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Y. Lou, Y. Yang, Y. Ye, W. Zuo, J. Wang 2021. “The Effect of Building Retrofit Measures 1
on CO2 Emissions Reduction – A Case Study with U.S. Medium Office Buildings.” Energy and 2
Buildings, 253, pp. 111514. https://doi.org/10.1016/j.enbuild.2021.111514 3
4
The effect of building retrofit measures on CO2 emissions reduction – A case study 5
with U.S. medium office buildings 6
Yingli Loua, Yizhi Yanga, Yunyang Yeb, Wangda Zuoa,c,*, Jing Wanga 7
a Department of Civil, Environmental and Architectural Engineering, University of Colorado Boulder, 8
Boulder, CO 80309, U.S.A. 9
b Pacific Northwest National Laboratory, Richland, WA 99354, U.S.A. 10
c National Renewable Energy Laboratory, Golden, CO 80401, U.S.A 11
* Corresponding author. Email address: Wangda.Zuo@colorado.edu (Wangda Zuo) 12
Abstract 13
Building retrofits have great potential to reduce CO2 emissions since buildings are responsible for 36% 14
of emissions in the United States. Several existing studies have examined the effect of building retrofit 15
measures on CO2 emission reduction. However, these studies oversimplified emission factors of electricity 16
by adopting constant annual emission factors. This study uses hourly emission factors of electricity to 17
analyze the effect of building retrofit measures on emission reduction using U.S. medium office buildings 18
as an example. We analyzed the CO2 emission reduction effects of eight building retrofit measures that 19
related to envelope and mechanical systems in five locations: Tampa, San Diego, Denver, Great Falls, and 20
International Falls. The main findings are: (1) estimating CO2 emission reduction with constant emission 21
factors overestimates the emission reduction for most measures in San Diego, while it underestimates the 22
emission reduction for most measures in Denver and International Falls; (2) The same retrofit measure may 23
have different effects on CO2 emission reduction depending on the climate. For instance, improving lighting 24
efficiency and improving equipment efficiency have less impact in emission reduction in cold climates than 25
hot climates; and (3) The most energy efficient measure may not be the most efficient emission measure. 26
For example, in Great Falls, the most energy efficient measure is improving equipment efficiency, but the 27
most efficient emission measure is improving heating efficiency. 28
Keywords: CO2 emissions, Building, Retrofit, Building energy model, Simulation 29
1. Introduction 30
The United States (U.S.) is the second-largest contributor to CO2 emissions [1] and reducing emissions 31
in the U.S. is necessary to mitigate the risk of catastrophic climate change. Intergovernmental Panel on 32
Climate Change (IPCC) declared that the CO2 emissions humans spew into the atmosphere leads to climate 33
2
change. By the end of the 21st century, the current CO2 emissions will cause global warming to around 1.5–34
2 °C if we do not drastically limit CO2 emissions by mid-century and beyond [2]. Global warming is 35
associated with many physical and biological damages, such as receding glaciers, bleached corals, 36
acidifying oceans, killer heat waves, and hurricanes [3][4][5]. The U.S. outlined a pathway to reduce CO2 37
emissions by 50% below 2005 levels by 2030 [6], and 80% below 2005 levels by 2050 [7]. 38
Buildings are critical for emission reduction because the U.S. buildings sector accounted for 36% of 39
energy-related CO2 emissions [8]. At present, there are plenty of buildings have poor energy performance 40
and lead to a bulk of CO2 emissions [9][10]. Most of these buildings will still be in function until 2025 or 41
even 2050 [11]. Retrofitting existing buildings is crucial for emission reduction in the U.S. Langevin et al. 42
[12] found that the combination of aggressive efficiency measures, electrification, and high renewable 43
energy penetration can reduce CO2 emissions in the U.S. building sector by 72%–78% relative to 2005 44
levels. 45
Several existing studies have examined the CO2 emission reduction effect of building retrofit measures. 46
In the case study conducted by Tettey et al. [13], CO2 emission reduction is about 6–8% when the building 47
insulation material is changed from rock wool to cellulose fiber. Murray et al. [14] treated CO2 emission 48
factors of electricity as an uncertainty variable and investigated the optimal set of building measures to 49
