Content uploaded by Amirhosein Jafari
Author content
All content in this area was uploaded by Amirhosein Jafari on Nov 06, 2017
Content may be subject to copyright.
ReseaRch
Journal of Green Building 69
SuStainable impact of building
energy retrofit meaSureS
Amirhosein Jafari1 and Vanessa Valentin2
ABSTRACT
Energy retrofitting is argued to be the most feasible and cost-effective method for
improving existing buildings’ energy efficiency. As a sustainable development, build-
ing energy retrofits require the consideration and integration of all three sustainability
dimensions: environmental, economic and social. e objective of this study is to
estimate and compare the sustainable impact of building energy retrofits to determine
the maximum sustainable benefit when implementing different energy-related mea-
sures. e proposed analysis consists of integrating three approaches for evaluating
these benefits. Economic benefits are measured by estimating the payback period of
energy-related measures, environmental benefits are measured by estimating the CO2
equivalent saving per year due to the implementation of energy-related measures,
and social benefits are measured by defining a “social impact index” that establishes
the impact of energy-related measures on buildings’ users. A case study is used to
demonstrate the framework for four potential scenarios. e results show that for the
case study, energy-related “controlling” and “upgrading mechanical system” measures
have the highest sustainable impact among the identified energy retrofitting measures.
KEYWORDS
sustainable impact, energy improvement, energy measure, building retrofits.
1. INTRODUCTION
In the U.S., over 60 percent of the housing inventory is more than 30 years old (USCB 2013)
and a large number of these homes are energy inefficient. Currently, energy retrofitting is argued
to be the most feasible and cost-effective method to improve building energy efficiency (Ahn et
al. 2013; Ramos et al. 2015). An energy retrofit is the physical or operational change in a build-
ing itself, its energy-consuming equipment, or its occupants’ behavior to reduce the amount of
energy needed and convert the building to a lower energy facility (Jafari and Valentin 2017).
As a sustainable development, building energy retrofits require the consideration and inte-
gration of all three sustainability dimensions: environmental, economic and social (Jafari et al.
2016; Santoyo-Castelazo and Azapagic 2014). Green retrofits for increasing energy efficiency
can benefit the environment by reducing air emissions, can benefit a building’s economy by
1. Department of Civil Engineering, University of New Mexico, Albuquerque, NM 87131, USA. Email: jafari@unm.edu
2. Department of Civil Engineering, University of New Mexico, Albuquerque, NM 87131, USA. Email: vv@unm.edu
05_RES_Jafari_6748.indd 69 9/7/17 10:16 AM
70 Volume 12, Number 3
reducing operating and energy consumption costs, and can benefit society and occupants by
enhancing occupants’ comfort and health and providing jobs (Jafari et al. 2016; USEPA).
Generally, in order to retrofit an existing building in terms of improving its energy effi-
ciency, the following energy-related measures need to be implemented (GBA 2015; Jafari and
Valentin 2017): (1) “Controlling” measures that provide appropriate controls for the mechani-
cal systems; (2) “Mechanical System Upgrade” measures that upgrade the mechanical systems;
(3) “Insulation” measures that insulate and air-seal the roof or ceiling, walls, and floor; (4)
“Windows & Doors Replacement” measures that replace the windows and doors with energy-
efficient models; (5) “Fixtures & Appliances Replacement” measures that replace fixtures, appli-
ances, and lighting with energy-efficient models; and (6) “Renewable Options” measures that
provide renewable-energy sources. In addition to the above energy measures, human factors
such as changes of energy consumption patterns of occupants can be considered as another
energy retrofit measure category (Ma et al. 2012).
e selection of a combination of retrofitting measures for a specific building is a complex
process. e selection process of a retrofitting strategy is a trade-off between the capital invest-
ment (the investment required to implement that retrofitting strategy) and the benefits obtained
from energy retrofitting (Ma et al. 2012). ese energy retrofitting benefits can be categorized
into economic, environmental, or social (Jafari et al. 2016). When choosing among a variety
of sustainable benefits, the decision maker has to consider environmental, energy related, eco-
nomic, and social factors to reach the optimum possible solution that satisfies the final occupant
needs and requirements (Asadi et al. 2012).
e literature about the economic and environmental impacts of building energy efficiency
is quite rich. ere are numerous studies that highlight the impacts of building energy efficiency
in terms of the environment (Dong et al. 2005; Jafari et al. 2014; Junnila and Horvath 2003;
Junnila et al. 2006; iel et al. 2013; Wang et al. 2010; Wu et al. 2012) and the economy
(Abdallah et al. 2014; Chai and Chen 2013; Jafari and Valentin 2015; Jafari et al. 2014; Jafari
et al. 2016; Kansal and Kadambari 2010; Karatas and El-Rayes 2014; Kumbaroglu 2012). e
measurement of social impacts of energy efficiency is under-developed (Jafari et al. 2016). While
there is considerable information about energy reduction strategies for new housing buildings,
there is limited knowledge about these strategies attributable to retrofitting existing buildings.
Prior research has not addressed the total sustainable impact of energy retrofits in build-
ings. is study integrates three approaches in terms of energy retrofitting impacts on economic
benefits (Jafari et al. 2014), environmental benefits (Jafari et al. 2014), and social benefits
(Jafari et al. 2016) to address this gap in the literature. e following study calculates and
compares the amount of sustainable benefit that a homeowner may receive when implement-
ing different energy-related measures for a specific home, in order to obtain the most effective
sustainable level. In order to accomplish these tasks, the case study of a house, built in 1960’s
in Albuquerque, New Mexico, is considered to study economic and environmental impacts. In
addition, a survey is used to study social impacts.
