Evaluation and Analysis of Passive Energy Saving Renovation Measures for Rural Residential Buildings in Cold Regions: A Case Study in Tongchuan, China

Abstract

Energy-saving renovation of rural residences is an effective means of promoting sustainable rural development. This study focuses on a single-story rural residential building located in Tongchuan City, Shaanxi Province, China (a cold region), as a case study. Retrofits were conducted on the exterior windows, roof, and exterior walls, with the addition of a sunroom. Using life cycle assessments (LCAs) and orthogonal experimental methods combined with value engineering principles, we calculated various indicators including the energy efficiency improvement rate, implied carbon emissions, proportion of implied carbon emissions, carbon footprint, carbon reduction rate, carbon payback period, and investment payback period. The impact of traditional retrofitting measures on these indicators was analyzed. The results indicate that carbon emissions from the production of building materials are a key concern among the embodied carbon emissions from the retrofits, while transportation, construction, and demolition contribute minimally. Changes in the depth of the sunroom had the most significant impact on comprehensive indicators, followed by changes to the roof. After retrofitting, the carbon reduction rate was underestimated by 9.35% to 12.02% due to embodied carbon emissions. The carbon payback period for all schemes is estimated to be between 3.27 and 4.21 years. Based on current market conditions, developing corresponding carbon economics can enhance the economic viability of the project. This approach extends the investment payback period by more than 7% while also helping to narrow the income gap between urban and rural residents to some extent. Overall, the environmental impact assessment of the alternative schemes promotes sustainable rural development and provides scientific and effective guidance for the construction of project decision-making evaluation systems and architectural designers.

1. Introduction

Buildings are a key driver of growing global energy demand [1]. According to calculations by the International Energy Agency, in 2020, global building operational energy consumption accounted for approximately 36% of total societal energy consumption, with carbon dioxide emissions accounting for 37% of total emissions [2,3]. The Research Report on Building Energy Consumption in China (2022) indicates that China’s building and construction sectors consumed 45.5% of the country’s total energy in 2020, with life cycle carbon emissions reaching about 50.9% of the nation’s total carbon emissions, making it one of the industries with the highest carbon emissions [4]. With economic development and the continuous improvement of rural economic levels, the quality of life for rural residents has significantly improved, increasing the demand for indoor thermal comfort in residential buildings. This trend directly leads to higher energy demands, primarily in heating, cooling, and ventilation [5], thereby exacerbating the contradiction between rural building energy consumption and national low-carbon sustainable development goals.

According to the 2023 Annual Report on China Building Energy Efficiency, rural residential energy consumption was 232 million tons of standard coal, accounting for 20.90% of the country’s total building operational energy consumption that year. Carbon emissions related to energy consumption in rural residential buildings were as high as 490 million tCO₂e. Therefore, rural residential buildings have enormous potential for improving building energy efficiency and reducing carbon emissions. Renovating residential buildings is one of the critical means to reduce energy consumption and address climate change [6,7], which will also be an important direction for the construction of new rural areas and sustainable development.

To achieve the goals of peaking carbon emissions before 2030 and achieving carbon neutrality before 2060, national requirements for various industries have been upgraded from dual control of energy consumption to dual control of carbon emissions (controlling both carbon emission intensity and total carbon emissions). Under these dual constraints of “energy and carbon emissions”, China has promoted low-carbon renovations in the construction sector [8]. In addition to zero-energy buildings (ZEBs) [9], the concept of net-zero-carbon buildings (NZCBs) [10] has also been proposed. A zero-energy building refers to a structure where operational energy consumption is offset by on-site renewable energy generation, typically relying on solar power or other renewable sources. A net-zero-carbon building, on the other hand, not only considers the carbon emissions during the building’s operational phase but also comprehensively accounts for the embodied carbon emissions associated with the construction process. The ultimate goal is to achieve zero carbon emissions over the entire life cycle of the building.

The main sources of carbon emissions in the construction industry include building material production, building construction, building operation, and other related processes [11]. Effective research on the impact of building renovation can leverage appropriate life cycle assessment (LCA) methods, such as ISO 14040, ISO 14044, ISO 21930, and RICS WLCA standards [12]. These methods enable stakeholders and policymakers to promote environmentally beneficial practices [13]. Research by Decorte Yanaika et al. [14] found discrepancies between the results of simplified LCA and comprehensive LCA. This is primarily because the operational phase of a building accounts for 81–99% of its life cycle impact, making simplifications in material calculations negligible for life cycle evaluation. In the field of architecture, this assessment method holds significant practical relevance [15]. Life cycle (LC) thinking helps promote the concept of a circular economy, enhance resource utilization efficiency, reduce raw material consumption in construction processes, and uncover the value of waste [16]. Tharindu Prabatha et al. [17] found that from the perspective of the entire building life cycle, measures to reduce carbon emissions in building renovations may not always be economically justifiable, necessitating careful consideration by all parties involved. Amoruso, Fabrizio M. et al. [18] found that for building renovations, the energy demand of building operations is the main source of greenhouse gas emissions throughout the building’s life cycle, followed by embodied emissions from windows and emissions from material transportation. The findings by Galimshina Alina et al. [19] indicate that replacing heating systems during building renovations can significantly reduce the environmental impact of buildings, with modifications to the building envelope being the optimal choice for low-energy renovation projects. Additionally, whether to construct new buildings or renovate existing ones requires initial research and evaluation. Research by Karel Struhala and Milan Ostrý [20] confirms that renovation and the use of environmentally friendly energy sources play a crucial role in achieving sustainable development in the construction industry. It also suggests that under specific circumstances, constructing new agricultural houses to replace inefficient older ones may be advisable.

