The Impact of Residential Building Insulation Standards on Indoor Thermal Environments and Heat-Related Illness Risks During Heatwaves: A Case Study in Korea

Abstract

This study investigates the impact of building insulation standards on indoor thermal environments and the risk of heat-related illnesses during heatwaves in South Korea. Indoor temperatures were measured in residential buildings located in Chuncheon and Gwangju during the 2022 heatwave, with outdoor temperature data sourced from the Korea Meteorological Administration. Probability distribution fitting was used to estimate the likelihood of indoor temperatures exceeding the critical threshold of 27 °C. Additionally, a linear regression analysis was conducted to examine the relationship between the probability of exceeding the threshold and heat-related illness data from 2017 to 2023 provided by the Korea Disease Control and Prevention Agency. The findings reveal significant variations in indoor thermal conditions during heatwaves, influenced by factors such as building type, year of construction, and climate region, which affect the thermal insulation performance. Buildings with a lower thermal insulation performance were associated with higher indoor temperatures, increasing the likelihood of exceeding the critical threshold and contributing to a higher incidence of heat-related illnesses, particularly in provincial non-metropolitan areas. These results underscore the need for region-specific building insulation standards that address both winter energy efficiency and summer heatwave resilience. Enhancing thermal insulation in vulnerable regions could significantly reduce the risk of heat-related illnesses and improve public health resilience to extreme heat events.

1. Introduction

The global average temperature has steadily increased by approximately 1 °C since the Industrial Revolution. In June 2024, the World Meteorological Organization (WMO) warned that the global average temperature is expected to rise more rapidly than anticipated. The report also indicated that there is an over 80% probability that the average annual temperature between 2024 and 2028 will exceed 1.5 °C above pre-industrial levels [1].

From 1880 to 2012, the global average surface temperature rose by approximately 0.85 °C, but in Korea, the temperature increased by approximately 1.8 °C from 1912 to 2017, showing a higher rise than the global average. This temperature increase has led to a rise in the number of heatwave days in Korea [2] and, consequently, an increase in casualties.

According to the Korean Heat Illness Surveillance System, the number of heat-related illnesses increased from 556 cases in 2014 to 4526 cases in 2018, nearly a ninefold increase. Particularly, from 2018 to 2021, there were a total of 8815 heat-related illness cases, with an annual average of over 2200 cases and an annual average of around 20 deaths, indicating a significant impact caused by heatwaves [3]. (See

Table 1. Statistics of heat-related illness patients and deaths due to heatwaves [3].

	Year	2014	2015	2016	2017	2018	2019	2020	2021	Total (2018–2021)	Yearly Average (2018–2021)
Case
Heat-Related Illness Patients		556	1056	2125	1574	4526	1841	1078	1370	8815	2204
Heat-Related Illness Deaths		1	11	17	11	48	11	9	10	78	20

).

In September 2018, Korea designated heatwaves as a natural disaster under Article 3 of the Basic Act on Disaster and Safety Management [4] and as a result, a comprehensive government-wide plan to address heatwaves has been implemented. As part of this plan, various mitigation measures have been implemented, such as promoting the use of cooling shelters and restricting outdoor work during heatwave periods to prevent heat-related illnesses occurring outdoors. However, despite indoor heat-related illnesses accounting for approximately 30% of all cases, there is a relative lack of understanding of their causes and corresponding countermeasures.

A study on 315 indoor heat-related deaths during the 2003 European heatwave in France [5] found that the risk of death was 2.26 times higher in poorly insulated homes and 1.83 times higher in older homes. This suggests that the characteristics of buildings have a significant impact on indoor thermal environments and the occurrence of heat-related illnesses during heatwaves, yet related research remains insufficient.

White-Newsome et al. [6] measured and analyzed hourly indoor and outdoor temperatures in 30 different homes in Detroit, Michigan, during the summer of 2009. They found that the indoor temperature in poorly insulated homes with vinyl panels or wooden exteriors was more sensitive to changes in outdoor temperature and indoor heat increases compared to brick houses. Loughnan et al. [7] studied 20 homes of elderly residents in an Australian town in 2012 and found that poorly insulated wooden and cement panel homes retained more heat throughout the day, keeping indoor temperatures high until the evening. Zuurbier et al. [8] measured living room and bedroom temperatures, as well as outdoor temperatures, in the homes of 113 elderly individuals in the Dutch cities of Arnhem and Groningen and found that indoor temperatures had a moderate correlation with outdoor temperatures (R² = 0.34–0.36). They also confirmed that building characteristics such as the number of floors, building age, and window orientation influence indoor temperatures during heatwaves. Gonzalez et al. [9] monitored indoor temperatures in four households in southern Spain and found that better thermal insulation could extend indoor thermal comfort during heatwaves. Oetomo et al. [10] analyzed indoor temperature trends during heatwaves in Vancouver and Ottawa, Canada, in 2022 and highlighted that housing characteristics increase the risk of overheating. However, while these studies qualitatively analyzed the impact of housing characteristics (e.g., type of building materials, building age) on indoor thermal environments, they did not quantitatively analyze the impact of building thermal insulation performance on changes in indoor thermal environments.

Thermal insulation standards for residential buildings in Korea were first established in December 1980 under the “Enforcement Rules of the Building Act” [11]. These initial regulations required that all exterior walls, as well as the ceilings or roofs of top-floor rooms in buildings, achieve a minimum thermal transmittance (U-value) of 0.58 W/m²·K. In 1984, the standards were refined by distinguishing between the mainland and Jeju (Korea’s southernmost island) regions, setting U-values at 0.58 W/m²·K for the mainland and 1.16 W/m²·K for Jeju [12]. By 1987, the mainland was further divided into two regions (Central and Southern), with U-values set at 0.58 W/m²·K for the Central region and 0.76 W/m²·K for the Southern region, while the Jeju standard remained at 1.16 W/m²·K [13]. During this period, insulation materials such as mineral wool, glass wool, and flame-retardant expanded polystyrene foam were commonly used.

In the 2000s, the focus shifted to continuous insulation techniques, emphasizing the reduction in thermal bridging and the improvement of airtightness. In 2001, the standards were further strengthened, specifying U-values of 0.47 W/m²·K for the Central region, 0.58 W/m²·K for the Southern region, and 0.76 W/m²·K for Jeju [14]. To comply with these standards, materials such as extruded polystyrene (XPS), rigid polyurethane (PU), and expanded polystyrene (EPS) boards became widely used.

From the 2010s onward, the introduction of high-performance building standards, including zero-energy and passive house standards, led to even stricter thermal insulation requirements. In 2010, U-values for exterior walls were reduced to 0.36 W/m²·K for the Central region, 0.45 W/m²·K for the Southern region, and 0.58 W/m²·K for Jeju [15], and these values were incorporated into the “Energy Saving Design Standards for Buildings”. Revisions in 2016 and 2018 further subdivided the Central region and updated U-values based on evolving regional climatic conditions and building types, such as apartments and detached houses [16,17]. While conventional insulation materials like XPS, PU, and EPS remained in use, advanced insulation materials such as aerogels and vacuum insulation panels (VIPs) were introduced. Innovations like air barrier layers, moisture control layers, and continuous insulation systems also helped to maximize thermal performance.

Korea’s thermal insulation performance standards have evolved significantly over time, reflecting technological advancements in insulation materials and construction techniques. Understanding the impact of these developments on indoor thermal environments and their contribution to heat-related illnesses during heatwaves is essential.

Indoor heat-related illnesses are influenced by the extent of exposure to temperatures exceeding the critical threshold. Quinn et al. [18] measured indoor temperature and humidity in 285 low- to middle-income households in New York City during the summer of 2006 and through a regression analysis and numerical modeling confirmed that many households could be exposed to hazardous indoor thermal environments during heatwaves. Farahani et al. [19] evaluated indoor overheating in over 6000 apartments in Helsinki, Finland, during a typical summer in 2020 and a hot summer in 2021, setting the critical temperature threshold at 27 °C. They found that 96% of the apartments exceeded this threshold temperature in 2021, showing that indoor spaces could be highly vulnerable to heatwaves.

Ahrentzen et al. [20], Kenny et al. [21], and Beckmann et al. [22] analyzed the impact of indoor thermal environments on human health, suggesting that temperatures exceeding the critical threshold of 27 °C could be particularly harmful to vulnerable groups. Kenny [23] further highlighted that heat-sensitive populations should avoid continuous exposure to temperatures above 31 °C. These studies discuss the relationship between indoor thermal environments and heat-related illnesses during heatwaves using indirect evidence, such as the physiological burden caused by exceeding the critical threshold and residents’ subjective evaluations of heat stress. However, they did not clearly establish a direct relationship between exposure to temperatures exceeding the indoor critical threshold and the occurrence of indoor heat-related illnesses.