minimize emissions for the Swiss building stock. An average CO2 emission factor of electricity in Spain 50
was adopted by Garriga et al. [15] to study the optimal carbon-neutral retrofit of residential communities 51
in Barcelona, Spain. Huang et al. analyzed the CO2 emission payback periods of external overhang shading 52
in a university campus in Hong Kong [16]. An average emission factor of electricity in recent years in Hong 53
Kong was adopted in this research. An average emission factor of electricity in the last five years in Finland 54
was used by Niemelä et al. [17] to determine the cost-optimal renovation from the CO2 emission reduction 55
potential perspectives. Life-cycle CO2 emission reduction of retrofit measures in new commercial buildings 56
was studied by Kneifel and a state-level annual emission factor of electricity was adopted in this study [18]. 57
However, the CO2 emission factor of electricity is oversimplified in existing studies and a constant 58
factor throughout the whole year is adopted. In fact, the emission factors can potentially change every day, 59
even every hour, especially in areas with a high renewable energy penetration [19][20][21]. For example, 60
if solar power generation is prevalent in one area, CO2 emission factors of electricity will be low during the 61
daytime and high at nighttime. If a region has extensive hydropower generation, emission factors of 62
electricity will be lower during the rainy season than the dry season. As a result, using a constant average 63
emission factor may underestimate or overestimate the emission reduction of some building retrofit 64
measures. 65
The above literature review shows that there is a lack of study on the emission reduction of building 66
retrofit measures with dynamically changing electricity emission factors. Existing research adopted a 67
constant emission factor, while electricity emission factors are dynamically changing. The impact of 68
electricity emission factors on building emissions is significant since electricity is the major energy source 69
of buildings. Therefore, it is crucial to investigate the emission reduction difference between using 70
dynamically changing emission factors and a constant factor. 71
In this study, hourly CO2 emission factors of electricity are adopted to analyze the effect of building 72
retrofit measures on emission reduction. U.S. medium office buildings are used as an example in this study. 73
3
This paper is organized as follows: Section 2 introduces the design of the case study including location 74
selection, building retrofit measures selection, and the method to estimate the emission reduction effect of 75
individual measures. Section 3 presents the hourly CO2 emission reduction by applying individual measures 76
using one location as an example. And the annual CO2 emission reduction effect of individual measures in 77
all locations is analyzed in Section 3. Section 4 discusses the impact of climates on emission reduction 78
effect, the difference between energy efficient measures and emission efficient measures, and the difference 79
between using the hourly CO2 emission factors of electricity and the annual factor. Finally, interesting 80
findings are concluded in Section 5. 81
2. Study Design 82
This section first introduces studied locations and building retrofit measures. Then, we introduce the 83
method to estimate the CO2 emission reduction effect of individual measures. To support commercial and 84
residential building energy codes and standards, the U.S. Department of Energy (DOE) has been dedicating 85
to the development of prototype building models. The prototype models include 16 commercial building 86
types in 19 climate locations (16 in the U.S. and 3 international locations) for different editions of ASHRAE 87
Standard 90.1 and IECC. Those models are widely used to investigate energy saving 88
[22][23][24][25][26][27], power consumption [28][29], and emission reduction [18]. And the results based 89
on these models are also accepted by the community. Therefore, this study adopted DOE Commercial 90
Prototype Building Models for medium office buildings [30] to estimate CO2 emissions. Fig. 1 shows the 91
geometry and thermal zones of the model, which has a rectangular shape with three stories. Each story 92
contains five thermal zones. Table 1 summarizes the key model parameters. 93
