Lead Author:
Elizabeth L. Malone, Independent Researcher
Contributing Authors:
Michele Betsill, Colorado State University
Sara Hughes, University of Toronto
Rene Kemp, Maastricht University
Loren Lutzenhiser, Portland State University
Mithra Moezzi, Portland State University
Benjamin L. Preston, RAND Corporation
Tristram O. West, DOE Office of Science
Expert Reviewers:
John Robinson, University of Toronto
Sarah Burch, Waterloo University
Hal Wilhite, University of Oslo
Nicole Woolsey Biggart, University of California, Davis
Benjamin Sovacool, University of Sussex and Aarhaus University
Science Lead:
Paty Romero-Lankao, National Center for Atmospheric Research (currently at National Renewable Energy Laboratory)
Review Editor:
Christine Negra, Versant Vision
Federal Liaison:
Elisabeth Larson, North American Carbon Program; NASA Goddard Space Flight Center, Science Systems and Applications Inc.

Social Science Perspectives on Carbon

Although social scientists have investigated the social processes responsible for growth in carbon emissions and decline in the capacity of carbon sinks, enlarging and enriching this knowledgebase would provide better guidance for policy that addresses systems, technology design, and other efforts to reduce overall carbon emissions. In addition to energy production, expansive urban settlements, and transport systems and activities (see Ch. 3: Energy Systems, and Ch. 4: Understanding Urban Carbon Fluxes), researchers have considered the acquisition and accumulation of goods, as well as their embodied energy and carbon contents. Demand-side research has focused on the technical characteristics and uses of energy-powered devices, in addition to the patterns of energy demand and carbon emissions resulting from the use of buildings and appliances (Sovacool 2014). Economics work aside, the bulk of social and behavioral sciences research and attention with respect to energy demand has been concerned with encouraging energy conservation and emissions reductions predominantly by individuals and households (Dietz et al., 2009; Stern et al., 2016; i.e., generally, behavior at the level of devices). There has been less attention to the structure and evolution of energy demand and its carbon emissions implications. For example, research on people’s role in residential air conditioning has focused on how people use their air conditioning systems and how to get people to use less, rather than on the social processes involved in housing construction, device design, and lifestyles that encourage increased installation of air conditioning in buildings and vehicles.

6.2.1 What Does the Research Show?

In contrast to relating energy use and carbon emissions to devices, social science researchers have emphasized that energy use and carbon emissions are deeply interwoven—“embedded”—features of social life. Energy consumption and emissions are part of people’s routines and habits, within patterns of social interaction, and are governed largely by social norms and expectations, without regard for or reference to energy sources or carbon emissions resulting from these activities. Moreover, in North America, although energy infrastructure (e.g., power lines and electrical cords) is visible, energy itself is virtually invisible to people except in special cases (e.g., cooking with a gas flame) or under unusual circumstances (e.g., appliance or system failures, grid blackouts, or energy-supply crises; Nye 2013; Rupp 2016; Shove 1997; Trentmann 2009). Although modern North American lifestyles are constrained somewhat by available energy sources and costs, they have come to represent a set of living standards and desires—normal expectations that exert growing “demands” for easily accessible energy that currently almost always is supplied across long distances and often requires considerable, yet invisible to the user, carbon emissions. Increasing installations of solar microgeneration, discussed below, could shift users’ relationships with energy systems to some extent, making the sources and limitations of energy supply clearer. However, if users are to contribute to major reductions in carbon emissions, they also will modify their living standards and daily activities in the name of what they now may see as intangible environmental benefits. Thus, even if emissions were visible and easily accountable, major change would not necessarily occur, unless people see that the benefits will improve their lives in measurable ways.

As noted, both the nature of energy-using behaviors and their susceptibility to change (mostly through formal interventions) have been investigated in studies by researchers and analysts in the energy-efficiency field as well as by social scientists working in other realms. Economics has provided the most generalizable theories of investment decisions and of change (i.e., reduced consumption in response to increased unit price of energy), but the strength of relationships is often quite low (Bernstein and Griffin 2006; Kriström 2008; Lijesen 2007), related to aggregate rather than individual patterns, and compromised by what economics literature identifies as market and nonmarket failures (Jaffe and Stavins 1994).

