Content uploaded by Michael Vandenbergh
Author content
All content in this area was uploaded by Michael Vandenbergh on Feb 26, 2014
Content may be subject to copyright.
Household actions can provide a behavioral wedge
to rapidly reduce US carbon emissions
Thomas Dietz
a
, Gerald T. Gardner
b
, Jonathan Gilligan
c
, Paul C. Stern
d,1
, and Michael P. Vandenbergh
e
aDepartment of Sociology and Environmental Science and Policy Program, Michigan State University, East Lansing, MI 48864; bDepartment of Behavioral
Sciences, University of Michigan, Dearborn, MI 48128; cDepartment of Earth and Environmental Sciences, Vanderbilt University, Nashville TN 37235;
dDivision of Behavioral and Social Sciences and Education, National Research Council, Washington, DC 20001; and eClimate Change Research
Network, Vanderbilt University Law School, Nashville, TN 37203
Edited by Elinor Ostrom, Indiana University, Bloomington, IN, and approved September 11, 2009 (received for review August 2, 2009)
Most climate change policy attention has been addressed to
long-term options, such as inducing new, low-carbon energy tech-
nologies and creating cap-and-trade regimes for emissions. We use
a behavioral approach to examine the reasonably achievable
potential for near-term reductions by altered adoption and use of
available technologies in US homes and nonbusiness travel. We
estimate the plasticity of 17 household action types in 5 behav-
iorally distinct categories by use of data on the most effective
documented interventions that do not involve new regulatory
measures. These interventions vary by type of action and typically
combine several policy tools and strong social marketing. National
implementation could save an estimated 123 million metric tons of
carbon per year in year 10, which is 20% of household direct
emissions or 7.4% of US national emissions, with little or no
reduction in household well-being. The potential of household
action deserves increased policy attention. Future analyses of this
potential should incorporate behavioral as well as economic and
engineering elements.
climate mitigation 兩climate policy 兩energy efficiency 兩
household behavior 兩energy consumption
Global greenhouse gas emissions and associated climate
change have been increasing at accelerating rates in recent
years. For example, atmospheric CO
2
concentration increased by
an annual average of 1.5 ppm/yr in 1980 –1999, 2.0 ppm/yr in
2000–2007, and 2.2 ppm in 2007 (1). Prompt change in this
trajectory is necessary to reach the ambitious stabilization
targets now being discussed, but most policy attention has been
directed to slow-acting options. New technologies for low-carbon
energy supply, energy efficiency, and carbon sequestration must
overcome various technical, economic, institutional, and societal
obstacles and will take decades to develop and penetrate markets
(2, 3). The most prominent policy approaches to the climate
commons dilemma—national and international cap-and-trade
regimes—face issues of implementation feasibility that could
delay achievement of carbon emissions reduction objectives for
years (4–6). For the United States, these include setting mean-
ingful caps, promulgating regulations to implement the program,
monitoring emissions and emissions offsets, and controlling
offshoring and other responses of covered entities that could
undercut the objectives of the regime (7, 8).
Cap-and-trade programs and policies to induce technologic
innovation may not be sufficient to achieve ambitious near- and
long-term emissions reduction targets. Time lags likely from
implementation of complex policy (e.g., the 1,400-page Clean
Energy and Security Act of 2009) and from getting to emissions
caps that are substantially more stringent than business-as-usual
levels also may make it difficult for the United States to
demonstrate international leadership. Complementary strate-
gies are probably needed and certainly advisable. Among these,
opportunities for short-term emissions reductions have been
relatively neglected.
We focus on a short-term option with substantial potential for
carbon emissions reduction: altering the adoption and use of
available technologies in US homes and nonbusiness travel by
means of behaviorally oriented policies and interventions. This
potential ‘‘behavioral wedge’’ can reduce emissions much more
quickly than other kinds of changes and deserves explicit con-
sideration as part of climate policy (9). It can potentially help
avoid ‘‘overshoot’’ of greenhouse gas concentration targets;
provide a demonstration effect; reduce emissions at low cost;
and buy time to develop new technologies, policies, and institu-
tions to reach longer-term greenhouse gas emissions targets and
to develop adaptation strategies.
