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The economic impacts of carbon emission trading scheme on building retrofits: A case study with U.S. medium office buildings

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Abstract

As a popular emission reduction tool, the carbon emission trading scheme (ETS) can potentially add an economic incentive for building owners to retrofit buildings in addition to the cost savings in energy. However, the additional economic benefits of building retrofits brought by ETS has not been quantitively investigated yet. To fill this gap, this study proposed a systematic economic evaluation method to investigate the economic impacts of ETS on building retrofits. The reduction of the payback period and the increase of the return on investment are adopted as evaluation metrics. Using medium office buildings as an example, this study predicted the economic impacts of ETS on building retrofits at four locations in the U.S., and three different carbon prices were investigated. The results show that carbon prices have significant economic impacts on building retrofits. With the relatively low forecasted time-variant carbon prices (around 10 USD per ton), the economic impacts of ETS on building retrofits are small. When carbon prices increase, the impacts of ETS would be up to 25% for 50 USD per ton (current prices in European Union) and 51% for 100 USD per ton. Furthermore, locations with more fossil energy have higher relative changes in the payback period and ROI but are more sensitive to carbon prices.
1
Yingli Loua, Yizhi Yanga, Yunyang Yeb, Chuan Hec, Wa ng da Z uo d,e,*
a Department of Civil, Environmental and Architectural Engineering, University of Colorado Boulder,
Boulder, CO 80309, USA.
b Pacific Northwest National Laboratory, Richland, WA 99354, USA.
c Leeds School of Business, University of Colorado Boulder, Boulder, CO 80309, USA.
d Department of Architectural Engineering, Department of Mechanical Engineering, Pennsylvania
State University, University Park, PA 16802, USA.
e National Renewable Energy Laboratory, Golden, CO 80401, USA.
*Corresponding author. Email address: wangda.zuo@psu.edu (Wangda Zuo)
Abstract
As a popular emission reduction tool, the carbon emission trading scheme (ETS) can potentially add an
economic incentive for building owners to retrofit buildings in addition to the cost savings in energy.
However, the additional economic benefits of building retrofits brought by ETS has not been quantitively
investigated yet. To fill this gap, this study proposed a systematic economic evaluation method to
investigate the economic impacts of ETS on building retrofits. The reduction of the payback period and the
increase of the return on investment are adopted as evaluation metrics. Using medium office buildings as
an example, this study predicted the economic impacts of ETS on building retrofits at four locations in the
U.S., and three different carbon prices were investigated. The results show that carbon prices have
significant economic impacts on building retrofits. With the relatively low forecasted time-variant carbon
prices (around 10 USD per ton), the economic impacts of ETS on building retrofits are small. When carbon
prices increase, the impacts of ETS would be up to 25% for 50 USD per ton (current prices in European
Union) and 51% for 100 USD per ton. Furthermore, locations with more fossil energy have higher relative
changes in the payback period and ROI but are more sensitive to carbon prices.
Keywords: Building retrofits; Carbon emission trading scheme; Carbon prices; Payback period; Return
on investment.
Y. Lou, Y. Yang, Y. Ye, C. He, W. Zuo. 2022. "The Economic Impacts of Carbon Emission
Trading Scheme on Building Retrofits: A Case Study with US Medium Office Buildings.
Building and Environment, 221, pp. 109311. https://doi.org/10.1016/j.buildenv.2022.109311
The economic impacts of carbon emission trading scheme on building retrofits: A
case study with U.S. medium office buildings
2
Nomenclature
𝑑
Number of differencing required to make the time series stationary
𝐷!
Forecasted data at time 𝑖
𝐸"#$
Reduction of electricity consumption brought by the building retrofit in month 𝑗 at time 𝑑
𝐹𝑒" #$
Emission factors of electricity in month 𝑗 at time 𝑑
𝐹𝑛
Emission factor of natural gas
𝐼
Retrofit investment
π‘š
Lifespan of the retrofit measure with the unit of month
𝑛
Number of months
π‘›π‘’π‘š
Number of intrinsic mode functions
𝑁"#$
Reduction of natural gas consumption brought by the building retrofit in month 𝑗 at time 𝑑
𝑝
Order of the autoregressive
𝑃%
Carbon prices
𝑃&
Electricity retail prices
𝑃'
Natural gas retail prices
π‘ž
Order of the moving average
π‘Ÿ
Monthly discount rate of money
𝑅!
Real data at time 𝑖
𝑅𝑂𝐼
Return on investment
𝑅𝑂𝐼(
Return on investment without carbon emission trading scheme
𝑅𝑂𝐼()
Return on investment with carbon emission trading scheme
𝑠"
Cost savings in month 𝑗
𝑆'
Cumulative cost savings until month 𝑛
𝑆)
Additional economic benefits from carbon credits via the carbon emission trading scheme
𝑆(
Cost savings considering energy only
𝑆()
Total cost savings when considering both energy and carbon
𝑇
Payback period
𝑇(
Payback period without carbon emission trading scheme
𝑇()
Payback period with carbon emission trading scheme
βˆ†π‘…π‘‚πΌ
Absolute increase of the return on investment brought by carbon emission trading scheme
%βˆ†π‘…π‘‚πΌ
Relative increase of the return on investment brought by carbon emission trading scheme
βˆ†π‘‡
Absolute reduction of the payback period brought by carbon emission trading scheme
%βˆ†π‘‡
Relative reduction of the payback period brought by carbon emission trading scheme
3
1. Introduction
The Intergovernmental Panel on Climate Change [1] declared that climate change presents one of the
world’s most pressing challenges. The United States (U.S.) Environmental Protection Agency [2] claimed
that carbon dioxide is the primary greenhouse gas contributing to recent climate change. Therefore,
reducing carbon emissions is crucial for the mitigation of climate change. In response to that, the U.S. has
outlined a pathway to reduce carbon emissions 50% by 2030 [3] and 80% by 2050 [4].
Retrofitting buildings has a great potential to reduce carbon emissions in the U.S. since buildings
account for approximately 36% of carbon emissions [5]. Current research shows that many existing
buildings have poor energy performance and thus lead to a large amount of carbon emissions [6,7].
Furthermore, most of the existing buildings will still be used until 2050 [8]. Consequently, there is a great
potential to reduce carbon emissions by retrofitting buildings. Langevin et al. [9] estimated that this
potential can be as large as 80% reduction relative to 2005 levels by 2050.
The carbon emission trading scheme (ETS) has been considered as the top promising instrument to
reduce carbon emissions. Thirty-eight national jurisdictions have implemented ETS in 2021, covering 16%
of global carbon emissions [10]. The effect of ETS on carbon emission reduction has been studied by
existing research. By analyzing the panel data from the German production census, Petrick and Wagner [11]
found that ETS caused treated firms to abate one-fifth of their carbon emissions relative to non-treated firms.
By studying the city-level panel data in China, Zhang et al. [12] found that ETS adopted in pilot cities
reduced carbon emissions by approximately 16%. Villoria-SΓ‘ez et al. [13] reviewed the existing ETS from
major countries including the EU, Australia, New Zealand, Japan, the U.S., and Canada. They found that
carbon emissions decreased around 1.6% per year since ETS implementation and around 23.4% of carbon
emission reduction can be reached after 10 years of ETS implementation, compared to the trend when ETS
was not implemented. Existing research indicates that ETS can help the reduction of carbon emissions.
Therefore, ETS can also potentially help the emission reduction in the building sector by adding additional
economic incentives for building owners to retrofit buildings. It’s also necessary for building sectors to
participate in the ETS since easy measures which were profitable in a short term, β€œthe low hanging fruit”,
had already been carried out [14].
There are a few studies about ETS in the building sector. By comparing the existing mechanisms, Wang
et al. [15] established a feasible mechanism to improve the ETS for renewable energy application in
buildings. Song et al. [16] investigated reasons for the lack of ETS in the building sector in China by
exploring building owner’s optimal strategies including adopting low-carbon technologies, purchasing
emission credits from the ETS market, and non-compliance. Chen et al. [17] analyzed the ETS in the
building sector in China and recommended a β€œstep-by-step” approach for promoting ETS.