The other, less-explicit economic explanations for energy-use behaviors and susceptibility to change given so far tend to be general and cannot be readily applied as mechanisms for reducing rates of carbon emissions, ranging from the abstract and macrohistorical (e.g., aggregate conditions and factors such as “affluence,” “consumer preferences,” and “institutional barriers”; NRC 2010) to the micropsychological (e.g., “motivations,” “intentions,” “values,” “beliefs,” and “propensities to adopt”; Shove 2010). These explanations often come with the assumption that actions are driven by these micropsychological properties (Ignelzi et al., 2013; Sussman et al., 2016). The descriptive layers do present ways of “seeing” people as diverse and evolving participants in energy use. Unclear, however, are how and how much the underlying qualities described in these analyses might be deliberately changed and, if they were, whether the desired reductions of energy use and carbon emissions might be achieved.

Leading-edge research has focused on diversity across individuals and households and on the layered structure of this diversity as opposed to simpler explanations rooted in isolated choices, with a particular emphasis in recent literature on populations, practices, patterns, and behavioral economics.

  • Observed energy-use levels vary dramatically across populations (e.g., households or firms) due to differences in activity patterns, technical efficiency, and environmental conditions. Energy-using activity patterns are shared within groups, and different groups may have widely varying patterns of activity and modes (Lutzenhiser et al., 2017; Sonderegger 1978).

  • Activities and practices, many involving energy-using equipment, emerge and are elaborated over time; some decline while others persist (Shove et al., 2012) as people modify and adapt physical systems to better meet social and cultural purposes and, in turn, modify what they do as they are “recruited” by and adopt practices (Shove et al., 2012).

  • Patterns are stabilized and constrained by the characteristics of their energized technologies and infrastructure, much more so than being clusters of discrete personal behavioral choices (e.g., Shove et al., 2012).

  • Insights from behavioral economics may be useful in designing instruments for energy-related behavioral change (Allcott and Mullainathan 2010) by focusing on the microstructure of decisions.

However, the complex and nuanced dynamics of energy use are not reported with much clarity in the literature. Future research could focus on understanding what influences the self-organizing nature of daily activity rather than directly engaging individuals and their behaviors.

Reviews find no overarching theory or set of consensus research methods (Lutzenhiser 1993; Wilson and Dowlatabadi 2007) and no cumulative practical understanding of “what works.” Instead, there are compartmentalized disciplinary knowledgebases guided by divergent perspectives and distinct methodological preferences. In the area of applied research, narrow perspectives of program- and policy-centered research have focused on the efficacy of specific interventions or instruments, finding that certain actions may be more amenable to ­intervention-based change within some groups (Abrahamse et al., 2005; Ehrhardt-Martinez and Laitner 2010). Applied research on energy-conservation actions, such as equipment purchase decisions, has long been dominated by short-term policy objectives (such as responding to demand or meeting utility-savings goals) even as these goals are increasingly translated to the longer timelines of supply planning and climate change. Energy use is represented typically as averages and norms, making calculations and planning appear more tractable but generally hiding the dynamic sources, forms, and logics that create energy use.

Programs and projects that focus on or pay attention to “behavioral energy-savings potential” usually are not connected to relevant insights and framings from the social sciences or accompanied by serious considerations of how this potential might be achieved. (For a history and critique, see Wilhite et al., 2000.) These programs typically focus on discrete actions relative to assumed normative behavior—parallel to notions of technical potential via efficiency—rather than attending to how behaviors are organized (e.g., as addressed by social practice theory; see Section 6.9). Thus, they miss opportunities provided by recognizing how systems, rather than individuals, create energy use. The findings of behavioral analysts have been used in experiments and case studies on behavioral economics (Ariely 2010; Alcott and Mullainathan 2010; Alcott and Rogers 2014), concept of “influence” (Cialdini 2010), social marketing (McKenzie-Mohr and Smith 2007), primary motivations (Pink 2010), and “nudges” (Thaler and Sunstein 2009). But that use has been without broad influence on programs and projects (Frederiks et al., 2015). Interestingly, behavioral economics experiments have found that economic incentives and awards are weak motivators compared to, for instance, friendship ties (Ariely 2010).

