Sustainable Cities and Society 26 (2016) 383–392

Contents lists available at ScienceDirect

Sustainable Cities and Society
journal homepage: www.elsevier.com/locate/scs

Assessing the effects of CO2 reduction strategies on heat islands in
urban areas
Yujiro Hirano a,∗ , Yukiko Yoshida b
a
b

National Institute for Environmental Studies, 16-2, Onogawa, Tsukuba, Ibaraki 305-8506, Japan
Osaka University, 1-1 Yamadaoka, Suita, Osaka 565-0871, Japan

a r t i c l e

i n f o

Article history:
Received 17 January 2016
Received in revised form 22 April 2016
Accepted 23 April 2016
Available online 27 April 2016
Keywords:
Urban heat island
Energy saving
Air-conditioning load
CO2 reduction
Urban thermal environment
Built environment
Numerical simulation

a b s t r a c t
There has been a wide range of low-carbon solutions proposed to mitigate climate change. However, such
measures must be compatible with the local environment and living standards of residents to be brought
to fruition. Measures that adversely affect residential environments will be difficult to implement, so the
impacts of measures on the local environment must be taken into consideration during implementation.
This study assessed the effects on urban heat islands of efforts to reduce CO2 emissions, as one environmental impact associated with climate change. A simulated assessment was conducted, using an urban
canopy model coupled with a building energy model (CM-BEM), to evaluate the effects of five specific
measures: solar shading of windows using curtains and blinds, improvement of the thermal insulation
of building walls and roof surfaces, implementation of energy-saving measures related to indoor appliances, installation of solar photovoltaic (PV) panels, and adjustment of preset cooling temperatures. The
study focused on these effects as they occur within typical urban districts of office buildings, fire-resistant
housing, and wooden housing. Results indicated that many of the energy-saving measures have slight
temperature lowering effects, but solar panel installation and improved heat insulation, both associated
with changes in surface heat balances, tend to raise daytime temperatures to some extent. However,
effects on daytime temperatures were in the range of 0.1–0.2 ◦ C and, as such, none of the CO2 reduction
measures considered was deemed a significant factor in raising urban temperatures.
© 2016 Published by Elsevier Ltd.

1. Introduction
The need to reduce CO2 emissions from daily activities has
increased in urgency as global warming has become more severe
(Murakami et al., 2006; “2050 Japan Low-Carbon Society” Scenario
Team, 2009). Recent years have seen an increase in proposals
to improve energy efficiency and introduce renewable energy
technologies; however, atmospheric concentrations of CO2 have
continued to rise (World Data Centre for Greenhouse Gases, 2015)
and further efforts to reduce emissions are required to stabilize
the climate system. Following the Great East Japan Earthquake and
subsequent tsunami disaster, Japan faces additional and important
policy challenges, associated with securing stable energy supplies
during disasters as well as lowering peak electricity demand, due
to the shutdown of its nuclear power plants. This has led to an
increased focus on the creation of regional distributed energy

∗ Corresponding author.
E-mail address: yhirano@nies.go.jp (Y. Hirano).
http://dx.doi.org/10.1016/j.scs.2016.04.018
2210-6707/© 2016 Published by Elsevier Ltd.

networks and the introduction of renewable energy technologies. However, numerous energy-saving proposals have proven
extremely difficult to implement and current CO2 reductions are
far from adequate. Indeed, beyond the effects of the recent global
financial crisis and the Great East Japan Earthquake, Japan’s business and household sectors have seen an increase in CO2 emissions
during the 20 years since the signing of the UN Framework Convention on Climate Change (National Institute for Environmental
Studies, 2015).
These circumstances call for proposals to reduce CO2 emissions
and for concrete research into how such reductions can be realistically achieved. Toward this end, it is our belief that responses
to climate change that seek to protect the environment for future
generations should align with proposals for urban environments
that appeal to the current generation. Regardless of whether a
specific CO2 reduction measure benefits future generations, relevant decisions are made by the current generation such that any
measures adversely affecting residential environments will be difficult to implement. Accordingly, the introduction of any measure
must consider not only the future global environment but also