4
However, the existing research about ETS in the building sector mainly focuses on qualitative analysis.
There is no quantitative prediction on the economic impacts of ETS. ETS can add additional economic
incentives for building owners to retrofit buildings. But are these additional incentives big enough to
motivate building owners for more retrofits? Before incorporating the building sector into the ETS, it’s
important to quantitatively investigate the economic impacts of ETS on building retrofits.
To fill this gap, this study developed a systematic economic evaluation method to quantitatively
investigate the economic impacts of ETS on building retrofits for the future adoption of this tool. A
prototypical U.S. medium-size office building was used as an example to illustrate the method. The
following of this paper was organized as follows: section 2 reviews the ETS and its implementation; section
3 introduces the method of evaluating the economic impacts of ETS on building retrofit; section 4 describes
the design of the case study including building energy models, investigated locations, and examined
building retrofit measures; section 5 presents the results for the economic impacts of ETS on building
retrofits; section 6 summarizes key findings and discusses the policy implication, finally, section 7 makes
a conclusion.
2. Overview of the Emission Trading Scheme
ETS is a market-based approach to control carbon emission by providing economic incentives for
reducing carbon emissions [18]. ETS works by first setting a limit on the overall amount of emissions that
is allowed to emit into the environment. Major participants in an ETS [19] include: (1) the government who
sets the coverage of sectors or regions targeted by the ETS and the emission limit; (2) enterprises from
different sectors who attempt to find optimal decisions regarding emission reduction in order to either
maximize the profits or minimize the cost; (3) third parties to conduct the measuring, reporting, and
verification work for estimating the emission reductions reported by enterprises; (4) the ETS market
through which various participants interact with each other and jointly determine the carbon price.
Introduced in 2005, the European Union (EU) ETS is the world's first major carbon market and remains
the biggest one [20]. The EU ETS is a key tool for the EU to reduce carbon emissions cost-effectively. The
New Zealand ETS is the government’s main tool for reducing carbon emissions and it has the broadest
sectoral coverage of any ETS. Korea launched its national ETS in 2015, which is the first national ETS in
East Asia. The Korea ETS plays an essential role in meeting Korea's 2030 target of 37% carbon emission
reduction [21].
The Regional Greenhouse Gas Initiative (RGGI) is the first mandatory ETS in the U.S. It is a
cooperative effort among 11 states in the U.S. to cap and reduce carbon emissions in the power sector [22].
In 2021 the carbon price in RGGI is 8.69 USD per ton [10]. Starting in 2012, California Cap-and-Trade
Program (California CaT) is another impactful ETS in the U.S. It covers approximately 80% of California’s
5
carbon emissions in the industry, power, transport, and building sectors. The carbon price in California CaT
is up to 17.94 USD per ton in 2021. A few more ETSs are scheduled to be implemented in the U.S. For
example, the state of Washington approved legislation to establish an ETS program starting in 2023. The
Transportation and Climate Initiative Program (TCI-P) is a collaboration of northeastern and mid-Atlantic
U.S. jurisdictions to develop an ETS for the transportation sector. It is scheduled to be implemented in
Connecticut, Massachusetts, Rhode Island, and Washington D.C.
Although the earlier implementation of ETS mainly focuses on the power sector, transportation, and
other energy-intensive industries, a trend to expand ETS to more sectors is evident in emerging schemes
[23]. For example, the EU ETS, the first major ETS introduced in 2005, only covers electricity and
industrial sectors. Tokyo ETS, introduced in 2010, started to cover all large installations such as office
buildings and factories. In 2015, the New Zealand ETS pushed its coverage to all sectors of the economy,
apart from agriculture [24]. Similarly, the building sector is expected to be widely covered by the ETS in
the U.S. soon.
3. Methods
Fig. 1 presents a general description of evaluating the economic impacts of ETS on building retrofits.
This section first introduces the method of estimating the investments of building retrofits in subsection 3.1.
Then subsection 3.2 introduces the method of predicting cost savings brought by building retrofits. Finally,
based on the building retrofit investment and the cost savings brought by retrofits, subsection 3.3 introduces
the economic evaluation metrics.
Fig. 1. General description of evaluating the economic impacts of ETS on building retrofits.
3.1. Investments of building retrofits
Two methods were proposed to estimate the investment of building retrofit: 1) retrofit fee and 2) retrofit
fee minus the replacement fee of the non-efficiency one. The first method was used for the retrofit measure
that doesn’t need to be replaced periodically over the lifespan of the building, for example, adding wall
insulation. The second method was used for the retrofit measure that needs to be replaced periodically over
the lifespan of the building, for example, replacing lamps. The retrofit fee includes material cost, labor cost,
3.3. Economic evaluation
Economic evaluation without ETS
Economic evaluation with ETS
Impact of ETS on the profits of
building retrofits
3.2. Cost savings due to
building retrofits
Cost savings without ETS
Cost savings with ETS
3.1. Building retrofit
Investments
6
and operating expenses for necessary equipment and facilities, which can be estimated by the RSMeans
Online database [25]. RSMeans is a construction estimating tool and it has a commercial renovation
database to estimate the retrofit fee for commercial buildings.
3.2. Cost savings brought by building retrofits
This subsection first introduces the method of forecasting energy and carbon prices. Then, based on the
forecasted prices, this subsection introduces the method of estimating cost savings without ETS and cost
savings with ETS.
3.2.1. Energy and carbon price forecasting
To investigate the economic impacts of ETS on building retrofits, this study examined three different
carbon prices: forecasted carbon prices in the U.S.; a fixed carbon price of 50 USD per ton (current prices
in EU); a fixed carbon price of 100 USD per ton. There are many energy price models, for example, fixed
prices, peak load prices [26], and time-variant prices [27]. Because building owners usually pay the utility
bill monthly and the energy prices may vary in different months, this study adopted monthly time-variant
energy prices. This subsubsection introduces the method of forecasting time-variant prices for energy and
carbon.
The current resource does not provide the future monthly energy and carbon prices in the U.S. Thus,
this study developed models to forecast energy and carbon prices based on existing research [28–31]. Fig.
2 explains the method of price forecasting. First, the time series of historical prices are decomposed into
different intrinsic mode functions (IMF) using the ensemble empirical mode decomposition (EEMD). The
main concept of EEMD is to decompose a complex time series into a sum of oscillatory components based
on the local characteristic timescale of the series. Then, autoregressive integrated moving average (ARIMA)
is used to create a forecasting model for each IMF. Finally, the forecasting result of each IMF is aggregated
to the final forecasting results. This method was used to forecast the electricity retail prices (
𝑃&
), natural gas
retail prices (
𝑃'
), and carbon prices (
𝑃%
). The following four paragraphs explain how to determine the
number of IMF (
π‘›π‘’π‘š
), and the parameters (
𝑝
,
𝑑
,
π‘ž
) in the ARIMA models.
7
Fig. 2. Method of energy and carbon price forecasting.
An ARIMA model is characterized by three terms:
𝑝
,
𝑑
,
π‘ž
. The
𝑝
is the order of the autoregressive (AR)
term. The
9
𝑑
is the number of differencing required to make the time series stationary. The
π‘ž
is the order of
the moving average (MA) term. In price forecasting, the raw data need to be divided into three data sets:
training set, validation set, and test set. The data of the training set is used to fit the price forecasting model;
the data of the validation set is used to adjust the parameters of the price forecasting model; the data of the
test set is used to measure the performance of the price forecasting model. This study adopted the proportion
0.70:0.15:0.15 for the training set, validation set, and test set, which is usually used in the research of energy
and carbon price forecasting [32].
Two types of error indicators were used to determine of number of IMF (
π‘›π‘’π‘š
) and the parameters of
the ARIMA model (
𝑝
,
𝑑
,
π‘ž
): mean absolute percentage error (MAPE) and root mean square error (RMSE),
which are expressed in equations (1) and (2):
𝑀𝐴𝑃𝐸=1
π‘˜?@𝑅!βˆ’π·!
