Investigation into the Methodology and Implementation of Life Cycle Engineering under China's Carbon Reduction Target in the Process Industry

Engineering ›› 2024, Vol. 40 ›› Issue (9) : 95 -107. DOI: 10.1016/j.eng.2023.08.025

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2023-03-13	2023-08-10	2024-09-30
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2024-11-11	2023-05-09

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Abstract

The industrial sector is the primary source of carbon emissions in China. In pursuit of meeting its carbon reduction targets, China aims to promote resource consumption sustainability, reduce energy consumption, and achieve carbon neutrality within its processing industries. An effective strategy to promote energy savings and carbon reduction throughout the life cycle of materials is by applying life cycle engineering technology. This strategy aims to attain an optimal solution for material performance, resource consumption, and environmental impact. In this study, five types of technologies were considered: raw material replacement, process reengineering, fuel replacement, energy recycling and reutilization, and material recycling and reutilization. The meaning, methodology, and development status of life cycle engineering technology abroad and domestically are discussed in detail. A multidimensional analysis of ecological design was conducted from the perspectives of resource and energy consumption, carbon emissions, product performance, and recycling of secondary resources in a manufacturing process. This coupled with an integrated method to analyze carbon emissions in the entire life cycle of a material process industry was applied to the nonferrous industry, as an example. The results provide effective ideas and solutions for achieving low or zero carbon emission production in the Chinese industry as recycled aluminum and primary aluminum based on advanced technologies had reduced resource consumption and emissions as compared to primary aluminum production.

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Carbon neutrality / Life cycle engineering / Process Industry / Carbon reduction technology / Eco-design

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Mingyang Li, Feng Gao, Zuoren Nie, Boxue Sun, Yu Liu, Xianzheng Gong. Investigation into the Methodology and Implementation of Life Cycle Engineering under China's Carbon Reduction Target in the Process Industry. Engineering, 2024, 40(9): 95-107 DOI:10.1016/j.eng.2023.08.025

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1. Introduction

Globally, climate change is an issue faced by humanity that impedes sustainable development and must be addressed urgently. The Paris Agreement proposed that countries need to substantially reduce global greenhouse gases (GHGs) emissions to limit the global temperature increase in this century to 2.0 °C while pursuing efforts to limit the increase even further to 1.5 °C [1]. Before December 2021, 136 countries, 115 regions, 235 cities, and 682 enterprises had planned to achieve zero carbon emissions [2]. In September 2020, China announced its targets for achieving carbon neutrality: peak carbon emissions by 2030 and zero carbon emissions by 2060 [3]. China’s timeline for achieving zero carbon emissions is shorter than that of developed countries, making this task more daunting. However, the ambition and determination of China appear to contribute to the capacity of humanity to solve the climate change issue.

According to the carbon emission structure among different sectors of China given by the International Energy Agency (IEA), emissions from the energy sector (electricity and heat, 56.39%) and industry sector (28.65%) were primary carbon producers [4]. The total energy consumption of the four basic materials, including steel, building materials, petrochemical and chemical industries, and nonferrous metals, accounts for more than 30% of the total nationwide energy consumption and more than 50% of the energy consumed by industries [5]. It is extremely important for the industrial sector, which is a typical process manufacturing industry, to reduce carbon emissions from material production. Hence, promoting the transformation and upgrading of ecological material manufacturing processes, reducing resources and energy consumption, and achieving zero carbon emissions from material production processes would be a substantial measure for achieving green, low-carbon, and circular development.

In this study, the technological demands and developments in carbon reduction and the methodologies of life cycle engineering technology were investigated. Furthermore, a method for the comprehensive analysis of carbon emissions in the material process industry was introduced. As a case study, this method was adopted to design a low-carbon manufacturing process in a nonferrous industry. Moreover, this study aimed to provide effective concepts and solutions for the design of low- or zero-carbon emission processes in the industrial sector.

1.1. Development of carbon reduction technology in material processing industries

Given the urgency to reduce carbon emissions, the basic material manufacturing sector is required to determine more sustainable raw material replacements and greener process reengineering strategies. For example, this could involve the mitigation of carbon emissions from chemical reactions, such as carbonate decomposition, by changing the raw materials used, reaction mechanisms, and manufacturing methodologies. Currently, some of the main potential carbon-reduction technologies include the categories described in the following sections.

1.1.1. Materials alternative technology (MAT)

Materials that generate CO₂ may be replaced by those that do not generate CO₂ or by biomass materials, thus reducing or avoiding the production of CO₂. For example, coke can be replaced with hydrogen during steel production [6]. In cement production, solid waste or noncarbonate materials can be used to replace raw calcium carbonate [7], [8], [9]. This type of technology is mainly utilized in the building materials, metallurgy, and chemical industries. The technology can simultaneously dispose of solid waste while substantially reducing carbon emissions.

1.1.2. Fuel alternative technology (FAT)

Carbon emissions from coal consumed in the process of calcining cement clinker account for 25%-40% of the carbon emissions from the cement industry. By adopting biomass fuel to replace fossil fuels, the cement industry can reduce carbon emissions from fuel consumption [10], [11]. The main forms of biomass fuel include agricultural waste (straw, rice husks, and rape rods), municipal waste, and blast furnace biomass carbon fuel. Furthermore, fossil fuels such as coal and natural gas can be replaced by renewable and clean fuels. For example, hydrogen can replace fossil fuels in the steel, cement, and glass production processes to reduce carbon emissions [12], [13].

1.1.3. Energy recycling and reuse technology (ERRT)

Excess energy generated by production industries, such as industrial waste heat, flammable gas, and energy-containing solid media (steel slag), can be recycled using specialized technical methods, processes, and equipment [14], [15], [16]. In some industrial processes, energy, such as methane, escapes as a byproduct [17]. Recycling the excess energy produced can improve overall energy utilization and help achieve carbon reduction.

1.1.4. Material recycling and reuse technology (MRRT)

Utilizing advanced technologies and methodologies to effectively recycle and reuse valuable waste such as sewage, solid waste, and scrap metal from industrial production and modern society can potentially produce waste byproducts of high value. Key technological approaches include industrial solid waste recovery and recycling technology, as well as low-carbon waste metal recycling [18], [19]. Furthermore, raw materials and products associated with high carbon emissions can be substituted with recycled materials, thereby reducing or avoiding emissions from further production. Additionally, a decrease in GHG emissions from conventional waste disposal methods, such as incineration or landfilling, can be achieved, thus obtaining indirect benefits.

