society–the INSEE Journal [78] 

 

move beyond the epistemic focus on individuals and broaden the scope of 
analysis. Individual decisions regarding energy use are deeply embedded in 
societal-level processes. 

Individuals do not seek to ‘consume’ energy for its own ends, but rely on it 
to facilitate everyday practices such as commuting to work, being 
comfortable at home, or laundering clothes. These energy-using practices 
have become embedded in contemporary social life, and rely on complex 
and embedded infrastructures such as national road networks, the domestic 
building stock and national electricity grids. (Hampton and Adams 2018, 
215). 

Previous studies, have, therefore, emphasized the multi-level determinants 
of energy transitions and how altering energy use requires broad-based 
changes in “energy cultures”, infrastructure, markets, and institutions in 
addition to changes in user behaviour (Stephenson et al. 2015, 117–123; 
Geels et al. 2017, 463–479; Klaniecki et al. 2020, 111092). Moreover, 
transitions in energy use are likely to have contradictory effects on 
stakeholders and are thus likely to be deeply conflictual processes, which 
unless managed, could have consequences for long-term energy usage 
patterns (Siciliano et al. 2018). Behavioural economics provides an 
interesting vantage point to address some of these issues; but even while 
several strands within it recognize the crucial ways in which the social 
environment matters, much of the existing work is wedded to the 
methodological individualism of neoclassical orthodoxy and thus 
undertheorizes the links between social structure and individual agency 
(Frerichs 2018). Given that energy consumption is deeply embedded in 
everyday cultural practices (Geels et al. 2017; Klaniecki et al. 2020) and is 
affected by social provisioning processes (Mattioli et al. 2020), and given 
that individual choices are inescapably enmeshed in complex power 
struggles that are ongoing within society, there is considerable room to 
build epistemic bridges between the sociological and behavioural strands of 
the energy transition debate (Hampton and Adams 2018). Doing so is 
particularly crucial to extend the geographical scope of behavioural studies, 
which have thus far been restricted to the developed world. In non-Western 
contexts, the distinction between public and private spheres is less clear, 
and conceptions of possessive individualism often co-exist along with 
strong collective identities. Here, the social environment within which 
decision-making occurs is likely to have a far more salient influence than 
elsewhere. Perhaps—precisely because of these realities—the behavioural 
turn in economics has not been extensively extended to the developing 
world yet. In fact, a striking feature in most of the studies mentioned thus 
far is the dearth of research in the context of developing nations.  



[79] Chandra Sekhar Bahinipati, Rahul A Sirohi, Sagarika S Rao 
 

Concerning India, in an empirical study, Suryawanshi and Jumle (2016) used 
a primary survey to highlight the role of socio-economic variables in 
determining household energy-conservation behaviour. Tyagi et al. (2020) 
surveyed the effectiveness of behavioural interventions in developed 
countries and discussed their applicability in the context of developing 
countries. Sudarshan (2017) adopted a nudging approach to assess 
household energy consumption behaviour in Delhi and found that 
informational interventions helped reduce energy consumption by 7%. 
However, the analysis also found that households did not reduce their 
energy consumption when they were given both information as well as 
pecuniary benefits—which indicates that monetary benefits crowd out non-
price-based behavioural interventions. Further, a tariff is observed to be 
more significant in the case of nudged households (Sudarshan 2017). These 
results correspond with a field experiment conducted by Chen et al. (2017), 
which found evidence of pro-environmental preferences in urban India. 
Their study found that non-pecuniary information regarding the health and 
environmental effects of energy consumption can significantly reduce 
energy consumption. While the studies cited here all seem to suggest the 
effectiveness of behavioural interventions, Prabhu et al. (2013) paint a more 
complex picture. They studied a large-scale programme launched in rural 
Kerala to greater awareness about energy efficiency. The study revealed that 
sustained campaigns, training classes, and local government involvement 
can play an important role in raising awareness regarding energy use. The 
study also highlighted, however, that habits and preferences for the status 
quo exerted a strong inertia on energy-related decisions and acted as a 
barrier to the adoption of energy-efficient lifestyles.  