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any potential impacts on the immediate environment. Informed by
this perspective, this study explored a co-benefit model of urban
planning that balances CO2 reductions with the comfort and convenience of residents in their immediate surroundings.
One particularly important aspect when considering this level
of comfort relates to combating rising temperatures associated with increased urbanization, or, in other words, the heat
island phenomenon (Ichinose, 2005; Rizwan, Dennis, & Liu, 2008;
Santamouris, 2015). In Japan, with its blistering summer temperatures, heat islands are associated with a number of serious problems
that require constant attention, such as increased energy consumption for air conditioning and peak electricity demand and
discomfort caused by heat, heatstroke, and sleeping disorders.
While it is widely known that increasing the albedo of buildings
helps to prevent heat islands (Akbari & Matthews, 2012; Akbari,
Konopacki, & Pomerantz, 1999; Akbari, 2003; Costanzo, Evola,
Marletta, & Gagliano, 2014; Cotana et al., 2014; Georgakis, Zoras,
& Santamouris, 2014; Kolokotsa, Diakaki, Papantoniou, & Vlissidis,
2011; Porritt, Cropper, Shao, & Goodier, 2012; Pisello, Rossi, &
Cotana, 2014; Synnefa & Santamouris, 2012; Synnefa, Saliari, &
Santamouris, 2012; Zinzi & Agnoli, 2012), installing solar photovoltaic (PV) panels, which have a low surface albedo, as a means to
reduce CO2 emissions may in fact exacerbate heat island effects. On
these occasions, it is important to design for a comfortable urban
environment by estimating the potential impacts on temperature,
while avoiding – as much as possible – any negative effects on the
thermal environment. Nonetheless, there is significant capacity for
energy efficiency measures in urban settings to alleviate heat island
effects from anthropogenic heat, in addition to achieving reductions in CO2 emissions. If such impacts can be made achievable, then
these measures should be actively introduced for their co-beneficial
effects. This is important not only to improve the immediate environment of residents, but also to increase the feasibility of measures
to reduce CO2 emissions.
In terms of research related to heat islands, many studies have
looked at the relationship between energy consumption and the
outdoor thermal environment. However, most of these studies have
sought to explain the effects of anthropogenic heat, caused by conventional energy consumption, on urban heat islands (Boehme,
Berger, & Massier, 2015; Chen, Yang, & Zhu, 2014; Ichinose,
Shimodozono, & Hanaki, 1999; Kimura & Takahashi, 1991), or the
effects that heat islands have on the consumption of energy for
air conditioning (Assimakopoulos, Mihalakakou, & Flocas, 2007;
Hassid et al., 2000; Hirano & Fujita, 2012; Santamouris et al., 2001;
Santamouris, Cartalis, Synnefa, & Kolokotsa, 2015; Santamouris,
2014). Previous studies have not evaluated the effects on heat
islands of specific CO2 reduction measures implemented to tackle
global warming. Furthermore, while research has been carried out
on urban heat island mitigation measures, with the aim of improving energy efficiency or reducing CO2 emissions, the majority of
this research has concentrated on measures to lower the energy
consumption associated with air conditioning (Akbari, Pomerantz,
& Taha, 2001; Akbari, 2002; Akbari & Konopacki, 2004, 2005;
Rosenfeld, Akbari, Romm, & Pomerantz, 1995; Rosenfeld, Akbari,
Romm, & Pomerantz, 1998; Taha, Akbari, Rosenfeld, & Huang,
1988; Sailor & Dietsch, 2007; Taha, Konopacki, & Gabersek, 1999).
The direct effects of heat island mitigation measures, in terms of
reducing air conditioning energy consumption as well as CO2 emissions, are, of course, significant. Nonetheless, as mentioned, for a
successful implementation it is essential that these measures be
incorporated into broader CO2 reduction strategies as a means to
improve the lives of local residents. Existing studies do not reflect
this particular viewpoint.
Thus, the purpose of this study was to assess the effects on urban
heat islands of various CO2 reduction measures at a building- and
an urban district-scale. This goal was consistent with an impact

assessment aimed at increasing the feasibility of such measures as
well as improving the comfort of inhabitants. Specifically, this study
undertook a simulation of thermal environments within typical
urban districts to quantify the impacts of CO2 emissions reduction
measures on urban thermal environments. It also considered the
main factors behind the observed effects based on estimations of
atmospheric heat balance within city districts.

