1. Introduction
In recent years, the issue of climate change caused by global warming has attracted significant attention from all countries and regions worldwide. According to the International Energy Agency [
1], in 2023, global energy-related CO
2 emissions increased by 1.1%, an increase of 410 million tons, to reach a record high of 37.4 billion tons. Carbon emissions would have tripled without the growing deployment of five key clean energy technologies since 2019 (solar PV, wind, batteries, electrolyzes, and heat pumps). As more than 30% of the total carbon emissions come from the manufacturing industry, under the background of “carbon peak” and “carbon neutrality” [
2,
3], manufacturing enterprises are facing enormous economic pressure and environmental challenges. Therefore, the research on manufacturing carbon emissions aiming at energy conservation and emission reduction has become one of the most crucial research hotspots. The research on energy saving and emission reduction in the manufacturing industry mainly focuses on the product, equipment, and production management layers. With the continuous development of manufacturing technology, more and more manufacturing enterprises have begun to focus on process planning and workshop scheduling research. Xiao et al. [
4] introduced the concept of the work step to represent the features of the factored parts, established a multi-objective optimization model, and realized the energy saving and low cost of machining. Li et al. [
5] set up a scheduling model that minimizes completion time, carbon emission, and machine load, considering the impact of transportation time on scheduling results during workshop processing. In the traditional manufacturing process, process planning and workshop scheduling are independent and serial, which quickly causes target conflict, unreasonable resource utilization, etc. Zhang et al. [
6] studied the multi-objective energy-saving integrated process planning and scheduling problem of parallel disassembly, flexible job shop reprocessing, and the parallel reassembly-integrated remanufacturing system to minimize energy consumption cost and completion time.
The manufacturing industry is the core of the national economy and the focus of policies and actions to address climate change. Formulating accurate accounting methods for manufacturing carbon emissions and implementing reasonable control measures is essential [
7]. Carbon emissions in manufacturing are characterized by the diversity of carbon emission sources, unclear evaluation boundaries, and difficulty in accurate quantification. Therefore, research on carbon emission accounting is the basis for carbon reduction optimization and low-carbon technology innovation. Currently, relevant research on carbon emission accounting can be roughly divided into two categories [
8,
9]: one is carbon emission accounting based on feature design or model improvement, and the other type is carbon emission accounting, which is based on the process or system level.
A reasonable and practical feature design or model can reduce carbon emissions from subsequent manufacturing processes. In terms of carbon emission accounting for design or model improvement, Sun et al. [
10] innovatively established a quantitative analysis model for carbon footprint assessment from the perspective of the life cycle, which carried out a comprehensive calculation and analysis of carbon footprints at various stages, including production, physical and chemical processes, construction, operation and maintenance, and treatment and recycling. You et al. [
11] conducted a comparative analysis of carbon emissions of three typical large-span steel structures. They proposed a layered hybrid life cycle assessment method, which promoted the practice of low-carbon design. Tian et al. [
12] constructed a carbon emission accounting model for internet service providers, which considered both direct and indirect influences and discussed the impact of by-product gases on interprocess carbon metabolism in steel production. In addition, an integrated iron-making process based on the headgear recovery and oxygen blast furnace technology is proposed, and its carbon emission reduction potential is analyzed. Li et al. [
13] proposed a quantitative analysis method of discrete manufacturing carbon footprints based on energy data space, which supports the real-time quantitative analysis of product carbon footprint. Xiao et al. [
14] adopted a double-layer game model to carry out the collaborative optimization of the low-carbon product series and its manufacturing process, analyzed the conflict and coordination between low-carbon product series architecture and manufacturing process configuration, and took microwave oven product series as an example for verification. Kannan et al. [
15] introduced a multi-criterion decision model to identify and evaluate the obstacles in carbon emission management. They verified and analyzed the model in combination with the actual production situation of several manufacturing enterprises. Chen et al. [
16] proposed a fuzzy evaluation model based on the processing performance to promote various tool industries to improve processing quality, reduce costs, and reduce carbon emissions. Kaur et al. [
17] proposed a carbon accounting framework for complex supply chains and applied it to define, calculate, and report carbon emission ranges. Kennelly et al. [
18] analyzed the existing carbon accounting methods from two aspects: input–output-based and process-based life cycle assessment, established a practical hybrid model, and analyzed its impact on accuracy. Panagiotopoulou et al. [
19] proposed a model-based carbon emission accounting framework for additive manufacturing processes from the level of each process, machine tool, and system based on carbon emission factors and manufacturing hierarchy principles, guiding carbon emission accounting in manufacturing systems. Romain et al. [
20] analyzed the overall flow of goods quality according to the factory’s inventory and flow of materials. They proposed a carbon accounting method based on multistage material flow analysis (MFA). Pawanr et al. [
21] developed an empirical model for treating carbon emissions of cylindrical parts and verified the practicability of this model by analyzing cylindrical parts with three different process schemes. Zheng et al. [
22] proposed a complete carbon emission evaluation model for the feature design of sand castings to guide the reduction in carbon emission in the planning stage of the sand-casting process. Wang et al. [
23] proposed an online automatic carbon emission accounting method in the production of aluminum castings. They verified the method’s effectiveness by taking an aluminum casting workshop to produce subframes of electric vehicles as an example.
