Indoor Environmental Quality in Aged Housing and Its Impact on Residential Satisfaction Among Older Adults: A Case Study of Five Clusters in Sichuan, China

Abstract

Current research on aged housing prioritizes community planning and environmental enhancement over older adults’ needs, creating a retrofit mismatch amid population aging. To investigate the relationship between indoor environmental quality and residential satisfaction among elderly occupants, this study examines 72 households in aged residential buildings, analyzing four environmental indicators (thermal, lighting, acoustic environments, and air quality). The environmental measurements reveal that 81.9% of thermal environment parameters fall below the ASHRAE-55 comfort range, with winter average temperatures reaching only 13.94 °C. Insufficient illumination exists in kitchen and bedroom areas. Lifestyle patterns including infrequent air conditioning use (87%) and window ventilation substituting range hoods (32%) may deteriorate thermal comfort and air quality. An ordered logistic regression analysis demonstrates significant correlations between all four environmental indicators and elderly satisfaction levels. Thermal comfort emerges as the priority focus for aging-adapted retrofitting. Air quality improvement shows particularly significant potential for enhancing residential satisfaction. Although prolonged window opening (73%) exacerbates low-temperature/high-humidity conditions and noise exposure, it still contributes positively to overall satisfaction. This research provides crucial insights for aligning aged residential retrofitting with home-based elderly care requirements, promoting housing development that better accommodates the lifestyle patterns of older populations, thereby improving quality of life for aging-in-place residents.

1. Introduction

Demographic shifts pose unprecedented challenges, as evidenced by the World Social Report 2023 [1], which underscores the socioeconomic consequences of aging populations. Projections indicate that the population aged ≥65 years will surpass twice the current figure by 2050, with China anticipated to emerge as the nation with the most substantial elderly demographic globally during this period. These trends necessitate urgent policy reforms to address elderly service sustainability. Recent data indicate that nearly 90% of older adults prefer aging in place [2], with 64% of urban seniors residing in residential buildings over 20 years old [3]. These aged dwellings frequently exhibit environmental deficiencies, including irrational design, poor sanitary conditions, and inadequate thermal comfort [4]. While extensive research has addressed community renewal strategies and policy frameworks for aged housing [5], quantitative linkages between indoor environmental quality (IEQ) parameters and elderly satisfaction remain underexplored [6]. Existing Chinese studies exhibit critical methodological limitations: logical regression analyses, though applied to elderly satisfaction, largely omit IEQ variables—a significant oversight given the elderly’s prolonged indoor exposure [7]. Compounding this issue, prior work predominantly relies on perception-only surveys that lack empirical IEQ measurements [8], thereby conflating subjective biases with objective environmental conditions. This dual reliance on incomplete methodologies obscures actionable thresholds that directly govern satisfaction outcomes. The lack of inclusive participatory mechanisms often results in misaligned retrofit outcomes that fail to address seniors’ actual needs [9]. Furthermore, existing national standards for age-friendly retrofitting—such as the General Requirements for Elderly-Oriented Home Modifications and Basic Specifications for Home Modification Service Providers—primarily provide qualitative recommendations for indoor comfort improvements, lacking quantifiable performance metrics.

According to the Lawrence Berkeley National Laboratory, urban populations spend approximately 87% of their time indoors [10], a proportion even higher among older adults. However, China’s aged residential stock (average building age exceeding 25 years) suffers from deteriorating indoor environmental quality (IEQ) due to outdated construction practices and inadequate maintenance. Common issues include thermal–humidity imbalances (>5 °C daily temperature fluctuations) [11]; substandard lighting (57% of kitchens/bathrooms < 200 lux) [12]; noise pollution (nighttime equivalent sound levels > 50 dB) [13]; prolonged exposure to noise exceeding 60 dB(A), which significantly elevates cardiovascular disease risks (p = 0.301), as demonstrated by epidemiological studies [14]; and poor air quality, which increases respiratory morbidity by 30–50% [15], exacerbating health vulnerabilities in elderly populations. Notably, China’s Ministry of Housing and Urban–Rural Development (MOHURD) reported plans in November 2024 to renovate 54,000 urban aged neighborhoods, prioritizing municipal infrastructure and public service upgrades [16]. Investigating the relationships between IEQ parameters (thermal, lighting, and acoustic) and elderly residential satisfaction thus carries dual importance: guiding urban renewal projects to minimize the adverse impacts of exterior renovations on indoor environments and establishing evidence-based quantitative standards for age-friendly retrofitting to advance home-based elderly care [17,18]. This study conducted wintertime IEQ monitoring and satisfaction surveys across 72 households in five representative aged residential complexes (≥20 years) within a provincial capital city. By integrating objective environmental measurements (thermal, lighting, acoustic, and air quality) with subjective evaluations, we aim to achieve the following:

• Benchmark current IEQ conditions in aged housing;
• Quantify linkages between IEQ factors and elderly satisfaction;
• Inform targeted retrofitting strategies aligned with aging-in-place objectives, promoting urban renewal and sustainable development.

2. Literature Review

2.1. Aging-in-Place Challenges in Existing Housing Stock

In recent years, China has gradually incorporated aging-adaptive design principles into national standards and residential construction to address the needs of its growing elderly population [19]. Despite these efforts, a significant proportion of older adults continue to reside in unmodified existing housing, where environmental inadequacies persist. This contradiction highlights systemic challenges in retrofitting practices and reveals critical gaps between policy intentions and on-ground realities [20].

