Analysis of Influencing Factors and a Refined Calculation Method of Carbon Emission in the Materialization Stage of a Steel–Concrete Composite Beam Bridge

Abstract

To clarify the carbon emission characteristics of steel–concrete bridges and address the gap in carbon emission accounting standards within the bridge sector, this study focuses on carbon emissions during the materialization phase of bridge engineering. Employing inventory analysis and emission factor methods, combined with the specific carbon emission patterns of this phase, a carbon emission accounting model was developed, a carbon emission factor database was established, and carbon emissions were calculated for a prestressed steel–concrete bridge on a highway in Shandong Province. The calculation results indicate that the carbon emissions during the materialization phase of prestressed reinforced concrete bridges amount to 3,092,237.79 kg CO₂e. Of this total, 77.74% of carbon emissions are concentrated in the building materials production stage, with emissions from building materials in the superstructure and lower structure accounting for 81.25% of the emissions across materials production stages. Material transportation and on-site construction account for 9.87% and 12.39%, respectively. Among these, high-carbon equipment constitutes less than 10% of the total quantity but contributes over 70% of emissions. Due to the coal-dependent energy structure in North China, the total carbon emissions are 31.90% higher than the national average. The multi-factor sensitivity analysis shows that the aggregate transportation distance becomes the most sensitive parameter, which changes the traditional cognitive paradigm of “material quantity dominance”. Some emission reduction measures are provided for the sources of key carbon emissions in the materialization stage of bridges.

1. Introduction

At present, the rapid development of railway bridge construction, which has both construction and transportation attributes, results in significant carbon emissions during the construction phase of railway bridge projects [1]. Globally, emissions generated during infrastructure construction account for 79% of total greenhouse gas emissions, with the construction sector alone responsible for 23% of global carbon dioxide emissions [2]. By the end of 2024, China had 1.1081 million highway bridges with a total length of 101.9758 million linear meters. Therefore, systematically analyzing the carbon emission characteristics of highway bridge engineering not only provides critical data support for low-carbon construction of railway infrastructure but also holds significant strategic importance for promoting green transformation in the transportation sector and achieving sustainable development goals.

The research on carbon emissions in the field of construction has formed a relatively perfect system [3,4,5]. Despite the Ministry of Transport’s proactive promotion of green and low-carbon transportation development since the 14th Five-Year Plan period, and the release of the “Calculation Standard for Carbon Emissions During Highway Construction” T/CHSDA 0001-2024 by the China Highway Survey and Design Association in 2024 to provide a basis for calculating carbon emissions in highway projects [6], there is still a lack of corresponding standards for the calculation of carbon emissions in the materialization stage of bridges. Therefore, conducting in-depth research on carbon emissions in this field holds significant importance.

In recent years, scholars have conducted extensive research on bridge carbon emissions, focusing on in-depth discussions in terms of calculation methods, life cycle assessment, stage characteristics, and intelligent prediction.

(1) In terms of carbon emission accounting methods, Life Cycle Assessment (LCA) has been widely adopted in research on bridge carbon emissions. Some scholars, based on the LCA framework, divide the bridge life cycle into stages such as material production, construction, operation, and demolition, and establish corresponding carbon emission calculation models. For example, Bouhaya et al. [7] divided the bridge life cycle into six stages: material production, transportation, construction, maintenance, demolition, and waste disposal, and systematically evaluated the environmental impacts of new bridges. Hammervold et al. [8] conducted a comparative study on three different types of bridges in Norway and found that carbon emissions in the material production stage exert the greatest influence on the overall environmental impact. Cao et al. [9] divided the life cycle of highway bridges into six stages: preliminary preparation, material production and processing, transportation, on-site construction, operation, and waste recycling, and carried out carbon emission accounting for two actual bridges based on the bill of quantities, verifying the feasibility of the model.

(2) In terms of life-cycle carbon emission calculation, Wang et al. [10] divided the bridge engineering stage based on the whole life cycle theory and studied the carbon emission flow of various bridge types. Ma et al. [11] established a five-stage carbon emission calculation model for the whole life cycle of cable-stayed bridges, conducted case studies, and proposed emission reduction measures. Qian et al. [12] studied and compared the life cycle carbon emissions of hollow slab bridges and T-beam bridges. The calculation model was established and analyzed. It is concluded that the LCCE of T-beam bridges is lower under the same span, and the production and operation stages are the key to emission reduction. Yang et al. [13] introduced a BIM-based rapid calculation and analysis system for bridge life cycle carbon emissions and proposed a systematic carbon emission reduction strategy after verifying the effectiveness with actual cases. Although the above studies have addressed carbon emissions during the materialization phase of bridges, they generally focus on the full life cycle. Targeted research on carbon emissions in the materialization phase remains insufficient, and widely applicable calculation methods have not yet been established, making it difficult to accurately and comprehensively reflect the carbon emission characteristics of this phase.

(3) In terms of the stage characteristics of carbon emissions, existing studies generally agree that the material production stage is the primary source of carbon emissions throughout the entire life cycle of bridges [14,15]. Meanwhile, although carbon emissions during the operation stage account for a considerable proportion and can be continuously optimized and adjusted, carbon emissions in the construction and materialization phase are characterized by a one-off nature and are difficult to substantially modify once construction is completed. Therefore, the optimization of carbon emissions at this stage is particularly critical. The research on carbon emissions in the materialization stage can be regarded as the further expansion and refinement of the research on carbon emissions in the construction period. The existing research has carried out some exploration around carbon emissions in the bridge construction period. Li et al. [16] established a carbon emission calculation framework for the railway bridge construction period. Combined with the intercity railway bridge project in the Guangdong-Hong Kong-Macao Greater Bay Area, the study found that the carbon emission per unit mileage of the segmental prefabricated continuous rigid frame bridge scheme is lower than that of the whole prefabricated simply supported beam bridge scheme, which can reduce the carbon emission by 3.96%. Song et al. [17] and Zhang et al. [18], respectively, studied the carbon emissions during the construction phase of continuous steel bridges in the Guangdong-Hong Kong-Macao Greater Bay Area and T-beam bridges. Through sensitivity analysis of key carbon emission factors, they concluded that the sensitivity coefficient for cement’s carbon emission factor was the highest. Qin et al. [19] combined LCA and LEAP to evaluate the carbon emissions and emission reduction potential of cable-stayed bridges, and developed a variety of emission scenarios, showing that different schemes have significant emission reduction effects. Most existing studies have focused on railway bridges within specific regions or on a limited number of bridge types. Furthermore, differences in computational methods and boundary definitions across studies have resulted in a lack of comparability among research outcomes. It is not conducive to the accurate statistics and analysis of the overall carbon emission data of the industry.

(4) In terms of carbon emission prediction and intelligent analysis, deep learning methods have been gradually introduced into this field in recent years. Liu et al. [20] established a carbon emission prediction model for bridge reconstruction and extension projects based on Autoencoder neural networks, realizing the dynamic prediction of carbon emissions from key components such as pile foundations and main girders. The prediction error of the model was controlled within 11%. Liu et al. [21] proposed a machine learning-based carbon emission prediction framework for bridges at the design stage, in which the GBRT ensemble model performed optimally. SHAP and PDP analyses revealed that the volume of the main girder, volume of pile foundations, and bridge length were the key influencing factors, providing a scientific basis for low-carbon design. Xie et al. [22] further integrated machine learning with SHAP interpretability analysis to construct an integrated framework for carbon emission prediction and impact mechanism interpretation. The marginal contributions of factors such as concrete consumption, steel consumption, and material type to carbon emissions are quantified.

Although the above studies have achieved important progress in bridge carbon emission accounting methods, stage characteristics, and intelligent prediction, the following limitations still exist. First, most existing studies focus on specific bridge types or single construction methods, lacking a systematic comparison of carbon emission characteristics under different bridge types and construction technologies. Second, the emission factors adopted in carbon emission calculations are mostly based on national average values, resulting in insufficient regional adaptability. Third, existing studies mostly concentrate on carbon emissions at the full life cycle or construction stage, and systematic and refined accounting for the materialization phase of bridges remains inadequate. Moreover, differences among studies in stage division, boundary definition, and calculation methods make results difficult to compare horizontally, which is not conducive to the accurate statistical analysis of carbon emission data at the industry level.

In summary, although current research on bridge carbon emissions has established a certain foundation, further in-depth studies are still needed in terms of methodological consistency, regional adaptability, model generalization ability, and refined accounting of the materialization phase. This study aims to establish a carbon emission accounting model for the materialization stage of bridges and construct a corresponding carbon emission factor database. Taking a prestressed concrete bridge in Shandong Province as the research object, an empirical analysis is conducted to quantify the carbon emission contribution rate of each stage, identify key emission sources and sensitive parameters, and propose targeted emission reduction measures accordingly. This research provides a reference for enriching the quantitative theory of carbon emissions in the bridge materialization stage and filling the gaps in carbon emission accounting standards for the bridge engineering field. It can also offer methodological support and data references for promoting the green and low-carbon development of bridge construction.

2. Construction of Carbon Emission Model for Bridge Materialization Stage

Based on the theory of carbon emission calculation, it is necessary to accurately define the boundary and time span of the bridge materialization stage. On this basis, the carbon emission sources of the whole process of bridge materialization stage are systematically sorted out, covering key links such as transportation, processing and manufacturing, and on-site construction. Finally, according to the relevant national standards and specifications, combined with the latest research findings and practical experience [23,24,25], the calculation model of carbon emissions suitable for the materialization stage of bridges is scientifically determined, which provides solid data support for the accurate calculation of carbon emissions.