1.1.5. Process reengineering technology (PRT)

Achieving rapid decarbonization in the industrial sector before achieving the zero-carbon emission target will need to be based on transformative manufacturing methods and the adoption of technologies that promote quality and efficiency. By integrating this approach with MAT and FAT, such as all-scrap electric furnace smelting, hydrogen-enriched blast furnaces, new energy metallurgy, and carbon cycle and cross-industry co-production technologies, the challenges associated with fossil fuel consumption and process emissions can be addressed [20]. Additional advancements include hydrometallurgical cleaner production, large-scale preparation with shortened processes, biometallurgical reduction technology in the non-ferrous industry [21], [22], [23], [24], dry powder technology, CO₂ recycling, multi-energy complementary calcination, and other technologies and processes within the building materials industry [25], [26]. Currently, transformative process refactoring technologies in the basic materials industry are largely in the research and development (R&D) phase.

From a production quality perspective, the implementation of carbon reduction technologies introduces uncertain impacts on resource consumption and material performance. For example, in the case of recycled aluminum (RA), although the carbon emission of RA is only 5% [27], factors such as impurities or contamination accumulated during the recycling process will decrease the performance of the RA alloy. The performance degradation becomes more obvious after many recycling cycles [28]. Hence, characterizing the relationships between performance, resource and energy consumption, and carbon emissions during the initial stages of designing new technologies and processes in the process industry is a scientific issue that should be paid close attention to during project design and the selection of carbon reduction technology.

1.2. Life cycle engineering technology

Life cycle engineering is dominated by eco-design, also called life cycle design or green design, and aims to satisfy the requirements of performance, resource savings, and environmental protection. It applies technologies and theories such as poison substitution, green process planning, clean production, and resource recycling. Life cycle engineering is presently an important international scientific research frontier formed at the intersection of materials, manufacturing, and environmental sciences [29]. The main idea of eco-design is to incorporate environmental factors into the product design. This method reduces the environmental impact of the entire product life cycle and ensures good product performance and economic feasibility [30], [31].

Since the beginning of the 21st century, research on life cycle engineering has developed rapidly worldwide. A theoretical framework and system structure were constructed based on the optimization of multiple factors and objectives under the guidance of the life cycle theory and eco-design. It considers multiple factors, such as performance, cost, and social and environmental issues, guiding the comprehensive optimization of entire industry chains [32], [33], [34], [35]. The eco-design method has been applied in the design of various products and industries [36], [37], [38], [39], [40], [41]. Ramani et al. [42] extended this eco-design method and examined some of its defects and proposed that it is important to integrate downstream life cycle data into ecological design. Kamalakkannan and Kulatunga. [43] proposed a method to parameterize life cycle assessment (LCA) and applied it to the eco-design method. Belucio et al. [44] proposed a new decision-support method that assesses environmental and other costs during the design step. The proposed method was verified and applied to a thermal insulation material in a case study. Borge-Diez et al. [45] and Ratner et al. [46] applied the eco-design method to an energy system to solve the issue of renewable energy capacity recovery and energy-saving design of power grids.

Material manufacturing is an important field in life cycle engineering applications. Carbon neutrality is effectively promoted throughout the entire process of material manufacturing by constructing the technical process of material ecological design; optimizing the resource and environmental problems of each link from the source; guiding the development of material component design; optimizing and reconstructing the process and recycling technology; and seeking an optimal solution for material properties, resource consumption, and environmental impacts throughout the entire life cycle.

2. Methodology used for the comprehensive analysis of carbon emissions in the material process industry

In this section, the relationships between carbon emissions, performance, and resource consumption are quantified. Different design schemes are evaluated and selected based on a comprehensive model that characterizes multiple factors including resource consumption, carbon emissions, and performance.

2.1. Comprehensive performance evaluation model

Performance evaluation aims to quantify the extent to which the material performance meets the design requirements. A requirement-performance matrix as shown in Table 1, was constructed to measure the importance of the indicator parameters and normalize each indicator. W_Ri represents the importance of the design requirements in the application field, that is, the requirement weights. C_ij represents the extent of the contribution of performance P_j to the requirement R_i. P_j represents the value of the test index of the jth performance for the target object.

The performance weight coefficient indicates the extent and correspondence relation between multiple performances and the overall application requirements of the materials. This can be calculated using Eqs. (1), (2):

(1)$ \mathrm{PD}_{j}=C_{1 j} \times W_{R_{1}}+C_{2 j} \times W_{R_{2}}+\ldots+C_{i j} \times W_{R_{i}}$

(2)$ \mathrm{PW}_{j}=\frac{\mathrm{PD}_{j}}{\sum_{\varphi=1}^{\varphi_{\max }} \mathrm{PD}_{\varphi}}$

Where i represents the number of requirements, i ∈ {1, 2, …, i_max}. j represents the number of performances, j ∈ {1, 2, …, j_max}. i_max and j_max depend on the demand of performances and requirements. PD_j or PD_φ represents jth or φth performance requirement, PW_j represents jth performance weights. φ represents the number of performance types, φ ∈ {1, 2, …, φ_max}, φ_max = j_max.

The comprehensive performance index of a certain material can be obtained using the weight coefficient and test index values of the various performances of the material, as shown in Eq. (3):

(3)$ \mathrm{PI}=\sum_{j=1}^{j_{\max }}\left(\mathrm{PW}_{j} \times \frac{P_{j}}{P_{j}^{\prime}}\right)$

where PI represents the value of the unitary performance index of the material, and P_j^′ represents the value of the test index of the jth performance for the reference object.

2.2. Comprehensive resource consumption assessment model

Mineral, fossil, water, and land resources exist in different forms in nature and have different measurement standards and physical units. Hence, none of the resource types could be directly quantified. However, the loss of all types of natural resources caused by material production can be characterized scientifically and objectively using a model that characterizes resource depletion based on the thermodynamic function of exergy. It is possible to identify the intensity of resource dependence in material production and characterize the resource depletion process [47]. The application of this model can characterize the loss of mineral, fossil fuel, water, and land resources in the material life cycle as a uniform index with the same physical units. It can also reflect regular variations in the “quantity” and “quality” of resources used in the material production processes. All natural resources can be systematically quantified based on a calculation model, reference environment, and elemental chemistry.