The objective of this study was to clarify the architectural impact on heat-related illnesses during heatwaves, particularly regarding thermal insulation performance standards, which had not been clearly addressed in previous research. To achieve this, thermal insulation performance standards for Korean buildings were collected, and indoor temperatures in two representative climate regions were measured to quantitatively analyze the impact of insulation on indoor thermal environments during heatwaves. The ratio of indoor to outdoor temperatures in these regions was also evaluated, and the average probability of exceeding the indoor critical temperature across administrative districts (i.e., provinces and metropolitan cities) in Korea was simulated. Subsequently, the correlation between the number of indoor heat-related illness cases in each district and the simulated results was analyzed. Through these analyses, the impact of residential building insulation on indoor thermal environments and heat-related illnesses during heatwaves was identified, and policy directions to reduce such illnesses were discussed.

2. Materials and Methods

2.1. Research Procedure

This study evaluated the impact of thermal insulation performance on indoor thermal environments and the occurrence of heat-related illnesses during heatwaves in Korea. The research flowchart is shown in Figure 1.

First, the national thermal insulation performance standards for Korean buildings were analyzed. Statistical data on building types and construction years for major administrative regions (i.e., provinces and metropolitan cities) were collected from open databases provided by the Ministry of Land, Infrastructure, and Transport and Statistics Korea. These data were used to determine the average thermal insulation performance of buildings in these regions.

Next, one city from each of two climate regions, Chuncheon and Gwangju, was selected. Indoor temperatures in buildings located in these cities were measured during the heatwave period in 2022. Outdoor temperature data for the same period were obtained from the Korea Meteorological Administration database. Using the collected data on building thermal insulation performance and indoor/outdoor temperature measurements, key factors related to thermal insulation performance—such as construction year, building type, and climate region—were quantitatively analyzed to assess their impact on indoor thermal environments during heatwaves. Additionally, the impact of air conditioning usage during heatwaves was analyzed by correlating daily air conditioning usage time with daily average indoor temperatures.

Furthermore, based on the modeled probability distribution of the indoor-to-outdoor temperature ratio derived from data collected in the two representative regions, the average probability of exceeding the indoor critical temperature threshold in buildings located in major administrative regions was simulated under a heatwave scenario. Lastly, the simulated results were compared with the number of heat-related illness cases collected by the Korea Disease Control and Prevention Agency from 2017 to 2023. This comparison was used to analyze and interpret the impact of regional thermal insulation performance standards on indoor temperature increases and the occurrence of indoor heat-related illnesses

The data collection, validation, and analysis methods used at each stage are detailed in Section 2.2, Section 2.3, Section 2.4 and Section 2.5.

2.2. Data Collection and Processing

The methods of data collection and processing for thermal insulation performance and heat-related illness data, as well as temperature measurements in and out of buildings within the representative cities, are described below.

2.2.1. Thermal Insulation Performance of Residential Buildings

Figure 2 illustrates the three climate regions (Central, Southern, and Jeju), along with the corresponding provinces and metropolitan cities, as defined in the 2015 “Energy Saving Design Standards for Buildings” [24].

Table 2. Averaged minimum U-values of exterior walls by climate region, building type, and construction year (calculated based on references [11,12,13,14,15,16,17]).

Climate Region	Residential Building Type	Construction Year	Averaged Minimum U-Value (W/m2·K)
Central Region	Apartment	Post-2010	0.24
		2000–2010	0.47
		Pre-2000	0.58
	Detached House	Post-2010	0.27
		2000–2010	0.47
		Pre-2000	0.58
Southern Region	Apartment	Post-2010	0.30
		2000–2010	0.58
		Pre-2000	0.76
	Detached House	Post-2010	0.33
		2000–2010	0.58
		Pre-2000	0.76
Jeju Region	Apartment	Post-2010	0.40
		2000–2010	0.76
		Pre-2000	1.16
	Detached House	Post-2010	0.44
		2000–2010	0.76
		Pre-2000	1.16

summarizes the averaged minimum U-values of exterior walls for residential buildings, categorized by climate region, construction year, and residential building type (i.e., apartments and detached houses), reflecting the revisions over the years [11,12,13,14,15,16,17]. These U-values are critical because exterior walls serve as the primary barrier against external heat, especially during heatwaves. Therefore, the thermal insulation standards for exterior walls have been applied as representative indicators of the overall thermal insulation performance of the buildings in this study. As shown in the table, thermal insulation performance standards have been strengthened in more recent years and in regions further north.

Korea is administratively divided into 9 provinces and 7 metropolitan cities, as shown in Figure 2.

Table 3. Average U-values of exterior walls in residential buildings by administrative district (provinces and metropolitan cities), calculated based on Table 2 and reference [25].

Provinces/Metropolitan Cities	Number of Residential Buildings	Averaged U-Value (W/m2·K)
Seoul Metropolitan City	3,083,922	0.45
Busan Metropolitan City	1,295,159	0.59
Daegu Metropolitan City	824,717	0.60
Incheon Metropolitan City	1,079,136	0.45
Gwangju Metropolitan City	549,449	0.59
Daejeon Metropolitan City	505,112	0.61
Ulsan Metropolitan City	393,469	0.59
Gyeonggi Province	4,690,911	0.42
Gangwon Province	653,937	0.47
Chungcheongbuk Province	649,270	0.46
Chungcheongnam Province	883,608	0.57
Jeollabuk Province	744,521	0.62
Jeollanam Province	810,975	0.62
Gyeongsangbuk Province	1,089,355	0.61
Gyeongsangnam Province	1,294,435	0.59
Jeju Province	246,651	0.77

presents the average thermal insulation performance of exterior walls in residential buildings across these administrative regions. These averages were calculated by weighting the minimum U-values from Table 2 according to the number of residential buildings of different types and construction years in each region, as provided by the Statistics Korea database [25], and then averaging these values across the total number of buildings in the respective year. In Table 3, the blue, yellow, and red shadings correspond to the climate regions shown in Figure 2, representing the Central, Southern, and Jeju regions, respectively. The average U-values of exterior walls in residential buildings across provinces and metropolitan cities range from 0.42 to 0.47 W/m²·K in the Central region, 0.57 to 0.62 W/m²·K in the Southern region, and 0.77 W/m²·K in the Jeju region.

The thermal insulation performance of exterior walls by climate region, building type, and construction year, as shown in Table 2, was used to analyze the impact of insulation on indoor thermal environments during heatwaves. The average thermal insulation performance by province and metropolitan city, as shown in Table 3, along with the information in Table 2, was employed to simulate the probability of exceeding the indoor critical temperature threshold across administrative regions.

2.2.2. Indoor and Outdoor Temperatures in Representative Residential Buildings

• Representative Regions

This study assessed indoor temperatures in residential buildings during the summer of 2022 to evaluate the impact of thermal insulation performance on indoor thermal environments and the likelihood of exceeding indoor threshold temperatures. The experimental work focused on the Central and Southern regions, where the majority of Korea’s residential buildings are concentrated. Chuncheon (indicated by the black circle in Figure 2) and Gwangju (indicated by the red circle in Figure 2) were selected as representative study sites for these two major climate regions. For the Jeju region, indoor temperature was not measured directly due to the small population of residential buildings. However, the impact of thermal insulation performance on exceeding the indoor threshold temperature for Jeju was generalized based on simulation results derived from findings in Chuncheon and Gwangju.

Chuncheon, located in the inland area of Gangwon Province, has experienced an average minimum winter temperature of −7.6 °C (December to January, 2014–2023), while Gwangju, in the southwestern part of Korea, had an average minimum winter temperature of −1.5 °C. These winter temperature differences form the basis of the Energy Saving Design Standards for Buildings, which classify Chuncheon in the Central climate region and Gwangju in the Southern climate region, each with distinct thermal insulation requirements. During the summer months (July to August, 2014–2023), Chuncheon had an average maximum temperature of 30.3 °C with 80% humidity, while Gwangju recorded an average maximum temperature of 30.9 °C with 84% humidity, indicating relatively similar summer climate conditions. These two cities provide ideal conditions for studying the impact of different winter insulation standards on indoor thermal environments during heatwaves, as they effectively represent the Central and Southern climate regions of Korea.