Fig. 1. building model Geometry and thermal zones of the prototype medium office building model 95
Table 1. Key parameters of the prototype medium office building model 96
Parameter Name
Value
Total floor area
4982 m2 (49.91 m × 33.27 m × 3)
Aspect ratio
1.5
Number of floors
3
Window-to-wall ratio
33%
Floor-to-floor height
3.96 m
Envelope type
Exterior walls: steel-frame walls
Roof: insulation above deck
HVAC system type
Heating: gas furnace inside the packaged air conditioning unit
Thermal zone 1
Thermal zone 2
Thermal zone 3
Thermal
zone 4
Thermal
zone 5
(a) Geometry (b) Thermal zones (each floor)
4
Parameter Name
Value
Cooling: packaged air conditioning unit
Terminal Units: VAV terminal box with damper and electric reheating coil
Service water heating type
Storage tank using natural gas as fuel
2.1. Location selection 97
The selected locations should cover different climates and compositions of electricity generation. Using 98
this principle, five locations are selected: (1) Tampa, Florida; (2) San Diego, California; (3) Denver, 99
Colorado; (4) Great Falls, Montana; and (5) International Falls, Minnesota. As shown in Fig. 2, they 100
represent five different climates (from hot humid to very cold). Their compositions of electricity generation 101
vary from fossil fuel dominated (e.g., Tampa) to renewable energy dominated (e.g., Great Falls). The 102
consumption of fossil fuel, like coal and natural gas, produces direct CO2 emissions, while the consumption 103
of renewable energy, like hydropower, solar power, wind power, and nuclear, doesn’t produce direct 104
emissions. 105
Fig. 2. Locations selection for the case study 107
2.2. Building retrofit measure selection 108
This subsection introduces building retrofit measures that are examined in this study. Existing research 109
has provided a rich set of building retrofit measures for U.S. commercial buildings [32][33][27][34][35][36]. 110
Based on our previous research [23][22], eight building retrofit measures for U.S. medium office buildings 111
are included in this study, as shown in Table 2. Based on literatures [22], these eight building retrofit 112
measures potentially have significant impacts on the CO2 emissions for medium office buildings across 113
Tam pa
San Diego
Great Falls International
Falls
Denver
Cold dry
64%
32%
Very cold
24%
22%
19%
16%
Hot humid
78%
12%
Warm marine
46%
21%
12%
Cool dry
33%
30%
28%
Climate feature
Wind power
Coal power
Nuclear power
Natural gas power
Hydropower
Solar power
Others
10%
9%
21%
4%
19%
Note: Climate features are obtained from [30]; compositions of electricity generation are obtained from [31].
5
different climate feature locations. The abbreviation for each measure will be used in the rest of this paper. 114
The values of model inputs will be introduced in Section 2.3. 115
Table 2. Building retrofit measures examined in the case study 116
No.
Building Retrofit Measure
Abbreviation
Model Input
1
Add wall insulation
WALL
Wall insulation R-value
2
Add roof insulation
ROOF
Roof insulation R-value
3
Replace windows
WINDOW
Window U-factor,
Window SHGC
4
Replace interior lights with higher efficiency lights
LIGHT
Lighting power density
5
Replace office equipment with higher efficiency equipment
EQUIP
Plug load density
6
Replace cooling coil with higher efficiency coil
COOLING
Nominal coefficient of
performance (COP)
7
Replace heating burner with higher efficiency burner
HEATING
Burner efficiency
8
Replace service hot water system with higher-efficiency
system
SWH
Heater thermal efficiency
2.3 CO2 emission reduction 117
The CO2 emission reduction effect of the individual measure () can be obtained using the following 118
formula: 119
=
×100%, = 1,2,3,4,5,6,7,8,
(1)
where, is CO2 emissions of baseline building model; and is CO2 emissions of retrofit building 120
model by applying the retrofit measure . The and can be obtained using the following formula, 121
which is also illustrated in Fig. 3. 122
=,
=(,+,)
=(,×+,×),
(2)
where, , is CO2 emissions at time for the building with retrofit measure . For the baseline building, 123
= 0. The is the total number of hours in a year, which is 8784 in this study. The , is CO2 emissions 124
from electricity at time for the building with retrofit measure . The , is CO2 emissions from natural 125
gas at time for the building with retrofit measure . The
, is electricity consumption at time for the 126
building with retrofit measure . The
is electricity CO2 emission factor at time .