5. CONCLUSION  

The consensus that a fundamental shift in energy-use patterns is crucial for 
building a sustainable future is undeniable and has been widely recognized. 
That such a shift requires a paradigm change in energy efficiency facilitated 
through innovative and radical new technologies has also been widely 
commented upon. What is often not taken into consideration, however, is 
the potential role of demand-side factors that influence how end-users 
make decisions regarding energy use. Many existing policy frameworks 
emphasize the role of supply-side technological solutions in achieving 
energy transitions and realizing low-carbon futures. However, recent studies 
in behavioural economics suggest that influencing consumer choice through 
behavioural nudges can play an important role in this regard. Given these 
reflections, there is an immediate need to design policies that aim at 
reducing consumer end-use through innovative behavioural programmes, 



Ecology, Economy and Society–the INSEE Journal [80] 

 

especially in the context of developing and market economies. Rather than 
thinking of energy sustainability solely in terms of supply-side solutions, it is 
becoming increasingly incumbent on policymakers to propose holistic, 
multi-dimensional solutions that seek to tackle demand-side as well as 
supply-side determinants of energy use.  

ACKNOWLEDGEMENTS  

The authors would like to acknowledge the research grant received from 
the Indian Council of Social Science Research (ICSSR), New Delhi, under 
the Impactful Policy Research in Social Science scheme 
(IMPRESS/P1652/269/2018–19/ICSSR) to carry out this study. Earlier 
versions of the manuscript were presented at several conferences such as 
Urban ARC 2021, IIHS, Bengaluru; 17th Globelics International 
Conference, Heredia, Costa Rica; and Environmental Research 2021. The 
views expressed in this paper are those of the authors and not of those of 
the supporting or collaborating institutions. The usual disclaimers apply. 

Availability of data and materials This is a review paper, and all the 
materials used in this study are included in the article. 

Author contribution SSR collected all the papers required for this review 
article, and these papers were distributed equally among CSB, RAS, and 
SSR to read and summarize the main arguments and findings. CSB and 
RAS authored the original draft. All authors have read and approved the 
final manuscript. 

Funding This work was supported by the Indian Council of Social Science 
Research (ICSSR), India, under the Impactful Policy Research in Social 
Science scheme (IMPRESS/P1652/269/2018–19/ICSSR). 

DECLARATIONS 

Ethics approval and consent to participate: Not applicable 

Consent for publication: Not applicable 

Competing interests: The authors declare no competing interests. 



[81] Chandra Sekhar Bahinipati, Rahul A Sirohi, Sagarika S Rao 
 

REFERENCES 

Abadie, Alberto, and Sebastien Gay. 2006. “The Impact of Presumed Consent 
Legislation on Cadaveric Organ Donation: A Cross-country Study.” Journal of Health 
Economics 25 (4): 599–620. https://doi.org/10.1016/j.jhealeco.2006.01.003. 

Abrahamse, Wokje, Linda Steg, Charles Vlek, and Talib Rothengatter. 2005. “A 
Review of Intervention Studies Aimed at Household Energy Conservation.” Journal 
of Environmental Psychology 25 (3): 273–291. 
https://doi.org/10.1016/j.jenvp.2005.08.002. 

Ajit Prabhu, V, Sumesh R Nair, Pervaiz K Ahmed, and C Ganesh. 2013. “Energy 
Conservation in Rural India: The Impact of Context and Attitudes on Behaviour.” 
Journal of Asian and African Studies 48 (4): 469–483. 
https://doi.org/10.1177/0021909613493605. 

Alberini, Anna, Silvia Banfi, and Celine Ramseier. 2013. “Energy Efficiency 
Investments in the Home: Swiss Homeowners and Expectations about Future 
Energy Prices.” Energy Journal 34 (1). https://doi.org/10.5547/01956574.34.1.3. 

Allcott, Hunt. 2011a. “Consumers’ Perceptions and Misperceptions of Energy 
Costs.” American Economic Review 101 (3): 98–104. 
https://doi.org/10.1257/aer.101.3.98. 

Allcott, Hunt. 2011b. “Social Norms and Energy Conservation.” Journal of Public 
Economics 95 (9–10): 1082–1095. https://doi.org/10.1016/j.jpubeco.2011.03.003. 

Allcott, Hunt, and Michael Greenstone. 2012. “Is There an Energy Efficiency 
Gap?” Journal of Economic Perspectives 26 (1): 3–28. 
https://doi.org/10.1257/jep.26.1.3. 

