Demand-side Mitigation Policies: The Role of the Buildings Sector

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Demand-side Mitigation Policies:

The Role of the Buildings Sector

vorgelegt von

Diplom-Wirtschaftswissenschaftler Antoine Levesque

ORCID: 0000-0003-2059-6318

an der Fakultät VI – Planen Bauen Umwelt der Technischen Universität Berlin zur Erlangung des akademischen Grades

Doktor der Wirtschaftswissenschaften Dr. rer. oec.

genehmigte Dissertation

Promotionsausschuss:

Vorsitzender: Prof. Dr. Timo Hartmann Gutachter: Prof. Dr. Ottmar Edenhofer Gutacther: Prof. Dr. Andreas Löschel

Tag der wissenschaftlichen Aussprache: 25. März 2021

Berlin 2021

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In 2015, the international community committed to limiting global warmingwell below 2°C. Since 2015, however, and before the coronavirus pandemic stroke, GHG emissions have continued on their growing track and the achievement of ambitious climate targets has become even more arduous. In order to rein in global warmingwell below2°C, energy systems must reach net-zero emissions by mid-century. The energy supply, in particular the electricity sector, offers a great potential for reducing emissions. But in the absence of large transformations on the energy demand side, achieving the Paris Agreement’s target would necessitate an extensive recourse to debated negative emission technologies.

The interest in demand-side solutions has therefore risen over the last few years. Today, buildings account for 28% of CO₂emissions in the energy system. This sector is therefore an essential building block of any successful mitigation strategy. The aim of this thesis is to investigate the contribution of buildings to limit climate change.

The widespread view on the role of buildings is that there is a large and cost-effective potential for energy demand reductions, and that this potential remains unexploited due to some barriers, which policies should remove.

This thesis relies on energy modeling to shed a new light on that widespread view. It uses the strengths of both an energy simulation model and of an integrated assessment model representing the energy, economy and climate systems. In order to assess the role of buildings in climate policies, the thesis addresses the following complementary questions: How will buildings energy consumption evolve in the future? What is the technological and behavioral potential for demand reductions? What are optimal climate change mitigation pathways for the buildings sector in the context of the overall energy system, and when the energy efficiency gap is taken into account?

This thesis shows that the landscape of buildings energy demand will undergo major changes in the 21^st century: while cooking and other heating purposes account for the bulk of the demand today; space cooling, appliances and lighting will represent the lion’s share tomorrow. Similarly, despite its current weight in demand, traditional biomass will gradually leave the stage. Against this background, radical changes in technologies and behaviors could lead to a halving of energy demand. The decarbonization of the sector however does not only pass through energy demand reductions. In the scenarios presented in this thesis, most of the decarbonization is attributed to the decline in the emissions per unit of energy consumed — a topic under-represented in the literature dealing with buildings energy demand.

In light of the thesis’ results, and supported by the literature, we challenge the widespread view on the role of buildings in climate change mitigation. Indeed, the widespread nar-

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6 Summary rative focuses mostly on energy demand reductions and does not embrace the strategy consisting in decreasing the amount of emissions per unit of energy — in particularvia electrification and fuel switching. This strategy accounts however for a substantial part of the sector’s decarbonization. We therefore propose an alternative narrative:

Two complementary and interacting strategies can lead to a deep decarbonization of buildings energy demand: reducing energy demand and decreasing the carbon content of energy demand through energy supply decarbonization and fuel switching. Virtually all energy services in buildings could be provided by carbon-free energy carriers. How- ever market incentives as well as barriers do not allow for a widespread uptake of clean energy carriers and efficient technologies. Policies should remove barriers to the uptake of efficient and low-carbon technologies, and design markets to give the right incentives in favor of these options.

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Zusammenfassung

Im Jahr 2015 hat sich die internationale Gemeinschaft verpflichtet, die globale Erwär- mung deutlich unter 2°C zu begrenzen. Seit 2015 und vor der Corona-Krise sind die Treibhausgasemissionen jedoch weiter gestiegen und der Weg zu ehrgeizigen Klimazielen ist noch beschwerlicher geworden. Um die globale Erwärmung deutlich unter 2°C einzu- dämmen, müssen die Energiesysteme bis Mitte des Jahrhunderts Netto-Null-Emissionen erreichen. Die Energieversorgung, insbesondere der Elektrizitätssektor, bietet ein großes Potenzial für Emissionsreduktionen. Ohne große Veränderungen auf der Seite der Ener- gienachfrage, würde es einen umfangreichen Rückgriff auf die umstrittenen Technologien zur CO₂-Entnahme erfordern, um das Ziel des Pariser Abkommens zu erreichen. Das In- teresse an Lösungen auf der Seite der Nachfrage ist daher in den letzten Jahren gestiegen.

Heute sind Gebäude für 28% der gesamten Emissionen im Energiesystem verantwortlich.

Dieser Sektor ist daher ein wesentlicher Baustein jedes erfolgreichen Klimaschutzes. Das Ziel dieser Dissertation besteht darin, den Beitrag zu untersuchen, den Gebäude zur Be- grenzung des Klimawandels leisten könnten.

Die weit verbreitete Sichtweise zur Rolle von Gebäuden beim Klimaschutz lässt sich wie folgt zusammenfassen: Es gibt ein großes und kostenwirksames Potenzial für die Verrin- gerung der Energienachfrage, und dieses Potenzial bleibt aufgrund einiger Hindernisse ungenutzt. Die Politik sollte diese Hindernisse beseitigen.

Diese Dissertation beruht auf Energie- und Integrated Assessment Modellen, um ein neu- es Licht auf diese verbreitete Sicht zu werfen. Um die Rolle von Gebäuden in der Klima- politik zu bewerten, befasst sich die Arbeit mit den folgenden ergänzenden Fragen: Wie wird sich der Energiebedarf im 21. Jahrhundert entwickeln? Was ist das technologische und verhaltensbedingte Potenzial für die Reduzierung des Energiebedarfs in Gebäuden?

Was sind optimale Wege zur Eindämmung des Klimawandels für den Gebäudesektor im Kontext des Gesamtenergiesystems, und wenn die Energieeffizienzlücke berücksichtigt wird?

Diese Dissertation zeigt, dass sich die Energienachfragelandschaft der Gebäude im 21.

Jahrhundert stark verändern wird: Während heute Kochen und andere Heizbedarfe den Großteil der Nachfrage ausmachen, werden zukünftig Raumkühlung, Geräte und Be- leuchtung den Löwenanteil des Bedarfs ausmachen. In ähnlicher Weise wird die tradi- tionelle Biomasse trotz ihres derzeitigen Gewichts bei der Nachfrage allmählich die Büh- ne verlassen. Vor diesem Hintergrund könnten radikale Veränderungen der Technologien und Verhaltensweisen zu einer Halbierung des Energiebedarfs führen. Die Dekarbonisie- rung des Sektors geht jedoch nicht nur über die Reduzierung der Energienachfrage. Die in dieser Dissertation vorgestellten Szenarien zeigen, dass der größte Teil der Dekarbo-

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8 Zusammenfassung nisierung darauf zurückzuführen ist, dass weniger Kohlenstoff pro Einheit verbrauchter Energie emittiert wird — ein Thema, das in der Fachliteratur zur Energienachfrage im Gebäudesektor unterrepräsentiert ist.