• Residential Buildings for Indoor Thermal Environment Experiments

To clarify the impact of climate region, building type, and construction year—factors influencing the thermal insulation performance of building exteriors—on indoor thermal environments during heatwaves, a preliminary survey was conducted on 110 residential buildings in the study area using Google Forms in March–May 2022. The survey consisted of the following five sections: (A) household Information: location, number of members, and ages of household members; (B) characteristics of the building: type, structural form, and construction year; (C) housing environment: floor area, number of floors, existence of a rooftop, building orientation, window size, and availability of air conditioning; (D) lifestyle habits: frequency of cooking, window opening times, and air conditioning usage; and (E) willingness to participate in the indoor thermal environment measurement experiments.

Based on the responses, participants were selected by considering the diversity of responses in sections A through D and those who expressed willingness to participate in section E. Ultimately, 46 households from Chuncheon and Gwangju participated in the study from June to August 2022.

For the indoor temperature measurement experiment, 30 residential buildings (18 apartments and 12 detached houses) in Chuncheon and 16 residential buildings (all apartments) in Gwangju were selected. Figure 3 shows the locations and representative images of the buildings used in the experiment, while

Table 4. Information on buildings used for indoor temperature measurement.

City	Residential Building Type	Construction Year	Index	Structural Form	Floor Area (m2)
Chuncheon	Apartment	Post-2010	CA11	RC	66–90
			CA12	RC	99+
			CA13	RC	99+
			CA14	RC	99+
			CA15	RC	99+
			CA16	RC	99+
			CA17	RC	33–66
			CA18	RC	99+
			CA19	RC	99+
		2000–2010	CA21	RC	66–99
			CA22	RC	99+
			CA23	RC	99+
			CA24	RC	99+
		Pre-2000	CA31	RC	33–66
			CA32	RC	99+
			CA33	RC	99+
			CA34	RC	33–66
			CA35	RC	66–99
	Detached House	Post-2010	CD11	Wooden	99+
			CD12	Wooden	99+
			CD13	Masonry	≤33
			CD14	RC	≤33
			CD15	RC	≤33
			CD16	RC	≤33
		2000–2010	CD21	Masonry	≤33
			CD22	RC	≤33
			CD23	Masonry	33–66
			CD24	Masonry	≤33
		Pre-2000	CD31	Wooden	33–66
			CD32	Wooden	66–99
Gwangju	Apartment	Post-2010	GA11	RC	33–66
			GA12	RC	66–99
			GA13	RC	99+
		2000–2010	GA21	RC	99+
			GA22	RC	66–99
			GA23	RC	99+
			GA24	RC	99+
			GA25	RC	99+
			GA26	RC	99+
		Pre-2000	GA31	RC	66–99
			GA32	RC	66–99
			GA33	RC	99+
			GA34	RC	99+
			GA35	RC	99+
			GA36	RC	≤33
			GA37	RC	99+

presents details on the construction year, structural form, and floor area of the measured residential buildings in each city.

In Chuncheon, the distribution of apartment buildings by construction year includes 9 buildings constructed after 2010, 4 buildings constructed between 2000 and 2010, and 5 buildings constructed before 2000. For detached houses, 6 buildings were constructed after 2010, 4 between 2000 and 2010, and 2 before 2000. The primary structural form of the apartment buildings is reinforced concrete (RC), while the detached houses are constructed using wooden, masonry, and RC structures. In Gwangju, the apartment buildings consist of 3 buildings constructed after 2010, 6 between 2000 and 2010, and 7 before 2000, all of which are RC structures.

• Indoor Temperature Data During Heatwaves

Indoor temperature measurements were conducted under conditions that allowed for the unrestricted control of indoor temperature and ventilation, including the use of air conditioning, shading, and window opening/closing. This approach was designed to capture typical temperature variations in the primary living spaces of residential buildings during heatwaves. Temperature data loggers were placed at a height of at least 80 cm from the floor and at least 50 cm away from windows in locations not directly exposed to sunlight within living rooms or bedrooms. The indoor temperature was continuously measured at 10 min intervals during the observation period. The Tzone Digital Technology, Co., Ltd. (Shenzhen, China). TempU 03 data logger shown in Figure 4 was used for temperature measurement. As specified in

Table 5. Specifications of the TempU 03 temperature data logger.

Classification	Specifications
Scale	°C or °F
Range	−30 °C~+60 °C
Accuracy	±0.5 °C (−20 °C~+40 °C)
Resolution	0.1 °C
Data Storage Capacity	32,000 Readings
Report Generation	PDF/CSV file
Dimensions/Weight	89 mm × 36 mm × 16 mm/25 g

, this device can measure temperatures within a range of −20 °C to +40 °C, with an accuracy of ±0.5 °C and a resolution of 0.1 °C.

• Outdoor Temperature Data During Heatwaves

The outdoor temperature data for the selected cities were collected through the Korea Meteorological Administration’s Open MET Data Portal [26]. The data were obtained from the Chuncheon Synoptic Meteorological Observation Station ASOS 101 and Gwangju ASOS 156, which provided minute-by-minute outdoor temperature readings, with accuracy verified according to the standards set by the Korea Meteorological Administration.

These data were used to analyze the relationship between outdoor and indoor temperature variations during heatwaves.

2.2.3. Regional Indoor Heat-Related Illness Statistics

The Korea Disease Control and Prevention Agency operates a heat-related illness emergency room surveillance system in collaboration with over 500 emergency room medical institutions across the country, working with local health centers and city/provincial disease control agencies. This system monitors the health impacts of heatwaves by tracking heat-related illnesses in emergency room patients.

Table 6. Average annual heat-related illness cases by province and metropolitan city: total, indoor, and percentage of indoor cases per million population (2017–2023).

Provinces/ Metropolitan Cities	Heat-Related Illness Patients Visiting the Emergency Room
	Number of Total Heat-Related Illness Cases (per Million Population)	Number of Indoor Heat-Related Illness Cases (per Million Population)	Percentage of Indoor/Total Heat-Related Illness Cases (%)
Seoul Metropolitan City	20.3	6.5	32.1
Busan Metropolitan City	27.3	7.0	25.5
Daegu Metropolitan City	19.6	5.1	26.1
Incheon Metropolitan City	33.8	10.1	29.9
Gwangju Metropolitan City	41.7	7.3	17.6
Daejeon Metropolitan City	26.6	5.3	19.8
Ulsan Metropolitan City	39.1	10.3	26.2
Gyeonggi Province	29.7	8.0	26.8
Gangwon Province	62.1	9.5	15.4
Chungcheongbuk Province	67.8	12.1	17.8
Chungcheongnam Province	61.0	15.5	25.5
Jeollabuk Province	66.5	13.6	20.5
Jeollanam Province	100.1	15.9	15.9
Gyeongsangbuk Province	64.0	13.8	21.5
Gyeongsangnam Province	61.9	15.9	25.7
Jeju Province	105.8	22.5	21.2

presents the average annual number of total and indoor heat-related illness cases per million and the percentage of indoor cases among total heat-related cases, as provided by the Korea Disease Control and Prevention Agency [3,27,28,29,30,31,32] from 2017 to 2023.

In Table 6, the blue, yellow, and red shadings represent the provinces and metropolitan cities corresponding to the Central, Southern, and Jeju climate regions, respectively.

The number of indoor heat-related illness cases per million population ranges from 6.5 to 12.1 (average 9.23) in the Central region, 5.1 to 15.9 (average 11.0) in the Southern region, and 22.5 in Jeju. The percentage of indoor cases among total heat-related illness cases varies by region, ranging from 15.4% to 32.1%. In Seoul, of the 20.3 heat-related illness cases per million, 6.5 occur indoors, resulting in an indoor rate of 32.1%, the highest in the country. Conversely, in Gangwon Province, of the 62.1 total cases per million, 9.5 occur indoors, leading to the lowest indoor rate of 15.4%.

The average annual number of indoor heat-related illness cases per million population by region, summarized in Table 6 for the years 2017–2023, was used in conjunction with the simulated probability of exceeding the indoor critical temperature threshold based on the average thermal insulation performance of buildings by region (Table 3) to analyze the correlation between indoor heat-related illness occurrence and thermal insulation performance.

2.3. Method for Analyzing Impact of Thermal Insulation Performance and Air Conditioning Usage on Indoor Thermal Environments During Heatwaves

To analyze the impact of construction year, building type, and climate region—factors that influence the thermal transmittance (U-value) of building exteriors—and the usage of air conditioning on indoor thermal environments during heatwaves, heatwave advisories and warnings issued in Chuncheon and Gwangju from June to August 2022 were investigated. The analysis period was established based on these findings.