Allcott, Hunt, and Sendhil Mullainathan. 2010. “Behavior and Energy Policy.” 
Science 327 (5970): 1204–1205. https://doi.org/10.1126/science.1180775. 

Allcott, Hunt, and Todd Rogers. 2014. “The Short-run and Long-run Effects of 
Behavioral Interventions: Experimental Evidence from Energy Conservation.” 
American Economic Review 104 (10): 3003–3037. 
https://doi.org/10.1257/aer.104.10.3003. 

Allcott, Hunt, and Dmitry Taubinsky. 2013. “The Lightbulb Paradox: Evidence 
from Two Randomized Experiments” NBER Working Paper no. 19713. 
https://doi.org/10.3386/w19713. 

Allcott, Hunt, Sendhil Mullainathan, and Dmitry Taubinsky. 2014. “Energy Policy 
with Externalities and Internalities.” Journal of Public Economics 112: 72–88. 
https://doi.org/10.1016/j.jpubeco.2014.01.004. 

Allen, Robert C. 2012. “Backward into the Future: The Shift to Coal and 
Implications for the Next Energy Transition.” Energy Policy 50: 17–23. 
https://doi.org/10.1016/j.enpol.2012.03.020. 

Andor, Mark A, and Katja M Fels. 2018. “Behavioral Economics and Energy 
Conservation – A Systematic Review of Non-Price Interventions and their Causal 
Effects.” Ecological Economics 148: 178–210. 
https://doi.org/10.1016/j.ecolecon.2018.01.018. 

https://doi.org/10.1016/j.jhealeco.2006.01.003
https://doi.org/10.1016/j.jenvp.2005.08.002
https://doi.org/10.1177/0021909613493605
https://doi.org/10.5547/01956574.34.1.3
https://doi.org/10.1257/aer.101.3.98
https://doi.org/10.1016/j.jpubeco.2011.03.003
https://doi.org/10.1257/jep.26.1.3
https://doi.org/10.1126/science.1180775
https://doi.org/10.1257/aer.104.10.3003
https://doi.org/10.3386/w19713
https://doi.org/10.1016/j.jpubeco.2014.01.004
https://doi.org/10.1016/j.enpol.2012.03.020
https://doi.org/10.1016/j.ecolecon.2018.01.018


Ecology, Economy and Society–the INSEE Journal [82] 

 

Andor, Mark, Andreas Gerster, and Lorenz Goette. 2020. “Disaggregated 
Consumption Feedback and Energy Conservation”. CEPR Discussion Paper: 14954. 
Washington. D.C.: Center for Economic and Political Research. 

Aydin, Erdal, Nils Kok, and Dirk Brounen. 2017. “Energy Efficiency and 
Household Behavior: The Rebound Effect in the Residential Sector.” The RAND 
Journal of Economics 48 (3): 749–782. https://doi.org/10.1111/1756–2171.12190. 

Bahinipati, Chandra Sekhar, and PK Viswanathan. 2019a. “Can Micro-irrigation 
Technologies Resolve India's Groundwater Crisis? Reflections from Dark-regions 
in Gujarat.” International Journal of the Commons 13 (2): 848–858. 
https://doi.org/10.5334/ijc.888. 

Bahinipati, Chandra Sekhar, and PK Viswanathan. 2019b. “Incentivizing Resource-
Efficient Technologies in India: Evidence from Diffusion of Micro-irrigation in the 
Dark Zone Regions of Gujarat.” Land Use Policy 86: 253–260. 
https://doi.org/10.1016/j.landusepol.2019.04.017. 

Blasch, Julia, Massimo Filippini, and Nilkanth Kumar. 2019. “Boundedly Rational 
Consumers, Energy and Investment Literacy, and the Display of Information on 
Household Appliances.” Resource and Energy Economics 56: 39–58. 
https://doi.org/10.1016/j.reseneeco.2017.06.001. 

Brandon, Alec, Paul J Ferraro, John A List, Robert D Metcalfe, Michael K Price, 
and Florian Rundhammer. 2017. “Do the Effects of Social Nudges Persist? Theory 
and Evidence from 38 Natural Field Experiments.” NBER Working Paper 23277. 
Cambridge, US: National Bureau of Economic Research. 
https://doi.org/10.3386/w23277. 