Individual and household behavioral change faces well-known
barriers (10), but more is known about how to overcome these
barriers than is commonly recognized (11–14). Lack of famil-
iarity with this knowledge among scholars and policy makers is
a major obstacle to achieving prompt, large, low-cost emissions
reductions. We apply a behavioral approach that complements
engineering and economic approaches to estimate the reason-
ably achievable potential for near-term emissions reduction from
behavioral change in households. We focus on US households
because they are a major emitter and because there is a
significant body of knowledge about the potential to achieve
near-term reductions in that sector.
Direct energy use by households accounts for approximately
38% of overall US CO
2
emissions, or 626 million metric tons of
carbon (MtC) in 2005 (15, 16). This is approximately 8% of
global emissions and larger than the emissions of any entire
country except China. National policy initiatives have addressed
households only indirectly, mainly through setting motor vehicle,
lighting, and appliance efficiency standards. Recent reviews of
the available research suggest a large near-term potential for
emissions reductions from behavioral changes involving the
adoption and altered use of available in-home and personal
transportation technologies, without waiting for new technolo-
gies or regulations or changing household lifestyle (15, 17). We
develop a quantitative estimate of this potential at the national
level, aggregated across behaviors.
Results
We find that the national reasonably achievable emissions
reduction (RAER) can be approximately 20% in the household
sector within 10 years if the most effective nonregulator y
interventions are used. This amounts to 123 MtC/yr, or 7.4% of
total national emissions—an amount slightly larger than the total
national emissions of France (18). It is greater than reducing to
zero all emissions in the United States from the petroleum
Author contributions: T.D., G.T.G., J.G., P.C.S., and M.P.V. designed research, performed
research, analyzed data, and wrote the paper.
The authors declare no conflict of interest.
This article is a PNAS Direct Submission.
Freely available online through the PNAS open access option.
1To whom correspondence should be addressed. E-mail: pstern@nas.edu.
This article contains supporting information online at www.pnas.org/cgi/content/full/
0908738106/DCSupplemental.
18452–18456
兩
PNAS
兩
November 3, 2009
兩
vol. 106
兩
no. 44 www.pnas.org兾cgi兾doi兾10.1073兾pnas.0908738106
refining (69 MtC), iron and steel (38 MtC), and aluminum (13
MtC) industries, each of which is among the largest emitters in
the industrial sector (19). The cost of achieving such a reduction
through behavioral change may be far lower than the cost of
many alternatives (15, 17).
We analyzed 17 types of household action that can appreciably
reduce energy consumption using readily available technology,
with low or zero cost or attractive returns on investment, and
without appreciable changes in lifestyle. We first estimated the
potential emissions reduction (PER) from each action, that is,
the reduction that would be achieved nationally from 100%
adoption of the action (15, 17). We then estimated plasticity
(20)—the proportion of current nonadopters that could be
induced to take action—from data on the most effective proven
interventions. This introduces a behavioral realism to our esti-
mates that is not included in analyses grounded solely in engi-
neering or economics.
We based our plasticity estimates on empirical studies of
responses to interventions at the individual and household levels
aimed at changing energy consumption and related environmen-
tally significant behaviors (12, 14, 21, 22) and on studies of
interventions to induce adoption of health-promoting behaviors
that resemble energy-saving behaviors (23–25). These studies
make it possible to consider how plasticity is affected by types of
intervention (e.g., media campaigns, information, and financial
incentives) separately and in combinations and also by the type
of behavior (12–14). Our approach contrasts with methods that
rely on generic indicators of plasticity, such as price elasticity of
demand. It facilitates consideration of the effects of both eco-
nomic and non-economic stimuli in the same analysis. This is
important because evidence from past energy efficiency inter-
ventions indicates that responsiveness to price can vary by a
factor of 10, depending on nonfinancial aspects of policy imple-
mentation (21).