The Korea Meteorological Administration issues heatwave advisories when the perceived temperature reaches 33 °C or when rapid increases in perceived temperature, prolonged heatwaves, or other factors are expected to cause significant damage. A heatwave warning is issued when the perceived temperature is expected to remain above 35 °C for more than two consecutive days or when rapid increases in perceived temperature or prolonged heatwaves are expected to cause significant damage over a wide area. In this study, the status of heatwave advisories and warnings in the two cities was identified by reviewing the “Daily Situation Report” provided by the Disaster Safety Data Sharing Platform of the Ministry of the Interior and Safety [33], as well as related articles and news reports.

Table 7. Status of heatwave advisories and warnings (Chuncheon, Gwangju, June–August, 2022).

Month	City	Day
		1	2	3	4	5	6	7	8	9	11	12	13	19	20	21	22	25	26	27	28	29	30	31
June	Chuncheon
	Gwangju
July	Chuncheon
	Gwangju
August	Chuncheon
	Gwangju
				Heat products									Heatwave watch							Heatwave warning

summarizes the heatwave advisories and warnings issued from June to August 2022.

• Analyzing Impact of Thermal Insulation Performance on Indoor Thermal Environments during Heatwaves

To assess the impact of thermal insulation performance on indoor thermal environments, the analysis focused on heatwave warning periods lasting at least two consecutive days. The study concentrated on two periods, namely 3–4 July and 4–6 August 2022 (marked by the bold black lines in Table 7). The average, maximum, and minimum outdoor temperatures for these heatwave periods in Chuncheon and Gwangju are presented in

Table 8. Statistics of outdoor temperatures during heatwave warning periods (Chuncheon, Gwangju, June–August, 2022).

Weather Observation Stations	Heatwave Warning Period
	3–4 July			4–6 August
	Average Temp. (°C)	Maximum Temp. (°C)	Minimum Temp. (°C)	Average Temp. (°C)	Maximum Temp. (°C)	Minimum Temp. (°C)
Chuncheon (ASOS 101)	27.96	34.01	24.19	28.60	33.18	24.94
Gwangju (ASOS 156)	28.34	34.33	25.17	29.25	33.96	26.30

.

Indoor temperature data collected during these heatwave periods were grouped by factors influencing thermal transmittance (U-value), such as construction year, building type (apartment, detached house), and climate region. Ensemble averaging for each of the groups was applied to minimize the impact of factors not directly related to the U-value—such as building orientation, solar exposure, temperature control capacity, and occupant activity—on indoor temperature variability. This approach ensured that the observed effects predominantly reflected thermal insulation performance.

• Analyzing Impact of Air Conditioning Usage on Indoor Thermal Environments during Heatwaves

To understand how active temperature control systems, particularly air conditioning, mitigate indoor thermal environments during heatwaves, the correlation between air conditioner usage time and indoor temperatures were analyzed. This analysis focused on 12 pre-2000 residential buildings in Chuncheon and Gwangju, which represent older, heat-vulnerable houses. The daily air conditioning usage time and corresponding daily average indoor temperatures were estimated based on time series data collected during heatwave warning days in July and August 2022. A regression analysis was performed to assess the impact of air conditioning on indoor temperatures during these heatwave periods.

While the influence of active cooling systems was thoroughly examined, other dynamic factors such as passive temperature controls (e.g., window openings, shading adjustments) and the number and activity levels of occupants were not included in the analysis. These variables were excluded due to limitations in measurement and observation, falling outside the scope of this study.

2.4. Methodology for Assessing Indoor Thermal Exposures During Heatwaves

To quantitatively assess the impact of indoor thermal environments on the occurrence of heat-related illnesses during heatwaves, it is necessary to analyze how often the indoor temperature exceeds a critical threshold. In this study, a threshold temperature associated with heat-related illnesses was established based on the literature reviews, and a method was developed to numerically simulate the probability of indoor temperatures exceeding this threshold, considering the thermal insulation performance of buildings during heatwaves. This approach was used to evaluate the average level of thermal exposure in buildings across provinces and metropolitan cities based on their average exterior wall thermal insulation performance (as shown in Table 3). The indoor threshold temperature and the method for simulating the probability of exceeding this threshold are described in the following subsections.

2.4.1. Establishing Indoor Threshold Temperature for Vulnerable Populations During Heatwaves

Table 9. Summary of the literature on indoor threshold temperatures affecting heat-vulnerable populations.

Category	The Literature	Indoor Threshold Temp.
Indoor Temperature Affecting Heat-Vulnerable Populations	Reducing the number of days when the indoor temperature exceeds 27 °C improves health, quality of life, reduces emotional distress, and increases sleep duration [20]. Chronic disease patients experience heat stress when the indoor temperature exceeds 27 °C [22]. Prolonged exposure to indoor temperatures above 27 °C can pose health risks to some adults [23]. When the indoor temperature exceeds 27 °C, elderly people should take preventive measures to avoid heat-related illnesses [34]. The ASHRAE 55 standard specifies 26 °C as the threshold for safe indoor temperatures [35]. The NHS (National Health Service) recommends maintaining indoor temperatures below 26 °C in shelters for heat-vulnerable populations [36]. New York City proposed limiting indoor temperatures in rental housing to 26 °C during extreme heatwaves [37].	26–27 °C

provides a summary of key studies, standards, and recommendations related to indoor temperatures affecting heat-vulnerable populations. Several studies, including those by Ahrentzen et al. [20], Beckmann et al. [22], and Kenny [23], have reported that indoor temperatures exceeding 27 °C can adversely affect the health and quality of life of elderly individuals and those with chronic conditions. Additionally, the Health in Aging Foundation [34] advises that elderly people take preventive actions when indoor temperatures surpass 27 °C to reduce the risk of heat-related illnesses.

On the other hand, standards like those from ASHRAE [35] and the NHS [36] establish the threshold for safe indoor temperatures at 26 °C or below. While ASHRAE’s guidelines are designed to ensure general indoor thermal comfort, the NHS focuses on protecting heat-vulnerable populations by maintaining safe indoor environments, particularly in places like shelters or care facilities. In these settings, keeping temperatures lower can help prevent heat-related health risks.

Given the range of indoor temperature thresholds from various sources, this research has chosen 27 °C as the critical indoor temperature threshold for evaluating the probability of heat-related illnesses during heatwaves. The decision to use 27 °C is based on studies that highlight this temperature as the point at which negative health outcomes are more likely to occur, particularly in the context of heatwaves.

2.4.2. Method for Simulating Impact of Thermal Insulation Performance on Exceeding Indoor Threshold Temperature

A simulation model was developed to quantitatively evaluate the impact of building thermal insulation performance on the probability of exceeding the indoor threshold temperature (27 °C) during heatwaves. The simulation was conducted using Matlab R2021b, based on measured data, and employed statistical and optimization toolboxes to derive and analyze the probability distributions of the indoor-to-outdoor temperature ratio.

• Data Collection and Calculation of the Indoor-to-Outdoor Temperature Ratio

Indoor and outdoor temperature data were collected from 46 residential buildings in Chuncheon and Gwangju during periods when the outdoor maximum temperature exceeded 27 °C and air conditioning was not in use. The indoor-to-outdoor temperature ratios (indoor temperature/outdoor temperature) were calculated for each time interval, and for further analysis, the temperature ratio values were classified according to the average thermal transmittance values of the buildings used for measurement, which ranged from 0.24 W/m²·K to 0.76 W/m²·K, as shown in Table 2.

• Derivation and Analysis of Probability Distribution Functions

Probability distribution functions were derived based on the calculated indoor-to-outdoor temperature ratios for each time interval. The analysis revealed that the distribution of indoor-to-outdoor temperature ratios was best fitted by a normal distribution (Equation (1)) during the morning and afternoon and a Weibull distribution (Equation (2)) during the late evening and early morning. Examples of these probability distributions for a thermal transmittance of 0.76 W/m²·K are presented in Figure 5.

$$\mathrm{Normal} \mathrm{Distribution}: \mathrm{f}\left(\mathrm{x}\right)={\displaystyle \frac{1}{ \mathsf{\sigma }\sqrt{2\mathsf{\pi }}}}{\mathrm{e}}^{{\left({\displaystyle \frac{\mathrm{x}-\mathsf{\mu }}{{2\mathsf{\sigma }}^2}}\right)}^2}$$

(1)

$$\mathrm{Weibull} \mathrm{Distribution}: \mathrm{f}\left(\mathrm{x}\right)={\displaystyle \frac{\mathrm{k}}{\mathsf{\lambda }}}{\left({\displaystyle \frac{\mathrm{x}}{\mathsf{\lambda }}}\right)}^{\mathrm{k}-1}{\mathrm{e}}^{-{\left({\displaystyle \frac{\mathrm{x}}{\mathsf{\lambda }}}\right)}^{\mathrm{k}}}$$

(2)

where x represents the indoor-to-outdoor temperature ratio, μ is the mean, σ is the standard deviation, and k and λ are the shape and scale parameters of the Weibull distribution, respectively.