Brandon, Alec, John List, Robert D Metcalfe, Michael K Price, and Florian 
Rundhammer. 2019. “Testing for Crowd Out in Social Nudges: Evidence from a 
Natural Field Experiment in the Market for Electricity.” Proceedings of the National 
Academy of Sciences 116 (12): 5293–5298. https://doi.org/10.1073/pnas.1802874115. 

Brennan, Timothy J. 2007. “Consumer Preference Not to Choose: Methodological 
and Policy Implications.” Energy Policy 35 (3): 1616–1627. 
https://doi.org/10.1016/j.enpol.2006.04.023. 

Brookes, Len. 1990. “The Greenhouse Effect: The Fallacies in the Energy 
Efficiency Solution.” Energy Policy 18 (2): 199–201. https://doi.org/10.1016/0301–
4215(90)90145–T. 

Bruckner, T, IA Bashmakov, Y Mulugetta, H Chum, A de la Vega Navarro, J 
Edmonds, A Faaij, B Fungtammasan, A Garg, E Hertwich,  et al. 2014. “Energy 
Systems.” In Climate Change 2014: Mitigation of Climate Change. Contribution of Working 
Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, 
edited by O Edenhofer, R Pichs-Madruga, Y Sokona, E Farahani, S Kadner, K 
Seyboth, A Adler, I Baum, S Brunner, P Eickemeier, et al. Cambridge, UK, and 
New York: Cambridge University Press. 

Brülisauer, Marcel, Lorenz Goette, Zhengyi Jiang, Jan Schmitz, and Renate 
Schubert. 2020. “Appliance-specific Feedback and Social Comparisons: Evidence 
from a Field Experiment on Energy Conservation.” Energy Policy 145: 111742. 
https://doi.org/10.1016/j.enpol.2020.111742. 

https://doi.org/10.1111/1756–2171.12190
https://doi.org/10.5334/ijc.888
https://doi.org/10.1016/j.landusepol.2019.04.017
https://doi.org/10.1016/j.reseneeco.2017.06.001
https://doi.org/10.3386/w23277
https://doi.org/10.1073/pnas.1802874115
https://doi.org/10.1016/j.enpol.2006.04.023
https://doi.org/10.1016/0301–4215(90)90145–T
https://doi.org/10.1016/0301–4215(90)90145–T
https://doi.org/10.1016/j.enpol.2020.111742


[83] Chandra Sekhar Bahinipati, Rahul A Sirohi, Sagarika S Rao 
 

Chen, Victor L, Magali A Delmas, Stephen L Locke, and Amarjeet Singh. 2017. 
“Information Strategies for Energy Conservation: A Field Experiment in India.” 
Energy Economics 68: 215–227. https://doi.org/10.1016/j.eneco.2017.09.004. 

Davis, Lucas W, Alan Fuchs, and Paul Gertler. 2014. “Cash for Coolers: Evaluating 
a Large-scale Appliance Replacement Program in Mexico.” American Economic 
Journal: Economic Policy 6 (4): 207–238. https://doi.org/10.1257/pol.6.4.207. 

European Environment Agency. 2013. “Achieving Energy Efficiency through 
Behaviour Change: What Does It Take?” EEA Technical Report no. 5 (December). 
https://www.eea.europa.eu/publications/achieving–energy–efficiency–through–
behaviour/file.  

Fanghella, Valeria, Matteo Ploner, and Massimo Tavoni. 2019. “Nudges: 
Complements or Substitutes of Traditional Energy Policy Instruments?” European 
Association of Environmental and Resource Economists 2019. Manchester, UK: University 
of Manchester. 

Filippini, Massimo, and Shonali Pachauri. 2004. “Elasticities of Electricity Demand 
in Urban Indian Households.” Energy Policy 32 (3): 429–436. 
https://doi.org/10.1016/S0301–4215(02)00314–2. 

Fishman, Ram, Upmanu Lall, Vijay Modi, and Nikunj Parekh. 2016. “Can 
Electricity Pricing Save India's Groundwater? Field Evidence from a Novel Policy 
Mechanism in Gujarat.” Journal of the Association of Environmental and Resource 
Economists 3 (4): 819–855. https://doi.org/10.1086/688496. 

Foster, John Bellamy. 2001. “Capitalism’s Environmental Crisis: Is Technology the 
Answer?” Hitotsubashi Journal of Social Studies 33 (1): 143–150. 
https://doi.org/10.14452/mr–052–07–2000–11_1. 