For rural areas, some research primarily focuses on the efficient renovation of building envelope structures. BJ He et al. [21] analyzed the deficiencies in rural building energy efficiency, identified the challenges faced by rural energy conservation efforts, and proposed measures to improve energy efficiency. Wang M and Wei C [22] found that enhancing the building envelope structure in rural areas is crucial for achieving future energy-efficient and environmentally friendly designs. It is estimated that improving the building envelope can reduce carbon emissions by approximately 50.4 to 77.5 million tons. Generally speaking, common renovation aspects in building envelope structure renovations include exterior walls, roofs, types of exterior windows, window-to-wall ratios, and air tightness [23,24,25]. Y. Cui et al. [24] renovated the roof, exterior walls, doors, and windows of buildings and added a sunroom. Using DesignBuilder, they simulated the effects of different renovation measures on rural commercial buildings. The results showed that performance improvements in roofs, exterior walls, doors, and windows, as well as the addition of a sunroom, could increase indoor temperatures by 6–7 °C and save more than 70% of energy. Z. Liu et al. [25] also conducted insulation renovations on the enclosure structure of reference buildings and added sunrooms. Their findings indicated that improving the performance of the enclosure structure could further raise the average temperature of two bedrooms and sunrooms to 16.7 °C, 15.9 °C, and 15.6 °C, respectively, meeting indoor space heating requirements. However, due to the economic backwardness of rural areas in plateau regions, the implementation of these transformations relies heavily on government support. Based on the characteristics of rural heating, X. Chen et al. [26] explored the energy-saving effects of intermittent heating modes on rural residential enclosure structure renovations, airtightness renovations, and the elimination of indoor heat sources from the perspectives of heating duration and heating space. The results showed that retrofitting the building envelope structure could achieve an energy-saving rate exceeding 20%. Additionally, priority should be given to renovating roofs. P. Peng and H. Wang [27] designed a passive solar energy system: a solar chimney. Their results demonstrated that applying solar chimneys in buildings could reduce energy consumption by 27.6%, with greenhouse gas emissions generated by construction being recoverable within one year.

Some studies have also focused on the influence of architectural form. The architectural form or shape can be defined by the so-called area-to-volume (A/V) ratio, which represents the relationship between the total enclosure area of the building and its total internal volume. The impact of architectural form on building performance is primarily reflected in thermal bridges. Moreno Rangel [28] pointed out that the main factors affecting the thermal performance of buildings are related to building shape, insulation, thermal bridges, and airtightness. Compact building forms generally require less energy consumption. C Marincu et al. [29] conducted research on three different building shapes and found that increasing the A/V ratio from 0.67 to 0.72 can lead to an approximate 50% increase in thermal-bridge-related heat transfer. An increase from 0.67 to 1.05 can result in a nearly fourfold increase in thermal-bridge-related heat transfer. These results emphasize the importance of considering the A/V ratio during the design phase of buildings. Pathiran S et al. [30] simulated 300 models and found that the window-to-wall ratio, building appearance, and staircase position all impact building thermal comfort and energy consumption.

In summary, reasonable design should be implemented in the early stage of building construction to create favorable shape conditions that promote low energy consumption. To reduce energy consumption in existing buildings, it is necessary to consider energy efficiency improvements, particularly focusing on enclosure structures. In rural areas, retrofitting these structures represents an efficient and cost-effective method for achieving this goal.

This paper draws on previous scholarly experiences and results, utilizing local Chinese data to study the renovation of farmhouse buildings in specific regions. The innovation of this study lies in its focus on rural residential buildings located in cold regions of China. Employing a life cycle assessment (LCA) approach, the study calculates and analyzes carbon emissions from the renovations. It also utilizes an orthogonal experiment method to explore the effects of various renovation measures on evaluation indicators. By applying value engineering principles, the study analyzes the economic efficiency and carbon payback period of the renovation measures, taking into account the potential influence of the carbon emission rights trading market. The findings provide valuable references and a scientific basis for formulating effective renovation strategies and policies, contributing to sustainable development in rural areas.

2. Case Study

2.1. Location and Climate

This paper takes typical single-story rural residential buildings in Shaanxi Province, China (Figure 1), as research reference buildings. The exterior walls are constructed with a 20 mm thick layer of cement mortar on both sides of a 240 mm thick solid clay brick core, resulting in a heat transfer coefficient of 2.13 W/(m²·K). The internal walls consist of 240 mm thick solid clay bricks and have a heat transfer coefficient of 1.269 W/(m²·K). The external windows are aluminum alloy single-layer windows with a heat transfer coefficient of 6.4 W/(m²·K).

The roof construction includes a 100 mm thick reinforced-concrete floor slab topped with a 30 mm thick cement mortar leveling layer, yielding a heat transfer coefficient of 3.81 W/(m²·K). The ground is constructed with a 20 mm thick layer of cement mortar over a 120 mm thick reinforced-concrete base, resulting in a heat transfer coefficient of 0.34 W/(m²·K).

The bedroom doors are ordinary 25 mm thick single-layer wooden doors with a heat transfer coefficient of 0.175 W/(m²·K). The outer doors are metal doors with a heat transfer coefficient of 6.4 W/(m²·K).

The single-story building is located in the central part of Shaanxi Province, China Figure 2), in Zone II of the building climate (cold regions, with an average temperature of −10~0 °C in January and 18~28 °C in July; refer to the Standard of Climatic Regionalization for Architecture (GB 50178)) [31].

2.2. Enclosure Structure

In 2021, the reference farmhouses were renovated. Field research was conducted on 467 rural households, revealing that the majority of rural houses (Figure 3) were constructed after 2000. There were 86 houses (18.4%) built before 2000; between 2000 and 2014, 232 houses (49.7%) were constructed; and 149 houses (31.9%) were built between 2014 and 2020.

As shown in Figure 4, 221 houses (47.3%) were self-built by villagers, 202 houses (43.3%) were constructed by nearby craftsmen, and 44 houses (9.4%) were built by professional construction teams.

The main materials used for exterior walls are red bricks (solid clay bricks, usually coated with cement mortar on the inner surface), while some use brick soil, and a few have pure soil walls. Roofs come in two types: flat roofs and sloping roofs, with flat roofs accounting for 73%. These flat roofs consist of a 100 mm thick reinforced-concrete floor slab topped with a 30 mm thick cement mortar leveling layer, without any insulation measures. Flat roofs with opposite sloping surfaces are more susceptible to outdoor environmental influences and lack a buffer space.

