The Effect of Low-Carbon Technology on Carbon Emissions Reduction in the Building Sector: A Case Study of Xi’an, China

Abstract

Efficient carbon reduction pathways in the building sector are critical for urban decarbonization. This study predicts urban carbon emissions and establishes models to evaluate the carbon emission reduction potential of applying building low-carbon technologies (LCTs) at the urban scale. The models under consideration encompass a spectrum of active strategies, specifically heat pump (HP), rooftop photovoltaic (PV) systems, and smart heating, ventilation, and air conditioning (HVAC) systems, alongside passive strategies encompassing advanced building materials and building envelopes. The predictive calculations consider building typologies, technological evolution, adoption rates, and local policy constraints. Results indicate that by 2030, the building sector in Xi’an will account for over 30% of the city’s total carbon emissions. The integrated emission reduction effect of LCTs reaches 25.8%, with building materials contributing the most significantly at 9%. Notably, rooftop PV systems demonstrate the highest carbon reduction potential among active strategies, while HP exhibits the fastest annual growth rate in mitigation. Furthermore, the study evaluates the feasibility of these LCTs to accelerate progress toward carbon reduction goals in the building sector.

1. Introduction

In response to the challenge of climate change, reducing greenhouse gas (GHG) emissions is an important issue in the world. China proposed achieving carbon peaking by 2030 and carbon neutrality by 2060, a goal that necessitates collaborative efforts across all sectors [1]. On a global scale, the three primary sectors responsible for substantial carbon emissions are buildings, industry, and transportation. Of these, buildings account for the highest share of energy demand, approximately 35% [2]. In China, the cumulative carbon emissions across the entire lifecycle of buildings reached 5.13 billion tons, accounting for a significant proportion of 48.3% of the total carbon emissions in 2021 [3]. A study of 28 provinces in China found that housing retrofits can reduce household energy consumption and carbon emissions by 17.87% compared to unretrofitted housing, significantly contributing to building energy efficiency and carbon reduction [4]. Given that cities serve as pivotal platforms for production and various activities, it is imperative to scrutinize the trend of carbon emissions from the building sector and assess the low-carbon potential of buildings. This analysis is crucial for the development of effective carbon emissions reduction technologies, which are indispensable in the fight against climate change, especially to achieve the goal of urban carbon neutrality [5].

To evaluate the contribution of LCTs in reducing urban carbon emissions, it is necessary to predict the future trajectories of carbon emissions within both the city and building sectors and to explore effective decarbonization pathways. For this purpose, top-down and bottom-up models are widely utilized in the development of prediction models aimed at estimating urban and building carbon emissions. Specifically, top-down models provide carbon emissions from cities and building stocks by considering factors such as carbon emissions, economics, and relevant statistics [6]. In contrast, bottom-up modeling establishes a baseline for the carbon emissions status of the assessed subject. When combined with scenario analysis, this approach can elucidate the direct impact of energy-saving and carbon-reduction measures on building energy consumption and emissions, encompassing technologies for enhancing building energy efficiency and energy substitution-related technologies. Notable bottom-up approaches include the Long-range Energy Alternatives Planning (LEAP), China Building Carbon Emission Model (CBCEM), and more. Researchers have made substantial progress in predicting building carbon emissions from a bottom-up perspective. However, this scenario analysis possesses limitations. For instance, Zhang and Luo [7] introduced a carbon emission prediction framework for public buildings in China, grounded in the LEAP model. This framework predominantly considers historical factors such as building area and energy intensity while neglecting the policy background of carbon reduction measures and the constraints of geographical natural resource conditions. Consequently, it establishes various carbon emissions scenarios for public buildings. Moreover, Tan, Lai et al. [8] utilized the bottom-up model from the Chinese Academy of Sciences to anticipate residential building carbon emissions up to 2050, including heating, urban buildings, and rural buildings. Factors such as population size, building area, urbanization rate, building energy efficiency, and changes in energy structure exert considerable influence on the overall carbon emissions during the operational phase of a building. Yuan, Chen et al. [9] found that in pursuing carbon emissions peaking within the building sector, four pivotal measures: advocating for the adoption of low-carbon and clean heating methods, extensive utilization of renewable energy sources, implementation of low-carbon retrofitting initiatives, and rational regulation of building scale. Xu, Sun et al. [10] evaluated the challenges associated with implementation, economic costs, and potential for widespread adoption of various technologies, following a rigorous calculation of carbon emissions from buildings across diverse scenarios. Their findings led to the proposal of the following prioritized strategies: firstly, enhancing the energy efficiency standards of newly constructed buildings; secondly, fostering the integration of renewable energy in building applications; and lastly, advancing energy-saving retrofits of existing buildings.

Research has demonstrated the effectiveness of clean energy and renewable energy in facilitating the decarbonization of the urban building sector. However, there is a paucity of studies focused on the selection of LCTs for achieving efficient decarbonization in buildings. The majority of existing research has primarily concentrated on the impacts of implementing LCTs on individual buildings or confined geographical areas. Consequently, the effects of integrating multiple technologies at the urban scale, and the contribution of the carbon reduction effect of different technologies to the city’s carbon-peaking goals, remain in need of thorough quantification.