2.1. System Boundary of the Materialization Stage of the Bridge

According to the life cycle assessment method, the calculation boundary of carbon emissions in the materialization stage of bridge engineering is divided into three links: building material production, material transportation, and on-site construction, as shown in Figure 1. Carbon emissions in the production of building materials are mainly due to energy consumption and industrial process emissions in the process of raw material processing, building material making, and prefabricated component production. The material transportation refers to carbon emissions generated by vehicle energy consumption during off-site and on-site transportation activities. Off-site transportation includes raw material transport, building material transport, and prefabricated component transport. On-site construction refers to the carbon emissions generated by the consumption of energy during the construction of various construction machinery, during the construction of bridge engineering.

2.2. Sources of Carbon Emissions in the Materialization Stage of Bridges

In the life cycle of the bridge, the carbon emissions generated in the materialization stage are the most significant [26]. This study will analyze the sources of carbon emissions in the materialization stage of bridges from three aspects: raw material mining, building material production and processing, material transportation, and on-site construction activities.

2.2.1. Raw Material Mining and Processing

In this study, the bridge project is divided into three parts: lower structure, superstructure, and bridge deck system. Each part contains multiple sub-projects. The raw materials used in each sub-project are shown in

Table 1. Raw materials for each sub-project.

Subdivision Engineering	Sub-Project	Types of Building Materials	Main Raw Material
Lower structure	Pile foundation	Underwater concrete	Cement, coarse and fine aggregates, water, and concrete admixtures
		Reinforcing steel bars, steel plate	Iron, carbon
		Mud	Bentonite, sodium hydroxide
	Bearing platform	Concrete	Cement, coarse and fine aggregates, water, and concrete admixtures
		Reinforcing steel bars, Template	Iron, carbon
	Pier column	Concrete	Cement, coarse and fine aggregates, water, and concrete admixtures
		Reinforcing steel bars, Template, Embedded parts	Iron, carbon
Superstructure	Box girder	Concrete	Cement, coarse and fine aggregates, water, and concrete admixtures
		Reinforcing steel bars, steel strand, and anchorage	Iron, carbon
		Bellows	PP, PA, PE
	Cover beam, Stopper piece	Concrete	Cement, coarse and fine aggregates, water, and concrete admixtures
		Reinforcing steel bars, Template	Iron, carbon
Bridge deck system engineering	Bridge deck system	Concrete	Cement, coarse and fine aggregates, water, and concrete admixtures
		Reinforcing steel bars, Template	Iron, carbon
	Guardrail	Concrete	Cement, coarse and fine aggregates, water, and concrete admixtures
		Reinforcing steel bars, Template	Iron, carbon

.

As shown in the above table, the bridge construction project mainly involves related raw materials such as cement, steel, coarse and fine aggregates, and mineral admixtures. Therefore, this study will systematically analyze the carbon emission mechanism of the above materials in the production process, as well as the carbon emission characteristics in the production process in detail.

• Cement

The raw materials for cement production include calcareous raw materials such as limestone, clay, shale, and iron ore. Limestone mining involves processes such as drilling and blasting; clay extraction requires mechanical operations and energy-intensive drying; iron ore beneficiation consumes electricity; and the clinker calcination link, which accounts for more than 70% of the total energy consumption in the production process. Significant carbon emissions are produced. The carbon emission sources of each link are shown in Figure 2.

b.

Steel

In bridge engineering, steel is the key structural material, and its core raw materials are iron ore, coking coal, and metallurgical flux. Iron ore mining, which has high energy consumption of mining equipment, coking coal mining, which has energy consumption of underground ventilation, washing, processing, and crushing and screening of flux raw materials, all produce carbon emissions. During the steel smelting stage, blast furnace ironmaking generates substantial carbon emissions through the reduction of iron ore using coke, with 70% originating from the direct reduction reaction and 30% from coke combustion [27]. Rolling process furnaces and heat treatment rely on fossil fuels, resulting in relatively high carbon emissions per ton of hot-rolled steel; auxiliary processes also generate minor carbon emissions (see Figure 3 for details).

c.

Coarse and fine aggregates

In bridge engineering, coarse and fine aggregates are the main materials of concrete. Although the carbon emission factor per ton is lower than that of cement and other building materials, due to the large amount of concrete, the cumulative carbon emissions of aggregate production account for an important proportion of the carbon footprint of the project. Therefore, when calculating the carbon emissions of bridge engineering, it is necessary to systematically consider the emission characteristics of the whole process of aggregate production. The mining process and carbon emission sources are shown in Figure 4.

2.2.2. Material Transportation

In bridge construction, material transportation carbon emissions primarily stem from the energy consumption of transport vehicles. This study takes concrete as an example to analyze its transportation carbon emission characteristics. As shown in Figure 5, the concrete transportation carbon emission system encompasses three key stages: raw materials transported by heavy-duty trucks from quarries to mixing plants; finished products delivered by mixer trucks from mixing plants to construction sites; and short-distance secondary transfers at construction sites using construction machinery.

2.2.3. On-Site Construction Activities

The carbon emissions of bridge construction mainly come from the energy consumption of mechanical equipment in each stage. Based on the four key links of bridge foundation engineering, lower structure, superstructure, and bridge deck system, this study systematically analyzes the carbon emission characteristics of construction machinery in each link.

Taking the construction of bored piles as an example in bridge foundation engineering, the carbon emissions mainly come from the three links of pile hole drilling, concrete pouring, and pile head breaking. During the construction of the pile cap, the operation of the diesel engine of the hydraulic excavator during the excavation of the foundation pit produces direct emissions, and the earthwork transportation of the dump truck produces mobile source emissions. In the formwork erection phase, carbon emissions stem from both diesel combustion in truck cranes and electricity consumption by welding machines. In the concrete pouring process, the fuel consumption of the concrete pump truck and the power consumption of the vibrating rod group constitute a mixed emission source (see Figure 6).

Taking the pier column of the lower structure of the bridge as the research object, the carbon emission of the construction machinery mainly comes from the following links: when the formwork is supported, the direct carbon emission is produced by the combustion of the fuel of the tower crane or the automobile crane, and the indirect emission of electricity is brought by the operation of the electric welding machine; the use of electricity in the steel processing stage constitutes the main emission source; in the concrete pouring process, the fuel consumption of the concrete pump truck and the power consumption of the vibrating rod group form a mixed carbon emission (see Figure 7).

The carbon emissions of construction machinery for box girder, capping beam, and bridge abutment blocks of the bridge superstructure are mainly due to the energy consumption of heavy equipment in each link. When the support is erected, the fuel combustion of the truck crane or tower crane produces direct carbon emissions, and the fixed power consumption of the welding machine template forms indirect emissions. In the construction of prestressing, the hydraulic system of tensioning equipment and the power consumption of grouting machinery account for a large proportion of carbon emissions; the binding of steel bars depends on the power consumption of processing equipment, and the concrete pouring has the fuel of pump trucks and the power emission of vibrating equipment. The carbon emissions from the construction of the abutment block are mainly from the intermittent operation of small vibrating and auxiliary hoisting machinery. The specific construction carbon emissions are detailed in Figure 8.

In the construction of bridge deck system engineering, the power system of spraying equipment in the construction stage of the waterproof layer produces direct fuel discharge. During the construction of the bridge guardrail foundation, the drilling equipment power system and the concrete transport vehicle produce fuel emissions. When the panel is installed, the fuel combustion of the lifting platform and the power use of the fastening equipment form a mixed emission, as shown in Figure 9.

2.3. Calculation Model of Carbon Emission in the Materialization Stage of the Bridge

Based on the theory of life cycle assessment, this study integrates the three core carbon sources of material production, transportation, and on-site construction, and a carbon emission calculation model was constructed for the bridge materialization stage.

2.3.1. Production and Processing Links

Carbon emissions in the production and processing of building materials are mainly derived from industrial energy consumption and process emissions. For example, cement production involves fuel combustion and carbonate decomposition, and steel production covers the whole process emissions of high-temperature smelting and rolling of coke-reduced iron ore. Its carbon emissions can be calculated according to the equation:

$$E_{\mathrm{sc}}={\displaystyle \sum _{i=1}^n}\left(M{Q}_i\times E{F}_{\mathrm{sc},i}\right)$$

(1)

In the formula, E_sc represents the carbon emissions generated in the production and processing stage, kgCO₂e; MQ_i is the production quantity of the i building material, t or m³; EF_sc,i is the carbon emission factor of the i building material production.

2.3.2. Transport Links

The transportation of the bridge materialization stage covers raw materials, building materials, and short-distance transportation on the site. Carbon emissions mainly come from the consumption of electricity, gasoline, and diesel by means of transportation, and are closely related to energy carbon emission factors, transportation modes, and transportation distances. Its carbon emissions can be calculated according to the equation:

$$E_{\mathrm{ys}}={\displaystyle \sum _{i=1}^n}{L}_i{M}_{i}E{F}_{\mathrm{ys},i}$$

(2)

In the formula, E_ys refers to the carbon emissions of the transportation stage, kgCO₂e; L_i is the transport distance of the i material, km; M_i is the usage amount of the i building materials, t or m³; EF_ys,i is the carbon emission factor of transport.