2.2.1. Consumption model of mineral resources based on the exergy index

The chemical compositions of minerals mined from nature are complex. For example, iron ore mined from nature is not pure magnetite; it is a compound consisting of many ores, such as magnetite, quartz, feldspar, hornblende, tremolite, and so forth. Hence, it does not meet the scope of the LCA, which characterizes the resource properties of a total mineral using one type of pure ore. The characterization model is given by Eq. (4). Calculation of the chemical exergy of natural minerals includes two steps. The first was to sum the exergy values of the different pure minerals, and the second was to calculate the exergy loss caused by the mixing of pure minerals.

(4)$ \mathrm{Ex}_{\mathrm{Na}}=\sum \mathrm{x}_{\mathrm{Ai}} \mathrm{Ex}_{\mathrm{Ai}}+R_{\mathrm{t}} T_{0} \sum n_{\mathrm{Ai}} \operatorname{In} m_{\mathrm{Ai}}$

where Ex_Na represents the chemical exergy of natural minerals per unit mass, Ex_Ai represents the chemical exergy of pure minerals per unit mass, x_Ai represents the mass percentage of a pure mineral in a natural mineral, R_t is the thermodynamic constant (R_t = 8.314 J·mol⁻¹·K⁻¹), T₀ is the normal temperature, n_Ai is the number of moles of pure mineral per unit mass of natural mineral, and m_Ai is the molar percentage of pure minerals in a natural mineral.

2.2.2. Consumption model of fossil energy based on the exergy index

It is difficult to calculate the chemical exergy of fossil fuels precisely using the natural mineral chemical exergy model. However, it is possible to calculate the chemical exergy of organic fuels with complex compositions using a specific exergy and energy ratio method. The core concept of this method is to estimate the chemical exergy by calculating the ratio of chemical exergy to the heating value of the organic fuel, as shown in Eq. (5):

(5)$ P_{\mathrm{b}}=\frac{\mathrm{Ex}_{\mathrm{eq}}}{\mathrm{Ex}_{\mathrm{t}}-\mathrm{Ex}_{\mathrm{t}_{\mathrm{r}}}}$

where β represents the exergy ratio of the organic fuel; Ex_orgrepresents the exergy value of the organic fuel; and H_l is the low heating value of the organic fuel.

2.2.3. Consumption model of water resources based on the exergy index

The concept of the water resource consumption impact assessment model is based on the basic variation after water resource consumption in the material production process. Water generates three types of variations after use, which include the incoming product being solidified, such as chemical bonds being broken; loss, such as evaporation and dispersion loss; and discharge into the environment as wastewater. The resource damage characterization model is given by Eq. (6):

(6)$ \mathrm{WRDI}=\Delta \mathrm{Ex}_{1} \times \sum m_{\alpha}+\Delta \mathrm{Ex}_{2} \times \sum m_{\beta}+\Delta \mathrm{Ex}_{3} \times \sum m_{\gamma}$

where WRDI represents the index of water resource depletion; m_α, m_β, and m_γ represent the wastewater quantity, evaporation, and curing of different production processes, respectively; and ΔEx₁, ΔEx₂, and ΔEx₃ represent the resource loss factors corresponding to the three types of consumption.

2.2.4. Consumption model of land resources based on the exergy index

The first step in the land resource consumption characterization model, was to determine the influence of human behavior on the capacity of land to sequester carbon, then the loss of land carbon sequestration as a result of human behavior was quantified and converted into the loss of land exergy sequestration capacity. The land carbon sequestration capacity was converted into chemical exergy sequestration capacity through an analysis of the energy fluctuations inherent in photosynthetic reactions, as shown in Eq. (7):

(7)$ \operatorname{ExLF}_{\text {occ,res }}=\mathrm{BL}_{\text {occ,res }} \times \mathrm{CR}_{\mathrm{C} \rightarrow \mathrm{Ex}}$

where ExLF_{occ, res} represents the resource loss factor during the occupation and recovery phases of land resources. CR_C→Ex represents the transfer coefficient of carbon and exergy, and BL_{occ, res} represents loss of carbon sequestration in land occupation and recovery phases.

2.2.5. Comprehensive assessment model

The resource impact index system of material eco-design was constructed based on the resource consumption comprehensive assessment model, as shown in Table 2. The index system divides the resource impact (primary index) into resource consumption and resource quality decline indices. The resource consumption index includes ore, renewable resource, and fossil energy consumption. The resource quality decline index includes two three-level indices: water resource consumption and land resource use. Through the index system, the influence of materials on resources over the entire life cycle can be characterized comprehensively and objectively.

The resource consumption intensity (RCI) of a unit metric ton (t) of product after the application of advanced technology and processes was calculated using Eq. (8):

(8)$ \mathrm{Ex}_{\mathrm{TA}}=\mathrm{Ex}_{0}+\mathrm{Ex}_{\mathrm{t}}-\mathrm{Ex}_{\mathrm{t}_{\mathrm{r}}}$

where Ex_TA represents the RCI after the application of the advanced technology, Ex₀ represents the RCI before the application of the advanced technology, Ex_t represents the variation in RCI by the advanced technology, and Ex_tr represents the RCI of the process, which was replaced by advanced technology.

The application of advanced technology may require the use of new equipment and equipment production may cause resource depletion. Ex_eq represents the RCI of equipment manufacturing, and the critical output P_b where advanced technologies begin to generate resource-saving benefits, was calculated using Eq. (9). When the product’s output (P₀) > P_b after the application of advanced technology, the installation and operation of advanced technology have the benefit of conserving resources.