The process layer and system layer of parts in the machining process are one of the primary sources of carbon emission. In terms of carbon emission accounting at the process or system level, Liu et al. [
24] introduced the life cycle assessment of carbon generated in the casting stage in detail. They proposed a carbon emission assessment model of the casting manufacturing process based on life cycle assessment and oriented to the 3D topology optimization structure of parts. Liu et al. [
25] proposed a carbon emission model for manufacturing process expansion based on considering energy, material, and environmental emissions and integrated considerations of capital, labor, and other factors. Cai et al. [
26] proposed a new benchmark assessment method for sustainable development to quantify the sustainability level of manufacturing systems. Ge et al. [
27] made a comprehensive decision on the welding parameters and sequence of multi-feature laser welding units, considering carbon emission and processing time. They proposed a two-layer algorithm combining state compression dynamic programming and a multi-objective marine predator algorithm to solve the problem. Tian et al. [
28] established a multi-objective optimization model of cutting parameters, including low-carbon target parameters, taking the cutting parameters during tool wear as the optimization objective, considering the perceptible influence of tool wear on the selection of cutting parameters and carbon emissions in the production process. Agrawal et al. [
29] evaluated the carbon emission of Ti-6Al-4V titanium alloy under different turning environments (low temperature/humidity). The results show that the total carbon emission of low-temperature turning at high cutting speed is reduced by 22% compared with wet turning. He et al. [
30] built a collaborative, low-carbon, and efficient dual-objective optimization model considering the interaction between cutting parameters and production scheduling. They adopted an improved genetic algorithm based on Pareto optimization. Fang et al. [
31] studied the multi-step parameter optimization problem of milling, analyzed the carbon emission, processing cost, and processing time of the processing process, established a multi-objective optimization model with cutting parameters as variables, and proposed an improved particle swarm optimization algorithm for solving the problem. Ge et al. [
32] proposed the concept of a “meta carbon emission block” composed of static and variable carbon emission blocks. Based on this, they suggested a data-driven carbon emission accounting method for manufacturing systems. Yao et al. [
33] proposed a framework for quantifying carbon emissions in four-layer machining based on Internet of Things (IoT) and energy flow analysis (MEFA) technology. They verified the effectiveness of the proposed method through a boss case. Liu et al. [
34] analyzed the carbon emission characteristics of each system used in the directed energy deposition process and built an optimization model of the directed energy deposition manufacturing process. Chi et al. [
35] established the objective function of carbon emission and surface roughness in turning processing, solved the optimization model by genetic algorithm, and analyzed the influence of cutting parameters on optimization objectives.
In summary, scholars have conducted some relevant studies on carbon emissions in the manufacturing process of products. However, there are two fundamental research gaps. On the one hand, the source analysis of carbon emissions in the specific manufacturing process of mechanical products needs to be more comprehensive, and the cited literature mainly focuses on the optimization effect of carbon emissions. It needs to be translated into intuitive carbon emissions accounting. On the other hand, the models established in the above literature tend to be for specific products, and manufacturing systems still need a standard carbon emissions accounting method.