For external window frames, there are three main types of materials: wood, plastic steel, and aluminum alloy. Specifically, aluminum alloy windows account for 62.2%, plastic steel windows for 36.2%, and wooden windows for 1.6%. Most exterior windows use single-layer glass (79%), which has poor thermal insulation performance (heat transfer coefficient of 6.4 W/m²·K). Insulated glass accounts for 21% but is less commonly used due to its higher cost.

The investigation found that the thermal performance of the enclosure structure of the farmhouse is poor, making it highly susceptible to external temperature fluctuations. There is significant potential for energy efficiency improvements.

2.3. Strategy Selection and Parameters

After systematic research on existing rural houses, we have obtained a detailed understanding of their basic situation. The survey revealed many shortcomings in indoor comfort and energy efficiency in rural residential areas. Based on the research results and the provisions of the Design Standard for Energy Efficiency of Rural Residential Buildings (GBT 50824-2013) [32], the following strategies for renovation will be implemented: adding an insulation layer outside the gable wall, installing an additional insulation layer on the roof, replacing external windows, and adding a sunroom to the south corridor of the farmhouse.

In this study, the sunroom is considered as an integrated component, with calculations for its foundation, doors, windows, and low walls included together. The depth of the sunroom significantly impacts the building’s energy efficiency. When assessing its influence on the building’s energy consumption, one must also consider the 1.0 m wide corridor located on the southern side of the residence.

The operational lifespan of rural residential buildings is consistent with the design service life, both set at 50 years. The evaluation period for renovation measures is 35 years, as the original building had been in use for 15 years at the time of renovation [33].

The renovation plan considers four factors: external wall insulation, roof insulation, U-value of external windows, and depth of sunroom penetration. The thickness of the insulation board ranges from 30 mm to 60 mm, with a step size of 10 mm. The U-values of the external windows are 6.4 W/m²·K, 4.7 W/m²·K, 3.7 W/m²·K, and 2.6 W/m²·K. The depths of the sunroom are 0.8 m, 1.0 m, and 1.2 m.

3. Method

3.1. Life Cycle Assessment Calculation

Life cycle assessment (LCA) is defined as the systematic statistical and analytical evaluation of environmental emissions generated throughout the entire life cycle of a product, process, or industry. The boundary range of LCA [34] includes the following stages: the material production stage (raw material acquisition and processing A1, raw material transportation A2, and raw material production A3), the construction stage (product transportation A4 and product installation A5), the operation stage (product operation B1, product maintenance B2, and product repair B3), and the waste stage (product disassembly C1, waste transportation C2, recycling C3, and waste C4). Additionally, there is an outside system boundary stage (D), which primarily evaluates the expected benefits and negative impacts of the reuse and recycling of building products and materials. Due to limitations in domestic databases, this study will not consider this stage temporarily.

Differences in raw material production methods and construction processes between domestic and international contexts result in significant variations in carbon emission factors. Therefore, foreign carbon emission databases cannot be directly applied to domestic projects in China [35]. The Construction Lifecycle Database (CLCD) is not specifically designed for the construction industry; many building materials lack comprehensive carbon emission coefficients. Additionally, China still lacks a comprehensive database for the carbon emission coefficients of buildings. As a result, the primary carbon emissions for the renovation schemes in this paper are primarily sourced from the China Statistical Yearbook (2010–2020), the China Energy Statistical Yearbook (2010–2020), the China Statistical Yearbook on Construction (2011–2020), the China Transport Statistical Yearbook (2011–2020), the China City Statistical Yearbook, the China Rural Statistical Yearbook, the Standard for Building Carbon Emission Calculation (2019), China. The Carbon Footprint Factors Database (CPCD), the Chinese Local Life Cycle Database (CLCD), and the China Carbon Emission Accounting Databases (CEADs),

Table 1. Materials used in different renovation options in this study.

Position	Renovation Measures and Materials	Material Production		Construction		Operation			Waste	Specifications/Dosage
		A1–A3		A4	A5	B1	B2	B3	C1
		Carbon Emission Factors	Unit	Distance /km	Method	Method	Service Life/Year	Method	Method	Area /m2	Thickness /mm
Wall	EPS	61.2	kg CO2e /m3	30	*	Calculated according to the simulation results of DeST-h	30	Default no repair	Calculated by reconstruction measures and parts	70	40–60
	Cement mortar	558.1		35			x				40
Exterior window	Aluminum alloy single-layer window	30.4	kg CO2e /m2	30			25			5.4	x
	Plastic steel single-layer window	68.5
	Aluminum alloy hollow window	56.3
	Plastic steel hollow window	94.4
Roof	XPS	70.2	kg CO2e /m3	30			30			126.5	40–60
	Cement mortar	558.1		35			x				30
	Asphalt	91.74									6
Sunroom	Section steel	2.05	kg CO2e /kg	30			25			0.51–0.53	4
	Plastic steel hollow window	94.4	kg CO2e /m2							30–32	x
	Aluminum composite panel	8.06	kg CO2e /m2							11.8–12.5	4
	Rock wool insulation board	1980	kg CO2e /t							11.8–12.5	60

* The consumption of unit construction machinery per shift is calculated according to the Consumption Quota for Building Construction and Decoration Works in 2019. The energy consumption of the mechanical shift of the unit project is calculated according to the National Unified Cost Quota for Construction Machinery Shift in 2018. Carbon emissions from temporary facilities are calculated as 5% of carbon emissions from construction machinery.

.

The carbon emissions at each stage are calculated using the emission factor method. The carbon footprint (CF) calculation method (Equation (1)) for each scheme is as follows:

$${CF}_i={CP}_i+{CC}_i+{CO}_i+{CW}_i,$$

(1)

where CF_i is the carbon emissions of the entire life cycle of the i-th renovation plan for the building (kg-CO₂e), CP_i is the carbon emissions during the material production phase of the i-th renovation plan (kg-CO₂e), CC_i is the carbon emissions during the construction phase of the i-th renovation plan (kg-CO₂e), CO_i is the carbon emissions during the operational phase of the i-th renovation plan (kg-CO₂e), and CW_i is the carbon emissions during the abandonment phase of the i-th renovation plan (kg-CO₂e).