(9)$ P_{\mathrm{b}}=\frac{\mathrm{Ex}_{\mathrm{eq}}}{\mathrm{Ex}_{\mathrm{t}}-\mathrm{Ex}_{\mathrm{t}_{\mathrm{r}}}}$

2.3. Carbon emission comprehensive assessment model

The main sources of industrial carbon emissions include GHG emissions from energy combustion and consumption during industrial production processes, indirect emissions from the generation of electricity used in industrial production processes, and direct GHG emissions from reactions in various technological processes. The analysis and quantification of carbon emissions originating from the industrial sector necessitates a comprehensive understanding of their primary source and characteristics. Key points include the categorization of energy sources, consumption rates, emission coefficients, electricity structure, emission factors of different methods of generating electricity, and output of intermediate or final products. The construction of a characteristic model to calculate carbon emissions in a process industry are based on these parameters, as shown in Eq. (10):

(10)$ \begin{aligned} G= & \sum_{\eta}\left[\sum _ { \varepsilon } \left[\sum_{p=1}^{\theta}\left(f_{p, \varepsilon} \times Q_{p}\right) \times E_{\varepsilon, \eta}+\sum_{p=1}^{\theta}\left(e_{p} \times Q_{p}\right) \times E_{e, \eta}\right.\right. \\ & \left.\left.+\sum_{p=1}^{\theta}\left(P_{p} \times Q_{p}\right)\right] \times \mathrm{Cl}_{\eta}\right] \end{aligned}$

where G represents the result of GHG emission, η represents GHG emission types, ε represents energy types, p represents product, and θ represents the number of products. f_p,ε represents the consumption of energy types ε in the unit process of product p, E_ε,η represents the ηth GHG emission factor of energy type ε, e_p represents the consumption of electricity in the unit process of product p, E_e,η represents the ηth GHG emission factor of electricity generated, P_p represents the emission of the process per unit process of product p, Q_p represents the consumption of product p in the entire production process, CI_η represents the characteristic factor of GHG emission type, and Q_θ = 1.

There are two main ways to decarbonize the advanced technologies applied in the process industry. First, carbon emissions were reduced by reducing energy consumption. Second, carbon is indirectly reduced or eliminated by process optimization and innovation. Carbon emissions can be calculated by quantizing characteristic parameters when advanced technology is applied in the process industry. Energy consumption and carbon emissions after the application of advanced technology can be calculated indirectly using Eqs. (11), (12), (13).

(11)$ f_{p, \varepsilon}=f_{o, p, \varepsilon}+f_{\mathrm{t}_{2}, \varepsilon}$

(12)$ e_{p}=e_{\mathrm{o}, p}+e_{\mathrm{t}_{\mathrm{z}}}$

(13)$ P_{p}=P_{\mathrm{o}, p}+P_{\mathrm{t}_{\mathrm{z}}}$

where f_o,p,ɛ represents the consumption of ɛ before the application of advanced technology, f_tz,ɛ represents the variation in energy consumption after the application of advanced technology, e_o,p is the consumption of electricity before the application of advanced technology, e_tz represents the variation in electricity consumption after the application of advanced technology, P_o,p represents the carbon emissions directly before the application of advanced technology, and P_tz represents the variation in carbon emissions after the application of advanced technology.

2.4. Comprehensive and multi-factor characterization model: resource, carbon emissions, and performance

Once the single factors of resource intensity, carbon emissions, and material performance are obtained, the comprehensive characterization model (Eq. (14)) can be constructed using the comprehensive index value of eco-design, which is synthesized by the following three factors:

(14)$ \begin{aligned} \mathrm{EDI} & =\frac{P}{\mathrm{RC}+E} \\ & =\frac{\sum_{j=1}^{j_{\max }}\left(\mathrm{PW}_{j} \times p_{j}\right)}{\left[\left(\sum_{u=1}^{n_{u}} \mathrm{Ex}_{u} \times \mathrm{RC}_{u}\right)+\left(\sum_{r=1}^{n_{r}} \mathrm{PE}_{r} \times A_{r}\right)\right]+\left[\sum_{\omega=1}^{n_{\omega}} E_{\omega} \times \mathrm{Cl}_{\omega}\right]} \end{aligned}$

where EDI represents the comprehensive index of eco-design, RC represents the value of energy consumption, E represents the value of carbon emissions. RC_u represents the consumption of the μth unrenewable resource (kg), Ex_u represents the exergy value of the μth unrenewable resource (MJ·kg⁻¹), n_u is the maximum number of u, PE_r represents the exergy value of the rth renewable resource (MJ·kg⁻¹ or MJ·m⁻²), A_r represents the consumption of the rth renewable resource (kg or m², where kg is the unit of water resources and m² is the unit of land resources), n_r is the maximum number of r, E_ω represents the carbon emissions of the wth GHG (kg), and CI_ω represents the characteristic factor of the wth GHG (CO₂ equivalent (eq)ċkg⁻¹), n_ω is the maximum number of ω.

In addition, a comprehensive characterization was achieved using the matrix model aimed at each factor, as illustrated by Eq. (15):

(15)$ \boldsymbol{A}=\left[w_{\mathrm{PI}} w_{\mathrm{RI}} w_{\mathrm{GI}}\right]\left[\begin{array}{llll} \mathrm{PI}_{1} & \mathrm{PI}_{2} & \ldots & \mathrm{PI}_{n} \\ \mathrm{RI}_{1} & \mathrm{RI}_{2} & \ldots & \mathrm{RI}_{n} \\ \mathrm{GI}_{1} & \mathrm{GI}_{2} & \ldots & \mathrm{GI}_{n} \end{array}\right]=\left[\begin{array}{llll} a_{1} & a_{2} & \ldots & a_{n} \end{array}\right]$

where w_PI,w_RI,and w_GI represent the weight coefficients of performance, resource intensity, and carbon emissions, respectively. n represents the number of products or projects, PI_n represents the index value of the performance factor of products or projects, RI_n represents the index value of the resource intensity factor of products or projects, GI_n represents the index value of the carbon emission factor of products or projects, a_n represents the comprehensive result of products or projects.

Owing to the impact of the weight coefficient, it is necessary to dequantify each factor before being fully characterized to achieve the overall calculation for each factor. This method is expressed in Eq. (16) below:

(16)$ \text { index }_{n}=\frac{\text { ind }_{n}}{\text { ind }_{\mathrm{re}}}$

where index_n represents the result after the dequantification of n products or projects, ind respresents indicator, ind_n represents the primary index result of n products or projects, and ind_re represents the reference index result of n products or projects. The maximum value should be selected in advance from the compared project or product as a reference index. Using this method, it would be easy to conduct data analysis and display the results because the index would be between zero and one.

An important issue associated with this method is requirement orientation. For example, it is optimal for performance value to be high. Resource intensity and carbon emissions should be smaller, and it is not possible to compare them if the orientations are different. To solve this issue, one orientation should be inverted to make all indexes have the same orientation to determine the resultant orientation of each project or product based on the index orientation. For example, taking the inverse of PI. This will be better if the $ \frac{1}{\mathrm{PI}}$ is smaller. Full calculations with $ \frac{1}{\mathrm{PI}}$, RI, and GI, a_n will also be better if they are smaller.