Anhand der in dieser Arbeit vorgestellten Ergebnisse und unterstützt durch die Litera- tur stellen wir die konventionelle Sichtweise in Frage. Tatsächlich konzentriert sich die verbreitete Erzählung auf die Reduzierung der Energienachfrage und geht nicht auf die Strategie ein, die darin besteht, den Kohlenstoffgehalt der Energie zu verringern, insbesondere durch Brennstoffwechsel. Diese Strategie macht jedoch einen wesentlichen Teil der Dekarbonisierung des Sektors aus. Wir schlagen daher eine alternative Erzählung vor:

Zwei komplementäre und interagierende Strategien können zu einer tiefgreifenden Dekar- bonisierung des Energiebedarfs von Gebäuden führen: die Verringerung des Energiebe- darfs an sich und die Verringerung des Kohlenstoffgehalts des Energiebedarfs. Praktisch alle Energiedienstleistungen in Gebäuden könnten durch kohlenstofffreie Energieträger bereitgestellt werden. Die Marktanreize und -barrieren erlauben jedoch keine breite Nut- zung von Energieeffizienz und kohlenstofffreien Energieträgern. Die Politik sollte Hin- dernisse für die Einführung effizienter und kohlenstoffarmer Technologien beseitigen und Märkte so gestalten, dass die richtigen Anreize für diese Technologien gegeben werden.

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Chapter 1

Introduction

In order to limit global warming below 2°C anda fortiori below 1.5°C, deep and rapid transformations towards full decarbonization until mid-century are necessary (Rogelj et al., 2015). The decarbonization of buildings will be of utmost importance to achieve this goal. Buildings energy consuming activities are an essential driver of anthropogenic GHG emissions. In 2018, buildings accounted for 31% of global final energy demand (IEA, 2019) and 9% of the energy and process-based CO₂ emissions. When adding the indirect emissions from electricity and commercial heat consumption, the sector’s share rose to 28%. If also including the impact of the construction industry, buildings represented up to 39% of emissions (Global Alliance for Buildings and Construction et al., 2019). But what exactly should be the role of buildings in climate change mitigation?

This is the question at the heart of this thesis.

Until recently, the academic discussion about decarbonization, in particular in the In- tegrated Assessment modeling community, was concentrating on supply-side solutions.

Even in pathways reducing their emissions rapidly, negative emission technologies are considered unavoidable to remain within the limits of tight carbon budgets, in particular because of residual emissions on the demand side (Luderer et al., 2018). For instance, most of the pathways complying with stringent climate targets rely heavily on the widespread culture of biomass to capture carbon from the atmosphere (BECCS). But the extent to which negative emission technologies are employed in such scenarios has raised manifold concerns. Indeed, despite their prominent role in emissions scenarios, negative emission technologies are for the most part only at the development or demon- stration phase, which casts a shadow over their availability and reliability in the future (Anderson and Peters, 2016), and would require dedicated policies to develop them as of today (Minx et al., 2018). But more than the current state of the technologies, these are the side-effects of their extensive roll-out that have attracted the attention of commenta- tors, in particular as far as food security, biodiversity and the availability of CO₂ storage are concerned (Van Vuuren et al., 2017). While BECCS proves an affordable technology in terms of cost per ton of carbon captured and of energy produced (Fuss et al., 2018), it has much higher requirements in terms of land, water and fertilizers than other options (Smith et al., 2016). As an example, in mitigation scenarios, BECCS could require approximately 380–700 Mha of land (Smith et al., 2016), between once and twice the surface of India (Anderson and Peters, 2016). Other negative emission technologies exist, at least prospectively, but they also bear negative effects and potential limits.

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10 Chapter 1 Introduction The limits and risks related to negative emissions have pointed to the necessity of reducing residual emissions from the energy system. Expanding the share of renewables in power generation and applying technological solutions to compensate for remaining carbon emissions would not suffice to keep global warming within sustainable limits.

Against this backdrop, the interest in demand-side solutions has grown in recent years (Creutzig et al., 2016), and occupies a variety of disciplines, each bringing their own perspective (Creutzig et al., 2018). Integrated Assessment models (IAMs) are not exempt from this interest in demand-side solutions. IAMs are powerful models that bring into a global perspective the relative contributions of supply-side and demand-side technologies to mitigate climate change. They can therefore assess the extent to which a decrease in energy demand can rein in the need for negative emission technologies. Recently, IAM scenarios have explored alternative scenarios with lower energy demand (Vuuren et al., 2018; Grubler et al., 2018a). In particular, behavioral and lifestyle changes have become a new topic for these models (van Sluisveld et al., 2016). Following this trend, scenarios with rapid and aggressive declines in energy demand, and therefore with only little need for negative emissions, were mentioned as one of the four illustrative pathways presented in one of the latest IPCC special reports (Allen et al., 2018).

It is against this broader context that this thesis dives into the decarbonization of buildings.

In the literature as well as in the policy arena, there seems to be a widespread narrative regarding the contribution of buildings to climate change mitigation (see Chapter 6). We can summarize it in three points:

• There is a large potential for energy demand reductions. By using this potential, consumers would not only conserve energy, they would also save money.

• Barriers to energy efficiency prevail in many energy service markets and preclude consumers from taking savings opportunities

• Policies should therefore remove barriers to efficiency for the sake of energy consumers

This narrative is closely related to the concept of energy efficiency gap: the idea that consumers fail to invest sufficiently in efficient technologies that would be beneficial to them. This gap has fueled and continues to fuel a fierce debate among engineers and economists concerning its components, depth and policy implications.

To investigate the role played by buildings in mitigation strategies, and question the widespread view detailed above, we will proceed in three steps. First, we will project the influence of socio-economic, demographic and climatic drivers on energy demand and the building stock across the globe (Chapters 2 and 3). The first step intends to de- pict the future landscape in which climate action will take place. The second step will sound the behavioral and technological potential to reduce energy demand from buildings activities (Chapters 3 and 4). Finally, the third step will assess the economically optimal contribution of the buildings sector in ambitious mitigation pathways (Chapter 5).

This thesis adds three main contributions to the literature. First, it makes a detailed assessment of potential energy demand reductions both from state-of-the-art technologies and energy-saving behaviors. Thereby, it depicts an ambitious pathway for energy demand in buildings. Second, it addresses one of the most important blind spots of the

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1.1 Energy demand drivers and trends 11 scenario literature, and specifically the integrated assessment model literature: the energy efficiency gap. While being hotly debated within engineering and economic circles over the last decades, and despite its high relevance for energy efficiency, this gap has not been represented and analyzed in IAMs so far. This thesis includes the efficiency gap in the assessment of the role of buildings energy demand for the decarbonization of the sector. Third, based on the findings from the thesis as well as from the literature, the thesis challenges the widespread narrative on the role of buildings energy demand for climate change mitigation. We propose an alternative narrative which places decarbonization at its center instead of demand reductions. Based on this new narrative, we propose future research areas of priority for the decarbonization of buildings energy demand.

