Life Cycle Assessment of Environmental Emissions and Scenario Simulation of an Automotive Power Seat Considering Scrap Recycling

Abstract

As automobiles become more motorized and intelligent, multifunctional power seats have become an important part of the modern vehicle. Related environmental performance and green level throughout the entire life cycle of the vehicle, from design and manufacture to scrap recovery, have attracted much attention. Based on a vehicle life cycle assessment model established previously, this study proposed a mathematical model for evaluating the full life cycle of automotive power seat environmental emissions, considering scrap recycling. Environmental emissions throughout the full life cycle of a typical vehicle power seat in China were assessed, and two key factors with great uncertainty, material regeneration rate and motor efficiency, were selected for sensitivity analysis. By combining evaluation and sensitivity analysis results, both static and dynamic scenario simulations, which is a novel method, were carried out to investigate the electromotive process of the power seat. Results showed various emissions from the power seat during the usage phase were the greatest of its life cycle. Specifically, CO₂ emission was the largest, and steel regeneration rate and fuel efficiency were the most sensitive to the environmental impact equivalent. In addition, when the material regeneration rate and motor efficiency were increased to the same degree, the power seat with more motors exhibited less effect on the environment. Accordingly, the power seat with a higher electromotive degree was more sensitive to the rate of material regeneration and motor efficiency. This article provides quantitative reference for green design of the power seat.

Introduction

In recent years, climate change has been a serious problem, mainly caused by the transportation sector according to the Carbon Dioxide Information Analysis Center (Li et al., 2013; Liao and Deng, 2018). Over past decades, the manufacturing industry in China has been experiencing a boom; for example, the production and sales of automobiles in China reached a volume of about 23 million in 2014, ranking first in the world and making China the largest energy consumer in the world (Li et al., 2013; Deng et al., 2015). Many studies have shown that alternative fuels and advanced vehicle component technologies are being promoted to help solve air pollution problems, reduce greenhouse gas emissions, and reduce China's dependence on imported oil (Shen et al., 2012; Hernandez et al., 2015; Palencia et al., 2016). Many scholars have researched manufacturing systems, especially of automotive components, and concluded that a large portion of carbon emissions stems from materials and energies consumed in manufacturing processes (Liao et al., 2018). Therefore, analysis of the manufacturing process of automobile parts in depth is urgently needed.

Power seats have now become an indispensable and important component in modern automobiles, and must meet both comfort and safety requirements (Wang et al., 2006; Ge et al., 2009; Sun and Kong, 2014). Power seats have several motors driven by a control system that enables multidirectional movements, including gliding, tilting, and raising (Liu et al., 2010; Jin et al., 2013). Advanced power seats also possess position memory storage and rearview mirror adjustment functions (Nie et al., 2007). Due to their weight and motor operation, power seats inevitably result in corresponding fuel consumption and environmental emissions, which cannot be ignored. To build a greener automobile industry chain, power seats face enormous challenges in terms of saving energy and reducing emissions. Only through life cycle assessment (LCA) can power seat emissions be effectively evaluated to explore ways in which to reduce their environmental impact.

To date, many Chinese and foreign scholars have gained much insight by assessing the life cycle environmental impact of automobile components, including their design, manufacture/production, and service life. Such studies have investigated use of different metals (e.g., magnesium, aluminum, steel, and scrap) for parts (Tharumarajah and Koltun, 2007; Cecchel et al., 2016, 2018a, 2018b), grapheme versus carbon tires (Lin et al., 2016), and solar hub motors (Reinhardt et al., 2017; Yi and Tang, 2018), established scalable life cycle inventory models of a permanent magnet electrical machine (Nordelof et al., 2018), developed multimedia solutions for sustainable chassis design (Simoes et al., 2016), and compared different light-weight powertrain designs (Peng et al., 2015; Luk et al., 2017). Moreover, cradle-to-grave LCA of exhaust values have been used to develop cleaner production scenarios to reduce enviro burden and improve manufacturing eco-efficiency (Silva et al., 2018). Chinese scholars, in particular, have also reported on other important automotive life cycle issues, such as end-of-life remanufacturing (Deng et al., 2017; Liao et al., 2017, 2018), the importance of adopting advanced technologies and equipment measures for cleaner production (Li et al., 2015), the life cycle impact of different automotive components accounting for scrapping and recycling (Zhao et al., 2016), and the importance of optimizing the preparation process and composition of existing raw materials as well as improving structural design of auxiliary instrument panels to effectively reduce the environmental impact (Zhang et al., 2015). Furthermore, Tu et al. (2013a, 2013b) developed a valuable life cycle material-energy-pollution evaluation model of automobiles.

Although LCA research in general has proposed an LCA framework suitable for automotive components, literature using in-depth scenario LCA is lacking. Moreover, literature on static and dynamic scenario analysis of vehicle components, such as power seats, to explore the environmental effects of the electrification process is rare. Therefore, this study established a model for evaluating the full life cycle emission of a typical automotive power seat in China for empirical analysis. Since the energy consumption of the entire life cycle has been analyzed in detail previously, this study reviewed and cited related data on energy consumption and resource depletion for environmental impact assessment (Chen et al., 2014). These results will serve as a valuable reference for green design, manufacture, and improvement of automotive power seats.

System Boundary and Functional Unit

The environmental impact assessment model was established for calculating major emissions and pollutants at all life cycle stages of automotive power seats. By analyzing the generation process of power seats, this study divided all emissions and pollutants into two categories: gas emissions [e.g., CH4, SO_x, NO_x, CO, CO₂, particulate matter (PM), and nonmethane volatile organic compounds (NMVOCs)] and toxic/harmful substances (e.g., chemical oxygen demand, fly ash, industrial wastes, and slag). The system boundary (Fig. 1) was restricted to seven phases, including material production, material transformation, seat components manufacturing and assembly, operation and use, logistics process, and disposal.

FIG. 1.

System boundary of power seat.

Assuming the power seat is not replaced during the entire vehicle usage period, the functional unit was that power seats are normally used in vehicles for 15 years.

Inventory Analysis

Calculation model of gas emissions

First, to obtain a gas emissions intensity matrix, the intensity matrix of direct gas emissions corresponding to energy consumption, denoted \documentclass{aastex}\usepackage{amsbsy}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{bm}\usepackage{mathrsfs}\usepackage{pifont}\usepackage{stmaryrd}\usepackage{textcomp}\usepackage{portland, xspace}\usepackage{amsmath, amsxtra}\usepackage{upgreek}\pagestyle{empty}\DeclareMathSizes{10}{9}{7}{6}\begin{document} $${P_{00}}$$ \end{document} , was constructed. Then, since gas emissions induced by fuel combustion also exist upstream of energy production, an intensity matrix of indirect gas emissions in energy production, denoted \documentclass{aastex}\usepackage{amsbsy}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{bm}\usepackage{mathrsfs}\usepackage{pifont}\usepackage{stmaryrd}\usepackage{textcomp}\usepackage{portland, xspace}\usepackage{amsmath, amsxtra}\usepackage{upgreek}\pagestyle{empty}\DeclareMathSizes{10}{9}{7}{6}\begin{document} $${P_{01}}$$ \end{document} , was constructed. Finally, by superposing both direct and indirect emission intensity matrices, an intensity matrix of gas emissions, denoted P₀, was written as follows: \documentclass{aastex}\usepackage{amsbsy}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{bm}\usepackage{mathrsfs}\usepackage{pifont}\usepackage{stmaryrd}\usepackage{textcomp}\usepackage{portland, xspace}\usepackage{amsmath, amsxtra}\usepackage{upgreek}\pagestyle{empty}\DeclareMathSizes{10}{9}{7}{6}\begin{document} \begin{align*} {P_0} = {P_{00}} + {P_{01}} = { \left( {{P_{{{00}_{ij}}}}} \right) _{r \times d}} + { \left( {{P_{{{01}_{ij}}}}} \right) _{r \times d}} = { \left( {{P_{{0_{ij}}}}} \right) _{r \times d}} , \tag{1} \end{align*} \end{document}