𝑅!@
*
!+,
(1)
𝑅𝑀𝑆𝐸=B,
*βˆ‘(𝑅!βˆ’π·!)-
*
!+,
(2)
EEMD
Time series
of prices
IMF2
Final forecas4ng
results
IMF1IMFnum
ARIMA1ARIMA2ARIMAnum
Forecas4ng
result of IMF1
Forecas4ng
result of IMF2
Forecas4ng result
of IMFnum
Determine the
number of IMF
…
…
…
Determine
p1, d1, q1
Determine
p2, d2, q2
Determine
pnum, dnum,
qnum
8
where
9
π‘˜
is the number of data;
𝑅!
is the real data;
𝐷!
is the forecasted data. The smaller the MAPE and
RMSE are, the higher the forecast accuracy is.
MAPE is dimensionless, reflecting the relative error, which can be used as an indicator for comparing
the price forecasting models developed in this study with existing research. RMSE is not dimensionless,
but it works well for the data set that has values close to zero. This study adopted MAPE and RMSE to
determine the number of IMF (
π‘›π‘’π‘š
), adopted RMSE to determine the parameter of ARIMA (
𝑝
,
𝑑
,
π‘ž
), and
adopted MAPE to test price forecasting models.
3.2.2. Cost savings with/without ETS
This study only considered electricity and natural gas for the energy consumption of buildings because
these two are the most commonly used energy in commercial buildings in the U.S., which accounts for 93%
[33]. Therefore, the cost savings brought by a retrofit measure in month
𝑗
without ETS include costing
savings from electricity and natural gas, which can be expressed in equation (3). Since carbon reduction
brought by building retrofits can be traded in the ETS and get carbon credits. Therefore, the cost savings
brought by a retrofit measure with ETS include costing savings from electricity and natural gas, and carbon
credits, which can be expressed in equation (4):
𝑠"
(=F𝑃& #" Γ—?𝐸"#$
.
$+, +𝑃'#" Γ—?𝑁" #$
.
$+, IΓ—1
(1+π‘Ÿ)"
(3)
𝑠"
() =𝑠"
(+𝑃%#" Γ—?(𝐸"#$ ×𝐹𝑒"#$ +𝑁"#$ ×𝐹𝑛)
.
$+, Γ—1
(1+π‘Ÿ)"
(4)
where
𝑠(
represents monthly cost savings only considering energy and
𝑠()
represents monthly cost
savings considering both energy and carbon;
𝑃&
represents electricity retail prices,
𝑃'
represents natural gas
retail prices, and
𝑃%
represents carbon prices;
𝐸
represents the reduction of electricity consumption brought
by the building retrofit, and
𝑁
represents the reduction of natural gas consumption brought by the building
retrofit;
𝐹𝑒
represents the emission factors of electricity and
𝐹𝑛
represents the emission factor of natural
gas;
9
𝑑
represents time with a unit of one hour;
β„Ž
is the total number of hours in month
𝑗
;
π‘Ÿ
is the monthly
discount rate of money.
The data sources used for equations (3) and (4) are explained as follows: 1)
𝑃& #"
,
𝑃& #"
, and
𝑃% #"
are
forecasted in subsubsection 3.2.1; 2)
𝐸"#$
and
𝑁"#$
are predicted by running building energy models, which
will be introduced in subsection 4.1; 3)
𝐹𝑒" #$
9is9obtained9from the National Renewable Energy Laboratory’s
Cambium data [34]. Because the available
𝐹𝑒" #$
is on even years, the
𝐹𝑒" #$
on odd years is the average value
of previous and next year at the same time; 4)
𝐹𝑛
is a constant value, which is 1.97 kg/m3 [35]; 5)
9
π‘Ÿ
is 0.25%
in this study. An annual discount rate between 2% and 4% is advised to be used for energy efficiency
investments [36,37]. Furthermore, the real discount rate for the energy management program suggested by
9
the U.S. Department of Energy (DOE) is 3% in 2021. Therefore, this study adopted 3% for the annual
discount rate, which is 0.25% for the monthly discount rate because monthly discount rate equals to annual
discount rate divided by twelve.
3.3. Economic evaluation of building retrofits
Because ETS can add an additional economic incentive for building owners, this study accounted for
its impacts on the payback period and return on investment (ROI), which are introduced below.
The payback period refers to the time required to recoup the funds expended in an investment [38]. In
this study, the payback period (
𝑇
) of a building retrofit measure refers to the time required to recoup the
retrofit investment (
𝐼
). Since building owners usually pay the utility bill monthly,
𝑇
is a real number
expressed in the unit of month. The physical meaning of
𝑇
is shown in Fig. 3.
Fig. 3. Calculation of payback period.
The first step of calculating
𝑇
is to find an integer number of months (
𝑛
) that meet the following
conditions:
⎩
βŽͺ
⎨
βŽͺ
⎧
𝑆'=?𝑠"
'
"+,
𝑆'9≀𝐼
𝑆'/, 9β‰₯𝐼
(5)
where
𝑠"
is the monthly cost savings in month
𝑗
brought by a building retrofit measure;
𝑆'
is the
cumulative cost savings until month
𝑛
;
𝐼
is the investment of this retrofit measure.
According to the triangle similarity theorems, the variables (
𝑇
,
𝑛
,
𝐼
,
𝑆'
,
𝑠'/,
) in Fig. 3 have the
following feature:
π‘‡βˆ’π‘›
πΌβˆ’π‘†'9=1
𝑠'/,
9
(6)
Therefore,
𝑇
can be expressed as following:
𝑇=𝑛+πΌβˆ’π‘†'
𝑠'/,
(7)
10
Retrofit investment (
𝐼
) has been introduced in subsection 3.1 and cost savings has been introduced in
subsection 3.2, which includes cost savings considering energy only (
𝑆(
) and cost savings considering both
energy and carbon (
𝑆()
). Additional economic benefits of building retrofits from carbon credits via the
ETS is expressed as
𝑆)
. These cost saving components have the following relationship:
𝑆() =𝑆(+𝑆)
(8)
Therefore, the payback period without ETS (
𝑇(
) and the payback period with ETS (
𝑇()
) can be
calculated. This study adopted the absolute reduction of the payback period (
βˆ†π‘‡
) and the relative reduction
of the payback period (
%βˆ†π‘‡
) to evaluate the economic impacts of ETS on building retrofits, which can be
expressed in equations (9) and (10).
βˆ†π‘‡=𝑇(βˆ’π‘‡()
(9)
%βˆ†π‘‡=βˆ†π‘‡
𝑇(Γ—100%
(10)
ROI is a ratio between the net income (over a period) and the investment [39]. In this study, the ROI of
a building retrofit measure refers to a ratio between the cost savings brought by the retrofit measure (over
the lifespan of this measure) and the retrofit investment. The lifespans of different building retrofit measures
vary significantly. For example, the lifespan of the wall insulation is up to 30 years, while the lifespan of
the lamp is only a few years. Therefore, this study adopted the annual ROI as a metric, which can be
expressed as:
𝑅𝑂𝐼=𝑆0
πΌΓ·π‘š
12Γ—100%
(11)
where
π‘š
is the lifespan of the retrofit measure with the unit of month;
𝑆0
is the cumulative cost savings
brought by a retrofit measure in its lifespan;
9
𝐼
is the investment of this retrofit measure. The same as
payback period, this study calculated the ROI without ETS (
𝑅𝑂𝐼(
) and ROI with ETS (
𝑅𝑂𝐼()
). The
absolute increase of the ROI (
βˆ†π‘…π‘‚πΌ
) and the relative increase of the ROI (
%βˆ†π‘…π‘‚πΌ
) were adopted to evaluate
the economic impacts of ETS on building retrofits, which can be expressed in equations (12) and (13):
βˆ†π‘…π‘‚πΌ=𝑅𝑂𝐼() βˆ’π‘…π‘‚πΌ(
(12)
%βˆ†π‘…π‘‚πΌ=βˆ†π‘…π‘‚πΌ
𝑅𝑂𝐼(Γ—100%
(13)
If we combine equations (8) and (11)-(13), we can convert
%βˆ†π‘…π‘‚πΌ
into the form of cost savings as:
%βˆ†π‘…π‘‚πΌ=𝑆0
)
𝑆0
(Γ—100%
(14)
11
4. Study Design
Medium office building was selected an example in this study because commercial buildings have more
potential to participate in the ETS than residential buildings [16]. In addition, the medium office building
is more representative in commercial buildings. According to 2012 commercial buildings energy
consumption survey [40], office buildings have the largest area share of all building types (22.5%) in the
U.S. The average floor area of office building is 12,878 m2, which represents the medium-sized office
building.
