Factors influencing urban heat island in Surabaya, Indonesia

Factors influencing urban heat island in Surabaya, Indonesia

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ARTICLE IN PRESS

SCS-468; No. of Pages 7

Sustainable Cities and Society xxx (2016) xxx–xxx

Contents lists available at ScienceDirect

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

Factors influencing urban heat island in Surabaya, Indonesia Ayu Candra Kurniati (Lecturer) a,∗ , Vilas Nitivattananon (Associate Professor) b a b

Urban and Regional Planning, Sekolah Tinggi Teknologi Nasional, Yogyakarta, Indonesia Urban Environmental Management, Asian Institute of Technology, Thailand

a r t i c l e

i n f o

Article history: Received 21 August 2015 Received in revised form 14 June 2016 Accepted 16 July 2016 Available online xxx Keywords: Electricity consumption Green space PLS-R analysis Significant factors UHI

a b s t r a c t Increased population and rapid urban development have raised the consumption of energy and affected to the urban environment. It creates the rising temperature in certain areas that is called urban heat island (UHI) phenomenon. Mitigating UHI on the most influencing factors based on the characteristics of a certain city is important due to the effectiveness and efficiency of the purposing strategies. As a matter of fact, municipality and community are lack of awareness to the activities that worsening UHI in urban development. This research therefore analyzes significant factors influencing UHI in Surabaya city, one of the metropolitan cities in Indonesia. A mixed method using city development documents and statistical data related to (Urban Heat Island Intensity), changes in surface cover, use of air conditioning and greenhouse gases has been applied in this study. The results show that provision of green space, electricity consumption and use of asphalt are the significant factors that influence UHI in the city. Furthermore, consideration to development and management of environment related strategies and measures is being needed. Municipality can focus to implement or establish for emphasizing most significant factors. Hence, this result can be a reference to mitigate UHI in Surabaya or other cities with similar characteristics. © 2016 Elsevier Ltd. All rights reserved.

1. Introduction High energy consumption is combusted to heat and concentrated due to urban structure (high building, building material, urban structure, size of the city, urban greenhouse effect). Furthermore, it leads to heat island phenomenon that increases the temperature in densely development area (Gago, Roldan, PachecoTorres, & Ordonez, 2013). Increased urban temperature from urban heat island (UHI), can influence the environment of the people and quality of life. In addition, it brings positive impacts like a continuation of the plant growing season and negative impacts such as elevated consumption of energy, raised of air pollution and greenhouse gases, hamper to human health and amenities, also it can interfere water quality (Bisset, 2013). The UHI can affect urban dwellers in many ways, “influencing their health and comfort, energy cost, air quality, and visibility levels, water availability and quality, ecological services, recreation and overall quality of life” (Prilandita, 2009). Governance and community are lack of awareness to the activities that worsening urban heat in urban live (Memon, Leung, & Chunho, 2007). Therefore, most of adaptation and mitigation measures to reduce

∗ Corresponding author. E-mail address: [email protected] (A.C. Kurniati).

the effect are only considering on making the new technique to reduce UHI effects, instead of enhancing the local government’s responses and policies in terms of UHI in their environment. Furthermore, it is feared that some municipality are lack of understanding to reduce UHI effect. Moreover, local institutions policies and response related UHI effect need to be reviewed. Determining significant factors also becomes the important part to reduce UHI, while considering that there are several factors that influence it, like population shift, urban and peri urban development, change in zoning, production and disposal of anthropogenic emissions and pollutants which mix with regional climate as well as in the frequency and intensity of specific weather (Prilandita, 2009). Reducing UHI on the most influencing factors based on the characteristics of a certain city is important due to effectiveness and efficiency of the purposing strategies. Most of the past studies in terms of increasing temperature in urban area took urban heat island as the main concern (Prilandita, 2009; Fariz, 2012). The earlier researches tried to identify the characteristic of UHI by using one factor as cause (Borbora & Das, 2013; Lau, Ganesan, & Giridharan, 2004). Meanwhile several factors causes for urban heat island (Gago et al., 2013; Prilandita, 2009; Wong, Jusuf, & Syafii, 2011), as well as the measures to mitigate and adapt the UHI (Gago et al., 2013; Santamouris, 2013; Yamamoto, 2006). Furthermore, the effect of UHI was studied by Prilandita (2009).

http://dx.doi.org/10.1016/j.scs.2016.07.006 2210-6707/© 2016 Elsevier Ltd. All rights reserved.