Fowlie, Meredith, Michael Greenstone, and Catherine Wolfram. 2018. “Do Energy 
Efficiency Investments Deliver? Evidence from the Weatherization Assistance 
Program.” Quarterly Journal of Economics 133 (3): 1597–1644. 
https://doi.org/10.1093/qje/qjy005. 

Frederiks, Elisha R, Karen Stenner, and Elizabeth V Hobman. 2015. “Household 
Energy Use: Applying Behavioural Economics to Understand Consumer Decision-
Making and Behaviour.” Renewable and Sustainable Energy Reviews 41: 1385–1394. 
https://doi.org/10.1016/j.rser.2014.09.026. 

Frerichs, Sabine. 2018. “What Is the ‘Social’ in Behavioural Economics? The 
Methodological Underpinnings of Governance by Nudges.” In Research Methods in 
Consumer Law: A Handbook, edited by Hans-W Micklitz, Anne-Lise Sibony, and 
Fabrizio Esposito. Edward Elgar Publishing. 
https://doi.org/10.4337/9781785366611.00019. 

Frey, Bruno S, and Reto Jegen. 2001. “Motivation Crowding Theory.” Journal of 
Economic Surveys 15 (5): 589–611. https://doi.org/10.1111/1467–6419.00150. 

Galle, Brian. 2014. “Tax, Command or Nudge: Evaluating the New Regulation.” 
Texas Law Review 92: 837–894. 

https://doi.org/10.1016/j.eneco.2017.09.004
https://doi.org/10.1257/pol.6.4.207
https://doi.org/10.1016/S0301–4215(02)00314–2
https://doi.org/10.1086/688496
https://doi.org/10.14452/mr–052–07–2000–11_1
https://doi.org/10.1093/qje/qjy005
https://doi.org/10.1016/j.rser.2014.09.026
https://doi.org/10.4337/9781785366611.00019
https://doi.org/10.1111/1467–6419.00150


Ecology, Economy and Society–the INSEE Journal [84] 

 

Geels, Frank W, Benjamin K Sovacool, Tim Schwanen, and Steve Sorrell. 2017. 
“The Socio-Technical Dynamics of Low-Carbon Transitions.” Joule 1 (3): 463–479. 
https://doi.org/10.1016/j.joule.2017.09.018. 

Hampton, Sam, and Rob Adams. 2018. “Behavioural Economics vs Social Practice 
Theory: Perspectives from Inside the United Kingdom Government.” Energy 
Research & Social Science 46: 214–224. https://doi.org/10.1016/j.erss.2018.07.023. 

Hanna, Rema, Sendhil Mullainathan, and Joshua Schwartzstein. 2014. “Learning 
Through Noticing: Theory and Evidence from a Field Experiment.” Quarterly 
Journal of Economics 129 (3): 1311–1353. https://doi.org/10.1093/qje/qju015. 

Hansen, Pelle Guldborg. 2016. “The Definition of Nudge and Libertarian 
Paternalism: Does the Hand Fit the Glove?” European Journal of Risk Regulation 7 (1): 
155–174. https://doi.org/10.1017/S1867299X00005468. 

Hutton, R Bruce, Gary A Mauser, Pierre Filiatrault, and Olli T Ahtola. 1986. 
“Effects of Cost-related Feedback on Consumer Knowledge and Consumption 
Behavior: A Field Experimental Approach.” Journal of Consumer Research 13 (3): 327–
336. https://doi.org/10.1086/209072. 

International Energy Agency. 2013. World Energy Outlook 2013. Paris: IEA. 

International Energy Agency. 2018. World Energy Outlook 2018. Paris: IEA.Internat. 

International Energy Agency. 2020. India 2020. Energy Policy Review. Paris: IEA. 

Jain, Manisha, Anand B Rao, and Anand Patwardhan. 2018a. “Appliance Labeling 
and Consumer Heterogeneity: A Discrete Choice Experiment in India.” Applied 
Energy 226: 213–224. https://doi.org/10.1016/j.apenergy.2018.05.089. 

Jain, Manisha, Anand B Rao, and Anand Patwardhan. 2018b. “Consumer 
Preference for Labels in the Purchase Decisions of Air Conditioners in India.” 
Energy for Sustainable Development 42: 24–31. 
https://doi.org/10.1016/j.esd.2017.09.008. 