This section first introduces the building energy models used to predict energy savings in subsection
4.1. Then, subsection 4.2 introduces the locations investigated in this study. Finally, subsection 4.3
introduces the examined building retrofit measures.
4.1. Building energy models
This study adopted the U.S. DOE commercial prototype building model for medium office buildings
[41] as a starting point to predict the reduction of electricity and natural gas consumption brought by
building retrofits. Fig. 4 shows the geometry of the medium office building model, which has a rectangular
shape with three stories. The total floor area of this model is 4,980 m2, with a 1,660 m2 roof area, a 1,314
m2 wall area, and a 653 m2 window area. It has steel-frame exterior walls and insulation entirely above deck
roofs. Furthermore, it uses packaged air conditioning units. There are two types of energy used in this
building energy model: electricity and natural gas. Natural gas is used for air conditioning system heating
and service water heating. Electricity is used for others, such as air conditioning system cooling, zone
supply air reheats, lighting, and equipment. More detailed information of this model can be found in [41].
The lighting power density in this building energy model is 10.78 W/m2. In addition, office lighting
standards state that a normal workstation requires 500 lumens/m2 [42]. Therefore, it can be deduced that
there are 109 incandescent bulbs (300W, 3600 lumens) and 356 fluorescents (59W, 5900 lumens) in this
building. The plug load density in this building energy model is 8.07 W/m2. Therefore, if DELL XPS
Desktop (360W) is used in this building, there should have 112 desktops.
Fig. 4. Geometry of the U.S. medium office prototype building model.
12
4.2. Locations
To get more representative results, this study investigated locations with distinct climate feature, price
level, and clean energy adoption rate. Considering the difference of price level and clean energy adoption
rate among locations, this study selected 4 from 16 climate locations [43] in the U.S.: 1) Tampa, Florida
(FL); 2) San Diego, California (CA); 3) Denver, Colorado (CO); 4) Great Falls, Montana (MT). Fig. 5
shows that the locations investigated in this study represent different climates (from hot humid to cold dry)
and different price levels. Regional price parities (RPPs) measure the differences in price levels across
states for a given year and are expressed as a percentage of the overall national price level [44]. The RPPs
of these four locations in 2020 vary from 92.4 to 110.4 (U.S. = 100). San Diego has the highest RPP while
Great Falls has the lowest one. The RPPs in Denver and Tampa are a little higher than the national price
level.
Fig. 5. Locations investigated in this study.
Furthermore, the locations investigated in this study have distinct predictions for clean energy adoption
rates in the next two decades [34]. Fig. 6 shows the composition of electricity generation in these four
locations from 2022 to 2041. The electricity generation in San Diego is cleaner than in the other three
locations because San Diego will not have coal usage in the next two decades and its clean energy
penetration will be high. The electricity generation in Denver is dirtier than in the other three locations
because Denver is expected to have a large amount of fossil energy (coal and natural gas) used for electricity
generation. Tampa will have a small percentage of coal used for electricity generation, but its natural gas
usage percentage is very high. The percentage of coal usage in Great Falls will increase from 2022 to 2026
and then decrease gradually. The clean energy adoption rates in Tampa, San Diego, and Denver will
increase over time due to increased awareness of environmental protection and improved clean energy
technology. However, the clean energy adoption rates in Great Falls will decrease in the first few years.
This is because the clean energy in Great Falls is mainly from hydro resource, which is difficult to further
improve its capacity. Therefore, coal fired power plants are expected to provide additional electricity to
meet the quickly increased demand.
Tam pa
San
Diego
Great
Falls
Denver
Climate feature
Regional price
parities in 2020
Cool dry
102.9
Hot humid
100.7
Warm marin e
110. 4
Cold dry
92.4
13
Fig. 6. Composition of electricity generation in four investigated locations [34].
4.3. Building retrofit measures
Based on the existing research [45–48] and the sensitivity analysis done in our previous research [46],
this study selected six building retrofit measures to investigate, as shown in Table 1. These six measures
potentially have significant impacts on the carbon emissions of medium office buildings across different
climate feature locations. The specific retrofit options was determined by referring to Advanced Energy
Design Guide 50% Energy Savings [49], available options in RSMeans [25], and Energy Star certified
products [50]. The model input values of baseline models and retrofit models are shown in Table 2.
Table 1. Building retrofit measures and their lifespan.
Measure
Lifespan (Year)
Retrofit Option
Reference
Tampa, FL
San Diego,
CA
Denver, CO
Great Falls,
MT
WALL
30
Add insulation (2.77 m2-K/W) for 1,314 m2 exterior wall.
[25,49]
ROOF
30
Add insulation (1.32 m2-K/W)
for 1,660 m2 roof.
Add insulation (2.08 m2-
K/W) for 1,660 m2 roof.
[25,49]
WINDOW
20
Replace single pane glass with
double pane insulated glass for
653 m2 windows.
Replace double pane
insulated glass with triple
pane insulated glass for 653
m2 windows.
[25,49,51]
LIGHT
2.75 for
fluorescents
0.57 for
incandescent bulbs
Replace 109 incandescent bulbs (300W, 3600 lumens) with 68
fluorescents (59W, 5900 lumens), which accounts for 784 m2
floor area.
[25,49,52]
EQUIP
6.5
Replace 37 DELL XPS Desktop (360W) with Energy Star rated
energy efficient computers, DELL OptiPlex 3090 Micro (65W),
which accounts for 1645 m2 floor area.
[50,53]
HVAC
14
Replace all 23
packaged
units with
efficient ones
(4 ton/unit),
which covers
Replace all
22 packaged
units with
efficient ones
(4 ton/unit),
which covers
Replace all
20 packaged
units with
efficient ones
(4 ton/unit),
which covers
Replace all
20 packaged
units with
efficient ones
(4 ton/unit),
which covers
[25,54]
14
Measure
Lifespan (Year)
Retrofit Option
Reference
Tampa, FL
San Diego,
CA
Denver, CO
Great Falls,
MT
4980 m2 floor
area.
4980 m2
floor area.
4980 m2
floor area.
4980 m2 floor
area.
Table 2. Model input values of baseline models and retrofit models.
Measure
Model Input
Unit
Tampa, FL
San Diego, CA
Denver, CO
Great Falls,
MT
Base1
Retr2
Base1
Retr2
Base1
Retr2
Base1
Retr2
WALL
Wall insulation R-
value
m2-K/W
1.04
3.81
1.71
4.48
2.37
5.14
2.37
5.14
ROOF
Roof insulation R-
value
m2-K/W
3.47
4.79
3.47
4.79
3.47
5.55
3.47
5.55
WINDOW
U-factor
W/m2-K
6.20
2.73
6.20
2.73
2.73
2.05
2.73
2.05
Solar heat gain
coefficient (SHGC)
-
0.81
0.70
0.81
0.70
0.70
0.67
0.70
0.67
LIGHT
Lighting power
density
W/m2
10.78
5.02
10.78
5.02
10.78
5.02
10.78
5.02
EQUIP
Plug load density
W/m2
8.07
5.88
8.07
5.88
8.07
5.88
8.07
5.88
HVAC
Nominal coefficient
of performance
(COP)
-
3.45
3.75
3.45
3.75
3.45
3.75
3.45
3.75
1 Base: Baseline model based on ASHRAE 90.1-2007
2 Retr: Retrofit model
The retrofit investments of WALL and ROOF were estimated using the retrofit fee since they don’t
need to be replaced periodically over the lifespan of the building. The retrofit investments of WINDOW,
LIGHT, EQUIP, and HVAC were estimated using the retrofit fee minus the replacement fee of non-
efficiency one since they need to be replaced periodically over the lifespan of the building. Since the
lifespan of the non-efficiency light (incandescent bulbs) is shorter than that of the retrofitted one
(fluorescents), the replacement fee of non-efficiency lights also includes the periodic replacement fee of
incandescent bulbs within the 2.7 years.
