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Article

Statistically Validated Urban Heat Island Risk Indicators for UHI Susceptibility Assessment

by
Nawhath Thanvisitthpon
Department of Architecture, Rajamangala University of Technology Thanyaburi, Pathum Thani 12110, Thailand
Int. J. Environ. Res. Public Health 2023, 20(2), 1172; https://doi.org/10.3390/ijerph20021172
Submission received: 5 November 2022 / Revised: 4 January 2023 / Accepted: 5 January 2023 / Published: 9 January 2023
(This article belongs to the Section Climate Change)
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Abstract

:
This research proposes a collection of urban heat island (UHI) risk indicators under four UHI risk components: hazard, exposure, sensitivity, and adaptive capacity. There are 46 UHI risk indicators linked to three pillars of sustainability: social equity, economic viability, and environmental protection. In this study, the UHI risk indicators were first validated by experts to determine their relevancy and subsequently applied to randomly sampled dwellers of Thailand’s capital Bangkok. The UHI indicators were further validated with confirmatory factor analysis to determine the factor loadings (0–1) and reliability. Under the hazard component, the percentage of days when the daily minimum temperature is less than the 10th percentile exhibited the highest indicator-level factor loading (0.915). Vehicular traffic was the UHI exposure indicator with the highest factor loading (0.923), and the proportion of green space to build environment was the UHI sensitivity indicator with the highest factor loading (0.910). For the UHI adaptive capacity component, the highest factor loading (0.910) belonged to government policy and action. To effectively mitigate UHI impacts, greater emphasis should be placed on the indicators with highest factor loadings. Essentially, this research is the first to use statistical structural equation modeling to validate UHI indicators.

1. Introduction

An urban heat island (UHI) is an urban or metropolitan area with considerably warmer temperatures than its surrounding rural areas as a result of human activities [1]. The conversion of natural land cover into pavement, buildings and other impervious surfaces that absorb and retain heat contributes to the UHI phenomenon [2]. The major impacts of UHIs include increased energy consumption, elevated emissions of air pollutants and greenhouse gases, compromised human health and comfort, and impaired water quality [3,4,5].
UHI risk assessment enables the formulation of public policy and action to mitigate UHI-related impacts in an effective manner and also enhances individual-level UHI adaptive capacity [6,7,8]. According to the intergovernmental panel on climate change, IPCC, a higher adaptive capacity to climate change-induced UHIs reduces socioeconomic losses while improving individual resilience to excessive heat events [9].
As shown in Figure 1, risk arises from the interaction of hazard, exposure, and vulnerability (i.e., risk = hazard + exposure + vulnerability). Specifically, risk is conceptualized as an internal property of a system that is a function of its current endogenous lack of adaptive capacity to overcome the adverse impact (its sensitivity) of a stressor, and vulnerability is an endogenous characteristic of a system that is determined by its sensitivity and adaptive capacity [9]. In theory, a low adaptive capacity relative to sensitivity (i.e., adverse impacts) results in higher vulnerability and higher risk. As a result, enhancing adaptive capacity reduces vulnerability and subsequent risk.
UHI risk assessment plays a crucial role in limiting ecosystem degradation, social disruptions and economic losses. Specifically, UHI risk assessment provides information about the existing weaknesses of a natural or a socioeconomic system and the plausible causes of the weaknesses. The assessment outcomes enable the formulation of mitigation strategies to deal with or adapt to UHI risks. According to Change [9], climate risk vulnerability reduction is crucial to limiting losses and building resilience to long-term climate change-induced UHI risks.
Specifically, this research relied on the IPCC’s risk assessment framework to develop a collection of UHI risk indicators linked to three pillars of sustainability: social equity, economic viability, and environmental protection. There are 46 UHI risk indicators under four UHI risk components: hazard (consisting of 13 indicators), exposure (10), sensitivity (12), and adaptive capacity (11).
For the hazard component, the UHI indicator selection is based on 13 future temperature-based extreme climatic indexes, where six indexes are associated with temperature (HT1–HT6) and the remaining seven are associated with duration (HD1–HD7) (Table 1). Additionally, the UHI indicators under the exposure component are associated with the three pillars of sustainability: social equity (ES1–ES4), economic viability (EE1–EE3), and environmental protection (EV1–EV3) (Table 2).
The UHI indicators under the sensitivity component are related to the three pillars of sustainability: social equity (SS1–SS5), economic viability (SE1–SE3), and environmental protection (SV1–SV4) (Table 3). Likewise, for the adaptive capacity component, the UHI indicator selection is based on the three pillars of sustainability: social equity (AS1–AS4), economic viability (AE1–AE4), and environmental protection (AV1–AV3) (Table 4).
These 46 UHI risk indicators were first validated by experts to determine their relevancy using a relevant/irrelevant questionnaire (Supplementary Materials S1), and they were subsequently applied to a sample of Bangkok residents using an agree/disagree questionnaire (Supplementary Materials S2). Based on the agree/disagree survey data, the UHI indicators were further validated with confirmatory factor analysis (CFA) to determine the factor loadings (0–1).
By definition, CFA-based factor loading is the correlation coefficient for the variable (i.e., UHI indicators) and factor (i.e., UHI phenomenon). In structural equation modeling, a factor loading of 0.6 or higher indicates that the factor (i.e., UHI phenomenon) is significantly influenced by the variable (i.e., UHI indicators). In this research, the factor loadings were calculated by using the AMOS (analysis of a moment structures) statistical software. AMOS is an added SPSS module and is used for structural equation modeling, path analysis, and confirmatory factor analysis. In addition, the correlations (R2) between UHI indicators were determined with a structural equation model (SEM). Essentially, the proposed CFA-validated UHI indicators could be deployed to effectively assess the susceptibility of an urban area to UHI risks. Moreover, the proposed UHI indicators are applicable to urbanized areas of varying size (e.g., first-, second-, and third-tier cities) to assess the climate change-induced UHI susceptibility and future impacts. Furthermore, this research is the first to apply the SEM technique to statistically validate UHI indicators.

2. Research Methodology

In the development of UHI risk indicators, this research first reviewed the literature and publications related to the IPCC’s risk assessment framework (Figure 1). The UHI risk indicators considered in this study are linked to the three pillars of sustainability (i.e., social equity, economic viability, and environmental protection). There are 46 UHI risk indicators associated with four UHI risk components: hazard (consisting of 13 indicators), exposure (10), sensitivity (12), and adaptive capacity (11) [10,11,12,13,14,15].
The hazard indicators were obtained from the open-source RClimDex1.1 program (http://etccdi.pacificclimate.org/software.shtml, accessed on 12 January 2022), which provides a friendly graphical user interface to compute all core climate change indices defined by the joint CCl/WCRP/JCOMM Expert Team on Climate Change Detection and Indices (ETCCDI). For the hazard component, the indicator selection was based on 13 future temperature-based extreme climatic indexes, where six indexes are associated with temperature (HT1–HT6) and the remaining seven are associated with duration (HD1–HD7) (Table 1).
Meanwhile, the exposure (10), sensitivity (12), and adaptive capacity indicators (11) were obtained from the literature review, as indicated in the references column in Table 2, Table 3 and Table 4, respectively. For the exposure component, the UHI indicator selection was based on the three pillars of sustainability: social equity (ES1–ES4), economic viability (EE1–EE3), and environmental protection (EV1–EV3) (Table 2). Likewise, the UHI indicators for the sensitivity component are related to the three pillars of sustainability: social equity (SS1–SS5), economic viability (SE1–SE3), and environmental protection (SV1–SV4) (Table 3). For the adaptive capacity component, the UHI indicator selection was based on the three pillars of sustainability: social equity (AS1–AS4), economic viability (AE1–AE4), and environmental protection (AV1–AV3) (Table 4).
The 46 UHI risk indicators were converted into a relevant/irrelevant questionnaire. To evaluate the relevancy of the UHI risk indicators, a total of 32 experts in the fields of architecture, climate change, urban environment and urban planning were randomly selected from Google Scholar (https://scholar.google.com/, accessed on 12 January 2022), and copies of the relevant/irrelevant questionnaire were emailed to them. There were seven responses from a total of 32 experts, and a number of expert responses ≥3 is acceptable.
The expert-validated UHI indicators were subsequently converted into an agree/disagree questionnaire based on a 10-point Likert scale, where 1 denotes strongly disagree and 10 denotes strongly agree. According to Bayraktar and Tatoglu [16], Ismail Salaheldin [17], Thanvisitthpon and Shrestha [18], an ordinal scale can be used with agree/disagree questions.
The agree/disagree questionnaire was applied to a random sample of individuals to further validate the 46 UHI risk indicators. The sample size was 400 individuals, calculated with the following equation where n is the sample size, N is the number of people in Bangkok (10.72 million), and e is the sampling error (0.05) (Yamane [19]).
N = N/1 + N (e)2
Due to incomplete data, the actual sample size (i.e., questionnaire respondents) was 398 individuals who resided in Thailand’s capital Bangkok. In the data collection, the 398 questionnaire respondents were randomly recruited to answer the agree/disagree questionnaire, and the responses were input into the structural equation model (SEM) analysis.
Prior to SEM analysis, the Kolmogorov–Smirnov test was applied to the data from the 398 questionnaire respondents, with the null hypothesis (H0) being that the data are normally distributed [20]. H0 is accepted if the observed statistic is greater than the critical value (α > 0.05), indicating that the data are normally distributed [21,22,23,24].
In the SEM analysis, the 46 UHI risk indicators and four UHI risk components were validated with confirmatory factor analysis (CFA) to determine the factor loadings and reliability of the UHI risk indicators and dimensions. Indicator- and dimension-level factor loadings (0–1) were used to indicate the degree of relevancy of UHI risk indicators and dimensions. The reliability of the UHI risk indicators and dimensions were measured with indicator-level reliability (R2) and dimension-level composite reliability (CR).
Since the SEM technique requires a large quantitative dataset or sample size (i.e., 398 respondents in this research) (Dawson and Peppe [25], Thanvisitthpon [26]), its main advantage lies in the validity of the association, as indicated by the factor loadings between the UHI risk indicators and the UHI phenomenon. Additionally, SEM-based CFA relies on the means and variance–covariance matrix instead of on the correlation matrix, thereby minimizing both nonuniform and uniform bias [26]. Furthermore, SEM-based CFA is used when there is an array of variables (i.e., UHI indicators) measuring more than one dimension (i.e., four UHI components—hazard, exposure, sensitivity, and adaptive capacity). However, the limitations of SEM-based CFA include the necessity of a large sample size (despite higher reliability) and the required training and skills to use the AMOS statistical software.

