Incorporating Occupant Age Structure into Building Energy Simulation for Envelope Retrofit Evaluation in Existing Residential Buildings

Abstract

The retrofit of existing residential buildings plays a critical role in reducing energy consumption and carbon emissions in the building sector. However, previous retrofit evaluations often fail to account for the age-related thermal and lighting requirements of residents in aging residential buildings, thereby overlooking the substantial behavioral heterogeneity that shapes retrofit effectiveness. This study evaluates the comprehensive performance of different building envelope retrofit strategies, considering occupants’ thermal and visual comfort, from the perspectives of energy efficiency, economic feasibility, and environmental sustainability. First, age-specific differences in occupancy patterns, thermal preferences, and lighting requirements between elderly and non-elderly comparison group occupants were systematically extracted from the literature. Then, a typical high-rise residential building was modeled in EnergyPlus to serve as the reference building, within which the differentiated occupant behavior models were implemented, and the pre-retrofit condition was defined as the baseline scenario. Next, six commonly applied exterior wall insulation materials and different glass configurations and window frames were parameterized and evaluated under varying insulation thicknesses and remaining building service life scenarios. Finally, the energy-saving performance, economic benefits, and carbon reduction potential of envelope retrofit measures were quantitatively assessed across three primary functional zones (bedroom, living room, and study), using area-normalized indicators. The results indicate that, in the retrofit of existing residential buildings, bedrooms and study rooms exhibit greater retrofit benefits than living rooms, primarily due to longer occupancy durations and higher heating demand. In terms of retrofit strategies, exterior wall insulation consistently outperforms window retrofitting in energy-saving potential, with energy-saving rates of approximately 3.2–4.3% depending on functional zone, material type, and insulation thickness. Among the evaluated materials, vitrified microbead insulation performs best overall in terms of energy, economic, and carbon benefits at 40–60 mm thickness. These findings support occupant-informed, low-carbon retrofit decision-making for existing residential buildings.

1. Introduction

Buildings constitute a major source of energy consumption and carbon emissions in China, with urban areas contributing nearly 80% of national CO₂ emissions despite occupying only about 1.2% of the land area [1,2]. Urban residential buildings alone account for 47.40% of the building stock and 38.09% of total building energy use [2], highlighting substantial mitigation potential. Among them, existing residential communities exhibit particularly high energy intensity due to inadequate insulation, a range of envelope performance, and outdated building systems [3,4]. Therefore, energy-efficient retrofitting of existing residential communities is critical for reducing urban building energy use and achieving national carbon reduction targets.

Beyond physical factors such as climate conditions and envelope performance, occupant energy-use behavior is a major source of variability in residential energy consumption [2,5]. In aging residential communities, residents are predominantly older adults whose occupancy schedules, activity patterns, and comfort preferences differ substantially from those of working-age groups, thereby reshaping both the magnitude and structure of end-use energy demand. Empirical studies have further demonstrated that variations in occupancy patterns and daily activities constitute one of the dominant contributors to residential energy-use disparities [5]. However, many building energy simulations and retrofit evaluations still rely on standardized or “average” occupant assumptions, which are particularly problematic in aging communities. Age-related physiological changes—such as reduced metabolic heat generation, weakened thermoregulation, and declining visual acuity—lead elderly occupants to require higher indoor thermal stability and illuminance levels and to spend longer durations indoors [4,6,7,8]. These characteristics directly affect residential energy demand and imply that retrofit strategies developed under generic occupant assumptions may yield biased energy, economic, and environmental performance assessments, underscoring the need for occupant-adaptive retrofit evaluation in aging residential buildings [4,7].

Previous studies have provided quantitative evidence on the energy-saving potential of envelope retrofit measures for existing buildings. Ortiz et al. [9] evaluated residential building retrofits in Catalonia and found that exterior wall insulation combined with window upgrades could reduce annual operational energy consumption by approximately 25–40%, with insulation thickness playing a dominant role in heating-dominated climates. Ascione et al. [10] conducted transient simulations for retrofitted educational buildings and reported that improving envelope insulation alone achieved energy savings of up to 30–50%, substantially outperforming window-only retrofit schemes. Similarly, Altieri et al. [11] analyzed low-cost envelope optimization strategies for residential buildings and showed that moderate insulation levels could reduce heating energy demand by 20–35%, while further increases in insulation thickness yielded diminishing marginal returns. Valentini et al. [12], using a life-cycle assessment framework for residential retrofits in Italy, demonstrated that exterior wall insulation provided the largest contribution to operational carbon reduction, accounting for over 60% of total life-cycle emission savings compared with window replacement. In parallel, occupant behavior modeling studies have shown that incorporating realistic occupancy schedules and thermal control behaviors can alter simulated residential energy consumption by more than 20–30% relative to standardized occupant assumptions [13,14,15,16,17]. These findings indicate that both envelope retrofit effectiveness and predicted energy-saving outcomes are highly sensitive to occupancy duration and heating-related behavior, highlighting the necessity of integrating occupant characteristics into retrofit evaluation frameworks. However, most existing retrofit studies either neglect occupant behavior or represent it using generalized assumptions, while most occupant behavior studies do not explicitly examine how age-related differences reshape retrofit evaluation outcomes in aging residential contexts.

To address this gap, this study develops an age-sensitive retrofit evaluation framework for existing residential buildings in Changsha, a representative hot-summer and cold-winter region. Elderly occupants were compared with a unified non-elderly comparison group because the primary objective was to capture the energy-use implications of elderly-specific residential behavior under aging-community contexts while avoiding unnecessary subgroup complexity. Based on this framework, envelope retrofit strategies were evaluated from the perspectives of energy performance, economic feasibility, and operational carbon reduction.

Unlike previous occupant-behavior-based studies that mainly examine generic stochastic schedules or control actions, this study focuses on age-related occupant heterogeneity in aging residential communities and evaluates how such differences alter the relative performance of envelope retrofit measures. Therefore, the contribution of this study lies not in proposing a new occupant behavior algorithm, but in integrating elderly-oriented occupancy and comfort characteristics into a simulation-based retrofit evaluation framework covering energy, economic, and operational carbon dimensions.

2. Methodology

The overall research workflow of this study is illustrated in Figure 1. A simulation-based framework was developed to evaluate envelope retrofit strategies for existing residential buildings while incorporating occupant-related behavioral characteristics. First, age-specific behavioral parameters were extracted from the literature and integrated into the baseline building model to define comparative occupancy scenarios. Second, exterior wall insulation and window retrofit measures were simulated in EnergyPlus. Finally, retrofit performance was assessed from energy, economic, and environmental perspectives to identify optimal strategies and threshold effects. The main contribution of this study lies in integrating occupant heterogeneity into retrofit evaluation rather than developing a new occupant behavior algorithm.