With low carbon emissions of process industries, advancement in technology will gradually be applied in industrial production. However, after the application of advanced technological or decarbonization methods, a method to effectively quantify and compare the multiple factors of performance, carbon emissions, and resource intensity and to characterize their impacts is lacking. Therefore, this study aimed to systematically quantify the performance, carbon emissions, and resource intensity after the application of different types of decarbonization technology and explore the potential of environmentally friendly and decarbonizing technology using the method introduced in Section 2. Furthermore, this study aimed to demonstrate how the results for the three kinds of factors from the comprehensive characteristic model given in Section 2.4 could support determinations of feasibility and policy for popularizing the technology.

3. Case study

The advancement and application of energy-saving technologies and the decarbonization of manufacturing processes in non-ferrous industries are vital. In the aluminum industry, there is an ongoing investigation into the application of advanced technologies and the reuse of recycled resources. The aim was to develop a process industry life cycle engineering design for the aluminum production industry and seek an optimal solution for decarbonization in the process industry. The decarbonized technologies were sourced from the previous results of our research group [3] and are shown in Table S1 (Part S1 in Appendix A).

3.1. System boundary

An A356 cast aluminum alloy was selected for this study [48]. The resource intensities and carbon emissions of primary aluminum (PA), RA, and PA alloys based on the application of advanced technologies (PATA) were calculated and combined with the performance of the product to obtain a life cycle engineering design that was compared to select the best aluminum product from the perspective of performance, resource intensity, and carbon emissions. The functional unit was a 1-t A356 aluminum alloy.

The system boundary was identified based on the typical production processes of primary and RA in China, as illustrated in Fig. 1. The unit processes of the PA alloy include bauxite mining, alumina production, carbon anode production, electrolysis, aluminum ingots, and aluminum alloy processing. The unit processes of the PA alloy and the variations in energy and material impacts on the GHG emissions were also considered in the production of the PATA alloy, as shown in Fig. 1(a). The unit processes of RA alloy production mainly include transportation, preprocessing, melting and casting, and aluminum alloy processing, as illustrated in Fig. 1(b).

The fuels consumed in aluminum alloy production include coal, natural gas, electricity, fuel oil, steam, coal gas, petrol, and diesel. Energy-related GHG emissions include CO₂, CH₄, and N₂O generated by energy combustion. The GHG emissions from the process were considered, including the CO₂ emissions from the calcination of the limestone reaction and electrolysis process, and CF₄ and C₂F₆ emissions from the anode effect. Fixed asset inputs, such as plant construction, equipment production, and maintenance, were not considered within the scope of the system, and GHG emissions caused by the transportation of raw materials in the production process were ignored.

3.2. Data sources

The values of the technical parameters and energy consumption for PA and RA production were sourced from the Ref. [49], typical enterprise research reports, and literature. The values of the technical parameters and energy consumption of aluminum processing were sourced from typical enterprise survey data and literature. The bauxite mining values were sourced from the SinoCenter database [50]. The GHG emission calculations for electrolytic aluminum production were based on Greenhouse gas emissions accounting methodology and reporting guidelines for Chinese electrolytic aluminum producers [51]. The GHG emission calculations for alumina production and aluminum processing were based on the stationary source emission calculations of the Intergovernmental Panel on Climate Change (IPCC). The calculation methods for the low heating value of fossil fuels, carbon content per unit heating value, and carbon oxidation rate of the fuel were the same as those used for calculating the electrolytic aluminum production. The GHG emission factor of electric power production was taken as the nationwide average value from the 2019 Annual emission reduction project China regional grid baseline emission factors [52]. The GHG emissions from RA production was sourced from the transportation process, energy utilization, and indirect emissions from electricity generated by the smelting and casting processes. The GHG emissions calculation for fuel combustion refers to the method of calculating the stationary source emissions of the IPCC. The method for calculating the GHG emissions from electricity in RA production was the same as that used for the PA production process.

4. Results and discussions

4.1. RCI

Characterizing the inventory data of the PA alloy processing with the resource depletion factor exergy provided the resource consumption intensities for PA and PATA alloy production (Fig. 2). The resource consumption intensities for producing 1-t PA and PATA were 366 and 320 GJ∙t⁻¹ alloy, respectively. The potential for resource savings from the application of advanced technologies was 12.57%. The cumulative exergy demands (CExD) for PA and PATA production were 344 and 298 GJ∙t⁻¹ alloy, respectively. The exergy saving value of the resource was approximately 46 GJ∙t⁻¹ alloy from the application of advanced technologies, which is a decline of 13.37%. With the application of advanced technologies, the exergy values of the electrolysis process, carbon anode production, and alumina production decreased by 13.07%, 4.04%, and 29.49%, respectively.

The comparative analysis of PA and PATA resource depletion is shown in Fig. 3. From the results, it can be seen that exergy is proportional to energy consumption, because as energy consumption was reduced, so was exergy, accounting for 97.71%. Furthermore, the proportion of the exergy decreased with a reduction in resource consumption, which was 2.29%. Hence, the decline in CExD was mainly due to the exergy decline resulting from the energy consumption reduction. This also illustrates that the main characteristic of the 12 advanced technologies is energy saving. The decline in the exergy value with the reduction in resource consumption was mainly due to the reduction in the carbon anode used in PA production. The decline in the exergy value with the reduction in energy consumption was mainly due to the reduction in the consumption of electricity, gas, steam, and coal; the CExD decreases of these energies were 33.51%, 32.60%, 30.37%, and 13.54%, respectively.

Similarly, the inventory data of the RA alloy processing were characterized using the resource depletion factor-exergy. The RCI results for the RA alloy production are shown in Fig. 4. The RCI of 1-t RA alloy was 48.44 GJ∙t⁻¹ alloy with RA as the raw material. The RCI of the 1-t RA was 17.48 GJ∙t⁻¹ equivalently. The RCI of RA was approximately 4.78% of that of PA. The main sources of resource depletion in RA production were the consumption of natural gas, diesel, electricity, and solvent, at 44.59%, 37.46%, 10.98%, and 6.97%, respectively. The CExD proportions of the aluminum alloy processing, RA casting, transportation, and pretreatment processes were 46.89%, 31.76%, 19.90%, and 1.45%, respectively. The RCI of the RA alloy was approximately 13.22% of that of the PA alloy. Hence, the adoption of recycling and regeneration technologies that recycle RA provides obvious benefits.