3.2. Indoor Thermal Environment Simulation

This study employs DeST-h to simulate the indoor thermal environment and building energy consumption of reference buildings [36]. The software uses the state-space method to calculate the thermal load demand of buildings at different times. It received China’s standard certification in 2000 and passed the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) test in 2019. Recognized officially as an energy consumption simulation tool, DeST-h is widely used in scientific research and has undergone comprehensive evaluation and validation through code validation, inter-program comparison, and case study calibration.

The discrepancy between the simulated results and measured values adheres to ASHRAE guidelines [37], which stipulate that the statistical errors should not exceed 5%, and instantaneous errors should not exceed 15%. This high simulation accuracy [36] holds significant research value. Moreover, given that this study is based on practical engineering and constrained by conditions, no additional experiments will be conducted to verify the simulation results.

DeST-h can simulate room temperature, thermal property indices, and annual loads of buildings under natural conditions. Additionally, it allows for setting different thermal disturbances and parameters for rooms with varying functions, taking into account factors such as shading and ventilation. This capability aligns well with scenarios where only individual room temperatures are controlled during winter in reference buildings, thereby fitting the actual situation of the buildings studied. Figure 5 illustrates the process of simulating building energy consumption using DeST-h.

Specific parameters shall be uniformly referred to in the Design Standard for Energy Efficiency of Rural Residential Buildings (GBT 50824-2013) [32]:

• Model: Figure 6.
• Meteorological data: Typical meteorological data from Xi’an are selected. (Xi’an and Tongchuan are both located in cold regions with similar latitudes and a straight-line distance difference of less than 90 km, Figure 2. They have extremely similar annual temperature changes, so it is feasible to choose Xi’an as a substitute without typical temperature data from Tongchuan.)
• Indoor temperature: 14 °C in winter.
• Indoor ventilation: The ventilation rate in winter is 0.5 h⁻¹.
• Thermal disturbance of personnel, lighting, and equipment in each room: 1.
• Heating period: November 15 to March 15.
• Cooling period: June 1 to August 31.

It is worth noting that the energy consumption simulation for rural buildings only considers winter heating energy usage and does not account for summer cooling energy consumption. This study calculates the energy consumption of the main rooms, including the master bedroom, second bedroom, and living room. Additionally, the maximum number of people engaged in indoor activities in each room is limited to three.

To evaluate the impact of reconstruction on rural houses, simulations were conducted to analyze both the pre-reconstruction cooling and heating loads and the effects of various reconstruction strategies and materials on these loads. The energy efficiency improvement rate (Equation (2)) served as a key metric for assessing the energy-saving effectiveness of the different strategies.

$$\eta ={\displaystyle \frac{Q_1-Q_2}{Q_1}}\times 100,$$

(2)

where η denotes the energy efficiency improvement rate, %; Q₁ denotes the building heat consumption index before reconstruction, W/m²; and Q₂ denotes the heat consumption index of the reconstructed building, W/m².

3.3. Economic Benefits

The price of common building materials is obtained from market research conducted in the early stage of the reconstruction project (

Table 2. Renovation measures and costs.

Position	Alternative Renovation Measures	Thermal Transmittance [W/(m2·K)]	Specifications	Price	Unit
Exterior wall	EPS	0.041	Thickness: 40–60 mm	450	CNY/m3
Exterior window	Aluminum alloy single-layer window	6.4	/	436.8	CNY/m2
	Plastic steel single-layer window	4.7		327.6
	Aluminum alloy hollow window	3.7		517.4
	Plastic steel hollow window	2.6		409.5
Roof	XPS	0.028	Thickness: 40–60 mm	630	CNY/m3
Sunroom	Square steel column, plastic steel double-glazed window, plastic steel door, and thermal insulation wall	/	Depth: 0.8 m, 1.0 m, 1.2 m	740.85	CNY/m2

). Cost data are provided by the construction unit. The building’s service life is 50 years. To calculate the building’s energy consumption and energy costs over its entire life cycle, average values are calculated based on field survey data; for instance, the price of clean coal is set at 1400 CNY/t.

To evaluate the economic benefits of the reconstruction measures and schemes relative to the current state of the reference building, we calculate the static investment payback period [38] (Equation (3)). This project is funded as a local government policy initiative, with the total investment equally shared between the government and the villagers. Since the building has already been in use for 15 years, the static investment payback period is evaluated over the remaining 35 years [33].

$$P_t=T-1+{\displaystyle \frac{\left|{\sum }_{t=1}^{T-1}(CI-CO{)}_t\right|}{(CI-CO{)}_t}},$$

(3)

where P_t denotes the static investment payback period, years; T denotes the number of years in which the cumulative net cash flow of each year of the project is positive for the first time, years; CI denotes the cash inflow in the t-th year of the project; and CO denotes the cash outflow in year t of the project.

3.4. Carbon Economy

In project renovations, the prioritization of alternative options has always been a focal point for practitioners and researchers, with economic indicators serving as a crucial metric to guide project decisions. Traditionally, in the economic evaluation of energy-saving renovation projects, the benefits derived from energy savings have been used as the project’s returns for assessment. However, against the backdrop of China’s gradually heating carbon emission trading market, this study posits that the ‘carbon economy’ will become an influential factor in guiding future project decisions and designs. This forms the basis of our research hypothesis. This concept of the ‘carbon economy’ originates from the carbon emission trading market, which is a market mechanism utilized by China to control and reduce greenhouse gas emissions [39]. There are currently 25 operational carbon emission trading markets worldwide, among which the European Union Emission Trading Scheme (EU ETS) stands out as the largest, earliest, and most mature carbon market globally. It also holds the highest carbon price, with the benchmark carbon quota futures contract price (EUA) exceeding EUR 100 per ton. In 2024, China’s carbon price is expected to range between CNY 70 and CNY 100 per ton and currently cannot be aligned with international prices, indicating significant potential for an increase in carbon pricing.