• Heatwave Condition for Simulation

The simulation conditions were established using outdoor temperature data measured at Gwangju ASOS 156 during a heatwave from 20 to 22 June 2022. These data were used to apply the derived probability distributions and simulate the time series of the probability of exceeding the indoor threshold temperature, as illustrated in Figure 6. The simulation accounted for variability in the indoor-to-outdoor temperature ratio across different times of day within a building and the uncertainty in indoor temperatures among buildings with the same thermal insulation performance.

• Calculation of Indoor Temperature Probability Distribution and Exceedance Probability

Using the time series of exceedance probabilities for each time period obtained through the simulation, the daily average probability of exceeding the threshold temperature was calculated. Additionally, for cases such as pre-2000 residential buildings in Jeju with a thermal transmittance of 1.16 W/m²·K, which fall outside the simulation range, the exceedance probability was estimated using extrapolation.

Based on the simulation and extrapolation results for the probability of exceeding the indoor threshold temperature across a range of thermal transmittance values (0.24 W/m²·K to 1.16 W/m²·K), the probabilities corresponding to the average thermal transmittance of exterior walls in buildings within each administrative region (as shown in Table 3) were estimated. These estimates were then applied in the correlation analysis with regional heat-related illness occurrences shown in Table 6.

• Model Validation and Limitations

To verify the reliability of the model, the simulated probability of individual buildings exceeding an indoor temperature of 27 °C was compared with the actual measured probability of exceedance. The relative error between the simulation results and the measured data was found to be within 3%, indicating that the model accurately predicts the average probability of exceeding the indoor threshold temperature. However, the simulation model used in this study was designed to predict average exceedance probabilities and therefore does not fully capture the finer temporal variations within the divided time intervals and the nonlinear interactions between indoor and outdoor temperatures.

2.5. Method for Correlation Analysis Between Probability of Exceeding Indoor Threshold Temperature and Occurrence of Indoor Heat-Related Illnesses

A correlation analysis between the probability of exceeding the indoor threshold temperature of 27 °C and the occurrence of indoor heat-related illnesses was conducted by reviewing all possible combinations between regions, as shown in Figure 7, to identify areas with high and low correlations. Post-identifying the region with the highest correlation coefficient, a further correlation analysis was conducted on the remaining regions to examine the differences in correlation between the region with the highest coefficient and other regions. Pearson correlation coefficients (r) were calculated for each combination of regions, providing a measure of the strength and direction of the linear relationships. Additionally, p-values were also calculated to assess the significance of the correlations, and regions with statistically significant and high correlations between the probability of exceeding the indoor threshold temperature and the occurrence of indoor heat-related illnesses were identified.

3. Results

3.1. Impact of Thermal Insulation Performance on Indoor Thermal Environments During Heatwaves

Based on the regional minimum thermal conductivity standards for buildings in Korea (as shown in Table 2), it is observed that buildings in Southern regions and older buildings tend to have higher U-values. Detached houses built after 2010 also tend to have higher U-values compared to apartments. This suggests a greater potential for heat to penetrate indoor environments through the exterior walls under the same outdoor temperature conditions. Therefore, this study analyzes the impact of thermal insulation performance on indoor thermal environments during heatwaves, focusing on these factors.

• Impact of Construction Year

Table 10. Indoor temperature statistics and correlation with construction year during heatwaves for residential buildings in Chuncheon (July and August 2022).

Residential Building Type	Construction Year	U-Value (W/m2·K)	Heatwave Warning Period
			3–4 July 2022		4–6 August 2022
			Average Temp. (°C)	Maximum Temp. (°C)	Average Temp. (°C)	Maximum Temp. (°C)
Apartment	Post-2010	0.24	27.35	28.21	27.52	28.44
	2000–2010	0.47	27.32	30.10	27.64	28.97
	Pre-2000	0.58	29.29	30.42	28.60	30.12
	Correlation Coefficient (U-value, Indoor Temp.)		0.74	0.98	0.81	0.92
Detached House	Post-2010	0.27	27.97	29.22	28.44	29.68
	2000–2010	0.47	28.60	29.63	28.72	29.93
	Pre-2000	0.58	28.69	31.00	29.06	30.95
	Correlation Coefficient (U-value, Indoor Temp.)		0.97	0.89	0.98	0.88

provides the indoor temperature statistics for 30 residential buildings in Chuncheon during the heatwave warnings in July and August 2022. As shown in Table 10, indoor temperatures varied according to the construction year of the buildings. Except for the average indoor temperature in apartments during July, all building types exhibited a trend of higher average and maximum indoor temperatures as the construction year became older. Notably, both apartments and detached houses built before 2000, with an average U-value of 0.58 W/m²·K, recorded maximum indoor temperatures exceeding 30 °C, indicating that indoor temperatures are highly sensitive to thermal insulation performance.

Although the sample size includes only three construction year ranges, the correlation analysis revealed strong linear tendencies between thermal insulation performance and indoor temperatures, with correlation coefficients ranging from 0.81 to 0.98 for all building types, except for apartments in July. This analysis confirms that older buildings tend to have a lower thermal insulation performance, as initially designed, and that thermal insulation performance degrades over time [38,39], leading to increased indoor temperatures during heatwaves.

• Impact of Building Type

Table 11. Indoor temperature statistics by building type during heatwaves for residential buildings in Chuncheon (July and August 2022).

Residential Building Type	Construction Year	U-Value (W/m2·K)	Heatwave Warning Period
			3–4 July 2022		4–6 August 2022
			Average Temp. (°C)	Maximum Temp. (°C)	Average Temp. (°C)	Maximum Temp. (°C)
Apartment	Post-2010	0.24	27.35	28.21	27.52	28.44
Detached House		0.27	27.97	29.22	28.44	29.68
Temperature Difference (Detached House Temp.—Apartment Temp.) (°C)			0.62	1.01	0.92	1.24
Apartment	Pre-2010	0.53	28.31	30.26	28.12	29.55
Detached House		0.51	28.65	30.32	28.89	30.44
Temperature Difference (Detached House Temp.—Apartment Temp.) (°C)			0.34	0.05	0.77	0.89

presents the statistics for the ensemble-averaged time series of indoor temperatures by building type in Chuncheon. For buildings constructed after 2010, the indoor temperatures in detached houses, with an average U-value of 0.27 W/m²·K, were between 0.62 °C and 1.24 °C higher than those in apartments with an average U-value of 0.24 W/m²·K.

This suggests that differences in U-values significantly impact indoor temperatures. Conversely, in buildings constructed before 2010, despite the lower U-value of detached houses (0.51 W/m²·K) compared to apartments (0.53 W/m²·K), the average and maximum temperatures in detached houses were still between 0.05 °C and 0.89 °C higher. This result indicates that the higher indoor temperatures in detached houses compared to apartments in buildings constructed before 2010 may be attributed not only to U-values but also to the insulating effect of shared walls and floors between adjacent units [40,41]. The larger temperature difference (0.62 °C to 1.24 °C) between post-2010 apartments and detached houses compared to the temperature difference (0.05 °C to 0.89 °C) between pre-2010 apartments and detached houses is likely due to the combined effects of the U-values and the additional thermal insulation provided by neighboring units.

• Impact of Climate Region

Table 12. Comparison of indoor temperature statistics for apartments in Chuncheon (Central region) and Gwangju (Southern region) during heatwaves (July and August 2022).