4.2. Carbon emission intensity (CEI)

The carbon emissions from the three manufacturing methods are shown in Fig. 5, Fig. 6. The carbon emissions of 1-t aluminum alloy production were 17.4 and 14.8 t from PA and PATA, respectively; the decarbonized value was 2.6 t, a decline of 14.94%. Hence, carbon emissions decrease with the application of advanced technologies. Fig. 7 shows a comparison of the carbon emission contributions from unit PA and unit PATA production. Carbon emissions related to energy consumption of per ton alloy were reduced by 785 kg CO₂ eq when advanced technologies were applied. Indirect carbon emissions of per ton alloy were reduced by approximately 1692 kg CO₂ eq because of electricity savings.

Carbon emissions from alumina production, carbon anode production, and electrolysis decreased by 30.09%, 35.19%, and 11.98%, respectively, after the application of advanced technologies. The carbon emission of per ton alloy from the RA alloy production was 1.89-t CO₂ eq, which was 10.86% of the carbon emissions from the PA alloy production. Carbon emissions from RA production were 5.54% of those from PA production. Recycling and reusing aluminum scrap provided obvious benefits for reducing GHG emissions. The proportions attributable to the unit processes, that is, aluminum alloy processing, RA casting, transportation, and pretreatment processes, were 49.29%, 34.29%, 12.48%, and 3.95%, respectively. From the perspective of carbon emissions, CO₂, CH₄, N₂O, CF₄, and C₂F₆ generated from the electrolysis process in PA production were not generated in RA production.

4.3. Performance

Many impurities were attached to the aluminum scrap used to produce RA. Some impurities could not be eliminated after sorting and cleaning and persisted during the smelting phase. Non-metallic inclusions and dissolved impurity elements were present in the RA liquid, and the performance and quality of the RA and RA alloy products were influenced by the efficiency of elimination or effective control of impurities in the smelting phase. The resultant RA was porous with oxide slag inclusions due to inadequate purification. Stress concentration occurs easily and performance declines. Therefore, the performance of the RA alloy is theoretically weaker than that of the PA alloy because the purity of RA is lower than that of PA. With limited data on the relationship between advanced technology and product quality, it was assumed that the performances of the PA and PATA alloys were the same.

The tensile strength, yield strength, and elongation were selected as the main indicators. The three types of performances of A356 alloy production by RA and PA are shown in Table S2 (Part S2 in Appendix A). The performance of the RA alloy was inferior to that of the PA alloy. For a comprehensive characterization, it was necessary to construct the requirement-performance matrix of the A356 aluminum alloy based on the usage demand, which is illustrated in Table 3. The requirements included three main types: resistance to deformation, durability, and ease of processing and molding (with yield considered); the weights were 50%, 25%, and 25%, respectively. The weight coefficients of the three types of performance-tensile strength, yield strength, and elongation are 33.5%, 32.25%, and 34.25% using Eqs. (1), (2), respectively. Based on Table S2, performance weight coefficients and Eq. (3), the normalization performance indicators of the three aluminum alloy manufacturing methods were obtained. The normalization performance indicators of the three kinds of alloy, (PA, PATA, and RA alloy), were 1.000, 1.000, and 0.986, respectively.

4.4 Comprehensive characterization

The results of the RCI, CEI, and performance from the three-way production of the aluminum alloy are shown in Table S3 (Part S3 in Appendix A). Before a comprehensive characterization, it is necessary to dequantify the results and unify the demand orientations of the three factors. The results are summarized in Table 4. Because the weight coefficients of resource consumption, carbon emissions, and performance are influenced by subjective factors, four types of weight coefficients were investigated. The weight coefficients, calculation process, and results are provided in Part S4 in Appendix A.

The comprehensive results of A356 aluminum alloy production by the three methods were RA, PATA, and PA, in ascending order. The comprehensive performance of the A356 aluminum alloy produced by RA was better than that of the PA alloy. If only the optimization of PA production is considered, the A356 aluminum alloy produced by PATA could effectively save resources and energy. If only the replacement of the primary product with the recycled product is considered, the comprehensive performance of the A356 aluminum alloy produced by RA is clearly better than that produced by the PA alloy. The target of carbon peaking and neutrality could be better achieved with the RA-A356 alloy replacing the PA-A356 alloy because this would reduce carbon emissions and resource consumption with a minimum sacrifice of performance.

5. Limitations

The previous sections presented a comparison of three production methods using PA, PATA, and RA alloy. However, the results were influenced by the weight coefficients of performance, resource depletion, and carbon emissions and were strongly subjective. The comprehensive result that the RA alloy was always the best of the three production methods was mainly due to the differences in resource depletion and carbon emissions between the three types of alloys. If the parameters of several schemes are similar, different optimal solutions are obtained for different weight coefficients. Hence, developing an objective method to overcome this issue by weighting the coefficients and determining the optimal solution would be beneficial.

In this study, the comprehensive characteristic method was optimized based on the above method using Eq. (14) to explore the optimal weight coefficient subset for each project, as shown in Eq. (17), which is programmed in Python. The larger the number of factor sets k, the higher the ability of the project to satisfy most requirements and the better the project.

(17)$ S=\left\{c_{k}, d_{k}, g_{k}\right\}\left(c_{k}+d_{k}+g_{k}=1, k \in \mathbf{N}^{+}\right)$

where S represent the set of the weight coefficient, c_k, d_k, and g_k represent the weight coefficient, N⁺ stands for natural numbers expect zero.

Subsequently, a comprehensive characteristic model was constructed, as shown in Eq. (18) and programmed using Python. The program code is provided in Part S5 in Appendix A.

(18)$ \mathrm{EDI}=c \times \mathrm{PER}+d \times \mathrm{RCI}+g \times \mathrm{CEI}$

where c, d, and g represent the weight coefficients and PER represent preference results.

The results of the RA, PATA, PA codes are k_RA = 5148, k_PATA = 3, and k_PA = 1. Hence, the comprehensive performance of the RA alloy was the best, and that of the PATA alloy was better than that of the PA alloy.