Existing studies have demonstrated the contributions of clean and renewable energy systems to decarbonizing urban building sectors. However, there remains a paucity of research focused on technology selection strategies for LCTs to achieve effective building decarbonization. Current research predominantly concentrates on assessing the environmental impacts of implementing LCTs at either the individual building level or within limited geographical boundaries. Consequently, two critical knowledge gaps persist: (1) quantifying the implementation efficacy of LCTs at the urban scale; (2) investigating the impact and contribution of different technologies’ carbon reduction potential on urban carbon reduction. These unresolved scientific inquiries necessitate establishing systematic quantification methodologies through developing comprehensive urban-scale analytical frameworks.

This study attempts to clarify the carbon emissions reduction effect of applying LCTs in buildings, to address the following research questions: (1) What are the carbon emissions and future trends of the city and building sector? (2) What is the carbon reduction impact of typical LCT applications in urban buildings? (3) What are the effectiveness and feasibility of different LCTs in terms of reducing carbon emissions?

This study fills the research gaps in two aspects: Firstly, it quantifies the carbon reduction potential of pivotal LCTs applied in the target city, expanding the research scope from small-scale building clusters to the urban scale; Secondly, it conducts comprehensive quantitative analysis of the contribution of LCTs and carbon sinks towards achieving urban carbon peaking goals.

2. Materials and Methods

The framework of this study is shown in Figure 1. The first step is to construct the database, including the energy consumption of various industries and building-related data of the city, etc. The LEAP model is widely used in the medium and long-term national and city energy and environmental planning, and it is capable of forecasting the long-term energy supply and demand dynamics of society under the influence of various drivers. In addition, the model can estimate the pollution and greenhouse gas emissions generated by the energy distribution and consumption processes [11]. Utilizing the LEAP model, take into account local policies and objective conditions, and establish the scenario parameters of low-carbon measures under different intensities, to predict the multi-scenario carbon emission results that conform to the actual situation of the city. Secondly, select five typical LCTs in the city, considering the building types, technological iterations, renovations, and application rates in the city over time (year), and establish corresponding models to calculate the carbon emission reduction amounts resulting from the application. Thirdly, to conduct a comparative analysis of the effects of these technologies on urban carbon emissions reduction. Finally, examine the feasibility of technology applications and put forward suggestions for the city to achieve a low-carbon peaking target.

2.1. Research Area

This study takes Xi’an City, located in Shaanxi Province, as a case study. The operational carbon emissions of the construction industry in Shaanxi Province witnessed a substantial surge, rising from 0.5 billion tons in 2010 to 0.8 billion tons in 2020, marking a growth rate of 60%. Notably, the amount of new construction and Retrofittable in Xi’an has increased significantly in recent years. By the end of 2023, Xi’an’s resident population exceeded 13 million, accounting for more than 30% of the province’s population, and had been increasing except for a slight negative growth in 2021. Furthermore, Xi’an’s Gross Domestic Product (GDP) exhibited robust performance, reaching 1,201,076 billion yuan in 2023, maintaining high growth. During the period from 2016 to 2020, Xi’an achieved cumulative reductions of approximately 24% in energy consumption per unit of GDP and 19% in carbon emission intensity, exceeding the provincial target.

2.2. Carbon Emission Analysis of Xi’an

Calculation of the Carbon Emissions

The energy consumption data were obtained from the official report of the Urban Bureau of Statistics, as shown in Tables S1 and S2. The total carbon emissions from energy consumption were derived from aggregated activity-level data. The formula is as follows:

$${CE}_{ec}=\sum {EC}_{i,j}\times c_i$$

(1)

where ${CE}_{ec}$ represents the carbon emissions from net energy consumption, ${EC}_{i,j}$ is the net consumption of energy i in industry sector j, and $c_i$ is carbon emission factor of energy i. The same principle applies to solid waste treatment.

Methane emissions generated from urban wastewater treatment can be converted into carbon dioxide equivalent using Global Warming Potential (GWP) values. The formula is as follows.

$${CE}_{wt}=Q\times ∆COD\times c_{{CH}_4}\times {GWP}_{{CH}_4}$$

(2)

where ${CE}_{wt}$ represents the carbon emissions from the treatment of wastewater, Q is the annual treated wastewater volume, and $c_{{CH}_4}$ is the methane emission factor, and ${GWP}_{{CH}_4}$ is the GWP of methane.

The green space carbon sink calculations reference the “Technical Guidelines for Gross Ecosystem Product (GEP) Accounting of Terrestrial Ecosystems” [12].

2.3. Prediction of the Future Carbon Emissions Under Different Scenarios

The LEAP model serves as an energy system planning model that considers a variety of aspects. In this study, the LEAP model was employed to simulate future carbon emission scenarios, methodologically grounded in the aggregation of multi-sectoral energy consumption data and the incorporation of policy constraints (Figure 2). This approach enables the evaluation of the impacts of diverse low-carbon interventions on urban carbon peaking timelines and peak magnitudes.