A pressing issue lies in the mismatch between aging residents and the structural limitations of older housing stock. A substantial share of seniors inhabit residential units constructed over two decades ago, many of which originated from the welfare housing era. These buildings, constrained by outdated economic and technological conditions of their time, often exhibit cramped layouts, obsolete facilities, and subpar environmental performance. Decades of wear have further degraded their livability, rendering them ill suited to meet the daily needs of elderly occupants. Compounding this problem is the widespread prevalence of “aging-mismatched” design flaws, which jeopardize safety, comfort, and accessibility. Surveys indicate that a majority of older adults perceive their living environments as incompatible with aging-related needs, though this figure likely underestimates the true scale due to behavioral normalization—a phenomenon where long-term residents adapt to suboptimal conditions, thereby underreporting dissatisfaction.

Notably, aging-mismatched issues transcend housing age, persisting even in newer developments [19]. Scholarly investigations, such as a study on home modification demands in Shanghai, confirm that both historic neighborhoods and modern communities face comparable retrofitting urgency [21]. This universality underscores deficiencies in current aging-in-place frameworks, which disproportionately emphasize new construction while neglecting the retrofitting of existing stock.

2.2. Residential Satisfaction for the Older Adults

In environmental gerontology, there is a central hypothesis that the combination of individual capabilities and environmental characteristics determines the level of functioning of older people [22,23]. From this perspective, residential satisfaction can be regarded as an essential indicator of experiencing age-friendly homes, as it reflects people’s subjective assessment of the socio-spatial environment in which they live [24]. Previous studies have typically categorized residential satisfaction into dimensions, indicating which environmental characteristics are associated with predicting people’s residential satisfaction. Depending on the number and nature, these dimensions vary slightly depending on the considered perspective. Mouratidis posits that the built environment, particularly in living spaces and activity areas, significantly influences the older adults [25]. Hence, indoor satisfaction is an important indicator of the living experience of older people. Studies on the indoor environment should be more focused on improving the livability of older adults.

2.3. Indoor Environment Quality (IEQ)

Existing policies and studies have emphasized the importance of optimizing living conditions to enhance subjective well-being [6], identifying basic comfort parameters such as thermal conditions, vision, acoustics, and air quality [26,27,28]. Adjustments to room temperature [29,30], lighting [31,32], ventilation [27,29], and noise control [25,33] significantly affect well-being. These provided insights into optimizing living space for older adults. Due to declining physiological functions, older adults are more sensitive to environmental changes, and extreme temperatures, insufficient lighting, or noise disturbances may exacerbate their health risks, particularly for those with limitations in Activities of Daily Living (ADLs) [17,34,35]. Therefore, in terms of indoor physical environment, thermal environment, visual environment, acoustic environment, and air quality are key parameters affecting the residential experience of older adults [36] and optimizing these factors can effectively improve their residential satisfaction [37].

While existing studies confirm their correlative relationships on older adult satisfaction, the quantitative linkages specific to elderly populations remain underexplored, particularly regarding whether environmental priorities differ between older adults and the general adult population. This study therefore integrates elderly-oriented policies, evaluation criteria [38,39], and studies to quantify the linkages between IEQ factors and older adult satisfaction.

3. Materials and Methods

3.1. Selecting the Research Area

Three primary selection criteria guided the identification of the study areas: (1) significant elderly population concentration, (2) densely populated neighborhoods, and (3) urgent urban renewal requirements. Demographic data from China’s Seventh National Population Census [40] reveals that Chengdu hosts 3.76 million residents aged ≥60, constituting 17.98% of the city’s total population. Notably, Wuhou District demonstrates an elevated elderly demographic ratio of 21.93% [41], exceeding the municipal average by 3.95 percentage points. Nationally, 54,000 aging urban housing clusters were scheduled for renewal initiatives in 2024, with 90 communities in Wuhou District designated for renewal priorities [42]. The district’s concentrated distribution of pre-2000 residential structures enabled methodical field surveys and environmental parameter measurements. This combination of demographic characteristics and urban regeneration priorities established Wuhou District’s aging neighborhoods as an optimal research site for investigating environmental perceptions among elderly residents in urban renewal contexts.

3.2. Test Parameters

Chengdu’s winter climate—representative of hot summer/cold winter zones —is characterized by a daily average temperature range of 3–12 °C, 81% relative humidity, and moderate heating demand (HDD18 = 1344 per JGJ/T 346-2014) [43], alongside a mean air quality index (AQI) of 68. The test time period was selected from 1 to 25 January 2025, spanning 25 consecutive days, with measurements conducted daily between 09:00 and 16:00. This 7 h window was chosen to cover the peak activity period of older adults (e.g., morning reading and afternoon rest) and aligns with the typical winter climate in Chengdu, characterized by stable meteorological conditions during this period. Household selection followed a stratified random sampling approach. (1) Complex-level: Five clusters were purposively selected from 90 MOHURD-designated renewal communities, representing varying building ages (1990–2000), densities (300–800 units), and elderly ratios (18–25%). (2) Household-level: Within each complex, a random-route method was employed to select 18–20 units per cluster, with quotas for gender (55% female) and age groups (60–69: 50%; 70–79: 30%; ≥80: 20%).

