The 29th AIM International Workshop
Lab. SYNERGY

Data-Driven Monitoring and Evaluation of Effects of Urban Cooling Technologies
Seon Hyuk Kim*1, Chan Park*1*1 Department of Landscape Architecture, University Of Seoul

Introduction▪

MethodClimate change is causing global temperatures to rise steeply, and urban populations are increasingly exposed to climate extremes. Decision-makers and urban
planners are trying to adapt to climate change by improving the deteriorating urban heat environment through green infrastructure that can effectively reduceurban heat.

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In order to determine how much the land surface temperature (LST) of theAmong them, green roof technology is one of the most prominent technologies in urban structures where available land is scarce. Green roof technology has
been widely adopted for urban heat reduction under climate change, but there is a lack of scientific information on whether the effect of green roof technology
is sustained and whether the effect of individual projects is valid. Therefore, this study aims to quantify and evaluate the effectiveness of green roofs for
multiple projects by adopting a quasi-experimental method through satellite image panel data. 136 green roof sites built from 2014 to 2023 in Seoul, SouthKorea, were selected as research subjects. Among them, 20 sites suitable for the study were extracted, and their effects were evaluated.

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Can small-scale green roofs help mitigate long-term microclimates by reducing surface temperatures?

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Provide scientific evidence by evaluating whether small-scale green roofs can help mitigate long-term microclimates.

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method that is introduced as a useful way to obtain robust inferences fromto the control group, we generated LST data for May and September fromobservational data. It controls for unobservable bias by defining the policy

2013 to 2023. May and September are the months when the demand for

effect as the difference in the average change in the pre- and post-cooling energy occurs in Korea.

treatment means between the treatment and control groups.
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data, which cannot be controlled for through simple comparisons, but canthe cooling degree day (average temperature above 18℃).

be controlled for through DID methods. Since this study is a DID method

To remove cloud-affected cells, QA bands were utilized to remove values

using panel data, we applied a regression model with fixed effects, whichthat fall under the influence of clouds. As a result, a total of 38 LSTs were

was estimated using the panel DID regression function in the Stata 17

constructedversion of the statistical program. The regression equation for the DID
model is as follows :yist = γS + γt + zistβ + Dstδ + εist

Methodology
diagram옥상녹화사업지 데이터구축 및 선정

Installation date

2. Control sitesselection

Building height
difference (±3.5M)

1. Greenroofs데이터sites selection옥상녹화사업지구축 및 선정▪

3. Land surface
temperature(LST)
generation

Results

Of the 136 green roof data, 20 projects were extracted byexcluding projects that may cause bias, such as the period ofbuilding construction, whether the building was new during
the evaluation period, and restoration vegetation work

Year : 2013~2023
Month : 5 ~ 9* p<0.05 ** p<0.01 *** p<0.001

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GROUP
32
36
43

LST
0.248***
0.230***
0.192***

NDVI
0.155***
0.00633*
0.0136TYPE
Extensive
ExtensiveComplextemperature decreases simultaneously, and the second is48

0.105***

0.0199Complexwhen the NDVI index increases but no decrease in LST is52596061717374
134

0.159***
0.141
0.441***
0.08110.190***
0.493***
0.137***
0.151***

0.0385***0.0187***0.0421***0.0203***0.0108**0.000916
0.00285
0.00567

Extensive
Extensive
Extensive
Extensive
Extensive
Extensive
Extensive
Extensive

The DID regression analysis showed that the ability of green
roofs to adjust the microclimate can be divided into twotypes: the first is when the NDVI index increases and the LST

detected. To interpret these results, we summarize theregression coefficients of the DID analysis with the rooftop
recording type of the two results as follows

Excluding new
construction, underconstruction, andrestoration projects

Excluding controls withhigher NDVI than green
roof or with installedgreen roof

▪ The yellow color classification of green roofs increased
NDVI after the project, but no ability to regulate theCooling degree days

microclimate was detected when the type of green roofs

GROUPLSTNDVITYPE

was Extensive or Complex. These types of vegetation were38-0.0434*

0.0235***Intensive

41-0.273***

0.0116

Intensive

44-0.320*

0.00525

Intensive

56-0.0281

0.0160*Intensive58-0.259*** 0.0721***

Intensivefound to be dominated by herbaceous and grassy species.
Excluding sites withunknown geolocation

Buffer (100M < x < 270M )2. Control
selection
옥상녹화사업지sites데이터구축 및 선정

Remove cloud-affectedcells

The green roofs of the green color classification showed an▪

“680 controlsselection”“38 LSTgeneration”

To minimize spatial and climatic heterogeneity, a control group wasthis case, the vegetation type was found to be a mixture of

selected from a specific range of the green roof area, excluding

shrubs and grasses, and sometimes even shrubs were64-0.197*** 0.0270***

Complexnearby buildings that could be affected by green roofs and applying

planted.

72-0.0478** 0.0322***complex133-0.169*

Intensivebuffers of 270 meters and 100 meters.

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Green roof site

Control sites

Buildings

GROUP 64▪

0.00786

Buildings with a height difference of ±3.5m were selected as thebuilding height significantly affects temperature.
The control group selection also considered the presence of othergreen space projects and the influence of surrounding green spaces.
Control buildings with higher NDVI values than the green roof

4. Difference in differences (DID) : Evaluating Green roof
heat mitigation effectiveness

GROUP 60

increase in NDVI and a decrease in LST after the project,
and the types of green roofs were Intensive and Complex. In“20 sitesselection”

Spatial and temporal characteristics are unobserved heterogeneity in theobservation data on the day of the LST, and selected a day that satisfied

Method

1. Green roof sites
selection

The DID (Difference-in-Differences) method is a quasi-experimental

green roof sites was reduced before and after the project period comparedWe further checked the weather conditions through historical weather

Research Question & Purpose▪

4. Differences in differences : Evaluating옥상녹화사업지데이터 구축및 선정Greenroof heat
mitigation
effectiveness

3. Landsurfacetemperature
generation옥상녹화사업지데이터 구축및 선정

treatment site were removed, as well as those controls group withgreen roof, identified qualitatively

Discussion & Conclusion▪ Currently, many green roof projects in Seoul have been expanded due to their contribution to urban heat reduction, but the results of this study show that not allgreen roof projects can effectively contribute to urban heat reduction. In the Seoul climate zone, the intensive type of small-scale green roofs had an impact on
microclimate regulation, but the extensive type of green roofs had no detectable microclimate regulation in the satellite image data. Therefore, when expanding
small-scale green roofs for urban heat reduction, the intensive type of green roofs may contribute to creating a better urban thermal environment.
▪ In future building energy demand modeling studies, it is necessary to investigate the extent to which the impact of the microclimate changed by green roofs canaffect building energy demand in conjunction with climate change mitigation and adaptation.
This work was supported by the Korea Environment Industry & Technology Institute (KEITI) through the Climate Change R&D Project for New Climate Regime, funded by Korea Ministry of Environment (MOE; ) (RS-2023-00218794).