5. Results
This section first presents the investment estimation of building retrofits in subsection 5.1. Then,
subsection 5.2 shows the prediction results of the cost savings brought by building retrofits, which include
the cost savings without ETS and cost savings with ETS. Three different carbon prices were investigated:
forecasted time-variant carbon prices in the U.S.; a fixed carbon price of 50 USD per ton (current prices in
15
EU); a fixed carbon price of 100 USD per ton. Finally, subsection 5.3 illustrates the economic impacts of
ETS on building retrofits.
5.1. Investments of building retrofits
Using the method introduced in subsection 3.1 and data sources described in subsection 4.3, the
investments of six retrofit measures in the four studied locations were estimated, as shown in Fig. 7. The
investment of WINDOW is significantly higher than the other five retrofit measures for both total
investment and investment per area, while the investment of LIGHT is the lowest one. The investments of
retrofit measures in different locations have the following features. First, the investment of EQUIP is the
same for all locations because only the retail prices of computers is considered which are assumed the same
for all locations. Second, San Diego requires the highest investment than the other three locations for retrofit
measures WALL, WINDOW, and LIGHT due to its high price level. Third, the investments of ROOF in
cold climates (Denver and Great Falls) are higher than in hot climates (Tampa and San Diego) due to
additional insulation required by the Advanced Energy Design Guide (Table 1). Furthermore, the
investment of HVAC in hot climates is higher than in cold climates because more packaged units need to
be replaced due to their larger cooling needs (Table 1).
Fig. 7. Investment of building retrofit measures
5.2. Cost savings brought by building retrofits
This subsection first presents the forecasting results of electricity retail prices, natural gas retail prices,
and carbon prices. Based on the forecasted prices, this subsection predicts cost savings without ETS and
cost savings with ETS.
16
5.2.1. Energy and carbon price forecasting
Due to the availability of data, this study adopted state-level prices for energy price forecasting and
adopted the auction prices in the U.S. RGGI for carbon price forecasting. For electricity retail prices [55],
the data from 1990-2010 was used as a training set, the data from 2011-2015 was used as a validation set,
and the data from 2016-2020 was used as a test set. For natural gas retail prices [56], the data from 2009-
2016 was used as a training set, the data from 2017-2018 was used as a validation set, and the data from
2019-2020 was used as a test set. For carbon prices [57], the data from 2009-2017 was used as a training
set, the data from 2018-2019 was used as a validation set, and the data from 2020-2021 was used as a test
set.
The number of IMF determined for price forecasting is shown in Appendix Table A.1 and the
parameters (
𝑝
,
𝑑
,
π‘ž
) determined for price forecasting models are listed in Appendix Table A.2. Table 3
shows the test results of price forecasting models. The MAPE of forecasted electricity retail prices is within
0.019-0.035; the MAPE of forecasted natural gas retail prices is within 0.034-0.067; the MAPE of
forecasted carbon prices is 0.077. All MAPE is within the range of MAPE in existing literature for the
energy and carbon price forecasting, which is from 0.0003 to 0.192 [32]. Therefore, this study applied these
models (Appendix Table A.2) to forecast electricity retail prices, natural gas retail prices, and carbon prices
from 2022 to 2041.
Table 3.Test results of price forecasting models.
Price Type
Location
MAPE
Electricity
Florida (FL)
0.030
California (CA)
0.033
Colorado (CO)
0.035
Montana (MT)
0.019
Natural gas
Florida (FL)
0.034
California (CA)
0.050
Colorado (CO)
0.067
Montana (MT)
0.064
Carbon
U.S.
0.077
Fig. 8 shows the forecasted energy and carbon prices in the four studied locations. As shown in Fig. 8
(a), California has the highest electricity prices and will see the most increase in the future. Electricity prices
in Florida, Colorado, and Montana are relatively stable in the next two decades. Furthermore, California
and Colorado have higher prices in summer than in winter.
17
Fig. 8. Forecasted energy and carbon price in four studied locations.
(a) Electricity retail prices
(b) Natural gas retail prices
(c) Carbon prices in the U.S. RGGI
18
As shown in Fig. 8 (b), Florida and California have higher natural gas prices than the other two locations.
Natural gas prices will increase in California; keep stable in Florida; decrease in Colorado and Montana.
Furthermore, Colorado and Montana have higher prices in summer than in winter.
Fig. 8 (c) shows that the forecasted carbon prices in the U.S. will be around 10 USD per ton in the next
two decades, which is lower than the carbon prices in the EU ETS (50 USD per ton) [10]. The International
Energy Agency (IEA) [58] predicted that carbon prices in developed countries will rise to 250 USD per ton
in 2050 if the world is to achieve net zero emissions by that time. Carbon prices forecasted in this study
were based on historical data which did not consider potential stringent actions to reduce emissions in the
future. Therefore, the carbon prices in this study may be low. To better understand the economic impacts
of ETS on building retrofits, this study also investigated another two carbon prices: a fixed carbon price of
50 USD per ton based on the EU ETS and a fixed carbon price of 100 USD per ton which is a middle value
between the current EU ETS and the prices in 2050 predicted by IEA.
5.2.2. Cost savings with/without ETS
Denver was used as an example to illustrate the monthly energy and cost savings brought by building
retrofits, as shown in Fig. 9. The energy savings are the same every year since we used the same weather
files and schedules. Fig. 9 (a) shows that all retrofit measures reduce electricity consumption, with LIGHT
reducing the most. LIGHT, EQUIP, and HVAC reduce more electricity consumption in summer than in
winter, while WALL, ROOF, and WINDOW reduce more electricity consumption in winter than in summer.
By improving efficiency, LIGHT and EQUIP reduce electricity consumption and related internal heat gain.
This can reduce cooling loads in summer but increase heating loads in winter, and thus, LIGHT and EQUIP
reduce more electricity consumption in summer than in winter. Since the HVAC is only to improve cooling
coil efficiency, we see the significant saving in the summer when cooling is needed. In Denver, improving
the insulation of envelope (WALL, ROOF, and WINDOW) can reduce the electricity for reheat in the cold
winter, which is significantly more than the cooling energy saved in the summer which is typically mild.
19
Fig. 9. Monthly energy and cost savings without ETS in Denver.
Fig. 9 (b) shows that WALL and ROOF reduce natural gas consumption, but LIGHT, EQUIP, and
WINDOW increase natural gas consumption. Furthermore, HVAC doesn’t have impacts on the
consumption of natural gas. The reduction of natural gas consumption is due to the reduced heating loads.
WALL and ROOF reduce heating loads by the improving insulation of envelope. However, LIGHT and
EQUIP increase heating loads since the heat released from lights and computers is reduced. By reducing
the solar heat gain, WINDOW increases heating loads in Denver. HVAC in this study only improves
cooling efficiency, and thus it doesn’t have impacts on heating loads.
Cost savings brought by building retrofit measures (Fig. 9c) is dominated by the reduction of electricity
consumption since it has a similar trend with Fig. 9 (a). This domination is because electricity has higher
prices and more reduction than natural gas. The electricity prices (Fig. 8a) in Denver are 0.10 to 0.11 USD
per kWh from 2022 to 2041, while the natural gas prices (Fig. 8b) are 0.16 to 0.33 USD per cubic meters
(equals 0.02 to 0.03 USD per kWh). Meanwhile, the maximum monthly reduction of electricity (Fig. 9a) is
20
9792 kWh, while the maximum absolute value of monthly reduction of natural gas (Fig. 9b) is 185 thousand
cubic meters (equals 1952 kWh).
Fig. 10 shows the monthly emission reductions and cost savings under forecasted carbon prices.