In this study, the annual energy savings of buildings under the combined renovation measures are converted into the corresponding annual reduction in carbon emissions (Equation (4)).

$$ACER=AES\times EF,$$

(4)

where ACER denotes the annual carbon emission reduction of buildings, t-CO₂e; AES denotes the annual energy saving of buildings, MWh; and EF denotes the carbon emission coefficient of the power system, taking the northwest region as 0.6671, t-CO₂e/MWh.

3.5. Carbon Emission Payback Period

The concept of the carbon payback period can be traced back to the early stages of the photovoltaic industry’s development and serves as an important indicator for measuring the environmental benefits of photovoltaic power systems. It refers to the duration required for a photovoltaic system to offset its own life cycle carbon emissions through electricity generation. Low carbon emissions from components and high electricity output contribute to shortening this period. This concept can also be applied to building energy retrofit projects, serving as an evaluation metric for the environmental benefits of different retrofitting schemes. In the context of building energy retrofits, the carbon payback time is defined as the time needed for the reduced carbon emissions from using the retrofitted building to offset the embodied carbon emissions associated with the retrofitting solution. Throughout the life cycle of a building energy retrofit project, embodied carbon emissions include those generated from the production of materials, transportation, construction, maintenance and replacement, and the demolition phase. The calculation method for the carbon payback time is detailed in Equation (5).

$${CPBP}_i={\displaystyle \frac{{ICE}_i}{ACEB-{ACEA}_i}}$$

(5)

where CPBP_i denotes the carbon emission payback period of the i-th renovation plan, a; ICE_i denotes the implied carbon emissions of the i-th renovation plan, kg-CO₂e; ACEB denotes the annual carbon emissions of buildings before renovation, kg-CO₂e; and ACEA_i denotes the annual carbon emissions of the building after the i-th renovation plan, kg-CO₂e.

In the life cycle of building energy-saving renovation projects, implied carbon emissions refer to the carbon emissions resulting from material production, transportation, construction, maintenance, replacement, and demolition.

3.6. Orthogonal Experiment and Range Analysis

Significant simulation work is required to combine and calculate the different reconstruction schemes for the external walls, roof, external windows, and additional sunroom. The orthogonal experiment, a multi-factor and multi-level experimental method, is well suited for this task [40]. An orthogonal array composed of numerical values ensures that the horizontal combinations of each factor are arranged evenly and reasonably. This systematic arrangement facilitates the analysis of influence trends, primary and secondary relationships, and the optimal combination of each factor on the experimental results. By ensuring uniform distribution and comparability across factors and levels, this method can significantly reduce the number of tests needed to achieve a comprehensive evaluation. From all experimental combinations, the most representative combination mode is selected and arranged using a standardized orthogonal table. This method enables obtaining a more accurate assessment of the factors’ influence with minimal time and experiments. By conducting range analysis, the permutation and combination results from the orthogonal experiment are analyzed and sorted, leading to the identification of the optimal combination mode and the significance of the four influencing factors.

In this study’s orthogonal experiment, the thickness of external wall insulation, the thickness of roof insulation, the depth of the additional sunroom, and the type of external window were selected as four factors. These factors were placed in columns 1, 2, 3, and 4 of the orthogonal table, respectively. Each factor includes three levels, ensuring that each level of every factor is combined with each level of the other factors at least once, resulting in a minimum of nine combinations. The levels of each factor are shown in

Table 3. Factor and level value.

Level	Thickness of External Wall Insulation Layer (A, mm)	Thickness of Roof Insulation Layer (B, mm)	Sunroom Depth (C, m)	Exterior Window Type (D)
a	40	40	0.8	Plastic steel single-layer window
b	50	50	1.0	Aluminum alloy hollow window
c	60	60	1.2	Plastic steel hollow window

. The orthogonal experimental plan is shown in

Table 4. Orthogonal experimental plans.

Number	Thickness of External Wall Insulation Layer (A, mm)	Thickness of Roof Insulation Layer (B, mm)	Sunroom Depth (C, m)	Exterior Window Type (D)
1	40	40	0.8	Plastic steel single-layer window
2	40	50	1.2	Aluminum alloy hollow window
3	40	60	1	Plastic steel hollow window
4	50	40	1.2	Plastic steel hollow window
5	50	50	1	Plastic steel single-layer window
6	50	60	0.8	Aluminum alloy hollow window
7	60	40	1	Aluminum alloy hollow window
8	60	50	0.8	Plastic steel hollow window
9	60	60	1.2	Plastic steel single-layer window

.

Range analysis evaluates how various factors influence renovation outcomes and calculates the energy efficiency improvement rates at different levels by analyzing the structural parameters of specific factors. A greater index value of the calculated range R indicates a more significant impact of that factor on the results, whereas a smaller range suggests lesser importance [41].

4. Results

4.1. Implied Carbon Emissions and Energy Performance of Special Renovation Measures

In the context of life cycle assessment, embodied carbon emissions encompass stages A1–A5, B2–B3, and C1. After integrating and analyzing the data, this study visualizes the distribution of embodied carbon emissions associated with specific renovation measures (depicted in Figure 7). Several conclusions can be drawn from the Figure 7:

1.

The carbon emissions from building materials during the cradle-to-gate phase account for over 40% of the total embodied carbon emissions in energy-saving renovations. This underscores the importance of prioritizing low-carbon production methods for building materials in all aspects of energy-saving renovation projects.

2.

The proportions of carbon emissions from transportation, construction, and demolition are relatively low, not exceeding 2%, 5%, and 0.11%, respectively. This can be attributed to the simplicity of the construction process involved in rural residential building energy-saving renovations, which typically require minimal use of heavy machinery. Consequently, this emphasizes the necessity of pursuing more low-carbon building materials in future renovation efforts.

3.