2. Research design
2.1. Modeling and urban districts used in calculations
The assessment was carried out using an urban canopy model
coupled with a building energy model (CM-BEM; Hirano & Fujita,
2016; Ihara, Kikegawa, Asahi, Genchi, & Kondo, 2008; Kikegawa,
Genchi, Yoshikado, & Kondo, 2003; Kikegawa, Genchi, Kondo, &
Hanaki, 2006; Kikegawa, Tanaka, Ohashi, Ihara, & Shigeta, 2014;
Kondo & Liu, 1998; Kondo et al., 2005; Ohashi et al., 2007; Ohashi,
Kikegawa, Ihara, & Sugiyama, 2014; Ohashi, Ihara, Kikegawa, &
Sugiyama, 2016). The CM-BEM combines a local meteorological
model at the district-scale with a computational model of the
air conditioning load. The urban canopy model is a multi-layer
one-dimensional meteorological model at the district-scale; in the
model, the average distance between buildings, average building
width, and vertical floor density distribution were used as parameters to describe a district containing numerous buildings. The
model calculates surface heat balances at different orientations,
and considers blocking out and multi-layer reflection of sunlight by
buildings, as well as temperature changes within the district. The
building energy model is a computational model of air conditioning
load, which can be coupled with the urban canopy model. Through
the combination of models, it becomes possible to express interactions between ambient air and buildings, such as the increase in
cooling due to temperature increases, and temperature increases
caused by associated anthropogenic waste heat, to calculate a
building’s thermal load, air conditioning energy consumption and
anthropogenic waste heat.
Districts of office buildings, fire-resistant housing, and wooden
housing were selected as typical urban districts for use in the
assessment. Conditions for the composition of wall and roof
surfaces, heat-pump equipment for air conditioning, as well as
operating schedules were calculated as per Kikegawa et al. (2003),
Kikegawa et al. (2006) and Hirano and Fujita (2016).

2.2. Calculation days
The study was conducted under two weather conditions: standard conditions and fair weather conditions. If days of broadly
average temperature and solar radiation were selected, there
would have been significant irregular temporal variation, due to
the changes in weather conditions owing to the fact that cloud coverage would not be 0%; therefore, the results would be difficult to
interpret. Moreover, it is not always the case that hotter environments present the most acute conditions. Days of fair weather are
more appropriate for revealing the mechanisms being examined
because they exhibit fewer irregular weather variations and are
valuable as case studies of acutely hot environmental conditions.
Nonetheless, days with extremely high temperatures produce nonaverage values for air conditioning energy consumption and CO2
emissions, and, hence, are not suitable for assessments representing average conditions. Thus, to represent these two conditions,
we include results for both standard and fair weather conditions.
In addition, the detailed examination of diurnal variations focused
on results obtained under fair weather conditions.

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Table 1
CO2 reduction measure settings.
Window shading

Change in total solar transmission rate,
using blinds or curtains (office
buildings: 0.3 → 0.1; residential
buildings: 0.5 → 0.2)
Improved insulation
Insulation of roof and external walls to
Japanese Next Generation Energy
Efficiency Standards (for actual values
see Hirano and Fujita (2016))
Indoor energy savings
Twenty percent reduction in energy
consumption by indoor appliances
(heat generated by appliances and
lighting, excluding air conditioning)
PV panels
Installation of PV panels over 60% of
building rooftops
Adjustment of preset air-conditioning Increase of preset cooling temperature
temperature
by 1 ◦ C (office buildings: 26 ◦ C → 27 ◦ C;
residential buildings: 27 ◦ C → 28 ◦ C)
PV—photovoltaic.