3. Results and Discussion

3.1. UHI Indicator Relevancy Assessment

In the assessment of the UHI risk indicators, the randomly chosen experts (i.e., 32 experts) were asked to evaluate the relevancy of the 46 UHI risk indicators under four UHI risk components (i.e., hazard, exposure, sensitivity and adaptive capacity) on a relevant/irrelevant scale of −1 to 1, where −1, 0, and 1 denote irrelevant, uncertain, and relevant, respectively. Seven experts responded the relevant/irrelevant questionnaire: two architects, one climate change scientist, two urban planners, and two urban environmentalists.
Table 1, Table 2, Table 3 and Table 4 tabulate the UHI indicators, definitions, and item-objective congruence (IOC) indexes under the four UHI risk components of hazard, exposure, sensitivity, and adaptive capacity, respectively. The IOC index was used as the basis for screening the item quality, whereby experts were asked to determine the relevancy score of each item (−1, 0, or 1) given that IOC > 0.6 is acceptable [27,28].
Table
Table 1. The UHI hazard indicators, definitions and expert-validated IOC indexes.
Table 1. The UHI hazard indicators, definitions and expert-validated IOC indexes.
Hazard Indicators (13)
DimensionIDIndicatorsDefinition/DetailIOC
TemperatureHT1CSDICold spell duration index (CSDI) is defined as annual or seasonal count of days with at least 6 consecutive days when the daily minimum temperature falls below the 10th percentile in the 5-day calendar window for a 30-year period.0.692
HT2DTRDaily temperature range0.692
HT3TXxMonthly maximum value of the daily maximum temperature0.846
HT4TNxMonthly maximum value of the daily minimum temperature0.769
HT5TXnMonthly minimum value of the daily maximum temperature0.769
HT6TNnMonthly minimum value of the daily minimum temperature0.769
DurationHD1SUNumber of summer days0.692
HD2TRNumber of tropical nights0.769
HD3TX90pPercentage of days when the daily maximum temperature is greater than the 90th percentile0.692
HD4TX10pPercentage of days when the daily maximum temperature is less than the 10th percentile0.769
HD5TN90pPercentage of days when the daily minimum temperature is greater than the 90th percentile0.846
HD6TN10pPercentage of days when the daily minimum temperature is less than the 10th percentile (TN10p)0.923
HD7WSDIWarm spell duration index (WSDI) is defined as annual or seasonal count of days with at least 6 consecutive days when the daily maximum temperature exceeds the 90th percentile in the 5-day calendar window for a 30-year period.0.769
Table
Table 2. The UHI exposure indicators, definitions and expert-validated IOC indexes.
Table 2. The UHI exposure indicators, definitions and expert-validated IOC indexes.
Exposure Indicators (10)
DimensionIDIndicatorsDefinition/DetailIOCReferences
SocialES1Population sizeA metropolis with a population of over one million has an average annual temperature 1–3 °C higher than surrounding rural areas.0.615[29,30,31]
ES2Electricity consumptionElectricity use is positively correlated with the UHI phenomenon. Specifically, electricity consumption and land surface temperature are strongly correlated with an R2 of 70–90%.1.000[32,33,34]
ES3 Compromised human health and comfort The UHI phenomenon contributes to heat-related deaths and illnesses such as general discomfort, respiratory difficulties, heat cramps, heat exhaustion, and non-fatal heat stroke.
Sensitive populations are particularly at risk during excessive heat events, including the elderly, young children, those working outdoors, and those with preexisting health conditions.		0.615[35,36,37,38]
ES4Vehicular traffic Vehicular traffic is the aggregation of vehicles coming and going in a particular locality. Vehicular traffic is positively correlated with UHI intensity and air pollution. 1.000[32,33,34,35,36,37,38,39]
EconomicEE1Higher cost of living in urban areasThe UHI phenomenon is significantly positively correlated with population size (p ≤ 0.01), economic size (p ≤ 0.01), and urbanization (p ≤ 0.05).0.692[13,40]
EE2Land use typeThe conversion of agricultural areas into commercial and industrial areas contributes to the UHI phenomenon. Urbanization expansion and increased human activities also worsen the UHI phenomenon.0.846[41,42,43]
EE3Urban economic growthUrbanization and economic growth contribute to population growth and increased economic activities, exacerbating the UHI phenomenon.0.769[13,40,44,45]
EnvironmentEV1Land surface temperatureLand surface temperatures vary in tandem with ambient temperatures. Increasing urban green space lowers land surface temperatures, which consequently reduces ambient air temperatures. 0.846[46,47]
EV2Impervious surface areaImpervious cover is any type of human-made surface that cannot effectively absorb or infiltrate rainfall, such as driveways, paved roads, parking lots, rooftops, and sidewalks. The expansion of an impervious surface area contributes to worsening UHI problems. 0.846[32,45,47,48,49,50]
EV3Pervious surface areaA pervious surface is land not covered by buildings or other man-made infrastructure, thus allowing rainwater to percolate into the soil to filter out pollutants and recharge the groundwater. Pervious surface coverage and the UHI phenomenon are inversely related.1.000[32,46,47,48]
Table
Table 3. The UHI sensitivity indicators, definitions and expert-validated IOC indexes.
Table 3. The UHI sensitivity indicators, definitions and expert-validated IOC indexes.
Sensitivity Indicators (12)
ComponentsIDIndicatorsDefinition/DetailIOCReferences
SocialSS1Population density and growthPopulation density and growth is closely linked to human activities and the UHI phenomenon. A larger population contributes to higher greenhouse gas emissions as a result of increased vehicular traffic and electricity consumption, giving rise to dramatic temperature increases.0.769[31,32,44,51]
SS2Built environmentThe built environment, including buildings, public utilities and infrastructure, touches all aspects of human life. Specifically, the conversion of green area into built environment contributes to the UHI phenomenon and the situation worsens as the conversion intensifies.0.769[32,41,42]
SS3Total
energy consumption		Total energy consumption is the sum of energy used for electricity, transport and heating. Total energy consumption is positively correlated with human activities, which consequently contribute to UHI effects.1.000[32,52,53,54,55]
SS4Prevalence of noncommunicable diseasesNoncommunicable diseases (NCDs) are of long duration and are the result of a combination of genetic, physiological, environmental and behavioral factors. Examples of NCDs are cardiovascular disease, diabetes, high blood pressure, and obesity.
Individuals with NCDs or preexisting health conditions are highly susceptible to UHI-related excessive heat events.		0.692[37,38,56]
SS5Traffic congestion Increased vehicular traffic and road congestion exacerbates the UHI situation and air pollution.1.000[32]
EconomicSE1Low economic status Individuals with low income are highly susceptible to UHI impacts. Specifically, low-income households have inadequate income or wealth to mitigate the negative impacts of UHI-induced excessive heat events.0.615[13,40,57]
SE2Monthly electricity expenditureMonthly household electricity cost is in direct proportion to electricity consumption. Higher electricity consumption consequently aggravates the UHI situation.1.000[32,58]
SE3Monthly energy spendingHigher monthly household energy (motor fuels) spending is in direct proportion to increased vehicle use, which consequently contributes to the UHI phenomenon and air pollution.0.615[32,58]
EnvironmentSV1Proportion of green space to built environmentGreen space provides cool and shaded areas while moderating ambient temperatures. The proportion of green space to the man-made built environment is inversely correlated with the UHI phenomenon.1.000[32,42,45,59]
SV2AtmosphericpollutionAnthropogenic atmospheric pollutants contribute to rising temperatures and the UHI phenomenon. UHI-causing atmospheric pollutants include CO, NO2, O3, PM2.5, and PM10.0.846[32,60]
SV3Proportion of water body to built environmentTemperatures in and near water bodies are significantly lower than those covered by the built environment. Additionally, the proportion of water bodies to man-made built environments is inversely correlated with the UHI phenomenon.1.000[32,41,42]
SV4Building densityBuilding density (measured by dwelling units per km2) determines how crowded or built-up an area of land is.
An area with a high building density exhibits a high UHI intensity.		1.000[32,61]
Table
Table 4. The UHI adaptive capacity indicators, definitions and expert-validated IOC indexes.
Table 4. The UHI adaptive capacity indicators, definitions and expert-validated IOC indexes.
Adaptive Capacity Indicators (11)
ComponentsIDIndicatorsDefinition/DetailIOCReferences
SocialAS1Public understandingThe public understanding of the UHI phenomenon and its impacts enhances adaptive capacity to excessive heat events or UHI-induced temperature increases.0.846[62,63,64]
AS2Public awarenessA greater public awareness of the UHI phenomenon and its risks contributes to increased UHI adaptive capacity and resilience.0.846[62,63,64,65,66]
AS3Multi-agency collaborationMulti-agency collaboration on the dissemination of information and UHI impact mitigation enhances adaptive capacity to abrupt and dramatic temperature increases.0.846[63,65,67,68]
AS4Governmentpolicy and actionGovernments (i.e., local, regional and national levels) should establish UHI mitigation policies and measures to enhance adaptive capacity to UHI-induced excessive heat events.1.000[32,42,59,63,69]
EconomicAE1Green budgetingGreen budgeting refers to the adoption of budgetary tools to achieve environmental and climate goals. Higher green budget allocation enhances adaptive capacity to the UHI phenomenon.0.846[8,32,63,70]
AE2Private sector participation in green economyA green economy is an economy that aims to reduce environmental risks and ecological scarcities while achieving sustainable development without degrading the environment. Specifically, the active participation of the private sector in the green economy enhances adaptive capacity to UHI-related dramatic temperature increases.0.846[8,32,63,71]
AE3Access to climate controlappliancesAccess to climate control appliances, e.g., electric fans, cooling fans, and air conditioners, increases adaptive capacity to UHI-induced excessive heat events.0.846[72,73]
AE4Household medical budgetThe size of a household’s medical budget is positively correlated with adaptive capacity to UHI-related illnesses.0.615[37,74,75]
EnvironmentAV1Proportion of green space to living spaceGreen space is land that is partly or completely covered with grass, trees, shrubs, or other vegetation. Meanwhile, living space refers to areas in a dwelling unit that are livable spaces. A higher proportion of green space to living space improves adaptive capacity to UHI-induced temperature increases.0.846[8,32,76]
AV2Adequacy of urban green spaceUrban green space refers to open-space areas reserved for parks and recreational activities. Adequate urban green space improves adaptive capacity to UHI-related abrupt temperature increases.0.846[8,32,41,42]
AV3Resilience to air pollution and UHIIndividual resilience to atmospheric pollutants and extreme heat events enhances adaptive capacity to UHI-related illnesses and deaths.0.846[32,77]