Please cite this article in press as: Kurniati, A. C., & Nitivattananon, V. Factors influencing urban heat island in Surabaya, Indonesia. Sustainable Cities and Society (2016), http://dx.doi.org/10.1016/j.scs.2016.07.006

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Based on the references, it can be concluded that past researchers analyzed factors that influencing UHI, but not the significant factors that influence it based on the characteristics of a certain area. Meanwhile, others analyzed UHI with focusing on one factor like population or building density only. Furthermore, ignoring significant factors that influence UHI effects will make the solution difficult to be implemented effectively, as well as it is necessary to recommend the strategies from the “stick” approach, because it is legally and basic approach.

2. Overview of urban heat island phenomenon, causes and effects Definition of UHI is there is the temperature differences (2–6 ◦ C) in the urban area compared to the peri-urban area, that influenced by urban structure and urban parameters (e.g: materials of the construction, provision of green area) have a crucial impact to the climate change (Lee, Baek, & Cho, 2014). Measurement to UHI can be done by assessing the effects that occur due to UHI and the parameters/conditions of UHI itself. The parameters or conditions is indicating how bad is the UHI is called urban heat island intensity (UHII) (Memon et al., 2007). Measuring UHII is by comparing the average and maximum air temperature between urban and rural areas. The comparison time period used to be a season, a month, or a year, or in some cases using few selected days (Velazquez-Lozada, Gonzales, & Winter, 2006). Increasing consumption of energy can influence rising of UHII. Increasing of minimum temperature can lead the use of heating building, while the increasing of maximum temperature can increase the use of cooling building. Cooling degree days (CDD) (the gist of the divergences between the daily mean temperature and 65 ◦ F) is an approximation of the quantity of cooling needed to sustain a comfortable home environment. Cooling degree days have increased greatly over the years, contributing to rising energy demand to cool building interiors (Velazquez-Lozada et al., 2006). Prilandita (2009) has mentioned several general causing factors of UHI like population shift, urban and sub-urban growth, land use change, production and dispersal of anthropogenic emissions and pollution which interact with regional climate as well as with the frequency and intensity of specific weather event. Borbora and Das (2013) stated that urbanization is influencing UHII. Hence, the activities and physical form in the urban is effected to UHI effect. Energy consumption, greenhouse effect, anthropogenic heat, and loss of green space provision is the activities that effected UHI, while the man-made factors that represent the physical form of the city is urban structure, city size, density of population and built-up area, the width of the street and building material (Lau et al., 2004). The heat island phenomenon has a severe impact on the energy use of buildings, increase smog production, while leading to an increasing emission of pollutants from power plants, including sulfur dioxide, carbon monoxide, nitric oxides and suspended particulates (Mihalakakou, Flocas, Santamouris, & Helmis, 2002). Higher urban temperatures increase the energy consumption for cooling and raise the peak electricity demand (Akbari 2005; Hirano & Fujita, 2012; Kolokotroni, 2007; Santamouris, 2001).

3. Approach and methodology 3.1. Study area Surabaya city in Indonesia is suitable for the objective of this research. In addition, the city is facing urbanization and rapid development as well as compact city does. The quality of environment

