A Study on Carbon-Reduction Strategies for Rural Residential Buildings Based on Economic Benefits in the Gannan Tibetan Area, China

Abstract

The building sector contributes approximately half of all carbon emissions. The heating stage accounts for the largest proportion of building carbon emissions. The focus on carbon-reduction strategies in rural areas could not be copied from urban buildings due to different heating modes limited by economic factors. The Gannan region in Gansu province was selected to carry out an on-site survey on heating conditions, including the heating modes, the energy used for heating, heating fees, residents’ satisfaction with heating, and the thermal environment of the typical building. The results showed that local rural residents burnt scattered coal for heating using primitive heating stoves with low efficiency, causing low air temperatures and high heating fees. The carbon emissions generated by heating reached 5743.28 kgCO₂e·m⁻². Several strategies for reducing carbon emissions were proposed, considering the economic benefits limited by rural economic development. A parameter of reduced carbon emissions per investment input was proposed to evaluate the carbon-reduction strategies. The results showed that biomass was the most economical way to reduce carbon emissions. Reduced carbon emissions per investment input reached 44.19 kgCO₂e·CNY⁻¹ with energy efficiency of 50%, followed by thermal insulation design of 32.31 kgCO₂e·CNY⁻¹, natural gas furnaces of 26.08 kgCO₂e·CNY⁻¹, and air-source heat pumps of 20.27 kgCO₂e·CNY⁻¹. In addition, carbon emissions generated by biomass were 12.4% and 24% of those caused by coal and natural gas supplying the same energy. Moreover, building insulation should be increased according to economic benefits. The optimum energy efficiency was 55% in Gannan. The results provided a reference for building low-carbon heating in rural areas, which could help achieve the low-carbon goal with low investments.

1. Introduction

In this century, energy and environmental issues have become increasingly prominent, global carbon emissions have been increasing, and more and more countries have proposed carbon-neutral targets [1]. In 2020, the Chinese government promised to strive to achieve the goal of the “double control” of the emission intensity and total amount of carbon emissions, aiming for peak carbon emissions by 2030 and carbon neutrality by 2060 [2]. As one of the three “major energy consumers” (industry, transport, and buildings), the building sector contributes approximately half of the total carbon emissions [3]. Rural buildings were built on the basis of experiences, lacking scientific architectural design guidance, resulting in poor thermal performances and higher energy consumption than urban buildings, especially in northern rural regions [4]. Spatial heating in rural areas heavily relied on fossil fuels, causing heavy air pollution and carbon emissions [2]. Reference [5] concluded that rural areas in northern China consumed 200 million tons of standard coal, mostly from scattered coal. However, due to the different lifestyles of residents and heating modes in rural and urban areas, the focus on carbon-reduction strategies in rural areas could not be copied from urban buildings. Studying the carbon emissions of rural buildings was an important way to explore low-carbon development to achieve carbon neutrality in our country [6].

It was urgent to conduct on-site surveys and optimize building carbon emissions in rural areas. From a provincial perspective, household carbon emissions in Gansu province were relatively high [7]. Therefore, A village in Zhuoni County of the Gannan Tibetan Autonomous Prefecture of Gansu Province was selected to conduct a survey on heating situations. The annual average temperature in Zhnoni County was 3.6 °C, and the lowest temperature was −19.2 °C. Meanwhile, the heating periods lasted up to 5 months, resulting in high heating consumption and carbon emissions. In addition, one hundred and ten thousand people resided in Zhuoni County, with 63.11% population living in agriculture. The per capita regional GDP of Zhuoni County was only half of that of Lanzhou, the provincial capital of Gansu province. So, economic factors must be considered when reducing building carbon emissions (BCEs) in rural areas.

Therefore, this paper focused on improving heating effects and reducing carbon emissions while considering the economic benefits. A parameter of reduced carbon emissions per investment input was put forward to evaluate the carbon-reduction effects to propose appropriate carbon-reduction strategies (CRSs). The research framework is shown in Figure 1. The study optimized the heating energies to reduce carbon emissions and protect our environment in many ways by preventing global warming and maintaining the balance of the ecological environment, which will be conducive to the sustainable development of society.