Data from two summer days of roughly average solar radiation and clear and sunny were selected for this analysis: July 29,
2002 (standard conditions), and August 10, 2002 (fair weather
conditions). Our detailed observation within urban districts was
conducted on these days (Ohashi et al., 2007). For both days,
we conducted detailed meteorological observations in the urban
canopy, and captured diurnal variations in air temperatures inside
the canopy. We also measured the shortwave and long wave downward radiation, and used these measurements for the calculations
in this study. Approach calculations were made for two more days
leading up to both target days (i.e., July 27–28 for standard conditions, and August 8–9 for fair weather conditions). All parameters
related to buildings were set using workday conditions.
For each of these days, we had already conducted hourly mobile
meteorological observations within urban districts, and verified the
reproducibility of outdoor meteorological factors by CM-BEM based
on these observations (Ohashi et al., 2007). Simulations focusing on
heat island mitigation measures were carried out under the standard conditions analyzed in this study (Hirano & Fujita, 2016). The
business as usual (BAU) simulation under standard conditions in
this study contained the same calculation conditions as the standard case simulation conducted by Hirano and Fujita (2016).
2.3. Summary of cases in which CO2 reduction measures were
used
We assessed five specific CO2 reduction measures: solar shading of window surfaces, PV panel installation, increased thermal
insulation of walls and roofs, energy-saving operation of indoor
appliances, and adjustment of preset cooling temperatures. The
relevant settings for each of these measures are listed in Table 1.
These are typical CO2 reduction measures, and numerous efforts
to increase the awareness of such measures among Japan’s citizens
have already been implemented. In addition, these five measures
have different physical mechanisms: 1. solar shading of windows
reduces the transmission of solar radiation, 2. improving the thermal insulation of the external walls and roof surfaces decreases the
transmission of heat, 3. indoor energy savings reduce internal heat
generation, 4. installation of PV panels generates electric power and
provides shielding from solar radiation, and 5. alteration of preset
temperatures in the air-conditioning units reduces the operating
load of indoor cooling appliances. These measures were selected
for the case study to compare how each measure influences the
outdoor thermal environment.
In the window shading case, the total solar transmission rate
changed; it was assumed that blinds or curtains were employed
to block solar radiation. The PV panel scenario assumed cover-

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age of 60% of a building rooftop. Following the settings associated
with PV panels from a previous study (Genchi et al., 2003), this
study included deductions of heat generated from the surface heat
balance, as well as decreased building cooling loads due to solar
blocking by panels and reduced anthropogenic heat from air conditioning energy consumption. The generation efficiency of PV panels
was assumed to be 10%, as given in Genchi et al. (2003). This
generation efficiency value was based on actual measurements
(Genchi et al., 2002). The insulation scenario assumed improvements in wall and roof heat insulation to meet the Next Generation
Energy Efficiency Standards, while volumetric heat capacity and
thermal conductivity parameters kept the same settings as in
Kikegawa et al. (2003), Kikegawa et al. (2006) and Hirano and
Fujita (2016). The application of indoor energy efficiency measures
assumed a 20% reduction in heat production by indoor appliances,
excluding air conditioners. Reductions in indoor generated heat,
as well as anthropogenic heat, caused by a lower consumption of
cooling energy were included. The air conditioning temperature
adjustment scenario assumed a 1 ◦ C increase in the preset cooling
temperature.

3. Methods
CM-BEM allows for the simultaneous calculation of radiation
within the district. Factors include multiple reflections by the
buildings and road, heat balance between building surfaces, airconditioning unit loads for each building according to height, and
energy consumption of buildings based on air-conditioning load.
Other factors include wasted heat from the cooling units, heat balance of the wasted heat from the cooling units, the building surface,
and the heat balance within the district, where the balance of radiation has been taken into account as well as a one-dimensional
component of vertical diffusion of air.
In this research, it was necessary to calculate the interactions
between the outdoor thermal environment and the indoor energy
consumption by air-conditioning units. Whereas there are many
general types of meteorological and computational models to calculate the air-conditioning load, for the purpose of this research,
it was necessary to combine both model types to calculate such
interactions simultaneously. There are only a limited number of
simulation models that have the capability of calculating such interactions and, among those models, the CM-BEM is the only one to
incorporate PV panels; therefore, the CM-BEM was selected for this
study.
To understand the overall flow of energy across an entire district in an integrated manner by using CM-BEM, the shape of the
buildings needed to be simplified. Thus, the buildings were represented as squares with a fixed average width and average distance
between buildings. In addition, the height distribution was defined
by setting building density at each height, constructing vertical
one-dimensional model layers.
To define the average building width, the average building spacing, and the vertical distribution of building heights, we used the
geographic information system (GIS) data for urban planning in
the city of Kawasaki. Based on the building polygon data pertaining to building usage and structure, which was included among the
GIS data, the major types of buildings, including office buildings,
fire-resistant housing, and wooden housing were selected as study
targets, in the same way as Hirano and Fujita (2016). First, the entire
city of Kawasaki was divided into approximately 500 m × 500 m
grids using the GIS data. We then identified the grids as typical
office building districts, fire-resistant housing districts, or wooden
housing districts, based on the information on building usage in
each grid. Subsequently, we calculated the average width of each
building, and average the distance between them once they were

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Y. Hirano, Y. Yoshida / Sustainable Cities and Society 26 (2016) 383–392

Fig. 1. Composition of typical urban districts.