The scenario modeling incorporated three different scenarios: the business as usual (BAU) scenario, the Emission Control (EMC) scenario, and the Reinforced Emission Reduction (RFM) scenario. The BAU scenario outlines the anticipated carbon emissions trajectory in the future under the assumption of no policy interventions, characterized by the fastest GDP growth based on current policies. In contrast, the EMC scenario appropriately raises the share of clean energy and reduces the proportion of fossil energy, resulting in moderate GDP growth. Lastly, the RFM scenario achieves the most significant energy savings and emission reductions but is likely accompanied by a decline in the GDP growth rate.

The constant growth rate assumption was employed in the modeling of the scenario setting, given the availability of data, the accuracy of the data, the study objectives, and the regional characteristics. This assumption was made in order to simplify the modeling. In establishing the scenario parameters for this study, the process of “input-output-judgment result-iterative input-output” was repeated multiple times to ensure that the overall forecasting results would not be significantly influenced by the variation in a specific parameter.

The reference basis for the model parameter settings is shown in Table S3, and the parameter settings are presented in

Table 1. Simulation parameter settings of different scenarios.

Scenario	Classification	2025	2030	2035
Business as usual (BAU)	Fossil fuels	1. Excluding the transportation industry, fossil fuels such as crude oil, gasoline, diesel, and liquefied petroleum gas in other sectors will decrease by 0.8% annually. 2. In transportation, gasoline and diesel consumption decrease by 5% annually. 3. The consumption of natural gas in all sectors rises by 4% annually.
	Electricity	The consumption of electricity power in all sectors rises by 6.6% annually.
	Solid waste and sewage treatment	The annual rate of reduction for both solid waste generation and the Chemical Oxygen Demand (COD) in wastewater is 2%.
	Clean energy	The proportion of clean energy generation has reached 20%.	50%.	70%.
	Forestry carbon sink	The area covered by grassland and forest has been expanded to encompass 48.03%	49%	50%
	GDP	The annual growth rate of the GDP is 6.5%.
Emissions control (EMC)	Fossil fuels	1. Excluding the transportation industry, fossil fuels such as crude oil, gasoline, diesel, and liquefied petroleum gas in other sectors will decrease by 1.5% annually. 2. In the transportation sector, the consumption of gasoline and diesel decreases by 7.5% per year. 3. The consumption of natural gas in all sectors rises by 6% annually.
	Electricity	The electricity consumption of electricity power in all sectors rises by 10% annually.	10.5% annually.	11% annually.
	Solid waste and sewage treatment	The annual rate of reduction for both solid waste generation and the Chemical Oxygen Demand (COD) in wastewater is 2%.	3%.	4%.
	Clean energy	The proportion of clean energy generation has reached 25%.	55%.	75%.
	Forestry carbon sink	The area covered by grassland and forest has been expanded to encompass 50%	52%	54%
	GDP	The annual growth rate of the GDP is 6.5%.	6%.	5.5%.
Reinforce mitigation (RFM)	Fossil fuels	1. Excluding the transportation industry, fossil fuels such as crude oil, gasoline, diesel, and liquefied petroleum gas in other sectors will decrease by 3% annually. 2. In the transportation sector, gasoline and diesel consumption decrease by 10% per year. 3. The consumption of natural gas in all sectors rises by 7% annually.
	Electricity	The electricity consumption of electricity power in all sectors rises by 11% annually.	11.5% annually.	12% annually.
	Solid waste and sewage treatment	The annual rate of reduction for both solid waste generation and the Chemical Oxygen Demand (COD) in wastewater is 4%.	5%.	6%.
	Clean energy	The proportion of clean energy generation has reached 25%.	60%.	80%.
	Forestry carbon sink	The area covered by grassland and forest has been expanded to encompass 50%	53%	56%
	GDP	The annual growth rate of the GDP is 6%.	5%.	4%.

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2.4. Carbon Emissions Reduction Effect from Building Low-Carbon Technologies

2.4.1. Prediction of Construction-Related Data

(1)

The built area prediction in the city

The newly developed and completed built-up areas within Xi’an from 2013 to 2022 were sourced from the Xi’an Bureau of Statistics (Table S4). Linear regression and curve regression models are employed to predict the built-up area, newly constructed area, and completed built-up area, respectively. The p-value and R² results showed that the prediction models are verified to be used [13]. The expressions are shown in

Table 2. Prediction results of the built area data of Xi’an.

Category	Expression	p Value	Fitting Degree R2	F Test
Built-up area (10,000 m2)	S1 = 1685.053 × N − 3,383,658.772	0.000	0.943	F = 132.845 (p = 0.000 ***)
New built area (10,000 m2)	S2 = 291.313 × N − 583,264.641	0.001	0.851	F = 24.154 (p = 0.001 **)
Completed built area (10,000 m2)	S3 = 123.314 × N − 246,004.91	0.002	0.819	F = 20.455 (p = 0.002 *)

Note: N is the year. *: p < 0.05 (“significant”, moderate evidence for rejection of the original hypothesis.). **: p < 0.01 (“highly significant”, strong evidence for rejection of the original hypothesis.). ***: p < 0.001 (“highly significant”, very strong evidence for rejecting the original hypothesis.).