Climate Region	Residential Building Type	U-Value (W/m2 K)	Heatwave Warning Period
			3–4 July 2022		4–6 August 2022
			Average Temp. (°C)	Maximum Temp. (°C)	Average Temp. (°C)	Maximum Temp. (°C)
Central Region (Chuncheon)	Apartment	0.38	27.88	29.04	27.84	28.68
Southern Region (Gwangju)		0.60	29.17	30.47	29.20	30.12
Indoor Temp. Difference (Gwangju Temp.—Chuncheon Temp.) (°C)			1.29	1.43	1.36	1.44
Outdoor Temp. Difference (Gwangju Temp.—Chuncheon Temp.) (°C)			0.38	0.32	0.65	0.78

presents the results of indoor temperature measurements for apartments in Chuncheon (Central region) and Gwangju (Southern region) during the same heatwave warning periods. As shown in Table 12, the indoor average and maximum temperatures in Gwangju, located in the Southern region, were between 1.29 °C and 1.44 °C higher than those in Chuncheon, located in the Central region, during the heatwave warnings in July and August. Although the outdoor maximum temperature difference between the two regions during the heatwave was less than 0.78 °C, the indoor temperature difference exceeded 1.29 °C. This discrepancy is likely due to the higher average U-value (0.60 W/m²·K) of the apartments in Gwangju compared to the average U-value (0.38 W/m²·K) of the apartments in Chuncheon.

3.2. Impact of Usage of Air Conditioning During Heatwaves and Its Impact on Indoor Temperatures

Figure 8 illustrates the relationship between daily air conditioning usage and daily average indoor temperatures, as recorded in 12 pre-2000 residential buildings in Chuncheon and Gwangju during heatwave warning periods in the summer of 2022. The analysis reveals a strong negative correlation (r = −0.72, p-value < 0.0001) between air conditioning usage time and indoor temperatures.

In homes without air conditioning, indoor temperatures ranged from 29.5 °C to 32.3 °C, with an average of 30.3 °C. This variability can be attributed to factors such as the building’s thermal insulation, structural design, internal heat sources, natural ventilation, and external environmental conditions. As air conditioning usage increased, a reduction in indoor temperatures was observed. However, temperature variability also increased due to these same building characteristics, external conditions, and differing air conditioning settings across homes.

As observed in Section 3.1, buildings with a lower thermal insulation, such as pre-2000 constructions, experience more pronounced temperature fluctuations compared to post-2010 well-insulated buildings. This suggests that although air conditioning mitigates heat exposure, poorly insulated buildings remain more susceptible to fluctuations due to limited cooling retention, highlighting the stabilizing role of thermal insulation.

The regression analysis in Figure 8 further supports these findings, showing that for every 100 min of air conditioning use, the daily average indoor temperature decreases by approximately 0.6 °C. This result demonstrates the effectiveness of prolonged air conditioning usage during heatwaves, especially in low-insulation houses. However, the persistence of temperature variability in these buildings underscores the role of thermal insulation in achieving more stable indoor conditions. Enhancing insulation in such buildings could reduce the need for extended air conditioning and improve cooling efficiency.

3.3. Probability of Exceeding Indoor Threshold Temperature During Heatwaves Based on Thermal Insulation Performance

Figure 9 illustrates the probability of exceeding the indoor threshold temperature of 27 °C in relation to the thermal insulation performance levels detailed in Table 2.

The “X” symbols represent the simulated probability values for exceeding the threshold temperature based on the modeled indoor-to-outdoor temperature ratios derived from measured indoor and outdoor temperature data. The black solid line represents the linear curve fitting applied to values of these “X” symbols, with interpolation and extrapolation used to extend the trend. As shown in Figure 9, buildings with a thermal transmittance of 0.24 W/m²·K have approximately a 49% probability of exceeding the 27 °C threshold under simulated heatwave conditions. This indicates that for an individual building, there is a 49% chance of exceeding the critical threshold or that on average, 49% of such buildings are expected to exceed this threshold. In contrast, buildings with a thermal transmittance of 0.77 W/m²·K have approximately a 76% probability of exceeding the critical threshold, representing a 55% higher likelihood compared to the lower transmittance scenario. These simulation results demonstrate that a building’s thermal transmittance significantly influences indoor temperatures and the likelihood of exceeding the threshold. Buildings with higher thermal transmittance values are more likely to exceed the threshold temperature during heatwaves, potentially increasing the risk of heat-related illnesses.

3.4. Impact of Indoor Temperatures on Heat-Related Illnesses During Heatwaves

3.4.1. Correlation Between Indoor Temperatures During Heatwaves and Indoor Heat-Related Illnesses

Figure 10 and

Table 13. Correlation coefficients and p-values between the average probability of exceeding the indoor threshold temperature and the number of indoor heat-related illnesses across regions.

Region	Correlation Coefficient (r)	p-Value
All Prov. and Metro. Cities	0.54	0.0323
Prov. and Metro. Cities with Highest Positive Correlation, Region A	0.93	<0.0001
Regions Excluding Region A, Region B	−0.70	0.1915
All Provinces	0.93	0.0002

illustrate the regression analysis results showing the correlation between the probability of exceeding the indoor threshold temperature of 27 °C, as simulated in Section 3.2, and the number of indoor heat-related illness cases across various regions (as detailed in Table 6). The upper horizontal axis in Figure 10 represents the averaged U-values of buildings, the lower horizontal axis shows the probability of exceeding the 27 °C indoor threshold, and the vertical axis indicates the number of heat-related illnesses per million population. The black line represents the regression analysis for all provinces and metropolitan cities, while the red line shows the results for regions with a strong positive correlation and the lowest p-value (Region A). The blue line represents the results for other regions (Region B), and the purple line depicts the results for all provinces. The blue, yellow, and red shadings correspond to the climate regions shown in Figure 2, representing the Central, Southern, and Jeju regions, respectively.

The analysis for all provinces and metropolitan cities reveals that the correlation coefficient between the probability of exceeding the 27 °C indoor threshold temperature and the number of indoor heat-related illness cases (black line) is 0.54, with a p-value of 0.0323. This correlation and p-value, which is less than the significance level of 0.05, indicate a statistically significant moderate relationship between the probability of exceeding the 27 °C indoor threshold and the number of heat-related illness cases across all regions. The correlation value aligns with findings from Hu et al. [42], who reported a similar correlation (r = 0.50) between building age—an indicator of thermal insulation performance—and heat-related illnesses in the United States. This alignment underscores the importance of building thermal insulation performance as a critical factor contributing to heat-related illnesses.

To further investigate this relationship, the analysis was broken down into different regions.

• Region with Highest Correlation and Lowest p-value, Region A

In regions with a strong positive correlation (Region A), which excludes major Southern metropolitan cities (Busan, Daegu, Daejeon, Gwangju, Ulsan), the correlation coefficient between the probability of exceeding the 27 °C threshold and the number of heat-related illness cases is 0.93, with a p-value of less than 0.0001 (red line). This very low p-value provides strong evidence that exceeding the 27 °C threshold significantly impacts the occurrence of heat-related illnesses in these regions, indicating a statistically significant and consistent relationship.

• Provincial Region

In provinces with small and medium-sized cities and rural regions, the regression analysis (purple line) revealed a strong and statistically significant relationship between the probability of indoor temperatures exceeding the threshold and the number of heat-related illness cases, with a correlation coefficient of 0.93 and a p-value of 0.0002.

As shown in the figure, the average U-values for buildings in the Central, Southern, and Jeju climate regions within these provinces are 0.45, 0.60, and 0.77, respectively. Correspondingly, the number of indoor heat-related illness cases in these regions is 9.85, 14.95, and 22.48 per million population. This demonstrates a clear trend; as the thermal insulation performance of buildings decreases (i.e., as U-values increase), the number of indoor heat-related illness cases increases in a consistent upward pattern. This monotonic increase highlights the direct relationship between lower thermal insulation performance and a higher incidence of heat-related illnesses.

Notably, regions with buildings that have an average U-value of approximately 0.45 W/m²·K exhibited around a 60% probability of exceeding the threshold, with about 9.85 heat-related illness cases per million. In contrast, regions with buildings having a higher U-value of approximately 0.75 W/m²·K show a 75% probability of exceeding the threshold, with approximately 20.15 heat-related illness cases per million population. This represents a 2.05-fold higher incidence of heat-related illnesses in regions with lower thermal insulation performance. This pattern mirrors findings from broader research, such as the 2003 European heatwave study, where the risk of death was 2.26 times higher in poorly insulated homes [5]. These results underscore the critical role of insulation in mitigating severe heat-related health outcomes.