During the operational phase, carbon emissions generated from replacing materials at the end of their service life constitute more than 50% of the materialization phase’s emissions. Specifically, maintenance-related replacement emissions include those from demolition, cradle-to-gate emissions of new building materials, transportation, and construction, collectively representing a significant portion of overall emissions. These findings further reinforce the conclusions drawn from points 1 and 2.

This study also calculates the embodied carbon emissions and energy performance of specific renovation measures, as illustrated in Figure 8. The results indicate the following:

1.

Among individual renovation measures, the order of implied carbon emission intensity is as follows: sunroom, roof, gable, then external window. Notably, the carbon emissions associated with external windows are significantly lower than those of other measures. Conversely, the ranking of energy performance is as follows: roof, sunroom, gable, then external window. The energy performance of external windows is also significantly lower than that of other measures.

2.

From an energy performance perspective, to meet the requirements for improving building energy efficiency, roof renovation, adding a sunroom, and exterior wall renovation are identified as the most critical components of rural residential building renovations. These measures represent the core focus areas for energy-saving renovations in rural homes.

3.

It is noteworthy that sunrooms not only enhance building energy efficiency but also provide additional usable space, akin to the balcony function in urban residential buildings. This feature plays a positive role in increasing residents’ satisfaction and represents a meaningful renovation measure.

4.

Among the four specific renovation measures, the highest energy efficiency improvement rate did not exceed 40%, failing to meet the renovation requirements. Therefore, relying solely on one measure for the energy-saving renovation of rural houses is deemed unreasonable.

4.2. Carbon Emissions and Energy Performance During the Life Cycle of Combination Schemes

By employing orthogonal experiment design and range analysis, this study integrated the comprehensive renovation list for rural residential buildings with energy consumption simulation to calculate the energy consumption improvement rate and carbon footprint (CF) for various combination renovation schemes. The research yielded the average energy efficiency improvement rate and CF for each factor and level under four renovation measures (Figure 9, Figure 10 and Figure 11). The internal relationships among these measures and the following conclusions are clearly illustrated in the Figure 9, Figure 10 and Figure 11:

1.

From the perspective of energy consumption improvement rates, the sensitivity ranking of the impact on the energy efficiency improvement rate of rural houses is as follows: depth of sunroom > thickness of roof insulation layer > thickness of exterior wall insulation layer > type of exterior window. In other words, varying the depth of the sunroom has the greatest influence on building energy efficiency, particularly when changing from 1 m to 1.2 m.

2.

The solution with the highest energy efficiency improvement rate is A_cB_cC_aD_a (MAX), which means an external wall insulation layer thickness of 60 mm, a roof insulation layer thickness of 60 mm, a sunroom depth of 0.8 m, and a plastic steel single-layer window type. It was found that a lower heat transfer coefficient of the external window results in a more energy-efficient scheme regarding a sunroom, which differs from the results of special renovation. This discrepancy arises because the sunroom’s high transparency allows full utilization of daylight, causing temperatures in the sunroom to rise even higher than those inside the house. Consequently, a higher heat transfer coefficient of the external windows facilitates temperature transmission from the sunroom to the interior, thereby reducing daytime indoor heating demands.

3.

From the perspective of CF, the sensitivity ranking of the impact on rural houses’ CF is as follows: depth of sunroom > thickness of roof insulation layer > thickness of exterior wall insulation layer > type of exterior window. Notably, there is a significant difference between the sunroom at a depth of 1 m and that at a depth of 1.2 m.

4.

The scheme with the lowest CF corresponds to the same configuration as the scheme with the highest energy efficiency improvement rate, namely A_cB_cC_aD_a (MAX).

5.

The sensitivity ranking of implied carbon emissions from rural houses is as follows: type of external window > depth of sunroom > thickness of roof insulation layer > thickness of external wall insulation layer. The ranking highlights significant differences in the implied carbon emissions between various types of external windows (Figure 8).

Energy consumption simulations and calculations were conducted for all 81 combination schemes, with life cycle carbon emissions divided into operational carbon emissions and embodied carbon emissions to generate scatter plots (Figure 12). From the Figure 12, the following can be observed:

1.

After fully combining these limited measures, their CF ranges from 99.37 t-CO₂e to 140.92 t-CO₂e, with the corresponding annual average carbon emission intensity per unit area ranging from 24.69 kg-CO₂e/(m²∙a) to 35.01 kg-CO₂e/(m²∙a).

2.

As the energy efficiency improvement rate increases, CF generally shows a decreasing trend. However, upon detailed observation, there is not a strict correlation between CF and the energy efficiency improvement rate. This indicates that simply pursuing a higher energy efficiency improvement rate may not necessarily yield optimal environmental impact. To achieve environmental goals, differences among various factors cannot be ignored.

3.

The combination of specific renovation measures constitutes the embodied carbon emissions of the combination plan, accounting for 17.67% to 26.43%. This highlights the importance of embodied carbon emissions in rural residential energy-saving renovation projects, which should not be overlooked.

4.

While the overall change in embodied carbon emissions is not significant, the detailed trends are not strictly positively correlated, mainly influenced by the type of external window, consistent with the results in Figure 11.

4.3. Carbon Reduction Performance and Economy

To quantify the carbon reduction benefits associated with the renovation measures, annual carbon emissions for each plan were calculated (Figure 13). Before renovation, the annual carbon emissions amounted to 9638.36 kg-CO₂e/a. After fully integrating the four measures, the annual carbon emissions varied from 2839.20 kg-CO₂e/a to 4026.29 kg-CO₂e/a due to differing carbon reduction capacities of each combination scheme. However, while the carbon reduction rates and energy efficiency improvement rates remained consistent, they ranged from 66.67% to 77.82%. To accurately quantify the actual carbon reduction performance, it is imperative to account for embodied carbon emissions. The average embodied carbon emissions for each plan were divided into 35 parts over the calculation period and added to the corresponding plan’s annual carbon emissions. When these factors are considered, the annual carbon emissions range from 2839.19 kg-CO₂e/a to 4026.29 kg-CO₂e/a, resulting in carbon reduction rates between 58.22% and 70.54%. This adjustment reveals an overestimation of the carbon reduction rates by 9.35% to 12.02%.