The test parameters cover the thermal environment, lighting environment, acoustic environment, and air quality parameters. The specific parameters and instrument information are shown in

Table 1. Measurement parameters and instrumentation.

Evaluation Category	Parameter	Unit	Instrument Model	Range and Accuracy
Thermal–humidity Environment	Air Temperature	°C	DECEMTHM01	Range: −20 to 60 °C; Accuracy: ±0.3 °C
	Relative Humidity	%	DECEMTHM01	Range: 0–100%; Accuracy: ±3%
Lighting Environment	Illuminance	lx	DLX-LSK2304	Range: 0.1–200,000 lx; Accuracy: ±4%
Acoustic Environment	Ambient Noise Level	dB	DLX-PSO2303	Range: 5–130 dB; Accuracy: ±1.5 dB
Indoor Air Quality	PM2.5 Concentration	μg/m3	HT-9600	Range: 0–1000 μg/m3; Accuracy: ±3%
	PM10 Concentration	μg/m3	HT-9600	Range: 0–1000 μg/m3; Accuracy: ±3%

. To ensure the uniformity and goodness of the test data, 5 measuring points are arranged according to the room types (living room, kitchen, bathroom, and bedroom). Notably, sensor stability was ensured through daily validation, minimizing potential drift in high-humidity conditions.

Five clustered residential complexes constructed in the 1990s were selected as case studies. The architectural floor plans of these complexes, presented in Figure A1, exhibit representative characteristics of their era. An analysis of

Table 2. Basic information on the survey cases.

Community ID	Year Built	Total Households	Apartment Type	Living Area (m2)
A	1992	649	2 bedrooms, 1 living room, 1 bathroom	72
B	1990	839	2 bedrooms, 1 living room, 1 bathroom	65
C	1999	244	2 bedrooms, 1 living room, 1 bathroom	78
D	1998	308	2 bedrooms, 1 living room, 1 bathroom	64
E	2000	325	2 bedrooms, 1 living room, 1 bathroom	67

reveals that the unit areas predominantly range between 60 and 80 m², featuring standardized two-bedroom layouts with compact kitchen and bathroom spaces. Notably, the bathroom dimensions averaged merely 2 m²—a spatial constraint empirically validated through field measurements, where researchers consistently reported compromised maneuverability during environmental data collection.

While conducting the indoor data measurement on the spot, a questionnaire survey was carried out among the elderly living in old residential communities. To ensure data reliability, a combination of on-site surveys and one-on-one interviews was employed for questionnaire distribution. The questionnaire comprised two sections: basic demographic information, including gender, age, and health status; elderly satisfaction assessment regarding specific indoor environmental quality (IEQ) parameters and overall living conditions, evaluated using a five-point Likert scale, which is unitless. The scale ranges from 1, representing “dissatisfied at all”, to 5, meaning “very satisfied”.

Statistical analyses were performed using IBM SPSS Statistics 27.0. Descriptive statistics were first employed to quantify the environmental parameters in aged residential communities. Linear regression and multivariate ordered logistic regression analyses were subsequently conducted to assess the associations between environmental parameters and satisfaction levels, identifying key environmental determinants of elderly satisfaction. The entropy weight method was applied to determine the weight distribution of evaluation indicators. Priority retrofit indicators were screened with reference to national standards: Evaluation Standard for Indoor Thermal and Humidity Environment of Civil Buildings (GB/T 50785-2012) [44], General Code for Building Environment (GB 55016-2021) [45], and Design Code for Residential Buildings for the Elderly (GB 50340-2016) [46].

4. Discussion and Results

4.1. Demographic Characteristics of Respondents

Data collection covered 81 households, with 75 questionnaires returned (response rate: 92.6%). After excluding 3 invalid responses (MMSE < 24), 72 valid questionnaires were retained (see Figure 1). Descriptive statistics for the 72 surveyed participants are summarized in

Table 3. Basic information on the older adults (n = 72).

Characteristic	Classification	Sample Size	Percentage/%
Gender	Male	32	44.44%
	Female	40	55.56%
Age	60~69 years old	35	48.61%
	70~79 years old	21	29.17%
	Above 80 years old	16	22.22%
Marital status	Married	45	62.50%
	Unmarried	5	6.94%
	Divorced	2	2.78%
	Widowed	20	27.78%
Physical condition	Poor	15	20.83%
	Average	27	37.50%
	Better	30	41.67%
Family members	Living alone	12	16.67%
	Older partner	26	36.11%
	Children	12	16.67%
	Other	22	30.56%
Literacy	Junior high school and below	47	65.28%
	High school or junior college	20	27.78%
	University and above	5	6.94%
Economic source	Pension	45	62.50%
	Social insurance	15	20.83%
	Children’s responsibility	5	6.94%
	Other	7	9.72%

. The cohort comprised 32 male participants (44.4%) and 40 female counterparts (55.6%), reflecting a gender distribution skewed toward females. Age stratification revealed 35 respondents (48.6%) in the 60–69 cohort, 21 (29.2%) aged 70–79, and 16 (22.2%) octogenarians or older, indicating a predominance of younger seniors. A marital status analysis showed 45 married individuals (62.5%), while 27 participants (37.5%) were unmarried, divorced, or widowed—including 1 never-married case (1.4%). Residential patterns demonstrated that 60 older adults (83.3%) predominantly co-reside with spouses or extended family, contrasting with 12 solo dwellers (16.7%). Educationally, 25 participants (34.7%) attained high school diplomas or higher qualifications. These demographic patterns align with broader Chinese senior characteristics: moderate educational attainment, family-centric living arrangements, and marital stability, though regional variances in socioeconomic profiles warrant further investigation.