where \documentclass{aastex}\usepackage{amsbsy}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{bm}\usepackage{mathrsfs}\usepackage{pifont}\usepackage{stmaryrd}\usepackage{textcomp}\usepackage{portland, xspace}\usepackage{amsmath, amsxtra}\usepackage{upgreek}\pagestyle{empty}\DeclareMathSizes{10}{9}{7}{6}\begin{document} $${P_{{0_{ij}}}}$$ \end{document} denotes the amount (kg) of j-th gas emission per unit of i-th energy consumed (kg/MJ), r represents the number of different energy types, d denotes the number of gas emission types, \documentclass{aastex}\usepackage{amsbsy}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{bm}\usepackage{mathrsfs}\usepackage{pifont}\usepackage{stmaryrd}\usepackage{textcomp}\usepackage{portland, xspace}\usepackage{amsmath, amsxtra}\usepackage{upgreek}\pagestyle{empty}\DeclareMathSizes{10}{9}{7}{6}\begin{document} $${P_{{{00}_{ij}}}}$$ \end{document} denotes the amount (kg) of j-th gas emission per unit of i-th energy burned (kg/MJ; also referred to as direct emission), and \documentclass{aastex}\usepackage{amsbsy}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{bm}\usepackage{mathrsfs}\usepackage{pifont}\usepackage{stmaryrd}\usepackage{textcomp}\usepackage{portland, xspace}\usepackage{amsmath, amsxtra}\usepackage{upgreek}\pagestyle{empty}\DeclareMathSizes{10}{9}{7}{6}\begin{document} $${P_{{{01}_{ij}}}}$$ \end{document} denotes the amount (kg) of j-th gas emission per unit of i-th energy generated (kg/MJ; also referred to as indirect emission).

By taking the gas emission intensity matrix as the basic unit in calculating the gas emissions at all stages of the life cycle, the gas emissions from various processes, including raw material production, material transformation (included in LCA database), seat components manufacturing, seat assembly, operation and use, disposal, and logistics processes were calculated.

1. The matrix of gas emissions in the raw material production phase. According to the analysis of the material base database from Beijing University of Technology (Beijing, China) and Sichuan University (Chengdu, China) (Di, 2005; Di et al., 2007; Gao, 2008; Liu et al., 2010; Wang et al., 2011; Huang et al., 2012), the matrix of gas emissions from raw material production was integrated according to the corresponding matrix of gas emissions from energy consumption. The matrix of gas emissions results can be acquired by inputting the quality of raw materials. Therefore, the matrix of gas emission during the raw material production phase (PT) can be calculated as follows: \documentclass{aastex}\usepackage{amsbsy}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{bm}\usepackage{mathrsfs}\usepackage{pifont}\usepackage{stmaryrd}\usepackage{textcomp}\usepackage{portland, xspace}\usepackage{amsmath, amsxtra}\usepackage{upgreek}\pagestyle{empty}\DeclareMathSizes{10}{9}{7}{6}\begin{document} \begin{align*} PT = M \cdot { \left( {{p_{{1_{ij}}}}} \right) _{n \times d}} + M \cdot {E_2} \cdot { \left( {{p_{{0_{ij}}}}} \right) _{r \times d}} = M \cdot {P_1} + M \cdot {E_2} \cdot {P_0} , \tag{2} \end{align*} \end{document}

where P₁ denotes the gas emission intensity matrix of raw materials in the production stage, M denotes the mass matrix of the power seat, \documentclass{aastex}\usepackage{amsbsy}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{bm}\usepackage{mathrsfs}\usepackage{pifont}\usepackage{stmaryrd}\usepackage{textcomp}\usepackage{portland, xspace}\usepackage{amsmath, amsxtra}\usepackage{upgreek}\pagestyle{empty}\DeclareMathSizes{10}{9}{7}{6}\begin{document} $$\;{E_2}$$ \end{document} denotes the energy consumption intensity matrix of seat materials in the processing stage, and \documentclass{aastex}\usepackage{amsbsy}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{bm}\usepackage{mathrsfs}\usepackage{pifont}\usepackage{stmaryrd}\usepackage{textcomp}\usepackage{portland, xspace}\usepackage{amsmath, amsxtra}\usepackage{upgreek}\pagestyle{empty}\DeclareMathSizes{10}{9}{7}{6}\begin{document} $${p_{{1_{ij}}}}$$ \end{document} denotes the amount of the j-th gas emission per unit mass of i-th material produced (kg/kg). The detailed algorithm was described previously (Chen et al., 2014).

2. The matrix of gas emissions in the seat components manufacturing phase. Using a direct algorithm for energy consumption calculation, the matrix of gas emissions in the seat components manufacturing phase (PW) were calculated as: \documentclass{aastex}\usepackage{amsbsy}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{bm}\usepackage{mathrsfs}\usepackage{pifont}\usepackage{stmaryrd}\usepackage{textcomp}\usepackage{portland, xspace}\usepackage{amsmath, amsxtra}\usepackage{upgreek}\pagestyle{empty}\DeclareMathSizes{10}{9}{7}{6}\begin{document} \begin{align*} PW = E{W_1} \cdot {P_0} , \tag{3} \end{align*} \end{document}

Using an indirect algorithm for energy consumption calculation, the matrix of gas emissions in the seat components manufacturing phase can be calculated as follows: \documentclass{aastex}\usepackage{amsbsy}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{bm}\usepackage{mathrsfs}\usepackage{pifont}\usepackage{stmaryrd}\usepackage{textcomp}\usepackage{portland, xspace}\usepackage{amsmath, amsxtra}\usepackage{upgreek}\pagestyle{empty}\DeclareMathSizes{10}{9}{7}{6}\begin{document} \begin{align*} PW = e{w_2} \cdot { \left( {{p_{{0_{uj}}}}} \right) _{1 \times d}} = e{w_2} \cdot \left( {{p_{{0_{11}} , \; \; \;}}{p_{{0_{12}} , \; \; \;}}{p_{{0_{13}} , \; \;}} \cdots {p_{{0_{1j}} , \; \;}} \cdots {p_{{0_{1d}} \; \;}}} \right) , \tag{4} \end{align*} \end{document}

3. The matrix of gas emissions in the seat assembly phase. Similar to seat components manufacturing stage, the matrix of gas emissions in the seat assembly stage (PA) can be expressed as follows: \documentclass{aastex}\usepackage{amsbsy}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{bm}\usepackage{mathrsfs}\usepackage{pifont}\usepackage{stmaryrd}\usepackage{textcomp}\usepackage{portland, xspace}\usepackage{amsmath, amsxtra}\usepackage{upgreek}\pagestyle{empty}\DeclareMathSizes{10}{9}{7}{6}\begin{document} \begin{align*} PA = E{A_1} \cdot {P_0} , \tag{5} \end{align*} \end{document}