Our plasticity estimates reflect what has been achieved by the
most effective documented interventions that do not involve new
regulation of technology or behavior. These interventions have
been demonstrated in field experiments or in organized pro-
grams implemented at the community, city, regional, or state
level—many of them in response to the energy crises of the
1970s. Our estimates of emissions reductions are based on
scaling the interventions up to national application.
The most effective interventions typically (i) combine several
policy tools (e.g., information, persuasive appeals, and incen-
tives) to address multiple barriers to behavior change; (ii) use
strong social marketing, often featuring a combination of mass
media appeals and participatory, community-based approaches
that rely on social networks and can alter community social
norms; and (iii) address multiple targets (e.g., individuals, com-
munities, and businesses) (12, 14, 23, 26).* Single policy tools
have been notably ineffective in reducing household energy
consumption. Mass media appeals and informational programs
can change attitudes and increase knowledge, but they normally
fail to change behavior because they do not make the desired
actions any easier or more financially attractive. Financial in-
centives alone typically fall far short of producing cost-
minimizing behavior—a phenomenon commonly known as the
energy efficiency gap (27). However, interventions that combine
appeals, information, financial incentives, informal social inf lu-
ences, and efforts to reduce the transaction costs of taking the
desired actions have demonstrated synergistic effects beyond the
additive effects of single policy tools (12, 13, 28). The most
effective package of interventions and the strongest demon-
strated effects vary with the category of action targeted.
We combined PER and plasticity to estimate RAER for each
action. PER and RAER estimates for actions were corrected for
double-counting (e.g., lower thermostat settings yield smaller
emissions reductions when combined with more efficient fur-
naces).
†
Details of all our calculations are provided in the SI Text.
Table 1 shows the actions and the associated estimates of 10-year
emissions reductions.
*Multiple targets can create community-level effects that enhance behavioral change
above what can be achieved with a single target. We do not include ‘‘spillover’’ savings
from businesses and other organizations in our calculations, so we are underestimating
the overall impact of the approach we propose.
†Our estimates are not corrected for potential ‘‘takeback’’ (i.e., a portion of achievable
reductions from improved technical efficiency that consumers forgo to gain other bene-
fits, such as increased thermal comfort).
Table 1. Achievable carbon emissions from household actions
Behavior change Category* Potential emissions reduction (MtC)
†
Behavioral plasticity (%)
‡
RAER (MtC)
§
RAER (%I/H)
§
Weatherization W 25.2 90 21.2 3.39
HVAC equipment W 12.2 80 10.7 1.72
Low-flow showerheads E 1.4 80 1.1 0.18
Efficient water heater E 6.7 80 5.4 0.86
Appliances E 14.7 80 11.7 1.87
Low rolling resistance tires E 7.4 80 6.5 1.05
Fuel-efficient vehicle E 56.3 50 31.4 5.02
Change HVAC air filters M 8.7 30 3.7 0.59
Tune up AC M 3.0 30 1.4 0.22
Routine auto maintenance M 8.6 30 4.1 0.66
Laundry temperature A 0.5 35 0.2 0.04
Water heater temperature A 2.9 35 1.0 0.17
Standby electricity D 9.2 35 3.2 0.52
Thermostat setbacks D 10.1 35 4.5 0.71
Line drying D 6.0 35 2.2 0.35
Driving behavior D 24.1 25 7.7 1.23
Carpooling and trip-chaining D 36.1 15 6.4 1.02
Totals 233 123 20
*See text for definitions of categories W, E, M, A, and D.
†Effect of change from the current level of penetration to 100% penetration, corrected for double-counting. Measured in millions of metric tons of carbon (MtC).
‡Percentage of the relevant population that has not yet adopted an action that will adopt it by year 10 with the most effective interventions.