, is natural gas 127
consumption at time for the building with retrofit measure . is natural gas emission factor, which is a 128
constant value. 129
6
Fig. 3. Workflow to estimate the CO2 emissions of a building 131
The model input values of baseline models are based on ASHRAE Standard 90.1-2007 [37]. The model 132
input values of retrofit models are based on the Advanced Energy Design Guide 50% Energy Savings [38]. 133
Table 3 shows the model input values of baseline models and retrofit models, which result in 45 models (5 134
locations × (1 baseline model + 8 retrofit models)). The objective of this study is to investigate the emission 135
reduction effect due to building retrofit measures on different locations. Therefore, the embodied emissions 136
of building retrofit measures are not involved in this study. 137
Table 3. Model input values of baseline models and retrofit models 138
Model Input
Unit
Tampa
San Diego
Denver
Great Falls
International
Falls
Base1
Retr2
Base1
Retr2
Base1
Retr2
Base1
Retr2
Base1
Retr2
Wall insulation R-value
m2-K/W
1.04
2.75
1.71
2.75
2.37
4.19
2.37
4.76
2.37
4.76
Roof insulation R-value
m2-K/W
3.47
4.52
3.47
4.52
3.47
5.50
3.47
5.50
3.47
6.29
Window U-factor
W/m2-K
4.09
2.56
3.52
2.33
2.73
1.99
2.73
1.99
2.38
1.87
Window SHGC
-
0.25
0.25
0.25
0.25
0.4
0.26
0.4
0.35
0.45
0.40
Lighting power density
W/m2
10.76
8.07
10.76
8.07
10.76
8.07
10.76
8.07
10.76
8.07
Plug load density
W/m2
8.07
5.92
8.07
5.92
8.07
5.92
8.07
5.92
8.07
5.92
Nominal COP
-
3.23
3.37
3.23
3.37
3.23
3.37
3.23
3.37
3.23
3.37
Burner efficiency
-
0.80
0.90
0.80
0.90
0.80
0.90
0.80
0.90
0.80
0.90
Heater thermal efficiency
-
0.81
0.90
0.81
0.90
0.81
0.90
0.81
0.90
0.81
0.90
1 Base: Baseline model (Source: ASHRAE Standard 90.1–2007 [37]) 139
2 Retr: Retrofit model (Source: AEDG 50% Energy Savings [38]) 140
2.3.1. Energy prediction 141
As shown in Fig. 3, this study predicts energy consumption for (1) baseline building models and (2) 142
retrofit building models by adopting individual measures. In this study, the baseline models are the DOE 143
Commercial Prototype Building Models for medium office buildings [30], which were introduced in the 144
beginning of Section 2. Retrofit models are the updated baseline models by adopting the individual 145
measures listed in Table 2. The model input values of individual measures are listed in Table 3. Two types 146
of data are extracted after model simulation: (1) hourly electricity consumption (,) and (2) hourly natural 147
gas consumption (,). 148
Energy Prediction
Baseline building model
()
Retrofit building model
with individual measure
( )
Electricity
consumption during
each hour ( )
Natural gas
consumption during
each hour ( )
Building energy model ( )
Electricity CO2
emission factor during
each hour ( )
Natural gas CO2
emission factor ( )
CO2emissions
from electricity
( )
CO2emissions
from natural gas
( )
CO2Emission Estimation
CO2
emissions
during each
hour ( )
CO2
emissions in
one year ( )
7
2.3.2. CO2 emission estimation 149
Using the electricity and gas consumption data obtained in the subsection 2.3.1, this subsection 150
introduces the method to estimate CO2 emissions of baseline models and retrofit models. As shown in Fig. 151
3, CO2 emissions from electricity are calculated by multiplying hourly electricity consumption with hourly 152
emission factors of electricity, and CO2 emissions from natural gas are calculated by multiplying hourly 153
natural gas consumption with one natural gas emission factor. Hourly CO2 emission factors of electricity 154
are obtained from the National Renewable Energy Laboratory (NREL) website [31]. The emission factor 155
in each hour is the average values of emission factors during that hour. For example, Fig. 4 shows the hourly 156
emission factors of electricity in Great Falls. The horizontal axis in Fig. 4 represents each day of the year. 157
Vertical axis represents each hour of the day. The shade of the color represents the magnitude of the value 158
in a specific hour on one day. Fig. 5 shows hourly emission factors of electricity on two typical days 159
(summer day: 2020-06-19 and winter day 2020-12-21) for the five studied locations. Hourly emission 160
factors of electricity in Great Falls during the summer are almost always zero because there is abundant 161
hydropower during that time. The natural gas emission factor is a fixed value in the whole year for five 162
studied locations, which is 180 kg/MWh [39]. 163
Fig. 4. Hourly CO2 emission factors of electricity in Great Falls 165
166
(a) Summer day (b) Winter day 168
Fig. 5. Hourly CO2 emission factors of electricity on two typical days 169
8
3. Results 170
3.1. Energy prediction 171
This subsection shows the prediction results of hourly electricity and natural gas consumption in 2020 172
for the baseline models and retrofit models. We use the baseline model in Great Falls as an example to 173
illustrate the hourly electricity and natural gas consumption, as shown in Fig. 6 and Fig. 7. To make the two 174
types of energy consumption comparable, the unit of natural gas consumption is converted from MJ to kWh. 175
Fig. 6 (a) and Fig. 7 (a) shows that the electricity consumption is much higher than the natural gas 176
consumption in Great Falls. Electricity consumption is relatively even throughout the year, while natural 177
gas consumption primarily concentrates in winter. Fig. 6 (a) and Fig. 7 (a) also shows that there is a periodic 178