We will concentrate on the role of energy-consuming activities taking place within buildings, and will not address the role of the construction industry.

In the remainder of this introduction, we review the influence of demographic, socio- economic and climatic drivers on buildings energy demand. We also consider important trends such as the energy transition in developing countries or the dynamics of appliances ownership, which are likely to influence buildings energy demand in the future. We con- tinue with a review of a topic of utmost importance for buildings energy demand and the main inspiration for the widespread narrative for the role of buildings: the energy efficiency gap. Where does this concept come from, how can we account for it, what are its policy implications, what is the real depth of the gap, how has it been represented in energy modeling so far? We then briefly introduce both models used in this thesis and present the outline of the thesis.

1.1 Energy demand drivers and trends

Energy is demanded for a variety of purposes that shape individual lives, work processes, economies. Societies require energy for transportation of goods and persons, for trans- forming materials, for heating, cooling indoor spaces. Understanding the reasons why energy is demanded requires not only to identify the various purposes underlying the demand, but also the factors that drive consumption (Schipper et al., 2001). Defining the right drivers for energy demand and identifying the right energy services is essential for understanding past trends (Schipper et al., 1996) and for designing trustful energy demand projections (Wolfram et al., 2012), which are a key component of scenarios used for climate change and climate change mitigation analyzes.

In the following sections, we briefly review the influence of demography, economic income, and climate on buildings energy consumption. We also discuss some trends that will probably shape the evolution of demand in the future.

1.1.1 Population

Population projections for the 21^st century cover a wide range of uncertainties on key elements like fertility and mortality rates, age and sex composition, education and in particular the education level of women. The global population is expected to keep rising over the next decades (KC and Lutz, 2017). However, as the birth rates are expected to

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12 Chapter 1 Introduction fall in developing countries, world population is likely to start declining in the course of the 21^stcentury.

Population dynamics are an important contributor to the environmental impact of human activities (Ehrlich and Holdren, 1971). Buildings energy demand can be impacted by demography in several ways. Obviously, the sheer population size is of utmost importance for the level of demand, but the size of households (Moura et al., 2015) or the distribution across age and sex (Tonn and Eisenberg, 2007; Kingma and van Marken Lichtenbelt, 2015) also matter.

1.1.2 Economic growth and energy demand

The bond between energy demand and economy is a complex one, which has spurred the interest of the energy modeling community early on. The relationship between energy demand and the economic system was the central topic discussed at the first Energy Mod- eling Forum in 1977, a meeting gathering energy modelers, meanwhile globally. Some of the authors of the meeting report (Hogan and Manne, 1977) asked whether the energy and economy sectors could be compared with an elephant-rabbit stew which would consist of one rabbit (the energy sector) and one elephant (the rest of the economy) and would taste very much like an elephant stew. The fact that the energy sector weighs only about five percent of the economy leads easily to the thought that this sector is only of minor relevance for the whole economy. With standard macroeconomic production functions, the cost share of a factor equates its output elasticity. If one assumes the energy cost share to stay constant — as is the case in a Cobb-Douglas function — strong variations in energy quantities would have only modest repercussions for the whole economy. But if one allows the cost share to vary — more specifically if the elasticity of substitution is below unity —, variations in energy demand may produce large variations on the economy (Hogan and Manne, 1977). Another assumption which could combine a small energy cost share with a stronger macro-economic impact are technological constraints (Ayres et al., 2009).

The issue of the substitution between energy inputs and other macro-economic factors also influences the ability of the economic output and the energy demand to decouple.

As noted by Stern (2004), however, there are numerous limitations to the substitution between energy and other factors. These limitations are rooted in thermodynamic constraints, complementarity of resources and capital in processes, the resource-intensive production of capital. Macro-economically, we can identify several mechanisms that could lead to a decoupling of energy demand and economic growth: substitution between energy and other factors like capital, technological change, shifts in the composition of the output towards less energy intensive sectors, shift in energy towards more efficient carriers. Stern (2004) finds that when correcting for the switch towards higher quality fuels — higher quality fuels lead to a higher economic output—, energy and economic trends are however tightly coupled. This lets the author think that further large reductions in energy intensity would be limited.

As suggested by Stern’s (2004) findings, when dealing with the link between energy demand and economic growth, it is important to specify which definition of energy is meant.

This has strong implications as for the results found. The energy system is often described

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1.1 Energy demand drivers and trends 13 as a succession of transformations from primary energy to energy services (Grubler et al., 2012; Madureira, 2014). Primary energy resources are the raw inputs to the economy covering chemical energy in fossil fuels and biomass, electromagnetic energy of solar ra- diation, potential energy from water in dam reservoirs, etc. The primary energy is then converted into secondary energy which takes the form of energy carriers like electricity, gasoline, heating oil, but which is not yet available to the end-consumer on markets. Fi- nal energy is the energy that is delivered to the end-consumer and ready to be used. In buildings, this would for instance be the electricity that powers a heat pump, or the natural gas that fuels a boiler. Final energy is then converted into useful energy which is the energy available to provide a service, e.g. the heat that radiates into a room from a radia- tor. Energy services — the ultimate rationale behind the whole chain of conversions from primary to useful energy — cover as various purposes as mobility of persons and goods, an agreeable room temperature, the production of a ton of steel, lighting, etc. Across this whole chain, there are conversion losses in the sense that only a share of the primary energy stays in the conversion chain up to the delivery of the service.

The reconstruction of long term trends for useful, final and primary energy shows that the improvements in energy intensity have been logically much lower for useful energy estimates than for primary and final energy estimates (De Stercke, 2014). The decoupling of economic output and energy demand is thereby much lower for useful energy than for primary energy. Assessing the long term trends in terms of exergy —the availability to provide work— instead of energy, Warr et al. (2010) even found that the useful work intensity per unit of GDP rose until the oil-crisis before declining in some industrialized countries.

Including useful energy (Ayres and Voudouris, 2014) or useful work (exergy) (Ayres and Warr, 2005) instead of primary or final energy into macro-economic functions help reducing to a large extent the unexplained part of growth which is not accounted for in models using only capital and labor. There appears to be a strong connection between growth and energy demand, especially when considering energy or exergy demand at its useful level:

the level that is relevant for economic activity in turn.

Another fruitful perspective on the relationship between energy demand and economic income are historical studies that explore the evolution of energy services over several centuries. The historical perspective gives a sense of the long term trends and evolutions that have affected energy demand. Historical studies are therefore a crucial source of inspiration for long term studies. Reconstructing the long-run demand trends in the United Kingdom — the first country to experience industrialization — Fouquet (2014) inves- tigated the evolution of income and price elasticities for three types of energy services:

domestic heating, lighting and passenger transport over two centuries. He shows that elasticities have not remained constant over the centuries. The income elasticities followed an inverse U-shaped curve. In a first phase, rising incomes allowed people to meet a la- tent demand for comfort which could not be realized before. In a second phase, further energy consumption transformed the lives of people,e.g.the design of houses underwent deep changes related to the new heating technologies, cheaper lighting allowed activities to be undertaken at night. In a third phase, new attributes of energy technologies affected the demand for energy services. In particular, energy sources and technologies distinguish themselves in their energy density, ease of storage, health and environmental impacts, etc.