4. The matrix of gas emissions in operation and use phase. The matrix of gas emission in operation and use phase can be calculated in accordance with fuel consumption as described in a previous study (Chen et al., 2014): \documentclass{aastex}\usepackage{amsbsy}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{bm}\usepackage{mathrsfs}\usepackage{pifont}\usepackage{stmaryrd}\usepackage{textcomp}\usepackage{portland, xspace}\usepackage{amsmath, amsxtra}\usepackage{upgreek}\pagestyle{empty}\DeclareMathSizes{10}{9}{7}{6}\begin{document} \begin{align*} P{U_{vj}} = f* \left( {{p_{{{00}_{11}}}}{p_{{{00}_{12}}}} \cdots {p_{{{00}_{1j}}}} \cdots {p_{{{00}_{1d}}}}} \right) \times { \left\{ { \begin{matrix} {{a_{1d}}} \\ {{a_{2d}}} \\ \vdots \\ {{a_{vd}}} \\ \vdots \\ {{a_{ld}}} \\ \end{matrix} } \right\} ^T} \\+ f* \left( {{p_{{{01}_{11}}}}{p_{{{01}_{12}}}} \cdots {p_{{{01}_{1j}}}} \cdots {p_{{{01}_{1d}}}}} \right) , \tag{6} \end{align*} \end{document}

where f represents total fuel consumption in the vehicle usage stage (detailed algorithm described previously) (Chen et al., 2014), and \documentclass{aastex}\usepackage{amsbsy}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{bm}\usepackage{mathrsfs}\usepackage{pifont}\usepackage{stmaryrd}\usepackage{textcomp}\usepackage{portland, xspace}\usepackage{amsmath, amsxtra}\usepackage{upgreek}\pagestyle{empty}\DeclareMathSizes{10}{9}{7}{6}\begin{document} $$P{U_{vj}}$$ \end{document} represents all kinds of gas emissions when the v-th fuel was burned during vehicle use. Since fuel cannot be completely burned during vehicle use, various emissions from different vehicles are completely different under different conditions. By introducing the emission correction matrix \documentclass{aastex}\usepackage{amsbsy}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{bm}\usepackage{mathrsfs}\usepackage{pifont}\usepackage{stmaryrd}\usepackage{textcomp}\usepackage{portland, xspace}\usepackage{amsmath, amsxtra}\usepackage{upgreek}\pagestyle{empty}\DeclareMathSizes{10}{9}{7}{6}\begin{document} $${ \left( {{ \alpha _{ij}}} \right) _{l \times d}}$$ \end{document} of fuel combustion, whose values can be flexibly adjusted according to the combustion efficiency of the real vehicle engine, the direct emissions of unit mass fuel consumption can be acquired by the product obtained by cross multiplication between direct emission matrix and correction matrix, while the indirect emissions of unit mass fuel consumption can be acquired by multiplying fuel consumption by the indirect emission intensity matrix of energy production. Superposition of both direct and indirect gas emissions is the total gas emissions in the operation and use stage.

5. The matrix of gas emissions in the disposal phase. The matrix of gas emissions from the disposal process (PR) can be calculated according to the matrix of energy consumption in this stage. The present study divided the disposal phase into five processes: dismantling, remanufacturing, reusing, burning, and burying. Energy consumption in four of these processes (remanufacturing, reusing, burning, and burying) have both payout and payoff. To carry out the above four processes, a lot of energy consumption must first be invested, which is called positive benefits. However, from the perspective of resource exhaustion, much energy consumption can be saved in the raw materials production and seat components manufacturing phases after remanufacturing, reusing, and recycling, which is called environmental benefits (negative value for the indicator). The formula for calculating PR is as follows: \documentclass{aastex}\usepackage{amsbsy}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{bm}\usepackage{mathrsfs}\usepackage{pifont}\usepackage{stmaryrd}\usepackage{textcomp}\usepackage{portland, xspace}\usepackage{amsmath, amsxtra}\usepackage{upgreek}\pagestyle{empty}\DeclareMathSizes{10}{9}{7}{6}\begin{document} \begin{align*} PR = \left[ { \mathop \sum \limits_{i = 1}^4 \left( {ER_{2i}^ + + ER_{2i}^ - } \right) + E{R_{11}}} \right] \cdot {P_0} , \tag{7} \end{align*} \end{document}

where \documentclass{aastex}\usepackage{amsbsy}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{bm}\usepackage{mathrsfs}\usepackage{pifont}\usepackage{stmaryrd}\usepackage{textcomp}\usepackage{portland, xspace}\usepackage{amsmath, amsxtra}\usepackage{upgreek}\pagestyle{empty}\DeclareMathSizes{10}{9}{7}{6}\begin{document} $$E{R_{11}}$$ \end{document} denotes the energy consumption matrix for the dismantling process without upstream energy, and \documentclass{aastex}\usepackage{amsbsy}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{bm}\usepackage{mathrsfs}\usepackage{pifont}\usepackage{stmaryrd}\usepackage{textcomp}\usepackage{portland, xspace}\usepackage{amsmath, amsxtra}\usepackage{upgreek}\pagestyle{empty}\DeclareMathSizes{10}{9}{7}{6}\begin{document} $$ER_{2i}^ + $$ \end{document} and \documentclass{aastex}\usepackage{amsbsy}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{bm}\usepackage{mathrsfs}\usepackage{pifont}\usepackage{stmaryrd}\usepackage{textcomp}\usepackage{portland, xspace}\usepackage{amsmath, amsxtra}\usepackage{upgreek}\pagestyle{empty}\DeclareMathSizes{10}{9}{7}{6}\begin{document} $$ER_{2i}^ - $$ \end{document} denote the positive benefits of energy consumption in the four processes of seat scrap recovery and the environmental benefits (negative value for the indicator) of energy conservation in the four processes of seat scrap recovery, respectively. The related algorithm was previously described in detail (Chen et al., 2014).

6. The matrix of gas emissions in the logistics process. The matrix of gas emissions in the logistics process (PD) was calculated according to the matrix of energy consumption in this process: \documentclass{aastex}\usepackage{amsbsy}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{bm}\usepackage{mathrsfs}\usepackage{pifont}\usepackage{stmaryrd}\usepackage{textcomp}\usepackage{portland, xspace}\usepackage{amsmath, amsxtra}\usepackage{upgreek}\pagestyle{empty}\DeclareMathSizes{10}{9}{7}{6}\begin{document} \begin{align*} PD = \left[ {{{ \left( { \mathop \sum \limits_{j = 1}^n {m_{1j}} , \mathop \sum \limits_{j = 1}^n {m_{2j}} , \cdots \mathop \sum \limits_{j = 1}^n {m_{ij}} , \cdots \mathop \sum \limits_{j = 1}^n {m_{kj}}} \right) }^T} \cdot D \cdot {E_3} + E{D_{31}}} \right] \cdot {P_0} , \tag{8} \end{align*} \end{document}

where D denotes the transport distance matrix, E₃ denotes the energy intensity matrix of logistics transportation, \documentclass{aastex}\usepackage{amsbsy}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{bm}\usepackage{mathrsfs}\usepackage{pifont}\usepackage{stmaryrd}\usepackage{textcomp}\usepackage{portland, xspace}\usepackage{amsmath, amsxtra}\usepackage{upgreek}\pagestyle{empty}\DeclareMathSizes{10}{9}{7}{6}\begin{document} $$\mathop \sum _{j = 1}^n {m_{ij}}$$ \end{document} denotes the quality of the i-th component transported using the j-th transport mode, and \documentclass{aastex}\usepackage{amsbsy}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{bm}\usepackage{mathrsfs}\usepackage{pifont}\usepackage{stmaryrd}\usepackage{textcomp}\usepackage{portland, xspace}\usepackage{amsmath, amsxtra}\usepackage{upgreek}\pagestyle{empty}\DeclareMathSizes{10}{9}{7}{6}\begin{document} $$E{D_{31}}$$ \end{document} denotes the matrix of energy consumption in the reverse logistics process.