change in the electricity and natural gas consumption: electricity and natural gas consumption is intensive 179
during the workday, while they are almost zero over the weekend. Fig. 6 (b) and Fig. 7 (b) shows that 180
electricity consumption is concentrated from 7:00 to 22:00 in winter and 8:00 to 16:00 in summer; natural 181
gas consumption is concentrated from 8:00 to 22:00 in winter and almost no consumption in summer. 182
(a) Whole year (b) Two typical days 184
Fig. 6. Hourly electricity consumption of the baseline model in Great Falls 185
(a) Whole year (b) Two typical days 187
Fig. 7. Hourly natural gas consumption of the baseline model in Great Falls 188
3.2. CO2 emission estimation 189
Based on the hourly electricity and natural gas consumption predicted in subsection 3.1, hourly CO2 190
emissions of baseline models and retrofit models in five locations can be obtained using equation (2). Here 191
9
we use Great Falls as an example to discuss the relationship between energy consumptions and CO2 192
emissions. The hourly CO2 emissions of the baseline model in Great Falls is shown in Fig. 8. There are 193
some interesting findings in two different time scales for Great Falls. 194
(a) Whole year (b) Two typical days 196
Fig. 8. Hourly CO2 emissions of the baseline model in Great Falls 197
For a period of one year, the change of CO2 emissions is not consistent with the energy consumption. 198
The emissions in Great Falls mainly occur on some days during winter while almost always zero during 199
summer. On the contrary, Fig. 6 (a) shows that electricity consumption is intensive during the whole year 200
in Great Falls. This inconsistency is due to time-variant emission factors: hourly CO2 emission factors of 201
electricity in Great Falls are almost always zero during summer and high in winter, as shown in Fig. 4. As 202
a result, the emissions from electricity consumption in summer are almost always zero despite the amount 203
of electricity consumption. Emissions from natural gas are also almost always zero during summer due to 204
low natural gas consumption as shown in Fig. 7. Therefore, total CO2 emissions in Great Falls during 205
summer are almost always zero. 206
For a period of one whole day in winter, the variation of CO2 emissions (Fig. 8) is consistent with 207
energy consumption (Fig. 6 and Fig. 7): emissions from the building mainly happen during the daytime, as 208
shown in Fig. 8, and energy consumption from the building also mainly happens during the daytime, as 209
shown in Fig. 6 and Fig. 7. This is because hourly emission factors of electricity in Great Falls on one whole 210
day are relative constant (Fig. 4) and the natural gas emission factor is a constant value. It is worth noting 211
this phenomenon may not occur for other locations, such as San Diego, where electricity is largely provided 212
by solar. 213
Fig. 9 shows the annual CO2 emissions of baseline building models and retrofit building models in five 214
studied locations. “MEASURE_e” represents emissions from electricity and “MEASURE_g” represents 215
emissions from natural gas. There are some interesting findings among different locations. 216
10
Fig. 9. Annual CO2 emissions of baseline models and retrofit models 218
First, the CO2 emissions in San Diego and Great Falls are much lower than the other three locations. 219
This is because San Diego and Great Falls have high renewable energy penetration, which is 46% and 97% 220
respectively. 221
Moreover, International Falls has the largest CO2 emissions from natural gas, followed by Great Falls, 222
Denver, San Diego, and Tampa. CO2 emissions from natural gas increase as the climate gets colder since 223
natural gas is used for heating. When the climate gets colder, heating loads increase accordingly [40][41]. 224
So, natural gas consumption for heating increases when the climate gets colder, which leads to the increase 225
of CO2 emissions. 226
The CO2 emissions from natural gas only account for a small part of total emissions in Tampa, San 227
Diego, Denver, and International Falls, but they account for more than 30% of total emissions in Great 228
Falls, as shown in Fig. 9. One of the reasons is that natural gas consumption in Great Falls is large due to 229
the cold climate feature mentioned above. Another reason is that hourly emission factors of electricity in 230
Great Falls are very low due to the high penetration of hydropower and wind power. 231
3.3. CO2 emission reduction 232
CO2 emission reduction by applying individual measures can be obtained by subtracting emissions of 233
the retrofit building from emissions of the baseline building. For example, CO2 emission reductions by 234
applying individual measures in Great Falls are shown in Fig. 10. Red means this measure reduces 235
emissions, while blue indicates the increase of emissions. Fig. 10 shows that: (1) building retrofit measures 236
in Great Falls reduce CO2 emissions in winter due to the high emission factors of electricity; (2) HEATING 237
reduces CO2 emissions more significantly than the other seven measures since natural gas is used for heating; 238
(3) COOLING hardly reduces CO2 emissions since emission factors of electricity in summer are almost 239
zero when cooling is needed; (4) SWH also has little impact on CO2 emissions because only a little amount 240
of energy is used for service water heating; (5) by improving the efficiency, LIGHT and EQUIP reduce 241
Note: Renewable energy (RE) penetration is obtained from [31].