Given the calls for absolute reductions in greenhouse gas (GHG) emissions rather than relative savings from energy efficiency, there is a need for a broader multidisciplinary social scientific and applied view (Keirstead 2006; Lutzenhiser et al., 2017). However, efforts to identify theoretically grounded and evidence-based “design principles” for carbon-reduction interventions are just beginning (Stern et al., 2016). Three factors hamper such efforts: 1) the absence of a systematic social science carbon-reduction research agenda, 2) the lack of adequate support from science and environmental policy agencies for social science contributions as a core component of energy-transition and carbon-mitigation research, and 3) insufficient experience in drawing together disparate scientific perspectives to address such complex high-level problems. Programs that are beginning to integrate scientific perspectives include those discussed throughout this chapter; findings from such programs are reiterated in Section 6.11.

6.2.2 Learning from the Energy-Efficiency Experience

A good deal of the research on energy use to date has been the result of U.S. federal, state, and local policy initiatives to encourage energy efficiency (Lutzenhiser and Shove 1999). Those initiatives have recognized since the 1970s that “energy services” such as cooking, washing, heating, and cooling could be provided via technologies that, technically at least, consume much smaller amounts of energy than then-current models (e.g., Gillingham et al., 2006). Thus, public policy has focused on increasing the efficiency of appliances and buildings to displace a fraction of current consumption and delay the need for new sources of energy. Emissions reduction can be a co-benefit of energy-efficiency improvement. However, differences between efficiency improvements and reductions in absolute emissions over time are easily overlooked.

Also, because interventions to improve the energy efficiency of technologies have been funded largely by utility ratepayers under the scrutiny of public regulators, the primary focus has been on hardware upgrades and “cost-effectiveness”—not on energy users or their habits, desires, or social practices. The kinds of research needed to support these efforts have been engineering studies and economic cost-benefit analyses. Emphasis has been placed on energy cost savings.

However, behavioral science research related to interventions has shown that energy demand is not particularly price sensitive (Kriström 2008). This research has pointed to the importance of environmental values, social influences, and concerns for others as more frequent and actionable motivations for carbon-reducing equipment purchases and energy-use behaviors (Abrahamse et al., 2005; Stern et al., 2016).

Large “efficiency gaps”—gaps between predicted rates of economically attractive purchases of more efficient technology and actual realized adoption rates—have been reported regularly (Allcott and Greenstone 2012; Gillingham and Palmer 2014; Jaffee and Stavins 1994; Shove 1998). In short, energy appears to be an area where markets do not function as predicted by rational economic behavior as envisioned by classical economics—or these definitions are too simple, and there are inadequate data and understanding to represent sufficiently the complex decision processes. Programmatic explanations point to “barriers” to efficiency program participation (Golove and Eto 1996). Lists of barriers (e.g., “high discount rates” or “risk aversion”) often are labels or glosses that say more about policy perspectives and program priorities than the nonadoption behaviors of actual energy users or their relationships to the energy uses targeted for change (Blumstein et al., 1980). Also, recurrent questions have been raised about “rebound effects”—the case in which expected savings from technology adoption may not be realized because of choices, behaviors, and intervening developments not predicted by efficiency-intervention planners (Gillingham et al., 2016; Herring 1999). In addition, traditional definitions of energy efficiency are not necessarily closely aligned with issues related to carbon emissions because not only do they not take into account the carbon content of supply, they focus on relative savings rather than absolute emissions (Moezzi and Diamond 2005). More recently, scholars have stressed the importance of the “macrorebound” of carbon and energy in a growth economy (Wilhite 2016).