2. Literature Review

Although the average carbon emissions of urban households were higher than those of rural households [7,8], reducing the carbon emissions of rural buildings and clarifying the influencing factors were important [6]. Scholars have sought various carbon-reduction strategies. According to the literature [9], the life-cycle assessment was divided into three stages: building material production and transportation, building construction and demolition, and the operational stage, including HVAC, domestic hot water, lighting and elevators, and renewable energy. Xu [10] found that carbon emissions generated by building production, transportation, and building operational stages accounted for 99.5% of those caused during the building life cycle. The material production stage accounted for about 98% of the building materialization stage and carbon emissions by building heating occupied 80–90% of those caused by the use stage. Therefore, this paper will review carbon emissions generated by material production and building heating.

First, local ecological building materials, such as bamboo [11] and straw bale [10], have been employed in rural residential buildings to reduce carbon emissions. Both these materials reduced carbon emissions due to their carbon sequestration properties. Li [12] concluded that low-carbon straw bale buildings with wood and light-steel structures reduced net carbon emissions by 96.75% and 76.92% compared with reference buildings.

Second, carbon emissions were mainly influenced by heating [7]. The state proposed coal-to-electricity and coal-to-gas projects in 2017, first applied in the Beijing-Tianjin-Hebei region [2]. It was concluded that villagers highly recognized the convenience and safety of electric heating [13]. As the most popular electric heating equipment, air-source heat pumps (ASHPs) were popular in northern China due to their low installation requirements, simple operation, and reliability [2], which were suitable for rural areas [14]. Moreover, they could be applied in severely cold areas with frost-free technologies for outdoor air–heat exchangers [15]. Wu [16] verified that ASHPs could provide enough heating protection in Xinjiang. Meanwhile, some scholars [17,18,19] found that the costs of current electric heating technologies were relatively high due to poor economic benefits and insufficient government investment. A “coal-to-gas” heating scheme was considered more suitable for severely cold northeast China [20]; however, in the literature [21], it was concluded that the initial investment of natural gas was about CNY 30,000 for each house without government investment, which was unaffordable for most residents. About CNY 10,000 was paid for each house with government funding. So, for natural gas, government subsidies were crucial. Solid biofuels were the most common heating sources, especially in rural areas, for their low cost, high efficiency, and low emissions [22]. Household biomass heating stoves were commonly used for heating in remote rural areas because of their excellent fuel flexibility and high combustion efficiency [23], priced at CNY 2000–5000 to heat a 60–120 m² space [2]. For biomass, subject to geographical restrictions, it was only available in biomass-rich areas. Most people residing in Zhuoni County lived on agriculture with large areas available for crops. Wheat and highland barley were the main food crops. Both could be used as biomass sources.

In addition, renewable energy, such as solar energy, could effectively reduce carbon emissions from building heating. It was commonly used in building heating designs. The combined use of active and passive utilization of a solar air collector and an attached sunspace provided more environmental benefits [24], and solar photovoltaics combined with heat pumps could reduce carbon emissions by up to 50% [25].

To summarize, carbon-reduction strategies such as ecological materials, ASHPs, biomass heating stoves, and solar heating could effectively reduce BCEs. Moreover, the economic aspects of CRSs need to be further studied. Gao [4] chose the optimum CRSs of envelope parameters and PV systems considering carbon emissions and cost-effectiveness. Tahsildoost [26] in Iran proposed optimal constructional parameters and the application of renewable energy technologies with a low cost to reduce BCEs in rural areas. He [21] concluded that the installation and operational costs of ASHPs were moderate and suitable for use in rural areas. In this paper, CRSs for envelope structures and heating optimization were evaluated while considering the economic benefits.