• Southern Metropolitan Cities, Regions (Region B, Excluding Region A)

In the Southern metropolitan cities (Region B), a negative correlation was observed between the frequency of exceeding the 27 °C indoor threshold and the number of heat-related illness cases, with a correlation coefficient of −0.70 and a p-value of 0.1915 (blue line). Since the p-value is above the significance threshold of 0.05, this suggests that the relationship between indoor temperatures and heat-related illnesses in these regions is not statistically significant. This indicates that factors beyond indoor heat exposure—such as infrastructure, protective measures, sociodemographic, and socioeconomic factors—may play a more significant role in reducing heat-related illness cases in these metropolitan cities. Supporting this observation, the average number of heat-related illness cases in the Southern metropolitan cities is approximately 53% lower than in the Southern provinces.

A comparison between the metropolitan cities in the Central and Southern climate regions shows that the average U-values are 0.45 and 0.60, respectively, while the number of indoor heat-related illness cases is 8.31 and 6.99 per million population. Despite the lower thermal insulation performance (i.e., higher U-values) in the Southern metropolitan cities compared to those in the Central region, the number of indoor heat-related illness cases is slightly lower in the Southern region. This difference may be influenced by factors beyond indoor heat exposure; however, further investigation is required to fully understand these contributing factors.

The relatively small difference of 1.32 cases per million population in heat-related illness between the Central and Southern metropolitan cities contrasts with the larger difference of 5.10 cases per million population observed between provincial regions in the Central and Southern climate regions. In provincial areas, where less developed infrastructure and fewer protective measures are prevalent, the number of heat-related illnesses is notably higher in the Southern region. In contrast, metropolitan cities show a smaller difference between the Central and Southern regions, likely due to urban-specific factors such as better infrastructure, more widespread access to air conditioning, and other protective measures that mitigate the effects of extreme heat.

These findings suggest a complex interplay of factors influencing the incidence of heat-related illnesses across different regions. In Southern metropolitan cities, where the negative correlation was observed, further investigation is needed to identify the specific infrastructure, healthcare accessibility, and social factors that may mitigate the effects of high indoor temperatures. Moreover, the results underscore the need for region-specific strategies, such as strengthening thermal insulation performance standards, to improve indoor thermal environments during heatwaves and reduce the incidence of heat-related illnesses, particularly in areas with a strong positive correlation between indoor temperatures and health outcomes.

3.4.2. Mechanisms Linking Thermal Insulation Performance and Indoor Heat-Related Illnesses

The relationship between thermal insulation performance and heat-related illnesses can be understood through several key mechanisms, building on the results of Section 3.1, Section 3.2, Section 3.3, Section 3.4 and Section 3.4.1 and supported by the existing heat-related literature.

• Heat Retention and Thermal Insulation Efficiency

From the experiments conducted in Chuncheon and Gwangju in this study, thermal insulation was found to be crucial for regulating heat transfer between indoor and outdoor environments during heatwaves. Buildings with lower thermal insulation (indicated by higher U-values) allowed heat to infiltrate more easily, leading to rapid indoor temperature rises. This increased the likelihood of indoor temperatures exceeding the critical temperature threshold, such as the 27 °C threshold identified for heat-related illnesses, as shown in the simulation results. In contrast, buildings with higher thermal insulation (i.e., lower U-values) were more effective at minimizing heat infiltration, maintaining stable indoor temperatures, and reducing the risk of exceeding this critical temperature threshold. This mechanism plays a significant role in the occurrence of heat-related illnesses, such as heat exhaustion and heat stroke, especially in vulnerable populations like the elderly and those with chronic health conditions, as noted by Kenny et al. [21,23].

• Thermal Comfort and Physiological Stress

The experiments showed that houses with lower thermal insulation performance (e.g., pre-2000 houses in Chuncheon) not only experienced higher average indoor temperatures but also greater fluctuations, including higher maximum temperatures, compared to houses with better insulation (e.g., post-2010 houses in Chuncheon). These temperature fluctuations place significant stress on the body’s ability to regulate heat, triggering physiological responses such as an elevated heart rate, dehydration, and impaired thermoregulation. Studies have shown that such physiological stress is particularly harmful to heat-vulnerable populations [23,43]. Poor insulation exacerbates these risks by allowing indoor temperatures to fluctuate more widely beyond comfort levels, increasing the likelihood of heat-related illnesses. Adequate insulation helps mitigate these effects by maintaining more stable indoor temperatures within safer and more comfortable ranges, thus reducing physiological stress and the associated health risks.

• Ventilation and Humidity Control

Thermal insulation performance also affects ventilation and humidity control within buildings. Poorly insulated homes often rely on natural ventilation, which can be insufficient during heatwaves. In Korea, residents living in older homes with poor insulation have been reported to earn about one-fourth less income compared to those in better-insulated homes [44]. According to Shin et al. [45], household income is a key determinant of electricity consumption, particularly for cooling appliances like air conditioners. As a result, households with lower incomes and poor insulation often depend on natural ventilation rather than air conditioning to cool indoor spaces. However, during extreme heat events, opening windows to promote natural ventilation may allow more hot air and humidity to enter the home, worsening indoor conditions. High humidity further complicates the body’s ability to cool down via sweating, exacerbating heat stress [46]. Proper thermal insulation, combined with controlled ventilation systems, helps stabilize indoor temperature and humidity levels, offering a healthier and more stable environment during heatwaves.

4. Limitations and Discussion

4.1. Limitations

This study assessed indoor temperatures in residential buildings during the summer of 2022 to evaluate the impact of thermal insulation performance on indoor thermal environments and the likelihood of exceeding indoor threshold temperatures during heatwaves. While this study offers valuable insights, certain limitations should be kept in mind when interpreting the results.

• Geographic Scope and Selection of Study Sites

This experimental study focused on two cities, Chuncheon and Gwangju, representing the Central and Southern climate regions of Korea. These two climate regions were chosen based on the concentration of residential buildings in each area. Chuncheon and Gwangju were selected specifically due to their contrasting winter insulation standards and similar summer climates, making them ideal for examining how winter insulation standards impact indoor thermal environments during heatwaves.

However, since the study was limited to these two cities, the findings may not fully reflect variations in micro-climate conditions and thermal behaviors during heatwaves across other regions of Korea. Expanding the study to include a broader range of climate regions and more study sites in future research would enhance the generalizability and robustness of the results.

• Sample Size of Residential Buildings

A preliminary survey of 110 residential buildings in Chuncheon and Gwangju was conducted, with 46 households participating in indoor temperature measurements. Although the sample was designed to capture a range of building types, construction years, and structural characteristics, the relatively small sample size may limit the extent to which the findings represent indoor thermal environments across Korea’s broader housing stock. Future studies would benefit from increasing the sample size and including a wider variety of building types across different climate regions to provide more comprehensive insights.

• Validation of Simulation Model

While this study primarily relies on a simulation model to evaluate the impact of thermal insulation performance on indoor thermal exposures during heatwaves, it is acknowledged that the absence of field validation or comparative measurements from other cases may limit the precision of the findings. Due to logistical and resource constraints, field measurements in other regions or comparative case studies were not conducted as part of this research.

However, the simulations were carefully calibrated, achieving a less than 3% relative error between the simulated probability of exceeding the indoor temperature threshold of buildings and the actual measured probabilities. These simulations were based on temperature measurements from Chuncheon and Gwangju, two cities representing contrasting climate regions, each with region-specific minimum transmittance values defined within the thermal insulation standards. The simulation results aim to provide a robust foundation for assessing indoor thermal exposures during heatwaves across various regions of Korea.

While the results provide valuable insights, future studies could be further strengthened by incorporating field validation and comparative measurements from a broader range of locations, thereby enhancing the accuracy and generalizability of the simulation findings.

• Generalization to Jeju Climate Region

Due to the concentration of residential building stocks and logistical constraints, direct measurements of indoor temperatures in the Jeju region were not conducted. Instead, simulation results from Chuncheon and Gwangju were used to extrapolate the impact of thermal insulation performance on indoor environments in Jeju. While this method provides valuable estimates, future research that incorporates direct measurements from Jeju, given its unique climate characteristics, would strengthen the robustness and generalizability of the study’s findings.

4.2. Discussion

This study highlights the critical role of thermal insulation in mitigating indoor temperature risks and reducing heat-related illnesses during heatwaves. As Korea faces escalating climate challenges, including more frequent and intense heatwaves, there is a pressing need to reassess insulation standards and related policy measures. Findings suggest that improving insulation, particularly in regions with high heat exposure, could significantly reduce indoor temperature risks. Additionally, factors such as infrastructure development, socio-demographic differences, and heat illness protection methods are likely to influence health outcomes.