Calculation model of toxic/harmful substance emissions

In addition to gas emissions produced by energy consumption, some solid and liquid toxic/harmful substances are also emitted during raw materials production, energy production, seat components manufacturing, seat assembly, and disposal. Toxic/harmful substances in the raw materials and energy production stages have been included in the material base database of Beijing University of Technology and Sichuan University (Di, 2005; Yuan, 2006; Di et al., 2007; Gao, 2008; Liu et al., 2010; Wang et al., 2011; Huang et al., 2012). First, the intensity matrix of toxic/harmful substances in raw materials production, denoted P₂, was constructed. Then, the matrix of toxic/harmful substances during raw materials production, denoted PHT, was written as follows: \documentclass{aastex}\usepackage{amsbsy}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{bm}\usepackage{mathrsfs}\usepackage{pifont}\usepackage{stmaryrd}\usepackage{textcomp}\usepackage{portland, xspace}\usepackage{amsmath, amsxtra}\usepackage{upgreek}\pagestyle{empty}\DeclareMathSizes{10}{9}{7}{6}\begin{document} \begin{align*} PHT = M \cdot { \left( {{p_{{2_{ij}}}}} \right) _{n \times e}} = M \cdot {P_2} , \tag{9} \end{align*} \end{document}

To calculate the volume of toxic/harmful substances in energy production (PHE), the intensity matrix (P₃) of the toxic/harmful substances in energy production must first be constructed. Then, total emissions of toxic/harmful substances were equivalent to dot-multiply between energy consumption in various stages of the life cycle and P₃ as follows: \documentclass{aastex}\usepackage{amsbsy}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{bm}\usepackage{mathrsfs}\usepackage{pifont}\usepackage{stmaryrd}\usepackage{textcomp}\usepackage{portland, xspace}\usepackage{amsmath, amsxtra}\usepackage{upgreek}\pagestyle{empty}\DeclareMathSizes{10}{9}{7}{6}\begin{document} \begin{align*} PHE = \mathop \sum \limits_{i = 1}^6 {E_i} \cdot { \left( {{p_{{3_{ij}}}}} \right) _{r \times e}} = \mathop \sum \limits_{i = 1}^6 {E_i} \cdot {P_3} , \tag{10} \end{align*} \end{document}

where E_i denotes the sum of various energy sources in all stages of the entire life cycle and is a \documentclass{aastex}\usepackage{amsbsy}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{bm}\usepackage{mathrsfs}\usepackage{pifont}\usepackage{stmaryrd}\usepackage{textcomp}\usepackage{portland, xspace}\usepackage{amsmath, amsxtra}\usepackage{upgreek}\pagestyle{empty}\DeclareMathSizes{10}{9}{7}{6}\begin{document} $$k \times r$$ \end{document} order matrix, i represents the six stages of the life cycle (i = 1, 2,…,6), and \documentclass{aastex}\usepackage{amsbsy}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{bm}\usepackage{mathrsfs}\usepackage{pifont}\usepackage{stmaryrd}\usepackage{textcomp}\usepackage{portland, xspace}\usepackage{amsmath, amsxtra}\usepackage{upgreek}\pagestyle{empty}\DeclareMathSizes{10}{9}{7}{6}\begin{document} $${p_{{3_{ij}}}}$$ \end{document} represents the amount of j-th toxic/harmful substance per unit of i-th energy produced (kg/MJ).

Other toxic/harmful substances in the other stages of the life cycle (PHS) can be calculated as follows: \documentclass{aastex}\usepackage{amsbsy}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{bm}\usepackage{mathrsfs}\usepackage{pifont}\usepackage{stmaryrd}\usepackage{textcomp}\usepackage{portland, xspace}\usepackage{amsmath, amsxtra}\usepackage{upgreek}\pagestyle{empty}\DeclareMathSizes{10}{9}{7}{6}\begin{document} \begin{align*} PHS = { \left[ {{{ \left( {ph{s_{ij}}} \right) }_{k \times e}}} \right] _{s = w , a , r , d}} , \tag{11} \end{align*} \end{document}

where s denotes the seat components manufacturing (w), seat assembly (a), disposal (r), and logistics process (d) stages; \documentclass{aastex}\usepackage{amsbsy}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{bm}\usepackage{mathrsfs}\usepackage{pifont}\usepackage{stmaryrd}\usepackage{textcomp}\usepackage{portland, xspace}\usepackage{amsmath, amsxtra}\usepackage{upgreek}\pagestyle{empty}\DeclareMathSizes{10}{9}{7}{6}\begin{document} $$ph{w_{ij}}$$ \end{document} , \documentclass{aastex}\usepackage{amsbsy}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{bm}\usepackage{mathrsfs}\usepackage{pifont}\usepackage{stmaryrd}\usepackage{textcomp}\usepackage{portland, xspace}\usepackage{amsmath, amsxtra}\usepackage{upgreek}\pagestyle{empty}\DeclareMathSizes{10}{9}{7}{6}\begin{document} $$ph{a_{ij}}$$ \end{document} , \documentclass{aastex}\usepackage{amsbsy}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{bm}\usepackage{mathrsfs}\usepackage{pifont}\usepackage{stmaryrd}\usepackage{textcomp}\usepackage{portland, xspace}\usepackage{amsmath, amsxtra}\usepackage{upgreek}\pagestyle{empty}\DeclareMathSizes{10}{9}{7}{6}\begin{document} $$ph{r_{ij}}$$ \end{document} , and \documentclass{aastex}\usepackage{amsbsy}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{bm}\usepackage{mathrsfs}\usepackage{pifont}\usepackage{stmaryrd}\usepackage{textcomp}\usepackage{portland, xspace}\usepackage{amsmath, amsxtra}\usepackage{upgreek}\pagestyle{empty}\DeclareMathSizes{10}{9}{7}{6}\begin{document} $$ph{d_{ij}}$$ \end{document} represent the j-th toxic/harmful substance from the corresponding i-th component in all stages of the whole life cycle.

Calculation model of environmental impact weighted value

Based on the calculation models of gas emissions and toxic/harmful substances from vehicle power seats established above, this study combined advantages of various impact assessment methods, mainly eco-indicator, midpoint, and endpoint destruction, in this life cycle impact assessment, and selected six kinds of environmental impact indices (global warming, acidification, eutrophication, photochemical smog, human health, and solid waste) of auto parts (Yang, 2009; Wang, 2013; Yin et al., 2014). After classification, characterization, and standardization, all kinds of gas emissions and toxic/harmful substances were weighted to environmental impact equivalents based on weight value.