3. Research Method

3.1. Field Investigation

A village in Zhuoni County was selected for field research. The annual average temperature in Zhuoni was 3.6 °C, with a high altitude ranging from 2000 to 4900 m above sea level, resulting in a severely cold winter with an average temperature of −8.2 °C in January and a severely cold area of building thermal design zone. Hourly temperature in Zhuoni County is shown in Figure 2 [27]. An on-site survey was conducted to investigate heating modes, the energy used for heating, heating fees, residents’ satisfaction with heating, and the factors considered in heating optimization. Photographs taken during the on-site survey are shown in Figure 3.

3.2. Thermal Environment Test

A typical building was selected for field testing on the thermal environment on the 6–8 of January to verify the building heating effects, including the indoor and outdoor air temperature and outdoor solar radiation. The selected typical building is shown in Figure 4. During the survey it was found that there existed three types of houses. One was an earth building constructed with raw earth before the 1960s, where only a small proportion of people lived. Another was the brick building built based on the old earth house. The building was composed of two envelope structures, and the internal space was relatively complex. The third was the new building constructed after 2000. The building was a reinforced concrete frame structure filled with brick walls, whose structure was the most typical one without insulation. The building’s internal spaces were relatively complete, and the building was the development trend of rural architecture. So, the third building was selected to conduct further study. The total covered area of the building with two stories was 192 m². Figure 4 depicts the design drawings with a frame structure fabricated with block components. Structure details, including the thermal conductivity of various materials and K-values of building envelopes, are shown in

Table 1. Structures of building envelopes.

Building Envelopes	Structures			K-Values/W·m−2·K−1
	Thickness	Materials	Thermal Conductivity λ /W·m−1·K−1
Wall	20 mm	cement plaster	0.93	1.68
	300 mm	fly ash block	0.74
	20 mm	cement plaster	0.93
	5 mm	limestone	1.16
Roof	10 mm	cement tile	0.74	1.70
	/	waterproof	/
	120 mm	reinforced concrete floor	1.74
	100 mm	air	0.28
	10 mm	wooden ceiling	0.17
Ground	/	Compacted plain soil	/	0.13 (non-surrounding ground) 0.34 (surrounding ground)
	120 mm	crushed stone concrete	1.51
	10 mm	wooden floor	0.17
Window 1	6 mm	glass	/	2.70
	/	wood	/
	6 mm	glass	/
	/	wooden frame	/
Window 2	6 mm	glass	/	4.70
	/	aluminum alloy frame	/

. K-values were calculated as follows [28].

$$K={\displaystyle \frac{1}{R_{\mathrm{i}}+{\displaystyle \sum \frac{d_{\mathrm{i}}}{{\lambda }_{\mathrm{i}}}}+R_{\mathrm{ag}}+R_{\mathrm{e}}}}$$

(1)

where K represents heat transfer coefficients of envelopes, W·m⁻²·K⁻¹; R_i denotes thermal resistances of inner surfaces of envelopes, m²·K·W ⁻¹; R_e denotes thermal resistances of outer surfaces of envelopes, m²·K·W⁻¹; d_i represent thicknesses of envelop components, m; λ_i denote thermal conductivities of envelop components, W·m⁻¹·K⁻¹; R_ag denote thermal resistances of air layer, m²·K·W⁻¹.

The thermal environment test sensors are shown in Figure 5. Solar radiation, indoor and outdoor air temperature, and surface temperatures were recorded. The specifications and accuracies of used sensors are shown in

Table 2. Specifications and accuracies of sensors.

Instrument Devices	Parameters	Specifications	Accuracies
Temperature recorder	Air temperature	−20–60 °C	±0.1 °C
Thermocouple temperature recorder	Surface temperature	−20–85 °C	±0.1 °C
Solar radiation recorder	Solar radiation	0–2000 W/m2	≤±2%

.

3.3. Numerical Calculation of Building Carbon Emissions

According to the literature [9], the life-cycle assessment was divided into three stages: building material production and transportation, building construction and demolition, and the operational stage, including HVAC, domestic hot water, lighting and elevators, and renewable energy. The carbon emission calculation processes of three stages were calculated as follows [9].