This effect is especially noticeable in Southern metropolitan areas, where despite similar indoor temperature exceedance probabilities compared to non-metropolitan areas, rates of heat-related illnesses remain notably lower. These findings suggest that region-specific approaches addressing not only thermal insulation performance but also broader factors—including socio-economic influences—could be beneficial in enhancing health outcomes during heatwaves. Metropolitan areas may benefit from indirect advantages, such as improved healthcare infrastructure, public awareness, and greater access to cooling systems, which help reduce heat-related illnesses despite high indoor heat exposure. In contrast, non-metropolitan regions might face greater risks due to limited infrastructure and fewer protective measures.

To further explore these observations, the following sections discuss region-specific policy pathways, necessary adjustments to insulation standards in light of climate projections, and the broader socio-economic and health implications associated with rising air conditioning costs, particularly for vulnerable populations.

• Region-Specific Policy Implementation Paths

Insulation plays a vital role in stabilizing indoor temperatures and reducing heat-related illnesses, particularly in provincial areas. Given the differences in indoor temperature exposure and the occurrence of heat-related illnesses across regions, it is essential to establish region-specific thermal insulation guidelines.

The process of setting regional thermal insulation standards typically begins by analyzing external temperature patterns and the duration of extreme weather conditions, followed by determining target indoor comfort temperatures. Simulations of the indoor–outdoor temperature differential help identify the optimal thermal insulation performance (U-value) to minimize heat transfer. Based on the findings of this study, factors such as heatwave duration and regional temperature averages can be used to set targets, such as the probability of exceeding indoor temperature thresholds. These simulations can guide the recalibration of thermal insulation standards to reduce the likelihood of indoor temperature exceedances and mitigate heat-related illnesses.

The Southern and Jeju provinces, where the probability of exceeding the indoor temperature threshold and the incidence of heat-related illnesses are higher, could serve as pilot regions for implementing region-specific thermal insulation policies. After evaluating the effectiveness of these policies, adjustments can be made before introducing nationwide standards.

To align these regional insulation adjustments with Korea’s broader energy efficiency goals, the zero-energy building (ZEB) framework [47] can be expanded to incorporate region-specific needs. Tailoring thermal insulation standards within the ZEB framework will enhance thermal performance, lower cooling demands, and help maintain stable indoor temperatures during heatwaves, particularly in regions more vulnerable to extreme heat.

Additionally, energy efficiency programs [48] that currently offer insulation and window upgrades for vulnerable populations in cities like Seoul and Yongin could benefit from nationwide expansion. Providing financial support—such as tax incentives, low-interest loans, or direct subsidies—would assist homeowners and building owners in making essential insulation improvements, encouraging broader participation and adoption.

By implementing these strategies, Korea may enhance resilience to heatwave risks by establishing thermal insulation standards that are both practical and effective.

• Adjusting Building Thermal Insulation Standards for Future Climate Trends

Projections from the Korea Meteorological Administration (KMA), based on IPCC climate change scenarios, indicate that by mid-century, average temperatures could rise by approximately 3 °C compared to the 1850–1900 baseline, with heatwave days becoming more frequent and affecting a wider range of regions. This anticipated increase highlights the urgent need for a comprehensive reassessment of building thermal insulation standards, which have traditionally prioritized winter energy efficiency.

While current standards focus on minimizing heat loss during cold winters, future adjustments should incorporate resilience for both winter and summer conditions, in line with the region-specific policies proposed earlier. Key recommendations include the following:

Dual-season thermal insulation criteria: establishing standards that address both winter heat retention and summer heat protection, ensuring materials and designs are optimized for extreme seasonal variations.

Lower U-value thresholds: recalibrating U-value thresholds in regions projected to experience significant temperature increases, as part of the region-specific policy implementation based on IPCC climate projections.

Additionally, the adoption of advanced materials such as vacuum insulation panels (VIPs) and phase-change materials (PCMs), combined with passive cooling strategies like reflective surfaces and enhanced natural ventilation, could significantly reduce heat infiltration and minimize dependence on mechanical cooling systems.

• Impact of Air Conditioning on Indoor Temperatures and Costs for Vulnerable Populations

The study shows that the prolonged use of air conditioning effectively reduces indoor temperatures in heat-vulnerable older houses during heatwaves, which can help lower the risk of heat-related illnesses. However, rising electricity costs, particularly during the summer months, pose a significant financial burden, especially for economically disadvantaged groups. In August 2024, the average household electricity bill in Korea increased by 13% [49] compared to the previous year, primarily due to the increased demand for cooling caused by heatwaves. Vulnerable groups, such as the elderly and low-income households, are especially affected, as many face substantial increases in their electricity bills under the progressive rate system.

To mitigate this burden, the government currently offers subsidies, including special summer rates for vulnerable populations. However, further targeted measures—such as differentiated electricity rates based on income and increased subsidies during periods of extreme heat—could better ensure that vulnerable populations can afford air conditioning without undue financial strain. Such initiatives would not only help reduce the health risks associated with heatwaves but also address the economic challenges posed by rising electricity costs.

5. Conclusions

This study comprehensively examined the impact of thermal insulation performance in residential buildings on indoor temperatures during heatwaves and its association with heat-related illnesses in South Korea. Through empirical data and simulation analyses, several key findings were identified.

First, significant differences in indoor thermal conditions during heatwaves were observed across construction years, building types, and climate regions. Older buildings, especially those constructed before 2000 in Southern regions, exhibited notably poorer thermal insulation performance. Detached houses demonstrated a lower insulation efficiency than apartments, primarily due to structural and insulation differences. A strong correlation (r = 0.74 to 0.98) was identified between U-values (thermal transmittance) and both average and peak indoor temperatures, emphasizing that lower thermal insulation significantly contributes to higher indoor temperatures during heatwaves.

Second, the role of air conditioning in managing indoor temperatures during heatwaves was examined. Even when unrestricted, buildings with lower thermal insulation maintained higher indoor temperatures compared to those with better insulation. In heat-vulnerable homes built before 2000, each 100 min of air conditioning reduced daily average temperatures by about 0.6 °C (r = −0.72, p-value < 0.0001). However, temperature variability also increased, likely due to factors such as thermal insulation performance, structural and environmental characteristics, and varied air conditioning settings across homes. These findings suggest that while extended air conditioning use effectively lowers indoor temperatures, enhancing insulation could reduce fluctuations, improve comfort, and lower reliance on prolonged cooling.

Third, a direct relationship was confirmed between exceeding the 27 °C indoor temperature threshold and the incidence of heat-related illnesses. Buildings with lower thermal insulation were more likely to surpass this threshold, raising the risk of heat-related illnesses. This correlation was particularly strong in non-metropolitan provinces, where a high correlation (r = 0.93, p-value = 0.0002) underscores that improving thermal insulation in these areas could be essential for mitigating health risks during extreme heat events.

Fourth, this study highlights the importance of implementing tailored thermal insulation strategies that account for regional heatwave impacts. In metropolitan areas, factors like infrastructure, protective measures, sociodemographic characteristics, and socioeconomic conditions appear to buffer the effects of thermal insulation performance on rates of heat-related illness. However, in non-metropolitan provincial areas with lower thermal insulation performance (e.g., U-value: 0.75 W/m²·K), the incidence of heat-related illnesses was 2.05 times higher than in areas with better insulation (e.g., U-value: 0.45 W/m²·K). These findings emphasize the necessity of adopting region-specific insulation standards, particularly in Southern provincial regions with lower thermal insulation performance, to reduce indoor heat exposure and mitigate heat-related illnesses.

Moreover, the study provides insights that could guide adjustments to regional building insulation standards to enhance public health protection during heatwaves. Future research may benefit from broadening the geographic scope to encompass a wider range of building types and climate regions. A field validation of simulated indoor thermal exposures, alongside an in-depth examination of factors beyond indoor heat exposure—such as infrastructure, protective measures, and sociodemographic and socioeconomic factors—could further illuminate effective strategies for protecting vulnerable populations. Additionally, exploring the performance of various insulation materials under diverse heat and humidity conditions could refine region-specific insulation strategies, supporting optimal material selection for particular climates and promoting both energy efficiency and public health.

In conclusion, improving thermal insulation performance in residential buildings, especially in non-metropolitan provincial regions, appears critical for reducing risks associated with extreme indoor temperatures during heatwaves. Establishing region-specific insulation standards may not only strengthen resilience to heatwaves but also significantly contribute to public health by lowering the incidence of heat-related illnesses.