2.3.3. On-Site Construction Links

In the construction stage, the diesel consumption of construction machinery is the dominant emission source, and the electric equipment is the indirect emission source; the emission characteristics are closely related to the mechanical type, working condition, and energy structure. In view of the fact that the proportion of hydropower consumption in the field life office area is less than 5% [28], this study only calculates the direct operation emissions of construction machinery. In the previous research on carbon emission factors, the carbon emission factors of construction machinery and equipment have comprehensively considered factors such as energy consumption and power of various types of equipment. Its carbon emissions can be calculated according to the equation:

$$E_{\mathrm{SG}}={\displaystyle \sum _{i=1}^n}\left(S{G}_i\times E{F}_{\mathrm{SG},i}\right)$$

(3)

In the formula, E_SG represents the carbon emissions from the on-site construction stage, kgCO₂e; SG_i is the class consumption of the i type of construction machinery; EF_SG,i is the carbon emission factor of the i construction machinery.

This study divides the materialization stage of bridges into three links: building material production, material transportation, and on-site construction. This division is derived from the inherent engineering characteristics of the bridge materialization process, rather than a customized solution for a specific bridge type. The embodied process of any bridge type consists of three irreducible fundamental activities: material production, spatial transportation, and on-site forming. All bridge types, including simply supported girder bridges, continuous girder bridges, cable-stayed bridges, suspension bridges, and arch bridges, fully cover the above three activities. These three links form a complete causal chain of bridge materialization and carbon emissions. There is no fourth independent activity that can be separated from the above links, nor any bridge type whose materialization process can be completed with only two links. The differences among various bridge types are only reflected in the specific contents within each link. Such particularities can be well adapted by adjusting input parameters such as the bill of quantities and carbon emission factors, without modifying the three-link accounting framework.

2.4. Analysis Methods

This study comprehensively adopts the following four analytical methods:

(1)

The inventory analysis method is adopted to obtain basic activity data. The consumption of building materials, construction machinery shifts, and other basic data are derived from the bill of quantities and construction organization design documents of bridge engineering, which are classified and summarized as the input parameters for carbon emission calculation.

(2)

The emission factor method is applied for carbon emission quantification. The carbon emissions of each stage are calculated based on the basic formula of activity data multiplied by the corresponding carbon emission factors, and the specific accounting model is presented in Section 2.3. All carbon emission factors are derived from the factor database established in Section 3, with data sources including national standards, IPCC guidelines, and datasets released by the National Climate Center.

(3)

The scenario analysis method is used to quantify the marginal effects of external parameters. By keeping core activity data such as the bill of quantities and mechanical configuration unchanged, only multiple possible values of a single parameter are replaced, and the variation range of total carbon emissions is calculated one by one, so as to separate the net impact of this parameter. Taking the regional power grid carbon emission factor as the scenario variable, seven scenarios are established in this study, including North China, Northeast China, East China, Central China, Northwest China, South China, and the national average level.

(4)

Monte Carlo simulation is adopted for the multi-parameter uncertainty analysis. Probability distribution types and fluctuation ranges are defined for eight core parameters, including cement consumption, steel consumption, and aggregate transportation distance. A total of 10,000 random sampling trials is conducted to calculate the mean carbon emission, coefficient of variation, and 95% confidence interval. Meanwhile, standardized regression coefficients are applied to complete the sensitivity ranking of influencing factors.

3. Carbon Emission Factor Database of Bridge Materialization Stage

Carbon emission factors are key parameters for quantifying the greenhouse gas emission intensity per unit of product or activity, reflecting the emission characteristics of a specific substance or energy source throughout its entire life cycle [29]. At present, there is no standardized carbon emission factor database specifically tailored for bridge engineering, which restricts the accurate accounting of carbon emissions throughout the whole life cycle and each stage of bridge projects. General databases present multiple limitations in bridging carbon emission accounting. First, the Chinese data in mainstream international LCA databases suffer from outdated timeliness. Carbon emission factors of regional power grids vary greatly across regions, with regional differences reaching several times or even more than ten times. The adoption of national average factors will cause significant accounting deviations, and the current carbon emission factors still have prominent deficiencies in spatial accuracy and temporal effectiveness [30]. Second, there is a lack of data on bridge-specific materials. Carbon emission factors for bridge-exclusive building products, such as prestressed steel strands, anchors, corrugated ducts, and rubber bearings, are rarely directly available in general databases. Third, the emission factors in conventional LCA databases are generally compiled based on historical production statistics, which fail to reflect the dynamic changes brought by the improvement of building material production technologies and the optimization of power supply structure. Although research on the basic data of energy consumption and emission in other fields has made progress [31,32,33], the differences in energy structure and production process in different countries and regions will lead to different values of carbon emission factors, which will affect the accounting results. Therefore, based on current international standards and localized regional data, this study establishes a dedicated carbon emission factor database for the materialization stage of bridges, covering four categories: energy, building materials, transportation, and construction machinery.

3.1. Carbon Emission Factors of Materials

Cement and steel, as primary structural materials in bridge construction, exhibit significantly higher carbon emissions during production compared to auxiliary materials. Given their substantial usage volumes and energy-intensive manufacturing processes, the accurate selection of carbon emission factors is crucial for ensuring the scientific validity of carbon emission calculations during the materialization phase of bridge construction. To this end, this study refers to national standard standards such as ‘Standard for building carbon emission calculation’ GB/T51366-2019 [4] and ‘Standard of Carbon Emission Calculation for Highway Construction’ T/CHSDA 0001-2024 [6], combined with regional quota data and the latest results, systematically constructs the carbon emission factor database of main raw materials of bridge engineering. The specific values are shown in

Table 2. Carbon emission factors for primary raw materials.

Material Varieties	Unit	CO2 Emission Factor (kg CO2e/Unit)
Acoustic pipe	t	2430
Steel strand	t	2340
Coal	t	0.67
C50 concrete	m3	385
C40 concrete	m3	362
Asphalt concrete	m3	81.2
Cement	t	735
C30 concrete	m3	295
C20 concrete	m3	265
Ordinary carbon steel	t	2050
Hot-rolled ribbed steel bar	t	2340
Hot-rolled plain steel bars	t	2375
Hot-rolled carbon steel medium section steel	t	2365
Welded bar-mesh reinforcement	t	2530
Asphalt	t	174.2
Steel strand	t	2340
Limestone	t	17.2
Gravel	t	2.18
Sand	t	2.51
Natural gypsum	t	32.8
Waterproof coating	m2	0.43
Waterproof rolled	m2	0.85
Steelmaking ferrous alloy	t	9530
Converter carbon steel	t	1990
Electric furnace carbon steel	t	3030
C-55 corrugated tubes	m	1.1
Rubber bearing	set	3.7
Hot-rolled carbon steel	t	2350
Anchorage	kg	11.3
Coal fly ash	t	0.1
Pig-iron	t	1660
Iron ore	t	54

.

The main materials used for bridge deck pavement include asphalt and asphalt concrete. According to Eurobitume, the carbon emission factor of asphalt is 285 kgCO₂e/t, while the carbon emission factor of asphalt concrete is 81.2 kgCO₂e/m³ [34]. Meanwhile, in the carbon emission factor list of this study, concrete is classified into four strength grades: C20, C30, C40, and C50. Steel materials are further divided into multiple types, including hot-rolled ribbed steel bars, hot-rolled plain steel bars, hot-rolled carbon steel, and hot-rolled medium-carbon steel sections. The carbon emission factor of cement, at 735 kgCO₂e/t, is adopted as the industrial average value specified in national standards, which covers the weighted average results of cement products with different strength grades. Accordingly, cement is not subdivided by strength grade. The carbon emissions of crushed stone and sand mainly derive from energy consumption during mining and crushing processes and show a weak correlation with particle-size classification; thus, these materials are also not classified by grade.

3.2. Energy Carbon Emission Factors

• Carbon emission factor of electricity

The carbon emission factor of electricity is a key parameter to quantify the greenhouse gas emission intensity per unit of electricity production, which is expressed by the CO₂ equivalent emission per kWh [35]. Its value is closely related to the energy structure of power generation. At present, China is dominated by thermal power. Although the proportion of renewable energy is increasing [36], the emission intensity of different power generation technologies varies greatly, resulting in obvious differences in the energy structure of regional power grids. Therefore, the accurate selection of power carbon emission factors is crucial for indirect emissions accounting. This study employs the regional grid average carbon emission factors released by the National Climate Center in 2012 [37], as shown in

Table 3. Carbon emission factor of electricity.

Regional Grid	Regional Characteristics	Carbon Emission Factors (kgCO2/kWh)
North China Grid	Primarily flat terrain, with a developed economy and high industrial density. The energy structure is dominated by coal-fired power generation, resulting in relatively high energy costs.	0.8843
Northeast China Power Grid	A traditional industrial base characterized by a mix of plains and rolling hills, with a high proportion of traditional heavy industries and significant reliance on coal, featuring moderate energy costs.	0.7769
East China Power Grid	Highly developed coastal economic regions exhibit substantial energy demand, characterized by a diversified energy mix that remains predominantly reliant on coal-fired power generation, resulting in relatively high energy costs.	0.7035
Central China Power Grid	Predominantly mountainous and hilly terrain with abundant hydropower resources, a high proportion of hydropower generation, low clean energy costs, and low carbon emissions intensity.	0.5257
Northwest Power Grid	Vast land with sparse population, abundant scenic resources, coexistence of coal-fired power and renewable energy, low energy costs, but challenging grid coverage.	0.6671
South China Power Grid	It covers coastal and mountainous areas, with significant economic differences, abundant hydropower resources (Yunnan-Guizhou-Guangxi), a high proportion of nuclear power and hydropower, low cost of clean energy, and low carbon emission intensity.	0.5271

.

The bridges in this study are located in the North China region; calculations were performed using the North China Power Grid carbon emission factor of 0.8843 kgCO₂e/kWh.

b.