Different from energy savings (Fig. 9), emission reduction of retrofit measures decreases over time. This is
because the emission factors of electricity decrease over time due to the increased penetration of clean
energy (Fig. 6). The same as cost savings with ETS, the cost savings without ETS decrease over time. This
is because the cost savings are discounted to obtain the present value, as explained in equations (3) and (4).
Fig. 10. Monthly emission reductions and cost savings with ETS under forecasted carbon prices in
Denver.
Fig. 11 shows the average annual cost savings with ETS brought by retrofit measures within their
lifespan (or 20 years for WALL and ROOF). Three different carbon prices were investigated: forecasted
time-variant carbon prices in the U.S.; a fixed carbon price of 50 USD per ton; a fixed carbon price of 100
USD per ton. With the relatively low forecasted time-variant carbon prices (around 10 USD per ton), the
cost savings from carbon only account for a small part (<5%) of total cost savings. This is because the
forecasted carbon prices in the U.S. in the next 20 years are low, which is around 10 USD per ton (Fig. 8c).
When carbon prices increase, the cost savings from carbon would account for up to 20% for 50 USD per
ton and 38% for 100 USD per ton. There are two interesting findings from Fig. 11:
21
Fig. 11. Comparison of average annual cost savings brought by six building retrofits at four locations
under three carbon prices.
First, LIGHT generates the highest cost savings for all locations, followed by EQUIP and WINDOW.
These three measures generate the highest energy cost savings in San Diego due to the significantly higher
electricity prices (Fig. 8a). However, these three measures generate the lowest carbon cost savings in San
Diego. This is because San Diego utilizes a large amount of clean energy for electricity generation.
Furthermore, WINDOW generates an unexpected less cost savings in Denver. The electricity and
natural gas prices in Denver are not significantly lower than in other three locations. Therefore, this because
WINDOW generates less energy savings in Denver due to the following three reasons. First, the insulation
of WINDOW (Table 2) in Denver and Great Falls is improved less than in Tampa and San Diego. Second,
the reduced SHGC further increases energy consumption in cold climates. Therefore, WINDOW generates
less energy savings in Denver and Great Falls. Moreover, improving the insulation of windows generates
less energy savings in Denver than in Great Falls because Great Falls is colder than Denver. As a result,
WINDOW generates minimal energy savings in Denver.
5.3. Economic impacts of ETS on building retrofits
Table 4 shows the payback periods of building retrofit measures with and without ETS. For the ETS
scenario, three different carbon prices were investigated: forecasted time-variant carbon prices based on the
historical data in the U.S. (around 10 USD per ton); a fixed carbon price of 50 USD per ton (current prices
in EU); a fixed carbon price of 100 USD per ton. The results show that the payback periods of building
retrofits with ETS are shorter than that without ETS. In addition, with the increase of carbon prices, the
payback periods of building retrofits decrease accordingly.
22
Table 4. Payback periods of building retrofits
Measure
WALL
ROOF
WINDOW
LIGHT
EQUIP
HVAC
Lifespan (Year)
30.0
30.0
20.0
2.750
6.50
14.00
Studied Period (Year)
20.0
20.0
20.0
2.750
6.50
14.00
Payback
Period
(Year)
Without ETS
Tampa, FL
>20.0
>20.0
15.4
0.110
1.74
11.65
San Diego, CA
>20.0
>20.0
10.9
0.184
1.06
9.85
Denver, CO
>20.0
>20.0
>20.0
0.237
1.99
>14.00
Great Falls, MT
19.6
>20.0
>20.0
0.224
2.39
>14.00
With ETS
(Forecasted
Time-variant
Carbon
Pricesβ‰ˆ10
USD/t)
Tampa, FL
>20.0
>20.0
14.7
0.106
1.68
11.34
San Diego, CA
>20.0
>20.0
10.8
0.182
1.04
9.79
Denver, CO
>20.0
>20.0
>20.0
0.228
1.85
>14.00
Great Falls, MT
18.9
>20.0
>20.0
0.220
2.35
>14.00
With ETS
(Fixed Carbon
Price = 50
USD/t)
Tampa, FL
>20.0
>20.0
12.6
0.094
1.49
9.58
San Diego, CA
>20.0
>20.0
10.4
0.174
1.00
9.56
Denver, CO
>20.0
>20.0
>20.0
0.203
1.59
>14.00
Great Falls, MT
15.4
>20.0
16.9
0.206
2.19
>14.00
With ETS
(Fixed Carbon
Price = 100
USD/t)
Tampa, FL
>20.0
>20.0
10.6
0.083
1.30
8.12
San Diego, CA
>20.0
>20.0
9.9
0.165
0.94
9.05
Denver, CO
>20.0
>20.0
>20.0
0.177
1.37
>14.00
Great Falls, MT
14.2
17.8
14.0
0.187
1.92
>14.00
Since the average annual cost savings of WALL are low (Fig. 11), the payback periods of WALL are
longer than 20 years for all the three carbon pricing scenarios in all locations except Great Falls. Because
Great Falls has long and cold winter, improving the insulation of envelope (e.g., WALL) can lead to
significant energy savings for heating, which reduces the payback periods accordingly. The ETS can reduce
the payback period of WALL in Great Falls for up to 5.4 years (from 19.6 years to 14.2 years).
Retrofitting ROOF has longer payback period than WALL. The retrofit investment of ROOF can be
recouped within 20 years only in Great Falls when the carbon price is 100 USD per ton. The same as WALL,
by improving the insulation of envelope, ROOF generates higher energy savings for heating in colder
climates. However, ROOF has the lowest average annual cost savings among the six measures (Fig. 11).
Together with its high investment, it takes a longer time to recoup the retrofit investment. Therefore, the
investment of ROOF can be recouped within 20 years only in the coldest climate under a significantly high
carbon price.
LIGHT has the shortest payback period, followed by EQUIP for all locations under all scenarios. As
discussed in subsubsection 5.2.2, LIGHT generates the highest average annual cost savings for all locations,
23
followed by EQUIP. Furthermore, these two measures require minimal retrofit investments (Fig. 7). Thus,
their investments can be recouped less than 2.4 years.
The investment of WINDOW can be recouped within its lifespan in Tampa and San Diego under all
scenarios. However, in Great Falls, it can only be recouped under the scenarios when the carbon price is 50
or 100 USD per ton, and it cannot be recouped in Denver under all scenarios. This is because WINDOW
generates significantly less cost savings in Denver, as explained in subsubsection 5.2.2.
The investment of HVAC cannot be recouped within its lifespan in Denver and Great Falls. Since
HVAC only improves cooling efficiency, it generates a small amount of energy savings in cold climates
due to their small cooling needs. Therefore, HVAC generates significantly less cost savings in Denver and
Great Falls (Fig. 11), and its investment cannot be recouped within its lifespan.
Fig. 12 illustrates the impacts of ETS on the relative reduction of the payback period with different
carbon prices. The retrofit measure that doesn’t show up in the figure means that the payback period of this
measure is out of its lifespan or longer than 20 years when ETS is not applied.
Fig. 12. Relative reduction of payback period with difference carbon prices.
Fig. 12 shows that the ETS with a high carbon price leads to a high relative reduction of the payback
period. With the relatively low forecasted time-variant carbon prices (around 10 USD per ton), the relative
reduction of the payback period is lower than 7%. However, the economic impacts of ETS on the six
building retrofit measures would be up to 21% when the carbon price is 50 USD per ton, and it would be
up to 31% when the carbon price is 100 USD per ton. Furthermore, the sensitivity of the payback period to
carbon prices varies from location to location. It is less sensitive in San Diego. This is because a building
retrofit measure leads to less carbon emission reduction in San Diego due to the high penetration of clean
energy.
24
For the same retrofit measure, ETS brings about the highest relative reduction of payback period in
Denver, while it brings about the lowest relative reduction in San Diego. This is because the emission
factors of electricity in Denver are high due to a large amount of fossil energy used for electricity generation
(Fig. 6), while the emission factors of electricity in San Diego are low due to the high penetration of clean
energy. Therefore, ETS has more impacts on the payback period in Denver. Fig. 14 will explain this in
detail.