.

(2)

The heating demand prediction

The annual steam and hot water demand data, heating area, and residential area of the city from 2013 to 2022 were selected from the Xi’an Bureau of Statistics (Table S5). The average value was adopted as the predicted value of the total steam volume, while the linear regression model was utilized for the remaining variables. The expressions are shown in

Table 3. Prediction results of the heating demand of Xi’an.

Category	Expression	p-Value	Fitting Degree R2
Total steam volume (10,000 GJ)	Q1 = 1843.927	-	-
Total hot water volume (10,000 GJ)	Q2 = 661.782 × N − 1,329,014.105	0.000 ***	0.920
Heating area (10,000 m2)	S4 = 2603.1 × N − 5,228,494.216	0.000 ***	0.887
Residential heating area (10,000 m2)	S5 = 1905.564 × N − 3,825,721.505	0.001**	0.846

Note: N is the year. **: p < 0.05 (“significant”, moderate evidence for rejection of the original hypothesis.). ***: p < 0.01 (“highly significant”, strong evidence for rejection of the original hypothesis.).

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2.4.2. Carbon Emissions Reduction Effect from Building Materials Carbon Sink

Building materials carbon sink encompasses the active role that buildings and associated activities play in the carbon cycle, involving the absorption, storage, and reduction of carbon dioxide emissions [14]. Zhao, Huang et al. [15] formulated a generalized model to predict the materials’ carbon sink of urban buildings across vast areas. Drawing Fick’s second law, it is established that the carbonation depth scales proportionally with $t^{0.5}$. Employing Pearson correlation analysis [16,17], among the eight independent variables, the effective exposed area $A_e$ (with the best correlation) was incorporated as another parameter of the prediction model. The power function regression model with the optimal goodness of fit R² was utilized to predict the building carbon sink in Xi’an. The formula is as follows:

$${CER}_{bm}={CER}_{bm,RB}+{CER}_{bm,IB}+{CER}_{bm,OB}+{CER}_{bm,CB}+{CER}_{bm,PB}$$

(3)

$${CER}_{bm,i}=a_1\times A_{i,t}^{a_2}\times t^{0.5}$$

(4)

where ${CER}_{bm}$ represents the carbon emission reduction from building materials carbon sink, ${\mathrm{A}}_{\mathrm{i},\mathrm{t}}$ is the exposed area of building type i with a completion age of t years (Table S6), RB is residential buildings, IB is industrial buildings, CB is commercial buildings, PB is public service buildings, OB is other buildings, $a_1$ and $a_2$ are known constant coefficients

Given the correlation between the building materials’ carbon sink and their completion time, this study focuses specifically on the carbon sinks associated with buildings that were completed in 2013 and thereafter.

2.4.3. Carbon Emissions Reduction Effect from Building Heat Pumps

The building sector consumes a significant amount of heat, with the heat source structure mainly coal-fired [18]. As a green and low-carbon heat supply scheme, heat pumps (HPs) are essential for the building sector to replace fossil energy for heating and achieve carbon-peaking goals and neutrality. The carbon emissions reduction effect from HP is the difference between the carbon emissions in the Baseline Scenario (BS) and the carbon emissions from using HP for heating in the Application Scenario (AS). The formula is as follows:

$${CER}_{hp}={CER}_{hp,Q_1}+{CER}_{hp,Q_2}$$

(5)

$${CER}_{hp,i}={CE}_{hp,BS}-{CE}_{hp,AS}$$

(6)

$${CE}_{hp,BS}={\displaystyle \frac{\sum Q_j}{q_i}}\times c_i$$

(7)

$${CE}_{hp,AS}={\displaystyle \frac{\raisebox{1ex}{$\sum Q_j\times {TAP}_i$}\!\left/ \!\raisebox{-1ex}{${HSFP}_k$}\right.}{q_i}}\times c_i+\left(1-{TAP}_i\right)\times {CE}_{hp,BS}$$

(8)

where ${CER}_{hp}$ represents the carbon emission reduction from HP, $Q_1$ is the total annual steam demand, $Q_2$ is the total annual hot water demand, q_i is the average calorific value of energy i, $c_i$ is the carbon emission factor of energy i, HSFP is the Heating Season Performance Coefficient of type k, and TAP is the Technology Application Proportion.

The HSPF of different types of HP are as follows: for heating, the HSPF of household HP is 2.8, and that of commercial HP is 3; for hot water, the HSPF of household HP is 2.4, and that of commercial HP is 2.5 [19]. The baseline scenario settings are shown in

Table 4. Baseline scenario settings for heating and hot water supply.