3.3.1. Building Material Production and Transportation

(1)

Building material production

The carbon emissions from building material production, CE_material, were calculated as follows:

$$C{E}_{\mathrm{material}}={\displaystyle \sum \left(1+{\alpha }_{\mathrm{i}}\right)\times m_{\mathrm{i}}\times E{F}_{\mathrm{i}}}$$

(2)

where m_i is the material amount i used in the building (unit); EF_i represents the carbon emission factor of material i (kgCO₂e·unit⁻¹). The carbon emission factors of the various materials were obtained from [9,10,29]. α_i denotes the building material loss rate during construction.

(2)

Building material transportation

The carbon emissions during the transportation process CE_trans were calculated as follows:

$$C{E}_{\mathrm{trans}}={\displaystyle \sum \left[\left({\displaystyle \sum _{i=1}^n{L}_{\mathrm{i},\mathrm{j}}\times m_{\mathrm{i}}}\right)\times E{F}_{\mathrm{trans},\mathrm{j}}\right]}$$

(3)

where L_i,j represents the transportation distance of material i transported via vehicle j (km); EF_trans,j denotes the carbon emission factor of vehicle j (kgCO₂e·t⁻¹·km⁻¹); and m_i is the quality of the materials transported (t). All building materials were assumed to be transported via road, and the carbon emission factor of trucks was valued at 0.115 kgCO₂e·t⁻¹·km⁻¹ [9].

3.3.2. Construction and Demolition

(1)

Building construction

The carbon emissions during construction, CE_construction, were calculated as follows:

$$C{E}_{\mathrm{construction}}={\displaystyle \sum E{F}_{\mathrm{i}}}\times E_{\mathrm{construction},\mathrm{i}}$$

(4)

where EF_i represents the carbon emission factor of energy i (kgCO₂e·MJ⁻¹); E_{construction,i} denotes the energy consumption of energy i during construction (MJ). Since few mechanical equipment details were recorded in rural areas, the carbon emissions during construction calculated were multiplied by the carbon emissions per unit area [10] and the building areas.

(2)

Building demolition

The carbon emissions during demolition included those generated during demolition and waste transportation. The carbon emissions during the demolition stage, CE_demolition, could be estimated as follows:

$$C{E}_{\mathrm{demolition}}=C{E}_{\mathrm{construction}}\times 90\%$$

(5)

The carbon emissions generated during waste transportation refer to those generated during the transportation process. The calculation method and results were the same as those in the transportation stage of the building materials.

3.3.3. Building Operation Stage

The building operation stage included the following parts: building heating and cooling, domestic hot water, building lighting and elevators, and renewable energy utilization. In Gannan, there was no demand for cooling in summer, and in addition, the rural residential buildings had no elevators. Therefore, the carbon emissions during the operational stage required calculating the building heating, domestic hot water, and building lighting.

(1)

Heating

The carbon emissions generated by heating could be calculated as follows:

$$C{E}_{\mathrm{heating}}=Q\times E{F}_{\mathrm{i}}\times 50$$

(6)

where CE_heating represents the carbon emissions of building heating (kgCO₂e); Q represents the energy consumed for heating (J); EF_i denotes the carbon emission factor of heating energy i (kgCO₂e·J⁻¹); and 50 represents the building life service limit of 50 years.

Two methods—steady-state calculation and dynamic simulation—are used to calculate the energy consumption for heating. In this paper, the steady-state calculation method was adopted. On the one hand, the steady-state method was easy for architects to understand; on the other hand, the research [30] showed that the difference between the steady-state calculation and dynamic simulation is within rationality in Gansu province. The energy consumed for heating Q was calculated as follows:

$$Q=24\times 3600\times Z\times q_{\mathrm{H}}\times A_0/\eta$$

(7)

Here, Q represents the energy consumed for heating (J); q_H denotes the building heat consumption index (W·m⁻²), calculated according to the literature [31]; Z is the heating period of 192 days [31]; η represents the heating equipment efficiency, recommended as 0.75 in the literature [10]; and A₀ represents the building area of 192 m².

$$q_{\mathrm{H}}=q_{\mathrm{HT}}+q_{\mathrm{INF}}-q_{\mathrm{IH}}$$

(8)

where q_H presents building heat consumption index, W·m⁻²; q_HT represents heat loss through envelopes per unit, W·m⁻²; q_INF presents heat loss of cold air permeation, W·m⁻²; q_IH presents indoor heat gain, it was recommended 3.8 m⁻² in the literature [31]. q_HT and q_INF should be calculated according to [31].