2. CASE STUDY
e house used as the case study was originally constructed in 1964 as a ranch style home (which
is one of the most popular styles in the area) in Albuquerque, New Mexico. All of the repairs
on the home have been intended to keep the facility habitable and no major energy conserving
features have been added. e home is 150 m2, has 3 bedrooms, 2 bathrooms, and is made of
05_RES_Jafari_6748.indd 70 9/7/17 10:16 AM
Journal of Green Building 71
concrete block constructed on a crawlspace. e current heating is by gas furnace and cooling
is provided by an evaporative cooling (swamp cooler) system. e house also uses a gas water
heater and electric kitchen and laundry appliances.
In this study, data is gathered from the case study house to evaluate the effectiveness
of various green remodeling techniques. e “Build Green New Mexico criteria for a Green
Building” (BGNM 2012) document is used to evaluate the steps that could be taken to renovate
the house. Table 1 provides a summary of the activities that could be implemented to retrofit
the house.
e energy performance of the home is modeled by an energy simulation software, called
eQuest v3.65. When the house was occupied by a family of three for two years, the annual utility
usage had been approximately 9,000 kWh of electricity and 70 MBtu of gas. As the results show,
the annual electricity and gas consumption of the study house is simulated to be 9,550 KWh
and 73.42 MBtu, respectively. erefore, the simulated energy consumption for the home is
directly in line with the average actual usage of utilities. Considering the unit price of 0.113
$/KWh and 10.6 $/MBtu for electricity and natural gas during the study period, respectively,
the annual utilities bill of the study house is $1,857.20.
3. METHODOLOGY
is study attempts to evaluate the sustainable impact of housing energy retrofit measures
in terms of economic, environmental, and social impacts. e scope of this study focuses on
TABLE 1. Planned retrofitting activities.
Retrofitting Measures Energy-Related Activities
Controlling Install programmable thermostat
Mechanical System Upgrade Tune up HVAC
Install ground source heat exchanger
Upgrade evaporative cooler
Insulation Insulate Ceilings
Insulate walls
Insulate Attic
Windows & Doors Replacement Replace doors with insulated core
Replace windows with energy efficient glass
Fixtures & Appliances Replacement Replace all lighting with CFLs
Replace refrigerator with an energy star one
Replace clothes washer with an energy star one
Replace dishwasher with an energy star one
Renewable Options Install solar thermal equipment
Install solar electricity equipment
05_RES_Jafari_6748.indd 71 9/7/17 10:16 AM
72 Volume 12, Number 3
quantifying the sustainable benefits that an owner may receive when implementing different
energy-related measures for a specific building. e aforementioned case study is used to assess
economic and environmental impacts. In addition, a survey is used to evaluate the social impacts
of a green energy retrofit and the results are incorporated into the analysis.
3.1. Economic Criteria Analysis
Payback period has been used as an effective tool to analyze the economic viability among avail-
able alternatives (Chidiac et al. 2011; Malatji et al. 2013; Wang et al. 2014). e payback period
is the time required for an activity to recover its initial costs by considering expected savings.
In this study, the time value of money is considered to accurately calculate the payback period
which is used as the indicator of the economic impact for each retrofitting measure (Equation 1):
IIC =AECS ×1−(1+d)−n
d
⎛
⎝
⎜⎞
⎠
⎟ or n=−log(1+d)1−IIC
AECS ×d
⎛
⎝⎞
⎠ (1)
where n is payback period in years, IIC is the initial investment cost of a retrofitting measure,
AECS is the expected annual energy cost savings results from implementing an energy retrofit
measure, and d is the discount rate. IIC refers to the cost of implementing a retrofitting measure
including materials, equipment, and labor costs among others. Expected AECS is the economic
benefit of implementing each energy retrofitting measure in terms of utility cost savings per
year. As a result of implementing each energy retrofitting measure, AECS can be estimated
using energy simulation tools. Only energy consumption costs and initial investment cost are
considered to calculate the payback period, based on data availability for the case study. Other
cost categories such as maintenance costs, operation costs, and tax rebates could be considered
for more accurate results.
In this study, a PERT distribution is used to establish the estimated costs (both IIC and
AECS). PERT distribution is a modified Beta distribution, which is typically used in con-
struction to estimate completion time and cost based on a three point estimate (minimum,
maximum, and most likely values). In this study, the estimated costs are established using three
point estimates: optimistic (xmin), most probable (xm), and pessimistic (xmax) expected costs.
ese values are then used to construct a PERT probability distribution for IIC and AECS for
each retrofitting measure.
After estimating the IIC and AECS, the payback period for each measure can be calculated.
Using the Monte-Carlo Simulation and @Risk software, the payback period of each activity is
calculated as a probability distribution.
A lower payback period is more desirable for retrofit projects. To evaluate the economic
impact of each measure, a factor is defined as ECI (economic impact) for each energy retrofit
measure, as shown in Equation 2.
ECIi=
1
ni
1
n
i
i=1
k
∑
(2)
where ECIi is the economic impact of the ith energy retrofitting measure, ni is the payback
period of the ith energy retrofitting measure, and k is the number of selected energy retrofitting
05_RES_Jafari_6748.indd 72 9/7/17 10:16 AM
Journal of Green Building 73
measures (k = 6 in this case). ECI is equal to the multiplicative inverse of the payback period,
which is normalized in a scale from 0 to 1.