Carbon emission factors of fossil energy

The carbon emission factor of fossil energy is a key parameter to measure the amount of CO₂ emission per unit calorific value of fuel combustion, and the value is determined by the fuel type and physical and chemical properties. As the basis of energy carbon emission accounting, it is standardized by CO₂ equivalent per unit energy (GJ or kWh). To facilitate subsequent calculations, this study calculates carbon emission factors for fossil energy sources using the methodology provided in the 2006 IPCC National Greenhouse Gas Inventory Guidelines. The specific calculation formula is

$$E{C}_i=H{C}_i\times O{F}_i\times A{L}_i\times {\displaystyle \frac{44}{12}}$$

(4)

In the formula, ECi is the carbon emission factor of fossil fuel i, HCi is the carbon content per unit calorific value of fossil fuel i, OFi is the carbon oxidation rate of fossil fuel i, ALi is the average low calorific value of fossil energy i, and 44/12 is the molecular weight ratio of carbon dioxide to carbon. The carbon emission factors of commonly used fossil fuels are calculated as shown in

Table 4. Common fossil energy carbon emission factors m³.

Type of Fuel	Carbon Content per Unit Calorific Value (tC/TJ)	Carbon Oxidation Rate (%)	CO2 Carbon Emission Factors (kgCO2/kg or kgCO2/m3)
Crude oil	20.1	0.98	3.02
Gasoline	18.9	0.98	2.93
Diesel oil	20.2	0.98	3.10
Natural gas	15.3	0.99	2.16
Coal char	29.5	0.93	2.86

Note: The data source is the ‘standard for building carbon emission calculation’ GB/T 51366-2019 [4].

.

3.3. Determination of Carbon Emission Factors for Construction Machinery

This study adopts the machine shift-based carbon emission factor method for construction machinery, mainly based on the following considerations. Current domestic carbon emission accounting standards universally take the machine shift as the basic unit, and the machine shift method has become a standardized industry paradigm. The mechanical configuration specified in the bill of quantities and construction organization design is compiled in terms of machine shifts. The application of the machine shift method enables seamless connection between carbon emission calculation and cost data. There is a clear conversion relationship between machine shifts and working hours, with one machine shift defined as eight working hours. Essentially, the machine shift emission factor represents the comprehensive carbon emission intensity for an eight-hour operation. Therefore, in this study, the carbon emission factor of construction machinery is defined as: the comprehensive carbon emission intensity of unit shift operation is equal to the sum of the product of various energy consumption and its carbon emission coefficient. Based on the non-engineering entity attributes of construction machinery and the characteristics of long depreciation cycles (usually ≥10 years), referring to the relevant national standards [38], the fossil energy and power consumption of each shift in machinery are determined. Combined with the energy carbon emission factor, the carbon emission factor of each shift in machinery operation was calculated, as detailed in

Table 5. Carbon emission factors of construction machinery.

Machinery		Energy Types	Energy Consumption (kg or kWh)	CO2 Carbon Emission Factors (kgCO2e/Unit)
Track-mounted Hydraulic Excavator		Diesel oil	63.00	195.3
Rotary Drilling Rig	SR220R	Diesel oil	198.00	613.8
	SR250R		264.00	818.4
	SR280R		330.00	1023
Caterpillar Crane	10 t	Diesel oil	23.56	73.04
	15 t		29.52	91.51
	20 t		30.75	95.33
	25 t		36.98	114.64
	30 t		41.61	128.99
	40 t		42.46	131.63
	50 t		44.03	136.49
	60 t		47.17	146.23
Gantry Crane	10 t	Electricity	88.29	78.07
	70 t	Electricity	277.71	245.58
Truck Crane	25 t	Electricity	40.65	35.95
Mud Pump	50 mm	Electricity	40.90	36.17
	100 mm		234.60	207.46
Sprinkler car		Diesel oil	114.40	354.64
Dumping Truck	5 t	Gasoline	31.34	91.83
	15 t	Diesel oil	52.93	164.08
Mud Tank car		Gasoline	31.57	92.50
A.c. arc welder	21 kV·A	Electricity	60.27	53.30
	32 kV·A		96.53	85.36
	40 kV·A		132.23	116.93
75 kV·A Butt Welding Machine		Electricity	122.00	107.88
Concrete Spreader	15 m	Electricity	55.92	49.45
	20 m		89.47	79.12
	30 m		111.83	98.89
Plug-in Concrete Vibrating Rod		Electricity	5.59	4.94
Electric Rock Drill		Electricity	12.75	11.27
Rock cutting machine		Electricity	11.28	9.97
Concrete Mixing Station HZS-180		Electricity	1514.02	1338.85
Loader ZLM40		Diesel oil	92.86	287.87
Concrete Delivery Pump	60 m3	Electricity	371.83	328.81
	80 m3		463.11	409.53
Concrete Mixer Truck 12 m3		Diesel oil	120.69	374.14
Intelligent Grouting Equipment		Electricity	80	70.74
Corrugated Tube Winding Machine		Electricity	25.81	22.82
Reinforcing Steel Cutters		Electricity	13.36	11.81
Steel Bar Cutting Machine		Electricity	29.9	26.44
Steel Bar Bender		Electricity	14.00	12.38
Electric Concrete Grinding Machine		Electricity	5	4.42
Steel Strand Stretching Equipment		Electricity	19.36	17.12
Prestressed Stretching Machine	650 kN	Electricity	19.36	17.12
	900 kN		27.92	24.69
	1200 kN		32.26	28.53
	3000 kN		45.16	39.93
	5000 kN		70.97	62.76
Diesel Generator Set	Within 120	Diesel oil	138.67	429.88
	Within 160		182.25	564.98
	Within 200		246.63	764.55
	Within 250		291.21	902.75
Hydraulic Pipe Bender		Electricity	27.00	23.88
Electric Planer for Woodworking		Electricity	10.10	8.93
Woodworking Circular Saw		Electricity	25.96	22.96
Cantilever crane		Electricity	188.10	166.34

.

3.4. Transportation Carbon Emission Factors

The transportation of building materials for bridge engineering relies on freight cars, trains, and ships, and the power sources are mainly diesel, gasoline, and electricity. According to the ‘Standard for building carbon emission calculation’ GB/T 51366-2019, the default transportation distance of concrete is set to 40 km, and other building materials is 500 km. The carbon emission factors of various modes of transportation are shown in

Table 6. Carbon emission factors of transportation modes.

Transportation Modes	Means of Transport	CO2 Carbon Emission Factors (kgCO2e/t·km)
Highway transportation	Light gasoline truck transport (2 t)	0.334
	Medium gasoline truck transport (8 t)	0.115
	Heavy gasoline truck transport (10 t)	0.104
	Heavy gasoline truck transport (18 t)	0.104
	Light diesel truck transport (2 t)	0.286
	Medium diesel truck transport (8 t)	0.179
	Heavy diesel truck transport (10 t)	0.162
	Heavy diesel truck transport (18 t)	0.129
	Heavy diesel truck transport (30 t)	0.078
	Heavy diesel truck transport (46 t)	0.057
Railway transportation	Electric locomotive transport	0.010
	Diesel locomotive transport	0.011
	Railway transport	0.010
Waterway transportation	Liquid cargo ship transport (2000 t)	0.019
	Dry bulk carrier transport (2500 t)	0.015
	Container shipping (200 TEU)	0.012

.

4. Case Calculation of Carbon Emissions in Bridge Materialization Stage

To verify the validity and applicability of the general carbon emission accounting model established in this study, a typical prestressed concrete bridge in North China is selected for empirical analysis. This study aims to demonstrate the refined calculation process based on the bill of quantities and regionalized factor database, and quantitatively analyze the carbon emission contribution rate of each stage, so as to provide methodological references and data benchmarks for carbon accounting of bridge engineering.

4.1. Bridge Engineering Overview

The case of this paper is a prefabricated prestressed concrete bridge in North China, with a total length of 14.39 km, of which the bridge section accounts for 21%. The length of the bridge is 60 m, the span is 3 × 20 m, and the net width of the left and right decks is 15.5 m. No. 0 and No. 3 abutments are equipped with 80-type expansion joints. The upper part is an assembled prestressed concrete simply supported box girder, and the lower part is a column platform/pier and bored pile foundation, as shown in Figure 10.

The data on building materials and construction machinery in this study are sourced from the bill of quantities. This section only counts the main materials, such as steel and concrete, calculates them according to the total amount, and does not subdivide the labels and varieties, ignoring the carbon emissions of trace materials. Carbon emission factors are derived from the factor library established in Section 2.

4.2. Calculation of Carbon Emissions in Each Link of the Materialization Stage

4.2.1. Calculation of Carbon Emissions in Building Materials Production

Based on the summary data of main building materials in the bill of quantities (excluding turnover materials), this study uses Equation (1) to calculate the total carbon emissions by multiplying the consumption of building materials by the corresponding carbon emission factors. The accounting results are shown in

Table 7. Building materials consumption and carbon emissions.