New attributes are often instrumental in energy transitions. Importantly, while the elastic-

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14 Chapter 1 Introduction ities dropped below the unity threshold around 1950 — meaning that from this time on, the share of energy expenditures in total income would start decreasing with income — they do not converge to zero. Demand does not show satiation.

Over the long run, the increase in energy demand for specific energy services can be dra- matic: in the UK since 1700, demand in freight transport has increased 250-fold, domestic heating demand has been multiplied by 220, land passenger travel by 48000 and lighting by 295000 (Fouquet, 2014). Over the same period, the population experienced only an approximate nine-fold increase.

Energy demand and economic growth seem therefore closely tight, especially at the useful energy level. Future decreases in energy intensities seem limited. Fouquet summarizes his results as follows: “While the global economy’s appetite for energy may not be insatiable, it is probably still far from being satiated” (Fouquet, 2014).

1.1.3 Energy (service) prices, and the rebound effect

Energy prices also influence the demand for energy. Ultimately however, consumers demand energy services rather than final energy carriers. It is therefore the price of energy services which is relevant to assess the influence of price on demand, and further on welfare. Correcting for the efficiency is therefore essential as higher energy efficiency will translate into a lower energy service price while it will not affect the energy carrier price.

As shown by Nordhaus (1996) and Fouquet and Pearson (2006), energy service prices have fallen much stronger than energy prices due to energy efficiency improvements. En- ergy service prices have declined over the last 250 years (Fouquet, 2011). Fouquet (2016) considers that “the important drivers for the energy transitions were the opportunity to produce cheaper and/or better energy services”. The translation of price declines into demand increases depends on the price elasticity. The price elasticity is of particular importance to energy demand as it is associated with therebound effect.

Rebound effects can be classified in three categories (A. Greening et al., 2000; Sorrell, 2007; Gillingham et al., 2016):

• Direct rebound effect: because the price of the energy service decreases following an improvement in efficiency, the demand for that service might decrease less than expected. This effect can be decomposed between a substitution effect and an income effect. As the relative price of the energy service declines, the consumer might make adjustment in her consumption basket: consumers will substitute towards the energy service whose relative price fell. Following the price decline, consumers enjoy a higher purchasing power which they will partly spend on the energy service from which the monetary savings originate.

• Indirect effect: the higher purchasing power of consumers also translates into higher demand for other goods, which also require energy consumption. Among others, the production of the efficient technology might “embody” more energy than the technology it replaces.

• Macroeconomic effect: Gillingham et al. (2016) identify two effects at the macroeconomic level. First, a lower demand for energy at the macroeconomic level will

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1.1 Energy demand drivers and trends 15 depress energy prices, which in turn will encourage higher demand. Second, higher energy efficiency might lead to higher productivity and economic growth, and economic growth requires higher energy demand. This effect can be fueled via several channels: a sectoral reallocation towards more energy intensive industries that benefit most from an efficiency improvement, induced innovations and spill-overs following an efficiency policy.

Rebound effects vary across time, space, actors, sectors. They are therefore difficult to assess in their magnitude, casting some uncertainty of the potential of the contribution of energy efficiency to climate change mitigation. The different assessments of the rebound effect tend to show that it is non-negligible and ranges approximately between 0 and 50%

for the sole direct effect (A. Greening et al., 2000; Sorrell et al., 2009; Chakravarty et al., 2013; Gillingham et al., 2016). The work led by Fouquet (2016) tends to show that rebound effects have been large in the past, with episodes of backfire — an increase in consumption following an improvement in efficiency — in the early stages of UK industrialization. Rebound effects also decline with economic development but they remain between 10% and 50% even at a developed stage. However, it does not necessarily follow that raising energy efficiency as part of a mitigation strategy will necessarily show high rebound effects. Some authors for instance suggested that rebound effects might be especially large for general-purpose technologies as steam engines, railroads, computers (Sorrell, 2007, p. 83), and might be lower for consumers (as opposed to producers) and already ripe technologies. Other authors consider that policy-driven measures aiming at reducing energy consumption are unlikely to lead to large rebound effects (Grubb, 1990).

From a welfare point of view, rebound effects are not a negative outcome. They might help improving health and comfort. But the rebound effect might limit the impact of non-pricing efficiency policies on energy demand and carbon emissions.

1.1.4 The impact of climate and climate change

Thermal comfort is an important part of buildings energy demand worldwide. Tempera- ture is, together with other factors like humidity, a primary determinant of thermal comfort (Fanger and Toftum, 2002; Nicol and Humphreys, 2002). Climate and climate change are therefore important drivers for heating and cooling needs across the globe.

The strong influence of climate on heating and cooling demand has prompted many studies regarding the impact of climate change on thermal demand in buildings. On the one hand, a warmer climate is likely to reduce space heating demand. On the other hand, it could increase cooling demand in two ways (Davis and Gertler, 2015). First, for those who would already own an air conditioner, it could increase the intensity with which they use it (the intensive margin). Second, a hotter climate could lead more consumers to acquire space conditioning equipment (extensive margin). People are less tempted to make an expensive investment in air conditioning if temperatures exceed 30°C only a few days in a year. As climate warms up, the number of hot days at a given place increases encouraging higher ownership rates of air conditioners (McNeil and Letschert, 2008).

Some studies have tried to quantify the impact of climate change on buildings energy demand based on calibrated models rather than on econometric estimates published in

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16 Chapter 1 Introduction economic journals. In these studies, the impact of climate change is found likely to be asymmetric across regions (Isaac and van Vuuren, 2009; Clarke et al., 2018). While residential cooling demand will increase, mostly in developing and hot countries, heating demand in developed and cold regions will decrease (Isaac and van Vuuren, 2009). The aggregate effect on buildings energy demand is unclear. Isaac and van Vuuren (2009) project a small aggregated impact at the global level with however important effects for space cooling and space heating individually. By contrast, Clarke et al. (2018) project that the increase in cooling demand would outweigh the decrease in heating demand by 2050, globally, and in almost all regions excluding Russia by 2100. Zhou et al. (2013) anticipate that the U.S. and Chinese thermal energy demand would decline due to climate change, while Clarke et al. (2018) find the opposite. The literature is therefore still incon- clusive regarding the extent to which cooling demand will increase and heating demand will decrease. The differences are explained by different assumptions as for the relevant equations, data used to calibrate models, climate scenarios, etc.

Instead of relying on engineering models reviewed above, van Ruijven et al. (2019) use econometric estimates based on aggregate national statistics, and not on end-use con- sumptions from individuals and organizations. They find a substantial energy demand increase of 11-58% economy-wide as a response to climate change. Unlike the afore- mentioned literature however, they do not distinguish between the various end-uses but only estimate the sensitivity to climate of the aggregate buildings demand. Surprisingly, the economy-wide increase in energy demand does not come from the residential sector, but mainly from the commercial and industrial sectors. But owing to their top-down methodology, it is hard to trace back these trends to specific dynamics.