Table 5 shows that the ROIs of building retrofits with ETS are higher than that without ETS. In addition,
with the increase of carbon prices, the ROIs of building retrofits increase accordingly. LIGHT generates
significantly higher ROI than the other five retrofit measures, which is up to 1226%. This is a combined
effect of less retrofit investment (Fig. 7) and more cost savings (Fig. 11). The ETS can increase ROI for
LIGHT up to 322% (from 904% to 1226%).
Table 5. Annual ROIs of building retrofits
Measure
WALL
ROOF
WINDOW
LIGHT
EQUIP
HVAC
Lifespan (Year)
30.00
30.00
20.00
2.75
6.50
14.00
Studied Period (Year)
20.00
20.00
20.00
2.75
6.50
14.00
ROI
(%)
Without ETS
Tampa, FL
3.12
1.05
6.09
904
54.4
8.28
San Diego, CA
1.57
1.26
8.37
590
88.0
9.66
Denver, CO
3.11
2.96
2.91
519
46.7
3.82
Great Falls, MT
5.09
3.96
4.80
614
40.6
2.86
With ETS
(Forecasted
Time-variant
Carbon
Prices
β‰ˆ
10
USD/t)
Tampa, FL
3.22
1.08
6.27
936
56.2
8.53
San Diego, CA
1.58
1.26
8.44
596
88.7
9.73
Denver, CO
3.24
3.08
3.03
544
48.8
3.97
Great Falls, MT
5.27
4.09
4.96
625
41.7
2.95
With ETS
(Fixed Carbon
Price = 50
USD/t)
Tampa, FL
3.65
1.21
7.06
1065
64.3
9.72
San Diego, CA
1.64
1.30
8.72
620
92.3
10.04
Denver, CO
3.83
3.64
3.57
651
58.2
4.67
Great Falls, MT
6.09
4.71
5.69
674
46.5
3.35
With ETS
(Fixed Carbon
Price = 100
USD/t)
Tampa, FL
4.18
1.37
8.02
1226
74.1
11.17
San Diego, CA
1.71
1.34
9.06
649
96.7
10.42
Denver, CO
4.56
4.31
4.22
783
69.8
5.51
Great Falls, MT
7.08
5.47
6.59
734
52.4
3.83
The ROIs of retrofit measures in different locations have the following features. First, WALL and
ROOF generate the highest ROIs in Great Falls because these two measures can generate a large amount
of energy savings for heating in cold climates. Second, the ROI of LIGHT in Tampa is higher than in the
25
other three locations. Although LIGHT in San Diego generates higher cost savings than in Tampa, it
requires more investments in San Diego due to its higher installation cost. As a result, LIGHT generates
the highest ROI in Tampa. Third, WINDOW, EQUIP, and HVAC generate the highest ROI in San Diego.
This is because WINDOW and EQUIP can generate the highest cost savings in San Diego due to the
significantly high electricity prices. Although HVAC in San Diego generates less cost savings than in
Tampa, it requires less investments in San Diego because of less packaged units to be replaced due to its
smaller cooling needs (Fig. 7). As a result, HVAC generates the highest ROI in San Diego.
Fig. 13 illustrates the impacts of ETS on the relative increase of the ROI with different carbon prices.
ETS brings about the highest relative increase of ROI in Denver, while it brings about the lowest relative
increase in San Diego. This is because San Diego has a high clean energy adoption rate, and thus building
retrofits generate less emission reduction with the same amount of energy savings. Therefore, ETS has less
impacts on the ROI in San Diego. Fig. 14 will explain this in detail.
Fig. 13. Relative increase of ROI with different carbon prices.
Fig. 13 indicates that the ETS with a higher carbon price has greater impacts on the ROI. With the
relatively low forecasted time-variant carbon prices (around 10 USD per ton), the relative increase of ROI
is lower than 5%. However, it would be up to 25% when the carbon price is 50 USD per ton, and it would
be up to 51% when the carbon price is 100 USD per ton. The same as the payback period, the sensitivity of
ROI to carbon prices varies from location to location, and it is less sensitive in San Diego.
Furthermore, the sensitivity of ROI to carbon prices is similar across six retrofit measures for all
locations except Great Falls. The ROI of LIGHT in Great Falls is less sensitive to carbon prices than the
other five measures. This is because the emission factors of electricity in Great Fall is low in the first two
years due to a small amount of coal used for electricity generation (Fig. 6). Furthermore, the lifespan of
LIGHT is only 2.7 years, which is shorter than the other five retrofit measures. As a result, with the same
26
amount of energy savings, LIGHT leads to less carbon emission reduction than other measures, and thus it
is less sensitive to carbon prices.
To explain why ETS has more economic impacts on building retrofits in Denver, while it has less
impacts in San Diego, Fig. 14 compares the energy savings, emission reductions, and cost savings of
LIGHT in Denver and San Diego in 2022. The horizontal axis in Fig. 14 represents each day of the year,
and the vertical axis represents each hour of the day. The shade of the color represents the magnitude of the
value at a specific hour on a specific day. Fig. 14 shows that emission reductions were obtained by
integrating the reduction of electricity and natural gas consumption with the emission factors of these two
kinds of energy. Energy cost savings were obtained by integrating the reduction of electricity and natural
gas consumption with the prices of these two kinds of energy. Similarly, carbon cost savings were obtained
by integrating emission reductions with carbon prices.
27
Fig. 14. Energy savings, emission reductions, and cost savings of LIGHT
in San Diego and Denver in the year of 2022
50.15
kg/GJ
Calculation of cost saving
Calculation of emissions
Multiply
Plus
28
As explained in equation (14), the
9
relative increase of ROI depends on the proportion between carbon
cost savings and energy cost savings. Energy cost savings of LIGHT in Denver are lower than in San Diego
due to lower energy prices and less energy savings. However, carbon cost savings of LIGHT in Denver are
higher than in San Diego due to higher emission factors of electricity. Therefore, the proportion between
carbon and energy cost savings is higher in Denver than in San Diego and thus ETS has more economic
impacts on building retrofits in Denver.
6. Discussion
6.1. Key findings of the economic impacts of ETS on building retrofits
Based on the current auction prices in RGGI, carbon prices in the U.S. in the next twenty years is
predicted to be around 10 USD per ton. With these low carbon prices, the economic impacts of ETS on
building retrofits are small. The relative reduction of payback period brought by ETS is lower than 7% and
the relative increase of ROI is lower than 5%. Furthermore, the economic impacts are different at different
locations in the U.S. The locations with more fossil energy have higher relative changes in payback period
and ROI but are more sensitive to carbon prices. Saving the same amount of energy in those locations can
lead to more carbon emission reduction compared to locations with less fossil energy. Therefore, building
owners in those locations could get more carbon credits with the same amount energy saving compared to
their peers in locations with cleaner energy. Of course, their additional economic benefits brought by ETS
from building retrofits could vary a lot depending on the carbon prices. In summary, by participating in the
ETS, building owners could get more economic benefits by retrofitting buildings. This will motivate
building owners to adopt more retrofit measures besides β€œthe low hanging fruit” measures.
6.2. Policy implication for incorporating the U.S. building sector into the ETS
This study found that the current carbon prices in the U.S. are relatively low and do not have significant
economic impacts on building retrofit decisions. Thus, a higher carbon price would motivate the building
owners on building retrofit and carbon reduction to achieve the new carbon reduction goal raised by the
U.S. government [3]. The government should also give necessary financial incentive to encourage building
owners to participate in the ETS in the early stage, to help them adapt to the ETS. The current focus should
be the regions with high fossil energy.
In addition, third parties to conduct the measuring and verification work for buildings should be
introduced to help building owners to get their reduced carbon emissions certified. A third party that has
ability to help building owners to estimate their carbon credits is suggested since it difficult for building
owners to do that by themselves. To accelerate the deployment of ETS, it is also necessary to streamline
the measurement, verification and transaction process and minimize the related costs.