Jevons, William S. (1906 [1865]). “The Coal Question: An Inquiry Concerning the 
Progress of the Nation, and the Probable Exhaustion of Our Coalmines.” Third 
edition. London: Macmillan. 

Kahneman, Daniel. 2003. “Maps of Bounded Rationality: Psychology for 
Behavioral Economics.” American Economic Review 93 (5): 1449–1475. 
https://doi.org/10.1257/000282803322655392. 

Kahneman, Daniel, Jack L Knetsch, and Richard H Thaler. 1991. “Anomalies: The 
Endowment Effect, Loss Aversion, and Status Quo Bias.” Journal of Economic 
Perspectives 5 (1): 193–206. https://doi.org/10.1257/jep.5.1.193. 

Klaniecki, Kathleen, Ioana Alexandra Duse, Lotte M Lutz, Julia Leventon, and 
David J Abson. 

2020. “Applying the Energy Cultures Framework to Understand Energy Systems in 
the Context of Rural Sustainability Transformation.” Energy Policy 137: 111092. 
https://doi.org/10.1016/j.enpol.2019.111092. 

https://doi.org/10.1016/j.joule.2017.09.018
https://doi.org/10.1016/j.erss.2018.07.023
https://doi.org/10.1093/qje/qju015
https://doi.org/10.1017/S1867299X00005468
https://doi.org/10.1086/209072
https://doi.org/10.1016/j.apenergy.2018.05.089
https://doi.org/10.1016/j.esd.2017.09.008
https://doi.org/10.1257/000282803322655392
https://doi.org/10.1257/jep.5.1.193
https://doi.org/10.1016/j.enpol.2019.111092


[85] Chandra Sekhar Bahinipati, Rahul A Sirohi, Sagarika S Rao 
 

Leard, Benjamin. 2018. “Consumer Inattention and the Demand for Vehicle Fuel 
Cost Savings.” Journal of Choice Modelling 29: 1–16. 
https://doi.org/10.1016/j.jocm.2018.08.002. 

Malm, Andreas. 2012. “Steam: Nineteenth-century Mechanization and the Power 
of Capital. In Ecology and Power: Struggles over Land and Material Resources in the Past, 
Present, and Future”, edited by A Hornborg, B Clark, and K Hermele, 108–123. 
London: Routledge. 

Malm, Andreas. 2016. Fossil Capital: The Rise of Steam Power and the Roots of Global 
Warming. London: Verso Books. 

Mattioli, Giulio, Cameron Roberts, Julia K Steinberger, and Andrew Brown. 2020. 
“The Political Economy of Car Dependence: A Systems of Provision Approach.” 
Energy Research & Social Science 66: 101486. 
https://doi.org/10.1016/j.erss.2020.101486. 

McCalley, LT, and Cees JH Midden. 2002. “Energy Conservation through Product-
integrated Feedback: The Roles of Goal-setting and Social Orientation.” Journal of 
Economic Psychology 23 (5): 589–603. https://doi.org/10.1016/S0167–
4870(02)00119–8. 

Midden, Cees JH, Joanne F Meter, Mieneke H Weenig, and Henk JA Zieverink. 
1983. “Using Feedback, Reinforcement and Information to Reduce Energy 
Consumption in Households: A Field-experiment.” Journal of Economic Psychology 3 
(1): 65–86. https://doi.org/10.1016/0167–4870(83)90058–2. 

Newman, Peter, and Jeff Kenworthy. 2000. “The Ten Myths of Automobile 
Dependence.” World Transport Policy and Practice 6 (1): 15–25. 

Pablo-Romero, María del P, and Josué De Jesús. 2016. “Economic Growth and 
Energy Consumption: The Energy-environmental Kuznets Curve for Latin 
America and the Caribbean.” Renewable and Sustainable Energy Reviews 60: 1343–1350. 
https://doi.org/10.1016/j.rser.2016.03.029. 

Paul, Anthony, Blair Beasley, and Karen Palmer. 2013. “Taxing Electricity Sector 
Carbon Emissions at Social Cost.” RFF DP 13-23-REV. Washington, D.C.: 
Resources for the Future. 