(2)

Domestic hot water

The carbon emissions generated by domestic hot water were calculated as follows:

$$C{E}_{\mathrm{hotwater}}={\displaystyle \frac{Q_{\mathrm{r}}}{{\eta }_{\mathrm{r}}\times {\eta }_{\mathrm{w}}}}\times EF\times 50$$

(9)

$$Q_{\mathrm{r}}=4.187{\displaystyle \frac{m{q}_{\mathrm{r}}\left(t_{\mathrm{r}}-t_{\mathrm{l}}\right){\rho }_{\mathrm{r}}}{1000}}\times T$$

(10)

where CE_hotwater represents the carbon emissions generated by hot water (kgCO₂e); Q_r represents the annual consumption of hot water (kWh·a⁻¹); η_r represents the domestic hot water transmission and distribution efficiency (%); η_w represents the average annual heat source efficiency of a domestic hot water system (%); EF represents the carbon emission factor of the energy used to produce hot water (kgCO₂e·kWh⁻¹); m represents the number of residents (five persons living in the studied building); q_r represents the hot water quota (20 L·person⁻¹·d⁻¹, according to the national standard GB50555 [32]); t_r represents the design hot water temperature of 55 °C; t_l represents the design cold water temperature of 5 °C; ρ_r represents the hot water density (kg·L⁻¹); T represents the annual usage hours (h); and 50 represents the building life service limit of 50 years.

(3)

Building lighting

The carbon emissions generated by lighting were calculated as follows:

$$C{E}_{\mathrm{light}}={\displaystyle \frac{{\displaystyle \sum _{j=1}^{365}{\displaystyle \sum _i{P}_{\mathrm{i},\mathrm{j}}{A}_{\mathrm{i}}{t}_{\mathrm{ij}}}}}{1000}}\times E_{\mathrm{i}}\times E{F}_{\mathrm{i}}\times 50$$

(11)

where CE_light represents the annual energy consumption of a lighting system (kWh·a⁻¹); P_i,j denotes the lighting density of room i on day j (W·m⁻²) recommended in the literature [9], as shown in

Table 3. Lighting density of main rooms.

Spaces	Lighting Density/W·m−2	Monthly Lighting Time/h
Living room	6	165
Bedroom	6	135
Dining room	6	75
Kitchen	6	96

; A_i is the lighting area of room i (m²); t_i,j represents the lighting time of room i on day j (h); EF_i denotes the carbon emission factor of the lighting energy (kgCO₂e); and 50 represents the building life service limit of 50 years.

4. Results

4.1. On-Site Surveys

4.1.1. Heating Conditions

An investigation on the heating situations of 45 households was conducted on the 6–8 of January. The results are shown in Figure 6 and Figure 7.

(1)

Heating mode

The results showed that 84.4% of households adopted heating stoves and Chinese kang for heating, and 11.11% of residents used heating stoves and electric blankets. In total, 82.22% of the energy used for heating was scattered coal, and the rest was coal and straw.

(2)

Heating fees

The average heating cost was CNY 2934, and 42.22% of residents spent CNY 2500–3000 on heating. The other 17.78% spent CNY 1500–2000. A key barrier to rural heating optimization was the high cost, which was unaffordable for most households. Therefore, investment input must be considered when reducing carbon emissions in rural areas.

(3)

Heating temperature and factors influencing heating optimization

The indoor temperature of 53.3% of the surveyed rooms was below 10 °C, which was lower than the minimum comfortable temperature of 14 °C recommended in the standard [33,34]. In total, 84.4% of households considered that the temperature was too low in winter, and all considered heating costs were too high. Therefore, 82.2% of residents were willing to change their heating mode, and 98% of people would consider economic benefits when changing their heating mode.