6. Conclusions

Carbon reduction technology in the processing industry is critical to achieving peak carbon and carbon neutrality in the industrial sector. Thus, it is necessary to utilize life cycle engineering technology to develop processing industries with low carbon emissions. Starting from a holistic view of manufacturing, analyzing the effects of combinations of carbon emission reduction technologies could foster the full exploitation of the advantages of technology integration and guide the low-carbon development of processing industries. The theoretical framework and methods of life cycle engineering technology are illustrated in this paper, providing a theoretical basis for the efficient combination and reconfiguration of energy-saving and carbon reduction technology systems in processing industries. These technologies include alternative materials, fuel, energy recycling and reuse, material recycling and reuse, and process reengineering technologies.

Taking the A356 aluminum alloy product as an example, a multidimensional analysis of ecological design was conducted from the perspectives of resource and energy consumption, carbon emissions, product performance, and recycling of secondary resources in a manufacturing process. The results provide a scientific basis and solutions for integrating low-carbon technologies into basic material processes, design, and production of zero-carbon products. Compared with the PA, both PATA and RA had substantially reduced resource consumption and carbon emissions. The application of carbon reduction technology reduced the RCI of the 1-t A356 aluminum alloy by 13.37%, and the RCI of RA was only 13.22% of that of PA. In terms of carbon emissions, the application of carbon reduction technology reduced carbon emissions by 14.94%. The carbon emissions of RA were only 10.86% of that of PA, and the carbon emissions of RA were 5.54% of that of PA production. The performance of the RA alloy was inferior to that of the PA alloy. A comprehensive characterization matrix model was constructed using three indicators. Under these four requirements, the comprehensive performance of RA was better than those of PATA and PA.

Acknowledgments

This study was supported by the National Key Research and Development Programs (2021YFB3704201 and 2021YFB3700902).

Compliance with ethics guidelines

Mingyang Li, Feng Gao, Zuoren Nie, Boxue Sun, Yu Liu, and Xianzheng Gong declare that they have no conflict of interest or financial conflicts to disclose.

Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.eng.2023.08.025.

References

Publishing order | Descend order by publishing year | Descend order by cited within

[1]	Mitchell D, Allen MR, Hall JW, Muller B, Rajamani L, Le Quéré C. The myriad challenges of the Paris Agreement. Philos Trans R Soc A 2018; 376 (2119):20180066.

[2]	Gao Z, editor. Boao forum for Asia—sustainable development:Asia and the world annual report 2022—Asia in action towards green transition. Beijing: University of International Business and Economics Press; 2022.

[3]	Li M, Gao F, Sun B, Liu Y, Gong X, Nie Z. Zero carbon-emission technology route construction and multifactor analysis of aluminum production in China. J Clean Prod 2022; 370:133535.

[4]	International Energy Agency (IEA). Energy statistics data browser. Report. Paris: IEA; 2023.

[5]	Department of Energy Statistics of the National Bureau of Statistics.China energy statistical yearbook 2013. Report. Beijing: China Statistics Press; 2013. Chinese.

[6]	Pistorius PC. Steelmaking decarbonization options with current technology. Metall Mater Trans B 2022; 53(3):1335-8.

[7]	Wang L, Jin Y, Nie Y, Li R. Recycling of municipal solid waste incineration fly ash for ordinary Portland cement production: a real-scale test. Resour Conserv Recycl 2010; 54(12):1428-35.

[8]	Tan Q. Co-benefit analysis of greenhouse gases mitigation in China major industrial sectors [dissertation]. Bejing: Tsinghua University. 2015. Chinese.

[9]	Wang Y, Cui S, Liu Y, Sun B, Gong X, Gao F. Evaluation and comparison of exergy-based resource consumption for fly ash utilisation in a cement clinker production system and landfill disposal. Int J Exergy 2021; 36(2-4):414-29.

[10]	Mathioudakis D, Karageorgis P, Papadopoulou K, Lyberatos G. LCA and LCC of dried and shredded food waste as an alternative fuel for the cement industry. Waste Manag Res 2021; 39(10):1264-9.

[11]	Rahman A, Rasul MG, Khan MMK, Sharma S. Recent development on the uses of alternative fuels in cement manufacturing process. Fuel 2015; 145:84-99.

[12]	Meng X, Chen M, Gu A, Wu X, Liu B, Zhou J, et al. China’s hydrogen development strategy in the context of double carbon targets. Nat Gas Ind B 2022; 9(6):521-47.

[13]	Li Z, Zhang W, Zhang R, Sun H. Development of renewable energy multi-energy complementary hydrogen energy system (a case study in China): a review. Energy Explor Exploit 2020; 38(6):2099-127.

[14]	Chen H, Wang Y, An L, Xu G, Zhu X, Liu W, et al. Performance evaluation of a novel design for the waste heat recovery of a cement plant incorporating a coal-fired power plant. Energy 2022; 246:123420.

[15]	Christodoulides P, Agathokleous R, Aresti L, Kalogirou SA, Tassou SA, Florides GA. Waste heat recovery technologies revisited with emphasis on new solutions, including heat pipes, and case studies. Energies 2022; 15(1):384.

[16]	Alex TC, Mucsi G, Venugopalan T, Kumar S. BOF steel slag: critical assessment and integrated approach for utilization. J Sustain Metall 2021; 7(4):1407-24.

[17]	Yang X, Gao Y, Zhu M, Springer C. Assessing methane emissions from the natural gas industry:reviewing the case of China in a comparative framework. Curr Clim Change Rep 2022; 8(4):115-24.

[18]	Hartmann S, Koval’ L, Škrobánková H, Matýsek D, Winter F, Purgar A. Possibilities of municipal solid waste incinerator fly ash utilisation. Waste Manag Res 2015; 33(8):740-7.

[19]	Lu X, Zhang Z, Hiraki T, Takeda O, Zhu H, Matsubae K, et al. A solid-state electrolysis process for upcycling aluminium scrap. Nature 2022; 606 (7914):511-5.

[20]	Gao J, Qi Y, Yan D, Wang F, Xu HC. Development path and key technical problems of low carbon ironmaking in China. China Metall 2021; 31:64-72. Chinese.

[21]	Luo J, Duan N, Xu F, Jiang L, Zhang C, Ye W. System-level analysis of the generation and distribution for Pb, Cu, and Ag in the process network of zinc hydrometallurgy: implications for sustainability. J Clean Prod 2019; 234:755-66.

[22]	Shen L, Zhao H, Qiu G. Review of low-carbon bio-hydrometallurgical technology. J China Univ Min Technol 2022; 51:419-33. Chinese.