Many of the problems with adoption of efficient technologies can be traced to the existing situation. Regulatory logics and institutional constraints push the energy-efficiency industry, itself a socially structured enterprise, to assume that choices made by energy users are well informed and economically rational (Lutzenhiser 2014). This assumption has encouraged efforts to improve the quantity and quality of information available to energy users, with an emphasis on communicating the economic benefits of energy savings. But psychological research has shown that the “delivery” of information is far from a simple matter and that even the highest-quality information supplied as directly as possible, whether via old media or new, frequently is not acted on in the way that program developers imagine that it should, or would, be (Owens and Driffill 2008; see Section 6.10). Even well-informed social actors routinely pass over clear and simple “rational” choices that would save money by saving energy.

This disconnect between assumptions and outcomes is as true for large firms and governmental agencies that have sophisticated information systems, analytic capacities, and strong economic interests (Biggart and Lutzenhiser 2007) as it is for individuals, households, and other groups. Explanations point to organizational structure, competing priorities and internal conflicts, risk and trust issues, and weak regulation (Stern et al., 2016). However, there also are instances of organizations leading the way in carbon reduction through corporate investment in renewable energy sources, supply-chain efficiency improvements, and energy-conscious acquisition and operation of buildings and other capital equipment (Prindle 2010; Stern et al., 2016). Research to determine how organizations variously relate to and manage carbon emissions, often in ways that defy simple explanation (e.g., by reference to cost and benefits, regulatory influence, or competition) is in its initial stages.

6.2.3 Expanding the Efficiency Policy Framework: Insights about Energy and Social Systems

Evidence suggests that various energy-efficiency technology innovations and policy initiatives undertaken over 40 years of activity in this field have saved energy (e.g., NRC 2001). However, the narrow regulatory focus and underperformance of these innovations and initiatives relative to idealized models, as discussed above, reinforce the importance of moving beyond a traditionally narrow energy-efficiency industry focus on producing energy reductions at less cost than supply (Lutzenhiser 2014). Future research and institutional changes need to recognize the social nature of energy use—including the social organization of technologies and energy systems, the social patterning of energy demands, the social nature of energy-conservation choices, and the social delivery of energy-efficiency programs and policies.

Although these social issues have rarely been explicitly considered in energy-efficiency policy or associated research, the “market transformation” strand of efficiency intervention is an important exception and success story. These activities are aimed at “upstream” actors and organizations in supply chains that engage with technology designers, manufacturers, wholesalers, and retailers to encourage, facilitate, and provide financial incentives for bringing more efficient technologies to the marketplace at appealing prices (Blumstein et al., 2000). Also, efforts by some states and the U.S. federal government to regulate the energy-using characteristics of appliances and buildings through codes and standards have had wider systemic impacts on technology efficiency. These upstream changes to improve efficiency have occurred despite strong political opposition from an array of groups and interests holding stakes in existing technologies, infrastructures, and supply arrangements (Sovacool 2008). Considerable social science research is needed on carbon management and the market systems, supply chains, and organizational networks involved in shaping and delivering technologies (Janda and Parag 2013).

Several other strands of social research on energy use and conservation also hold promise. One has focused on the considerable variation in energy use across populations and among subgroups of energy users. Utilities and other efficiency industry actors have sometimes identified “segments” of energy users to target marketing and communications to their interests. But these efforts, redefined as the lifestyle dimension of energy—how people’s behaviors socially vary and place different loads on even the most efficient energy-using equipment—offer opportunities for a better understanding of the invisible and embedded dimensions of social carbon management. In addition, periodic energy-supply crises, such as the 2001 to 2002 California electricity shortages and the 2008 loss of a substantial fraction of electricity supply to Juneau, Alaska, have provided “natural experiments” that highlight variations in energy use and in people’s willingness or ability to conserve. Also shown is the malleability of taken-for-granted practices when supply is suddenly called into question (Lutzenhiser et al., 2004; Pasquier 2011) or general economic conditions worsen such as in the 2007 to 2009 recession (see Ch. 2: The North American Carbon Budget). In addition, the past decade has seen a growing appreciation of “behavioral potentials” for energy savings (e.g., in equipment-use patterns and practices). Utility regulators and efficiency advocates have responded by adding the modification of what people actually do with energy-using equipment to the technology-efficiency improvements in their agenda.