Parrish, Bryony, Phil Heptonstall, Rob Gross, and Benjamin K Sovacool. 2020. “A 
Systematic Review of Motivations, Enablers and Barriers for Consumer 
Engagement with Residential Demand Response.” Energy Policy 138: 111221. 
https://doi.org/10.1016/j.enpol.2019.111221. 

Pausas, Juli G, and Jon E Keeley. 2009. “A Burning Story: The Role of Fire in the 
History of Life.” Bioscience 59 (7): 593–601. 
https://doi.org/10.1525/bio.2009.59.7.10. 

Platchkov, Laura M, and Michael G Pollitt. 2011. “The Economics of Energy (and 
Electricity) Demand.” Cambridge Working Paper in Economics no. 1137. 

Polimeni, John M, and Raluca Iorgulescu Polimeni. 2006. “Jevons’ Paradox and the 
Myth of Technological Liberation.” Ecological Complexity 3 (4): 344–353. 
https://doi.org/10.1016/j.ecocom.2007.02.008. 

https://doi.org/10.1016/j.jocm.2018.08.002
https://doi.org/10.1016/j.erss.2020.101486
https://doi.org/10.1016/S0167-4870(02)00119-8
https://doi.org/10.1016/S0167-4870(02)00119-8
https://doi.org/10.1016/0167–4870(83)90058–2
https://doi.org/10.1016/j.rser.2016.03.029
https://doi.org/10.1016/j.enpol.2019.111221
https://doi.org/10.1525/bio.2009.59.7.10
https://doi.org/10.1016/j.ecocom.2007.02.008


Ecology, Economy and Society–the INSEE Journal [86] 

 

Puffert, Douglas. 2008. Path Dependence in Technical Standards. In the New 
Palgrave Dictionary of Economics, edited by M Vernengo, EP Caldentey, and BJ Rosser 
Jr. London: Palgrave Macmillan. https://doi.org/10.1057/978–1–349–95121–
5_2846–1. 

Ricardo, David. 2015 [1817]. On The Principles of Political Economy, and Taxation. 
Cambridge, UK: Cambridge University Press. 
https://doi.org/10.1017/CBO9781107589421. 

Sallee, James M. 2014. “Rational Inattention and Energy Efficiency.” The Journal of 
Law & Economics 57 (3): 781–820. 

Sammer, Katharina, and Rolf Wüstenhagen. 2006. “The Influence of Eco‐Labelling 
on Consumer Behaviour-Results of a Discrete Choice Analysis for Washing 
Machines.” Business Strategy and the Environment 15 (3): 185–199. 
https://doi.org/10.1002/bse.522. 

Siciliano, Giuseppina, Frauke Urban, May Tan-Mullins, and Giles Mohan. 2018. 
“Large Dams, Energy Justice and the Divergence between International, National 
and Local Developmental Needs and Priorities in the Global South.” Energy 
Research & Social Science 41: 199–209. https://doi.org/10.1016/j.erss.2018.03.029. 

Shen, Junyi, and Tatsuyoshi Saijo. 2009. “Does an Energy Efficiency Label Alter 
Consumers' Purchasing Decisions? A Latent Class Approach Based on a Stated 
Choice Experiment in Shanghai.” Journal of Environmental Management 90 (11): 3561–
3573. https://doi.org/10.1016/j.jenvman.2009.06.010. 

Sorrell, Steve. 2009. “Jevons’ Paradox Revisited: The Evidence for Backfire from 
Improved Energy Efficiency.” Energy Policy 37 (4): 1456–1469. 
https://doi.org/10.1016/j.enpol.2008.12.003. 

Stephenson, Janet, Barry Barton, Gerry Carrington, Adam Doering, Rebecca Ford, 
Debbie Hopkins, Rob Lawson, et al. 2015. “The Energy Cultures Framework: 
Exploring the Role of Norms, Practices and Material Culture in Shaping Energy 
Behaviour in New Zealand.” Energy Research & Social Science 7: 117–123. 
https://doi.org/10.1016/j.erss.2015.03.005. 

Sudarshan, Anant. 2017. “Nudges in the Marketplace: The Response of Household 
Electricity Consumption to Information and Monetary Incentives.” Journal of 
Economic Behavior & Organization 134: 320–335. 
https://doi.org/10.1016/j.jebo.2016.12.015. 