§Reduction in national CO2emissions at year 10 due to the behavioral change from plasticity, expressed in MtC/yr saved and as a percentage of total US
individual/household sector emissions (%I/H). Both estimates are corrected for double counting.
Dietz et al. PNAS
兩
November 3, 2009
兩
vol. 106
兩
no. 44
兩
18453
SUSTAINABILITY
SCIENCE
The 17 types of actions include both adoption of more efficient
equipment and changes in use of equipment on hand. We divide
the actions into 5 categories on the basis of behaviorally relevant
attributes: W (home weatherization and upgrades of heating and
cooling equipment); E (more efficient vehicles and nonheating
and cooling home equipment); M (equipment maintenance); A
(equipment adjustments), and D (daily use behaviors). This
behavioral classification elaborates on previous ones that do not
distinguish W from E or A from D (29 –31). W and E both involve
adoption of equipment, but the equipment differs in the salience
of product attributes other than energy savings and cost. A and
D both involve changes in equipment usage but differ in the ease
of maintaining emission reductions: adjustments made once
maintain their effects automatically, but D behaviors must be
repeated over and over to achieve their potential.
W actions [weatherizing with attic insulation, by sealing drafts,
and installing high-efficiency windows, and replacing inefficient
home heating, ventilating, and central air conditioning (HVAC)
equipment] are one-time investments in energy-efficient build-
ing shells and equipment that have few salient product attributes
other than energy savings and financial costs and benefits.
Plasticity is estimated from the most effective documented
weatherization programs, which have combined financial incen-
tives (grants or rebates covering most of the retrofit cost),
convenience features (e.g., one-stop shopping), quality assur-
ance (e.g., certification for contractors, inspection of work), and
strong social marketing. The highest recorded plasticity is 85%
over 27 months (28); rates of 15–20% per year have been
recorded several times (21). Assuming the most effective inter-
ventions are deployed, we estimate plasticity of 80% in 5 years
and 90% in 10 years, except for furnaces and central AC
equipment, for which we assume replacement only at the end of
the useful life of existing equipment, resulting in 80% plasticity
in 10 years. RAER for W is thus estimated at 5.1% of total
household use, or 32 MtC. Strong financial incentives are
necessary but insufficient to achieve this plasticity; in the past,
plasticity with identical strong incentives has varied by a factor
of ⬎10, depending on other aspects of their implementation
(21). By supplementing financial incentives with program ele-
ments such as energy audits, convenience, and quality assurance,
the most effective programs significantly reduce nonfinancial
costs of action as well as financial ones (14, 21).
E actions (e.g., adopting more energy-efficient appliances,
equipment, and motor vehicles) involve purchases to upgrade the
energy efficiency of household equipment, but in most cases
product attributes other than cost and energy savings matter to
consumers.
‡
We assume replacement at the end of useful life
with products of the same type (e.g., size, performance, conve-
nience, and appearance features) that are more efficient. As with
heating, ventilating, and AC (HVAC) equipment, we estimate
10-year plasticity at 80% for most equipment classes. We esti-
mate only 50% plasticity for motor vehicle efficiency. The new
vehicle fleet may not change fast enough to allow higher
plasticity unless consumers forgo other product attributes (e.g.,
size and acceleration). The most effective inter ventions probably
combine improved rating/labeling systems, other information for
households and retailers, financial incentives for households and/or
vendors, and strong social marketing (14, 26). RAER for this class
of actions is 9.0% of total household emissions, or 56 MtC.
M actions (e.g., changing air filters in HVAC systems, vehicle
maintenance) are infrequent, low-cost, or no-cost actions that
can be maintained by habit. A actions (reducing laundry tem-
peratures, resetting temperatures on water heaters) are infre-
quent, no-cost actions that, once taken, are maintained auto-
matically. D actions (e.g., eliminating standby electricity,
thermostat setbacks, line drying, more efficient driving, carpool-
ing, and trip chaining) are frequently repeated actions main-
tained by habit or repeated conscious choice.