Different strategies have been proposed to encourage those changes. A primary focus has been on mass delivery of energy usage–related information enabled by advances in electronic metering and data warehousing. The results indicate some modest aggregate reductions in overall electricity demands (Karlin et al., 2015; Power System Engineering 2010; Todd et al., 2014), even in a number of states where utility regulators only mandated delivery of information to allow persons to compare their usage to that of others (Allcott 2011; Allcott and Rogers 2014). However, these efforts have been limited in depth and aims—at least, when measured against goals—and represent small investments compared to technology-focused efficiency activities.

Despite an explicit linking of behavior changes to climate change by some academic and public-sector actors (e.g., within the Behavior Energy and Climate Change Conference, held annually since 2007 (ACEEE/BECC 2016)), the social sources and logics of energy-using practices, habits, lifestyles, and behaviors, as well as their organization and how they change continue to receive little systematic attention in U.S. scholarship. There is progress, for example, in the biannual European Summer Study on Energy Efficiency and in other efforts to “push the envelope” of energy-efficiency thinking and intervention by augmenting the classic economics framework (Frederiks et al., 2015), but this work tends to be siloed. However, there is valuable experience that can be gained from careful attention to successes and failures of energy-efficiency policy interventions, and that experience can serve as a starting point for broader and more universal carbon-reduction initiatives in the future.

6.2.4 Energy and Carbon Emissions Embedded in Complex Systems

Apart from efficiency, the other main route to reducing emissions from energy use has been developing and fostering lower-carbon energy sources. Human-centered research on this topic has focused on social acceptance of these alternatives. As much higher market shares of renewables start to become realized, researchers have started to pay closer attention to the intermittency and time-variability of renewable energy sources and how supply dynamics can synchronize with energy use rooted in temporal patterns of daily living. The social dimensions of technology acceptance (e.g., rooftop solar and wind farms, among newer technologies; nuclear power, among established technologies) and the social dynamics of routines and demand patterns (e.g., the locus of work and the cultural definition of approved practices) will require concerted attention in social science research, carbon policy development, and energy system management. These efforts also must contend with the fact that the energetic structure of the modern North American society has developed with the experience and expectation of ready and virtually unlimited availability of energy at any time of day to fuel homes, cars, work, and play in any and all locations (see Ch. 4: Understanding Urban Carbon Fluxes for a discussion of urban forms).

The social-technical-environmental systems and systemic interactions involved in even the simplest energy-using and carbon-emitting human activities are complex and resistant to change via deliberate interventions—particularly on short time scales. And in that complexity, there is a “chicken and egg” quality to the relationships between supply (e.g., of goods, appliances, energy, buildings, vehicles, and transport options) and demand (i.e., for energy services). Demands are shaped and constrained by what is available, and effective supply requires that households and organizations actually consume what is offered. At the same time, suppliers attempt to encourage and increase demand through marketing, while consumers (certainly households but, most effectively, organizations) attempt to shape supply, such as through energy-related choices, regulations, and efficiency requirements. Capturing this complexity to show effective and democratic paths to reduced carbon emissions clearly requires more inclusive integrated models and increased understanding of the systems involved. This need for better models and understanding reflects earlier arguments (Douglas et al., 1998; Meadows 2008) and echoed in recent work on energy and climate change (Labanca and Bertoldi 2013; Shove et al., 2012). This also will require renewed attention to how evidence is evaluated. Next-generation analytic models and policy approaches will need to draw on new collaborations among research disciplines and between the scientific community and the social worlds in which energy is used and carbon is released to the atmosphere.

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