1. Construction of environmental impact equivalent factor matrix. Essentially, calculation of the influence equivalent factor of each kind of gas emission and toxic/harmful substances on each type of environmental impact involves classification and characterization. The global warming equivalent factor matrix, denoted GWP, consists of the following parts: \documentclass{aastex}\usepackage{amsbsy}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{bm}\usepackage{mathrsfs}\usepackage{pifont}\usepackage{stmaryrd}\usepackage{textcomp}\usepackage{portland, xspace}\usepackage{amsmath, amsxtra}\usepackage{upgreek}\pagestyle{empty}\DeclareMathSizes{10}{9}{7}{6}\begin{document} \begin{align*} GWP = { \left( {gw{p_{{1_{ij}}}}} \right) _{d \times 1}} + { \left( {gw{p_{{2_{ij}}}}} \right) _{e \times 1}} = GW{P_1} + GW{P_2} , \tag{12} \end{align*} \end{document}

where \documentclass{aastex}\usepackage{amsbsy}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{bm}\usepackage{mathrsfs}\usepackage{pifont}\usepackage{stmaryrd}\usepackage{textcomp}\usepackage{portland, xspace}\usepackage{amsmath, amsxtra}\usepackage{upgreek}\pagestyle{empty}\DeclareMathSizes{10}{9}{7}{6}\begin{document} $$gw{p_{{1_{ij}}}} $$ \end{document} and \documentclass{aastex}\usepackage{amsbsy}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{bm}\usepackage{mathrsfs}\usepackage{pifont}\usepackage{stmaryrd}\usepackage{textcomp}\usepackage{portland, xspace}\usepackage{amsmath, amsxtra}\usepackage{upgreek}\pagestyle{empty}\DeclareMathSizes{10}{9}{7}{6}\begin{document} $$gw{p_{{2_{ij}}}}\; $$ \end{document} denote the environmental impact factor of the i-th emission and toxic/harmful substance, respectively, on global warming, and \documentclass{aastex}\usepackage{amsbsy}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{bm}\usepackage{mathrsfs}\usepackage{pifont}\usepackage{stmaryrd}\usepackage{textcomp}\usepackage{portland, xspace}\usepackage{amsmath, amsxtra}\usepackage{upgreek}\pagestyle{empty}\DeclareMathSizes{10}{9}{7}{6}\begin{document} $$GW{P_1}$$ \end{document} and \documentclass{aastex}\usepackage{amsbsy}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{bm}\usepackage{mathrsfs}\usepackage{pifont}\usepackage{stmaryrd}\usepackage{textcomp}\usepackage{portland, xspace}\usepackage{amsmath, amsxtra}\usepackage{upgreek}\pagestyle{empty}\DeclareMathSizes{10}{9}{7}{6}\begin{document} $$GW{P_2}$$ \end{document} stand for the global warming equivalent factor matrix of gas emissions and toxic/harmful substances, respectively. For calculation uniformity, the matrix element was set as 0 when a certain emission imposed no effect on global warming, while the dimensions of the matrices (d × 1 and e × 1) were constant. Accordingly, acidification (AP), eutrophication (EP), photochemical smog (POCP), ozone depletion (ODP), human health (HTP), and industrial waste (SW) equivalent factor matrices can be constructed in a similar way.

2. Calculation of environmental impact equivalent. According to the environmental impact equivalent factor matrix and calculation models of gas emissions and toxic/harmful substances, the equivalent effects of the power seat on six kinds of environmental impacts can be calculated. According to the gas emission matrices (PT, PW, PA, PU, PR, and PD) of all stages of the entire life cycle (with the same k × d dimension, in which blank parts were set as 0) and emission matrices (PHT, PHW, PHA, PHU, PHR, and PHD) of toxic/harmful substances (with the same k × e dimension, in which blank parts were set as 0), the global warming equivalent matrix, denoted \documentclass{aastex}\usepackage{amsbsy}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{bm}\usepackage{mathrsfs}\usepackage{pifont}\usepackage{stmaryrd}\usepackage{textcomp}\usepackage{portland, xspace}\usepackage{amsmath, amsxtra}\usepackage{upgreek}\pagestyle{empty}\DeclareMathSizes{10}{9}{7}{6}\begin{document} $$IS{ _{ \left( {GWP} \right) }}$$ \end{document} , can be written as follows: \documentclass{aastex}\usepackage{amsbsy}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{bm}\usepackage{mathrsfs}\usepackage{pifont}\usepackage{stmaryrd}\usepackage{textcomp}\usepackage{portland, xspace}\usepackage{amsmath, amsxtra}\usepackage{upgreek}\pagestyle{empty}\DeclareMathSizes{10}{9}{7}{6}\begin{document} \begin{align*} I{S_{ \left( {GWP} \right) }} = \left( {PT + PW + PA + PU + PR + PD} \right) \cdot GW{P_1} + \left( {PHT + PHW + PHA + PHU + PHR + PHD} \right) \cdot GW{P_2} \tag{13} , \end{align*} \end{document}

where the order of \documentclass{aastex}\usepackage{amsbsy}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{bm}\usepackage{mathrsfs}\usepackage{pifont}\usepackage{stmaryrd}\usepackage{textcomp}\usepackage{portland, xspace}\usepackage{amsmath, amsxtra}\usepackage{upgreek}\pagestyle{empty}\DeclareMathSizes{10}{9}{7}{6}\begin{document} $$IS{ _{ \left( {GWP} \right) }}$$ \end{document} is at the k-th row and 1-th column. The sum of each row represents the global warming equivalent of one component among all power seat components.

Similarly, acidification ( \documentclass{aastex}\usepackage{amsbsy}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{bm}\usepackage{mathrsfs}\usepackage{pifont}\usepackage{stmaryrd}\usepackage{textcomp}\usepackage{portland, xspace}\usepackage{amsmath, amsxtra}\usepackage{upgreek}\pagestyle{empty}\DeclareMathSizes{10}{9}{7}{6}\begin{document} $$I{S_{ \left( {AP} \right) }}$$ \end{document} ), water eutrophication ( \documentclass{aastex}\usepackage{amsbsy}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{bm}\usepackage{mathrsfs}\usepackage{pifont}\usepackage{stmaryrd}\usepackage{textcomp}\usepackage{portland, xspace}\usepackage{amsmath, amsxtra}\usepackage{upgreek}\pagestyle{empty}\DeclareMathSizes{10}{9}{7}{6}\begin{document} $$IS{ _{ \left( {EP} \right) }}$$ \end{document} ), photochemical smog ( \documentclass{aastex}\usepackage{amsbsy}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{bm}\usepackage{mathrsfs}\usepackage{pifont}\usepackage{stmaryrd}\usepackage{textcomp}\usepackage{portland, xspace}\usepackage{amsmath, amsxtra}\usepackage{upgreek}\pagestyle{empty}\DeclareMathSizes{10}{9}{7}{6}\begin{document} $$IS{ _{ \left( {POCP} \right) }}$$ \end{document} ), human health ( \documentclass{aastex}\usepackage{amsbsy}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{bm}\usepackage{mathrsfs}\usepackage{pifont}\usepackage{stmaryrd}\usepackage{textcomp}\usepackage{portland, xspace}\usepackage{amsmath, amsxtra}\usepackage{upgreek}\pagestyle{empty}\DeclareMathSizes{10}{9}{7}{6}\begin{document} $$IS{ _{ \left( {HTP} \right) }}$$ \end{document} ), and industrial waste ( \documentclass{aastex}\usepackage{amsbsy}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{bm}\usepackage{mathrsfs}\usepackage{pifont}\usepackage{stmaryrd}\usepackage{textcomp}\usepackage{portland, xspace}\usepackage{amsmath, amsxtra}\usepackage{upgreek}\pagestyle{empty}\DeclareMathSizes{10}{9}{7}{6}\begin{document} $$IS{ _{ \left( {SW} \right) }}$$ \end{document} ) equivalent matrices can be calculated. After calculating the equivalents of the six kinds of environmental impacts, the six equivalents should be standardized based on reference values and normalized in accordance with the weight coefficients. Reference values come from the CML method. The specific weight coefficient was not an emphasis in this study, and the weight coefficients in a previous study (Wang, 2013) were used for normalizing the seven kinds of environmental impact equivalents.