Incorporating the burgeoning carbon trading market and adjusting for the rate of carbon reduction, we have calculated the static investment payback period for 81 alternative solutions. The results are depicted in Figure 14, which reveals several insights.

1.

When factoring in the carbon market, there is a substantial improvement in the project’s static investment payback period. Specifically (Figure 15), when carbon reductions are traded at prices of 70 CNY/t-CO₂e, 80 CNY/t-CO₂e, 90 CNY/t-CO₂e, and 100 CNY/t-CO₂e, the investment payback period decreases by 6.84%-7.03%, 7.74%-7.92%, 8.62%-8.86%, and 9.49%-9.75%, respectively. Correspondingly, as energy efficiency improves, the expected additional annual benefits are projected to range from CNY 392.85 to CNY 475.94, CNY 448.97 to CNY 543.93, CNY 505.09 to CNY 611.93, and CNY 561.21 to CNY 679.92, respectively. It is noteworthy that in the context of the carbon trading market, the primary purchasers of carbon reduction credits are typically enterprises subject to carbon emission constraints. When rural residential energy conservation and carbon reduction amounts are traded with these enterprises, the proceeds go to farmers, positively impacting the income gap between urban and rural residents. Additionally, rural residents benefiting from carbon inclusivity may become more proactive in carbon reduction efforts, thereby playing a crucial role in achieving environmental objectives.

2.

From the perspective of the energy efficiency improvement rate, it is found that an increase in this rate does not fully reduce the static investment payback period of the project. The lowest payback period scheme has an energy efficiency improvement rate of 71.86%.

3.

Our analysis indicates that sunroom depth has the most significant impact on the investment payback period. A sunroom with a depth of 1.2 m exhibits the longest payback period, while one with a depth of 0.8 m demonstrates the shortest.

4.

As illustrated in Figure 16, the sensitivity ranking of factors affecting the investment payback period is as follows: depth of sunroom > thickness of roof insulation layer > type of external window > thickness of external wall insulation layer. Notably, the high construction costs associated with deeper sunrooms should not be overlooked, as they can significantly extend the investment payback period. Surprisingly, increasing the thickness of the roof insulation layer can actually lengthen the payback period, underscoring the need for balanced considerations in design decisions.

The carbon payback period was calculated to evaluate the carbon emission reduction performance of the scheme (Figure 17). The graph reveals that in limited combination schemes, the variation in carbon payback periods and the optimal scenario differ due to the presence or absence of an investment payback period. The carbon payback period ranged from 3.27 years to 4.21 years. The solution with the shortest carbon recovery period currently exhibits the highest energy efficiency improvement rate. Based on Figure 12, differences are observed between changes in the carbon recovery period and the cash flow factor (CF). This indicates that relying solely on traditional investment payback periods for selecting the optimal solution is not aligned with contemporary goals. While the carbon footprint remains a key metric for scholars, it has its limitations. Therefore, future optimization of project plans should incorporate the carbon recovery period as a critical criterion. Similarly, Figure 18 and Figure 19 show that the selection of sunroom type has a crucial impact on both the carbon reduction rate and the carbon recovery period.

5. Discussion

In the context of energy-saving renovations for rural residential buildings, common measures include roof insulation, exterior wall insulation, replacement of external windows, and the construction of an additional sunroom. Due to the simplicity and convenience associated with constructing rural residential buildings, material production accounts for the vast majority of embodied carbon emissions during renovation. Therefore, optimizing the energy performance of roofs, exterior walls, and sunrooms is a critical consideration in practical engineering applications. However, relying solely on these measures has limited effectiveness. The choice of sunroom plays a significant role in improving energy efficiency rates, carbon payback periods, carbon footprints, embodied carbon, the proportion of embodied carbon, carbon reduction rates, and investment payback periods, necessitating special attention.

Notably, the energy performance of external windows is extremely low, making solutions with lower heat transfer coefficients more favorable when a sunroom is present. This factor must also be considered in practical engineering design.

1.

Carbon Emission Considerations

From a carbon emissions perspective, the cradle-to-gate phase of building materials constitutes a significant portion—over 90%—of the renovation’s embodied carbon emissions. Materialization accounts for 17.67% to 26.43% of the total carbon footprint (CF). Consequently, achieving high-performance and low-carbon emissions in materials is an inevitable goal for future endeavors. Neglecting embodied carbon emissions can lead to an overestimation of the carbon reduction effect of renovation plans. Therefore, a comprehensive life cycle evaluation of the carbon reduction rate is crucial to avoid misjudgment of the actual benefits.

2.

The Emergence of the Carbon Economy

This study suggests that the carbon economy will increasingly influence project decisions in the future [42]. The carbon economy arises from carbon emission reductions and may bring additional benefits beyond energy conservation, which are related to both policy frameworks and market conditions. Current research has corrected the actual annual carbon reduction rates and analyzed the economic benefits over a 35-year building life cycle. Additional income from the carbon economy makes projects more economically viable, effectively shortening the static investment payback period. As carbon prices escalate, this impact will become even more pronounced.

Given the large number of rural areas in China requiring energy efficiency improvements and renovations, government investment should become the mainstream approach for similar rural renovation projects. Market economies must also be fully considered to align with government goals, enhance livelihoods, narrow the urban–rural income gap, and positively impact sustainable rural development [43].

It should be noted that research on the carbon economy is hypothetical, as tradable carbon reduction values require methodological support. China remains in its infancy regarding this aspect. With the development and refinement of relevant policies, the significance of such research will eventually be confirmed.

3.

Conceptual Framework: Carbon Payback Period

The concept of the carbon payback period originates from value engineering principles, extending their application to carbon emission assessment. It provides a convenient and intuitive method reflecting the environmental investment value of alternative solutions throughout their life cycle. In this study, all alternative solutions achieved carbon recovery within their life cycle. The carbon payback period is significantly negatively correlated with the carbon reduction rate, positively correlated with the carbon footprint (CF), and negatively correlated with embodied carbon emissions.