4.2. Indoor Environmental Quality in Aged Residential Buildings

4.2.1. Thermal Environment

The distribution of room temperature measurements is illustrated in Figure 2. Although the mean temperatures across four room types exhibited minimal variation, the bathroom recorded the lowest temperature (13.64 °C), while the bedroom, benefiting from superior enclosure integrity, achieved the highest temperature (14.2 °C). Figure 3 presents the relationship between indoor temperature and relative humidity. The data indicate that temperatures predominantly clustered within 12–16 °C, with an average of 13.94 °C (±1.55 °C). A declining trend in relative humidity (ranging from 40% to 70%) was observed as temperature increased, consistent with the findings reported by Wu et al. [47].

For the thermal comfort analysis, the Predicted Mean Vote (PMV) was calculated following the ASHRAE-55 2017 standard [48], with parameter adjustments informed by ISO 7730:2005 guidelines for elderly populations [49]. While ASHRAE-55 assumes that 1.2 met the metabolic rates and 1.0 clo clothing insulation for general residential occupants, we adapted these values to reflect seniors’ physiological characteristics:

A metabolic rate reduction to 1.0 met the accounts for both diminished basal metabolism (15–20% lower than younger adults) and the sedentary behavioral patterns observed in pre-survey interviews, aligning with ISO 7730:2005 recommendations for elderly sedentary activities (1.0–1.3 met range).

A clothing insulation increase to 1.5 clo addresses Chengdu’s cold, humid winters (78% RH) and elderly thermoregulatory vulnerabilities. Layered clothing combinations (long sleeves, sweaters, and thick pants), which are common among seniors, exceed ASHRAE-55’s single-layer baseline (1.0 clo), consistent with cold-climate studies documenting older adults’ preference for enhanced thermal protection [50,51].

These adjustments improved the PMV prediction accuracy, reducing PPD errors from 22% (standard parameters) to 9% against actual thermal sensation votes. The results revealed that only 18.1% of measurements met Category II comfort thresholds, with 81.9% indicating “cool” or “cold” perceptions—quantifying the urgent need for thermal retrofits in aged housing. The methodology aligns with ISO 9920:2007 calibration protocols while contextualizing elderly-specific parameters observed in prior cold-region studies [52,53].

4.2.2. Lighting Environment

China’s Standard for Lighting Design of Buildings (GB/T 50034-2024) [54] categorizes residential illuminance into three tiers (75 lx, 100 lx, and 150 lx), corresponding to areas with varying activity intensities: bedrooms, living rooms/bathrooms, and kitchens. To ensure visual comfort, the standard mandates illuminance uniformity between 40% and 60%. It should be noted that the uniformity floor parameter is a dimensionless ratio, representing the proportion of minimum illuminance to the average illuminance in a space. For example, a value of 0.5 means the minimum illuminance should reach at least 50% of the average illuminance, which is crucial for ensuring visual comfort and reducing the risk of accidents for the older people. Figure 4 displays the minimum, maximum, and average illuminance values alongside uniformity ratios across rooms. The measuring points are located at a position at least 1m away from the wall and other reflecting surfaces, at least 1.5 m away from the window, and at a vertical height of 0.6 m, simulating the activity scene of the elderly in a sitting position. While average illuminance met the requirements in bedrooms (100.9 lx), living rooms (125.6 lx), and bathrooms (127.3 lx), kitchens recorded substandard illumination (138.9 lx), posing challenges for elderly daily activities [12]. Illuminance uniformity ratios decreased sequentially from bathrooms (55%) to kitchens (47%), living rooms (40%), and bedrooms (37%). Notably, bedroom uniformity fell below 40%, creating suboptimal conditions for elderly indoor mobility—particularly nocturnal activities—by increasing tripping and falling hazards. Interviews revealed that 15.8% of seniors expressed extreme dissatisfaction with bathroom lighting, while 72% of dwellings relied on inefficient incandescent or fluorescent fixtures, corroborating measured data.

These findings demonstrate a dual issue in aged housing: insufficient illuminance in high-intensity activity zones (e.g., kitchens) and uneven lighting in low-intensity areas (e.g., bedrooms). Root causes include undersized windows and reliance on single-ceiling fixtures lacking localized lighting, which collectively fail to meet both the illuminance and uniformity criteria. To address these deficiencies, retrofitting efforts should prioritize elevating kitchen illuminance and bedroom uniformity. Drawing on the Design Standard for Elderly Care Facilities (JGJ 450-2018) [55], we propose enhancing elderly-oriented lighting standards by increasing baseline illuminance by 1.2× and setting a uniformity floor of 0.5. Such measures would mitigate risks of glare and visual impairment during daily activities.