Heat Pump Market Type		Baseline Scenario	Main Applicable Scenarios
Heating	Household heat pump heating unit	100% coal burning	for residential heating in severe cold and cold regions
		100% gas wall-hung boiler	for residential heating in hot summer and cold winter regions
	Heat pump air heater	100% coal burning	for residential heating in severe cold and cold regions
	Commercial heat pump unit	50% cogeneration + 10% gas boiler + 40% coal-fired boiler	for central heating in severe cold and cold regions
	Water source heat pump unit
Hot water	Household heat pump water heater	50% gas water heater + 50% electric water heater	for the hot water supply in all regions
	Commercial heat pump water heater

and is applied from the China Energy Conservation Association’s “Heat Pump Development Roadmap for China’s Building Sector”.

The market growth rate of HP is estimated using nodal sales data for hot water and heating applications under carbon neutrality scenarios (Table S7). The curvilinear regression model with a goodness-of-fit of 0.996 is selected as the predictive model for simulated sales, with it is shown in

Table 5. Prediction model for China’s heat pump sales.

Type	Expression	p Value	Fit Degree R2
Hot water sales	Q3 = 0.334 × N2 − 1308.235 × N + 1,282,101.84	0.000 ***	0.996
Heating sales	Q4 = 1.368 × N2 − 5509.464 × N + 5,548,091.723	0.000 ***	0.996

Note: N is the year. ***: p < 0.001 (“highly significant”, very strong evidence for rejecting the original hypothesis.).

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The growth in HP market sales typically directly reflects the prevalence of technology adoption; therefore, its annual growth rate is utilized as a proxy for the TAP (Table S8).

2.4.4. Carbon Emissions Reduction Effect from Rooftop Photovoltaic Systems

Given the vigorous development of the urban economy, the high concentration of power demand, and the abundant building roof resources [20], roof PV systems not only refrain from interfering with the original functions of the building but also have the potential to achieve self-sufficiency in power supply [21]. The formula is as follows:

$${CER}_{pv}=\Sigma \left(RUC\times {RA}_i\right)\times SI\times CEM\times OE\times c$$

(9)

where ${CER}_{pv}$ represents the carbon emission reduction from PV, ${RUC}_i$ is the Roof Utilization Coefficient, ${RA}_i$ is the roof area of buildings, i is the building category, $SI$ is the total solar irradiance received by PV systems, $CEM$ is the conversion efficiency of PV modules, and $OE$ is the PV operational efficiency of, $c$ is the carbon emission factor of electricity.

The RUC is 0.4 for residential land, 0.6 for public and commercial land, and 0.8 for industrial land [22]. The average total horizontal irradiation in Xi’an in 2022 is 1459.7 kWh/m². The conversion efficiency of PV modules in 2024 is assumed to be 16.7%, with an annual growth rate set at 0.7% [23,24]. The operational efficiency of PV is defined as 0.8 [25]. An average tilt angle of 20° is assumed in the roof PV system model. This angle is corrected by considering the discrepancy between the theoretical optimal tilt angle (34°) and the error caused by assuming 0°, as well as practical limitations such as roof load-bearing capacity, esthetic requirements, multi-row installation spacing constraints, and the need to balance power generation efficiency with high-temperature attenuation, to ensure both theoretical and engineering feasibility.

2.4.5. Carbon Emissions Reduction Effect from Smart Heating, Ventilation, and Air Conditioning Systems

There is a significant demand for air conditioning in commercial buildings, office buildings, and large shopping malls [26]. Smart heating, ventilation, and air conditioning (HVAC) systems dynamically regulate indoor environmental parameters (e.g., temperature, humidity, and airflow), saving energy while maintaining thermal comfort [27]. The formula is as follows:

$${CER}_{hvac}=\Sigma {EC}_{c,i}\times A_i\times {ES}_i\times c$$

(10)

where ${CER}_{hvac}$ represents the carbon emission reduction from HVAC, ${EC}_{c,i}$ is the unit energy consumption for building cooling, $A_i$ is the area of building type i, ${ES}_i$ is the energy-saving rate, and $c$ is the carbon emission factor of electricity.

The annual cooling energy consumption is quantified as 8 kWh/(m²·a) for RB, 37 kWh/(m²·a) for CB, 17 kWh/(m²·a) for PB, and 20 kWh/(m²·a) for other building typologies. The current energy-saving rate is set at 20%, while mandatory energy performance standards mandate a 50% improvement by 2035 relative to 2023 technological baselines. Based on these constraints, the annual energy efficiency improvement rate is calibrated. The analysis encompasses buildings completed in or after 2013 [28].

2.4.6. Carbon Emissions Reduction Effect from Building Envelope Improvement

Common technologies for building envelope improvement include external wall insulation systems, reflective coatings, and thermal insulation coatings [29]. The application of high-performance materials enhances building thermal performance primarily through the integration of materials with superior thermal insulation, soundproofing, fire resistance, and other properties [30]. Representative materials encompass aerogels, vacuum insulation panels (VIPs), and phase change materials (PCMs) [31]. This study quantifies the carbon emission reduction benefits of upgrading to more efficient insulation materials using the following formula:

$${CER}_{ei}=∆HL\times \sum {\displaystyle \raisebox{1ex}{$q_i$}\!\left/ \!\raisebox{-1ex}{$c_i$}\right.}$$

(11)

$$∆HL=∆U\times HFA\times ∆T\times \sum q_i\times c_i$$

(12)

where ${CER}_{ei}$ represents the carbon emission reduction from envelope improvement, $∆HL$ is the reduced heat loss amount, $U$ is the thermal transmittance, $HFA$ is the heated floor area. $∆T$ is the average indoor/outdoor temperature difference during winter heating in Xi’an. $q_i$ is the average calorific value of energy i, and $c_i$ is the carbon emission factor of energy i.