The most effective interventions for M, A, and D actions
generally involve combinations of mass-media messages, house-
hold- and behavior-specific information, and communication
through individuals’ social networks and communities, with
specifics depending on the target behavior (14, 22, 26). Plasticity
is probably behavior-specific in ways not fully understood at
present. Few studies exist of interventions targeting single
behaviors of these types. However, studies of daily or continuous
energy-use feedback, a form of household-specific information,
typically show reductions in total in-home energy consumption
by 5–12%, probably by inducing change in multiple A and D
behaviors (32). Multipronged interventions have produced re-
ductions of 15% or more of home energy use by changing these
behaviors (e.g., 33, 34). We conser vatively estimate that feed-
back supplemented by other communication can achieve the
plasticities shown in Table 1 for A and D behaviors, which reduce
total energy use in homes and in driving by approximately 4%.
Very little is known about the plasticity of the M behaviors.
Analogies from health behavior campaigns suggest plasticity of
8% to 9% from mass media campaigns (24, 25) and more if
communication uses individuals’ social networks and commu-
nities (23). We believe 30% plasticity is achievable for these
maintenance actions because they involve less-difficult changes
than most health behaviors. RAER for M, A, and D, respectively
is 1.5%, 0.2%, and 3.8%, or 34 MtC/yr in total for these actions.
Discussion
Our estimates of RAER are based on the best available behav-
ioral evidence and provide a reasonable initial guide to what can
be achieved by active promotion of household behaviors to
reduce greenhouse gas emissions. More precise estimates can be
developed with better data. However, decades of research on
proenvironmental and health behaviors demonstrate that be-
havioral interventions can have substantial impacts and show
how to design them for maximal effect.
Two kinds of knowledge seem most critical for developing firmer
estimates of RAER and achieving more of the potential. One
concerns the current penetration of energy-ef ficient equipment and
practices. We could find no data to estimate the penetration of trip
chaining, low rolling resistance (LRR) replacement tires, and
reduction of standby electricity and very limited data on other
actions, including important ones such as water heater tempera-
tures. Better data would yield better estimates.
The other type of knowledge, perhaps even more important,
is knowledge related to plasticity, in particular about how the
features of interventions, including incentives, education, infor-
mation, social marketing, quality assurance, and convenience
improvements, work separately and together to affect adoption
of specific emissions-reducing activities. Energy conservation
policies often go without evaluation or are evaluated in ways that
are not useful for understanding plasticity or learning how to
make interventions more effective. On a related note, there is
insufficient information on the costs and institutional require-
ments (e.g., staffing, program management) of highly effective,
large-scale behavioral change initiatives. The experience of
successful programs over the last several decades suggests that
these are not insurmountable barriers, but additional data would
be valuable.
The American Recovery and Reinvestment Act of 2009,
commonly known as the stimulus package, and the Consumer
Assistance to Recycle and Save (CARS) Act of 2009, better
‡More efficient lighting is omitted from our analysis because the 2007 Energy Indepen-
dence and Security Act mandates phaseout of incandescent lighting and forces a shift to
compact fluorescents, yielding PER of 30.2 MtC or 4.8% of household sector emissions in
year 10. Further savings can be obtained by voluntary shifts to solid-state (light-emitting
diode) lighting, but this technology is new to consumers and we have no basis for
estimating plasticity.
18454
兩
www.pnas.org兾cgi兾doi兾10.1073兾pnas.0908738106 Dietz et al.