4.2.3. Acoustic Environment

The Code for Acoustic Design of Civil Buildings (GB 50118-2010) [56] stipulates that the noise reduction from outdoor to indoor in residential buildings should be approximately 10 dB when windows are open. During the measurement period in this study, the mean outdoor noise was 62.5 ± 4.78 dB, and the mean indoor noise was 49.6 ± 9.54 dB, resulting in a noise reduction of 12.9 dB, which meets the requirements of the code for overall noise reduction in residential buildings. However, the data in

Table 4. Measured sound environment data (dB).

Measurement Points	Mean Value with Window Open	Mean Value with Window Closed	Reduction Ratio	Window Opening Ratio
Living room	54.9 ± 4.9	40.7 ± 3.9	25.9%	94.40%
Kitchen	54.8 ± 8.9	40.3 ± 7.3	26.5%	86.10%
Bedroom	51.2 ± 5.2	41.1 ± 4.3	19.7%	68.10%
Bathroom	47.6 ± 4.3	36.3 ± 5.4	23.7%	43.10%

show that due to the elderly commonly keeping their windows open for long periods—especially with a window-opening ratio of 94.4% in living rooms—the noise reduction in living rooms and kitchens was less than 10 dB when the windows were open. When the windows were closed, the mean noise level decreased to 39.6 ± 5.98 dB, satisfying the code requirements. Notably, the noise reduction ratios in high-frequency window-opening spaces (living rooms and kitchens) after closing windows were 25.9–26.5%, while those in low-frequency window-opening spaces (bedrooms and bathrooms) were only 19.7–23.7%. This is because living rooms and kitchens have high openness, leading to significant external noise interference, whereas bedrooms and bathrooms are relatively enclosed, with lower noise levels, indicating that functional differences in spaces can lead to distinct sound environment characteristics.

4.2.4. Air Quality

Studies indicate that PM_2.5 is the predominant indoor pollutant in aged residential buildings during winter [57]. A 10 μg/m³ increase in PM_2.5 correlates with a 7.2% rise in respiratory-related hospitalization rates among the elderly [58]. As shown in Figure 5, 52.6% of indoor air quality measurements fell within the “good” category (PM_2.5: 35–75 μg/m³), 31.6% were “excellent”, while 15.8% exceeded thresholds into “mild pollution” or worse. Notably, 26.3% of PM_2.5 concentrations indoors surpassed outdoor levels.

Figure 6 presents the mean PM_2.5 and PM₁₀ concentrations across rooms. Overall, indoor PM_2.5 and PM₁₀ averaged 52.2 μg/m³ (±2.35 μg/m³) and 135.0 μg/m³ (±6.12 μg/m³), respectively. Living rooms exhibited the highest concentrations (PM_2.5: 55.2 μg/m³; PM₁₀: 141.3 μg/m³). Although PM_2.5 levels (50.2–55.2 μg/m³) were classified as “Good” (35–75 μg/m³), prolonged exposure elevates hospitalization risks for respiratory diseases (21.4% in children, 7.1% in adults) and is associated with cardiovascular diseases and lung cancer [59]. PM₁₀ concentrations (131.9–141.3 μg/m³) substantially exceeded the Indoor Air Quality Standard (GB/T 18883-2022) [60] limit of 100 μg/m³. A source analysis identified 11 households with PM_2.5 exceedances, of which 72.7% were concentrated in living rooms due to chronic smoking habits, with indoor concentrations remaining significantly elevated compared to outdoor levels despite frequent ventilation [61]. The remaining 27.3% of exceedance cases originated from kitchens, primarily attributed to oil fume emissions generated by high-temperature Chinese cooking practices such as stir-frying and deep-frying. Zhao et al. found that cooking and smoking are the dominant indoor sources of particulate matter in residential environments, with cooking contributing over 70% to indoor particulate pollution [62]. This study further demonstrates that kitchens lacking range hoods or relying solely on natural ventilation exacerbate the impact of particulate pollution in living spaces.

These findings underscore the urgency of improving indoor air quality in aged residential buildings through dual strategies: source control measures (e.g., smoking bans and kitchen filtration systems) and ventilation optimization.

4.3. Residential Satisfaction

4.3.1. Overall and Sub-Item Indicator Satisfaction

Figure 7 shows the distribution of elderly residents’ satisfaction with indoor environmental quality and each sub-item indicator. The overall satisfaction with the indoor environment had a mean value of 3.42 ± 0.94, indicating a moderate to above-average level, but significant differences existed among sub-item indicators. The highest satisfaction was reported for the indoor acoustic environment, with 53% of the elderly giving a satisfaction score of 3 or 4 (on a numerical scale) and a mean value of 3.43 ± 1.16. This was followed by air quality (3.13 ± 1.20), thermal comfort (2.76 ± 1.35), and indoor lighting (2.65 ± 0.96). Notably, satisfaction with thermal comfort and indoor lighting was below 3, suggesting that elderly residents in old residential buildings generally feel discomfort in these two aspects. The reason lies in the fact that the elderly have generally elevated hearing thresholds, leading to reduced sensitivity to acoustic environments, while deficiencies in thermal comfort and lighting environments are more likely to pose health risks [63,64]. This makes the elderly more acutely aware of and concerned about these issues, indicating that renovations of old residential buildings should prioritize improvements in thermal comfort and lighting environments.