3.2. Confirmatory Factor Analysis of UHI Hazard Indicators

Two dimensions constitute the UHI hazard component: temperature and duration. The temperature dimension consists of six UHI hazard indicators, and the duration dimension consists of seven indicators. The UHI hazard indicators (13 indicators) were validated with confirmatory factor analysis (CFA) to determine their factor loadings and reliability (first-order) and their dimensions (second-order). Structural equation modeling (SEM) was used to analyze the correlations between the UHI indicators.
Figure 2 shows the structural equation model and the factor loadings of the UHI hazard indicators, where chi-squared = 4413.707, degree of freedom (df) = 9, p-value = 0.114, root mean square residue (RMR) = 0.050, root mean square error of approximation (RMSEA) = 0.038, goodness of fit index (GFI) = 0.995, adjusted goodness of fit index (AGFI) = 0.945, normed fit index (NFI) = 0.997, and confirmatory fit index (CFI) = 0.999. According to Baumgartner and Homburg [78], Gatignon [79] and Hooper and Coughlan [80], GFI, AGFI, NFI and CFI should be close to 1, while RMSEA and RMR should not exceed 0.050.
Table 5 tabulates the first- and second-order factor loadings of the UHI hazard indicators and dimensions. The factor loadings of the HT1–HT6 and HD1–HD7 indicators were 0.757–0.915 (R2 = 0.617–0.837) and 0.764–0.888 (R2 = 0.583–0.788), respectively, while those of the temperature and duration dimensions (b) were 0.937 and 0.991, respectively. In comparison, the factor loading of duration was slightly higher, suggesting that the duration dimension plays a more significant role in the UHI hazard component, consistent with [81,82]. According to Kim and Mueller [83], a factor loading of >0.3 is statistically significant. The composite reliability (CR) of the temperature and duration dimensions was 0.921 and 0.932, respectively, with corresponding average variance extracted (AVE) values of 0.660 and 0.663. According to Fornell and Larcker [84], a CFA construct is valid if CR > 0.6 or AVE > 0.5.
On the indicator level, the highest factor loading (0.915) belonged to HT6 (percentage of days when the daily minimum temperature is less than the 10th percentile, TN10). According to Shrestha and Bajracharya [85], Zhang and Aguilar [86], Wong and Mok [87], Manalo and Matsumoto [88], and Dong and Xu [89], TN10 is a key indicator of UHI trends and is commonly used in climate change-induced UHI trend analysis.

3.3. Confirmatory Factor Analysis of UHI Exposure Indicators

The UHI exposure component comprises three dimensions: social, economic and environment. The three exposure dimensions are linked to three pillars of sustainability, i.e., social equity, economic viability, and environmental protection. Specifically, the social, economic and environment dimensions consist of four, three and three UHI exposure indicators, respectively. The UHI exposure indicators (10 indicators) were validated with CFA to determine their factor loadings and reliability (first-order) and their dimensions (second-order), and SEM was used to analyze the correlations between the UHI indicators.
Figure 3 shows the structural equation model of the UHI exposure indicators, where chi-squared = 5925.738, df = 45, p = 0.112, RMR = 0.028, RMSEA = 0.040, GFI = 0.996, AGFI = 0.960, NFI = 0.998 and CFI = 0.999.
Table 6 presents the first- and second-order factor loadings of the UHI exposure indicators and dimensions. The factor loadings of ES1–ES4, EE1–EE3 and EV1–EV3 were 0.860–0.923 (R2 = 0.739–0.853), 0.808–0.908 (R2 = 0.683–0.825), and 0.879–0.917 (R2 = 0.772–0.840), respectively, while those of the social, economic and environment dimensions (b) were 0.997, 0.999, and 0.994, respectively; a factor loading >0.3 is statistically significant (Kim and Mueller [83]. The CR values of the social, economic, and environment dimensions were 0.930, 0.887, and 0.925, respectively, with corresponding AVE values of 0.770, 0.724, and 0.804. According to Fornell and Larcker [84], a CFA construct is valid if CR >0.6 or AVE > 0.5.
The highest indicator-level factor loading (0.923) belonged to ES4 (vehicular traffic). Vehicular traffic is the aggregation of vehicles coming and going in a particular locality and it was positively correlated with the UHI intensity and air pollution. According to Hoehne and Chester [90], vehicular traffic and transportation network design play crucial roles in the UHI phenomenon. Hart and Sailor [91] reported that road surface temperatures during weekdays are 2 °C higher than during the weekend. This finding could be attributed to the higher level of vehicular traffic during weekdays. Sailor and Lu [92] reported that heat from vehicles in six U.S. cities accounted for 47%–62% of total heat emissions.
Specifically, increased vehicular traffic contributes to higher greenhouse gas emissions, thereby worsening the UHI situation. As a result, individual-, community-, and institutional-level adaptation measures have been proposed to mitigate the impacts of UHIs [93,94,95,96,97,98].
The second highest factor loading (0.917) belonged to EV3 (pervious surface area). By definition, a pervious surface is land not covered by buildings or other man-made infrastructure, thus allowing rainwater to percolate into the soil to filter out pollutants and recharge the groundwater. Pervious surface coverage and the UHI phenomenon were found to be inversely related.
The conversion of pervious to impervious surfaces as a result of economic development and urbanization expansion causes temperatures in urban areas to be significantly warmer than surrounding rural areas [99,100]. Impervious surface expansion raises urban land surface temperatures (Haselbach and Boyer [100], Shuster and Bonta [101], Gorsevski and Taha [102], Taha [103], Sen and Roesler [104], and Cao and Li [105]).

3.4. Confirmatory Factor Analysis of UHI Sensitivity Indicators

The UHI sensitivity component comprises three dimensions linked to three pillars of sustainability, i.e., social equity, economic viability, and environmental protection. The social, economic and environment dimensions of the UHI sensitivity component consist of five, three, and four UHI sensitivity indicators, respectively. The 12 UHI sensitivity indicators were validated with CFA to determine their factor loadings and reliability (first-order) and their dimensions (second-order), and SEM was used to analyze the relationships between the UHI indicators. Figure 4 shows the SEM of the UHI sensitivity indicators, where chi-squared = 5026.587, df = 66, p = 0.197, RMR = 0.040, RMSEA = 0.027, GFI = 0.992, AGFI = 0.957, NFI = 0.996 and CFI = 0.999.
Table 7 tabulates the first- and second-order factor loadings of the UHI sensitivity indicators and dimensions. The factor loadings of SS1–SS5, SE1–SE3 and SV1–SV4 were 0.745–0.887 (R2 = 0.500–0.727), 0.790–0.867 (R2 = 0.582–0.751), and 0.846–0.910 (R2 = 0.721–0.827), respectively, while those of the social, economic and environment dimensions (b) were 0.936, 0.981, and 0.999, respectively; a factor loading > 0.3 is statistically significant (Kim and Mueller [83]. The CR values of the social, economic, and environment dimensions were 0.915, 0.876, and 0.982, respectively, with corresponding AVE values of 0.684, 0.702, and 0.765. According to Fornell and Larcker [84], a CFA construct is valid if CR >0.6 or AVE > 0.5.
The proportion of green space to a built environment indicator (SV1) had the highest factor loading (0.910). Urban green space provides cool and shaded areas while moderating ambient temperatures [106]. Urban green spaces, including rooftop and vertical gardens, help tackle UHI problems [107,108]. Specifically, UHI impacts are inversely correlated with the proportion of urban green space to a built environment [109,110,111,112].
According to Costanzo and Evola [113], Jamei and Chau [114], and Odli and Zakarya [115], the cooling effects provided by rooftop and vertical gardens mitigate UHI-induced dramatic temperature increases. In addition, rooftop and vertical gardens trap atmospheric pollutants, thus improving air quality [108,116].
In Japan’s capital Tokyo, several UHI mitigation measures have been implemented, including green premises, green roofs, green building walls, enhanced rooftop reflectivity, water-retentive pavement, and reductions in heat loss from buildings [117]. In Hong Kong, UHI mitigation strategies have primarily focused on preserving vegetation, improving ventilation routes, and reducing heat load in built-up regions by regulating urban morphology factors [118,119].
The second highest factor loading (0.887) belonged to SS5 (traffic congestion). According to [39], increased vehicular traffic and road congestion exacerbate the UHI situation and air pollution. Increased vehicular traffic also results in increased energy consumption, elevated emissions of greenhouse gases, and compromised human health and comfort [120]. Simpson [121] attributed a surge in vehicular traffic to the affordability and ease of ownership of private cars, as well as inefficiency of public transport.