known as the ‘‘cash for clunkers’’ program, represent a missed
opportunity in this regard. The stimulus package provided $5
billion for low-income home weatherization, $4.3 billion for a
30% tax credit for certain home energy-efficiency investments,
and $300 million in rebates for the purchase of Energy Star
appliances, as well as additional funds that state and local
governments could use for various purposes, including residen-
tial energy efficiency (35). The CARS program provided $3
billion for incentives for owners to trade in qualifying older
vehicles for more fuel-efficient new ones, with the old ones being
scrapped. It is too soon to estimate the effects in terms of
emissions reduction, but 2 observations are worth making. First,
the programs are quite different in behavioral terms. Although
there are questions about the cost effectiveness of CARS, it was
a great behavioral success, probably due in part to outstanding
marketing, paid for by the industry, and convenience (it featured
one-stop shopping, removed all paperwork burdens from the
consumer, and provided an instant rebate). This contrasts, for
instance, with the tax credit program, which also provides a large
financial incentive but has not been as well marketed, does not
make shopping easy, and requires paperwork and up to a 1-year
delay in collecting the credits. More could have been done to
apply the lessons of past behavioral research. Second, they do not
include an evaluation component that would allow for learning
from these major policy experiments. Of course, these programs
were intended primarily to provide quick stimulus to the econ-
omy, so these deficiencies are not surprising, but the opportu-
nities for more effective, behaviorally based programs and for
learning by doing should not be missed in future policies.
Our analysis suggests that most of the 10-year RAER (⬎13%
of total household emissions or 5.2% of total US emissions) can
be achieved in 5 years because most actions will ramp up quickly
in the early years of an effective program. An average of 60% of
the 10-year plasticity could probably be achieved by year 5 in all
categories except weatherization, for which, as noted, we antic-
ipate 80% plasticity by year 5. The significance of these reduc-
tions can be illustrated in relation to the metaphor of climate
‘‘stabilization wedges.’’ Pacala and Socolow (9) argued that
adoption of any 7 of 15 existing technologies could be ramped up
sufficiently over 50 years to stabilize CO
2
emissions at approx-
imately 7 billion tons of carbon (GtC)/yr to allow time for the
development of new technologies that could reduce emissions
further. Each wedge would provide a cumulative total of 25 GtC
in reductions over the 50-year period compared with ‘‘business
as usual.’’ If the United States, which emits roughly 20% of
global greenhouse gas, were to take a corresponding share of the
burden of emissions reduction, it would contribute 7 US wedges
of 200 MtC/yr each after 50 years, or 40 MtC each after 10 years.
The changes in household behavior outlined above result in a 123
MtC total year-10 RAER or roughly 3 such wedges—44% of the
US contribution at year 10.
Extending beyond the United States, similar percentage re-
ductions are likely possible in Canada and Australia, which have
carbon profiles roughly comparable to that of the US, and
percentage savings of perhaps half the US level may be achiev-
able in the European Union countries and Japan, where the
household sector is less energy intensive. Analyses similar to this
one would be needed to estimate the potential in other countries.
Because the behavioral wedge can ramp up in 10 years, and
significant portions of it even more quickly, it provides both a
short-term bridge to gain time for slower-acting climate mitiga-
tion measures and an important component of a long-term
comprehensive domestic and global climate strategy.
Our estimate for US households is conservative. Further
10-year emissions reductions will be achieved by adoption of
technologies now almost ready for mass market penetration (e.g.,
heat pump water heating and space conditioning, electric vehi-
cles, light-emitting diode lighting). Reductions are also likely
from household actions that are already being taken but that may
not meet our cost criterion, such as purchases of solar technol-
ogy, green electricity services, carbon offsets, and consumer
products with low life-cycle emissions. Still other reductions are
possible from behavioral changes that moderately alter lifestyle,
such as travel mode changes, telecommuting, and downsizing
larger homes and cars. The potential reductions from shifts in
consumer purchase patterns, downsizing, and some of these
other actions are calculable, but available data are inadequate
for making the estimations. For example, carbon calculators, which
typically include estimates of emissions associated with purchases of
food and other consumer goods, yield inconsistent results and so far
do not provide enough information for validation (36).