After weighting, the environmental impact equivalent matrix of the full life cycle (AIP) can be expressed as follows: \documentclass{aastex}\usepackage{amsbsy}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{bm}\usepackage{mathrsfs}\usepackage{pifont}\usepackage{stmaryrd}\usepackage{textcomp}\usepackage{portland, xspace}\usepackage{amsmath, amsxtra}\usepackage{upgreek}\pagestyle{empty}\DeclareMathSizes{10}{9}{7}{6}\begin{document} \begin{align*} AIPx = { \frac { \left[ { I { S_ { \left( { GWP } \right) } , } I { S_ { \left( { AP } \right) } , } I { S_ { \left( { EP } \right) } , } I { S_ { \left( { POCP } \right) } , } I { S_ { \left( { ODP } \right) } , } I { S_ { \left( { HTP } \right) } , } I { S_ { \left( { SW } \right) } } } \right] \cdot { { \left( { { \beta _ { ij } } } \right) } _ { 7 \times 1 } } } { { \gamma _i } } } \tag { 14 } \end{align*} \end{document}

where AIP is a k × l matrix, and \documentclass{aastex}\usepackage{amsbsy}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{bm}\usepackage{mathrsfs}\usepackage{pifont}\usepackage{stmaryrd}\usepackage{textcomp}\usepackage{portland, xspace}\usepackage{amsmath, amsxtra}\usepackage{upgreek}\pagestyle{empty}\DeclareMathSizes{10}{9}{7}{6}\begin{document} $${ \beta _{ij}} $$ \end{document} and \documentclass{aastex}\usepackage{amsbsy}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{bm}\usepackage{mathrsfs}\usepackage{pifont}\usepackage{stmaryrd}\usepackage{textcomp}\usepackage{portland, xspace}\usepackage{amsmath, amsxtra}\usepackage{upgreek}\pagestyle{empty}\DeclareMathSizes{10}{9}{7}{6}\begin{document} $${ \gamma _i}$$ \end{document} represent the weight coefficients and reference values, respectively, of the seven environmental impact equivalents (i = 1, 2,…,7). Finally, the environmental impact equivalent of the power seat can be obtained by summing the elements in the i-th row of the AIP: \documentclass{aastex}\usepackage{amsbsy}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{bm}\usepackage{mathrsfs}\usepackage{pifont}\usepackage{stmaryrd}\usepackage{textcomp}\usepackage{portland, xspace}\usepackage{amsmath, amsxtra}\usepackage{upgreek}\pagestyle{empty}\DeclareMathSizes{10}{9}{7}{6}\begin{document} \begin{align*} \sum aip = \mathop \sum \limits_{i = 1}^k { \left( {ai{p_{ij}}} \right) _{k \times 1}} , \tag{15} \end{align*} \end{document}

Acquisition of inventory data

In this study, a representative power seat typically used in China was selected as an example for empirical study. Based on the established evaluation model, research and development, production and assembly workshop, and testing centers were investigated in depth to collect inventory data, such as the power seat Bill of Material (BOM) chart, manufacturing energy consumption, corresponding automotive types, and basic parameters of power seat motors. According to the evaluation model, energy consumption in each stage of the life cycle should be determined when calculating power seat emissions and pollutants at each stage.

Then, emissions and pollutants in various stages of the life cycle can be obtained by multiplying energy consumption in each stage by the emission intensity matrix of energy consumption in each stage. The power seat BOM chart, basic parameters, and energy consumption at various stages were described in detail in a previous study (Chen et al., 2014).

Impact Assessment and Results

Calculation of environmental emissions

Relevant data related to the power seat itself were mainly sourced from an investigation of a specific enterprise, while other basic and background data were mainly derived from the relevant literature (Wang et al., 2011; Huang et al., 2012; Liu et al., 2010), including SinoCenter, Ecoinvent, and Chinese Life Cycle databases of Sichuan University. Factors, reference values, and weights of environmental impact assessment are shown in Table 1. The specific calculation model of environmental emission was compiled with MATLAB software. Next, the above list data were input into the calculation model, and the final evaluation results were acquired. According to Equations (1) through (15) above, various gas emissions and toxic/harmful substances in all stages of the full power seat life cycle can be calculated as shown in Tables 2 and 3, respectively. From the results of the above calculations, emissions and pollutants assessment results for each stage were analyzed separately.

Table 1.

Factors, Reference Values, and Weights of Environmental Impact Assessment

Environmental impact categories	Environmental impact substances	Characterization factor	Unit	Reference value kg/year	Weight
GWP	CO₂	1	kg CO₂ equivalent	4.18E+13	0.2155
	N₂O	298
	CH₄	25
	CFC	4750
AP	SO₂	1.2	kg SO₂ equivalent	2.39E+11	0.1418
	NO_x	0.500
	HCl	0.749
	NH₃	1.600
EP	Phosphate	1	kg PO₄⁻³ equivalent	1.58E+11	0.0697
	Ammonia	0.350
	Nitrate	0.100
	NO_x	0.130
	Phosphorus	3.060
	Ethanol	0.044
	Biochemical Oxygen Demand/Chemical Oxygen Demand	0.022
POCP	Ethylene	1	kg Ethylene equivalent	3.68E+10	0.2155
	NMVOC	0.364
	NO_x	0.028
	SO₂	0.048
	CO	0.027
	Hydrocarbon	0.113
	CH₄	0.006
HTP	Particulate Matter	0.820	kg 1,4-Dichlorobenzene equivalent	2.58E+12	0.0417
	NO_x	1.200
	SO₂	0.096
	NMVOC	0.059
SW	Fly ash	1	kg SW equivalent	2.39E+11	0.3157
	Slag	1
	Tailings	1
	Industrial waste solid	1

AP, acid potential; EP, eutrophication potential; GWP, global warming potential; HTP, health toxic potential; NMVOC, nonmethane volatile organic compounds; POCP, photochemical ozone creation potential; SW, solid waste.

Table 2.

Summary of Various Gas Emissions at All Stages of Life Cycle of the Power Seat (kg)

			Operation and use stage
Various stages of the full life cycle	Raw materials production stage	Seat components manufacturing stage	Seat weight	Motor operation	Disposal stage	Logistics process	Total
CH₄	0.380	0.001	0.051	0.038	−0.123	1.718E-05	0.348
SO_x	0.317	0.559	0.929	0.688	−0.068	2.665E-03	2.427
NO_x	0.188	0.380	1.474	1.093	−0.049	2.511E-03	3.088
CO	0.160	0.116	14.083	10.441	0.055	7.285E-04	24.855
CO₂	78.233	82.400	1438.700	1066.600	9.074	0.681	2675.700
PM	0.888	0.031	0.211	0.156	−0.431	2.717E-04	0.855
NMVOC	0.020	0.003	0.707	0.524	0.052	6.979E-05	1.307

Table 3.

Summary of Various Toxic/Harmful Substances in All Stages of Life Cycle of the Power Seat (kg)

			Operation and use
Various stages of the life cycle	Raw materials production stage	Seat components manufacturing stage	Seat weight	Motor operation	Scrap recovery stage	Logistics process	Total
COD	0.214	0.006	0.967	0.717	−0.108	1.860E-05	1.796
Fly ash	25.519	1.800	1.896	1.406	−14.223	5.792E-03	16.404
Industrial wastes	130.470	0.000	0.000	0.000	−67.752	0.000	62.714
Slag	6.240	0.000	0.000	0.000	−3.488	0.000	2.753

1. Emissions and pollutants in the raw materials production stage. As shown in Table 1, PM emits the most, except carbon dioxide, among all gaseous emissions in the raw material production stage, suggesting that a great amount of PM was discharged. Therefore, both control and treatment of PM emission in the raw materials production process should be strengthened. In addition, among four kinds of toxic/harmful substances, industrial waste was the largest (Table 3), and mainly caused by a lot of tailings in the raw ore mining process. Strategies for effective recycling of the tailings are an important task that needs to be solved.