3.5. Confirmatory Factor Analysis of UHI Adaptive Capacity Indicators

The UHI adaptive capacity component comprises three dimensions linked to three pillars of sustainability: social, economic and environment. The social, economic, and environment dimensions consist of four, four, and three UHI adaptive capacity indicators, respectively. The 11 UHI adaptive capacity indicators were validated with CFA to determine their factor loadings and reliability (first-order) and their dimensions (second-order), and SEM was used to analyze the relationships between the UHI indicators.
In the context of climate change, adaptive capacity refers to the ability of individuals to adjust to climate variability and extremes in order to mitigate potential impacts and cope with the consequences [122]. According to Abrar, Sarkar [123] UHI adaptive capacity refers to individuals’ capability to adapt to increased UHI exposure.
Figure 5 shows the SEM of the UHI adaptive capacity indicators, where chi-squared = 4092.786, df = 55, p = 0.109, RMR = 0.034, RMSEA = 0.039, GFI = 0.994, AGFI = 0.952, NFI = 0.996 and CFI = 0.999.
Table 8 tabulates the first- and second-order factor loadings of the UHI adaptive capacity indicators and dimensions. The factor loadings of AS1–AS4, AE1–AE4 and AV1–AV3 were 0.783–0.910 (R2 = 0.613–0.828), 0.822–0.854 (R2 = 0.639–0.729), and 0.834–0.860 (R2 = 0.696–0.739), respectively, while those of the social, economic, and environment dimensions (b) were 0.912, 0.998, and 0.958, respectively; a factor loading >0.3 is statistically significant (Kim and Mueller [83]. The CR values of the social, economic, and environment dimensions were 0.918, 0.903, and 0.886, respectively with corresponding AVE values of 0.738, 0.670, and 0.722. According to Fornell and Larcker [84], a CFA construct is valid if CR >0.6 or AVE > 0.5.
The highest factor loading (0.910) belonged to AS4 (government policy and action), followed by AS3 (multi-agency collaboration) with a factor loading of 0.887. According to [58,122,123,124,125,126], government policy and action is crucial to the UHI adaptive capacity of urban residents.
In addition, multi-agency collaboration on the dissemination of information and UHI impact mitigation enhances adaptive capacity to abrupt and dramatic temperature increases [127,128,129,130], Pineo and Zimmermann [131], and Smith and Levermore [69,132,133].

4. Conclusions

This research proposes a collection of 46 UHI risk indicators under four UHI risk components: hazard, exposure, sensitivity, and adaptive capacity. The UHI risk indicators were first validated by experts to determine their relevancy. The expert-validated UHI indicators were subsequently converted into a Likert-scale questionnaire and applied to randomly sampled residents of Thailand’s capital Bangkok. The respondents’ answers were input into SEM analysis.
In the SEM analysis, the 46 UHI risk indicators and four UHI risk components were validated with CFA to determine the factor loadings and reliability of the UHI risk indicators and dimensions. The indicator- and dimension-level factor loadings (0–1) were used to indicate the degree of relevancy of the UHI risk indicators and dimensions.
In the UHI hazard component, the CFA factor loadings of the duration and temperature dimensions were 0.991 and 0.937, respectively, indicating that the duration dimension plays a slightly more significant role in the UHI hazard component than the temperature dimension. Furthermore, the highest indicator-level factor loading (0.915) belonged to HT6 (percentage of days when the daily minimum temperature is less than the 10th percentile, TN10).
In the UHI exposure component, the highest indicator-level CFA factor loading (0.923) belonged to ES4 (vehicular traffic). Vehicular traffic is the aggregation of vehicles coming and going in a particular locality and was positively correlated with UHI intensity. The second highest factor loading (0.917) belonged to EV3 (pervious surface area). The conversion of pervious to impervious surfaces as a result of economic development and urbanization expansion causes temperatures in urban areas to be significantly warmer than surrounding rural areas.
Under the UHI sensitivity component, the proportion of green space to build environment indicator (SV1) had the highest factor loading (0.910). Urban green space provides cool and shaded areas while moderating ambient temperatures. Specifically, the UHI impacts were found to be inversely correlated with the proportion of urban green space to build environment. The second highest factor loading (0.887) belonged to SS5 (traffic congestion). Increased vehicular traffic and road congestion exacerbate the UHI situation and air pollution. Increased vehicular traffic also results in increased energy consumption, elevated emissions of greenhouse gases, and compromised human health and comfort.
In the UHI adaptive capacity component, the highest factor loading (0.910) belonged to AS4 (government policy and action), followed by AS3 (multi-agency collaboration) with a factor loading of 0.887. Government policy and action plays a crucial role in the UHI adaptive capacity of urban residents. In addition, multi-agency collaboration on the dissemination of information and UHI impact mitigation enhances adaptive capacity to abrupt and dramatic temperature increases.
In the implementation of the proposed statistically validated risk indicators to assess area-specific UHI susceptibility, the UHI indicators with high or very high factor loadings are converted into a questionnaire (i.e., UHI risk assessment tool) for primary data collection. In the event that the data are publicly available, the information related to the UHI indicators with high or very high factor loadings can be gathered from secondary sources (e.g., textbooks, reports, and news releases). The collected data (both primary and secondary data) are analyzed using normalized scores, percentages, or weighted average indexes to obtain area-specific UHI susceptibility index scores. The UHI susceptibility index scores are subsequently transformed into a UHI risk map that can be used by policymakers to formulate proper UHI mitigation measures.

Research Implications

Although the proposed UHI indicators could be applied to assess UHI susceptibility, UHI risk assessment was outside the scope of this current research given that the aim of this research was to propose the statistically validated UHI risk indicators under four UHI risk components: hazard, exposure, sensitivity, and adaptive capacity. Nevertheless, the UHI risk assessment of 50 districts of metropolitan Bangkok will be carried out in subsequent research.
In addition, since this current research was limited to one metropolitan area (i.e., Thailand’s capital Bangkok), future research could expand the study area to encompass diverse geographical locations, i.e., other major capital cities. Furthermore, in UHI risk assessment, the proposed 46 UHI risk indicators could be partially (<46 indicators) or wholly adopted, depending on data availability and the characteristics of the urban area.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ijerph20021172/s1.

Funding

This project (Grant No. RGNS N42A650209) is funded by National Resrarch Council of Thailand (NRCT) and Rajamangala University of Technology Thanyaburi (RMUTT).

Institutional Review Board Statement

The study was conducted in accordance with the RMUTT Review Board with Certificate of Approval (COA) no.38.

Informed Consent Statement

Not applicable.

Data Availability Statement

No new data were created.