In the economic literature, effort is still needed to qualify with more precision the impact of climate on energy demand both regarding the intensive margin, and even more concerning the extensive margin (Auffhammer and Mansur, 2014).

1.1.5 Appliances

Appliances cover a wide range of services in buildings: refrigeration of food, entertain- ment, computations, communication, space cooling, etc. Ownership rates of most appliances, if not all, follow a so-called S-shaped curve, i.e. the rate of increase evolves non-linearly by accelerating past a certain income threshold and decelerating past another one. As the bulk of the population distribution will pass the threshold at which ownership rates accelerate, a strong increase in ownership rates of appliances is expected in developing countries. Together, the income distribution and the thresholds for appliances acquisition result in non-linear trends in energy demand from appliances (Gertler et al., 2016), and could account for a faster-than-expected growth in energy demand in developing countries (Wolfram et al., 2012).

The penetration of air conditioners is related in a similar fashion to income distribution, but the interactions with climate adds another layer of complexity (Sailor and Pavlova, 2003; McNeil and Letschert, 2008). Nevertheless, the future rise in cooling demand will result primarily from income growth rather than from climate change (Davis and Gertler, 2015).

Crucially, the penetration of appliances depends on the access to electricity and the form

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1.1 Energy demand drivers and trends 17 of access: decentralized systems or grid access. This issue leads us to a discussion of the energy transition and energy ladder concepts.

1.1.6 The energy transition and the energy ladder

In 2017 at the global level, 21% of buildings energy demand was still met with traditional biomass. This figure rises to 34% if considering only developing countries (IEA, 2019).

This has strong implications not only for energy demand, but also for health because of indoor pollution, for economic development and education as the collection of wood, crop residues, dung takes a great amount of time which cannot be spent on other activities. In parallel, the access to clean energy like electricity remains far from universal. In 2018, 860 million people still had no access to electricity, of whom 600 million living in Africa (half the African population) (IEA, 2019). Nevertheless, these high figures hide a strongly declining trend in some parts of the globe. In 2018 alone for instance, almost 100 million Indians gained access to the grid.

The concept ofenergy ladderhas emerged to describe the energy transition in developing countries for households. According to this concept, households would have a preference ladder for energy sources, preferring modern and clean fuels over traditional and polluting fuels (highly controllable, versatile in tasks, no need for collecting and storing), and would switch from one energy source to another as their purchasing power and the access to modern fuels improve (Leach, 1992). However, this concept has been criticized as too simplistic because it does not reflect the fact that households tend to stack stove models and do not simply switch from the one to the other. Considerations about fuel availability, cultural aspects, and other characteristics of fuels and their economic consequences also flow into the decision and justify the use of multiple fuels (Masera et al., 2000). Theoretically, however, the relationship between income, well-being and the consumption of traditional fuels is not strongly established (Hanna and Oliva, 2015). The consumption of dirty fuels damages health, which is valued more as income increases.

Households would thereby substitute clean fuels for dirty fuels as income rises. The wealth effect of an increase in income would however induce households to acquire more dirty fuels. Which effect between the wealth and the substitution effect dominates remains however unclear theoretically. While the concept of energy ladder remains too simplistic in many regards and does not render properly the changes in consumption patterns at the micro-level, it is still helpful in describing the aggregate behavior of energy demand.

To conclude this section, we might underline the importance of these diverse trends and dynamics for the future of buildings energy demand. In particular, the strong tie between useful energy demand and economic growth seems a helpful characteristic to build upon.

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18 Chapter 1 Introduction

1.2 The Energy Efficiency Gap

One of the most prominent topics when dealing with buildings energy demand is the energy efficiency gap: the idea that there exists a potential for reducing energy demand at a negative cost. This idea has spread into the policy arena and contributes to the positive im- age of energy efficiency because this policy seems to have no cost. Identified in response to the oil crisis and growing concerns about energy security, the potential for energy efficiency has now turned a crucial ally for mitigating climate change. Following the oil crises, the interest of governments for energy conservation measures gained momentum and estimates of the potential for energy efficiency appeared in the literature (Rosenfeld and Poskanzer, 2009). In 1982, Meier et al. (1982) designed supply curves of conserved energy which showed how much energy could be saved cost effectively at which cost.

They found that in California, 34% of natural gas demand and 25% of electricity demand in the residential sector could be saved at a cost below the energy prices,i.e.with net savings. Compiling the results from various analyzes, Rosenfeld et al. (1993) found that 40%

of buildings electricity demand could be saved at a cost below the energy prices. More recently, the McKinsey and Company consulting firm gave the debate about the energy efficiency gap a new momentum (Granade et al., 2009). Following a similar methodology as Meier et al. (1982), Granade et al. (2009) identified a cost-efficient demand reduction potential of 23%. The complementary part of these findings — energy efficiency can strongly decrease energy demand at negative costs — is that some barriers prevent the cost-effective penetration of efficient technologies (Sorrell et al., 2000). Policies should therefore target these barriers to “unlock” energy efficiency.

To economists, the existence of a free lunch like unexploited cost saving opportunities is a conundrum. Without further explanations it contradicts the basic assumption of efficient markets. The topic of the energy efficiency gap has therefore soon attracted the attention of economists eager to understand the reasons for this phenomenon with several attempts to defend the efficient market hypothesis. For instance, some argued that the apparent underinvestment in efficiency was the result of an optimal choice as energy efficiency investments are risky and illiquid (Sutherland, 1991; Hassett and Metcalf, 1993; Metcalf, 1994). But this defense appeared limited and unable to account for the existence of the efficiency gap (Sanstad et al., 1995; Howarth and Sanstad, 1995; Golove and Eto, 1996).

Behind the question of the reasons for the energy efficiency gap, it is a question about the justification for efficiency policies that is asked. When do barriers to energy efficiency offer a sound rationale for policy intervention, or asked differently, when is a policy intervention likely to increase social welfare? To answer this question, the first step is to define what the optimal level of energy efficiency is. The answers to this question will depend upon the conceptual approach taken (Jaffe and Stavins, 1994). Jaffe and Stavins identify several optimality approaches. The economist’s potential considers that the optimal level of demand corresponds to the one where market failures pertaining to efficiency markets are solved. The technologist’s perspective would in addition consider the removal of barriers which are not market failures like the illiquidity of efficiency investment. The next level of optimality takes other market failures into account which however do not concern directly energy efficiency but energy markets. The three conceptions of optimal efficiency mentioned above do not, however, consider the potential complexity or infeasibility of removing some of the barriers. Policy interventions are costly (e.g. Joskow and Mar-

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1.2 The Energy Efficiency Gap 19 ron (1992)), and their effectiveness to remove barriers is limited by their implementation costs. A fourth level of optimal efficiency therefore only removes market barriers which can be removed cost-effectively. Finally, another set of market failures can be taken into consideration: externalities, especially environmental externalities. Energy prices do not necessarily reflect the costs that energy consumption incurs to environment for instance.