However, the relationship between the carbon payback period and the investment payback period is more complex. After combining and transforming conventional measures, both positive and negative correlations exist between the two. In practical engineering, economic evaluation remains the dominant factor in project decision-making, which does not meet the requirements of today’s era. Although the evaluation system for low-carbon buildings is relatively mature, regional development variations and field-specific emphases have led to a lack of scientific and effective evaluation systems in many regions, especially in rural areas where attention is relatively low. Therefore, addressing these gaps by combining the requirements of high-standard building components is a key direction for the future.

Therefore, this study advocates evaluating and optimizing projects based on both carbon recovery periods and economic viability. Decision-makers need to consider project goals and requirements, assigning appropriate weights to economic evaluations and carbon payback period evaluations. This weight cannot be generalized, as it is crucial for the sustainable development of rural areas.

4.

Uncertainty Factors of LCA

In the life cycle assessment (LCA) of building renovation, many variables are inherently difficult to determine [44], and further research is needed on uncertain factors, including evaluation scope, material data, operational assumptions, etc. [45,46,47,48]. Different evaluation methods may lead to significant differences in results [49]. The coverage of different methods varies; in this study, due to the lack of reliable databases, the evaluation scope ignored the impact of waste treatment and recycling (C2, C3, C4, and D), which could have an unknown impact on the results.

Research based on LCA relies on a large and accurate carbon emission factor library [45]. The data in this study are sourced from national standard statistical values, which are averages and do not match reality precisely. A more reliable source might be the environmental performance disclosure from manufacturers. The paper also omitted some materials with low usage and no carbon data, such as rivets and sealing strips. The uncertainty of these materials needs to be quantitatively calculated.

The assumptions made in the paper regarding operating conditions are based on standards, but predictions of human actions and equipment are relatively simple, potentially leading to errors in predicting operational energy consumption.

5.

Urban Renewal and Renovation

In the future, urban renewal and renovation will be an essential part of China’s infrastructure. Referencing the results of rural residential renovations discussed in this article, exploring how to establish a scientific and comprehensive low-carbon assessment method holds great practical significance for guiding low-carbon construction during urban renewal and renovation processes in China. At the same time, with the development of low-altitude economies, the contribution of drones to cities will gradually become significant. They have great potential to replace cars in certain tasks [50,51,52], promoting the sustainable development of cities and indicating an undeniable direction for future research. By establishing a comprehensive carbon emission accounting method, an energy efficiency evaluation system, green building evaluation standards, and transportation carbon emission evaluation methods, robust support can be provided for low-carbon construction during urban renewal and renovation processes in China.

6.

Limitations and Prospects

Of course, this study has certain limitations, mainly reflected in the following:

(1)

This study specifically focuses on buildings located in Tongchuan City, which has a cold climate. This implies that the quantitative results may only be applicable to this specific region. Given China’s wide longitudinal span and diverse climate zones, it is necessary to conduct comprehensive research on rural housing across different climate zones.

(2)

This paper focuses on passive energy-saving renovations, which should also include ground and chimney renovations. The types of external windows considered are relatively limited. Currently, there are various types of external windows on the market, such as low-E glass and gas-filled windows, whose effects are also worth studying. The development and application of active energy-saving technologies require increased investment.

(3)

The buildings in the paper are flat-roofed structures, while sloping roofs are still prevalent in rural areas. Additionally, many building envelope structures are made of bricks, requiring scientific research on transformation strategies.

(4)

This study did not consider psychological factors such as residents’ needs and satisfaction. Future research should supplement these aspects using scientific methods, as considering users’ feelings is just as important as the economy and environment.

In future research, multi-objective evaluation systems and optimization strategies can be explored, offering a more intuitive method for effectively guiding practical engineering. Meanwhile, combining reliable algorithms [53] for the accurate prediction of building operational energy consumption is also a promising direction. This approach can reduce reliance on simulation software.

6. Conclusions

This study conducted a comprehensive life cycle assessment (LCA)-based analysis of carbon emissions and other impacts associated with passive energy-saving renovation measures for rural residences in the cold regions of China. The life cycle of building renovation was segmented into five stages: production, transportation, construction, operation and maintenance, and disposal. This analysis focused exclusively on the behavior of renovation and its subsequent changes, without accounting for any impact on the original building structure. Passive renovation measures included external insulation of roofs, external insulation of exterior walls, the addition of a sunroom, and replacement of external windows. Principal evaluation indicators encompassed the energy efficiency improvement rate, carbon footprint (CF), embodied carbon emissions, proportion of embodied carbon, carbon reduction rate, carbon payback period, and investment payback period.

The primary findings of the study are as follows:

(1)

During the embodied carbon emissions phase of rural residential renovations, low-carbon production of building materials requires significant attention. Emissions from transportation, construction, and demolition constitute only a minor proportion and should not overshadow the critical focus on material production.

(2)

Considering all evaluation indicators, the depth design of sunrooms warrants priority, followed by the thickness of roof insulation and then wall insulation. The choice of external windows has the most substantial impact on the carbon recovery period.

(3)

Embodied carbon emissions from comprehensive renovations represent 17.67% to 26.43% of the total carbon footprint. Overlooking these emissions can result in an overestimation of the carbon reduction rate by 9.35% to 12.02%.

(4)

The estimated carbon payback period for the proposed renovation plan ranges between 3.27 and 4.21 years. This parameter should be incorporated alongside economic assessments for multi-objective optimization in future projects.

(5)

The concept of the carbon economy can effectively shorten the investment payback period of projects and mitigate income disparity between urban and rural residents to some extent. Investors and relevant governmental bodies should recognize its significance.

These findings underscore that both rural and urban renovations must not only concentrate on the environmental impact of technological interventions but also establish a more rigorous and comprehensive evaluation framework. Such a framework is essential for selecting and optimizing renovation strategies, thereby fostering sustainable development across environmental, economic, and cultural dimensions in transformed areas.