4.1.2. Thermal Environment

A typical building shown in Figure 4 was selected to test the outdoor solar radiation, air temperature, and indoor temperature to determine the outdoor climatic conditions and indoor temperature changes in 48 h. The results were depicted as follows.

(1)

Solar radiation

As is shown in Figure 6, the on-site test on solar radiation lasted from 8:00 to 16:00. It reached a maximum of 580 W·m⁻² at 13:00, and the solar radiation time exceeding 120 W·m⁻² reached 7 h. There were abundant solar energy resources.

(2)

Outdoor air temperature

The average and the lowest outdoor air temperatures were −3.6 °C and −12.5 °C, respectively, coupling with the high heating demand. The maximum temperature was 9 °C at 14:30.

(3)

Indoor air temperature

The master bedroom on the first floor was equipped with a stove for heating and cooking. Residents’ schedules, cooking times, and heating needs influenced the indoor air temperature. As can be seen in Figure 7, the average and the lowest temperatures for the sunspace were only 3 °C and −4 °C, respectively, due to the poor insulation performance of single glazing. The maximum temperature reached 16 °C at 13:30. The temperature was maintained above 12 °C when there was direct solar radiation from 11:00 to 14:30, which was higher than in other rooms without any heating measures. However, the temperature sharply decreased without sunshine because of the poor insulation performance of single glazing, which needed better insulation strategies.

To summarize, heating in rural areas heavily relied on fossil fuels, which caused heavy air pollution and high carbon emissions. The indoor temperature was low and distributed unevenly. The heating fees were high with traditional and inefficient heating equipment, and human thermal comfort could not be satisfied. Residents considered heating costs first when optimizing the heating mode constrained by conditions. Therefore, economic low-cost carbon-reduction strategies were required to improve the occupants’ living conditions.

4.2. Carbon-Reduction Strategies

4.2.1. Analysis of Carbon Emissions of Reference Building

(1)

Building energy consumption

The total heat loss calculated through building envelopes was 11,933.61 W, shown in

Table 4. The net heat losses of envelopes.

Building Envelope	Net Heat Loss/W	Percentage
Walls	5599.97	46.93%
Roof	2719.17	22.79%
Ground	436.40	3.66%
Windows	1794.93	15.04%
Infiltration	582.04	4.88%
Door	490.68	4.11%
Balcony	310.42	2.59%
Sum	11,933.61	100.00%

. With a building area of 192 m² and an internal heat source of 3.8 W·m⁻², the building heat consumption index q_H of the reference building calculated based on Equation (7) was 58.35 W·m⁻², which was much higher than the specified value in the standard [33]. The heat losses of the walls and roof accounted for 46.93% and 22.79% of the entire heat losses due to the lack of thermal insulation, on which architectural thermal insulation design should focus.

(2)

Carbon emissions of the reference building

The BCEs were calculated according to Formula (2)–(11). The results are shown in

Table 5. Carbon emissions at all stages.

Various Stages		Carbon Emissions/kgCO2e⋅m−2			Percentage
Material production + transportation	Production	486.94	96.86%	502.72	7.36%
	Transportation	15.78	3.14%
Construction + demolition	Construction	3.59	52.63%	6.82	0.11%
	Demolition	3.23	47.36%
Operation	Hot water	552.75	8.75%	6318.11	92.53%
	Heating	5743.28	90.90%
	Lighting	22.07	0.35%
Sum		6827.64			100.00%

. It showed that carbon emissions during the building operation stage held the largest proportion of 79.93%. The carbon emissions from heating accounted for 89.34% of the building operation stage, reaching 5743.28 kgCO₂e·m⁻². The carbon emissions generated by hot water ranked second to heating, reaching 552.75 kgCO₂e·m⁻², followed by the material production phase. The transportation, construction, and disposal phases had relatively minor impacts on the carbon emissions. Therefore, reducing the carbon emissions during the heating stage should be further optimized.

4.2.2. Carbon-Reduction Effects of Different Optimization Strategies

The heating effects were mainly affected by the building envelopes and heating conditions, including the heating energy and heating efficiency.