Acknowledgments

Special thanks also go to the students of the architecture faculty, RMUTT, for their contributions to data collection.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Oke, T.R. Boundary Layer Climates, 2nd ed.; Methuen & Co., Ltd.: London, UK, 1987. [Google Scholar]
  2. Fortuniak, K. Selected Characteristics of the Atmospheric Turbulence Over a Central European City Centre-Integral Statistics. In Proceedings of the 7th International Conference on Urban Climate, Helsinki, Finland, 29 June–3 July 2009. [Google Scholar]
  3. Bhargava, A.; Lakmini, S.; Bhargava, S. Urban Heat Island Effect: It’s relevance in urban planning. J. Biodivers. Endanger. Species 2017, 5, 2020. [Google Scholar]
  4. Tan, Z. Air Pollution and Greenhouse Gases: From Basic Concepts to Engineering Applications for Air Emission Control; Springer: Berlin/Heidelberg, Germany, 2014. [Google Scholar]
  5. Özyavuz, M.; Bilgili, B.; Elkıran, G. Temperature distribution and environmental impact of Tekirdag Ataturk Forest Nature Park. Int. J. Glob. Warm. 2015, 8, 102–113. [Google Scholar] [CrossRef]
  6. Xu, L.; Cui, S.; Tang, J.; Nguyen, M.; Liu, J.; Zhao, Y. Assessing the adaptive capacity of urban form to climate stress: A case study on an urban heat island. Environ. Res. Lett. 2019, 14, 044013. [Google Scholar] [CrossRef]
  7. Leal Filho, W.; Icaza, L.E.; Neht, A.; Klavins, M.; Morgan, E.A. Coping with the impacts of urban heat islands. A literature based study on understanding urban heat vulnerability and the need for resilience in cities in a global climate change context. J. Clean. Prod. 2018, 171, 1140–1149. [Google Scholar] [CrossRef] [Green Version]
  8. Tawsif, S.; Alam, M.; Al-Maruf, A. How households adapt to heat wave for livable habitat? A case of medium-sized city in Bangladesh. Curr. Res. Environ. Sustain. 2022, 4, 100159. [Google Scholar] [CrossRef]
  9. IPCC. Climate Change; IPCC: Geneva, Switzerland, 2014. [Google Scholar]
  10. Lin, P.; Lau, S.S.Y.; Qin, H.; Gou, Z. Effects of urban planning indicators on urban heat island: A case study of pocket parks in high-rise high-density environment. Landsc. Urban Plan. 2017, 168, 48–60. [Google Scholar] [CrossRef]
  11. Dong, W.; Liu, Z.; Zhang, L.; Tang, Q.; Liao, H.; Li, X. Assessing heat health risk for sustainability in Beijing’s urban heat island. Sustainability 2014, 6, 7334–7357. [Google Scholar] [CrossRef] [Green Version]
  12. Nastran, M.; Kobal, M.; Eler, K. Urban heat islands in relation to green land use in European cities. Urban For. Urban Green. 2019, 37, 33–41. [Google Scholar] [CrossRef] [Green Version]
  13. Cui, Y.; Xu, X.; Dong, J.; Qin, Y. Influence of urbanization factors on surface urban heat island intensity: A comparison of countries at different developmental phases. Sustainability 2016, 8, 706. [Google Scholar] [CrossRef] [Green Version]
  14. Inostroza, L.; Palme, M.; De La Barrera, F. A heat vulnerability index: Spatial patterns of exposure, sensitivity and adaptive capacity for Santiago de Chile. PLoS ONE 2016, 11, e0162464. [Google Scholar] [CrossRef] [Green Version]
  15. Voelkel, J.; Hellman, D.; Sakuma, R.; Shandas, V. Assessing vulnerability to urban heat: A study of disproportionate heat exposure and access to refuge by socio-demographic status in Portland, Oregon. Int. J. Environ. Res. Public Health 2018, 15, 640. [Google Scholar] [CrossRef] [PubMed]
  16. Bayraktar, E.; Tatoglu, E.; Zaim, S. An instrument for measuring the critical factors of TQM in Turkish higher education. Total Qual. Manag. 2008, 19, 551–574. [Google Scholar] [CrossRef]
  17. Ismail Salaheldin, S. Critical success factors for TQM implementation and their impact on performance of SMEs. Int. J. Product. Perform. Manag. 2009, 58, 215–237. [Google Scholar] [CrossRef]
  18. Thanvisitthpon, N.; Shrestha, S.; Pal, I.; Ninsawat, S.; Chaowiwat, W. Assessment of flood adaptive capacity of urban areas in Thailand. Environ. Impact Assess. Rev. 2020, 81, 106363. [Google Scholar] [CrossRef]
  19. Yamane, T. Statistics: An Introductory Analysis; Harper & Row: Manhattan, NY, USA, 1973. [Google Scholar]
  20. Marcoulides, K.M.; Foldnes, N.; Grønneberg, S. Assessing model fit in structural equation modeling using appropriate test statistics. Struct. Equ. Model. Multidiscip. J. 2020, 27, 369–379. [Google Scholar] [CrossRef]
  21. Drezner, Z.; Turel, O.; Zerom, D. A modified Kolmogorov–Smirnov test for normality. Commun. Stat. Simul. Comput. 2010, 39, 693–704. [Google Scholar] [CrossRef] [Green Version]
  22. Ahad, N.A.; Yin, T.S.; Othman, A.R.; Yaacob, C.R. Sensitivity of normality tests to non-normal data. Sains Malays. 2011, 40, 637–641. [Google Scholar]
  23. Öztuna, D.; Elhan, A.; Tüccar, E. Investigation of four different normality tests in terms of type 1 error rate and power under different distributions. Turk. J. Med. Sci. 2006, 36, 171–176. [Google Scholar]
  24. Mishra, P.; Pandey, C.M.; Singh, U.; Gupta, A.; Sahu, C.; Keshri, A. Descriptive statistics and normality tests for statistical data. Ann. Card. Anaesth. 2019, 22, 67. [Google Scholar]
  25. Dawson, R.J.; Peppe, R.; Wang, M. An agent-based model for risk-based flood incident management. Nat. Hazards 2011, 59, 167–189. [Google Scholar] [CrossRef]
  26. Thanvisitthpon, N. Statistically Validated Component- and Indicator-Level Requirements for Sustainable Thai Homestay Businesses. Sustainability 2021, 13, 936. [Google Scholar] [CrossRef]
  27. Turner, R.C.; Carlson, L. Indexes of item-objective congruence for multidimensional items. Int. J. Test. 2003, 3, 163–171. [Google Scholar] [CrossRef]
  28. Nafisi, N.; Noori, A.; Mokariantabari, M. Inter-Organizational Conflict (IOC) in Building Refurbishment Projects; an Exploratory Factor Analysis (EFA) approach. J. Constr. Res. 2021, 2. [Google Scholar] [CrossRef]
  29. Basics, U.H.I. Reducing Urban Heat Islands: Compendium of Strategies. US EPA. Available online: http://www.epa.gov/heatisland/resources/compendium.htm. (accessed on 14 January 2011).
  30. Zhang, X.; Chen, L.; Jiang, W.; Jin, X. Urban heat island of Yangtze River Delta urban agglomeration in China: Multi-time scale characteristics and influencing factors. Urban Clim. 2022, 43, 101180. [Google Scholar] [CrossRef]
  31. Molina-Gómez, N.I.; Varon-Bravo, L.M.; Sierra-Parada, R.; López-Jiménez, P.A. Urban growth and heat islands: A case study in micro-territories for urban sustainability. Urban Ecosyst. 2022, 25, 1379–1397. [Google Scholar] [CrossRef]
  32. Khamchiangta, D.; Dhakal, S. Physical and non-physical factors driving urban heat island: Case of Bangkok Metropolitan Administration, Thailand. J. Environ. Manag. 2019, 248, 109285. [Google Scholar] [CrossRef] [PubMed]
  33. Yomsatiankul, J.; Iabchoon, S.; Varnakovida, P. Study on Bangkok its Urban Heat Island effect and the Relationship with Electricity Consumption using Geoinformatics. Int. J. Build. Urban Inter. Landsc. Technol. BUILT 2018, 12, 35–46. [Google Scholar]
  34. Li, J.; Sun, R.; Liu, T.; Xie, W.; Chen, L. Prediction models of urban heat island based on landscape patterns and anthropogenic heat dynamics. Landsc. Ecol. 2021, 36, 1801–1815. [Google Scholar] [CrossRef]
  35. Glazer, J.L. Management of heatstroke and heat exhaustion. Am. Fam. Physician 2005, 71, 2133–2140. [Google Scholar]
  36. Hall, E.J.; Carter, A.; O’Neill, D. Incidence and risk factors for heat-related illness (heatstroke) in UK dogs under primary veterinary care in 2016. Sci. Rep. 2020, 10, 9128. [Google Scholar] [CrossRef]
  37. Xiang, J.; Bi, P.; Pisaniello, D.; Hansen, A. Health impacts of workplace heat exposure: An epidemiological review. Ind. Health 2014, 52, 91–101. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  38. Hyde, K.L. Assessing Urban Heat Island Mitigation Capacities of Green Infrastructure to Address Heat Vulnerability Inequities in San Francisco, California. Master’s Thesis, University of San Francisco, San Francisco, CA, USA, 2022. [Google Scholar]
  39. Zhu, R.; Wong, M.S.; Guilbert, É.; Chan, P.-W. Understanding heat patterns produced by vehicular flows in urban areas. Sci. Rep. 2017, 7, 16309. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  40. Miner, M.J.; Taylor, R.A.; Jones, C.; Phelan, P.E. Efficiency, economics, and the urban heat island. Environ. Urban. 2017, 29, 183–194. [Google Scholar] [CrossRef]
  41. Jang, J.; Lee, W.; Choi, M.; Kang, C.; Kim, H. Roles of urban heat anomaly and land-use/land-cover on the heat-related mortality in the national capital region of South Korea: A multi-districts time-series study. Environ. Int. 2020, 145, 106127. [Google Scholar] [CrossRef] [PubMed]