3.2. Environmental Criteria Analysis
Although construction activities consume large amounts of energy, more than 80% of energy
consumed by a building occurs during its operation phase, when the building is in actual
occupancy and use (Menassa 2011). As such, the amount of energy consumption of a building
during its operation significantly affects the environment. One of the most important environ-
mental impacts is the amount of greenhouse gases that are released to the atmosphere through
energy production, which is the focus of this study.
In the U.S., electricity is generated in many different ways, and therefore, environ-
mental impacts vary. Electricity generation from the combustion of fossil fuels contributes
towards air pollution, acid rain, and global climate change (EPA 2013). According to the U.S.
Environmental Protection Agency (EPA), power emissions factors are determined based on
the power grid region, and air emission rates of the electricity generated in the region are com-
pared with those of the national average. However, burning natural gas instead of other fossil
fuels emits fewer harmful pollutants, and an increased reliance on natural gas can potentially
reduce the emission of many of these harmful pollutants (NaturalGas 2013). e air emissions
analyzed in this study include carbon dioxide (CO2), sulfur dioxide (SO2), and nitrogen oxide
(NOx) (Jafari et al. 2014).
In order to describe different greenhouse gases in a common unit, the term of CO2-
equivalent reduction is used to analyze the environmental impact of energy retrofitting mea-
sures. e CO2-equivalent reduction is the amount of CO2-equivalent for CO2, SO2, and NOx
(equivalent global warming impact), that is not produced due to the energy saved by implement-
ing a retrofitting measure. In this study, CO2-equivalent reduction is calculated as Equation 3:
CO2-Eq =aAES ×ECO2
+AGS ×GCO2
( )
+bAES ×ESO2
+AGS ×GSO2
( )
+gAES ×ENOx
+AGS ×GNOx
( )
(3)
where CO2-Eq is the CO2-equivalent reduction per year, AES is the expected annual electricity
saving in Kwh, AGS is the expected annual natural gas saving in MBtu, Ei is the amount of air
emission releases for producing 1 KWh of electricity (lbs/KWh), Gi is the amount of air emis-
sion releases for consuming 1 MBtu of natural gas (lbs/MBtu), and
α
,
β
,
γ
are the conversion
factors (in terms of global warming impact) of CO2, SO2, and NOx respectively, in the CO2-
Equivalent calculation.
TABLE 1. Energy emissions factors.
Energy Reference
Air Emissions
NOxSO2CO2
Electricity (Ei)(EPA 2013) 1.52 lbs/MWh 0.62 lbs/MWh 1,191 lbs/MWh
Natural Gas (Gi) (NaturalGas 2013) 0.092 lbs/MBtu 0.001 lbs/MBtu 117 lbs/MBtu
05_RES_Jafari_6748.indd 73 9/7/17 10:16 AM
74 Volume 12, Number 3
Table 2 summarizes the air emissions quantities resulting from electricity and natural
gas generation for the state of New Mexico (the case study location). In addition, values of
1, 1/0.005, and 1/0.0025 are suggested for the same location for
α
,
β
, and
γ
, respectively
(ICBE 2017).
AES and AGS are the savings in electricity and natural gas, respectively, from implementing
each energy retrofitting activity. As a result of implementing each energy retrofitting measure,
AES and AGS can be estimated using energy simulation. After estimating the AES and AGS, the
CO2-Eq saving per year for each retrofitting measure can be calculated. Higher CO2-Eq savings
per year is more desirable environmentally for any retrofit project. To evaluate the environmen-
tal impact of each measure, a factor is defined as ENI (environmental impact) for each energy
retrofit measure, as shown in Equation 4:
ENIi=CO2
−
Eqi
CO2−Eqi
i=1
k
∑ (4)
where ENIi is the environmental impact of the ith energy retrofitting measure, and CO2-Eqi is
CO2 equivalent reduction per year for the ith energy retrofitting measure. In other words, ENI
is equal to CO2-Eq saving per year, which is normalized in a scale from 0 to 1.
3.3. Social Criteria Analysis
An energy retrofit may have several social effects in a community and society levels such as improv-
ing health by decreasing air emissions, providing job opportunities, and promoting the culture
of energy efficiency (Jafari and Valentin 2016). However, the social effects of energy housing
retrofit practices on the building occupants (at the building level) are the primary focus of this
study. Based on the characteristics of the project and prior research about positive outcomes
associated with the project features and attributes, the following social benefits are identified at
the building level (Jafari et al. 2016):
• Health: sustainable design mainly focuses on enhancing indoor air quality by improv-
ing ventilation rate, improving HVAC performance, and controlling humidity, which
can have a positive effect on occupants’ health (Noris et al. 2013; Sieber et al. 1996).
• Comfort and Satisfaction: using individual controls for temperature and ventilation as
well as positive perceptions of indoor air quality associated with high ventilation rates
can have a positive effect on the occupants comfort and satisfaction (Sieber et al. 1996;
Wyon 2004).
• Productivity: controlling over temperature, air movement, and lighting are important
components of an effective environment, that increases productivity (Kroner et al.
1992).
• Security: increasing energy-efficiency and using renewable energy sources could make
security improvements more affordable: e.g., improving reliability during utility system
outages by providing onsite power systems and improving thermal and optical perfor-
mance by upgrading existing windows for blast resistance (Harris et al. 2002).