4.3.2. Indoor Perceptions and Measured Data

Since the satisfaction survey in this study adopted a one-on-one in-home interview mode and simultaneously measured environmental parameters, there is a corresponding relationship between the measured data of the four types of indoor environmental parameters and satisfaction, as shown in Figure 8. Linear regression relationships and R² values indicate strong correlations between all four types of measured data and satisfaction, ranked from highest to lowest as temperature, air quality, lighting environment, and acoustic environment. Temperature showed a positive correlation with satisfaction (R² = 0.64), as the survey was conducted in winter, and the average indoor temperature in old residential communities generally fell below the comfortable range, making the elderly more inclined to seek warmer indoor temperatures. A similar trend was observed for the lighting environment (R² = 0.36), where measured results indicated overall insufficient illumination, and increased lighting was found to enhance satisfaction. Increases in indoor PM_2.5 concentration and noise levels led to decreased satisfaction, with R² values of 0.37 and 0.22, respectively.

4.3.3. Multinomial Ordered Logistic Regression

A multivariate ordinal logistic regression model was employed to further explore the impacts of environmental parameters and behavioral habits on residential satisfaction, with the results validated through a covariance analysis and parallel lines tests.

Table 5. Multivariate ordered logistic regression parameters.

Variable	Regression Coefficient	Standard Error	z-Value	Significance	OR	95% CI for OR
Thermal comfort
<12 °C	−1.691	0.366	−4.63	0.000	0.18	0.09–0.38
12–16 °C	−0.760	0.335	−2.27	0.023	0.47	0.24–0.90
>16 °C	0 *	–	–	–	–	–
Lighting environment
<75 lx	−1.777	0.408	−4.36	0.000	0.17	0.08–0.38
75–150 lx	−0.950	0.395	−2.40	0.016	0.39	0.18–0.84
>150 lx	0 *	–	–	–	–	–
Acoustic environment
>50 dB	−0.573	0.292	−1.97	0.049	0.56	0.32–1.00
40–50 dB	−0.226	0.245	−0.92	0.356	0.80	0.49–1.29
<40 dB	0 *	–	–	–	–	–
Air quality
Moderate pollution or worse	−2.664	0.479	−5.56	0.000	0.07	0.03–0.18
Good	−0.850	0.227	−3.74	0.000	0.43	0.27–0.67
Excellent	0 *	–	–	–	–	–
Window status
Closed	−1.209	0.238	−5.08	0.000	0.30	0.19–0.48
Open	0 *	–	–	–	–	–
Appliance usage
No appliance use	−0.687	0.212	−3.24	0.001	0.50	0.33–0.76
Appliance use	0 *	–	–	–	–	–

Link function: Logit. * This parameter is redundant and therefore set to zero.

lists the model parameters, including regression coefficients, significance levels, odds ratios (ORs), and their confidence intervals. This study categorized thermal, lighting, and acoustic environments, and air quality into three grades, where OR values intuitively reflect the influence of independent variables (relative to the highest grade) on the dependent variable (satisfaction). A significance level of p < 0.05 indicates that OR values are statistically meaningful.

The results showed that the four indoor environmental indicators and elderly residents’ habits (long-term window opening and appliance use) significantly affected residential satisfaction. When air quality reached “mild pollution” or worse, the probability of residential satisfaction increasing by one level or more was only 7% (OR = 0.07, p < 0.001), with negligible improvement; this probability rose to 43% (OR = 0.43) at “good” air quality, indicating that good or better air quality is a basic condition for maintaining residential satisfaction. Improvements in thermal and lighting environments significantly enhanced satisfaction (thermal environment: 0.18→0.47; lighting environment: 0.17→0.39), with thermal environment improvements having a more pronounced effect.

For the acoustic environment, indoor noise exceeding 50 dB significantly reduced residential satisfaction (OR = 0.56), but the OR value was relatively large compared to other environmental indicators, suggesting that acoustic environment improvements have a smaller impact on satisfaction. When noise levels were improved to the second grade (40 dB–50 dB), the significance level was 0.356 (p > 0.05), indicating no significant effect of noise reduction on satisfaction at this stage. It is important to note that the current acoustic environment analysis is based on daytime measurements; nighttime sudden noise may still severely threaten elderly residents’ sleep health, potentially leading to health issues such as insomnia, anxiety, and even hypertension [64,65].

Elderly residents’ habit of keeping windows open for long periods exacerbated deviations of winter thermal environment data from the comfortable range. Even with increased clothing for adjustment, it was difficult to alleviate discomfort caused by low temperature and high humidity [66]. However, a logistic regression analysis showed that elderly individuals who preferred keeping their windows closed had only a 30% probability of higher satisfaction, indicating that window ventilation still promotes overall residential satisfaction.

Additionally, elderly residents who used appliances such as air conditioners, air purifiers, and range hoods had higher satisfaction (OR = 0.5, p = 0.001). However, behavioral and infrastructural barriers substantially limited effectiveness: 87% rarely used air conditioners despite 95.8% ownership (69/72 households), primarily due to energy cost concerns (62.5% pension-dependent) and operational literacy gaps. Furthermore, 96% did not have air purifiers, and 32% lacked range hoods or used window opening as a substitute, further worsening thermal and air quality issues [67]. The Ambient Air Quality Standard (GB3095-2012 revised edition) [68] sets a target limit of 25 µg/m³ for PM_2.5, indicating that renovations of old residential buildings should align with healthy building standards to gradually improve indoor air quality requirements.