Based on the national standards for thermal performance benchmarks of building envelopes [32,33,34], Xi’an is classified as Cold Region Zone B, with the reference heat transfer coefficient (K₀) of its envelope structures set at 0.35 W/(m²·K) [35]. The technology is assumed to achieve approximately 15% load reduction. The average indoor/outdoor temperature difference during the heating season in Xi’an is defined as 20 °C, with a heating duration of approximately four months.

3. Results

3.1. Carbon Emissions Characters of Xi’an

The total carbon emissions comprise the following sectors: industry (1.65 × 10⁷ t), service industry (1.20 × 10⁷ t), transportation industry (8.31 × 10⁶ t), residential life (1.352 × 10⁷ t), construction industry (3.95 × 10⁶ t), and agriculture, forestry, animal husbandry, and fishery (7.09 × 10⁵ t). Among these, the industrial sector contributes a large proportion, accounting for 30.60% of total emissions, followed by residential living (25.07%), the service industry (22.19%), transportation (15.41%), construction (7.33%), and agriculture, forestry, animal husbandry, and fishery (1.31%).

The predicted results of carbon emissions in Xi’an under different scenarios are shown in Figure 3. Under the BAU scenario, Xi’an is projected to achieve its carbon peak target by 2029, with total carbon emissions reaching 5.62 × 10⁷ tons (Table S9). In contrast, the EMC scenario demonstrates accelerated progress, enabling the city to attain carbon peaking one year earlier in 2028, with corresponding emissions reduced to 5.45 × 10⁷ tons (Table S10)—representing a 3.02% reduction compared to the BAU baseline. Under the RFM scenario, through intensified energy conservation and emission reduction measures, Xi’an could advance its carbon peak timeline to 2026. This pathway would result in peak emissions of approximately 5.4 × 10⁷ tons (Table S11), corresponding to a 3.20% decrease relative to the BAU scenario.

3.2. Carbon Emissions Reduction Effect from Building Low-Carbon Technologies of Xi’an

As illustrated in Figure 4, the implementation of various low-carbon building technologies in Xi’an City has yielded a demonstrable impact on carbon reduction.

The building carbon sinks continue to accumulate in the research period (Table S12). By 2035, it is estimated that the reduction in carbon emissions due to building carbon sinks will reach 8,095,794 tons. Among these reductions, the contributions from residential buildings (RB), public service buildings (PB), and other buildings (OB) are relatively significant, accounting for 22.98%, 25.75%, and 31.59%, respectively. In 2024, building carbon sinks accounted for over 4% of Xi’an’s total carbon emissions. This proportion is projected to increase to approximately 9% by 2030.

With the expansion of the scale of building heating and hot water using HP, it is predicted that by 2035, the annual carbon emissions reduction contribution of HP in Xi’an will exceed 40% of the carbon emissions associated with heating and hot water in the baseline scenario (Tables S13 and S14). In 2024, the carbon emissions reduction achieved through the application of building HP accounted for approximately 1% of Xi’an’s total carbon emissions. By 2030, this proportion is anticipated to increase to around 4%.

As shown in the results (Table S15), the carbon emission reductions achieved through solar power generation by rooftop PV systems are projected to reach 3.5 × 10⁶ tons by 2030. In 2024, the carbon reduction from rooftop PV systems accounted for approximately 4% of Xi’an’s total carbon emissions. This proportion is projected to increase to around 6% by 2030.

The carbon emissions reduction from smart HVAC systems can reach 1.79 × 10⁶ tons in 2030 (Table S16). In 2024, the carbon emissions reduction attributed to the smart application of smart HVAC systems in buildings accounts for approximately 2.7% of the total carbon emissions in Xi’an. By 2030, this proportion will rise to around 3.3%.

By 2030, the reduction in heat loss achieved through envelope insulation improvements is quantified at 1.41 × 10³ GJ (Table S17). In 2024, carbon emission reductions attributed to enhanced thermal performance of building envelopes accounted for approximately 2.5% of Xi’an City’s total carbon emissions. This proportion is projected to rise to around 3.5% by 2030.

From the perspective of the carbon reduction potential of technology applications at the urban scale (Figure 5), in 2024, the carbon sink of building materials accounts for the highest proportion (approximately 30%) of total carbon reduction from all technologies. This is followed by rooftop PV systems (28%), smart HVAC systems (18%), envelope improvements (16%), and HP (~9%). In terms of growth rates, by 2030, building carbon sinks will remain the primary contributor to urban carbon reduction, representing approximately 35% of the total. The proportion of carbon reduction from HP rises to third place (15%), with its growth rate consistently ranking highest.