In this study, a survey is deployed to estimate the impact of implementing energy retrofit
measures on buildings’ occupants, according to the respondents’ perception. e tradeoffs
05_RES_Jafari_6748.indd 74 9/7/17 10:16 AM
Journal of Green Building 75
among these intangible benefits is not considered in this study. Opinion surveys have been
used as an effective tool to assess the impact of different subjects on people, in societies where
surveys are culturally appropriate (Vanclay et al. 2015). A questionnaire is developed, based on
the features of an energy retrofitting measures and their different social benefits. e question-
naire starts with an introduction about the purpose of the study, provides a definition of the
social benefits to be evaluated and asks the following questions:
• Question 1: Participants are asked to determine the level of importance of each identi-
fied social benefits of energy retrofitting. A five-point Likert scale—(1) very low impor-
tance, (2) low importance, (3) moderate importance, (4) high importance, and (5) very
high importance—is adopted to calculate the relative importance of each identified
social benefit; and
• Question 2: Participants are asked to estimate the level of impact that each housing
energy retrofit measures has on each identified social benefit of energy retrofitting.
Again, a five-point scale—(1) very low impact, (2) low impact, (3) moderate impact, (4)
high impact, and (5) very high impact—is also adopted to estimate the relative impact
of each retrofitting measure on each identified social area of influence.
e relative importance index (RII) method has been used to determine the relative impor-
tance of various factors is survey studies (Heravi and Jafari 2014; Sambasivan and Soon 2007).
In this study, RII is used to evaluate the importance of each social building retrofit benefit based
on the responses for the first question, using Equation 5:
RII =
W1
∑
A×N
(5)
where RII is the relative importance factor for a social benefit of energy retrofitting, W1 is the
level of importance given by the respondents to each social benefit category and ranges from
very low importance (1) to very high importance (5), A is the highest weight possible (i.e., 5);
and N is the total number of respondents.
e responses obtained for Question 2 are used to evaluate how different retrofitting
measures influence the social housing retrofit benefits through the proposed “Impact Factor”
(IF) index. e IF also can be calculated using Equation 6:
IF =
W2
∑
A×N
(6)
where IF is the impact factor of a retrofitting measure on each of the four identified social
benefits of energy retrofitting, and W2 is the level of impact given to each energy retrofitting
measure by the respondents and ranges from very low impact (1) to very high impact (5).
en, considering the social impact of a retrofitting measure as an aggregation of social
benefits, a “Social Impact Index” (SII) is defined using Equation 7:
SII =
RII i×IFi
i
∑
M
(7)
05_RES_Jafari_6748.indd 75 9/7/17 10:16 AM
76 Volume 12, Number 3
where SII is the social impact index for a retrofitting measure; i represents the social benefits of
energy retrofitting; RIIi is the relative importance index of the ith social benefit; IFi is the impact
factor of the retrofitting measure for its ith social benefit; and M is the number of social areas
of influence (equal to 4 in this study).
Higher SII is more desirable for any retrofit project. To evaluate the social impact, a factor
is defined as SOI (social impact) for each energy retrofit measure, using Equation 8:
SOIi=
SII i
SII i
i=1
k
∑ (8)
where SOIi is the social impact of ith energy retrofitting measure, and SIIi is the social impact
index of the ith energy retrofitting measure. In other words, SOI is equal to SII which is normal-
ized in a scale from 0 to 1.
3.4. Sustainable Criteria Analysis
e sustainable impact of energy retrofit measures will be the summation of its economical
(ECI), environmental (ENI), and social (SOI) impacts as shown in Equation 9:
SUIi=a×ECI i
( )
+b×ENI i
( )
+c×SOIi
( )
(9)
where SUIi is the aggregated sustainable impact for the ith energy retrofitting measure, ECIi, is
its economic impact, ENIi is its environmental impact, SOIi is its social impact, and a, b, and c
are the weight of economic, environmental, and social impacts in sustainable impact analysis,
respectively.
In terms of energy retrofitting, each decision-maker could choose different importance
weights for ECI, ENI, and SOI. In order to demonstrate the methodology, four different
potential scenarios are defined as follows:
• Equal importance to all criteria: where economic, environmental and social criteria are
rated with the same importance.
• Economic scenario: where economic impact has higher importance than environmental
and social criteria.
TABLE 3. Different weight factors for different scenarios.
No. Scenario
Importance Factor
a b c
1 Equal importance to all criteria 33% 33% 33%
2Economic scenario 80% 10% 10%
3Environmental scenario 10% 80% 10%
4Social scenario 10% 10% 80%
05_RES_Jafari_6748.indd 76 9/7/17 10:16 AM
Journal of Green Building 77
• Environmental scenario: where environmental impact has higher importance than eco-
nomic and social criteria.
• Social scenario: where social impact has higher importance than environmental and
economic criteria.
e importance weight factors are assumed based on the above definition for each sce-
nario and are defined in Table 3. Using these importance factors, energy retrofitting measures
are evaluated and ranked in terms of their sustainable impacts. Energy measures with higher
sustainable impact would be more desirable to be implemented in a building.
4. RESULTS AND DISCUSSION
4.1. Economic Impacts
Two types of costs were required to estimate the payback period of each retrofitting measure:
initial investment cost and expected annual energy cost savings.
e initial cost of each activity was estimated using available sources such as RS Means:
Green Building Cost Data (RSMeans 2012), Housing and Urban Development Website: Energy
Efficient Rehab Advisor (HUD 2013), and a cost estimator professional. e annual savings of
each activity was estimated using the following sources: (1) eQuest (Quick Energy Simulation
Tool) software (DOE2 2013), version 3.65, (2) Housing and Urban Development Website:
Energy Efficient Rehab Advisor (HUD 2013), and (3) Energy Star portal (EnergyStar 2013).
For each retrofitting activity, three points of cost were estimated for IIC and AECS using the
aforementioned sources.
Although discount rate is not a constant term and may vary over the service life of the
project, a discount rate of 2 or 3% above inflation is considered an appropriate value (Hojjat
2002). In this study, a discount rate of 3% was assumed.