wasdeteriorated and UHI phenomenon was occurred in Surabaya as well (Fariz, 2012). 3.2. Methodology A mix method of qualitative and quantitative aspects was used for identifying the candidate factors, analyzing the correlation among the factors and determining the significant factors, by using partial least square regressions (PLS-R) analysis. For determining the significant factors, the analysis was divided into two stages, as follows. 3.2.1. Stage 1 This is to identify the condition of UHI in the city and nature and man-made factors in Surabaya. The method used statistical data review for 20 years. In addition, the data include maximum and minimum temperature data, CDD data, carbon emission data, electricity consumption, provision of green space, total area of paving and asphalt surface. Furthermore, to identify the UHI phenomenon and the effects it uses DPSIR analysis. 3.2.2. Stage 2 This is to determine the significant factors that influence UHI effects in Surabaya. PLS-R analysis was used Smart-PLS software as an analytical tool. Input data were the outputs of Stage 1 and data from microclimate and man-made factors, such as demography data, area of green/open space, electricity record, transportation document, CO emission record and daily traffic average data. PLS-R in this research has been used to determine the significant factors in several variables, dividing into five independent variables and two dependent variables. 3.3. Data processing First step in this analysis is entering data into Statistical Package for the Social Science (SPSS) software, both independent and dependent variables. Next step is draw/set up the model then analysis process by using PLS-R. In addition PLS-R has last concern to kind of data, whether its distribution is normal or not. This data has ignoring missing data, considerate that there are no missing data in this research‘s data. Furthermore, structural model assessment procedure is assess structural model for collinearity issues, assess the significance and relevance of the structural model relationship, weight of each variables and assess the level of R2 . 4. Results and discussions 4.1. Characteristicsof Surabaya city development The city occupies coastal terrain and lies approximately 33,048 ha with consist of 31 sub-districts. Surabaya is the capital city of East Java Province with the most developed and most density population. Built up areas in Surabaya are almost reach 2/3 for all Surabaya area. Commonly, thus developing is concentrate on real estate building and commercial building. Up to now, the proportion of land use in Surabaya are divided into 42.00% for housing, 16.24% for paddy filed and moor, 15.20% for pond, 10.76% for commercial and services, 07.30% for industries and 05.50% for open space. Surabaya has divided into 12 development unit to sustain and enhance the development objectives. It is divided into several function, like housing, commercial and services, education, health, governance, recreation and conservation area. Nowadays Surabaya is a rapidly growing commercial and educational substance. Some of its chief industries are ship build-

Please cite this article in press as: Kurniati, A. C., & Nitivattananon, V. Factors influencing urban heat island in Surabaya, Indonesia. Sustainable Cities and Society (2016), http://dx.doi.org/10.1016/j.scs.2016.07.006

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Fig. 1. Location of Surabaya City (Ariyaningsih, 2012).

ing, heavy equipment, food-processing and agriculture, electronics, home furnishings, and handcrafts (Surabaya PDA, 2005). 4.2. UHI phenomenon in Surabaya Based on DPSIR (driving force-pressure-state-impact-response) analysis it can be found that the driving force in Surabaya is urbanization. Considering that the main function of Surabaya is in commercial-service and industry. In addition, that function make Surabaya attract people to come, that in the ends it makes increasing in population. Furthermore, urbanization leads the pressure to the provision of green space and using of energy which in the end of the day it can increasing the air pollution and house gases effects. The current condition-state of UHI in Surabaya is, there is 1.4 ◦ C temperature differences between urban and rural area, due to insignificantly differences of urban and rural area in Surabaya. Considering that Surabaya is developed city which is no bold boundary of built up area (field, farm) to build up area (buildings). The impact of UHI in Surabaya is increasing in energy use which embracing to consumption of air conditioning. Furthermore, according to the UHI condition in Surabaya, the municipality of Surabaya has given responses to address UHI through direct and indirect strategies, such as manage the zoning and building codes, green building standard, city regulation to protect the tree and reducing CO2 emission in transportation sector, procurement and greenery program.

of green space and increase heat storage due to construction materials such as asphalt and concentrate (Lormaneenopparat, 2002; Prilandita, 2009; Yamamoto, 2006). Based on data from Surabaya Cleanliness and Landscape Agency (2013), green space in Surabaya is including park, stadium/sport field, graveyard, and green. c. Heat of individual emitting. This research is using heat of individual emitting refers to anthropogenic heat source. Air conditioning is a common technical solution to problems of increasing temperature that increase the electricity consumption which is in the end it contribute to urban heat island effect and ambient heat exposure (Liu, Ma, & Li, 2011; Lundgren & Kjellstrom, 2013). d. Greenhouse gases effect. This research is using greenhouses because incoming solar radiation is reflected back to space and it trapped in the urban atmosphere due to smog that block the solar radiation to go to the higher atmosphere. In addition, greenhouse gases can increase cloudiness and smog effect Lormaneenopparat (2002). 4.4. Identification of factors influencing UHI in Surabaya 4.4.1. Dependent variables Dependents variables in this research are related with a micro climate that influences UHI effects. Both of that variables had been collected from secondary data.