If all these dimensions are taken into account, we arrive at the level of efficiency which is qualified by Jaffe and Stavins (1994) as being thetrue social optimal. What this classifica- tion illustrates is that the energy efficiency gap can be decomposed in several components corresponding to different, though sometimes overlapping, perspectives. The individual elements imply different outcomes in terms of policy recommendations. We review in the following some of the components of the energy efficiency gap identified in subsequent reviews of the issue (Golove and Eto, 1996; Sorrell et al., 2000; Gillingham et al., 2009;

Tietenberg, 2009; Gillingham and Palmer, 2014; Gerarden et al., 2017).

1.2.1 An energy efficiency gap smaller than expected?

The first set of explanations questions the idea that the energy efficiency gap challenges the market efficiency hypothesis, either because the gap is overestimated, or because it does not derive from market failures. Many a time, policy evaluations have witnessed a gap betweenex anteestimates of energy savings for a program and realized savings (Wade and Eyre, 2015; Davis et al., 2014; Grimes et al., 2016; Gerarden et al., 2017; Fowlie et al., 2018). This is insofar important for the depth of the energy efficiency gap as lower savings than expected reduce the profitability of investments in energy efficiency. One of the usual suspects for the observed gap are rebound effects. But in a comprehensive evaluation study, Fowlie et al. (2018) find that while actual savings are only one third of predicted savings, there was no significant increase in indoor temperatures, suggesting that rebound might not be so important in this specific case. Other explanations for the gap between projections and realized savings comprise the incorrect modeling of usage patterns, failure during installation, unanticipated reaction of users to new technologies (Wade and Eyre, 2015).

Similarly to the inaccuracies in estimating energy savings, the energy efficiency gap might be overestimated due to hidden costs in efficiency investments. These costs may include higher efforts to fetch information, the opportunity cost of alternative investments and of dedicating workforce to an activity not relevant for the core business of a firm. Paying for an energy manager to supervise efficiency investments also increases the bill of efficiency (Sorrell et al., 2000). Hidden costs might as well include losses in the quality of an energy service. Some users might for instance miss the warm glow of incandescent light bulbs when replacing the fixtures with more efficient LEDs. Hidden costs might cover as detailed motives as the wish to avoid having works at home or having to tidy the attic space before installing attic insulation (Gerarden et al., 2017). In an efficiency program implying zero monetary costs to households, Fowlie et al. (2015) show surprisingly low participation rates. Because no monetary costs are involved to the households, the authors attribute this low participation (up to 15%), in some cases despite an encouragement program, to non-monetary costs. One could however argue that the “hidden costs” argument bears the risk of being tautological and should be handled with care. These costs might for instance be the expression of institutional or organizational barriers which policies

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20 Chapter 1 Introduction could remove, but whose removal cost would be only transitory.

Another barrier to the penetration of energy efficiency that does not count to market failures is the heterogeneity of consumers. Often, the economic attractiveness of efficiency investments is assessed for average consumers. However, consumers with low energy bills will save less energy and therefore less energy expenditure from efficiency investments. Their incentive to invest in efficiency might therefore be lower than average. Even if an investment is coined beneficial for the average consumer, it will probably not be the case for the entire population. Symmetrically however, some users with a higher than average consumption could benefit from an efficiency investment that is unattractive to the average user. Because heterogeneity plays in both directions, the aggregate impact on the depth of the energy efficiency gap is not obvious. Heterogeneity also concerns preferences and time preferences (discount rates), non-monetary costs, purchasing power, capital borrowing conditions, etc.

As mentioned above, one of the aspects of efficiency investments that has been suspected by some economists to justify a rational low uptake of efficient technologies are risk and uncertainty. Some of the energy efficiency investments in the residential sector represent a significant share of households’ revenues. Households may therefore not have the financial capacity to diversify the risk from this efficiency investment with the rest of their assets portfolio. In order to compensate for the risk that cannot be diversified away, the household will require higher rates of return on the specific investment than it would from other, smaller, more liquid investments (Sutherland, 1991). In that sense, the riskiness of large efficiency investments might explain why rational market players might not take upa priori profitable efficiency investments. This mechanism would especially explain why low income households are more likely to require high rates of returns than other households. The idea that the riskiness of efficiency investments would justify higher profit rates has however been criticized as efficiency is a good hedge against energy expenditures. In particular, low income households are particularly exposed to the volatility of energy prices. Energy prices are also negatively correlated with the economic cycle (Metcalf, 1994). Another financial aspect of efficiency investments that might justify lower uptakes is the illiquidity of these investments: there is rarely a secondary market on which it would be possible to sell the efficient equipment if liquidity is needed. Hassett and Metcalf (1993) attempted to explain parts of the energy efficiency gap with this option value: the value of waiting for certainty to materialize. Beyond the fact that waiting for a year might not bring much certainty as for the future of energy prices, their argument has been shown to be relevant only for a limited set of cases (Sanstad et al., 1995).

1.2.2 Market failures

In the following, we will review some of the most important and most often cited market failures accounting for the energy efficiency gap. First, we will discuss the problem of imperfect or unavailable information. According to this argument, consumers and firms would tend to miss efficiency investment opportunities because they are unaware of the potential benefits. Second and related to the first point, we turn to principal-agent issues that arise when there is a divergence between those who choose the technologies and those who pay the energy bill. Third, we will briefly consider the limited capital access for some consumers: because they do not have the liquidity to invest, they have to forgo

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1.2 The Energy Efficiency Gap 21 profitable investments. Though we only discuss these three market failures, which we think the most relevant, other market failures affect energy service markets: learning-by- using, the inseparability of products’ features, adverse selection to the disadvantage of efficient products, regulatory failures, etc. (Golove and Eto, 1996; Sorrell et al., 2000;

Gillingham and Palmer, 2014; Gerarden et al., 2017).

Imperfect information

The efficient market hypothesis assumes that information is costless and that all economic actors are well aware about the characteristics of goods and services exchanged. For efficiency markets, as well as for many markets, this assumption needs not hold true. If consumers cannot observe the different levels of efficiency of technologies, and if they cannot assess the delivered savings across the lifetime of the technology, they will not be inclined to invest in the potentially more expensive but more efficient technology.

The lack of information may not only concern products but also the consumption by individuals. It is only rare that energy bills distinguish between the consumption of a refrigerator and of a light-bulb. Instead, consumers are only given information about the aggregate consumption of the whole household. Hewett, cited in Sorrell et al. (2000) considers that imperfect information may be most problematic when“the product or service is purchased infrequently; the performance characteristics are difficult to evaluate before or soon after purchase; the rate of technology change is rapid relative to the interval between purchases”. Sorrell et al. (2000) make the point that the costs of acquiring information about energy demand technologies is much greater than for retail energy supply, as the latter sector has much fewer actors and the characteristics of the products are much simpler and fewer. This would put energy supply solutions at an advantage compared to energy efficiency.