Table 4 summarizes the results of each payback period distribution using Equation 1,
in terms of the mean value and the interval of the payback period estimated with a 90%
confidence. For example by performing “Insulate Ceiling,” the initial investment with a 90%
probability will be recovered between 9.2 to 12 years; 10.2 years on average. In addition, the
average payback period of each energy measure was calculated separately assuming that all
energy improvement activities within that energy measure are implemented, using Equation 1.
e last column of Table 4 shows the normalized ECI for each energy retrofit measure. As
the results show, the controlling measures have the highest economic impact. at is because
the implementation of controlling measures is low cost; however, they can save a large amount
of the energy consumed by a building. After the “controlling” measure, “fixtures and appliances
replacement” and “insulation” measures have the highest economic impacts. On the other hand,
“renewable options” measure has the lowest economic impact. at is because these measures
have high initial costs and therefore their payback periods are usually high (however they can
save a huge amount of energy).
4.2. Environmental Impacts
According to the estimated amount of electricity and gas consumption of the building, the
amount of emissions that are released during the operation of the building were 21.3 lbs of
NOx, 6.0 lbs of SO2, and 19,962 lbs of CO2 per year. Also, the amount of CO2 emission during
05_RES_Jafari_6748.indd 77 9/7/17 10:16 AM
78 Volume 12, Number 3
TABLE 4. Summary of the payback periods for each retrofitting activity.
Measures Activities
Average
Payback
Period
(year)
Normalized
ECI
Payback Period (Year)
Percentile5 Mean Percentile95
Controlling Install
programmable
thermostat
0.4 0.6 1.0 0.6 0.835
Mechanical
System
Upgrade
Tune up HVAC 0.6 0.7 0.8 21.2 0.024
Install ground
source heat
exchanger
30.3 35.4 41.6
Upgrade evaporative
cooler
4.9 6.7 8.6
Insulation Insulate Ceilings 9.2 10.2 12.0 12.6 0.040
Insulate walls 7.9 17.4 33.0
Insulate Attic 8.8 11.1 13.5
Windows
& Doors
Replacement
Replace doors with
insulated core
31.5 74.7 145.6 69.2 0.007
Replace windows
with energy efficient
glass
43.0 63.7 9 7.7
Fixtures &
Appliances
Replacement
Replace all lighting
with CFLs
0.2 0.3 0.3 6.0 0.084
Replace refrigerator
with an energy star
one
14. 8 42 .9 103.2
Replace clothes
washer with an
energy star one
7.9 14.5 23.5
Replace dishwasher
with an energy star
one
27.9 64.5 133.7
Renewable
Options
Install solar thermal
equipment
21.5 32.4 50.1 48.4 0.010
Install solar
electricity
equipment
47. 6 54.4 62.4
05_RES_Jafari_6748.indd 78 9/7/17 10:16 AM
Journal of Green Building 79
the operation was equal to 13.3 lbs/ft2.year (64.9 kg/m2.year), which can be compared to the
results of prior studies which range from 13.9 kg/m2.year in Spain (Zabalza et al. 2013) to 260
kg/m2.year in China (Wu et al. 2012).
Considering the energy savings associated to each activity as well as energy emissions, the
amount of emission savings associated with the implementation of each retrofitting activity was
calculated using Equation 3. Table 5 summarizes air emission savings per year considering the
implementation of each activity.
As the results show, the “mechanical system upgrade” measures and “renewable options”
measure have the highest ENI and therefore the highest environmental benefits. at is, because
TABLE 5. Summary of the environmental impact for each retrofitting activity.
Measures Activities
Average Emission Saving
per year (lbs/year) CO2-Eq
Saving
(lbs/yea r)
Normalized
ENINOxSO2CO2
Controlling Install programmable
thermostat
0.355 0.155 2 9 7. 8 471 0.009
Mechanical
System
Upgrade
Tune up HVAC 1.614 0.489 1572 .5 20,842 0.388
Install ground source heat
exchanger
10.15 0 2.994 9975.0
Upgrade evaporative cooler 2.759 0.919 2604.9
Insulation Insulate Ceilings 1.194 0.333 119 2.8 5,980 0.111
Insulate walls 1.338 0.075 1640.1
Insulate Attic 1.388 0.348 14 2 7. 3
Windows
& Doors
Replacement
Replace doors with insulated
core
0.286 0.015 351.4 2,878 0.054
Replace windows with energy
efficient glass
1.578 0.301 1718.1
Fixtures &
Appliances
Replacement
Replace all lighting with
CFLs
2.967 1.417 2364.7 4,747 0.088
Replace refrigerator with an
energy star one
0.285 0.136 226.9
Replace clothes washer with
an energy star one
0.285 0.136 226.9
Replace dishwasher with an
energy star one
0.138 0.068 107.6
Renewable
Options
Install solar thermal
equipment
1.914 0.021 2433.6 18,832 0.350
Install solar electricity
equipment
11.786 5.14 6 9885.3
05_RES_Jafari_6748.indd 79 9/7/17 10:16 AM
80 Volume 12, Number 3
the implementation of these measures can save a large amount of energy consumed by a build-
ing. On the other hand, “controlling” measure has the lowest environmental impact because of
the small amount of energy that is saved from the building’s total energy consumption.
4.3. Social Impacts
In order to determine the social impact of an energy retrofit project, a survey was deployed
(Jafari et al. 2016). e target population of this pilot study consists of (1) academic research-
ers who are experts in building sustainable developments, and (2) industrial experts who are
working in sustainable areas. A group of 14 participants was involved in the pilot survey of
this study.