  42. Khamchiangta, D.; Dhakal, S. Time series analysis of land use and land cover changes related to urban heat island intensity: Case of Bangkok Metropolitan Area in Thailand. J. Urban Manag. 2020, 9, 383–395. [Google Scholar] [CrossRef]
  43. Nimish, G.; Bharath, H.; Ramachandra, T. Visualization of landuse change pattern and its impact on Urban Heat Islands. In Global Urban Heat Island Mitigation; Khan, A., Akbari, H., Fiorito, F., Mithun, S., Niyogi, D., Eds.; Elsevier: Amsterdam, The Netherlands, 2022; pp. 301–322. [Google Scholar]
  44. Faragallah, R.N.; Ragheb, R. Evaluation of thermal comfort and urban heat island through cool paving materials using ENVI-Met. Ain Shams Eng. J. 2022, 13, 101609. [Google Scholar] [CrossRef]
  45. Naikoo, M.W.; Islam, A.R.M.T.; Mallick, J.; Rahman, A. Land use/land cover change and its impact on surface urban heat island and urban thermal comfort in a metropolitan city. Urban Clim. 2022, 41, 101052. [Google Scholar]
  46. Lee, K.; Kim, Y.; Sung, H.C.; Kim, S.H.; Jeon, S.W. Surface urban heat island in South Korea’s new towns with different urban planning. Environ. Monit. Assess. 2022, 194, 360. [Google Scholar] [CrossRef]
  47. Srivanit, M.; Kazunori, H. The influence of urban morphology indicators on summer diurnal range of urban climate in Bangkok metropolitan area, Thailand. Int. J. Civ. Environ. Eng. 2011, 11, 34–46. [Google Scholar]
  48. Yuan, F.; Bauer, M.E. Comparison of impervious surface area and normalized difference vegetation index as indicators of surface urban heat island effects in Landsat imagery. Remote Sens. Environ. 2007, 106, 375–386. [Google Scholar] [CrossRef]
  49. Sekertekin, A.; Zadbagher, E. Simulation of future land surface temperature distribution and evaluating surface urban heat island based on impervious surface area. Ecol. Indic. 2021, 122, 107230. [Google Scholar] [CrossRef]
  50. Mathew, A.; Khandelwal, S.; Kaul, N. Spatial and temporal variations of urban heat island effect and the effect of percentage impervious surface area and elevation on land surface temperature: Study of Chandigarh city, India. Sustain. Cities Soc. 2016, 26, 264–277. [Google Scholar] [CrossRef]
  51. Naheed, S.; Eslamian, S. Urban Vulnerability to Extreme Heat Events and Climate Change. In Disaster Risk Reduction for Resilience; Eslamian, S., Eslamian, F., Eds.; Springer: Berlin/Heidelberg, Germany, 2022; pp. 413–434. [Google Scholar]
  52. Rossi, F.; Bonamente, E.; Nicolini, A.; Anderini, E.; Cotana, F. A carbon footprint and energy consumption assessment methodology for UHI-affected lighting systems in built areas. Energy Build. 2016, 114, 96–103. [Google Scholar] [CrossRef]
  53. Li, X.; Zhou, Y.; Yu, S.; Jia, G.; Li, H.; Li, W. Urban heat island impacts on building energy consumption: A review of approaches and findings. Energy 2019, 174, 407–419. [Google Scholar] [CrossRef]
  54. Arifwidodo, S.; Chandrasiri, O. Urban heat island and household energy consumption in Bangkok, Thailand. Energy Procedia 2015, 79, 189–194. [Google Scholar] [CrossRef] [Green Version]
  55. Ihara, T.; Kikegawa, Y.; Asahi, K.; Genchi, Y.; Kondo, H. Changes in year-round air temperature and annual energy consumption in office building areas by urban heat-island countermeasures and energy-saving measures. Appl. Energy 2008, 85, 12–25. [Google Scholar] [CrossRef]
  56. Zhu, W.; Yuan, C. Urban Heat Health Risk Assessment in Singapore to Support Resilient Urban Design—By Integrating Urban Heat and the Distribution of the Elderly Population. Cities 2023, 132, 104103. [Google Scholar] [CrossRef]
  57. Devarajan, S. Using Recurrent Neural Networks to Predict Future Land Surface Temperatures as an Approach to Urban Heat Island Mitigation; Massachusetts Academy of Math & Science: Worcester, MA, USA, 2022. [Google Scholar]
  58. Yamamoto, Y. Measures to Mitigate Urban Heat Islands; NISTEP Science & Technology Foresight Center: Tokyo, Japan, 2006. [Google Scholar]
  59. Khamchiangta, D.; Dhakal, S. Future urban expansion and local climate zone changes in relation to land surface temperature: Case of Bangkok Metropolitan Administration, Thailand. Urban Clim. 2021, 37, 100835. [Google Scholar] [CrossRef]
  60. Piracha, A.; Chaudhary, M.T. Urban Air Pollution, Urban Heat Island and Human Health: A Review of the Literature. Sustainability 2022, 14, 9234. [Google Scholar] [CrossRef]
  61. Keeratikasikorn, C.; Bonafoni, S. Urban heat island analysis over the land use zoning plan of Bangkok by means of Landsat 8 imagery. Remote Sens. 2018, 10, 440. [Google Scholar] [CrossRef]
  62. Hess, J.J.; McDowell, J.Z.; Luber, G. Integrating climate change adaptation into public health practice: Using adaptive management to increase adaptive capacity and build resilience. Environ. Health Perspect. 2012, 120, 171–179. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  63. Hoverter, S.P. Adapting to Urban Heat: A Tool Kit for Local Governments; Georgetown Climate Center: Washington, DC, USA, 2012. [Google Scholar]
  64. Susilawati, S.A.; Maytasari, F.; Sekartaji, A. Knowledge Exploration Study of Muhammadiyah School Students about Urban Heat Island Threats in Surakarta City. In Proceedings of the 4th Progressive and Fun Education International Conference (PFEIC 2019), Makassar, Indonesia, 6–8 August 2019. [Google Scholar]
  65. Diehl, J.A.; Sweeney, E.; Wong, B.; Sia, C.S.; Yao, H.; Prabhudesai, M. Feeding cities: Singapore’s approach to land use planning for urban agriculture. Glob. Food Secur. 2020, 26, 100377. [Google Scholar] [CrossRef]
  66. Yow, D.M. Urban heat islands: Observations, impacts, and adaptation. Geogr. Compass 2007, 1, 1227–1251. [Google Scholar] [CrossRef]
  67. Ghosh, S.; Byahut, S.; Masilela, C. An Overview of Climate Protection and Resiliency Planning in the New York Megacity Region. In Smart Global Megacities; Vinod Kumar, T.M., Ed.; Springer: Berlin/Heidelberg, Germany, 2022; pp. 287–313. [Google Scholar]
  68. Gilbert, H.; Mandel, B.H.; Levinson, R. Keeping California cool: Recent cool community developments. Energy Build. 2016, 114, 20–26. [Google Scholar] [CrossRef] [Green Version]
  69. Elgendawy, A.; Davies, P.; Chang, H.-C. Planning for cooler cities: A plan quality evaluation for Urban Heat Island consideration. J. Environ. Policy Plan. 2020, 22, 531–553. [Google Scholar] [CrossRef]
  70. Aina, Y.A.; Parvez, I.M.; Balogun, A.-L.; Adam, E. Urban heat island effects and mitigation strategies in Saudi Arabian cities. In Urban Heat Island (UHI) Mitigation; Enteria, N., Santamouris, M., Eicker, U., Eds.; Springer: Berlin/Heidelberg, Germany, 2021; pp. 235–248. [Google Scholar]
  71. Puchol-Salort, P.; O’Keeffe, J.; van Reeuwijk, M.; Mijic, A. An urban planning sustainability framework: Systems approach to blue green urban design. Sustain. Cities Soc. 2021, 66, 102677. [Google Scholar] [CrossRef]
  72. Patankar, A.; Patwardhan, A. Estimating the uninsured losses due to extreme weather events and implications for informal sector vulnerability: A case study of Mumbai, India. Nat. Hazards 2016, 80, 285–310. [Google Scholar]
  73. Kondo, K.; Mabon, L.; Bi, Y.; Chen, Y.; Hayabuchi, Y. Balancing conflicting mitigation and adaptation behaviours of urban residents under climate change and the urban heat island effect. Sustain. Cities Soc. 2021, 65, 102585. [Google Scholar] [CrossRef] [PubMed]
  74. Cresswell, K.J. Urban Heat Island Driving Forces and Vulnerability Assessment: A Study of Southeast Florida. Ph.D. Thesis, Florida Atlantic University, Boca Raton, FL, USA, 2022. [Google Scholar]
  75. Council, N.R. Understanding the Changing Planet: Strategic Directions for the Geographical Sciences; National Academies Press: Washington, DC, USA, 2010. [Google Scholar]
  76. Kadaverugu, R.; Lama, S.; Purohit, V.; Mopati, S.; Matli, C.; Biniwale, R. Potential of Green Roofs in Heat Stress Mitigation—A Case of Nagpur City. In Innovative Trends in Hydrological and Environmental Systems; Dikshit, A.K., Narasimhan, B., Kumar, B., Patel, A.K., Eds.; Springer: Berlin/Heidelberg, Germany, 2022; pp. 367–383. [Google Scholar]
  77. Thanvisitthpon, N.; Rintra, J.; Sittiprapaporn, P.; Bumrungpert, A.; Trakulkongsmut, P. Self-Assessment Adaptive Capacity Indicators of Health Risks from Air Pollution. Sustainability 2021, 13, 13141. [Google Scholar] [CrossRef]
  78. Baumgartner, H.; Homburg, C. Applications of structural equation modeling in marketing and consumer research: A review. Int. J. Res. Mark. 1996, 13, 139–161. [Google Scholar] [CrossRef] [Green Version]