2.1. Extraction and Modeling of Behavioral Characteristics of Older Adults

The age-group setting adopted in this study was designed to reflect the core contrast relevant to aging residential buildings, namely the difference between elderly occupants and the mainstream non-elderly comparison group resident population. Since elderly residents are typically retired, their occupancy schedules and comfort demands differ more fundamentally from other groups than the differences among non-elderly comparison group subgroups themselves. Therefore, this study treated the elderly group as the primary target group and used a single non-elderly comparison group to avoid unnecessary model complexity and parameter uncertainty.

To address limitations of conventional age-based assumptions, this study defines four age-specific occupant behavior dimensions—occupancy rate, illuminance requirement, activity intensity, and thermal-humidity preferences—that directly govern thermal and visual comfort as well as internal heat gains. These dimensions were explicitly parameterized as core occupant boundary conditions and embedded into the EnergyPlus model to represent age-dependent energy demand differences.

To improve transparency in parameter selection, the age-specific behavioral inputs used in this study were derived from peer-reviewed empirical studies and relevant standards according to three criteria: (1) relevance to residential buildings and occupant age differences; (2) consistency with the climatic and usage context of this study; and (3) direct applicability to simulation inputs in EnergyPlus. Priority was given to studies reporting clearly defined and quantifiable parameters for occupancy schedules, illuminance requirements, metabolic rates, and thermal preferences. When discrepancies were found across studies, extreme values from a single source were not directly adopted. Instead, parameter values were determined based on comparability, empirical reliability, and representativeness. If multiple studies showed a consistent trend but reported different magnitudes, representative intermediate values or reasonable ranges were used; if a reported value was highly context-specific, it was used only to identify the direction of age-related differences rather than being directly translated into simulation settings.

Table 1. Literature selection and parameter integration principles for age-specific behavioral inputs.

Behavioral Dimension	Main Data Source Type	Selection Criterion	Conflict-Handling Rule	Final Simulation Representation
Occupancy rate	time-use survey/residential occupancy model	age definition clear, residential context	keep common trend, avoid extremes	hourly weekday/weekend schedule
Illuminance requirement	standards + empirical studies	zone-specific and age-relevant	choose representative value/range	zone illuminance threshold
Activity intensity	empirical studies/compendium values	activity category comparable	preserve ranking, avoid extremes	metabolic rate by zone
Thermal preference	empirical comfort studies	elderly-specific evidence	use trend for schedule translation	seasonal setpoint schedule

summarizes the literature selection logic, conflict-handling rules, and final simulation representation of the four age-specific behavioral dimensions considered in this study.

2.1.1. Occupancy Rate

Elderly occupants generally exhibit higher indoor presence throughout the day than non-elderly comparison group occupants, whose daily routines are largely constrained by work-related activities. Based on national time-use surveys and representative residential occupancy models, age-specific hourly occupancy schedules were constructed for weekdays and weekends [14,18]. As shown in Figure 2, elderly occupants display a characteristic bimodal daily pattern, with activity peaks in the morning (08:00–09:00) and afternoon to early evening (15:00–19:00), separated by a midday rest periods [19,20].

2.1.2. Illuminance Requirements

Due to age-related visual degeneration, elderly occupants require higher indoor illuminance levels to maintain visual comfort during daily activities. Zone-specific illuminance requirements for bedrooms, living rooms, and study rooms were defined following relevant lighting standards and empirical studies [21,22,23,24]. As shown in Figure 3, the illuminance thresholds adopted for elderly occupants were consistently higher than those for the non-elderly comparison group in all three functional zones. The age-specific illuminance requirements defined in this study represent demand-side lighting thresholds rather than fixed indoor daylight assumptions. Daylight availability was calculated dynamically during simulation, whereas occupant-driven adaptive shading actions were not explicitly modeled.

2.1.3. Activity Intensity

Metabolic activity levels were used to represent age-dependent differences in internal heat gains. Elderly occupants were assigned lower metabolic rates across all functional zones, reflecting predominantly low-intensity indoor activities and age-related declines in basal metabolic rate [25,26]. As illustrated in Figure 4, elderly occupants consistently exhibit lower activity intensity than non-elderly comparison group occupants in bedrooms, living rooms, and study rooms [21,27,28,29]. These age-specific metabolic rates, combined with occupancy and illuminance schedules, are directly applied to internal heat gain calculations in EnergyPlus, influencing space heating and cooling demands throughout the simulation.

2.1.4. Thermal and Humidity Preferences

Age-specific thermal and humidity preferences were determined according to empirical comfort studies and physiological evidence. Elderly occupants are characterized by higher neutral indoor temperatures and wider acceptable humidity ranges than non-elderly comparison group adults, reflecting age-related declines in thermoregulation capacity and increased sensitivity to cold conditions [26]. Accordingly, elderly-oriented simulations adopted higher heating setpoints and more moderate cooling setpoints than those used for non-elderly comparison group occupants. Seasonal thermostat schedules reflecting these age-dependent preferences were consistently applied across all conditioned zones, as shown in

Table 2. Neutral values and acceptable ranges of indoor temperature and relative humidity for elderly occupants and the non-elderly comparison group [30,31].

Temperature and Humidity	Elderly Occupants	Non-Elderly Comparison Group
Neutral temperature	25.6 °C	23.2 °C
Acceptable temperature range	Winter: 14–26.8 °C Summer: 16.4–24.8 °C	Winter: 12–24.8 °C Summer: 14.4–22.8 °C
Neutral relative humidity	62%	57%
Acceptable relative humidity range	55–69%	48–66%

Note: This table was compiled and summarized by the authors based on Refs. [30,31]. The reported elderly neutral temperature is a summer adaptive neutral temperature obtained from the cited field study under naturally ventilated conditions, rather than a year-round HVAC setpoint. It is presented only to indicate age-related differences in summer thermal preference. The HVAC control settings used in the simulation were defined separately in Figure 5

and Figure 5, and were directly used to evaluate the energy-saving and comfort performance of envelope retrofit measures under elderly-oriented occupancy conditions.

2.2. Building Energy Modeling of Aging Residential Buildings Based on the Elderly Occupant Behavior

A representative high-rise residential building in Changsha, located in China’s hot summer–cold winter climate zone, was modeled in EnergyPlus to evaluate envelope retrofit performance under age-specific occupancy conditions. The baseline building model represents typical existing residential construction, while differentiated boundary conditions derived from elderly and non-elderly comparison group occupant profiles (Section 2.1) were applied to quantify the influence of occupant behavior on retrofit energy, comfort, and carbon performance.