4. Discussion

4.1. The Urban Building Sector’s Substantial Carbon Mitigation Potential

Under various scenarios, carbon emissions from building operations exceed 30% of total urban emissions. Across multiple scenarios, the urban building sector consistently demonstrates the highest emission reduction potential, which aligns with previous research findings [36]. Our findings demonstrate that implementing LCTs in Xi’an’s building sector can reduce CO2 emissions by 43.3% within the sector and 14.2% citywide by 2024, projected to rise to 76.3% and 25.8%, respectively, by 2030. Operational phases account for the largest share of building-related emissions, with LCTs capable of halving these emissions. Comparative analysis reveals that the reinforced mitigation scenario not only accelerates the carbon peaking timeline but also effectively reduces the magnitude of the emission peak, thereby aligning with economic development objectives and establishing a more sustainable pathway toward long-term climate goals.

The study comprehensively assessed the effects of multiple types of technologies, rather than a single measure, on the subject. This outcome serves to illustrate the combined impact of low-carbon technologies. The urbanization process is staged, and the projection year is situated in the middle stage of urbanization. The reduction in energy consumption is achieved through energy efficiency retrofits in existing buildings and the implementation of higher energy efficiency standards in new buildings. Collectively, these factors contribute to the decline in the sectoral share of emissions.

4.2. Differences in Emission Reduction Potential and Feasibility of Low-Carbon Technologies

Among the contributions of different LCTs, the carbon sink of building materials stands out. Green building materials, distinguished by their inherent advantages, have emerged as a pivotal strategy for the building materials industry to attain carbon reduction objectives [37]. The decarbonization potential of building materials is particularly noteworthy, given their inherent capacity for carbon sequestration. The production of building materials (e.g., cement, steel, glass) is known to be a significant consumer of energy and a substantial source of emissions. It has been estimated that the carbon emissions associated with the production of these materials account for more than 50% of the total carbon emissions emitted during the lifecycle of a building. However, when utilized in architectural settings, these materials possess the capacity to persist in reacting with airborne substances, thereby engendering an absorption effect. As a rapidly urbanizing area, Xi’an has a substantial building stock and a large scale of new construction, with a significant demand for materials. Consequently, there is considerable scope for optimizing material choices (e.g., promoting low-carbon concrete and recycled building materials) to reduce emissions. Given the scarcity of land resources, China and the majority of Asian countries share similarities in the structural composition of residential and commercial buildings, with reinforced concrete structures being prevalent. Conversely, in regions such as North America and Australia, wood structures are more commonly adopted for building construction [38].

Traditional energy sources such as coal, used for heating, power generation, and cooling, impose significant environmental burdens. Enhancing energy efficiency and increasing the adoption of renewable energy, particularly solar power, can mitigate these impacts by reducing emissions and conserving resources. Among the evaluated retrofitting solutions, the application of rooftop PV systems demonstrates a remarkable increase in clean energy integration, highlighting its exceptional potential for lowering carbon intensity. HP exhibits the highest annual growth rate in carbon reduction efficiency, underscoring its promising applicability in energy transition scenarios. Additionally, smart HVAC system upgrades and building envelope retrofits contribute significantly to reducing reliance on conventional energy sources. Notably, solar energy harnessed through PV systems not only supplies clean power for building operations but also enables synergies with other LCTs, such as energy storage and smart grid integration.

Among active LCTs, rooftop PV systems in Xi’an demonstrate the most significant carbon emission reduction, underscoring the low-carbon benefits of renewable energy systems. This phenomenon can be attributed to the synergistic effect of its climatic conditions, application scenarios, and policy strength. Xi’an experiences an annual mean of 2000 to 2500 h of sunshine, which is sufficient to support the basic performance of rooftop PV systems. The extensive roof area offers significant potential for integrating PV technology into the urban infrastructure and people’s lives. The city of Xi’an has clearly articulated a policy of “increasing the installed capacity of renewable energy to 30% by 2025” and has placed a significant emphasis on enhancing the proportion of PV energy. This commitment is further supported by the promotion of large-scale grid-supporting initiatives and financial subsidies to ensure that the city’s extensive rooftop PV potential is fully developed.

The HP system demonstrates a high degree of scalability. The energy efficiency ratio (COP) of the heat pump is notably elevated in comparison with conventional electric heating systems or coal-fired boilers. Consequently, the carbon emission intensity per unit of energy consumption is substantially mitigated. The heat pump has been demonstrated to be a viable solution for meeting Xi’an’s winter heating and hot water demands. Its suitability extends to both new construction and existing building renovation. Concurrently, the heat pump system demonstrates compatibility with renewable energy sources, such as photovoltaic power drives. This aligns with policy directives promoting the transition to electric heating and the pursuit of clean heating solutions. In the context of the northern clean heating transformation, the system benefits from financial support and market recognition.