Subdivision Engineering	Sub-Project	Types of Building Materials	Amount of Materials	Carbon Emissions (kgCO2e)
Lower structure	Pile foundation	HPB300	12.93 t	30,697.92
		HRB400	82.38 t	192,767.52
		C30 concrete	1644.54 m3	485,139.3
		Acoustic pipe	5.45 t	13,233.97
	Bridge abutment	C20 concrete	15.36 m3	4070.4
		C30 concrete	306.7 m3	90,476.5
		C40 concrete	213.2 m3	77,178.4
		HPB300	4.41 t	10,474.27
		HRB400	66.00 t	154,423.29
		Cold-rolled ribbed welded steel mesh	1.73 t	4389.15
	Bridge pier	C40 concrete	174.64 m3	63,219.68
		HRB400	29.30 t	68,568.27
Superstructure	Box beams	C40 concrete	10.2 m3	3692.4
		C50 concrete	762.24 m3	293,462.4
		HRB400	162.38 t	379,968.12
		Steel strand	22.56 t	52,791.38
		Anchorage	480 kg	5424
		Corrugated tube C-55	4710 m	5181
		Rubber bearing	60 Set	222
		Q235 steel plate	8.56 t	17,552.1
Deck system engineering	Bridge decks	C50 concrete	198 m3	76,230
		Asphalt concrete	185.4 m3	15,054.48
		HRB400	4.29 t	10,034.76
		Cold-rolled ribbed welded steel mesh	24.10 t	60,967.94
		Damp-proof course	1878 m2	2403.84
	Guard fence	C20 concrete	207.96 m3	55,109.4
		C40 concrete	362.1 m3	131,080.2
		HRB400	40.64 t	95,085.9
		Crash barrier	240 m	4860
Total				2,403,758.59

.

In this study, low-consumption materials such as corrugated ducts, anchors, waterproof layers, and rubber bearings are all included in the accounting. Their carbon emissions are 5181, 5424, 2403.84, and 222 kgCO₂e, respectively, with a total of 13,230.84 kgCO₂e for the four items, accounting for only 0.43% of the total embodied carbon emissions of 3,092,237.79 kgCO₂e. Only auxiliary materials with an extremely small consumption, such as welding rods and release agents, are excluded from accounting. Considering that the proportion of the counted trace materials is less than 0.5%, the carbon emission contribution of such materials with even lower consumption can be reasonably regarded as negligible, which exerts no significant influence on the total emission accounting and the identification of key emission sources.

4.2.2. Calculation of Carbon Emissions During Transportation

The concrete of bridge engineering in this project is centrally mixed at the mixing station, and the transportation radius of aggregate is controlled within 50 km. Steel from about 80 km away is sourced from the Jinan Iron and Steel Industry Base. The project is adjacent to many important highways, so it is assumed that the building materials are transported by road freight cars, and the average transportation distance is within 120 km.

The calculation of carbon emissions during the transportation phase involves unit conversions for concrete. The waterproof layer of the bridge in the case is 3 mm thick, and the asphalt waterproofing membrane is used with a density of 1015 kg/m³. Rubber density is 1200 kg/m³ [39]. The statistical results for carbon emissions during the transportation phase, calculated using Equation (2), are presented in

Table 8. Carbon emission statistics of the main building materials transportation.

Material Varieties	Amount of Materials/t	Transport Ways	Carbon Emission (kgCO2e)
HPB300	17.34	Heavy-duty diesel trucks (18 t)	268.36
HRB400	384.98	Heavy-duty diesel trucks (18 t)	5959.46
Acoustic pipe	5.45	Heavy-duty diesel trucks (18 t)	84.31
Cold-rolled ribbed welded steel mesh	25.83	Heavy-duty diesel trucks (30 t)	241.80
Q235 steel plate	8.56	Heavy-duty diesel trucks (30 t)	80.14
Steel strand	22.56	Light diesel truck (2 t)	774.27
Anchorage	0.48	Medium diesel trucks (8 t)	10.31
Corrugated tube C-55	0.43	Light diesel truck (2 t)	14.76
C30 concrete	38,043.71	Heavy-duty diesel trucks (46 t)	260,218.96
C40 concrete	1824.34	Heavy-duty diesel trucks (46 t)	12,478.46
C20 concrete	529.27	Heavy-duty diesel trucks (46 t)	3620.20
C50 concrete	2323.78	Heavy-duty diesel trucks (46 t)	15,894.66
Asphalt concrete	454.23	Heavy-duty diesel trucks (30 t)	4251.59
rubber bearing	0.25	Light diesel truck (2 t)	8.58
Damp-proof course	1.64	Medium diesel trucks (8 t)	35.23
Crash barrier	135.96	Heavy-duty diesel trucks (30 t)	1272.59
Total			305,213.68

.

4.2.3. Calculation of Carbon Emissions in On-Site Construction Machinery

The carbon emissions in the construction stage are mainly due to the energy consumption of various construction machinery. The diesel combustion of heavy machinery such as rotary drilling rigs during foundation excavation is the main emission source. The operation of concrete pumps and other equipment in the construction stage of piers and abutments produces significant emissions. The high-load operation of large equipment, such as bridge erectors in the construction of the superstructure, forms centralized emissions. The unit engineering machinery class consumption in this study is calculated based on the 2019 ‘Consumption Quotas for Building Construction and Decoration Engineering’ and ‘Budget Quotas for Highway Engineering’ JTG/T 3832-2018 [40]. The carbon emissions of construction machinery are calculated according to Equation (3), and the results are shown in

Table 9. Carbon emission statistics of the main construction machinery.

Machinery		Machinery Unit Shifts	Carbon Emission (kgCO2e/Machine-Shift)	Carbon Emission (kgCO2e)
1 m3 Track-mounted Hydraulic Excavator		0.07	195.3	2263.45
Rotary Drilling Rig		0.49	613.8	49,795.96
Caterpillar Crane 40 t		0.27	131.63	5884.23
Mud pump 100 mm		0.22	207.46	7556.63
A.c. arc welder 32 kV·A		0.14	85.36	1978.58
Concrete delivery pump 60 m3		0.1	328.81	12,806.95
25 t Truck crane		0.46	89.85	6843.01
Cast-in-place component round steel bar ≤ φ10	Bar straightener 40 mm	0.24	11.81	49.15
	Bar cutter 40 mm	0.11	26.44	50.43
	Bar bender 40 mm	0.35	12.38	75.13
Cast-in-place component ribbed steel bar ≤ φ10	Bar straightener 40 mm	0.27	11.81	142.04
	Bar cutter 40 mm	0.11	26.44	129.56
	Bar bender 40 mm	0.31	12.38	170.96
Cast-in-place component ribbed steel bar ≤ φ18	Bar cutter 40 mm	0.1	26.44	463.17
	Bar bender 40 mm	0.23	12.38	498.80
	Direct-current arc welder 32 kV·A	0.45	52.06	4103.89
	75 kV·A butt welding machine	0.11	107.88	2078.80
	Electric welding drying oven	0.045	4.62	36.42
Cast-in-place component ribbed steel bar ≤ φ25	Bar cutter 40 mm	0.09	26.44	307.56
	Bar bender 40 mm	0.18	12.38	288.02
	Direct-current arc welder 32 kV·A	0.4	52.06	2691.51
	75 kV·A butt welding machine	0.06	107.88	836.61
	Electric welding drying oven	0.04	4.62	23.89
Cast-in-place component ribbed steel bar ≤ φ40	Bar cutter 40 mm	0.09	26.44	85.67
	Bar bender 40 mm	0.13	12.38	57.94
Dumping truck 15 t		0.06	164.08	1629.96
Concrete spreader 20 m		0.38	79.12	11,710.37
Cement concrete mixing station HZS-180		0.3	1338.85	156,442.21
Loader ZLM40		0.08	287.87	3812.92
Plug-in concrete vibrating rod		0.2	4.94	384.82
Rock cutting machine		0.06	9.97	3.96
Concrete mixer truck 12 m3		0.74	374.14	107,836.71
Intelligent grouting equipment		0.03	70.74	4.79
Corrugated pipe rolling machine		7.71/10 t	22.82	396.93
Electric concrete grinding machine		8	4.42	1606.16
900 kN Prestressed stretching machine		17.43/100 t	24.69	97.09
Woodworking circular saw		0.44	22.96	121.23
Total				383,265.51

.

4.3. Analysis of Carbon Emission in Bridge Materialization Stage

4.3.1. Carbon Emission Analysis of Building Materials Production

The carbon emissions in the production and use of concrete and steel are not only affected by the dosage, but also closely related to the carbon strength of the material and the structural design parameters. Based on the above quantitative results, the carbon emissions and proportions of each part of the construction are shown in Figure 11a and Figure 11b, respectively. Furthermore, to analyze the carbon emission intensity of different materials in various components, the unit carbon emission factor of each material was calculated by dividing the total carbon emissions of each material by its consumption quantity, with the results presented in

Table 10. Unit carbon emission is measured by carbon emissions per ton of material.

Component	Material	Quantity (t)	Quantity Ratio (%)	Carbon Emissions (kg CO2e)	Carbon Emissions Ratio (%)	Unit Carbon Emission (kg CO2e)
Substructure	Concrete	5650	97	720,084.28	60.3	127.5
	Steel	202.19	3	474,554.39	39.7	2347.3
Superstructure	Concrete	1853.86	93	297,154.80	39.75	160.3
	Steel	193.98	7	450,311.61	60.25	2321.5

.

From the perspective of components, the substructure and superstructure are the fundamental sources of carbon emissions. Together, they account for 81.25% of the total emissions, among which the substructure accounts for 49.7% and the superstructure accounts for 31.55%. This distribution characteristic stems from the differences in mechanical functions and structural scales between the two components. The substructure, including pile foundations, bearing platforms, and piers, undertakes the core function of transferring the entire bridge load to the foundation. It requires mass concrete and dense reinforcement, resulting in a large base consumption of building materials. The superstructure is dominated by box girders. Although its concrete consumption only accounts for 12.3% of that of the substructure, C50 high-strength concrete is adopted to meet the flexural and shear resistance requirements under large spans. Its carbon emission factor is remarkably higher than that of C30 concrete commonly used in the substructure, leading to prominent carbon emissions. Meanwhile, the superstructure features large spans and numerous components, resulting in a considerable cumulative consumption of steel and concrete as well. In contrast, the deck system and auxiliary structures (waterproof layers, anti-collision guardrails, etc.) have relatively low absolute carbon emissions, but exhibit high carbon emission intensity per unit engineering quantity. For instance, modified asphalt waterproofing layers, due to their high energy consumption during production and large carbon emission factors, still generate considerable emissions even with a limited engineering quantity.