rearranged into a column of squares, while maintaining the coverage and number of buildings. District specifications are shown in
Fig. 1.
To confirm the accuracy of this model, Kikegawa et al. (2003)
compared the modelled temperature sensitivity of electricity
demand within an urban block of office buildings with actual
electricity supply data; the results were used to validate the
model. Furthermore, Kikegawa et al. (2006) compared calculations
from a building energy model with calculations by general airconditioning load calculation software for residential buildings,
and confirmed the correspondence of those calculation results.
Ohashi et al. (2007) conducted detailed meteorological observations within city blocks, and confirmed the reproducibility
of outdoor meteorological elements. Hirano, Ohashi, Kikegawa,
Kondo, and Genchi (2005) compared daily variation patterns of
cooling energy calculated by this model with hourly data on
energy consumption intensity, and confirmed correspondence of
the characteristics of variation patterns. Hirano, Kikegawa, Genchi,
and Kondo (2006) conducted inter-comparison among methods,
including this model, for estimating the temperature sensitivity of
energy consumption; the results were confirmed to be consistent.

4. Results and discussion
4.1. Results under business as usual (BAU)
Calculations of outdoor air temperatures and indoor heat balances under BAU and fair weather conditions are shown in
Figs. 2 and 3. As mentioned, results under standard conditions were
the same as those obtained in the standard case of Hirano and Fujita
(2016). As Fig. 2 illustrates, office and fire-resistant housing districts
showed less variation in diurnal temperature than the timber housing, and their nighttime temperatures were particularly high. These
higher nighttime temperatures are attributed to the accumulation
of heat and the obstruction of radiative cooling by the different
building structures, and clearly suggest heat island effects. These
trends largely match those in the standard case (Hirano & Fujita,
2016); however, under fair weather conditions, the daytime temperature reached around 36 ◦ C, producing extremely hot weather
conditions that differed from those in the standard case. Indoor
heat balances (Fig. 3) were largely in line with those of the standard case; however, as there was little irregular dispersion of solar
radiation penetrating windows, the indoor heat balance displayed
a clear, double-peaked pattern. This is a logical result, given that
solar radiation more easily penetrates windows during times of
low solar elevation.
The amount of removed sensible and latent heat, shown in Fig. 3,
refers to the quantity of heat removed from building interiors by
air conditioning equipment. In the building energy analysis model,
we calculated the amount of energy consumed for cooling purposes

Fig. 2. Outdoor air temperatures under business as usual (BAU) (fair weather conditions).

using the values for removed heat and the efficiency of air conditioning equipment. Fig. 4 shows the energy for cooling consumed
per unit of floor area, under fair weather and standard conditions. In
both cases, the most elevated results were from office building districts during the day. While office buildings are less susceptible to
the effects of outside air when compared to residential buildings,
the larger quantities of heat generated by indoor appliances and
occupants, as well as their higher rates of air conditioner operation
during the day, means that solar radiation from windows rapidly
adds to their air conditioning load. Also, the relatively large difference between office and residential districts under fair weather,
compared to standard conditions, was due to a higher susceptibility to the effects of indoor heat generation compared to those
of outside air. Considering the residential districts, we noted the
particularly high daytime energy consumption of wooden residential districts under fair weather conditions. This is mainly due to
the effects of heat transfer from roof and wall surfaces and solar
radiation through windows. Wooden housing has a lower thermal

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387

Fig. 3. Indoor heat balances under BAU (fair weather conditions).

insulation performance than fire-resistant housing and, due to its
smaller structural scale, has a higher ratio of surface area to volume, making it more susceptible to the effects of outside air. This
accounts for the larger amounts of cooling energy consumed per
unit of floor area.

4.2. Results of CO2 emission reduction measures

Fig. 4. Consumption of cooling energy under BAU.

Fig. 5 shows the differences in CO2 emissions per unit of floor
area under BAU and with reduction measures applied. As the figure
shows, in office building districts, indoor energy efficiency measures had the largest impact in reducing CO2 emissions, while in
residential districts, the installation of PV panels had the largest
impact. However, as we did not establish settings that would allow
each measure to be compared on equal terms, we could not conduct
a comparative assessment of the effect of each countermeasure.
For example, in the energy efficiency case, a straightforward fixed
proportional value was given for the rate of reduction in indoor
energy consumption, so that office districts, which consume significant amounts of energy for indoor appliances, showed a large
reduction in CO2 emissions. Furthermore, due to lower building
height in residential districts compared to office districts, residential districts have a larger roof area relative to floor area and hence
a large effect of PV panels per unit of floor area. Thus, both out-