(1)

Building envelopes

① Investment input of thermal insulation materials

EPS was selected as the insulation material for the walls, roof, and ground, with a thermal conductivity of 0.039 W·m⁻¹·K⁻¹. The price was 400 CNY·m⁻³. Double-glazed glasses and multi-cavity plastic window frames were selected for the windows to provide a better thermal insulation performance, the price of which was affected by the windows’ heat transfer coefficient and shading coefficient [35], as was shown below:

$$Y=2946.87-2208.56U+608.1U^2-56.83U^3+14.53/(0.87\times SC)$$

(12)

where Y represents the costs of the windows per area (CNY·m⁻²); U represents the heat transfer coefficient of the windows (W·m⁻²·K⁻¹); and SC represents the shading coefficient of the windows.

Energy efficiencies of 50%, 55%, 60%, 65%, and 70% with the different structures and heat transfer coefficients of the building envelopes are shown in

Table 6. The structures and heat transfer coefficients of the building envelopes.

Energy Efficiency	Envelope	Thermal Insulation Thickness	K-Value/W·m−2·K−1
50%	Wall	30 mm	0.73
	Roof	50 mm	0.54
	Window	/	3.00
55%	Wall	40 mm	0.61
	Roof	60 mm	0.47
	Window	/	3.00
60%	Wall	50 mm	0.53
	Roof	60 mm	0.47
	Window	/	2.70
65%	Wall	60 mm	0.47
	Roof	80 mm	0.38
	Window	/	2.40
70%	Wall	100 mm	0.32
	Roof	100 mm	0.32
	Window	/	2.40

. Energy efficiency refers to the ratio of energy consumption reduced by an energy-efficient building relative to the reference building to that of the reference building. The reference building is shown in Figure 4. The energy-efficient building was achieved by increasing the thermal insulation of envelopes.

② Result analysis

Cost analysis was a critical factor when deciding the CRSs. A parameter of reduced carbon emission per investment was proposed and calculated with Formula (13). The simple payback period helped select the appropriate retrofitting strategy; this referred to the period required to recoup the funds expended in an investment or to reach the break-even point.

$$RCE=(C{E}_{\mathrm{r}}-C{E}_{\mathrm{o}})/I_{\mathrm{o}}$$

(13)

Here, RCE represents the reduced carbon emissions per investment input (kgCO₂e·CNY⁻¹); CE_r represents the carbon emissions of the reference building (kgCO₂e); CE₀ represents the carbon emissions of the optimized building (kgCO₂e); and I_o represents the initial investment of various carbon-reduction strategies relative to the reference building.

$$P=I_{\mathrm{o}}/(F_{\mathrm{r}}-F_{\mathrm{o}})$$

(14)

Here, P denotes the payback period (y); I_o represents the initial investment of various carbon-reduction strategies relative to the reference building; F_r represents the heating fees of the reference building; and F_o represents the heating fees of the optimized building.

Reduced carbon emissions per investment input and payback period with various energy efficiencies were obtained. The results are shown in Figure 8. It showed that the reduced carbon emissions per investment input reached a maximum value of 32.25 kgCO₂e·CNY⁻¹ with an energy efficiency of 55%. The cost input significantly increased when the energy efficiency exceeded 55%. The carbon emissions did not linearly reduce; the reduced carbon emissions per investment input gradually decreased, and the investment payback period gradually increased. Therefore, it was not economical to blindly increase the building insulation to reduce the BCEs.

(2)

Heating modes and heating efficiency optimization

① Heating optimization

Electric heating devices were already commonly available. Growing attention was being paid to heat pumps to decarbonize buildings via electrification. China has established standards for installing ASHPs at different minimum ambient temperatures [36].

As a renewable energy source, biomass could significantly reduce carbon emissions. Household biomass heating stoves were commonly used for heating in remote rural areas because of their low cost, high efficiency, and low emissions, which were priced in the range of CNY 2000–5000 and could heat spaces of up to 60–120 m².

Natural gas-fired heating was considered a low-carbon method. The literature showed that annual carbon emissions could be reduced by 78.3% and 35.6% compared with coal-fired and ASHP heating, respectively.