In reaction to the lack of information about efficiency, a wealth of labels and information campaigns flourished across the globe to signal efficient products. Some well-known examples include the Energy Star program in the United States and the Energy labels in the European Union.

While imperfect information is often cited as a root cause for the energy efficiency gap, what is its weight in the gap? To answer this question, we might draw from the experience of information policies. Summarizing the effect of information provision on conservation behavior from past studies Delmas et al. (2013) find that, on average, information campaigns have achieved 7.4% electricity demand reductions. However, as the methodological quality of studies improves, the effect tends to be lower. Also, the framing of information turns out to be decisive in the outcome achieved. For instance, monetary information on consumption might lead to anincreasein consumption due to a licensing effect: becoming aware of the real cost of energy consumption, and finding it lower than expected, people could start consuming more energy. In a study assessing the impact of information provision on investments, Allcott and Taubinsky (2015) found that information increased the market share of efficient compact fluorescent lightbulbs (CFL) by 5 to 12% depending on the experimental design. However, the willingness-to-pay for CFL underrated its lifetime cost savings by a far margin. Davis and Metcalf (2016) study the impact of more detailed labels including local conditions into the estimates of cost savings. They find that the detailed information allows people to adjust their choice to

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22 Chapter 1 Introduction local conditions: consumers with low energy prices and consumption will invest less in efficiency andvice versa. This enhanced information allows saving costs to the consumers in the order of 1%, but it need not save energy in the aggregate as some consumers will consume more and other less. Allcott and Sweeney (2017) find that the market share of Energy Star water heater would not be increased much — below 5 percentage points — when sellers provide information about the sizable savings of these products. Their finding seems confirmed by subsequent survey showing that consumers actually overestimate the savings from Energy Star products. In this case, information does not seem to be the relevant barrier to the penetration of efficient technologies. Allcott and Rogers (2014) report on an information campaign targeting energy consumption. While they find only limited energy savings, about 2-3%, the program remains very cost-effective.

While the impact of information on energy savings seems limited to a couple of percentage points, these policies are certainly cost-effective owing to their low implementation cost. As we will see in the section discussing behavioral aspects, the impact of information campaigns however depends upon the framing of information.

Principal-Agent misalignment

One of the most often cited market failures regarding the energy efficiency gap is the asymmetry of information and the impossibility for investors to recoup the energy expenditure savings. This is for instance the case for efficiency upgrades in rented houses:

while the investment cost is incurred by the landlord, these are the renters who profit from the energy savings. We can distinguish four situations relevant to the energy efficiency gap: whether the consumer pays the bill or not, and whether the consumer purchases the technology or not (IEA, 2007). In case the consumer both pays the bill and chooses the equipment, there is no asymmetry. In all other cases, there are agency issues.

The relevance of information asymmetries for the energy efficiency gap is a produce of two factors: the share of energy consumption that is concerned by asymmetries, and the difference in consumption/investment patterns between cases with and without asymmetries. In 2007, the IEA estimated the amount of energy concerned by information asymmetries in eight case studies (IEA, 2007). They found estimates of about 5% in the commercial sector to about 30% for some of the studies in the residential sector. Gilling- ham et al. (2012) find that the difference between investment and consumption patterns between home owners who pay in relation to their consumption does exist and is statis- tically significant. But the change in the probability to invest in insulation is only about 10-20% higher. While there is evidence that the information asymmetry does impact energy consumption, the combined effect is likely to be small, in the order of magnitude of 2% (Davis, 2011; Gillingham et al., 2012).

Information asymmetries might however become a larger issue in case energy prices and/or the investment costs increase.

Restricted access to capital

Energy efficiency investments might represent large amounts of money to households, in particular to low-income households. While these investments might “pay for them-

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1.2 The Energy Efficiency Gap 23 selves” in case of high expected returns, the capital upfront costs might prevent households and small companies to invest because of limited access to capital. Banks may not be able to distinguish the risk associated with the efficiency investments from other risks and may limit loans if they consider the overall default risk too high. Nevertheless, if the default risk is truly higher, credit constraints, or at least higher interest rates on loans need not represent a market failure (Sutherland, 1996). There might also be higher relative transaction costs to the bank for small-sized loans that would justify higher interest rates for efficiency investments.

In companies, where the borrowing capacity is also limited, capital constraints on efficiency may be self-imposed to keep a borrowing to equity ratio, and therefore the capital costs, below a certain threshold (Sorrell et al., 2000).

The capital constraint failure is however unlikely to concern primarily energy efficiency, but it would affect a whole range of investment decisions. Solving this issue with dedicated efficiency policies might therefore not be the best policy design option.

1.2.3 Behavioral explanations

Next to the purported overestimation of the energy efficiency gap and market failures act- ing as barriers to efficiency, another factor behind the efficiency gap are behavioral issues.

In numerous instances, economic actors fail to act according to the principles of rationality as defined by economic theory (Kahneman, 2011). In particular, the way people process and evaluate information may have important impacts on economic choices. To what extent can behavioral biases be a systematic barrier to energy efficiency?

According to the prospect theory (Kahneman, 2011), losses are weighed much more than gains, and certain outcomes more than risky outcomes. This can lead to a preference for status quo, anchoring, loss aversion (Gillingham et al., 2009). In the case of efficiency investments, the certain losses from the upfront capital costs may therefore be weighted more than the uncertain gains.

Consumers are also faced with limitations regarding the cognitive burden they support, and therefore recourse to heuristics to simplify their decision process. These heuristics might possibly disadvantage efficient technologies: for instance in case the set of options considered is first reduced by selecting only technologies below a given cost (Gillingham et al., 2009). Or consumers may attach more importance to the most salient characteristics of products, potentially over-emphasizing the initial cost. Consumers might also be inattentive to their energy consumption (Palmer and Walls, 2015).

The framing of choices, i.e. how choices are presented, also plays a role on the decisions taken, even if the additional information or the ordering of information should be irrelevant from an economically rational point of view. The framing effect has become the basis for a popular type of policies inspired from libertarian paternalism. Libertar- ian paternalism insists that consumers should be given the full set of options to decide from. But the options should be framed such as to favor options that are deemed “good decisions”, leaving open the question regarding what a “good decision” is. Policies following this idea are often callednudges. Testing for the influence of nudges on energy labels, Newell and Siikamäki (2014) showed that the probability to purchase an efficient technology could increase depending on the characteristics of a label.

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24 Chapter 1 Introduction While many behavioral anomalies might play a role in energy service markets, it is an empirical question to assess whether these anomalies systematically bias decisions against energy efficiency technologies.

1.2.4 Social optimum considerations

In the previous sections, we have concentrated on the consumer’s choice and why these choices may not correspond to economic rationality, preventing the penetration of efficient technologies. But we might also ask whether the environment faced by consumers, in particular the prices, correspond to optimal conditions. The obvious candidate for such sub-optimalities are externalities, in particular those concerning the environmental impact of energy consumption or the innovation induced by a growing market for efficient technologies. Both externalities play differently on the choice for efficient technologies. In case the environmental externality is not internalized, the energy price is too low and the reward to the efficiency investment is too low. In case the technological externality is not internalized, the technology upfront cost is too high. Both externalities, if not internalized, disadvantage efficient technologies compared to the optimal situation.