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Fig. 5. CO2 emission reductions achieved under each reduction measure (measure case—BAU case).

comes were highly contingent on the settings for each emissions
reduction scenario and may not be generalizable. Nevertheless, the
aim of this study was to assess the effects of each countermeasure
on outdoor air temperatures and not on CO2 emissions; therefore, assessments were based on these reduction scenarios. Further
research is needed to establish scenario values to allow each measure to be compared on an equal basis, for example by using the
cost of application.
Next, we assessed the impacts of each measure on outdoor air
temperature. As stated, the scenario settings did not allow for comparison of each measure on an equal basis; thus, we have shown
the relationship between the effects of CO2 reduction and temperature change in scatterplots (Fig. 6). Despite significant variation
according to conditions, many cases indicated reductions in air
temperature. Nevertheless, the average daytime effect on temperature among the CO2 reduction measures considered was less than
0.1 ◦ C, which is not significant.
Looking at each case in turn, shading solar radiation from windows, indoor energy-saving measures and changing preset cooling
temperatures all reduced CO2 emissions by lowering the energy
consumption of air conditioners and appliances and, in many cases,
a small reduction in air temperature was produced. This is most
likely caused by a reduction in anthropogenic heat resulting from
lower energy use. Conversely, PV panel installation in many cases
produced an increase in air temperature. This implies that, despite
electricity generation and blocking of solar radiation lowering air
temperatures, the low albedo of PV panels cancels out these effects.
In the case of improved insulation, the range of variation in surface
temperatures differed significantly due to the lowered heat capacity of buildings, with impacts varying depending on conditions. In
these cases, it is difficult to consider them in detail based on daily
average values. Therefore, we conducted a detailed analysis based

on daily variations, focusing on fair weather conditions, in Section
4.3.
4.3. Analysis of sensible heat balances within districts
Fig. 7 shows diurnal variations in air temperature and differences against BAU for each case under fair weather conditions. Fig. 8
shows atmospheric sensible heat balances within the urban canopy
and differences against BAU for each case, also under fair weather
conditions. For atmospheric sensible heat balances, sensible heat
inflows into the urban canopy from each factor are shown as positive, and outflows as negative. Accordingly, if the total of each factor
(shown by the black line in Fig. 8) is positive, this indicates a rise in
temperature, and likewise a drop in temperature if negative. Fig. 8
shows column amounts totaled to the top of the urban canopy. The
total for each factor cannot be compared across the different district
types owing to their different canopy heights.
From Fig. 7, we can observe that despite significant variations
according to conditions, solar shading of windows, indoor energy
efficiency measures and adjustment of preset cooling temperatures
generally produced slight reductions in air temperature. As mentioned, all of these measures reduce CO2 emissions by lowering
energy consumption, and, hence, the main cause of these temperature reductions was the decrease in anthropogenic heat as a
consequence of lower energy consumption. Fig. 8 shows that, in
all cases, waste heat from air conditioning equipment was lower
compared to BAU, confirming a reduction in waste heat via the
application of the relevant measures (Fig. 8b, e, f). In addition, as
sensible heat flux via turbulent heat dispersion has a negative value,
in the figures showing differences a positive value indicates a reduction in heat flux. Accordingly, as the difference against BAU was
positive in the case of solar shading of windows, indoor energy effi-

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Fig. 6. Differences in CO2 emission (measure case—BAU case) and differences in daily average air temperature (measure case—BAU case).

Fig. 7. Air temperature calculations for each case and differences against BAU.

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Y. Hirano, Y. Yoshida / Sustainable Cities and Society 26 (2016) 383–392

Fig. 8. District sensible heat balances for each case and differences against BAU.

ciency measures, and adjustment of preset cooling temperatures
(Fig. 8b, e, f), sensible heat flux from the top of the urban canopy was
reduced as air temperatures fell. Sensible heat flux from building
surfaces during the daytime increased in the case of solar shading
of windows (Fig. 8b). This is due to the increased absorption of heat
into the canopy atmosphere proportional to the reduction of solar
radiation that would have penetrated windows absent any shading.