11
electricity consumption and related internal heat gain. This can reduce the cooling load in the cooling season 242
but increase the heating load in the heating season. As a result, they reduce CO2 emissions in the spring and 243
fall when cooling is still needed and electricity comes from fossil fuel, and they increase CO2 emissions 244
when natural gas is used for heating; and (6) by reducing the solar heat gain and increasing insulation, 245
WINDOW reduces the cooling load but increases the heating load. Therefore, it reduces CO2 emissions in 246
the spring and fall, and increases CO2 emissions when heating is needed. 247
WALL
ROOF
WINDOW
LIGHT
EQUIP
12
COOLING
HEATING
SWH
Fig. 10. CO2 emission reduction by applying individual measures in Great Falls 248
The relative reduction of each measure is calculated using the CO2 emission reduction effect () 249
defined in equation (1). The results are shown in Fig. 11. The difference of is small in cold locations 250
(within 2.4% for Great Falls and within 5.1% for International Falls). The difference of is relatively large 251
in the other three locations (from 7.9% in Denver to 9.9% in San Diego) since EQUIP and LIGHT have 252
significant impacts on . The reason for this phenomenon is explained in Section 4. The EQUIP and 253
LIGHT are the top two emission efficient measures in four locations except Great Falls where the top two 254
are HEATING and WINDOWS. 255
256
13
Fig. 11. CO2 emission reduction effect () by applying individual measures 258
4. Discussion 259
4.1. Impact of climates on CO2 emission reduction 260
In cold climates, improving lighting efficiency and improving equipment efficiency are less effective 261
in emission reduction than hot climates. Fig. 11 shows that the CO2 emission reduction effects of LIGHT 262
and EQUIP in International Falls (cold climate) are 4.4% and 5.1% respectively, while they are 6.6% and 263
8.8% respectively in Tampa (hot climate). 264
Using EQUIP as an example, Fig. 12. shows the hourly CO2 emission factors of electricity, the 265
reduction of electricity consumption, the reduction of natural gas consumption, and the reduction of CO2 266
emissions in Tampa and International Falls. Both locations have similar emission factors in electricity 267
generation (Fig. 12 a). However, the reduction of electricity consumption by applying EQUIP is more 268
effective in hot climates, such as Tampa (Fig. 12 b), since it also reduces the cooling load due to the reduced 269
internal heat gain from the equipment. For cold climates, like International Falls, additional heating will be 270
needed when internal heat gain resulted from equipment is reduced. This also leads to an increase of gas 271
consumption in the cold climate location, as shown in Fig. 12 (c). As a combined effect, Fig. 12 (d) shows 272
larger emission reduction resulted by improving efficiency of equipment in Tampa than International Falls. 273
8.8%
6.6%
9.9%
7.1%
7.9%
6.5%
2.4%2.4%
5.1%
4.4%
Note: Renewable energy (RE) penetration is obtained from [31].