  79. Gatignon, H. Statistical Analysis of Management Data; Springer: Berlin/Heidelberg, Germany, 2003. [Google Scholar]
  80. Hooper, D.; Coughlan, J.; Mullen, M. Structural equation modelling: Guidelines for determining model fit. Electron. J. Bus. Res. Methods 2008, 6, 53–60. [Google Scholar]
  81. Keramitsoglou, I.; Kiranoudis, C.T.; Maiheu, B.; De Ridder, K.; Daglis, I.A.; Manunta, P.; Paganini, M. Heat wave hazard classification and risk assessment using artificial intelligence fuzzy logic. Environ. Monit. Assess. 2013, 185, 8239–8258. [Google Scholar] [CrossRef] [PubMed]
  82. Dashkhuu, D.; Kim, J.P.; Chun, J.A.; Lee, W.-S. Long-term trends in daily temperature extremes over Mongolia. Weather. Clim. Extrem. 2015, 8, 26–33. [Google Scholar] [CrossRef] [Green Version]
  83. Kim, J.-O.; Mueller, C.W. Factor Analysis: Statistical Methods and Practical Issues; Sage: Newcastle upon Tyne, UK, 1978; Volume 14. [Google Scholar]
  84. Fornell, C.; Larcker, D.F. Evaluating structural equation models with unobservable variables and measurement error. J. Mark. Res. 1981, 18, 39–50. [Google Scholar] [CrossRef]
  85. Shrestha, A.B.; Bajracharya, S.R.; Sharma, A.R.; Duo, C.; Kulkarni, A. Observed trends and changes in daily temperature and precipitation extremes over the Koshi river basin 1975–2010. Int. J. Climatol. 2017, 37, 1066–1083. [Google Scholar] [CrossRef] [Green Version]
  86. Zhang, X.; Aguilar, E.; Sensoy, S.; Melkonyan, H.; Tagiyeva, U.; Ahmed, N.; Kutaladze, N.; Rahimzadeh, F.; Taghipour, A.; Hantosh, T. Trends in Middle East climate extreme indices from 1950 to 2003. J. Geophys. Res. Atmos. 2005, 110. [Google Scholar] [CrossRef]
  87. Wong, M.; Mok, H.; Lee, T. Observed changes in extreme weather indices in Hong Kong. Int. J. Climatol. 2011, 31, 2300–2311. [Google Scholar] [CrossRef]
  88. Manalo, J.A.; Matsumoto, J.; Takahashi, H.G.; Villafuerte, M.Q.; Olaguera, L.M.P.; Ren, G.; Cinco, T.A. The effect of urbanization on temperature indices in the Philippines. Int. J. Climatol. 2022, 42, 850–867. [Google Scholar] [CrossRef]
  89. Dong, S.; Xu, Y.; Zhou, B.; Shi, Y. Assessment of indices of temperature extremes simulated by multiple CMIP5 models over China. Adv. Atmos. Sci. 2015, 32, 1077–1091. [Google Scholar] [CrossRef]
  90. Hoehne, C.G.; Chester, M.V.; Sailor, D.J.; King, D.A. Urban heat implications from parking, roads, and cars: A case study of metro Phoenix. Sustain. Resilient Infrastruct. 2020, 7, 272–290. [Google Scholar] [CrossRef]
  91. Hart, M.A.; Sailor, D.J. Quantifying the influence of land-use and surface characteristics on spatial variability in the urban heat island. Theor. Appl. Climatol. 2009, 95, 397–406. [Google Scholar] [CrossRef]
  92. Sailor, D.J.; Lu, L. A top–down methodology for developing diurnal and seasonal anthropogenic heating profiles for urban areas. Atmos. Environ. 2004, 38, 2737–2748. [Google Scholar] [CrossRef]
  93. De Abreu, V.H.S.; Santos, A.S.; Monteiro, T.G.M. Climate Change Impacts on the Road Transport Infrastructure: A Systematic Review on Adaptation Measures. Sustainability 2022, 14, 8864. [Google Scholar] [CrossRef]
  94. Quinn, A.D.; Ferranti, E.J.; Hodgkinson, S.P.; Jack, A.C.; Beckford, J.; Dora, J.M. Adaptation becoming business as usual: A framework for climate-change-ready transport infrastructure. Infrastructures 2018, 3, 10. [Google Scholar] [CrossRef] [Green Version]
  95. Rowan, E.; Evans, C.; Riley-Gilbert, M.; Hyman, R.; Kafalenos, R.; Beucler, B.; Rodehorst, B.; Choate, A.; Schultz, P. Assessing the sensitivity of transportation assets to extreme weather events and climate change. Transp. Res. Rec. 2013, 2326, 16–23. [Google Scholar] [CrossRef]
  96. Santos, A.S.; Ribeiro, S.K.; de Abreu, V.H.S. Addressing Climate Change in Brazil: Is Rio de Janeiro City acting on adaptation strategies? In Proceedings of the 2020 International Conference and Utility Exhibition on Energy, Environment and Climate Change (ICUE), Pattaya, Thailand, 20–22 October 2020.
  97. Mallick, R.; Radzicki, M.J.; Daniel, J.S.; Jacobs, J.M. Use of System Dynamics to Understand Long-Term Impact of Climate Change on Pavement Performance and Maintenance Cost. Transp. Res. Rec. 2014, 2455, 1–9. [Google Scholar] [CrossRef] [Green Version]
  98. Bollinger, L.; Bogmans, C.; Chappin, E.; Dijkema, G.P.; Huibregtse, J.; Maas, N.; Schenk, T.; Snelder, M.; Van Thienen, P.; De Wit, S. Climate adaptation of interconnected infrastructures: A framework for supporting governance. Reg. Environ. Change 2014, 14, 919–931. [Google Scholar] [CrossRef] [Green Version]
  99. Vujovic, S.; Haddad, B.; Karaky, H.; Sebaibi, N.; Boutouil, M. Urban heat Island: Causes, consequences, and mitigation measures with emphasis on reflective and permeable pavements. CivilEng 2021, 2, 459–484. [Google Scholar]
  100. Haselbach, L.; Boyer, M.; Kevern, J.T.; Schaefer, V.R. Cyclic heat island impacts on traditional versus pervious concrete pavement systems. Transp. Res. Rec. 2011, 2240, 107–115. [Google Scholar] [CrossRef]
  101. Shuster, W.D.; Bonta, J.; Thurston, H.; Warnemuende, E.; Smith, D. Impacts of impervious surface on watershed hydrology: A review. Urban Water J. 2005, 2, 263–275. [Google Scholar] [CrossRef]
  102. Gorsevski, V.; Taha, H.; Quattrochi, D.; Luvall, J. Air pollution prevention through urban heat island mitigation: An update on the Urban Heat Island Pilot Project. In 1998 ACEEE Summer Study on Energy Efficiency in Buildings: Proceedings; ACEEE: Washington, DC, USA, 1998; Volume 9, pp. 23–32. [Google Scholar]
  103. Taha, H. Urban climates and heat islands: Albedo, evapotranspiration, and anthropogenic heat. Energy Build. 1997, 25, 99–103. [Google Scholar] [CrossRef]
  104. Sen, S.; Roesler, J.; Ruddell, B.; Middel, A. Cool pavement strategies for urban heat island mitigation in suburban Phoenix, Arizona. Sustainability 2019, 11, 4452. [Google Scholar] [CrossRef] [Green Version]
  105. Cao, L.; Li, P.; Zhang, L. Impact of impervious surface on urban heat island in Wuhan, China. In Proceedings of the International Conference on Earth Observation Data Processing and Analysis (ICEODPA), Wuhan, China, 28–30 December 2008. [Google Scholar]
  106. Oliveira, S.; Andrade, H.; Vaz, T. The cooling effect of green spaces as a contribution to the mitigation of urban heat: A case study in Lisbon. Build. Environ. 2011, 46, 2186–2194. [Google Scholar] [CrossRef]
  107. Thammapornpilas, J. Urban Spatial Development to Mitigate Urban Heat Island Effect in the Inner Area of Bangkok. Nakhara J. Environ. Des. Plan. 2015, 11, 29–40. [Google Scholar]
  108. Osmond, P.; Irger, M. Green roof retrofit and the urban heat island. In Green Roof Retrofit: Building Urban Resilience; Wilkinson, S., Dixon, T., Eds.; John Wiley & Sons: Hoboken, NJ, USA, 2016; pp. 37–61. [Google Scholar]
  109. Yu, C.; Hien, W.N. Thermal benefits of city parks. Energy Build. 2006, 38, 105–120. [Google Scholar] [CrossRef]
  110. Potchter, O.; Cohen, P.; Bitan, A. Climatic behavior of various urban parks during hot and humid summer in the Mediterranean city of Tel Aviv, Israel. Int. J. Climatol. J. R. Meteorol. Soc. 2006, 26, 1695–1711. [Google Scholar] [CrossRef]
  111. Zoulia, I.; Santamouris, M.; Dimoudi, A. Monitoring the effect of urban green areas on the heat island in Athens. Environ. Monit. Assess. 2009, 156, 275–292. [Google Scholar] [CrossRef]
  112. Georgi, J.N.; Dimitriou, D. The contribution of urban green spaces to the improvement of environment in cities: Case study of Chania, Greece. Build. Environ. 2010, 45, 1401–1414. [Google Scholar] [CrossRef] [Green Version]
  113. Costanzo, V.; Evola, G.; Marletta, L. Energy savings in buildings or UHI mitigation? Comparison between green roofs and cool roofs. Energy Build. 2016, 114, 247–255. [Google Scholar] [CrossRef]
  114. Jamei, E.; Chau, H.W.; Seyedmahmoudian, M.; Stojcevski, A. Review on the cooling potential of green roofs in different climates. Sci. Total Environ. 2021, 791, 148407. [Google Scholar] [CrossRef]
  115. Odli, Z.; Zakarya, I.; Mohd, F.; Izhar, T.; Ibrahim, N.; Mohamad, N. Green roof technology-mitigate urban heat island (UHI) effect; MATEC Web of Conferences. In Proceedings of the 2nd International Conference on Green Design and Manufacture 2016 (IConGDM 2016), Phuket, Thailand, 1–2 May 2016. [Google Scholar]
  116. Susca, T.; Gaffin, S.R.; Dell’Osso, G. Positive effects of vegetation: Urban heat island and green roofs. Environ. Pollut. 2011, 159, 2119–2126. [Google Scholar] [CrossRef] [PubMed]