Lifestyle changes may become necessary in the out-years
under constrained energy supply or economic growth scenarios,
and they may become more attractive as a result of changes in
social attitudes or national or community priorities, some of
which might evolve from grassroots efforts to achieve the
emissions reductions analyzed here. Additionally, policies that
add a financial incentive for carbon emissions reduction are
likely to increase behavioral plasticity and may also induce
downsizing of household equipment. A US demonstration of
leadership on achieving the behavioral wedge might help induce
other countries to do the same (37). The potential of behavioral
change deserves increased policy attention. Future analyses of the
potential of efficiency in meeting emissions goals should incorpo-
rate behavioral as well as economic and engineering elements.
Materials and Methods
We analyzed 33 specific actions that constituted the 17 action types (e.g.,
‘‘driving behavior’’ combines slower acceleration, 55 mph speed for highway
driving, and reduced idling in nontraffic situations). We defined each action
precisely enough to allow us to estimate its current penetration, or the
proportion of the relevant population that has adopted an action (e.g., the
proportion of motor vehicles being driven at 55 mph on the highway). We
estimated penetration from the strongest empirical evidence we could find.
The precise definitions of the actions and the bases for estimating emissions
reductions and current penetration for each are presented in SI Text. PER was
calculated by multiplying the PER from an action by the size of the population
that has not yet adopted it, aggregating across fuels weighted by carbon
emission factors for each, and correcting for double-counting of actions that
have overlapping effects (e.g., slower driving has a smaller effect in a more
energy-efficient vehicle). The methods are described further in SI Text. Plas-
ticity was estimated from data on the most effective documented nonregu-
latory interventions as described above. We estimated RAER by combining
plasticity and PER after recalculating the double-counting corrections for the
incomplete penetrations of overlapping actions. SI Text presents and illus-
trates the calculation method.
ACKNOWLEDGMENTS. We thank Jennifer Kelly for her assistance in manu-
script preparation. This research was funded in part by support from the
Michigan Agricultural Experiment Station.
1. Global Carbon Project (2008) Carbon budget and trends 2007. Available at: http://
www.globalcarbonproject.org/carbonbudget/07/index.htm. Accessed October 2, 2009.
2. National Research Council (2009) Electricity from Renewable Sources: Status, Prospects
and Impediments (National Academy Press, Washington, DC).
3. National Research Council (2009) Liquid Transportation Fuels from Coal and Biomass:
Technological Status, Costs and Environmental Impacts (National Academy Press,
Washington, DC).
4. Ostrom E (1990) Governing the Commons: The Evolution of Institutions for Collective
Action (Cambridge Univ Press, New York).
5. Tietenberg T (2002) in The Drama of the Commons, eds National Research Council
(National Academy Press, Washington, DC), pp 197–232.
6. Dietz T, Ostrom E, Stern PC (2003) The struggle to govern the commons. Science
301:1907–1912.
7. Northrop M, Sassoon D (2008) Clean air jumpstart. Environ Finance 10:18–19.
8. Stavins RN (2008) A meaningful U.S. cap-and-trade system to address climate change.
Harv Environ Law Rev 32:296–357.
9. Pacala S, Socolow R (2004) Stabilization wedges: Solving the climate problem for the
next 50 years with current technologies. Science 305:968–972.
Dietz et al. PNAS
兩
November 3, 2009
兩
vol. 106
兩
no. 44
兩
18455
SUSTAINABILITY
SCIENCE
10. Brown MA, Chandler J, Lapsa MV, Sovacool BK (2008) Carbon Lock-In: Barriers To
Deploying Climate Change Mitigation Technologies (Oak Ridge National Laboratory,
Oak Ridge, TN), Publ No ORNL/TM-2007/124.
11. Stern PC (1986) Blind spots in policy analysis: What economics doesn’t say about energy
use. J Policy Anal Manag 5:200–220.
12. National Research Council (2002) New Tools for Environmental Protection: Informa-
tion, Education, and Voluntary Measures (National Academies Press, Washington, DC).
13. Stern PC (2008) in The Cambridge Handbook of Psychology and Economic Behaviour,
ed Lewis A (Cambridge Univ Press, Cambridge, UK), pp 363–382.