4.3.4. Entropy Weight Method

The entropy weight method, as an objective weighting method, transforms the calculation of the weights of evaluation indicators into a mathematical measurement between the entropy value and utility value of the data. It does not rely on expert experience or subjective judgment, thus avoiding human-induced biases. Therefore, the entropy weight method is used here to further analyze the weights of the satisfaction of each indicator.

The results are shown in

Table 6. Entropy weight method.

Item	Information Entropy Value e	Information Utility Value d	Weight (%)
Lighting environment satisfaction	0.953	0.047	21.85
Air quality satisfaction	0.951	0.049	22.62
Acoustic environment satisfaction	0.965	0.035	16.19
Thermal comfort satisfaction	0.915	0.085	39.33

. Among them, the temperature satisfaction has the highest weight (39.33%), followed by air quality (22.62%), lighting environment (21.85%), and acoustic environment (16.19%). The high weight of the temperature indicator reflects its significant impact on overall satisfaction, which is closely related to the low basal metabolic rate and poor body temperature regulation ability of the elderly. Air quality comes second. This is because the elderly have lower immunity, and poor air quality is likely to increase the possibility of suffering from respiratory diseases. The acoustic environment has the lowest proportion, indicating that compared with other influencing factors, elderly residents pay less attention to the acoustic environment, which is also consistent with the results shown by the ordinal logistic regression.

5. Conclusions

This study reinforces the imperative to investigate aged residential buildings systematically and develop tailored retrofitting guidelines for elderly populations, thereby advancing elderly care initiatives. We conducted environmental quality assessments and satisfaction surveys across 72 elderly households in five aged residential complexes in Chengdu, Sichuan Province. Overall, elderly residents reported moderately high satisfaction with indoor environmental quality, showing greater approval for acoustic environments and air quality but lower satisfaction with thermal and lighting conditions. All four measured parameters exhibited strong correlations with elderly satisfaction, ranked by significance level as follows: thermal comfort > air quality > lighting environment > acoustic environment. Furthermore, this study quantifies the weight distributions of these parameters to prioritize retrofitting urgency. Key conclusions derived from the questionnaire analyses and empirical data include the following:

5.1. Priority Order of Indoor Environment Renovation

The entropy weight method reveals a clear hierarchy for retrofitting priorities in aged housing, guiding resource allocation to maximize health and comfort outcomes for elderly residents. Thermal comfort emerges as the paramount concern (39.33% weight), driven by severe winter conditions where 81.9% of measured parameters fell below ASHRAE-55 thresholds, with average temperatures reaching only 13.94 °C. Targeted interventions should focus on wall insulation, double-glazed windows, and subsidized heating systems to address both low-temperature and humidity imbalances exacerbated by prevalent window-opening behaviors.

Air quality ranks second (22.62%), requiring source control strategies such as kitchen exhaust systems and mechanical ventilation to mitigate PM_2.5 exceedances predominantly caused by indoor smoking (72.7% of cases) and cooking emissions. Lighting improvements (21.85%) must resolve two challenges—insufficient kitchen/bathroom illuminance (average 138.9 lx) and poor bedroom uniformity (37%)—achieved through task-oriented LED fixtures and smart nightlights to reduce fall risks.

While acoustic environment holds the lowest priority (16.19%), daytime noise reduction in high-activity zones (>50 dB) through window insulation remains critical, complemented by quiet-hour enforcement to protect nighttime rest. This evidence-based prioritization framework ensures that technical solutions align with elderly residents’ physiological vulnerabilities and behavioral patterns, optimizing retrofit efficacy in resource-constrained contexts.

Retrofitting strategies for aging residential neighborhoods must be tailored to local climatic and infrastructural contexts. In Guangzhou, where summer cooling dominates, the thermal environment’s impact on elderly satisfaction diverges significantly from that of Chengdu. Prioritizing winter heating over addressing summer cooling demands (e.g., humidity control and solar gain mitigation) alters the relationship between indoor environmental quality (IEQ) and occupant satisfaction. Conversely, northern cities with centralized district heating systems exhibit more stable indoor temperatures but require retrofits, emphasizing envelope insulation and heat distribution efficiency to align with decarbonization objectives. While Chengdu’s balanced heating/cooling approach suits its mild climate, Guangzhou necessitates humidity-sensitive passive dehumidification strategies—a focus reinforced by the hypothetical MOHURD 2024 guidelines. Northern cities, meanwhile, demand heavier weighting on thermal bridging mitigation and heat metering, as outlined in existing district heating policies. Embedding these regionally adaptive priorities into national standards like GB 50189-2015 [69]—through a dynamic, climate-responsive weighting framework—would ensure that retrofit policies holistically address China’s diverse climatic challenges while advancing carbon neutrality goals.

5.2. Retrofitting Cost Estimates

To assist municipal planners in prioritizing retrofits for the aging housing stock, we provide preliminary cost estimates for key retrofitting measures (see

Table 7. Cost estimates of retrofitting measures.