From the material characteristics shown in Table 10, the carbon emission contribution rate of steel is significantly higher than its mass proportion, revealing the decisive effect of material carbon intensity on the emission structure. In the substructure, steel accounts for only 3% of the total mass of this component but contributes 39.7% of its carbon emissions; in the superstructure, steel accounts for 7% of the total mass, with a carbon emission contribution rate as high as 60.25%. The contrast between “low mass proportion yet high carbon emission contribution” stems from the fact that the unit carbon emission factor of steel is much higher than that of concrete. Although concrete accounts for the majority of total emissions due to its massive consumption, its unit carbon intensity is relatively stable. Owing to its high carbon intensity, steel has become a key breakthrough for optimizing reinforcement ratios and promoting high-strength steel.

The high carbon intensity of steel further indicates that increasing the application ratio of recycled materials in the iron and steel industry is of great significance for carbon emission reduction in bridge engineering. The carbon emission factor of steel is closely related to its production process route. The blast furnace-converter process mainly takes iron ore as raw material and presents high carbon emission intensity, while the electric furnace process uses scrap steel as the main raw material and can significantly reduce carbon emissions per unit product. The currently adopted steel carbon emission factor is the industrial average value, which incorporates the existing average scrap steel ratio. Increasing the proportion of electric furnace steel and the input amount of scrap steel in the building material production stage can effectively lower the unit carbon emission factor of steel products, thereby cutting the total carbon emissions during the bridge materialization stage. Further research can establish scenarios with different scrap steel ratios to quantify the emission reduction potential brought by the application of recycled materials to bridge carbon emissions.

In summary, the superstructure and substructure are the fundamental components for carbon emission control, and the high carbon intensity of steel is the core factor affecting carbon emission efficiency. Accordingly, this study proposes focusing emission reduction efforts on the following three aspects: first, optimizing the concrete mix proportion of the substructure to reduce cement consumption or adopt low-carbon alternative materials on the premise of meeting bearing capacity requirements; second, optimizing the reinforcement design of the superstructure and promoting the application of high-strength steel bars to reduce the total steel consumption; third, conducting material substitution and process optimization for key technologies of the deck system with high carbon intensity but low engineering quantity. Fourth, select low-carbon cement types. Cement products of different types have distinct carbon emission factors due to differences in clinker content and admixture dosage. On the premise of meeting structural performance requirements, priority should be given to composite cement with high admixture content, which can further reduce carbon emissions under the same material consumption. Through the above measures, a significant reduction in carbon emissions can be achieved without compromising structural safety and service functions.

4.3.2. Carbon Emissions Analysis of Building Materials Transportation

From Table 8, the total carbon emissions in the transportation stage of building materials are 305,213.68 kgCO₂e. Based on this, the carbon emissions per ton of transportation materials under different transportation modes are calculated, as shown in

Table 11. Carbon emissions per ton of building materials transportation.

Transport Ways	Carbon Emissions (kg CO2e)	Amount	Carbon Emissions per Unit Weight
Heavy-duty diesel trucks (18 t)	6312.12	407.75946	15.48
Heavy-duty diesel trucks (30 t)	5846.11	624.58484	9.36
Heavy-duty diesel trucks (46 t)	292,212.27	42,721.0926	6.84
Light diesel truck (2 t)	797.61	23.24042	34.32
Medium diesel trucks (8 t)	45.54	2.12	21.48

.

In terms of total transportation volume, 46-ton heavy-duty diesel trucks are the primary source of carbon emissions during the transportation stage. The carbon emissions of such vehicles amount to 292,212.27 kg CO₂e, accounting for 95.7% of total transportation emissions. The fundamental reason is that 46-ton trucks undertake the vast majority of building material transportation for this project, with a total transport volume of 42,721.09 t, representing 98.2% of the total. In comparison, the combined transport volume of 30-ton and 18-ton trucks accounts for less than 2%, while light- and medium-duty trucks make up less than 0.1% of the total transport volume.

From the perspective of unit emission intensity, vehicle tonnage and carbon emission efficiency show a significant negative correlation, revealing the energy efficiency advantages of large-tonnage transportation. The carbon emission per unit weight of 46-ton trucks is 6.84 kg CO₂e/t, which is 26.9% lower than that of 30-ton trucks (9.36 kg CO₂e/t) and 55.8% lower than that of 18-ton trucks (15.48 kg CO₂e/t). Although the transportation volume of 8-ton medium-sized and 2-ton light trucks accounts for less than 0.1% of total freight volume, the carbon emissions intensity per unit of freight transported is as high as 21.48 kgCO₂e/t and 34.33 kgCO₂e/t, which are 3.1 times and 5.0 times that of 46-ton vehicles, respectively. The fundamental reason for this difference is as follows: per unit transport distance, vehicle energy consumption mainly depends on fixed losses such as engine idling and mechanical wear. The greater the load capacity, the more these fixed losses are diluted, resulting in lower carbon emission intensity per unit transport volume. Therefore, large-tonnage vehicles exhibit significant energy efficiency advantages under full-load conditions.

Accordingly, this study proposes optimizing the transportation process from the following three aspects: first, prioritize the use of large-tonnage trucks of 46 tons and above for the transportation of bulk building materials to fully leverage their energy efficiency advantages; second, integrate scattered transportation demands and reduce the frequency of using light and medium-duty trucks; third, under the same tonnage conditions, give preference to building material suppliers with shorter transportation distances to further reduce carbon emissions in the transportation stage. Through the above measures, the carbon emission reduction efficiency of the transportation process can be significantly improved on the premise of ensuring construction progress.

4.3.3. Carbon Emission Analysis of On-Site Construction Machinery

According to Table 9, the carbon emissions generated by different mechanical equipment are compared and analyzed. The comparison between mechanical energy consumption types and carbon emissions is shown in Figure 12.

In terms of emissions, cement concrete mixing stations and concrete mixing trucks are the primary sources of carbon emissions during the construction stage. As shown in Figure 12, the HZS-180 mixing station emits 156,442.21 kg CO₂e, and mixer trucks emit 107,836.71 kg CO₂e, which together account for 68.9% of total emissions in the construction stage. The fundamental reason is that concrete production and transportation constitute the core processes of bridge construction, running through the entire pouring process of all concrete structures, including pile foundations, bearing platforms, piers, and main girders, with operating hours far exceeding those of other equipment. Meanwhile, mixing stations are driven by electric motors, and mixer trucks rely on diesel engines; both are energy-intensive facilities with high energy consumption intensity per unit time.

In terms of equipment classification patterns, carbon emissions exhibit a distinct three-level differentiation characteristic. As shown in the figure, Class A high-carbon equipment (mixing station and mixer trucks) accounts for less than 10% of the total quantity but contributes over 70% of emissions. Mixing stations operate continuously to meet concrete production demands, while mixer trucks frequently travel between mixing stations and construction sites during peak pouring periods, resulting in long operating hours and high energy consumption. Class B medium-carbon equipment (rotary drilling rigs, concrete pump trucks, etc.) is characterized by intermittent operation. Although its working hours are concentrated, it does not run continuously, resulting in a moderate total emission volume. Class C low-carbon equipment (vibrators, cutters, electric welding machines, etc.) is abundant in quantity but serves as auxiliary equipment. Such equipment features low unit power, short single-operation duration, and mostly intermittent use, thus generating low emissions per machine. Therefore, the functional attributes of equipment determine its operating hours and energy consumption pattern, which in turn define its position in the carbon emission structure.

In summary, this study proposes a hierarchical emission reduction strategy. For Class A high-carbon equipment, priority should be given to promoting clean energy substitution (e.g., electric mixer trucks and electrically powered mixing stations) and establishing an IoT energy consumption monitoring system for precise management and control. For Class B medium-carbon equipment, operation scheduling should be optimized to avoid idling and repeated startup and shutdown, thereby improving single-operation efficiency. For Class C low-carbon equipment, efforts should focus on routine maintenance and management to ensure efficient operation. Through such classified measures, precise control and effective reduction of carbon emissions can be achieved without affecting construction progress.

4.3.4. Comprehensive Analysis of Carbon Emissions in the Materialization Phase

Integrating carbon emission models across all stages, the carbon emissions during the materialization phase of bridge engineering: Ere = E_ys + E_sc + E_SG = 3,092,237.79 (kgCO₂e). The proportion of carbon emissions in each link is shown in Figure 13, and the carbon emissions in each link of the materialization stage are shown in Figure 14.

The above figure shows that the carbon emissions in the production of building materials are the highest, reaching 2,403,758.59 kgCO₂e, accounting for 77.74% of the total, which is mainly due to the energy-intensive production process of cement and steel. The carbon emissions in the construction process were 383,265.51 kgCO₂e, accounting for 12.39%, of which the rotary drilling rig and mixing station are the main emission sources. The carbon emissions in the transportation link were 305,213.68 kgCO₂e, accounting for 9.87%, of which 46 tons of heavy trucks accounted for 96.5%. This is mainly because the material consumption in the building material production stage indirectly determines the emission scale of transportation and construction. That is, the greater the material consumption, the denser the transportation demand, and the more significant the construction energy consumption. During construction, the continuous operation of rotary drilling rigs in bored pile construction and the continuous running of mixing plants in concrete production both represent an emission pattern characterized by core processes, continuous operation, and high energy intensity. In the transportation stage, large-tonnage vehicles undertake the vast majority of building material transportation. Their absolute dominant position determines the emission distribution of this stage.