2.2.1. Typical Residential Building Model

The layout of a standard floor in an existing 28-story residential building with a two-staircase, four-dwelling-per-floor configuration was established for simulation, with the floor plan design referenced from typical residential layouts reported in previous studies [13] (Figure 6). This standard-floor modeling approach was widely recognized [32] and adopted in this study to balance computational efficiency with representativeness of existing residential buildings in the region, while allowing direct incorporation of age-specific occupant behavior.

The standard floor was subdivided into thermally independent zones based on functional use and spatial adjacency (Figure 7). Bedrooms, living rooms, and study rooms were selected as the primary conditioned zones due to their dominant contributions to occupancy duration, thermal comfort demand, and HVAC operation, particularly for elderly occupants. Kitchens and bathrooms were excluded from detailed energy analysis, as air-conditioning systems are typically absent or only intermittently operated in these spaces, and their energy contribution is limited.

2.2.2. Climate Data and Boundary Conditions

Chinese Standard Weather Data (CSWD) in EnergyPlus Weather (EPW) format was adopted as the typical meteorological year input, representing long-term climatic conditions in Changsha [33]. The hot summer–cold winter climate is characterized by pronounced seasonal variations in outdoor air temperature and solar radiation, resulting in substantial differences in heating and cooling demand across seasons (Figure 8).

Baseline envelope constructions and thermal performance parameters were defined according to typical construction practices of existing residential buildings in the region, as presented in

Table 3. Envelope constructions and thermal performance parameters of a typical residential building in Changsha.

Envelope Component	Envelope Construction	Performance Parameters
Exterior wall	20 mm cement mortar + 200 mm sintered perforated brick + 20 mm cement mortar + 15 mm flame-retardant extruded polystyrene (XPS) board + 15 mm cement mortar + 5 mm paint finish	Thermal transmittance, K = 1.0 [W/(m2·K)]
Exterior window	Thermally broken aluminum window frame + Low-E high-transmittance insulating glass (6 + 12A + 6)	Thermal transmittance, K = 3.2 [W/(m2·K]); Solar heat gain coefficient, SHGC = 0.4
Roof	20 mm cement mortar + 40 mm fine aggregate concrete + 90 mm flame-retardant extruded polystyrene (XPS) board + 6 mm waterproof membrane + 20 mm cement mortar leveling layer + 30 mm lightweight aggregate concrete slope layer + 120 mm reinforced concrete + 20 mm mixed mortar	Thermal transmittance, K = 0.4 [W/(m2·K)]
Partition wall	20 mm mixed mortar + 200 mm sintered perforated brick + 20 mm mixed mortar	Thermal transmittance, K = 1.5 [W/(m2·K)]

. A constant air change rate of 1.0 h⁻¹ was adopted to represent typical residential ventilation and infiltration conditions, following the mandatory requirement of the Design Standard for Energy Efficiency of Residential Buildings in Hot Summer and Cold Winter Zone (JGJ 134-2010) [34] for bedrooms and living rooms in both summer and winter [13]. This value was used as a regionally appropriate constant ventilation boundary for comparative simulation.

These parameters were combined with age-specific boundary conditions derived from Section 2.1 to enable occupant-oriented retrofit performance evaluation. In addition, daylight availability in each zone was dynamically determined in EnergyPlus based on hourly weather conditions, solar radiation, building geometry, orientation, and window optical–thermal properties. Therefore, the daylight-related component of lighting demand was not treated as a fixed static assumption.

It should be noted that the baseline case represents a regionally typical existing high-rise residential prototype in Changsha with a moderate envelope performance level, rather than an extremely under-insulated building stock case. Therefore, the reported retrofit gains should be interpreted as incremental improvements over an existing practical baseline.

2.2.3. HVAC Systems and Lighting-Related Internal Loads

An ideal air-conditioning system was employed for space heating and cooling to isolate the influence of HVAC system performance and control strategies. The operation schedules were linked to age-specific occupancy profiles and thermostat setpoints defined in Section 2.1, allowing a focused evaluation of retrofit-induced changes in thermal demand. Internal heat gains from occupants and lighting were defined on a zone basis, with occupant-related gains calculated using age-specific metabolic rates and lighting loads reflecting age-dependent illuminance requirements. This approach ensures that simulated energy consumption and thermal comfort outcomes directly reflect both envelope characteristics and elderly occupant behavior, providing a controlled framework for evaluating retrofit effectiveness.

To improve reproducibility, the lighting-energy model was defined using explicit assumptions on lighting power density (LPD), illuminance demand, control logic, and daylight interaction. The LPD values were assigned by functional zone as 4.5 W/m² for the bedroom, 5.0 W/m² for the living room, and 4.8 W/m² for the study. Age-related differences between elderly-oriented and non-elderly comparison group-oriented scenarios were represented through differentiated illuminance requirements (Figure 3), while the installed lighting power densities remained unchanged across scenarios.

Electric lighting operation was controlled by a rule-based strategy linked to occupancy and daylight availability. During daytime, lighting was switched on when occupants were present and available daylight was insufficient to satisfy the corresponding indoor illuminance requirement, i.e., daylight compensation was considered in the simulation. During nighttime, lighting was set to remain off from 22:00 to 07:00.

In the present study, daylight availability was dynamically influenced by hourly outdoor weather conditions and window properties, whereas occupant-driven dynamic shading or blind-control behavior was not explicitly modeled. Therefore, shading effects were treated as fixed boundary characteristics associated with the baseline building geometry and envelope configuration, rather than as adaptive behavioral variables. These assumptions were applied consistently across all retrofit scenarios to ensure comparability in lighting-energy evaluation.

Owing to the limited availability of measured operational data for the selected aging residential prototype, the baseline model was not calibrated against field measurements. Therefore, the simulation results should be interpreted primarily as comparative estimates for assessing the relative performance of retrofit options under consistent assumptions, rather than as exact predictions of real-world energy use.

2.3. Proposed Energy-Saving Measures

This study examines exterior wall insulation and window replacement as representative envelope retrofit measures, as they are widely adopted and technically mature strategies in existing residential retrofitting practice and can be implemented without major structural intervention. More importantly, these two measures directly regulate heat loss through the building envelope and daylight transmission through fenestration, which are closely linked to the elevated thermal stability and visual comfort requirements of elderly occupants. This makes them particularly suitable for evaluating envelope retrofit performance under elderly-oriented thermal and visual demand conditions [10,35].