The effect and growth rate of HVAC (heating, ventilation, and air conditioning) systems and envelope improvements in reducing emissions in the building sector are relatively unremarkable and are mainly constrained by factors such as technology, cost, implementation difficulty, and policy. At the technical level, HVAC system renovation is easily affected by the original layout of the building’s pipeline network, equipment compatibility, and load demand fluctuations. Part of renovating old buildings needs to be synchronized with upgrading infrastructure. The comprehensive benefits of releasing the cycle are longer. Although envelope improvement can reduce heat loss, the energy-saving effect is more evident in the long-term reduction in energy consumption. The short-term reduction of carbon emissions is less than one would intuitively expect from renewable energy alternative technology. In terms of implementation and cost, the initial investment for HVAC retrofitting is high due to the professional and technical complexity. Building owners lack motivation to transform due to the long cost recovery period. Envelope improvement involves the interests of many parties and is vulnerable to property rights division, construction nuisance, and other constraints when transforming existing buildings. Additionally, compared to the clear subsidies and installation targets of PV projects, the energy-saving retrofit support policy for HVAC and envelope structures lacks quantitative assessments and differentiated subsidies. This makes it difficult for market players to participate in the initiative and grow rapidly. However, as technology improves and the carbon trading market improves, the energy-saving potential and growth rate of the two are expected to increase.

A triad of factors undergirds regional disparities in technology implementation: climatic conditions predominate in determining technological aptness; the economic development level and energy structure are salient; and local policies and infrastructure are also significant. Local governments’ carbon peaking action programs, which delineate technology pathways, will directly impact the deployment pace.

While emission growth rates in the building sector show declining trends, achieving carbon peaking by 2030 requires further intervention to offset residual emissions. Xi’an’s decarbonization strategy must prioritize technological retrofits in the building sector. Data-driven predictive modeling should guide policy design to accelerate emission reduction trajectories and align with urban carbon peaking targets. Regionally tailored low-carbon pathways, informed by localized data analytics, offer replicable frameworks for national and global scalability. Strengthening intercity and international collaboration—particularly in resource sharing and policy innovation—is imperative to address climate change collectively.

4.3. Limitations and Prospects

Given the variations in carbon emission reduction policies and targets across different regions, this paper focuses on Xi’an as a case study and utilizes precise data spanning from 2013 onwards for future predictions and calculations. Initially, the insufficiency of historical data probably has a certain impact on the prediction accuracy. Furthermore, in evaluations of the carbon reduction benefits associated with LCTs in buildings, the calculation method for each building can be further refined and improved.

The calculation framework and results of this paper can provide valuable insights and serve as a reference for cities similar to Xi’an. Furthermore, they can assist cities in different regions to calculate the corresponding carbon emission reduction pathways within the building sector. In the future, this paper intends to collect more data to conduct more accurate predictions. By further categorizing building types, the calculations will be refined to enhance the accuracy of the results. Additionally, future research should select different types of cities for calculation and compare their results to explore the differences in low-carbon pathways for buildings in different cities. Concurrently, the expanding model database will assist city authorities in planning efficiently.

5. Conclusions

The building sector, responsible for over 30% of urban carbon emissions, holds critical accountability in mitigating its environmental footprint due to its substantial energy consumption. This study establishes a comprehensive framework to quantify emission reductions from LCTs in Xi’an, integrating the carbon sequestration potential of building materials, HP systems, rooftop PV systems, smart HVAC systems, and envelope improvements. Our city-scale simulation model projects emission trajectories from 2024 to 2035 under existing policy conditions, evaluating technological applicability, regional feasibility, and iterative innovation impacts. The framework integrates the characteristics of the urban environment and infrastructure in the modeling process, ensuring the universality of the calculation indicators and emphasizing the flexibility of local adaptation. The parameters are based on localized data, with scenarios based on local policies, solar radiation based on measured values, heat pump scenarios based on climate zoning codes, and key parameters of HVAC and envelope precisely matching regional climate conditions. The adaptability of the framework across regions, particularly in small and medium-sized cities with limited data resources or unique climate conditions, overcomes the constraints of traditional models.

The principal findings demonstrate that the building sector, as a primary urban carbon emission source, can achieve carbon peaking before 2030. Quantitative assessments identify building materials in Xi’an as having the highest decarbonization potential, underscoring the strategic imperative of embodied carbon management. Within active LCTs, rooftop PV systems demonstrate optimal carbon emission reduction effect, highlighting the low-carbon advantages of renewable energy systems. HP systems exhibit rapid scalability in both retrofitting and new construction applications, demonstrating significant emission reductions through energy efficiency gains. Regional applicability analysis further reveals technology-specific deployment potentials across geographic contexts. Under business as usual (BAU) scenarios, the sector’s contribution to urban emissions declines from 32.7% (2024) to 29.7% (2030), evidencing substantial mitigation effectiveness from implemented LCTs.

The synergistic deployment of these technologies not only reduces emission growth rates but also accelerates peaking timelines through amplified mitigation effects. Energy structures progressively transition toward cleaner alternatives, enhancing environmental and economic co-benefits. This research underscores the necessity for city-specific quantitative frameworks to prioritize retrofit measures across diverse climatic and socioeconomic contexts, offering actionable policy insights for data-driven low-carbon transitions. The methodology demonstrates replicable value in guiding regional climate cooperation and resource-sharing strategies to achieve urban decarbonization goals.