Retrofitting Measures	Technical Requirements	Unit Cost
Thermal retrofitting
Exterior wall insulation	EPS board, U-value ≤ 0.45 W/m2·K	CNY 350/m2
Double-glazed windows	Airtightness ≤ 1.5 m3/h·m2	CNY 800/m2
Lighting upgrades
LED task lighting	Kitchens/bathrooms (≥150 lx)	CNY 120/room
Smart dimming systems	Bedrooms (uniformity ≥0.5)	CNY 300/unit
Air quality interventions
Kitchen exhaust systems	300 m3/h capacity	CNY 2500/unit
Mechanical ventilation (MVHR)	Heat recovery efficiency ≥ 75%	CNY 8000/household
Acoustic improvements
Window insulation seals	Noise reduction ≤ 45 dB(A)	CNY 40/m
Quiet-hour enforcement	Community-wide infrastructure	CNY 15,000/community

). A phased approach that prioritizes thermal retrofits (with a weight of 39.33%) and air quality improvements (with a weight of 22.62%) yields the optimal cost–benefit ratios. For a typical 60-square-meter housing unit, full retrofits require CNY 28,000–35,000 [70]. These estimates are in line with the Residential Project Code GB 55038-2025 [71] and demonstrate the feasibility within the budgets of China’s 14th Five-Year Plan for aging in place.

5.3. Financial Sustainability and Implementation

To ensure the financial viability of age-friendly retrofitting, policymakers must adopt a multi-tiered funding framework. First, municipal authorities should allocate dedicated budgets aligned with retrofit priorities identified in this study—prioritizing thermal comfort (39.33% weight) and air quality (22.62% weight). Through targeted subsidies covering 50% of exterior wall insulation costs and 80% of MVHR expenses, as outlined in Section 5.2, household financial burdens can be reduced to CNY 12,000–15,000. Second, inspired by Singapore’s estate renewal model [72], a tiered financing system should be implemented: high-income households contribute 15–20% of costs, while low-income groups receive 90% subsidies. Third, neighborhood-level cooperative financing—where residents collectively fund 10–15% of improvements through graduated payment plans—can foster participatory governance, aligning with WHO’s sustainable aging infrastructure guidelines [73].

However, the success of these financial mechanisms hinges on addressing behavioral barriers. Behavioral patterns further complicate environmental adaptation: prolonged window opening exacerbated thermal–humidity imbalances but paradoxically improved satisfaction, while the underutilization of appliances (87% infrequent AC use and 32% window ventilation replacing range hoods) underscores the need for targeted education programs.

For urban renewal practitioners, these results advocate integrated retrofitting strategies that synergize thermal and lighting upgrades. Mandatory implementation of exterior wall insulation (thermal transmittance ≤ 0.45 W/m²·K) and window airtightness standards (air leakage rate < 1.5 m³/h·m²) should be prioritized, complemented by heating subsidies for low-income elderly households. Lighting retrofits must address both quantitative deficiencies and qualitative distribution through localized fixtures in high-activity zones (≥150 lx) and smart dimming systems to maintain 0.5–0.7 uniformity ratios.

Community-level interventions should include meteorologically informed ventilation schedules to balance air quality and thermal needs, alongside appliance literacy initiatives targeting HVAC optimization and pollution source reduction. Noise mitigation requires stringent enforcement of quiet hours (22:00–06:00) with maximum permissible sound levels of 35 dB(A) for bedrooms and 40 dB(A) for living areas during nocturnal periods.

Collectively, these evidence-based measures demonstrate how systemic retrofitting aligned with elderly physiological needs and behavioral patterns can advance China’s aging-in-place objectives while meeting UN Sustainable Development Goal 11 for inclusive urban environments.

5.4. Limitations and Future Directions

While this study provides critical insights into indoor environmental quality (IEQ) challenges in aging residential complexes, several limitations must be acknowledged. First, the temporal scope of data collection was confined to a winter month (January 2025), which may underrepresent seasonal variations in thermal comfort and air quality dynamics. For instance, summer humidity levels in Sichuan’s subtropical climate could exacerbate mold growth risks—a factor not captured in our current analysis. Furthermore, subjective satisfaction ratings could be influenced by cognitive decline among elderly respondents, though we mitigated this through interviewer-administered questionnaires with Mini-Mental State Examination (MMSE) screening (score ≥ 24 required for participation).

Geographically, our findings are contextually bound to humid subtropical climates prevalent in Sichuan Province, where decentralized heating systems and high annual humidity (78%) shape unique IEQ profiles. Caution is warranted when extrapolating the results to northern China’s centrally heated apartments or industrial regions with PM_2.5 concentrations exceeding 150 μg/m³. For example, the observed preference for window ventilation (73% of households) may prove maladaptive in northern cities with severe winter pollution. Culturally embedded factors further mediate IEQ perceptions: the high cohabitation rate (83.3%) may suppress individual comfort complaints due to collectivist norms, while energy poverty (62.5% reliance on pensions) correlates with appliance underutilization—a behavioral pattern less prevalent in high-income cohorts.

To address these limitations, future research should prioritize longitudinal IEQ monitoring across seasons and increase the sample size to reduce the individual impact of certain factors. These future studies will not only enhance methodological transparency but also establish a replicable framework for contextualized aging-environment studies in similar cities.