2. Emissions and pollutants in the seat components manufacturing phase. In terms of gas emissions, SO_x and NO_x were greatest in the vehicle usage phase, except CO₂ (Table 2). This is mainly due to the fact that this stage consumed a large amount of energy. This is also consistent with conclusions drawn from energy consumption evaluation results. Moreover, fly ash far exceeded all other toxic/harmful substances produced (Table 3). This can be attributed to high proportion of thermal power generation in this stage. Application of advanced desulfurization technology and clean fuel power to reduce the proportion of thermal power generation are important ways in which to reduce the amount of emissions and pollutants during seat components manufacturing.

3. Emissions and pollutants in the operation and use phase. Tables 2 and 3 show that various gas emissions and toxic/harmful substances corresponding to power seat weight were slightly larger than those corresponding to motor operation. Based on the principle of environmental load distribution, emission during the operation and use stage is allocated to the power seat based on the seat to vehicle weight ratio, while the emission during the use of the power seat motor is allocated to the seat 100%. This distribution principle is in line with the actual situation during the power seat operation stage.

Data analysis revealed that the proportion of toxic/harmful substances in the power seat weight exceeds the proportion of the seat motor running and differed from that of gas emissions. Toxic/harmful substances in the operation and use phase were mainly produced in the gasoline production process, while toxic/harmful substances produced by direct vehicle emissions can be ignored. Accordingly, direct and indirect gas emissions from the power seat during its use were analyzed and the results are shown in Table 4. While direct gas emission of CH4, CO, CO₂, PM, and NMVOCs exceeded that of corresponding indirect gas emissions, the opposite was found for SO_x and NO_x (Table 4). This suggests that large amounts of SO_x and NO_x were emitted upstream of gasoline production. The use of some advanced oil mining and smelting technologies can effectively lower SO_x and NO_x emissions. In the meantime, improvement of oil quality and increased fuel efficiency are other effective ways of reducing emissions.

Table 4.

Comparison Between Direct and Indirect Gas Emissions of the Power Seat in Operation and Use Stage (kg)

	Part of seat weight		Part of motor power consumption
Types of gas emissions	Direct gas emission	Indirect gas emission	Direct gas emission	Indirect gas emission
CH₄	0.045	5.630E-03	0.034	4.172E-03
SO_x	0.054	0.875	0.040	0.649
NO_x	0.649	0.825	0.481	0.612
CO	13.844	0.239	10.263	0.177
CO₂	1208.600	230.110	896.010	170.600
PM	0.122	0.089	0.090	0.066
NMVOC	0.684	0.023	0.507	0.0170

4. Emissions and pollutants in the disposal stage. In this study, only positive benefits and environmental benefits (negative value for the indicator) in the recovery of metal materials and incineration and landfill of nonmetallic materials were calculated. Summaries of emissions and pollutants in the disposal phase of the power seat are shown in Figs. 2 and 3, respectively.

FIG. 2.

Gas emissions in scrap recovery phase.

FIG. 3.

Toxic/harmful substances in scrap recovery phase.

Analysis of the results shows that the values of all gas emissions, except CO and CO₂, were negative in the disposal stage, with the absolute value of PM being the largest. This indicates that most gas emissions, especially PM, noticeably decrease by recycling and reuse of metal materials. In addition, the calculation results for four kinds of toxic/harmful substances were negative, demonstrating that recycling of metal materials prevented many other procedures, such as raw material mining and smelting, and effectively reduced the amount of toxic/harmful substances produced.

5. Emissions and pollutants in the logistics process. The road transport mode using a diesel truck as a tool was selected for the logistics process in this study. Similar to the operation and use phase, various gas emissions exhibited both direct and indirect emissions in the logistics process (Table 5). Direct emissions depend heavily on gas emissions during truck transportation, while indirect emissions are mainly determined by gas emissions in the diesel production process. Indirect gas emissions were much larger than direct gas emissions in both forward and reverse logistics, indicating that a variety of gas emissions were produced in the diesel production process.

Table 5.

Direct and Indirect Gas Emissions in Logistics Process (kg)

	Forward logistics		Reverse logistics
Types of gas emissions	Direct gas emissions	Indirect gas emissions	Direct gas emissions	Indirect gas emissions
CH₄	3.106E-10	1.031E-05	1.472E-10	6.870E-06
SO_x	2.423E-10	1.599E-03	1.148E-10	1.066E-03
NO_x	1.272E-08	1.507E-03	6.028E-09	1.005E-03
CO	2.196E-08	4.371E-04	1.041E-08	2.914E-04
CO₂	3.655E-05	0.409	1.732E-05	0.272
PM	2.878E-07	1.628E-04	1.364E-07	1.085E-04
NMVOC	1.215E-08	4.186E-05	5.758E-09	2.791E-05

Environmental impact equivalent evaluation results

Next, various gas emissions and toxic/harmful substances from the power seat at all stages of the full life cycle were substituted into the normalized calculation model of environmental impact, and the resultant environmental impact equivalents of the power seat are shown in Table 6. The environmental impact was largest in the operation and use phase, with GWP showing the largest value among all six types of environmental impacts. Moreover, the environmental impact equivalent in the disposal phase was positive.

Table 6.

Various Types of Environmental Impact Equivalents of the Power Seat in All Stages of Life Cycle (Dimensionless)

Various stages of the life cycle	Raw materials production stage	Seat components manufacturing stage	Operation and use stage	Disposal stage	Logistics process	Total
GWP	4.513E-13	4.2397E-13	1.2897E-11	3.0897E-14	3.5058E-15	1.3807E-11
AP	2.8137E-13	5.1072E-13	1.9126E-12	−6.3136E-14	2.6425E-15	2.6442E-12
EP	1.283E-14	2.1849E-14	1.6353E-13	−3.8501E-15	1.4419E-16	1.945E-13
POCP	2.0188E-13	2.4429E-13	7.3809E-12	8.8167E-14	1.4255E-15	7.9166E-12
HTP	1.5913E-14	8.654E-15	5.8311E-14	−6.7166e-015	5.6509E-17	7.6219E-14
SW	2.1429E-12	2.3777E-14	4.3621E-14	−1.1289E-12	7.6512E-17	1.0814E-12
Total	2.5715E-11

According to the environmental impact normalization calculation model, the corresponding environmental impact equivalents of seven kinds of gas emissions and four kinds of toxic/harmful substances were calculated (Tables 7 and 8, respectively). The proportion of various gas emissions was significantly greater compared with toxic/harmful substances. Among all gas emissions, CO₂ made the greatest contribution to the environmental impact equivalent, accounting for 53.5083%. Among all toxic/harmful substances, industrial waste made the greatest contribution to the environmental impact equivalent, accounting for 3.2208%.

Table 7.

Corresponding Environmental Impact Equivalents and Occupying Ratios of Various Gas Emissions of the Power Seat (Dimensionless)

Gas emissions	CH₄	SO _x	NO _x	CO	CO₂	PM	NMVOC
Environmental impact equivalent	5.6935 × 10⁻¹⁴	2.4143 × 10⁻¹²	1.6592 × 10⁻¹²	3.9299 × 10⁻¹²	1.3762 × 10⁻¹¹	1.1331 × 10⁻¹⁴	2.7872 × 10⁻¹²
The percentage of environmental impact equivalent	0.22	9.39	6.45	15.28	53.51	0.04	10.84

Table 8.