Nonetheless, during many time periods the reduction in waste heat
from air conditioning equipment surpassed this amount, resulting
in a slight drop in air temperature.
Conversely, the installation of PV panels tended to produce an
increase in daytime air temperature (Fig. 8c). The main cause of this
increase was the low albedo of PV panel surfaces and their tendency
to increase in temperature. Fig. 8 shows increasing heat flux from

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building surfaces in each of the different districts during morning
hours, highlighting the effect of rising surface temperatures. However, evening values for office and fire-resistant residential districts
were negative, indicating a slight drop in air temperature. This is
due to the reduction in accumulated heat by concrete roof surfaces,
which have a large heat capacity, caused by the low heat capacity of
PV panels and their blocking of solar radiation. Conversely, wooden
housing has a small heat capacity compared to reinforced concrete
structures, and hence does not produce this type of reduction in
heat flux. Also, due to the low insulation performance of wooden
housing, the effects of reduced air conditioning load due to blocking of solar radiation are relatively large. For this reason, within
wooden housing, the increase in heat flux and the reduction in
waste heat from air conditioning during the evening largely offset each other, such that the air temperature remained essentially
unchanged against BAU.
In the case of improved thermal insulation (Fig. 8d), we can see
a number of time periods over which air temperatures rose or fell,
determined by the balance between increasing building surface
temperatures and reductions in waste heat from air conditioning.
For office districts, which have high air conditioner operation rates,
the reduction of heat penetration during operational hours had a
significant effect, while heat flux from building surfaces increased.
Nevertheless, as air conditioning increased during morning hours,
the effect of reduced waste heat from air conditioning equipment
was greater, producing a temporary reduction in air temperature.
For residential buildings, the lower heat capacity of building surfaces created by insulation resulted in an increase in heat flux
from building surfaces during the day and a reduction during the
night. This effect was particularly pronounced for fire-resistant residential districts, which have a high heat capacity to begin with,
and a clear reduction in nighttime air temperature was observed.
Wooden residences have low insulation performance and sensitive
cooling loads, thus, improved insulation produced a slight increase
in air temperature, caused by increased heat flux from building surfaces during the morning. However, the reduction in waste heat
from air conditioning in the afternoon resulted in a slight drop in
air temperature.

5. Conclusions
In this study, we have assessed the effects on urban heat islands
of a range of measures aimed at reducing CO2 emissions. We conducted a simulation of the summertime thermal environment to
test CO2 reduction measures at the district- and building-scale
using an urban canopy model coupled with a building energy analysis model (CM-BEM). The purpose of this study was to provide
an impact assessment of CO2 emissions reductions strategies, not
simply to improve the comfort of residents but also to enhance the
feasibility of future measures aimed at ameliorating global warming.
We found that measures to reduce CO2 emissions via energysavings on the whole resulted in reductions in air temperatures,
while measures involving changes to a building’s heat capacity
or albedo, such as improving insulation or installing PV panels,
resulted in higher daytime air temperatures. However, in many of
these cases the effects on daytime air temperature were in the range
of 0.1–0.2 ◦ C, and we concluded that such measures were not major
factors in increasing temperatures in urban areas. At the same time,
the incentive to introduce these CO2 reduction measures, according
to any co-beneficial effects, is surely limited.
While not considered by this study, emissions of cold air from
heat-pump water heaters and the displacement of atmospheric
heat emissions by ground source heat pumps may be significant in
terms of heat island mitigation. As these factors could contribute

391

more directly to alleviating district thermal environments, conducting a similar assessment of these measures in the future is
considered worthwhile. Furthermore, while this study only considered measures at the district- and building-scales, many CO2
emissions reduction strategies that have been proposed may have
more large-scale impacts on urban thermal environments. These
include transferring untapped energy from industrial waste heat
to cities, as well as urban distributed generation and cogenerated
heat supplies, which produce waste heat from electricity generation. Moreover, the increasing shift towards compact cities, with
higher concentrations of people and buildings, will become a factor
in increasing the exposure of inhabitants to these potential impacts.
Accordingly, similar assessments will be required for measures
involving supply-side solutions and modifications to city structures.

Acknowledgments
We thank Dr. Toshiaki Ichinose (National Institute for Environmental Studies), Dr. Yukihiro Kikegawa (Meisei University), Dr.
Yukitaka Ohashi (Okayama University of Science), and Dr. Tomohiko Ihara (The University of Tokyo) for helpful discussions and
comments.
This research was supported by the Environment Research and
Technology Development Fund (2RF-1303) of the Ministry of the
Environment, Japan.

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