  117. Ren, C.; Lau, K.L.; Yiu, K.P.; Ng, E. The application of urban climatic mapping to the urban planning of high-density cities: The case of Kaohsiung, Taiwan. Cities 2013, 31, 1–16. [Google Scholar] [CrossRef]
  118. Ren, C.; Ng, E.Y.-y.; Katzschner, L. Urban climatic map studies: A review. Int. J. Climatol. 2011, 31, 2213–2233. [Google Scholar] [CrossRef]
  119. Ng, E.; Chao, R.; Katzschner, L.; Yao, R. Urban climatic studies for hot and humid tropical coastal city of Hong Kong. In Proceedings of the 7th International Conference on Urban Climate, Yokohama, Japan, 29 June–3 July 2009. [Google Scholar]
  120. Kotharkar, R.; Bagade, A.; Ramesh, A. Assessing urban drivers of canopy layer urban heat island: A numerical modeling approach. Landsc. Urban Plan. 2019, 190, 103586. [Google Scholar] [CrossRef]
  121. Simpson, B. Briefing: Making the connections: Final report on transport and social exclusion. Report by the Social Exclusion Unit 2003. In Proceedings of the Institution of Civil Engineers—Municipal Engineer; Thomas Telford Ltd.: London, UK, 2003. [Google Scholar]
  122. Corburn, J. Cities, climate change and urban heat island mitigation: Localising global environmental science. Urban Stud. 2009, 46, 413–427. [Google Scholar] [CrossRef]
  123. Akbari, H.; Cartalis, C.; Kolokotsa, D.; Muscio, A.; Pisello, A.L.; Rossi, F.; Santamouris, M.; Synnefa, A.; Wong, N.H.; Zinzi, M. Local climate change and urban heat island mitigation techniques—The state of the art. J. Civ. Eng. Manag. 2016, 22, 1–16. [Google Scholar] [CrossRef] [Green Version]
  124. Qi, J.; Ding, L.; Lim, S. Ontology-based knowledge representation of urban heat island mitigation strategies. Sustain. Cities Soc. 2020, 52, 101875. [Google Scholar] [CrossRef]
  125. Ariani, N.; Wijaya, M.; Priambudi, B. Understanding Spatial Planning Policies in Responding to Urban Heat Island Using GIS Analysis in The Kendal Industrial Area. IOP Conf. Ser. Earth Environ. Sci. 2021, 887, 012021. [Google Scholar] [CrossRef]
  126. Wang, C.; Wang, Z.-H.; Kaloush, K.E.; Shacat, J. Perceptions of urban heat island mitigation and implementation strategies: Survey and gap analysis. Sustain. Cities Soc. 2021, 66, 102687. [Google Scholar] [CrossRef]
  127. Mirzaei, P.A.; Haghighat, F. Approaches to study urban heat island–abilities and limitations. Build. Environ. 2010, 45, 2192–2201. [Google Scholar] [CrossRef]
  128. Akbari, H.; Matthews, H.; Seto, D. The long-term effect of increasing the albedo of urban areas. Environ. Res. Lett. 2012, 7, 024004. [Google Scholar] [CrossRef]
  129. Ulpiani, G. On the linkage between urban heat island and urban pollution island: Three-decade literature review towards a conceptual framework. Sci. Total Environ. 2021, 751, 141727. [Google Scholar] [CrossRef] [PubMed]
  130. Degirmenci, K.; Desouza, K.C.; Fieuw, W.; Watson, R.T.; Yigitcanlar, T. Understanding policy and technology responses in mitigating urban heat islands: A literature review and directions for future research. Sustain. Cities Soc. 2021, 70, 102873. [Google Scholar] [CrossRef]
  131. Pineo, H.; Zimmermann, N.; Davies, M. Integrating health into the complex urban planning policy and decision-making context: A systems thinking analysis. Palgrave Commun. 2020, 6, 21. [Google Scholar] [CrossRef] [Green Version]
  132. Smith, C.; Levermore, G. Designing urban spaces and buildings to improve sustainability and quality of life in a warmer world. Energy Policy 2008, 36, 4558–4562. [Google Scholar] [CrossRef]
  133. Hintz, M.J.; Luederitz, C.; Lang, D.J.; von Wehrden, H. Facing the heat: A systematic literature review exploring the transferability of solutions to cope with urban heat waves. Urban Clim. 2018, 24, 714–727. [Google Scholar] [CrossRef]
Ijerph 20 01172 g001 550
Figure 1. The IPCC’s risk assessment framework [9].
Figure 1. The IPCC’s risk assessment framework [9].
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Figure 2. Structural equation model and the factor loadings of the UHI hazard indicators and dimensions.
Figure 2. Structural equation model and the factor loadings of the UHI hazard indicators and dimensions.
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Figure 3. Structural equation model and the factor loadings of UHI exposure indicators and dimensions.
Figure 3. Structural equation model and the factor loadings of UHI exposure indicators and dimensions.
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Figure 4. Structural equation model and the factor loadings of UHI sensitivity indicators and dimensions.
Figure 4. Structural equation model and the factor loadings of UHI sensitivity indicators and dimensions.
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Figure 5. Structural equation model and the factor loadings of UHI adaptive capacity indicators and dimensions.
Figure 5. Structural equation model and the factor loadings of UHI adaptive capacity indicators and dimensions.
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Table
Table 5. First- and second-order CFA of UHI hazard indicators.
Table 5. First- and second-order CFA of UHI hazard indicators.
Dimension
(Latent Factors)		CFA Construct ValidityUHI
Hazard
Indicators		Factor LoadingR2
Composite Reliability (CR)Average Variance Extracted (AVE)
Temperature
(b = 0.937)		0.9210.660HT10.8060.682
HT20.7570.677
HT30.7850.660
HT40.7850.617
HT50.8180.670
HT60.9150.837
Duration
(b = 0.991)		0.9320.663HD10.7640.583
HD20.8210.674
HD30.8880.788
HD40.8400.706
HD50.7840.615
HD60.8220.675
HD70.7710.594
b is the factor loading of the second-order CFA. R2 is the reliability of the indicators.
Table
Table 6. First- and second-order CFA of UHI exposure indicators.
Table 6. First- and second-order CFA of UHI exposure indicators.
Dimension
(Latent Factors)		CFA Construct ValidityUHI
Exposure Indicators		Factor LoadingR2
Composite
Reliability (CR)		Average Variance Extracted (AVE)
Social
(b = 0.997)		0.9300.770ES10.8600.739
ES20.8640.747
ES30.8600.739
ES40.9230.853
Economic
(b = 0.999)		0.8870.724EE10.8080.683
EE20.9080.825
EE30.8330.695
Environment
(b = 0.994)		0.9250.804EV10.8940.799
EV20.8790.772
EV30.9170.840
b is the factor loading of the second-order CFA. R2 is the reliability of indicators.
Table
Table 7. First- and second-order CFA of UHI sensitivity indicators.
Table 7. First- and second-order CFA of UHI sensitivity indicators.
Dimension
(Latent
Factors)		CFA Construct ValidityUHI
Sensitivity
Indicators		Factor
Loading		R2
Composite
Reliability (CR)		Average Variance Extracted (AVE)
Social
(b = 0.936)		0.9150.684SS10.8520.727
SS20.8390.703
SS30.8040.647
SS40.7450.500
SS50.8870.658
Economic
(b = 0.981)		0.8760.702SE10.7900.582
SE20.8550.731
SE30.8670.751
Environment
(b = 0.999)		0.9820.765SV10.9100.827
SV20.8460.721
SV30.8650.749
SV40.8760.767
b is the factor loading of the second-order CFA. R2 is the reliability of indicators.
Table
Table 8. First- and second-order CFA of UHI adaptive capacity indicators.
Table 8. First- and second-order CFA of UHI adaptive capacity indicators.
Dimension
(Latent
Factors)		CFA Construct ValidityUHI Adaptive Capacity IndicatorsFactor LoadingR2
Composite
Reliability (CR)		Average
Variance
Extracted (AVE)
Social
(b = 0.912)		0.9180.738AS10.8500.723
AS20.7830.613
AS30.8870.787
AS40.9100.828
Economic
(b = 0.998)		0.9030.670AE10.8440.685
AE20.8220.639
AE30.8540.729
AE40.8260.724
Environment
(b = 0.958)		0.8860.722AV10.8600.739
AV20.8340.696
AV30.8550.730
b is the factor loading of the second-order CFA. R2 is the reliability of indicators.
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Thanvisitthpon, N. Statistically Validated Urban Heat Island Risk Indicators for UHI Susceptibility Assessment. Int. J. Environ. Res. Public Health 2023, 20, 1172. https://doi.org/10.3390/ijerph20021172

AMA Style

Thanvisitthpon N. Statistically Validated Urban Heat Island Risk Indicators for UHI Susceptibility Assessment. International Journal of Environmental Research and Public Health. 2023; 20(2):1172. https://doi.org/10.3390/ijerph20021172

Chicago/Turabian Style

Thanvisitthpon, Nawhath. 2023. "Statistically Validated Urban Heat Island Risk Indicators for UHI Susceptibility Assessment" International Journal of Environmental Research and Public Health 20, no. 2: 1172. https://doi.org/10.3390/ijerph20021172

APA Style

Thanvisitthpon, N. (2023). Statistically Validated Urban Heat Island Risk Indicators for UHI Susceptibility Assessment. International Journal of Environmental Research and Public Health, 20(2), 1172. https://doi.org/10.3390/ijerph20021172

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