2.3.1. Exterior Wall Insulation Retrofit

Six insulation materials commonly used in residential exterior wall retrofitting in China were selected to represent a broad range of thermal performance, material composition, and construction characteristics. These materials were chosen to reflect practical retrofit options applicable to aging residential buildings, where construction feasibility, cost sensitivity, and long-term thermal performance are critical considerations. These include vitrified microbead insulation (VMI), polyurethane composite panels (PU), flame-retardant extruded polystyrene boards (XPS), rock wool strips (RW), modified foamed cement insulation boards (MFCIB), and inorganic thermal insulation mortar (ITIM).

The key thermophysical properties of these materials—thermal conductivity, thermal resistance, apparent density, and specific heat capacity—are presented in

Table 4. Thermophysical properties of external wall insulation materials.

Insulation Material	Thermal Conductivity [W/(m·K)]	Apparent Density [kg/m3]	Specific Heat Capacity [J/(kg·K)]
Vitrified microbeads insulation (VMI)	0.030	300	1000
Polyurethane composite panel (PU)	0.036	40	1460
Flame-retardant extruded polystyrene board (XPS)	0.040	30	1400
Rock wool strips (RW)	0.050	48	750
Modified foamed cement insulation board (MFCIB)	0.070	330	1075
Inorganic thermal insulation mortar (ITIM)	0.085	370	1000

. Based on material composition and thermal characteristics, the selected insulation systems cover lightweight polymer-based, conventional synthetic, and mineral-based insulation categories, enabling comprehensive comparison under identical boundary conditions.

For each insulation material, thickness was varied parametrically from 20 to 130 mm to capture the non-linear relationship between insulation level and energy-saving performance within a practically feasible retrofit range. This range covers commonly implemented insulation thicknesses in residential retrofits and allows identification of diminishing marginal energy-saving effects under elderly-oriented thermal demand conditions. Retrofit measures were applied to the exterior walls of conditioned zones while maintaining all other building parameters unchanged.

Cost data for exterior wall insulation retrofitting, including material, transportation, and waste disposal costs, were derived from market surveys and references [36,37], as presented in

Table 5. Cost of exterior wall insulation materials at different retrofitting stages.

Insulation Material Type and Thickness for Exterior Wall Retrofitting	Material and Transportation Costs [CNY/m2]	Transportation and Disposal Costs of Waste Materials [CNY/m2]	Total Cost per Unit Area [CNY/m2]
20–130 mm vitrified microbeads	4.68–30.42	0.70–4.55	5.38–34.97
20–130 mm polyurethane composite panel	23.00–149.50	0.70–4.55	23.70–154.05
20–130 mm flame-retardant extruded polystyrene (XPS) board	5.60–39.00	0.70–4.55	6.30–43.55
20–130 mm rock wool strips	13.30–86.45	0.70–4.55	14.00–91.00
20–130 mm modified foamed cement insulation board	50.00–160.00	0.70–4.55	50.70–164.55
20–130 mm inorganic thermal insulation mortar	44.00–99.00	0.70–4.55	44.70–103.55

Note: Cost ranges correspond to insulation thicknesses varying from 20 to 130 mm.

. All costs represent initial retrofit investment and were normalized per unit area to ensure consistency with subsequent life-cycle economic evaluation.

2.3.2. Exterior Window Retrofit

Window retrofit scenarios were defined by combining nine representative glazing configurations (A–I) with five commonly used window frame types (I–V) in accordance with the Energy Efficiency Design Standard for Residential Buildings in Hunan Province (DBJ43/T025-2022) [38]. This configuration scheme enables systematic evaluation of window retrofit options that are compliant with local design practice while covering a wide range of thermal and optical performance levels. More importantly, the selected glazing–frame combinations allow simultaneous consideration of heat transfer, solar heat gain, and daylight transmittance, which are particularly relevant for elderly occupants with elevated thermal stability and visual comfort requirements.

The selected window frame types include thermally broken aluminum alloy frames with different thermal break widths (14.8–35 mm) and PVC frames with single and multi-chamber structures, resulting in frame thermal transmittance values (Kf) ranging from 1.8 to 4.2 W/(m²·K). When combined with the nine glazing configurations, the resulting whole-window U-values range from approximately 1.21 to 2.65 W/(m²·K), while solar heat gain coefficients (SHGC) values vary between 0.22 and 0.54 (as presented in

Table 6. Window performance of different window frame types combined with different glazing configurations.

ID		I		II		III		IV		V
	Window Frame	Thermally Broken Aluminum Alloy Frame: Thermal Break Width = 14.8 mm, Kf = 4.2 W/(m2·K), Frame Area Ratio = 20%, ρ = 0.4		Thermally Broken Aluminum Alloy Frame: Thermal Break Width = 24 mm, Kf = 2.8 W/(m2·K), Frame Area Ratio = 20%, ρ = 0.4		Thermally Broken Aluminum Alloy Frame: Thermal Break Width = 35 mm, Kf= 1.8 W/(m2·K), Frame Area Ratio = 20%, ρ = 0.4		PVC Frame: Thermal Break Width = 35 mm, Kf = 2.7 W/(m2·K), Frame Area Ratio = 25%, ρ = 0.4		Multi-Chamber PVC Frame: Thermal Break Width = 35 mm, Kf = 2.0 W/(m2·K), Frame Area Ratio = 25%, ρ = 0.6
	Visible Light Transmittance τᵥ (–)	U-Value [W/(m2·K)]	SHGC (–)	U-Value [W/(m2·K)]	SHGC (–)	U-Value [W/(m2·K)]	SHGC (–)	U-Value [W/(m2·K)]	SHGC (–)	U-Value [W/(m2·K)]	SHGC (–)
A	0.72	2.65	0.48	2.37	0.47	2.17	0.47	2.39	0.45	2.22	0.45
B	0.68	2.30	0.36	2.02	0.35	1.82	0.35	2.07	0.34	1.89	0.33
C	0.62	2.26	0.28	1.98	0.27	1.78	0.27	2.03	0.27	1.86	0.26
D	0.61	2.11	0.28	1.83	0.27	1.63	0.27	1.89	0.27	1.71	0.26
E	0.70	2.54	0.54	2.26	0.53	2.06	0.53	2.29	0.51	2.12	0.51
F	0.61	2.04	0.33	1.76	0.32	1.56	0.31	1.82	0.31	1.65	0.30
G	0.58	2.01	0.27	1.73	0.26	1.53	0.26	1.79	0.26	1.62	0.25
H	0.49	1.89	0.25	1.61	0.24	1.41	0.23	1.68	0.24	1.50	0.23
I	0.48	1.69	0.24	1.41	0.23	1.21	0.23	1.49	0.23	1.32	0.22

Note: The U-value represents the overall thermal transmittance of the window system, accounting for both glazing and frame effects. SHGC denotes the solar heat gain coefficient.