Therefore, in this paper, household biomass heating stoves, ASHPs, and natural gas combustion furnaces were selected to reduce heating carbon emissions. Their costs and carbon emission factors are shown in

Table 7. Cost analysis of various heating energy.

Heating Energy	Heating Efficiency	Calorific Value	Operating Cost	Carbon Emission Factor/kgCO2e·Unit−1
Coal	0.75	29,307 J·g−1	700 CNY·t−1	89 tCO2e·TJ−1
Natural gas	0.91	38,931 k J·m−3	2.4 CNY·m−3	55.54 tCO2e·TJ−1
Biomass	0.75	16,368 J·g−1	590 CNY·t−1	180 kgCO2e·t−1
Air-source heat pump	2.5	3600 kJ·kwh−1	0.5 CNY·kWh−1	0.66 kgCO2e·kWh−1

. The prices of 1 kWh of heat generated by coal, natural gas, biomass and ASHP are shown in Figure 9.

② Result analysis

The carbon emissions and economic effects of the three optimized heating modes were analyzed. Based on an on-site investigation of the building in Zhuoni County as a reference building, an energy efficiency of 50–70% was achieved by increasing the thermal insulation of envelopes. Reference building consumption was 11,933.61 W shown in Figure 4, and building consumption was 5966.81 W with of 50%. Then, carbon emissions, annual heating fees, RCEs with different heating energies, and different energy efficiencies were calculated. The results are shown in Figure 10, Figure 11 and Figure 12, based on which the following results could be concluded.

(a)

The carbon emissions generated by biomass energy were the lowest, followed by natural gas, ASHPs, and traditional coal.

(b)

The annual heating fees of natural gas were the highest, followed by ASHPs, biomass, and coal when supplying the same heat for buildings.

(c)

Biomass was the most economical way to reduce carbon emissions due to the low initial cost input and low carbon emission of biomass, followed by thermal insulation design, natural gas for heating, and ASHPs used for heating. The initial investment in natural gas was large with pipeline layouts.

5. Conclusions and Discussions

This study presented a life-cycle assessment of a rural residential building from the perspective of carbon–economic efficiency. According to the BCE calculations, the carbon emissions generated by heating accounted for approximately 90% of BCEs. Therefore, in this study, a heating-optimization design was conducted to study the optimum carbon-reduction strategies considering economic benefits. The following conclusions were made based on on-site surveys and the analysis of optimized heating modes:

(1)

Increasing building thermal insulation could effectively reduce carbon emissions. The optimum energy efficiency was 55% in Gannan, with 30 mm thermal insulation of walls and 50 mm thermal insulation of the roof. It was not economical to blindly increase building insulation to reduce BCEs. In addition, it was an economic way to reduce carbon emissions. RCEs of thermal insulation reached 32.31 kgCO₂e·CNY⁻¹ with an energy efficiency of 55%.

(2)

Traditional coal produced the maximum carbon emissions by supplying the same amount of energy, but it was the most commonly used heating source due to its availability and low heating costs. It takes some time to eliminate the use of coal in rural areas; therefore, optimizing coal’s burning efficiency needs further study.

(3)

Biomass was the most economical way to reduce carbon emissions due to the low initial investments. RCEs of biomass reached 44.19 kgCO₂e·CNY⁻¹ with energy efficiency of 50%. Carbon emissions generated by biomass were 12.4% and 24% of those caused by coal and natural gas when supplying the same energy.

(4)

The maximum RCEs of natural gas reached 26.08 kgCO₂e·CNY⁻¹ with relatively high pipeline layout and maintenance costs. The government investment was an important factor in popularizing natural gas in rural areas.

(5)

Carbon emissions and heating fees of AHSP were relatively high. Reducing the carbon emissions and costs of the electricity generation process further improved the economic benefits of electric heating.

Based on the above conclusions, biomass heating was first recommended as the optimum carbon-reduction strategy in rural areas, followed by the thermal insulation optimization of envelopes, natural gas furnaces, and ASHPs.