By using the calculated RII for each identified social benefit using Equation 5 (based on
survey responses for Question 1), the most important social indicators of a housing energy
retrofit are calculated. e most important social area of influence for a housing retrofit project
was found to be health, followed by comfort and satisfaction, security, and productivity (RII
of 0.91, 0.80, 0.71, and 0.66, respectively).
To evaluate how different retrofitting measures influence the social housing retrofit benefits
based on responses received for Question 2, the “Impact Factor” (IF) index is calculated, using
Equation 6. Table 6 shows the relative impact of each retrofitting measure on the identified
social areas of influence by calculating I F.
Table 6 shows that “mechanical system upgrade” measures has the highest impact on resi-
dents’ health, followed by “controlling” measures. In addition, the retrofitting measure that has
the highest impact on residents’ comfort and satisfaction is “controlling” measure followed by
“mechanical system upgrade” measure. e retrofitting measure that has the highest impact on
residents’ productivity is the “controlling” measure followed by “mechanical system upgrade”
and “fixtures & appliances replacement” measures. Furthermore, the retrofitting measure that
TABLE 6. Summary of the social impact for each retrofitting activity.
Measures
Social Impact Factor (IF) Social
Impact
Index
(SII)
Normalized
SOI
Health
(RII =
0.91)
Comfort &
Satisfaction
(RII = 0.80)
Productivity
(RII = 0.66)
Security
(RII =
0.71)
Controlling 0.756 0.884 0.734 0.584 0.573 0.180
Mechanical System
Upgrade
0.850 0.816 0.700 0.564 0.572 0.179
Insulation 0.750 0.766 0.634 0.600 0.535 0.168
Windows & Doors
Replacement
0.650 0.800 0.616 0.616 0.519 0.163
Fixtures & Appliances
Replacement
0.634 0.750 0.700 0.584 0.513 0.161
Renewable Options 0.666 0.650 0.516 0.634 0.479 0.150
05_RES_Jafari_6748.indd 80 9/7/17 10:16 AM
Journal of Green Building 81
has the highest impact on residents’ security is the “renewable options” measure, followed by
“windows and doors replacement” measure.
By using the calculated SII for each energy retrofit measure, their social impact is calcu-
lated and compared. e SOI results show that the “controlling” measure has the highest social
impact on residents, followed by “mechanical system upgrade” measure. In addition, “renewable
options” measure has the lowest social impact on residents, followed by “fixtures and appliances
replacement” measure.
4.4. Sustainable Impacts
Economic, environmental and social criteria were integrated for each retrofit measure by
multiplying their respective normalized factors by its associated weight them up as shown in
Equation 9. As shown in Table 3, four different scenarios were considered in this study with
their associated importance weights for each economic, environmental, and social criteria, as:
(1) equal importance to all criteria; (2) economic scenario; (3) environmental scenario; and (4)
social scenario.
e results of the sustainable index for each energy retrofit measure according to each
scenario are shown in Table 7.
When equal importance is assigned to all criteria (economic, environmental, and social),
the highest sustainable impact is found for “controlling” measures, followed by “mechanical
system upgrade” measures. e same result is obtained when the social criteria has the highest
level of importance. When the economic criteria has the highest level of importance, the highest
sustainable impact is obtained by “controlling” measures, followed by “fixtures and appliances
replacement” measures. Finally, when the environmental criteria has the highest level of impor-
tance, “mechanical system upgrade” measures have the highest sustainable impact, followed by
renewable options measures. On the other hand, in all scenarios, “windows and doors replace-
ment” measures have the lowest sustainable impacts and are ranked as the least effective ones.
TABLE 7. Summary of the sustainable impact for each retrofitting measure.
Retrofitting Measure
Equal
Importance to all
Criteria
Economic
Scenario
Environmental
Scenario Social Scenario
Index Rank Index Rank Index Rank Index Rank
Controlling 0.341 10.687 10.109 40.228 1
Mechanical System
Upgrade
0.197 20.076 30.331 10.184 2
Insulation 0.106 50.060 40.110 30.150 4
Windows & Doors
Replacement
0.075 60.027 60.060 60.137 6
Fixtures & Appliances
Replacement
0.111 40.092 20.095 50.146 5
Renewable Options 0.170 30.058 50.296 20 .156 3
05_RES_Jafari_6748.indd 81 9/7/17 10:16 AM
82 Volume 12, Number 3
5. CONCLUSION
is study integrated three approaches for evaluating and quantifying the total sustainable
impacts of energy retrofitting measures in terms of economic, environmental, and social ben-
efits. Using a case study and survey research, the study estimated and compared the amount of
sustainable benefit that a homeowner may achieve when implementing different energy-related
measures for a specific home in order to obtain the most effective sustainable level. e six
main energy retrofitting measures considered in this study include: (1) “Controlling” measures;
(2) “Mechanical System Upgrade” measures; (3) “Insulation” measures; (4) “Windows & Doors
Replacement” measures; (5) “Fixtures & Appliances Replacement” measures; and (6) “Renewable
Options” measures.
Four different potential scenarios were considered for establishing the weight of economic,
environmental, and social benefits: (1) “Equal importance to all criteria”; (2) “Economic sce-
nario”; (3) “Environmental scenario” and (4) “Social scenario.” e results showed that “con-
trolling” measures have consistently the highest or one of the highest impacts in each scenario,
followed by “mechanical system upgrade” and “renewable option” measures. e results of this
study are only applicable to the case study; however, the proposed framework could be replicated
and adapted for other cases and can be used as a tool for decision-making in energy retrofitting.