). These variations represent substantial differences in conductive heat transfer and solar heat gain control across glazing–frame combinations.

Meanwhile, visible light transmittance (τᵥ) of the selected window systems spans from 0.48 to 0.72, indicating notable variation in daylight availability that directly affects lighting energy demand. This parameter is particularly important in elderly-oriented retrofit evaluation, as higher illuminance requirements among elderly occupants can increase lighting energy demand and potentially offset thermal energy savings achieved through window insulation improvements. Therefore, the selected window configurations enable assessment of the trade-offs between thermal performance and daylight provision under age-specific lighting demand conditions.

To isolate the specific impacts of window retrofits on building energy performance, cost ranges for different glazing–frame combinations were collected based on market data and engineering practice (

Table 7. Cost of exterior window retrofit options with different window frame materials.

Window Frame Type and Glazing Configuration	Material and Transportation Costs [CNY/m2]	Transportation and Disposal Costs of Waste Materials [CNY/m2]	Total Cost per Unit Area [CNY/m2]
I + (A/B/C/D/E/F/G/H/I)	450–595	0.84–1.47	450.8–596.5
II + (A/B/C/D/E/F/G/H/I)	465–610	0.84–1.47	465.8–611.5
III + (A/B/C/D/E/F/G/H/I)	485–630	0.84–1.47	485.8–631.5
IV + (A/B/C/D/E/F/G/H/I)	350–555	0.84–1.47	350.8–556.5
V + (A/B/C/D/E/F/G/H/I)	380–575	0.84–1.47	380.8–576.5

Note: Cost ranges correspond to different window frame types combined with glazing configurations A–I.

). Window retrofit scenarios were evaluated independently from exterior wall insulation measures, allowing clear identification of their individual effects on heating, cooling, and lighting energy consumption, as well as associated economic and environmental performance.

2.4. Evaluation Indicators

To comprehensively evaluate the performance of different envelope retrofit schemes, a set of quantitative indicators was established covering three complementary dimensions: energy-saving performance, economic performance, and environmental performance. All indicators were normalized on a unit floor area basis to ensure comparability among different retrofit scenarios.

2.4.1. Energy-Saving Performance

The energy-saving performance of envelope retrofit measures was assessed using two indicators: annual energy saving per unit area and energy saving rate. The annual total energy consumption per unit area of the baseline building and that of the retrofitted building under the scenario were calculated according to Equations (1) and (2), respectively. The total energy consumption includes cooling, heating, and lighting energy use, expressed in kWh/m². Based on these values, the energy saving per unit area, $∆E$, is defined as the absolute reduction in total energy consumption attributable to envelope retrofit measures, as shown in Equation (3). To facilitate normalized comparison among different retrofit schemes, the energy saving rate was further introduced and defined as the ratio of energy savings to baseline energy consumption, as expressed in Equation (4).

$$E_{t0}=E_{c0}+E_{h0}+E_{l0}$$

(1)

$$E_{ti}=E_{ci}+E_{hi}+E_{li}$$

(2)

$$∆E=E_{t0}-E_{t\mathrm{i}}$$

(3)

$${\eta }_E={\displaystyle \frac{∆E}{E_{t0}}}$$

(4)

2.4.2. Economic Performance

The economic performance of envelope retrofit measures was evaluated by balancing the initial retrofit investment against the cumulative reduction in operational electricity costs over the remaining service life of the building [39].

Net economic benefit per unit area was adopted as the primary economic indicator. This indicator represents the total electricity cost savings achieved through envelope retrofitting over the building’s remaining service life, minus the initial retrofit investment cost. The net economic benefit is calculated using Equation (5).

$$IM=\left({EA}_{pre}-{EB}_{post}\right)\times Y_b\times P_e-M$$

(5)

where $IM$ denotes the net economic benefit per unit area after envelope retrofitting (CNY/m²); ${EA}_{pre}$ and ${EB}_{post}$ represent the annual electricity consumption per unit area before and after retrofitting, respectively (kWh/m²); $Y_b$ is the remaining service life of the building (years); $P_e$ is the average electricity price, 0.588 CNY/kWh, corresponding to the common residential electricity tariff in Hunan Province, China; and $M$ represents the retrofit cost per unit area (CNY/m²), including material, transportation, and disposal costs as described in Section 2.4.

It should be noted that the economic assessment in this study is a simplified static evaluation intended for comparative analysis of retrofit options under consistent assumptions. The current framework does not explicitly incorporate discount rate effects, maintenance/replacement costs, or time-varying energy prices. Therefore, the reported economic indicators should be interpreted as comparative estimates within the adopted boundary, rather than as a full discounted cash flow (DCF)-based life-cycle financial analysis.

2.4.3. Environmental Performance

The environmental performance of envelope retrofit measures was assessed by quantifying the reduction in operational carbon emissions associated with electricity consumption. As the focus of this study is on operation-stage performance, embodied carbon emissions of materials were not considered [39,40].

The annual carbon emission reduction per unit area was used as the primary environmental indicator and is calculated in Equation (6). Based on this, the annual carbon reduction intensity (ACRI) was introduced to characterize the absolute operational carbon reduction achieved per unit floor area per year.

To improve comparability among retrofit schemes with different baseline emission levels, a relative indicator—the relative annual carbon reduction intensity ($∆ACRI$)—was further defined in Equation (7). This dimensionless metric normalizes $ACRI$ by the baseline operational carbon emissions per unit area, enabling consistent comparison of environmental performance across different retrofit scenarios.

$$ACRI=({EA}_{pre}-{EA}_{post})\times EF$$

(6)

In Equation (6), where $ACRI$ denotes the annual carbon emission reduction per unit area (kgCO₂eq/(m²·year)); ${EA}_{pre}$ and ${EA}_{post}$ represent the annual electricity consumption per unit area before and after retrofitting, respectively (kWh/m²); and $EF$ is the carbon emission factor of electricity (kgCO₂eq/kWh).

$$∆ACRI={\displaystyle \frac{\mathit{ACRI}}{{\mathit{EC}}_{\mathit{base}}}}$$

(7)

where $∆ACRI$ denotes the relative annual operational carbon reduction intensity (–); and ${\mathit{EC}}_{\mathit{base}}$ is the annual operational carbon emissions per unit area of the baseline building (kg CO₂eq/(m²·year)).