In summary, as an upstream source with high-carbon production processes, the building material production stage constitutes the fundamental source of carbon emissions during the materialization phase. The emission characteristics of each stage follow a causal chain: material consumption drives transportation and construction, and emissions are gradually transmitted through transportation and construction activities.

Based on the above, this study proposes a hierarchical optimization strategy. The first level focuses on the building material production stage. By promoting high-strength materials (high-strength steel bars, high-performance concrete), the goal of “reducing consumption and carbon emissions” is achieved, so as to cut material usage and the downstream emissions it causes at the source. The second level targets the construction stage. For Class A high-carbon equipment such as rotary drilling rigs and mixing plants, the proportion of electric equipment will be increased to 40% to reduce dependence on fossil energy. The third level concentrates on the transportation stage. Large-tonnage vehicles of 46 tons and above are preferentially adopted, taking advantage of their high energy efficiency and low unit emission intensity. Through the progressive hierarchy of “source reduction, process efficiency improvement, and end-of-pipe optimization”, the above strategies can systematically reduce carbon emissions in the materialization phase.

• Analysis of carbon emissions from electricity

The construction stage of bridge engineering has large power consumption, concentrated equipment, and a long running time, which is an important source of indirect carbon emissions in the life cycle of the project. This phase involves continuous operation of high-energy-consumption equipment such as concrete batching plants, which feature independent electricity metering and clearly defined system boundaries, presenting significant potential for emissions reduction. This study adopts the single-factor controlled scenario analysis method to verify the influence of regional differences on carbon emission accounting results. Specifically, the bill of quantities and construction machinery configuration of the case bridge are set as invariant control variables. Only the carbon emission factor of electric power is replaced from the baseline value of the North China Power Grid (0.8843 kgCO₂/kWh) with the corresponding values of the Northeast, East China, Northwest, Central China, Southern China power grids, and the national average level. With all other conditions kept completely consistent, the total indirect carbon emissions from electric power consumption during the construction stage are recalculated, so as to accurately characterize the net impact of regional differences. Calculation results are presented in

Table 12. Carbon emissions by region.

Regional Grid	Carbon Emissions Factors (kgCO2/kWh)	Total Carbon Emissions (kgCO2)	Difference from the Average Value (kgCO2)	Difference Range	Ranking
North China Grid	0.8843	212,043.91	+51,280.71	+31.90%	1
Northeast China Power Grid	0.7769	182,684.33	+21,921.13	+13.64%	2
East China Power Grid	0.7035	165,424.67	+4661.47	+2.90%	3
Northwest Power Grid	0.6671	156,865.38	−3897.82	−2.42%	4
National Average	0.5839	160,763.20	-	-	-
South China Power Grid	0.5271	123,945.05	−36,818.15	−22.91%	5
Central China Power Grid	0.5257	123,615.85	−37,147.35	−23.11%	6

, regional carbon emission comparisons are shown in Figure 15, and power generation composition is illustrated in Figure 16.

As shown in the table and the figure, the total carbon emissions of North China Power Grid are the highest, which is 212,043.91 kgCO₂, which is 31.90% higher than the national average, mainly due to the coal-dependent power structure. The total carbon emission of the Central China Power Grid is the lowest (123,615.85 kgCO₂), which is 23.11% lower than the average, and the proportion of clean energy, such as hydropower, is high. The maximum value (North China) is 1.71 times the minimum value (Central China), highlighting the significant impact of grid structure on carbon emissions. This disparity fully reflects the decisive influence of the grid’s energy mix on carbon emissions.

Correlation analysis between carbon emission factors and total amount: carbon emission factors and total amount rank exactly the same (North China > Northeast China > East China > Northwest China > South China > Central China), indicating that factors dominate the difference in total amount. Based on the electricity consumption of this project, a reduction of 0.1 kgCO₂/kWh in the carbon emission factor results in a decrease of approximately 24,000 kgCO₂ in total carbon emissions. The difference in the carbon emission factor of the regional power grid directly leads to the fluctuation of the total carbon emission of the bridge construction machinery up to 55.01%.

When examining such differences against the total carbon emissions, regional fluctuations in power carbon emission factors can cause a fluctuation of 1.7% to 2.8% in the overall emissions of the entire materialization stage. Although this proportion appears relatively small, it produces a prominent cumulative effect in the large-scale construction of infrastructure clusters. Combined with the multi-factor sensitivity analysis results presented, the standardized regression coefficient of the regional power grid factor is 0.099, ranking 7th among the eight core parameters and categorized as a low-sensitivity parameter. This conclusion does not mean that regional differences are insignificant; instead, it reveals the hierarchical structure of carbon emission driving factors. High-sensitivity parameters such as material consumption and transportation distance dominate the major proportion of carbon emissions, while the regional power grid structure exerts a further moderating effect on this basis. Therefore, for specific engineering projects located in high-carbon power grid regions such as North China, the procurement of green electricity and the local utilization of renewable energy should still be regarded as important supplementary carbon reduction measures. Meanwhile, when formulating industrial carbon emission accounting standards, it is necessary to explicitly require the adoption of regional power carbon emission factors rather than national average values, so as to avoid systematic underestimation for regions with high-carbon power grids. At the project level, carbon reduction strategies should be implemented in a hierarchical manner. Priority shall be given to material optimization and supply chain management, followed by the localized promotion of construction electrification and clean energy substitution.

b.

Multi-factor sensitivity analysis

Based on the above carbon emission accounting results, this study identifies the key factors with the highest contribution to carbon emissions in each link as the object of sensitivity analysis. In the four links, eight core parameters are selected, including cement dosage, steel dosage, replacement rate of high-strength steel, aggregate transportation distance, proportion of electric equipment, energy efficiency of mixing station, proportion of large tonnage vehicles, and regional power grid factor. According to the research of Zhang [15], the influence mechanism of each numerical fluctuation on the total carbon emission of the materialization stage of the bridge is systematically analyzed. The detailed data are shown in

Table 13. Sensitivity parameter range.

Construction Links	Parameters	Mean (μ)	Fluctuation Range	Distribution Type	Variance (σ2)
Production of materials	Cement Dosage (CD)	1744.52 (t)	±15%	Normal Distribution	6.8 × 104
	Steel Dosage (SD)	680 (t)	±12%	Normal Distribution	6.7 × 103
	Replacement Rate of High Strength Steel (RRHSS)	0%	0–40%	Uniform Distribution	1.3 × 10−2
Raw material mining	Aggregate Transport Distance (ATD)	85 (km)	±25%	Uniform Distribution	1.5 × 102
Construction process	Proportion of Electric Equipment (PEE)	0%	0–40%	Uniform Distribution	1.3 × 10−2
	Energy Efficiency of Mixing Station (EEMS)	1.0	±10%	Normal Distribution	1.0 × 10−2
Transportation	Proportion of Large Tonnage Vehicles (PLTV)	0%	±20%	Normal Distribution	4.0 × 10−3
Electricity factor	Regional Power Grid Factor (RPGF)	0.58	±8%	Uniform Distribution	7.2 × 10−4

.

Due to the interaction among multiple parameters, this study employed MATLAB Version R2024a [41] to run Monte Carlo simulations to generate parameter distributions. By calculating the total carbon emissions for each sample, sensitivity analysis was ultimately conducted using standardized regression coefficients. The order of parameter sensitivity is shown in Figure 17, and the probability of total carbon emissions is shown in Figure 18.

Based on 10,000 Monte Carlo simulations, the average carbon emissions were calculated at 4,582,968 kgCO₂e, which is 3.7% lower than the baseline calculation value (4,761,156 kgCO₂e). From Figure 17, the driving effects of each influencing factor on carbon emissions during the materialization phase of bridges exhibit significant gradient differences. Aggregate transport distance (0.608) and cement consumption (0.595) serve as highly sensitive parameters that decisively influence total carbon emissions. Fluctuations in these parameters will directly lead to significant changes in carbon emissions. The high-strength steel substitution rate (0.397) and steel consumption (0.310) are both classified as highly sensitive parameters, indicating that material selection and consumption control are critical factors in carbon emission reduction. In contrast, the energy efficiency (0.107) and grid emission factor (0.099) of the mixing station in the construction stage are less sensitive, reflecting the limited carbon emission elasticity of energy consumption in the construction stage. Figure 18 shows that the 95% confidence interval is [4,248,513, 4,926,404] kgCO₂e, with a relative range of ±7.4%. The coefficient of variation is 3.8%, indicating that the total carbon emissions estimate possesses high precision, and parameter uncertainty has a limited impact on the overall results. The four most sensitive parameters were selected for carbon emission correlation analysis, as shown in Figure 19. The emission reduction effects at each stage are illustrated in Figure 20.

As shown in Figure 19, the scatter points of aggregate transportation distance and cement consumption are closely distributed along the fitting line with a relatively large slope. This indicates that these two parameters exert decisive impacts on the total carbon emissions and should be prioritized in the formulation of carbon emission reduction strategies. The aggregate transportation phase exhibits the strongest correlation with carbon emissions, accounting for 62% of raw material extraction emissions. For every 10% increase in transportation distance, total carbon emissions rise by approximately 6.1%. Cement production accounts for over 60% of emissions in the building materials manufacturing sector, with fluctuations in its consumption directly impacting total emissions. Compared to steel, cement has fewer alternative materials and technological pathways, leading to concentrated sensitivity. Continuous improvements in steel production processes have reduced emissions intensity per unit, while the recyclability of steel partially offsets the sensitivity of emissions from initial production. Observing Figure 20, a single logistics, material, and equipment electrification strategy can achieve emission reduction rates of 3.7%, 7%, and 13%, respectively, while the comprehensive optimization strategy can produce a synergistic emission reduction effect of 24%, which is far from the simple superposition of a single strategy. This shows that carbon emission reduction needs to adopt systematic solutions of logistics, materials, and energy, and maximize the benefits of low-carbon construction through targeted management involving multiple measures.

c.