Suryawanshi, P, and AG Jumle. 2016. “Relationship of Socio Economic Factors 
(SEF) and Energy Conservation Behavior in India: Empirical Study.” IRA-
International Journal of Management & Social Sciences 4 (1): 39–47. 
https://doi.org/10.21013/jmss.v4.n1.p4. 

Thaler, Richard H and Cass R. Sunstein. 2009. Nudge: Improving Decisions about 
Health, Wealth, and Happiness. Penguin Books. 

Tyagi, Ruchi, Suresh Vishwakarma, Kishan Kumar Singh, Chanan Syan. 2020. 
“Low-Cost Energy Conservation Measures and Behavioral Change for Sustainable 
Energy Goal.” In Affordable and Clean Energy: Encyclopedia of the UN Sustainable 

https://doi.org/10.1057/978-1-349-95121-5_2846-1
https://doi.org/10.1057/978-1-349-95121-5_2846-1
https://doi.org/10.1017/CBO9781107589421
https://doi.org/10.1002/bse.522
https://doi.org/10.1016/j.erss.2018.03.029
https://doi.org/10.1016/j.jenvman.2009.06.010
https://doi.org/10.1016/j.enpol.2008.12.003
https://doi.org/10.1016/j.erss.2015.03.005
https://doi.org/10.1016/j.jebo.2016.12.015
https://doi.org/10.21013/jmss.v4.n1.p4


[87] Chandra Sekhar Bahinipati, Rahul A Sirohi, Sagarika S Rao 
 

Development Goals, edited by Leal Filho W, Azul AM, Brandli L, Lange Salvia A, Wall 
T. Cham: Springer. https://doi.org/10.1007/978–3–319–71057–0_155–1. 

UNEP. n.d. “Goal 7: Affordable and Clean Energy.” United Nations Environment 
Programme. https://www.unenvironment.org/explore-topics/sustainable-
development-goals/why-do-sustainable-development-goals-matter/goal-7. 

Vance, Leisha, Tarsha Eason, and Heriberto Cabezas. 2015. “Energy Sustainability: 
Consumption, Efficiency, and Environmental Impact.” Clean Technologies and 
Environmental Policy 17 (7): 1781–1792. https://doi.org/10.1007/s10098–015–0932–
y. 

Veblen, Thorstein. 1898. “Why Is Economics Not an Evolutionary Science?” 
Quarterly Journal of Economics 12 (4): 373–397. https://doi.org/10.2307/1882952. 

Velez-Henao, Johan-Andres, Claudia-Maris Garcia-Mazo, Jaume Freire-Gonzalez, 
and David Font Vivanco. 2020. “Environmental Rebound Effect of Energy 
Efficiency Improvements in Colombian Households.” Energy Policy 145. 
https://doi.org/10.1016/j.enpol.2020.111697. 

Velthuijsen, Jan Willem, and Ernst Worrell. 2002. “The Economics of Energy.” In 
Handbook of Environmental and Resource Economics, edited by JCJM van den Bergh, 
177–194. Cheltenham, UK: Edward Elgar Publishing. 

Whitt, J Allen, and Glenn Yago. 1985. “Corporate Strategies and the Decline of 
Transit in US Cities.” Urban Affairs Quarterly 21 (1): 37–65. 
https://doi.org/10.1177/004208168502100106. 

Wrigley, Edward Anthony. 2013. “Energy and the English Industrial Revolution.” 
Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences 371: 1–
10. https://doi.org/10.1098/rsta.2011.0568. 

Zarri, Luca. 2010. “Behavioral Economics Has Two ‘Souls’: Do They Both Depart 
from Economic Rationality?” The Journal of Socio-Economics 39: 562–567. 

https://doi.org/10.1007/978-3-319-71057-0_155-1
https://www.unenvironment.org/explore-topics/sustainable-development-goals/why-do-sustainable-development-goals-matter/goal-7
https://www.unenvironment.org/explore-topics/sustainable-development-goals/why-do-sustainable-development-goals-matter/goal-7
https://doi.org/10.1007/s10098–015–0932–y
https://doi.org/10.1007/s10098–015–0932–y
https://doi.org/10.2307/1882952
https://doi.org/10.1016/j.enpol.2020.111697
https://doi.org/10.1177/004208168502100106
https://doi.org/10.1098/rsta.2011.0568