14
Fig. 12. Energy and CO2 emission reduction by applying EQUIP in hot and cold locations 275
4.2. Measures to reduce energy and emissions 276
Due to the variability of CO2 emission factors, the most energy efficient measure is not necessarily the 277
most efficient emission measure. For instance, the most energy efficient measure in Great Falls is EQUIP 278
(Fig. 13) while the most efficient emission measure is HEATING (Fig. 11). Improving equipment efficiency 279
reduces electricity consumption and related internal heat gain. This can reduce cooling loads but increase 280
Tampa (hot humid)International Falls (very cold)
(a) Hourly CO
2
emission factors of electricity
(b) Electricity reduction
(c) Natural gas reduction
(d) CO
2
emission reduction
15
heating loads. Therefore, improving equipment efficiency in Great Falls mainly reduces electricity 281
consumption in summer. However, this large energy reduction does not lead to corresponding emission 282
reduction because electricity in Great Falls in summer mainly comes from hydropower with zero emissions. 283
On the contrary, natural gas is used for heating in Great Falls, improving heating efficiency can directly 284
reduce emissions so that it becomes the most efficient emission measure. 285
A different example is San Diego, whose most efficient emission measure is the same as the most 286
energy efficient measure: EQUIP, as shown in Fig. 11 and Fig. 13. There are two reasons. First, San Diego 287
has little heating needs. Therefore, the emission reduction effect of HEATING is minimal. Second, only 288
46% of electricity comes from renewable energy. As a comparison, Great Falls gets 97% of its electricity 289
from renewable energy. Thus, reducing electricity consumption by adopting efficient equipment can still 290
lead to a good amount of emission reduction in San Diego. 291
Fig. 13. Site energy reduction by applying individual measures 293
If a location doesn’t have high renewable energy penetration of electricity generation, it is suggested to 294
select energy efficient measures for emission reduction because emission efficient measures are same as 295
energy efficient measures. For example, improving the efficiency of electric equipment and lighting are 296
suggested retrofit measures. If a location has high renewable energy penetration of electricity generation, it 297
is suggested to select retrofit measures that can reduce fossil fuel consumption for emission reduction. For 298
example, improving heating efficiency is a suggested retrofit measure for buildings that natural gas is used 299
for heating. 300
4.3. Impact of using hourly CO2 emission factor 301
By comparing the CO2 emission reduction difference between using our method and the existing 302
method (adopting constant annual factor on the current year grid emissions), we find that estimating CO2 303
emission reduction with the constant annual emission factor will overestimate or underestimate the 304
8.7%
6.7%
9.9%
7.8%
7.3%
5.8% 5.5%
4.4% 4.3%
3.4%
Note: Renewable energy (RE) penetration is obtained from [31].
16
reduction. Fig. 14 shows the estimation bias on emission reductions using the constant emission factor by 305
comparing with the one using hourly factors. 306
Fig. 14. Estimation bias on CO2 emission reduction using the annual emission factor 308
To quantitatively compare the difference of emission reduction by using hourly emission factors and 309
constant emission factor, Table 4 shows the CO2 emission reduction by using these two methods and their 310
difference. Fig. 14 and Table 4 shows that using the constant emission factor tends to overestimate the 311
emission reduction in San Diego (up to 1550 kg), underestimate in Denver (up to 692 kg) and International 312
Falls (up to 1165 kg), both over- or underestimating in Tampa and Great Falls. The largest difference occurs 313
in San Diego and the smallest difference in Tampa. 314
Table 4. CO2 emission reduction by using hourly emission factors and a constant emission factor 315
Location
Retrofit
Measures
Emission Reduction
using Hourly Emission
Factors (kg)
Emission Reduction using
A Constant Emission
Factor (kg)
Emission
Reduction
Difference (kg)
Tampa
WALL
2618
2490
-128
ROOF
525
495
-30
WINDOW
2647
2521
-126
LIGHT
17252
17404
152
EQUIP
22739
22853
114
COOLING
3717
3591
-126
HEATING
8
8
0
SWH
48
48
0
San Diego
WALL
265
271
6
ROOF
219
262
43
WINDOW
0
-57
-57
LIGHT
7919
9469
1550
Note: Renewable energy (RE) penetration is obtained from [31].