[23]	Zhang P, Guo Q, Wei G, Meng L, Han L, Qu J, et al. Leaching metals from saprolitic laterite ore using a ferric chloride solution. J Clean Prod 2016; 112:3531-9.

[24]	Jia Y, Tan Q, Sun H, Zhang Y, Gao H, Ruan R. Sulfide mineral dissolution microbes: community structure and function in industrial bioleaching heaps. Green Energy Environ 2019; 4(1):29-37.

[25]	Mezquita A, Monfort E, Ferrer S, Gabaldón-Estevan D. How to reduce energy and water consumption in the preparation of raw materials for ceramic tile manufacturing: dry versus wet route. J Clean Prod 2017; 168:1566-70.

[26]	Wang Y, Liu Y, Cui S, Sun B, Gong X, Gao F, et al. Comparative life cycle assessment of different fuel scenarios and milling technologies for ceramic tile production: a case study in China. J Clean Prod 2020; 273:122846.

[27]	Ding N, Gao F, Wang ZH, Gong XZ. Comparative analysis of primary aluminum and recycled aluminum on energy consumption and greenhouse gas emission. Chin J Nonferrous Met 2012; 22:2908-15. Chinese.

[28]	Helbig C, Huether J, Joachimsthaler C, Lehmann C, Raatz S, Thorenz A, et al. A terminology for downcycling. J Ind Ecol 2022; 26(4):1164-74.

[29]

Lee DE, Melkanoff MA. Issues in product life cycle engineering analysis. In: Proceedings of the 19th Design Automation Conference: Volume 1— Mechanical System Dynamics; Concurrent and Robust Design; Design for Assembly and Manufacture; Genetic Algorithms in Design and Structural Optimization; 1993 Sep 19-22; Albuquerque, NM, USA. New York: ASME; 1993. p. 75-86.

[30]	ISO/TR 14062:2002: Environmental management—integrating environmental aspects into product design and development. ISO standard. Geneva: ISO; 2002.

[31]

General Administration of Quality Supervision, Inspection and Quarantine of the People’s Republic of China, Standardization Administration of the People’s Republic of China. GB/T 24256-2009: General principle and requirements of eco-design for products. Chinese standard. Beijing: Standardization Administration of the People’s Republic of China; 2009. Chinese.

[32]	Kellens K, Jeswiet J. Life Cycle Engineering. In: Chatti S, Laperrière L, Reinhart G, Tolio T, editors. CIRP encyclopedia of production engineering. Berlin: Springer; 2019. p. 1502-3.

[33]	Cooper JS, Vigon BW. Life cycle engineering guidelines. Report. Cincinnati: US Environmental Protection Agency. 2001.

[34]	A. James Clark School of Engineering. College park:center for advanced life cycle engineering. Report. University of Maryland. College Park: University of Maryland. 2023.

[35]	Oh H, Han B, McCluskey P, Han C, Youn BD. Physics-of-failure, condition monitoring, and prognostics of insulated gate bipolar transistor modules: a review. IEEE Trans Power Electron 2015; 30(5):2413-26.

[36]	Buzuku S, Farfan J, Kässi T, Kraslawski A. Analysis and ranking of drivers for eco-design implementation in the finnish pulp and paper industry. Procedia Manuf 2018; 17:1049-57.

[37]	Princˆaud M, Cornier A, Froélich D. Developing a tool for environmental impact assessment and eco-design for ships. Proc Inst Mech Eng Part M 2010; 224 (3):207-24.

[38]	Li GF, Kong JY, Jiang GZ, Zhao G, Xie LX. Green design and manufacturing in electromechanical product. Appl Mech Mater 2010;29-32:985-90.

[39]	Bianco I, Thiébat F, Carbonaro C, Pagliolico S, Blengini GA, Comino E. Life cycle assessment (LCA)-based tools for the eco-design of wooden furniture. J Clean Prod 2021; 324:129249.

[40]	Navajas A, Uriarte L, Gandía L. Application of eco-design and life cycle assessment standards for environmental impact reduction of an industrial product. Sustainability 2017; 9(10):1724.

[41]	Yu WD, Cheng ST, Miao CM, Perng GY. Green innovation of green roof technology—a case study. Mater Sci Eng Technol 2017; 48(5):420-9.

[42]	Ramani K, Ramanujan D, Bernstein WZ, Zhao F, Sutherland J, Handwerker C, et al. Integrated sustainable life cycle design: a review. J Mech Des 2010; 132 (9):091004.

[43]	Kamalakkannan S, Kulatunga AK. Optimization of eco-design decisions using a parametric life cycle assessment. Sustain Prod Consum 2021; 27:1297-316.

[44]	Belucio M, Rodrigues C, Antunes CH, Freire F, Dias LC. Eco-efficiency in early design decisions: a multimethodology approach. J Clean Prod 2021; 283:124630.

[45]	Borge-Diez D, Ortega-Cabezas PM, Colmenar-Santos A, Blanes-Peiró JJ. Contribution of driving efficiency and vehicle-to-grid to eco-design. Energies 2020; 13(15):3997.

[46]	Ratner S, Gomonov K, Revinova S, Lazanyuk I. Eco-design of energy production systems: the problem of renewable energy capacity recycling. Appl Sci 2020; 10(12):4339.

[47]	Nie ZR. Exergy-based resource depletion analysis in materials life cycle assessment. Beijing: Science Press; 2021.

[48]

General Administration of Quality Supervision, Inspection and Quarantine of the People’s Republic of China, Standardization Administration of the People’s Republic of China.GB/T 1173-2013: Casting aluminum alloy. Chinese standard. Beijing: Standardization Administration of the People’s Republic of China; 2013. Chinese.

[49]	China Nonferrous Metals Industry Association.The yearbook of nonferrous metals industry of China (2020). Report. Beijing: China Nonferrous Metals Industry Association; 2021. Chinese.

[50]	CNMLCA. Material life cycle assessment databased-sinocenter. Report. Beijing University of technology; 2022.

[51]	National Development and Reform Commission. Greenhouse gas emissions accounting methodology and reporting guidelines for Chinese electrolytic aluminum producers (for trial implementation). Report. Beijing: National Development and Reform Commission; 2013. Chinese.

[52]	Ministry of Ecology and Environment of the People’s Republic of China. 2019 Annual emission reduction project China regional grid baseline emission factors. Report. Beijing: Ministry of Ecology and Environment of the People’s Republic of China; 2020. Chinese.

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