Therefore, this paper proposes a three-stage carbon accounting model for mechanical products based on life cycle assessment (LCA). By analyzing the carbon emission source distribution of mechanical products in the three stages of part–assembly–test (P-A-T), the corresponding carbon accounting model is established to evaluate and quantify the carbon emission of mechanical products. In this paper, the parts stage is further divided into the production stage of purchased parts (PPs) and homemade parts (HPs), and the corresponding mathematical models are established for various carbon emission sources in the production and transportation of purchased parts and the processing process of homemade parts. Meanwhile, the product assembly process is analyzed and modeled. It is worth noting that most mechanical products need to be tested before being put on the market, so this paper also establishes the corresponding carbon emission quantitative model according to the characteristics of the product test stage. Through the detailed stage division described above, the carbon footprint of the entire manufacturing process of the product can be more accurately captured and quantified. Finally, the proposed model is verified by taking the processing of a specific type of ball valve as an example. The primary sources of carbon emission in the manufacturing process of the ball valve are analyzed, and some suggestions for reducing carbon emission are put forward.
This paper is organized as follows.
Section 2 introduces the accounting boundary of product carbon emission.
Section 3 proposes the P-A-T three-stage product carbon emission quantitative model.
Section 4 conducts a case study on a model of a three-piece ball valve.
Section 5 presents the conclusions (
Table 1).
5. Conclusions
Low-carbon development in the manufacturing industry is one of the key areas for global sustainable development. With climate change and environmental issues becoming increasingly prominent, the industry must take proactive measures to reduce carbon emissions and achieve sustainability. Therefore, establishing a comprehensive carbon emissions quantification model for mechanical products is significant in controlling and reducing carbon emissions in the manufacturing industry.
This paper proposes a three-stage accounting model for quantifying carbon emissions of mechanical products based on the life cycle assessment (LCA) method. Starting from the carbon emissions in the product manufacturing process, the model divides product carbon emissions into three stages: parts, assembly, and testing. Detailed modeling is conducted for the production and transportation of purchased parts, processing steps of self-made parts, connection methods, and the installation and testing of products on testing equipment. The model has strong generality and can be used for the carbon accounting of everyday mechanical products. Through this model, mechanical manufacturing companies can calculate the carbon emissions at each stage and identify corresponding optimization solutions.
Finally, the effectiveness and feasibility of the model are validated using a specific three-piece ball valve as an example. The validation reveals that carbon emissions from material consumption, energy consumption, and transportation account for a significant proportion in the ball valve production process, namely 35.6%, 38.8%, and 17.6%, respectively. The corresponding carbon emissions are 17.854 kgCO2e, 19.405 kgCO2e, and 8.8 kgCO2e.
Based on the above analysis, the research significance of this paper is as follows:
(1) Based on the LCA method, this paper comprehensively considers the major carbon emissions sources in the product manufacturing process and establishes a corresponding carbon emissions accounting model. The model is strong and general and suitable for general mechanical manufacturing industries with low theoretical requirements for readers.
(2) This paper divides the carbon emissions of mechanical products into stages (P-A-T). Through this model, manufacturing companies can obtain carbon emissions information for each production stage of the product, providing low-carbon optimization directions for enterprises and improving the low-carbon competitiveness of their products.
Future research will aim to build a more comprehensive quantitative model of carbon emissions from product manufacturing workshops. In addition to considering the impact of factors such as the transportation route of the product and its components, the number of mass production, and the manufacturing and assembly sequence of the parts, the subsequent research should also include the impact of dynamic characteristics such as urgent order insertion, equipment aging failure, etc., on the carbon emissions of mechanical products. The research on these factors will further refine and optimize the carbon emissions accounting of each link in the product manufacturing process. In addition, based on the carbon emission model proposed in this paper, the optimization method of product process planning and workshop scheduling optimization technology will be deeply studied, and how to effectively coordinate resource utilization and production scheduling in a dynamic production environment will be explored to minimize the overall carbon emission. The research in these directions will provide new theoretical support and practical guidance for green manufacturing and help promote the manufacturing industry’s sustainable development.