Emission reduction control strategy

In order to achieve the goal of low-carbon sustainable development of bridge engineering, this study proposes a systematic emission reduction control strategy for the high-carbon emission characteristics of each link in the materialization stage.

Firstly, case studies in the raw material extraction phase reveal that diesel machinery accounts for only 25% of the equipment fleet yet contributes 95% of carbon emissions. This highlights the contradiction within mining machinery: a low proportion but high emissions from diesel equipment. This study proposes supporting measures such as advancing the electrification of high-energy-consumption equipment, developing dual-mode hybrid transition systems, and constructing mining micro-grids (integrating photovoltaic, energy storage, and fast charging) to resolve the conflict between process and energy cleanliness, thereby providing a quantifiable pathway for the low-carbon transformation of raw material extraction machinery.

Secondly, aiming at the carbon emission problem of building materials transportation, this study proposes to establish an 80 km radius regional building materials supply network, preferentially select aggregate suppliers with a distance of ≤50 km from the construction site, and control the average transportation distance within 30 km. Implementation of transport equipment upgrades, the full use of 46 tons of heavy-duty electric trucks, and equipped with an intelligent loading system to ensure that the loading rate is ≥95%. These measures can reduce transportation losses, reduce procurement costs, and achieve win-win environmental and economic benefits.

Finally, to effectively reduce carbon emissions from construction machinery in bridge engineering, this study implements tailored strategies based on equipment carbon intensity: high-carbon equipment (concrete batching plants, transport trucks) undergoes mandatory electrification retrofits, incorporating an electric mixing station and battery-charged transport vehicle; medium-carbon equipment (rotary drilling rigs, etc.) employs BIM-based intelligent scheduling to minimize idle energy consumption; low-carbon equipment utilizes shared leasing to enhance utilization rates.

5. Discussion

5.1. Considerations on the Setting of Research Boundaries

In this study, the carbon emission accounting boundary is limited to the stage, which is mainly based on the following considerations. First, carbon emissions in the materialization stage present a prominent locking effect. Once a bridge is completed, the carbon emissions generated by building material production and construction activities become unalterable established facts with no room for subsequent optimization and reduction. In contrast, carbon emissions during the operation and maintenance stage can be continuously optimized through the adjustment of management strategies, while the carbon emissions of the demolition and recycling stage will occur decades later with great uncertainty. Therefore, carbon emission control in the materialization stage is of decisive significance for the whole-life cycle carbon reduction in bridges. Second, there are gaps in current industrial standards. A relatively mature carbon emission accounting system has been formed in the building engineering sector, whereas unified standards for calculating materialization carbon emissions are still lacking in the bridge engineering field. This study aims to fill such research gaps and provide methodological support for the subsequent formulation of whole-life cycle accounting standards.

5.2. Model Reliability Analysis

This study did not adopt independent experimental datasets or additional engineering cases for cross-validation, due to the following objective reasons. The carbon emission accounting of bridge engineering involves cross-process data covering multiple links, including building material production, transportation, and construction. It is rather difficult to acquire independent case data of another bridge with the same bridge type, similar structural parameters, as well as complete bills of quantities and construction machine shift records. At the current stage, the data conditions required for multi-case validation are not yet available.

Although independent dataset validation is absent, this study ensures model reliability through five guarantee measures.

(1)

The proposed model is constructed based on life cycle assessment (LCA) theory and divided into three stages: building material production, material transportation, and on-site construction. This framework is consistent with the studies conducted by Bouhaya et al. [7] and Cao Jian et al. [9].

(2)

All core parameters are traceable. The baseline data for building material carbon emission factors are adopted from the Standard for Building Carbon Emission Calculation (GB/T 51366-2019). Energy-related carbon emission factors are calculated in accordance with IPCC guidelines, and regional power grid carbon emission factors are sourced from official data released by the National Climate Center. All the above parameters are established on the basis of life cycle assessment with unified accounting boundaries; thus, mixed citation of different types of data will not compromise data comparability.

(3)

The data selection reflects practical engineering conditions. The transportation distances in this study are derived from field engineering investigations, with the comprehensive weighted average transportation distance controlled within 85 km. The sensitivity analysis reveals that the standardized regression coefficient of aggregate transportation distance is 0.608, ranking first among all parameters. Nevertheless, the transportation stage only accounts for 9.87% of the total carbon emissions, and its fluctuation exerts a limited impact on the total emission volume. In the preliminary engineering stage, adopting the average transportation distance is a feasible, simplified treatment. When conditions permit, actual transportation distances are recommended for calculation to improve numerical accuracy.

(4)

This study systematically presents the calculation formulas, input parameters, and intermediate results of each stage. Other researchers can fully reproduce the calculation process and verify the research results by adopting the same bill of quantities and carbon emission factor database.

(5)

Multi-factor sensitivity analysis and Monte Carlo simulation are conducted in this study. The results show that the coefficient of variation in the average carbon emission is 3.8%, and the relative range of the 95% confidence interval is ±7.4%, which demonstrates the high robustness of the model outputs. The fluctuation ranges of various parameters set in this study are determined based on practical engineering experience. Artificially narrowing the fluctuation ranges will reduce the coefficient of variation, and vice versa. In addition, the sensitivity analysis in this study only focuses on parameter uncertainty, while the uncertainty of model structure remains to be further quantified in future research.

5.3. Limitations and Future Prospects

This study has the following limitations, which need to be improved in subsequent research.

(1)

The operation, maintenance, demolition, and recycling stages are not included in the research scope, so the full picture of bridge life cycle carbon emissions cannot be reflected. It should be noted that this boundary setting is a deliberate research design rather than an inherent defect of the model. On the basis of the proposed model, future research can expand the accounting scope to the full life cycle and realize a comprehensive evaluation of bridge carbon emissions.

(2)

The carbon emissions generated from the extraction and production of partial auxiliary materials are incorporated into the emission factors of major building materials instead of being listed separately, which may lead to the underestimation of the contribution of individual subdivided links. Nevertheless, this processing method conforms to the factor system specified in the Standard for Building Carbon Emission Calculation (GB/T 51366-2019), and the resulting deviation is within the allowable tolerance range of industrial standards.

(3)

This study does not separately distinguish the recycled content in raw materials, such as the influence of varying scrap steel ratios on the carbon emission factor of steel. The currently adopted emission factors inherently reflect the average recycling level of the industry. In the future, scenario analysis can be carried out for materials with different recycled component contents to further refine the accounting results.

In general, the focus on the stage is a targeted choice corresponding to the research objectives. The resulting deviation is a reasonable simplification of the research boundary, which does not affect the validity and generalization of the research conclusions. Based on the current model framework, subsequent studies can extend the research scope to the full life cycle and verify the model’s applicability with more engineering cases.

6. Conclusions

This study systematically explores carbon emissions in the materialization stage of bridge engineering, scientifically divides the system boundary and accounting scope, accurately identifies key carbon emission sources, establishes a carbon emission calculation model in the materialization stage, clarifies the emission path, and collects regional carbon emission factors. Taking a reinforced concrete composite continuous beam bridge in Shandong Province as an example, the carbon emissions of raw material mining, processing, construction, and transportation are calculated and analyzed, and the following conclusions are drawn:

(1)

In view of the aforementioned emission sources, this study establishes a carbon emission calculation model for the materialization stage of bridges based on life cycle assessment (LCA) theory. For the building materials production phase, baseline data from the ‘Standard for building carbon emission calculation’ GB/T51366-2019 is used, supplemented with industry-specific correction factors based on new research findings. Energy consumption factors employ regional grid and fossil fuel emission coefficients.

(2)

The building material production stage accounts for the largest share of carbon emissions, reaching 77.74%; followed by the construction stage at 12.39%, among which mixing plants and mixer trucks account for 68.9% of total emissions in the construction stage. The transportation stage contributes 9.87%, with heavy-duty diesel trucks generating the highest total carbon emissions, accounting for 96% of total transportation emissions, yet exhibiting the lowest carbon emission intensity per unit weight.

(3)

This study adopts the single-factor controlled scenario analysis method and verifies that the regional power grid structure exerts a significant impact on carbon emissions during the materialization stage of bridges. Due to the coal-dependent energy structure of North China Power Grid, the total carbon emission is the highest (212,043.91 kgCO₂), which is 31.90% higher than the national average (160,763.20 kgCO₂). Central China Power Grid relies on clean energy such as hydropower, and the total carbon emissions are the lowest (123,615.85 kgCO₂), which is 23.11% lower than the average. The carbon emission factors are completely consistent with the total emissions ranking (North China > Northeast China > East China > Northwest China > South China > Central China).

(4)

Through the multi-factor sensitivity analysis of the system, the key driving mechanism of carbon emissions in the materialization stage of bridge engineering was revealed: aggregate transport distance has become the most sensitive parameter, which has changed the traditional ‘material consumption-led’ cognitive paradigm. The substitution of high-strength steel shows a higher-than-expected emission reduction leverage effect and should be promoted as a key technology. The research findings validate the scientific validity of the dual-drive emission reduction pathway combining ‘material innovation and supply chain optimization’, providing quantitative decision-making support for low-carbon construction in bridge engineering.