It should be noted that the environmental assessment in this study focuses on operational-stage carbon reduction associated with energy savings during the use phase. Embodied carbon emissions related to retrofit materials (including production, transportation, installation, maintenance/replacement, and end-of-life processes) are not included in the current system boundary. Therefore, the reported environmental results should be interpreted as use-phase carbon mitigation performance rather than full life-cycle carbon outcomes.

3. Results and Discussion

3.1. Baseline Energy Consumption Characteristics

3.1.1. Analysis of Energy Consumption on Typical Winter and Summer Days

On a typical winter day, heating energy consumption in elderly-oriented dwellings is approximately 20–40% higher than that of the non-elderly comparison group across most hours, with heating operation remaining nearly continuous throughout both daytime and nighttime (Figure 9). This increase is primarily attributable to higher indoor occupancy rates (close to 100% for elderly residents during most daytime hours) and higher heating setpoints, reflecting reduced thermoregulatory capacity and greater sensitivity to cold conditions.

In contrast, summer cooling demand in elderly households is about 10–30% lower in peak intensity and exhibits a smoother diurnal profile than that of non-elderly comparison group occupants, despite longer indoor presence (Figure 10). This reduction is mainly driven by higher cooling setpoints and more conservative cooling behavior, which offset the effect of increased occupancy duration.

For lighting, elderly households consistently consume approximately 20–50% more lighting energy than the non-elderly comparison group during both summer and winter, particularly in the morning and evening periods (Figure 9 and Figure 10). This increase results from a combination of longer indoor occupancy durations and higher illuminance requirements associated with age-related visual degradation.

Overall, elderly-oriented residential energy demand is characterized by elevated heating and lighting loads and relatively moderated cooling demand, indicating that energy use is dominated by persistent transmission heat losses during the heating season rather than short-term cooling peaks. In conjunction with the outdoor environmental conditions shown in Figure 11, these findings further confirm that improving envelope thermal insulation is essential for reducing baseline energy loads in aging residential buildings.

3.1.2. Analysis of Monthly Energy Consumption

For heating, elderly-oriented dwellings show approximately 20–40% higher monthly heating energy consumption than non-elderly comparison group dwellings during peak winter months, and the difference remains evident during transitional periods at the beginning and end of the heating season (Figure 12). This extended heating demand duration is primarily driven by higher indoor occupancy rates, higher heating setpoints, and greater sensitivity to cold conditions among elderly occupants, leading to sustained heating operation beyond conventional heating periods.

In contrast, cooling energy consumption in elderly-oriented dwellings is generally 10–30% lower during summer months and exhibits a flatter monthly profile with reduced peak intensity (Figure 12). Despite longer indoor occupancy, this reduction reflects higher cooling setpoints and more conservative cooling behavior, which reduce sensitivity to short-term outdoor heat extremes.

Lighting energy consumption shows a stable year-round increase of approximately 20–50% in elderly-oriented dwellings compared with non-elderly comparison groups (Figure 13). The difference becomes more pronounced in winter months, when shorter daylight hours coincide with longer indoor occupancy durations and higher illuminance requirements associated with age-related visual decline.

Overall, the monthly results confirm that elderly-oriented residential energy demand is dominated by prolonged and continuous heating requirements, rather than by short-term cooling peaks. Consistent with the monthly outdoor environmental conditions shown in Figure 14, this pattern, consistent with the typical-day analysis, underscores the importance of improving baseline envelope thermal performance to reduce persistent heat losses in aging residential buildings.

3.1.3. Analysis of Annual Energy Consumption

The annual energy consumption results further confirm the age-related patterns observed at the typical-day and monthly scales. Compared with non-elderly comparison group occupants, elderly-oriented dwellings exhibit higher heating and lighting energy consumption and lower cooling demand, resulting in a distinctly different annual end-use energy structure (Figure 15 and Figure 16).

In terms of heating, elderly-oriented dwellings consume 25.90 kWh/m² annually, which is 24.10% higher than that of non-elderly comparison group dwellings (20.87 kWh/m²) (Figure 15). This increase reflects the cumulative effects of longer indoor occupancy durations, higher heating setpoints, and extended heating operation periods, consistent with the sustained heating demand identified at shorter time scales.

By contrast, annual cooling energy consumption decreases from 12.61 to 8.96 kWh/m², corresponding to a 28.95% reduction for elderly occupants (Figure 15). Despite longer indoor presence, this reduction is primarily attributed to higher cooling setpoints and more conservative cooling behavior, leading to lower peak intensity and reduced sensitivity to short-term outdoor heat extremes.

Lighting energy consumption increases from 10.10 to 12.81 kWh/m² (+26.83%) (Figure 15), driven by higher illuminance requirements and prolonged indoor occupancy. As a result, heating and lighting together account for over 80% of total annual energy use in elderly-oriented dwellings (Figure 16), confirming a “high heating–high lighting–low cooling” profile and reinforcing the importance of improving baseline envelope thermal performance in aging residential buildings.

3.2. Retrofit Case Simulation Results

To comprehensively evaluate envelope retrofit strategies under elderly-oriented occupancy conditions, the energy-saving, economic, and environmental performances of exterior wall insulation and window retrofit measures were analyzed across different functional zones, retrofit intensities, and remaining service life scenarios. The following results are interpreted in light of the underlying physical mechanisms and occupant-related characteristics.

3.2.1. Analysis of Energy-Saving Potential

Exterior Wall Insulation Retrofit

As shown in Figure 17 and Figure 18, exterior wall insulation retrofitting provides consistent and thickness-dependent energy-saving benefits across all functional zones under elderly-oriented occupancy. For all insulation materials, energy-saving rates increase monotonically with insulation thickness, confirming the effectiveness of reducing transmission heat losses through the envelope.

Nevertheless, the thickness–performance relationship exhibits clear diminishing marginal returns. Incremental energy-saving gains are highest at 20–40 mm (approximately 0.35–0.55%) and decline markedly at 40–60 mm (0.15–0.30%). Beyond 60 mm, marginal gains fall below 0.10–0.15%, indicating limited additional benefit from excessive insulation.

Functional-zone differences are evident. At the same thickness, the study room achieves the highest energy-saving rates (about 4.0–4.3% at 60 mm), followed by the bedroom (3.6–3.9%), while the living room shows the lowest improvement (3.2–3.5%). This hierarchy is mainly associated with differences in occupancy duration and heating demand.