29
6.3. Future research
The price forecasting method adopted in this study was only based on historical data and didn’t consider
the economic and policy uncertainties. In particular, carbon prices are highly impacted by the carbon
reduction goal set by the government. Accurate price forecasting models would be crucial to evaluate the
actual economic impacts of ETS on carbon reduction measures. Furthermore, this study examined each
building retrofit measure individually and didn’t consider constraints (e.g., budget). Future research can
examine the combined effect of various retrofit measures and study the optimal building retrofit strategy
under constraints. In addition, this study only investigated the economic impacts of ETS on building
retrofits in the U.S. Future research can examine the impacts of ETS in other countries.
7. Conclusion
This study proposed a systematic economic evaluation method to investigate the economic impacts of
ETS on building retrofits and the U.S. medium office was used as an example to demonstrate the method.
We adopted the reduction of the payback period and the increase of the ROI as evaluation metrics to analyze
the economic impacts of ETS, and three different carbon prices were examined. Using the relatively low
forecasted time-variant carbon prices (around 10 USD per ton), the economic impacts of ETS on building
retrofits are small, with the relative change of evaluation metrics lower than 7%. When carbon prices
increase, the impacts of ETS would be up to 25% for 50 USD per ton (current prices in European Union)
and 51% for 100 USD per ton. Furthermore, locations with more fossil energy have higher relative changes
in the payback period and ROI and are more sensitive to carbon prices. This is because building retrofits
bring about more carbon credits from the same amount of energy savings in these locations.
The contribution of this study mainly lies in the following three aspects. First, the results of this study
have policy implications for incorporating the U.S. building sector into the ETS for the future adoption of
this tool. Second, the method of evaluating the economic impacts of ETS on building retrofits can be applied
to other building types and regions. Using this method, the economic impacts of ETS on building retrofits
in the U.S. can be predicted by providing carbon prices, energy prices, building retrofit measures, predicted
reduction of building energy consumptions, and expected emission factors of other regions. Third, the
method of calculating carbon credits of building retrofits can be used by building owners to estimate their
additional economic benefits when the U.S. building sector is incorporated in the ETS. This can accelerate
the reduction of the carbon emission from the U.S. buildings.
Acknowledgement
This research was supported by the National Science Foundation under Awards No. CBET- 2217410.
30
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Appendix
Table A.1. Number of intrinsic mode functions (IMF) adopted for price forecasting
Price Type
Location
Number
of IMF
MAPE
RMSE
Selection in
this Study
Electricity
Florida (FL)
4
0.004
0.039
No
5
0.004
0.039
Yes
6
0.101
0.836
No
California (CA)
4
0.006
0.093
No
5
0.006
0.093
Yes
6
0.101
1.271
No
Colorado (CO)
4
0.005
0.048
No
5
0.005
0.048
Yes
6
0.040
0.312
No
Montana (MT)
4
0.007
0.062
No
5
0.007
0.062
Yes
6
0.100
0.776
No
Natural gas
Florida (FL)
4
0.002
0.025
No
5
0.002
0.025
Yes
6
0.860
9.426
No
California (CA)
3
0.003
0.035
No
4
0.003
0.035
Yes
5
0.061
0.504
No
Colorado (CO)
3
0.004
0.040
No
4
0.004
0.040
Yes
5
0.084
0.634
No
Montana (MT)
3
0.004
0.036
No
4
0.004
0.036
Yes
5
0.041
0.340
No
34
Price Type
Location
Number
of IMF
MAPE
RMSE
Selection in
this Study
Carbon
U.S.
2
0.019
0.085
No
3
0.019
0.085
Yes
4
0.046
0.204
No
Table A.2. Parameters (p, d, q) of price forecasting models
Price Type
Location
IMF
p
d
q
Electricity
Florida
(FL)
1
6
0
3
2
10
0
3
3
4
0
2
4
3
0
0
5
3
2
4
California
(CA)
1
7
0
4
2
7
0
3
3
8
0
2
4
0
0
1
5
2
2
1
Colorado
(CO)
1
9
0
3
2
14
0
3
3
1
0
0
4
1
0
0
5
6
2
4
Montana
(MT)
1
4
0
0
2
2
0
0
3
2
0
0
4
3
0
4
5
9
2
4
Natural gas
Florida
(FL)
1
4
0
3
2
7
0
1
3
4
0
1
4
0
0
1
5
1
0
0
California
(CA)
1
4
0
5
2
6
0
0
3
9
0
1
4
0
1
1
Colorado
(CO)
1
9
0
2
2
6
0
2
3
6
0
1
4
7
1
4
Montana
(MT)
1
3
0
4
2
6
0
3
3
2
0
0
4
4
1
2
Carbon
U.S.
1
4
0
3
2
1
0
0
35
Price Type
Location
IMF
p
d
q
3
2
2
2
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The European Union’s (EU) building stock is characterised by low energy efficiency and slow growth rates. To achieve EU’s greenhouse gas (GHG) emission targets, doubling building retrofit rates is one of the focuses of the European Green Deal. In this article, a real-world retrofit study was conducted, testing the limits towards carbon neutrality. A multi-objective optimisation process was developed, aiming to minimise the operating GHG emissions and the life-cycle cost. The process was applied to a typical multi-residential building and was tested in the four Greek climate zones. It was found that the cost-optimal retrofit agrees with the observed market trends (envelope insulation, double-glazed windows, air-to-air heat pumps (HP) and solar thermal collectors), leading to more than 60% reduction in GHG emissions. A maximum of 87% to 96% reduction was achieved by applying thicker envelope insulation, low-carbon (biomass boiler) or high-efficiency (gas-condensing boiler, air-to-air or air-to-water HPs) heating and cooling systems, photovoltaic-thermal and facade-integrated photovoltaic systems. A net-zero GHG emission retrofit could not be achieved within the building premises without considering the future decarbonisation of the electricity grid and the installation of efficient electricity-driven systems.
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The accurate prediction of energy price is critical to the energy market orientation, and it can provide a reference for policymakers and market participants. In practice, energy prices are affected by external factors, and their accurate prediction is challenging. This paper provides a systematic decade review of data-driven models for energy price prediction. Energy prices include four types: natural gas, crude oil, electricity, and carbon. Through the screening, 171 publications are reviewed in detail from the aspects of the basic model, the data cleaning method, and optimizer. Publishing time, model structure, prediction accuracy, prediction horizon, and input variables for energy price prediction are discussed. The main contributions and findings of this paper are as follows: (1) basic prediction models for energy price, data cleaning methods, and optimizers are classified and described; (2) the structure of the prediction model is finely classified, and it is inferred that the hybrid model and prediction architecture with multiple techniques are the focus of research and the development direction in the future; (3) root mean square error, mean absolute percentage error, and mean absolute error are the three most frequently used error indicators, and the maximum mean absolute percentage error is less than 0.2; (4) the ranges of data size and data division ratio for energy price prediction in different horizons are given, the proportion of the test set is usually in the range of 0.05-0.35; (5) the input variables for energy price prediction are summarized; (6) the data cleaning method has a more significant role in improving the accuracy of energy price prediction than the optimizer.
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Diverse quantitative models have been applied to analyse emission trading system, as the top effective climate change policy. This paper aims to present a comprehensive literature review on full-scale types of quantitative models in emission trading system research. The models dominating emission trading system-related literature can be categorized as optimization models, simulation models, assessment models, statistical models, artificial intelligences and ensemble models. Using different quantification and solution tools, these models complemented and enriched each other in serving the various agents involved in emission trading system and facilitating their respective emission trading system related works: the government to design emission trading system policies, enterprises to participate in emission trading system and goods markets, third parties to regulate emission trading system and emission trading system markets involving different agents. For each agent, a systematic analysis is provided on research hotspots (the challenges to address), quantitative models (to describe the problems and find the results), main findings (the policy implications from the models) and future research (potential improvements on existing models). Some conclusions are obtained. (1) Generally, China was the largest contributor to emission trading system research using quantitative models (representing 35.71% of the total articles). (2) The research hotspots were decision making by enterprises under an emission trading system (20.92%), spillovers amongst emission trading system and other markets (17.54%) and allowance allocation by the government (12.52%). (3) Popular quantitative models included various optimization models (32.00%) and simulation models (29.64%).
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