Most of GHG emissions come from the energy sector. It is therefore important that energy prices reflect the environmental impact of energy consumption. In 2019, 20% of global GHG emissions were covered by a carbon price. However, only 5% of emissions under a carbon pricing scheme were priced at a level consistent with the Paris Agreement (World Bank Group, 2019). Energy prices to the consumers do not, therefore, transmit the environmental impact of consumption to the consumer. Energy prices are subject to other taxes and subsidies that include a wedge between the marginal cost of production and the price to the consumer. Globally in 2018 fossil fuel consumption subsidies largely exceeded carbon price revenues (IEA, 2019). Overall, energy prices to consumers are for the most part inefficiently low, which leads to underinvestment in energy efficiency.

Another theoretical source of suboptimal pricing lies in spill-overs, as learning-by-doing:

when firms produce more of a good, they become more efficient and lower the costs of that product. Units sold at the beginning of the product’s lifecycle therefore create a positive externality that will decrease the costs of future goods. We do not know however, whether spill overs play an important role for the energy efficient technologies and if the latter are overpriced. In some sense, spill-overs reflect the property of some costs to be only transitional: once a hurdle has been taken, the costs tend to disappear.

Along these lines, environmental regulations can trigger innovations that would offset the additional costs originally caused by the regulation. This is the so-called Porter Hypoth- esis (Ambec et al., 2013). The Porter Hypothesis implicitly assumes that firms are not always profit maximizing and that spurs from regulations can move them in a direction beneficial for both firms and the environment. Similarly to the energy efficiency gap debate, the theoretical underpinnings of the Porter Hypothesis root in behavioral arguments, market and organizational failures, but apply them to the supply side of technologies. The impact of regulations on energy efficiency might be amplified by the fact that learning rates of technologies on the energy demand side are often higher than energy supply side (Wilson et al., 2012).

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1.3 The Energy Efficiency Gap in energy-economy models 25

1.2.5 Concluding remarks on the energy efficiency gap

In the above sections, we have briefly reviewed some of the important factors behind the energy efficiency gap concept: whether the barriers to efficiency are to be considered as failures or not, which market failures could explain the gap, and what the role of externalities is. Overall, there are many candidates to account for the energy efficiency gap.

Some of them challenge parts of the efficiency gap because it does not take into account hidden costs, or overestimate the projected energy savings. These and other factors have led some economists to question the very existence of the energy efficiency gap (Allcott and Greenstone, 2012). Based on a review of studies on the topic, Allcott and Greenstone (2012) come to the conclusion that “while investment inefficiencies do appear in various settings, the actual magnitude of the Energy Efficiency Gap is small relative to the assessments from engineering analysis”. The study from Allcott and Greenstone (2012) shows some shortcomings (Nadel and Langer, 2012) and the author’s skepticism about the energy efficiency gap might be too strong. But the various economic assessments of the energy efficiency programs, including high quality studies (Fowlie et al., 2018), cast some shadow on the depth of the gap. While the energy efficiency gap matters, it is probably smaller than first anticipated (Sorrell, 2015).

1.3 The Energy Efficiency Gap in energy-economy models

As seen in the previous section, the energy efficiency gap has fueled heated debates among economists and engineers across the past decades. But the energy efficiency gap has also represented a challenge to economic modelers. Indeed, influenced by the famous ’as if’

argument of Friedman (1953), modelers have tended to model the economy with agents maximizing their utility, whicha priori contradicts the very existence of the energy efficiency gap. As Huntington (1994) puts it: “If decision maker ’inertia’ and other related inefficiencies are excluded by assumption rather than by empirical measurement, can there be a meaningful discussion between economists and technologists on the apparent underinvestment in energy efficiency or the ’gap’”. Understanding and modeling the gap has been an early concern for modelers (Huntington, 1994; Blumstein and Stoft, 1995). Ideally, the various components of the energy efficiency gap reviewed in section 1.2 should be modeled individually (Huntington, 2011). In practice, however, the barriers are often modeled using a higher discount rate than the macro-economic discount rate, sometimes combined with more detailed components (Koopmans and te Velde, 2001;

Capros, 2016). The inclusion of the barriers to energy efficiency in models has helped a great deal to close the gap between engineering estimates and energy-economy models (Murphy and Jaccard, 2011).

Despite its importance for energy demand, global integrated assessment models (IAMs) have however not yet engaged with the energy efficiency gap. To some extent, this is not surprising as IAMs have long focused on supply-side issues. The interest in demand side issues has grown only recently in response to the sustainability challenges posed by a widespread roll-out of carbon capture and storage technologies. For now, the IAM demand side studies concentrated mostly on the issue of lifestyle changes (van Sluisveld

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26 Chapter 1 Introduction et al., 2016; Grubler et al., 2018b), and for the transport sector, on non-price barriers (McCollum et al., 2018).

1.4 EDGE and REMIND models

This thesis aims at improving our understanding of buildings energy demand in the future.

To that end, we rely primarily on scenarios which were designed using applied energy- economy models. These models, EDGE and REMIND, which were created or improved for the sake of this thesis, show complementary strengths and weaknesses. EDGE is a simulation model with a detailed representation of the demand for energy services in the buildings sector. It is therefore appropriate to study how socio-economic and climatic drivers would influence future energy demand. REMIND represents the linkages between energy demand and energy supply and can therefore assess the role of buildings in mitigation strategies in the economy-wide context of a climate target. In the following, we briefly describe both models. For a critical discussion of these methods, we refer to section 6.2 at the end of this thesis.

1.4.1 EDGE

EDGE (Levesque et al., 2018, 2019; Edelenbosch et al., 2019) is a simulation, bottom-up model. It covers five energy service categories which cover the full demand in buildings:

appliances and lighting, space cooling, space heating, cooking, water heating. The global demand is represented. In the latest versions, a total of 41 regions were included, with the 27 Member States of the European Union.

The relationships between the demand for the individual energy services with their underlying drivers — income, population, climate, floorspace, etc. — is calibrated to best fit the historical data. The future development of energy demand however does not only depend upon historical relationships, but also, and crucially, on how these will evolve. In the end, this is a question about the level of hot water demanded for showering at saturation, or whether appliances demand growth will outpace economic growth. EDGE therefore depends on the projections for the underlying drivers of demand, but also on behavioral and technological parameters that reflect certain transformations of society which could take place in the 21^st century (O’Neill et al., 2017). Thereby, the detail of the demand representation in EDGE allows getting a clear translation of future economic and social conditions into precise consumption patterns.

See Chapter 2 for further details on the EDGE model.

1.4.2 REMIND

In contrast with EDGE, REMIND (ADVANCE, 2016) is an optimization model. It be- longs to the Integrated Assessment Models that contribute to international reports on climate change (e.g. IPCC reports, UNEP Gap reports). REMIND depicts the connection between the economy, the energy system and climate.

Demand-side Mitigation Policies: The Role of the Buildings Sector