Material performance also varies. Vitrified microbead insulation consistently delivers the highest energy-saving rates, outperforming higher-conductivity materials by approximately 10–20%, particularly at moderate thicknesses (40–60 mm). The superior performance of vitrified microbead insulation observed in this study is broadly consistent with the general importance of wall optimization highlighted in advanced wall-system research. Nath et al. [41] showed that the energy performance of PCM-CLT wall systems is jointly influenced by climatic conditions, wall configuration, and material thermal properties; however, the present conclusion should not be directly extended to such advanced systems because of substantial differences in material composition and thermal mechanisms.

Window Retrofit Measures

As shown in Figure 19 and Figure 20, window retrofitting can reduce residential energy consumption under elderly-oriented occupancy, but its effectiveness is highly dependent on glazing–frame combinations and remains significantly lower than that of exterior wall insulation. Across functional zones, unit-area energy-saving rates generally range from 1.2% to 4.5% and do not increase monotonically with successive glazing upgrades.

Incremental analysis indicates clear diminishing and configuration-sensitive returns. The largest gains occur when upgrading from single glazing to basic double glazing, with incremental improvements of approximately 0.4–0.8%, whereas further upgrades to higher-performance glazing systems yield limited or even negative gains (typically within −0.4% to +0.2%), reflecting the combined effects of conductive heat loss reduction, altered solar gains, and the limited window-to-wall area ratio.

Functional-zone differences are modest: the study room and bedroom show slightly higher responses (by about 0.3–0.6 percentage points) due to longer occupancy durations, yet window retrofits remain less robust and less predictable than wall insulation under elderly-oriented occupancy conditions.

3.2.2. Economic Feasibility Analysis

For the economic analysis, full results were obtained for all evaluated insulation materials, functional zones, insulation thicknesses, and remaining service life scenarios. For clearer interpretation, the discussion highlights representative materials and the study room, which exhibits the highest energy-saving potential.

Exterior Wall Insulation Retrofit

Figure 21 and Figure 22 illustrate the economic performance of six exterior wall insulation materials under varying insulation thicknesses (20–130 mm) and remaining building service lives (10–70 years) across different functional zones. The results indicate that economic feasibility is jointly determined by material type, insulation thickness, remaining service life, and functional zone, and exhibits clear threshold effects rather than linear trends.

Among the evaluated materials, vitrified microbeads insulation (VMI) demonstrates the most favorable economic performance. In the study room, VMI achieves positive net economic benefits of approximately 80–100 CNY/m² at moderate thicknesses (40–60 mm) when the remaining service life exceeds 40–50 years. However, even for VMI, economic benefits decline rapidly with excessive thickness (>90–100 mm) or short remaining service life (<20 years), with net values decreasing to below 0 CNY/m² and reaching around −20 to −40 CNY/m² under unfavorable conditions.

In contrast, modified foamed cement insulation board (MFCIB) and inorganic thermal insulation mortar (ITIM) exhibit predominantly negative economic performance across most thickness–lifetime combinations. Their net economic outcomes typically range from −40 to −140 CNY/m², indicating that increased insulation thickness cannot offset their limited energy-saving potential and higher material costs. Only at minimal thicknesses (20–30 mm) and long service lives do these materials approach marginal economic viability.

Functional-zone differences are evident. The study room consistently shows the highest economic benefits, generally 20–40 CNY/m² higher than the living room under identical conditions, due to longer occupancy duration and higher heating demand. Overall, the economic optimum across materials and zones is concentrated at moderate insulation thicknesses (40–60 mm) combined with long remaining service lives (>30–40 years), highlighting the importance of balancing insulation depth, material selection, and lifecycle context in retrofit decision-making.

Window Retrofit Measures

Figure 23 and Figure 24 show that, under the assumed costs and elderly-oriented energy use, all window retrofit schemes yield negative net economic benefits, regardless of glazing configuration, frame type, functional zone, or remaining service life.

For glazing configurations A (best) and I (worst) combined with five frame types (Figure 23), the net economic performance per unit area typically lies between about −330 and −600 CNY/m². Even under the most favorable conditions—configuration A with the best-performing frame and a remaining service life of 70 years—the net benefit in the bedroom, living room, and study room remains negative, around −330 to −380 CNY/m². Shorter remaining service lives (≤20 years) further deteriorate the economics, with values commonly below −500 CNY/m². Configuration I is consistently worse, often 100–150 CNY/m² lower than configuration A under the same frame and lifetime, reflecting a higher cost but only modest additional energy savings. Functional-zone differences are relatively small, indicating that window retrofit economics are less sensitive to occupancy patterns than wall insulation.

Overall, these results demonstrate that, under current costs, window retrofits are economically unattractive, and their role in envelope retrofit strategies should be secondary to exterior wall insulation in aging residential buildings.

Overall, wall retrofit measures can achieve scenario-dependent economic viability, whereas window retrofits remain economically unfavorable under the present cost assumptions. Because economically suboptimal options may still provide operational carbon reduction benefits, environmental performance was further examined independently.

3.2.3. Environmental Performance Analysis

Exterior Wall Insulation Retrofit

As shown in Figure 25, exterior wall insulation retrofitting results in a clear and monotonic increase in annual carbon reduction intensity (ACRI) with increasing insulation thickness across all functional zones and materials. For example, when insulation thickness increases from 20 to 60 mm, ACRI rises from approximately 0.8–1.0 to 1.3–1.5 kg CO₂/m² in the bedroom and living room, and from 1.1–1.3 to 1.6–1.8 kg CO₂/m² in the study room, depending on material type. Further increases to 130 mm yield only moderate additional gains, with ACRI approaching 1.7–1.8 kg CO₂/m² in the study room and remaining below 1.2 kg CO₂/m² in the living room.

The relative annual carbon reduction intensity (ΔACRI) exhibits pronounced diminishing returns. The highest marginal benefits occur at low thickness increments (20–40 mm), where ΔACRI typically reaches 12–18% in the study room and 8–14% in the bedroom and living room. Within the 40–60 mm range, ΔACRI decreases to approximately 5–8%, indicating that a large proportion of achievable carbon mitigation has already been realized. Beyond 80–100 mm, marginal gains fall below 3–4% across all zones, approaching negligible levels.

Clear functional-zone differences are observed. The study room consistently achieves the highest ACRI and ΔACRI, reflecting the amplification effect of longer occupancy duration and sustained heating demand. Overall, these results demonstrate that moderate insulation thicknesses (40–60 mm) provide the most carbon-efficient retrofit solution under elderly-oriented occupancy conditions.