Although this study tried to fill the gap in the literature about combined sustainable impact
of existing buildings’ energy retrofitting by developing and testing the proposed integrated
approach, the study has some limitations. e study does not consider the interaction or impact
of different energy retrofitting measures on each other or the tradeoffs among intangible benefits
of energy retrofits. Besides, the study used investment costs and energy cost savings to calculate
payback periods. Additional cost categories such as operation costs and resale value could be
used for more accurate results in future research. e analysis considered environmental impacts
of energy retrofitting during building operation with a focus on air emissions. Although CO2-
equivalent reduction is one of the most significant environmental benefits of energy retrofit-
ting according to prior research, future research could consider additional impacts such as the
required energy for retrofitting, and fossil fuel conservation, among others. e survey used to
rank the social benefits of retrofitting had a limited number of respondents. e study could
also be expanded by defining additional energy-related activities to increase generalizability of
the results.
REFERENCES
Abdallah, M., El-Rayes, K., and Liu, L. (2014). “Optimal Selection of Sustainability Measures to Minimize
Building Operational Costs.” Construction Research Congress 2014, 2205–2213.
Ahn, Y. H., Pearce, A. R., Wang, Y., and Wang, G. (2013). “Drivers and barriers of sustainable design and construc-
tion: e perception of green building experience.” International Journal of Sustainable Building Technology
and Urban Development, 4(1), 35–45.
Asadi, E., da Silva, M. G., Antunes, C. H., and Dias, L. (2012). “Multi-objective optimization for building retrofit
strategies: A model and an application.” Energy and Buildings, 44, 81–87.
BGNM, B. G. N. M. (2012). <http://www.buildgreennm.com/>.
Chai, H., and Chen, W. (2013). “Uncertainty Analysis by Monte Carlo Simulation in a Life Cycle Assessment of
Water-Saving Project in Green Buildings.” Information Technology Journal, 12(13), 2593–2598.
Chidiac, S. E., Catania, E. J. C., Morofsky, E., and Foo, S. (2011). “A screening methodology for implementing
cost effective energy retrofit measures in Canadian office buildings.” Energy and Buildings, 43(2–3), 614–620.
DOE2 (2013). <http://www.doe2.com/equest/>.
05_RES_Jafari_6748.indd 82 9/7/17 10:16 AM
Journal of Green Building 83
Dong, B., Kennedy, C., and Pressnail, K. (2005). “Comparing life cycle implications of building retrofit and
replacement options.” Canadian Journal of Civil Engineering, 32(6), 1051–1063.
EnergyStar (2013). “Home Improvement: Improve Your Home’s Energy Efficiency with ENERGY STAR.” <http://
www.energystar.gov/index.cfm?c=home_improvement.hm_improvement_index&s=mega>.
EPA (2013). “Unites States Environmental Protect Agency:How clean is the electricity I use?,” <http://www.epa.
gov/cleanenergy/energy-and-you/how-clean.html>.
GBA (2015). “Remodel Project: Deep Energy Retrofit.” <http://www.greenbuildingadvisor.com/green-basics/
remodel-project-deep-energy-retrofit#ixzz3hEd3XTaM>.
Harris, J., Tschudi, W., and Dyer, B. (2002). “Securing Buildings and Saving Energy: Opportunities in the Federal
Sector.” <https://www1.eere.energy.gov/femp/pdfs/security_sustain_whitepaper_final_12aug.pdf>.
Heravi, G., and Jafari, A. (2014). “Cost of Quality Evaluation in Mass-Housing Projects in Developing Countries.”
Journal of Construction Engineering and Management, 140(5), 04014004.
Hojjat, A. (2002). “Life-cycle cost optimization of steel structures.” International Journal for Numerical Methods
in Engineering, 55(12), 1451.
HUD (2013). “Housing and Urban Development (HUD): Energy Efficient Rehab Advisor.” <http://rehabadvisor.
pathnet.org/audience.asp?Project=1&BuildingType=1&UserRole=10&ClimateType=4&BuildingAge=2>.
ICBE, I. C. B. a. E. (2017). “Calculating Greenhouse Cases.” <http://www.myendnoteweb.com/EndNoteWeb.
html?func=new&>. (2017).
Jafari, A., and Valentin, V. (2015). “Decision-making life-cycle cost analysis model for energy-efficient housing
retrofits.” International Journal of Sustainable Building Technology and Urban Development, 6(3), 173–187.
Jafari, A., and Valentin, V. “An Investment Allocation Approach for Building Energy Retrofits.” Proc., Construction
Research Congress 2016.
Jafari, A., and Valentin, V. (2017). “An optimization framework for building energy retrofits decision-making.”
Building and Environment, 115, 118–129.
Jafari, A., Valentin, V., and Berrens, R. (2017). “Estimating the Economic Value of Energy Improvements in
U.S. Residential Housing.” Under Revision by Journal of Construction Engineering and Management (ASCE).
Jafari, A., Valentin, V., and Bogus, S. (2016). “Assessment of Social Indicators in Energy Housing Retrofits.”
Construction Research Congress 2016, American Society of Civil Engineers, 1081–1091.
Jafari, A., Valentin, V., Howe, K. J., and Russell, M. (2014). “Environmental Impact of Housing Retrofit Activities:
Case Study.” World Sustainable Building (WBS) Conference, Oct 28–30, 2014, Barcelona, Spain.
Jafari, A., Valentin, V., and Russell, M. (2014). “Probabilistic Life cycle Cost Model for Sustainable Housing
Retrofit Decision-Making.” International Conference on Computing in Civil and Building Engineering;
Orlando, Florida.