4.3. Selection of factors and variables for analysis a Maximum and minimum temperature This research has used changes in surface cover, individual heat emitting and air pollutant that come from the number of vehicle as a man made factor. Meanwhile, cooling degree days and maximum minimum temperature has been used as microclimate factors. The main reasons for choosing the factors are: a. Urban Heat Island Intensity (UHII). To measure the condition of UHI in Surabaya with using CDD and maximum-minimum temperature as the variables. Based on Memon et al. (2007), the maximum temperature (day time) and minimum temperature (night time) are related to the absorption of the solar radiation. CDD is the increases of air conditioning consumption, with using 65 ◦ F as the baseline temperature. b. Changes in surface cover. This research is using changes in surface due to urban development effect. Change in surface cover can reduce surface evapotranspiration capacity due to less

The mean temperature in Surabaya is mostly in 28 ◦ C while the rising temperature happened in 2009 and 2010 by 1 ◦ C but then decreasing again in 2011. In addition, for last 20 years the mean temperature in Surabaya is increasing by 0.45 ◦ C (Fig. 2). b Cooling Degree Days (CDD) The average CDD for the last 20 years was increasing, with annual average is 330.62 CDD. In addition, during 20 years CDD was increasing by 10.79 CDD (Fig. 3). 4.4.2. Independent variables Independent variables in this research are related to man-made factors. Base on Prilandita (2009), the significant factors that influ-

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in 2013 is more than 37 km2 which is linearly used with the urban development. The increasing for the last 20 years is 97.69%. In addition, asphalt road construction is used for highways or primary road, while the others are used for paving and concrete.

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d Electricity Energy Consumption maximum temperatu re minimum temperatu re

28

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Electricity energy for cooling the building is used in this research. In addition, for 20 years there were increasing number of energy consumption, except declining in 1997 and 1998. In addition, for the last 20 years municipality of Surabaya has to provide more than 104 × 106 Mw h for cooling the buildings.

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e Carbon Emission

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1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013

Max and Min Temperature

34

Year

Fig. 2. Maximum and Minimum Temperature in Surabaya, 1993–2013 (www. tuitempo.net).

The result of carbon emission is from equivalent number of vehicles on the several main roads that had been taken by transportation department in Surabaya. Hence, the increasing number of vehicless will increase the number of carbon emission in the air. In addition, for last 20 years the total carbon emission in Surabaya is more than 137 × 106 metrics ton of carbon while the number of vehicles is more than 28 × 106 . Furthermore, it can be concluded that the number of carbon emission in Surabaya has increased 72.97% for the last 20 years. 4.5. Significant factors influencing UHI in Surabaya PLS-R path modeling estimation for determine the significant factors that influence UHI in Surabaya shown in Fig. 1, two numbers, in the circle and on the arrow. In addition, by looking the diagram, the results for determining the significant factors are (Fig. 4): a) Explanation of target endogenous variable variance

Fig. 3. CDD in Three Meteorology Stations of Surabaya, 1993–2013 (www.tutiempo. net).

encing UHI is the development of the city itself. The independent variables, are: a Area of Green Space The provision of green space is influence to reducing surface evapotranspiration capacity. Evapotranspiration process requires solar energy, meanwhile since both evaporation and transpiration amount are decreased, the solar energy that involved in this process will rise to the surface heating. In addition, the number or green space in Surabaya has increased 91.14% the last 20 years. The total area of green space in 2013 is 4.18 km2 . b Area of Paving Paving is use non-reflective construction material that will absorb heat, keep it and become radiating after sunset that raises night temperature. In this research the assumption is increasing use in paving will lead to increasing the heat. In addition, the total area of paving in Surabaya has increased 99% for the last 20 years. The total area of paving in 2013 is more than 8 km2 . c Area of Asphalt In this research the assumption is increasing use in asphalt will lead to increasing the heat. In addition, the total area of asphalt

The coefficient of determination, R2 is 0.407 for the factors endogenous latent variable. This means that the “factors” latent variable, which is contain of carbon emission, area of asphalt, area of paving, area of green space and energy consumption are explaining 40.7% of the variance in UHI. Meanwhile, 59.3% remaining is explaining by other factors that excluded in this research, such as wind speed, water body, urban structure and the others. Therefore, the small value of variance indicates that the data points tend to be close to mean and hence to each other. The R2 shows how much the variance of the latent variable is being explained by the other latent variables. The value of R2 can be improved by adding more data related the independent variables. The idea for this research is “The bigger number of dependent variable is making the highest value of variance.” For the example, the independent variables that can be added are wind speed, water body and urban structure. b) Inner model path coefficient sizes and significance The inner model shows that factors variable (carbon emission, area of asphalt, area of paving, area of green space and energy consumption) have 0.638 effects on UHI. Meanwhile, 0.362 remaining is affected by other factors that excluded in this research, such as wind speed, water body, urban structure and the others. Based on Kwong and Wong (2012), the effect size assesses the magnitude or strength or relationship between latent variables. Moreover, the effect size helps researchers to assess the overall contribution of a research. c) Indicator reliability

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Fig. 4. Path Diagram of Modeling Result.