Window Retrofit Measures

Figure 26 shows that window retrofit measures provide limited and strongly configuration-dependent carbon reduction benefits across all functional zones. The annual carbon reduction intensity (ACRI) achieved by window upgrades generally ranges from 0.4 to 1.6 kg CO₂/m², which is substantially lower than that of exterior wall insulation. Among functional zones, the study room consistently exhibits higher ACRI (approximately 1.3–1.6 kg CO₂/m² for double glazing), while values in the bedroom and living room are mostly below 1.0 kg CO₂/m².

Incremental carbon reduction efficiency (ΔACRI) demonstrates clear non-linear behavior. The largest marginal gains occur when upgrading from single glazing to basic double glazing, with ΔACRI typically reaching 8–15%. In contrast, further upgrades among high-performance glazing configurations often yield diminishing or even negative marginal efficiencies, with ΔACRI fluctuating between −15% and +10%.

Overall, window retrofitting delivers lower and less predictable carbon mitigation potential than exterior wall insulation, especially under elderly-oriented occupancy where heating-dominated transmission losses prevail.

3.2.4. Integrated Comparison and Implications

Integrating the energy-saving, economic, and environmental results reveals a clear hierarchy among envelope retrofit strategies. Exterior wall insulation consistently outperforms window retrofitting across all evaluated dimensions, providing more robust, predictable, and cost-effective performance improvements. Moderate insulation thicknesses (approximately 40–60 mm) represent an optimal range in which most energy and carbon reduction benefits can be achieved while maintaining economic feasibility.

Across all measures, retrofit effectiveness is highest in the study room and bedroom, reflecting the influence of prolonged occupancy duration and sustained heating demand in elderly-oriented dwellings. This result indicates clear differences in envelope retrofit benefits across functional rooms. Compared with a uniform whole-building retrofit approach, high-benefit spaces such as bedrooms and study rooms may be prioritized for targeted interventions, suggesting the feasibility of room-level retrofit strategies in existing residential renovation. The bedroom and study room were identified as high-benefit zones for envelope retrofit, which is consistent with previous studies on envelope thermal performance. Vighio et al. [42] showed that the combined effects of walls, windows, and solar gains can produce differentiated energy responses across spaces, which supports the present identification of priority retrofit zones from a thermal-mechanism perspective. These findings indicate that envelope retrofit strategies for aging residential buildings should prioritize moderate-thickness exterior wall insulation using high-efficiency materials and focus on high-occupancy spaces to maximize overall retrofit effectiveness.

In practical retrofit projects, wall insulation and window upgrading are often implemented as a combined strategy. Although such integrated retrofits may achieve greater overall energy savings than single measures, their benefits are not necessarily a simple linear addition of the individual effects, but are jointly influenced by thermal characteristics, window-to-wall ratio, spatial zoning, and occupant behavior.

Taken together, the results indicate that the comparative advantage of wall retrofits in this study is closely related to the elderly-oriented demand structure, in which prolonged heating demand and increased lighting use outweigh cooling-related savings. Under such conditions, measures that improve opaque-envelope thermal resistance provide more stable annual benefits than window upgrades, whose performance remains more sensitive to glazing–frame combinations, solar gains, and cost levels. This also explains why moderate wall insulation performs more robustly across the energy, economic, and environmental dimensions.

This finding is consistent with the energy-saving and carbon-reduction results, indicating that moderate insulation thicknesses provide the best overall balance between technical performance and lifecycle feasibility.

4. Conclusions

This study assessed envelope retrofit strategies for existing residential buildings in the hot-summer and cold-winter region by explicitly incorporating age-related differences in occupancy patterns, thermal preferences, and lighting demand. Based on EnergyPlus simulations of a representative existing high-rise residential building in Changsha, the results demonstrate that age-related occupant behavior materially affects both the baseline residential energy-use structure and the comparative performance of retrofit measures. In particular, elderly-oriented occupancy shifts residential energy demand toward more persistent heating- and lighting-related loads, thereby increasing the relative effectiveness of exterior wall insulation. Under this demand pattern, exterior wall insulation emerges as the more robust retrofit strategy, whereas window retrofit measures provide comparatively limited and less reliable overall benefits. From a practical perspective, retrofit planning for aging residential communities should therefore prioritize moderate-thickness exterior wall insulation using cost-effective materials, while window retrofits should be adopted selectively according to building conditions and project objectives.

The main conclusions are as follows:

(1)

Elderly-oriented occupancy produces a distinct baseline residential energy-use structure. Compared with the non-elderly comparison group, elderly-oriented dwellings exhibit higher heating demand, higher lighting demand, and relatively lower cooling demand. As a result, annual energy use is governed more strongly by persistent winter heat losses and age-related visual comfort requirements than by short-duration summer cooling peaks.

(2)

Exterior wall insulation retrofits show the most robust overall performance under elderly-oriented occupancy. Across energy, economic, and environmental evaluations, moderate insulation thicknesses—especially 40–60 mm—provide the best overall balance between technical performance and lifecycle feasibility. Among the evaluated materials, vitrified microbead insulation performs best overall, while the study room and bedroom exhibit higher retrofit sensitivity than the living room.

(3)

Window retrofit measures yield only limited overall benefits under the evaluated conditions. Although window upgrades can reduce energy consumption to a certain extent, their effects are substantially weaker and less stable than those of exterior wall insulation. Under the assumed cost conditions and elderly-oriented demand profile, all evaluated window retrofit schemes remain economically unfavorable, even under long-remaining service-life scenarios.

(4)

The quantitative conclusions are context-dependent, but the analytical framework is transferable. Because the analysis was based on one representative existing high-rise residential building in Changsha, the numerical results should be interpreted as most applicable to buildings with similar climatic and architectural characteristics. Nevertheless, the broader contribution of this study lies in the proposed retrofit evaluation framework, which incorporates age-related occupant behavior differences into envelope retrofit assessment and can support further applications in other climates, building types, and demographic contexts.

Several limitations should also be acknowledged. The relative performance of wall and window retrofit measures may vary with building type, shape coefficient, window-to-wall ratio, roof exposure, and functional layout. In buildings with larger window-to-wall ratios, the contribution of window heat transfer and solar gains may become more significant, potentially increasing the relative advantage of window retrofits. By contrast, in low-rise or multi-story buildings, the combined influence of roofs and external walls may be stronger. Climate conditions may likewise alter retrofit priorities. In Changsha’s hot-summer and cold-winter climate, exterior wall insulation can simultaneously mitigate summer heat gains and winter heat losses, thereby delivering relatively stable annual benefits. In severe cold climates, this advantage may be further strengthened, whereas in hot-summer and warm-winter climates, window performance, shading, and solar heat-gain control may assume greater importance.