Jafari, A., Valentin, V., and Russell, M. (2016). “Sensitivity Analysis of Factors Affecting Decision-Making for
a Housing Energy Retrofit: A Case Study.” Construction Research Congress 2016, American Society of Civil
Engineers, 1254–1263.
Junnila, S., and Horvath, A. (2003). “Life-Cycle Environmental Effects of an Office Building.” Journal of
Infrastructure Systems, 9(4), 157.
Junnila, S., Horvath, A., and Guggemos, A. A. (2006). “Life-Cycle Assessment of Office Buildings in Europe and
the United States.” JOURNAL OF INFRASTRUCTURE SYSTEMS, 12(1), 10–17.
Kansal, R., and Kadambari, G. (2010). “Green Buildings: An Assessment of Life Cycle Cost.” IUP Journal of
Infrastructure, 8(4), 50–57.
Karatas, A., and El-Rayes, K. (2014). “Optimal Trade-Offs between Social Quality of Life and Life-Cycle Cost
in Housing Units.” JOURNAL OF CONSTRUCTION ENGINEERING AND MANAGEMENT, 140(12),
04014058.
Kroner, W. M., Stark-Martin, J. A., and Willemain, T. R. (1992). Rensselaer’s West Bend Mutual study : using
advanced office technology to increase productivity, Center for Architectural Research, Troy, N.Y.
Kumbaroglu, G. M. R. (2012). “Evaluation of economically optimal retrofit investment options for energy savings
in buildings.” Energy and Buildings Energy and Buildings, 49, 327–334.
Ma, Z., Cooper, P., Daly, D., and Ledo, L. (2012). “Existing building retrofits: Methodology and state-of-the-art.”
Energy and Buildings, 55, 889–902.
Malatji, E. M., Zhang, J., and Xia, X. (2013). “A multiple objective optimisation model for building energy effi-
ciency investment decision.” Energy and Buildings, 61, 81–87.
05_RES_Jafari_6748.indd 83 9/7/17 10:16 AM
84 Volume 12, Number 3
Menassa, C. C. (2011). “Evaluating sustainable retrofits in existing buildings under uncertainty.” Energy and
Buildings, 43(12), 3576–3583.
NaturalGas (2013). “Natural Gas and the Environment.” <http://www.naturalgas.org/environment/naturalgas.
asp>.
Noris, F., Adamkiewicz, G., Delp, W. W., Hotchi, T., Russell, M., Singer, B. C., Spears, M., Vermeer, K., and
Fisk, W. J. (2013). “Indoor environmental quality benefits of apartment energy retrofits.” Building and
Environment, 68, 170–178.
Ramos, A., Gago, A., Labandeira, X., and Linares, P. (2015). “e role of information for energy efficiency in the
residential sector.” Energy Economics, 52, Supplement 1, S17-S29.
RSMeans (2012). Green Building Cost Data, Norwell, MA : RSMeans, c2010-.
Sambasivan, M., and Soon, Y. W. (2007). “Causes and effects of delays in Malaysian construction industry.”
International Journal of Project Management, 25(5), 517–526.
Santoyo-Castelazo, E., and Azapagic, A. (2014). “Sustainability assessment of energy systems: integrating environ-
mental, economic and social aspects.” Journal of Cleaner Production, 80, 119–138.
Sieber, W. K., Stayner, L. T., Malkin, R., Petersen, M. R., Mendell, M. J., Wallingford, K. M., Crandall, M. S.,
Wilcox, T. G., and Reed, L. (1996). “e National Institute for Occupational Safety and Health Indoor
Environmental Evaluation Experience. Part ree: Associations between Environmental Factors and Self-
Reported Health Conditions.” Applied Occupational and Environmental Hygiene, 11(12), 1387–1392.
iel, C. L., Campion, N., Landis, A. E., Jones, A. K., Schaefer, L. A., and Bilec, M. M. (2013). “A Materials Life
Cycle Assessment of a Net-Zero Energy Building.” Energies (19961073), 6(2), 1125–1141.
USCB (2013). “American Housing Survey for United States: 2011.” <http://www.census.gov/programs-surveys/
ahs.html>. (January, 2015).
USEPA “Why Build Green?,” <http://www.epa.gov/greenbuilding/pubs/whybuild.htm>. (July 26, 2015).
Vanclay, F., Esteves, A. M., Aucamp, I., and Franks, D. (2015). “Social Impact Assessment: Guidance document.”
International Association for Impact Assessment.
Wang, B., Xia, X., and Zhang, J. (2014). “A multi-objective optimization model for the life-cycle cost analysis and
retrofitting planning of buildings.” Energy and Buildings, 77, 227–235.
Wang, N., Chang, Y.-C., and Nunn, C. (2010). “Lifecycle assessment for sustainable design options of a com-
mercial building in Shanghai.” Building & Environment, 45(6), 1415–1421.
Wu, H. J. J., Yuan, Z. W. W., Zhang, L., and Bi, J. (2012). “Life cycle energy consumption and CO2 emission of an
office building in China.” INTERNATIONAL JOURNAL OF LIFE CYCLE ASSESSMENT, 17(2), 105–118.
Wyon, D. P. (2004). “e effects of indoor air quality on performance and productivity.” Indoor Air, 14(s7),
92–101.
Zabalza, I., Scarpellini, S., Aranda, A., Llera, E., and Jáñez, A. (2013). “Use of LCA as a Tool for Building
Ecodesign. A Case Study of a Low Energy Building in Spain.” Energies, 6(8), 3901–3921.
05_RES_Jafari_6748.indd 84 9/7/17 10:16 AM