Table 1 Reliability and Validity Checking. Check list

Smart-PLS

Analysis

Reliability Indicator Reliability

“Outer loadings” numbers

Internal Consistency Reliability

“Cronbachs Alpha”

It can be shows that based on outer loadings number, each of variables has a higher value than 0.70. Therefore, the higher is preferred (Wiyono, 2011). In addition, it can be concluded that indicator variables are highly correlated with latent variable and it has ability to measure latent variable. Umar (2002) said that an instrument of the research is reliable if the coefficient of reliability, r > 0.6. In addition, based on Cronbachs Alpha factors variable is exceedingly than 0.6. therefore, it shows that data has given the significant and consistency of the measurement

Validity Convergent Validity

“AVE” numbers

Kwong and Wong (2013) said that the AVE numbers should be higher than 0.5. Based on this research it is found that AVE values are greater (0.861 for factors and 0.995 for UHI variable) than the acceptable threshold of 0.5. Therefore, convergent validity is confirmed.

Source: Smart-PLS, 2014.

Table 1 shows the various reliability and validity items that should be checked and reported. Reliability and validity are needed to check the data that used in this research, whether it is valid (trustworthy) and consistent. Based on the reliability and validity checking, this research shows that all the data and the result is valid and reliable. d) Outer model loadings Outer model loadings is to view the correlations between latent variable and the indicators in its outer model. In this research, outer model loadings will be used for identify the most significant factors that influence UHI in Surabaya. Table 2 shows the weight of each indicator that influences the equation. In addition, the weight of each indicator assesses the magnitude or strength of indicators to the latent variable. Based on Table 2, it can be found that: 䊏 The result of dependent variables, both for CDD and mean temperature is 0.998. Those results is according to PLSR‘s calculation,

Table 2 Results of Significant Factors. Indicators

UHI

CDD Mean temperature Carbon emission Area of asphalt Area of green space Area of paving Energy consumption

0.998 0.998

Factors

0.827 0.970 0.988 0.872 0.971

Source: Smart-PLS, 2014.

which using Smart-PLS software. The data had obtained from the statistical data. The CDD‘s value were from the calculation of daily mean temperature minus base line temperature for using air conditioner in Surabaya. This research used 65 ◦ F as the base line data. Meanwhile, the maximum and minimum temperature gained from monthly maximum and minimum temperature measurement. 䊏 The most significant factors are area of asphalt (0.970), energy consumption (0.988) and area of green space (0.971) respec-

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tively. In addition, in Surabaya the most significant factors that influence UHI are the change of land cover such as use of asphalt and provision of green space as well as the electricity energy consumption. Furthermore, the provision of green space in this research is embracing to provide park and plant trees, considering that according to Fariz (2012) said that vegetation, in particular trees can be very effective as it delivers several mechanisms of cooling simultaneously and in a complementary manner. In Surabaya the area provision for green space is simultaneously increase in accordance with the Surabaya‘s regulatory to provide more trees. This research is limited for only determining the significant factors without excluding the relationship among the latent and indicator variables. Therefore, whereas the latent has a linier or inversely relation, it will not excluded and could be in the further research. The change of the surface can increase the sensible heat storage and decrease the evapotranspiration. The heat can trap in the materials like street (asphalt) during day time then it becomes radiating source after sunset that rises night temperature. Meanwhile, evapotranspiration is needs solar energy to do the process, while when the area of green space is limited it makes difficulties to absorb the water leadings to decrease the amount of evapotranspiration and in the end of the day it can rise the amount of solar energy resulting in the surface heating. Furthermore, electricity energy consumption is embracing to use of air conditioning for cooling the buildings. Whereas, cooling the building increase a building‘s energy consumption and associated carbon emission. e) Checking structural path significance in bootstrapping Bootstrapping checking result in Smart-PLS can generate tstatistic for significance testing of both the inner and outer model. In this procedure, a large number of subsamples (e.g., 5000) are taken from the original sample with replacement to give bootstrap standard errors, which in turn gives approximate t-value for significance testing of the structural path. The bootstrap results approximately the normality of data (Kwong & Wong, 2013). Using a two-tailed t-test with significant level of 5% the path coefficient will be significant if the t-statistic is larger than 2.093 (␣ = 0.05, degree of freedom is 19). The t-statistic1 is 5.165 which is larger than t-value2 of 2.093. Therefore, it can be concluded that the model loadings are highly significant and there is influence between manmade factors which are carbon emission, area of asphalt, area of green space, area of paving, and electricity energy use with UHI (Gago et al., 2013; Lormaneenopparat, 2002; Prilandita, 2009; Yamamoto, 2006). 4.6. Discussions and implications Based on DPSIR analysis, the temperature in Surabaya for over 20 years is raising 1 ◦ C and there is 1.4 ◦ C temperature difference between urban and rural areas. Therefore, the requirement of CDD is influenced by the temperature differences. The high and centralized development in the urban area makes difference temperature as well. Based on city regulation, zoning codes is important approach to manage the development within city. High development will influence to the change of green/open space to built-up area. Based on the National Act No26/2007 on Spatial Planning, every city should provide 30% of the city‘s area for green and open