17
Location
Retrofit
Measures
Emission Reduction
using Hourly Emission
Factors (kg)
Emission Reduction using
A Constant Emission
Factor (kg)
Emission
Reduction
Difference (kg)
EQUIP
11066
11965
899
COOLING
802
890
88
HEATING
1
2
1
SWH
82
82
0
Denver
WALL
3110
3107
-3
ROOF
3136
3105
-31
WINDOW
10126
9918
-208
LIGHT
19457
18765
-692
EQUIP
23753
23161
-592
COOLING
1851
1667
-184
HEATING
438
438
0
SWH
123
123
0
Great Falls
WALL
801
539
-262
ROOF
493
343
-150
WINDOW
998
523
-475
LIGHT
228
292
64
EQUIP
622
772
150
COOLING
72
97
25
HEATING
1010
1010
0
SWH
141
141
0
International
Falls
WALL
5103
4862
-241
ROOF
4200
3993
-207
WINDOW
7421
6952
-469
LIGHT
10631
9466
-1165
EQUIP
12381
11728
-653
COOLING
872
710
-162
HEATING
2443
2443
0
SWH
166
166
0
316
As shown in Fig. 2, San Diego has plenty of solar power during the daytime, thus, hourly CO2 emission 317
factors during daytime are lower than both the hourly emission factors during nighttime and the annual 318
factor (Fig. 5). This will lead to an overestimated emission for energy used in the daytime if the annual 319
factor is adopted. As a result, it will also overestimate the emission reduction for the proposed energy 320
efficiency measures since they mainly reduce energy consumption in the daytime. 321
On the contrary, hourly emission factors in Denver and International Falls during daytime are higher 322
than both the hourly emission factors at nighttime and the annual factors (Fig. 5). Since electricity 323
consumption mainly occurs during the day, applying annual emission factors to the reduced electricity 324
consumption will underestimate the CO2 emission reduction. 325
18
As shown in Fig. 2, Tampa’s electricity source is dominated by natural gas (78%) and nuclear (12%), 326
which leads to relative constant hourly emission factors (Fig. 5). Thus, using hourly or annual emission 327
factors only results in a relatively small difference in the predicted emission reduction. 328
Although estimating CO2 emission reduction with the constant annual emission factor can produce 329
biases, it takes less time for data collection and processing. The existing method (adopting annual factor) 330
is still applicable for locations where fossil fuel is dominated because using constant annual emission factor 331
in these locations only produce minor biases. However, our proposed method (adopting hourly factors) is 332
suggested for locations where renewable energy is dominated because using constant annual emission factor 333
in these locations leads to large biases. 334
5. Conclusion 335
This study analyzed the CO2 emission reduction of building retrofit measures that related to envelope 336
and mechanical systems in five locations: Tampa, San Diego, Denver, Great Falls, and International Falls. 337
Instead of using the constant annual CO2 emission factor of electricity, this study adopted hourly emission 338
factors. We found that using the constant emission factor cause estimation bias: it overestimates the 339
emission reduction for most measures in San Diego, while it underestimates the reduction for most 340
measures in Denver and International Falls. Another finding is that the same retrofit measure may have 341
different CO2 emission reduction depending on the climates: improving lighting and equipment efficiency 342
has less impact on CO2 emission reduction in cold climates than hot climates. Furthermore, the most energy 343
efficient measure is not necessarily the most efficient emission measure: in Great Falls, the most energy 344
efficient measure is improving equipment efficiency, but the most efficient emission measure is improving 345
heating efficiency. Those finding are applicable only for medium office that natural gas is used for heating 346
and electricity is used for cooling. 347
The innovation and contribution of this study mainly lie in the following two aspects. Firstly, it reveals 348
that hourly emission factors should be adopted in CO2 emission reduction analysis for locations where 349
renewable energy is dominated. Secondly, the method of estimating CO2 emission reduction of building 350
retrofit measures proposed in Section 2.3 can be applied to other building retrofit cases. Using this workflow, 351
future studies can estimate their CO2 emission reductions by providing electricity emission factors together 352
with their estimated building energy consumptions and retrofit measures. 353
This study analyzes the CO2 emission reduction effect of building retrofit measures based on one-year 354
simulation data. However, the composition of electricity generation may change over time, and CO2 355
emission factors will change accordingly. Thus, if a building retrofit measure reduces electricity 356
consumption, emission reduction resulting from it may change over time. With the increased penetration 357
of renewable energy in electricity generation, the annual reduction of emissions due to the building retrofits 358
will likely decrease. Since the effects of building retrofit measures will last for a few decades, it would be 359
interesting to study the CO2 emission reduction effect of building retrofit measures over a longer time frame. 360
19
Acknowledgement 361
This research was supported by the National Science Foundation under Award No. CBET-2110171. 362
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