14. Gardner GT, Stern PC (2002) Environmental Problems and Human Behavior (Pearson
Custom Publishing, Boston), 2nd Ed.
15. Gardner GT, Stern PC (2008) The short list: The most effective actions U.S. households
can take to curb climate change. Environment 50:12–23.
16. Energy Information Agency (2008) Emissions of Greenhouse Gases in the United States
2007 (U.S. Department of Energy, Washington, DC), Publ No DOE/EIA-0573, Table 5, p
13.
17. Vandenbergh M, Barkenbus J, Gilligan J (2008) Individual carbon emissions: The
low-hanging fruit. UCLA Law Rev 55:1701–1758.
18. Energy Information Administration (2006) International Energy Annual 2006 (U.S.
Department of Energy, Washington DC).
19. Energy Information Administration (2009) Annual Energy Outlook 2009 (U.S. Depart-
ment of Energy, Washington, DC), Publ No DOE/EIA-0383, Table A19, p 145.
20. York R, Rosa E, Dietz T (2002) Bridging environmental science with environmental
policy: Plasticity of population, affluence and technology. Soc Sci Q 83:18–34.
21. Stern PC, et al. (1986) The effectiveness of incentives for residential energy conserva-
tion. Evaluation Rev 10:147–176.
22. Abrahamse W, Steg L, Vlek C, Rothengatter T (2005) A review of intervention studies
aimed at household energy conservation. J Environ Psychol 25:273–291.
23. Abroms LC, Maibach EW (2008) The effectiveness of mass communication to change
public behavior. Annu Rev Public Health 29:219–234.
24. Snyder LB, Hamilton MS (2002) in Public Health Communication: Evidence for Behav-
ioral Change, ed Hornik RC (Erlbaum, Mahwah, NJ), pp 357–384.
25. Snyder LB, et al. (2004) A meta-analysis of the effect of mediated health communica-
tion campaigns on behavior change in the United States. J Health Communication
9(Suppl 1):71–96.
26. McKenzie-Mohr D, Smith W (1999) Fostering Sustainable Behavior: An Introduction to
Community-Based Social Marketing (New Society, Gabriola Island, BC, Canada).
27. Jaffe AB, Stavins RN (1994) The energy-efficiency gap: What does it mean? Energy
Policy 22:804– 810.
28. Hirst E (1988) The Hood River Conservation Project: An evaluator’s dream. Evaluation
Rev 12:310–325.
29. Stern PC, Gardner GT (1981) Psychological research and energy policy. Am Psychologist
36:329–342.
30. Kempton W, Harris CK, Keith JG, Weihl, JS (1984) in What Works: Documenting Energy
Conservation in Buildings, eds Harris J, Blumstein C (American Council for an Energy-
Efficient Economy, Washington DC), pp 429– 438.
31. Clayton S, Myers G (2009) in Conservation Psychology: Understanding and Promoting
Human Care for Nature (Wiley-Blackwell, Chichester, UK), pp 143–145.
32. Fischer C (2008) Feedback on household electricity consumption: A tool for saving
energy? Energy Efficiency 1:79–104.
33. Rothstein RN (1980) Television feedback used to modify gasoline consumption. Behav
Therapy 11:683–688.
34. Staats H, Harland P, Wilke HAM (2004) Effecting durable change: A team approach to
improve environmental behavior in the household. Environ Behav 36:341–367.
35. Alliance to Save Energy. American Recovery and Reinvestment Act of 2009. Fact Sheet
on the 2009 Act (Alliance to Save Energy, Washington, DC).
36. Padgett, JP, Steinemann AC, Clarke JH, Vandenbergh MP (2008) A comparison of
carbon calculators. Environ Impact Assessment Rev 28:106–115.
37. Sunstein C (2008) The world vs. the United States and China? The complex climate change
incentives of the leading greenhouse gas emitters. UCLA Law Rev 55:1675–1700.
18456
兩
www.pnas.org兾cgi兾doi兾10.1073兾pnas.0908738106 Dietz et al.