1 2

The result of PLSR analysis. The result of t-table.

space, while the rest is for development area. Ignoring this regulation will implement to environmental change, that in this research is influencing to the rising of UHI in the city. The change of surface will increase the sensible heat of storage and decrease evapotranspiration. Green space or vegetation can absorb air emission, absorb solar radiating, doing evapotranspiration and even can reduce the consumption of air conditioning. Whereas electricity energy consumption can associate with carbon emission, which can lead to increasing green gas house effects. Moreover, UHI phenomenon in Surabaya is causing the negative impact to increase the energy consumption for cooling buildings. Additionally, as the response to those condition, the municipality of Surabaya has implemented several strategies to address the situation by conducting greenery program, reducing carbon emission in the transportation sector, protecting trees, zoning and building codes, procuring and implementing green building standard. Next, based on PLS-R analysis the area of green space, electricity energy consumption and use of asphalt variables which contribute the most significant value to influence UHI in Surabaya. Moreover, these independent variables are influencing UHI (dependent variable) by 63.8%. This result indicates that man-made factors are highly effected the relationship to the magnitude of UHI. While the remaining is possibility affected to nature factors that excluded in this research, such as wind speed, water body and the others. In the end, municipality should implement, direct and indirect strategies as well to mitigate UHI, with emphasison land cover and electricity consumption. The municipality of Surabaya can implement several strategies, such as park-open space revitalization, use green building, and use high albedo for asphalt. In addition, to implement the strategies, the municipality should also consider the strategies that mitigating UHI, for the example tree location, tree density and suitable tree to mitigate the rising temperature.

5. Conclusions and recommendations The most influencing factors related to UHI in Surabaya are found to bechange on land cover (provision of green space and use of asphalt) and electricity energy consumption. Identifying the significant factors can support the implementation of strategies, which is nowadays the municipality of Surabaya implementing provision of green space, such as 30% of the city‘s area for green. Hence 70% of the city‘s area can be used as pavement area (building, road, etc). Those strategies can reduce the temperature in green space area. Moreover, the green space becomes the first influencing factor due to the importance to address UHI. This research can be a good source to identify current conditions of UHI and determine the significant factors for possible to enhace the current strategies to addres UHI. This research can be applied not only for mitigating UHI in Surabaya‘s municipality, but others city with similar characteristics as well. The next research can focus to selected significant factor only and possible enhancing current strategies to address UHI.

Acknowledgments The first author would like to acknowledge the master scholarship granted by Directorate General of Higher Education and Asian Institute of Technology (AIT) fellowship. Our appreciation is also extended to Dr. Shobhakar Dhakal and Dr. Djoen San Santoso of AIT for their helpful and insightful comments. The support of local organizations and respondents in Surabaya city for data collection has enhanced the quality of this research

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Please cite this article in press as: Kurniati, A. C., & Nitivattananon, V. Factors influencing urban heat island in Surabaya, Indonesia. Sustainable Cities and Society (2016), http://dx.doi.org/10.1016/j.scs.2016.07.006