Corresponding Environmental Impact Equivalents and Occupying Ratios of Toxic and Harmful Substances of the Power Seat (Dimensionless)

Toxic and harmful substances	COD	Fly ash	Industrial wastes	Slag
Environmental impact equivalent	1.7432 × 10⁻¹⁴	2.1669 × 10⁻¹³	8.284 × 10⁻¹³	3.6359 × 10⁻¹⁴
The percentage of total amount	0.07	0.84	3.22	0.14

Sensitivity analysis

In this study, environmental impact equivalents were used to evaluate the ecological benefits of the selected automotive power seat. The above analysis results showed that the material regeneration rate in the disposal phase and motor efficiency in the operation and use phase have great uncertainties. Therefore, sensitivity analysis was conducted on material regeneration rate and motor efficiency, and key factors with great environmental impact equivalent sensitivity were explored.

Sensitivity analysis of material regeneration rate

The sensitivity analysis of material regeneration rate in the disposal stage mainly includes changes in environmental impact equivalent trends when the regeneration rates of four metal materials (copper, steel, aluminum, and aluminum alloy) were increased from 1% to 10%. Since all nonmetallic materials were disposed by incineration and/or landfill after scrap of the power seat in this study, regeneration of nonmetallic materials was not considered. If some nonmetallic materials were recycled, their sensitivities can be analyzed in the same way (Fig. 4).

FIG. 4.

Variation of sensitivities for regeneration rates of various materials relative to environmental impact equivalent.

The regeneration rate of steel was the most sensitive to the environmental impact equivalent, followed by copper (Fig. 4). These two materials far exceeded aluminum and aluminum alloys in sensitivity to the environmental impact equivalent. Apparently, the variations in regeneration rates for different materials exhibited totally different sensitivities to resource depletion and environmental impact. Therefore, after the power seat was scrapped, improving the regeneration rates of steel and copper with the use of advanced material recycling and cyclic utilization technologies is an effective way to reduce the environmental impact of the power seat from the perspective of scrap recycling.

Sensitivity analysis of motor efficiency

According to the statistical data of the specific enterprise in motor experiments, fuel conversion efficiency from engine to battery was 40%, and the working efficiency of the motor was 45%. These two parameters were still low and will be promoted in the future. However, which parameter was more sensitive to the ecological environment required further analysis. Therefore, the sensitivities of fuel conversion and motor operation efficiency when increased by 0–20% to environmental impact equivalents were examined and the results are shown in Fig. 5.

FIG. 5.

Sensitivity of motor efficiency to environmental impact equivalent.

The sensitivity of fuel conversion efficiency to environmental impact equivalent was greater compared with the working efficiency of the motor. Therefore, when performing technical upgrades on power seats to reduce environmental impacts and improve greenness, various measures should be taken to first improve fuel conversion efficiency in terms of technical path selection, and then to improve the motor's working efficiency.

Simulations of power seat electrification

This empirical research was performed on a mature power seat in which four motors (horizontal, angle adjusting, seat elevating, and overall elevating) were all mounted. At present, the power seat loaded with these four motors is still relatively expensive and has not been extensively applied. Many vehicles have not been equipped with power motors; some are only installed with horizontal motors for automatic adjustment of anteroposterior position. Therefore, based on static evaluation results and sensitivity analysis, the ecological impact of the seat loading motor in the electrification process should be simulated and evaluated to better guide development of energy-saving power seats with low environmental impact.

Scenario setting

According to the actual motor loading process (horizontal, angle, and elevation adjustment), this study set up five scenarios for the electromotive process of the power seat (Table 9). Seat weight accounted for only about 2% of the completed vehicle curb weight, and the sum of the masses of four motors accounted for only 0.15% of the completed vehicle curb weight. Therefore, this study assumed that vehicles used under these five scenarios have the same vehicle curb weight so that fuel consumption per 100 km was constant.

Table 9.

Electromotive Process of Power Seat

	The number of motors
	0	1	2	3	4
Scenario 1	•
Scenario 2		•
Scenario 3			•
Scenario 4				•
Scenario 5					•

Solid dots correspond to the number of motors in various scenarios.

0: No motor was equipped, that is, a manual seat was used.

1: The power seat was equipped with a horizontal motor so as to realize the automatic adjustment between the front and back positions of the seat as a whole.

2: The power seat was equipped with horizontal motor and angle-adjusting motor for increasing the automatic adjustment function of back angle.

3: The power seat was equipped with horizontal motor, angle-adjusting motor, and seat elevating motor for increasing the automatic seat lifting function.

4: The power seat was equipped with horizontal motor, angle-adjusting motor, seat elevating motor, and overall elevating motor simultaneously for increasing the automatic lift function of the whole seat.

According to the calculation results, energy consumption in the seat manufacturing stage accounted for only about 3% of the life cycle energy consumption, and the energy allocated to each seat was even less. The induced energy consumption was slightly different among these five scenarios. Information on the manufacturing energy consumption for each motor was not obtained because it was an enterprise secret. Therefore, it was assumed that the average energy consumption of the entire seat was 100 kW h and remained unchanged in the seat components manufacturing stage in the five scenarios. The BOM chart of the power seat and electricity consumption value of motor data in the operation and use stage in the five scenarios is listed in Tables 10 and 11, respectively.

Table 10.

Bill of Material Chart of Vehicle Power Seat Under Five Scenarios (kg)

Types of materials	Scenario 1	Scenario 2	Scenario 3	Scenario 4	Scenario 5
Copper	0.110	0.160	0.195	0.245	0.295
Steel	20.200	20.480	20.775	21.150	21.525
Aluminum alloy	0.00	0.00	0.00	0.18	0.36
Plastic (PP)	1.745	1.775	1.895	1.925	1.955
Ferrite	0.000	0.078	0.123	0.198	0.273

Table 11.

Electricity Consumption Value of Motor in Operation and Use Stage Under Five Scenarios (kWh)

	Scenario 1	Scenario 2	Scenario 3	Scenario 4	Scenario 5
Electricity consumption value of motor	0.0000	271.6049	530.8642	615.1235	657.2531

Static scenario simulation

According to above settings of the five scenarios for the seat's electromotive process, the corresponding environmental impact equivalent under each scenario was calculated by substituting BOM data and electricity consumption values of motors in Tables 10 and 11, respectively, into the static evaluation model for each scenario. In addition, seven kinds of gas emissions (CH4, CO₂, NO_x, SO_x, CO, PM, and NMVOCs) and four kinds of toxic/harmful substances (chemical oxygen demand, fly ash, slag, and industrial waste) were calculated (Tables 12 and 13, respectively). In addition, six different types of environmental impact values (GWP, AP, EP, POCP, HTP, and SW) for five scenarios were calculated (Table 14).

Table 12.

Gas Emissions of the Power Seat Under Five Scenarios (kg)

Gas emissions	CH₄	CO₂	NO _x	SO _x	CO	PM	NMVOC
Scenario 1	0.286	1480.17	1.858	1.636	13.169	0.650	0.717
Scenario 2	0.306	1945.01	2.335	1.941	17.710	0.724	0.945
Scenario 3	0.328	2393.16	2.795	2.234	22.086	0.796	1.168
Scenario 4	0.291	2509.12	2.917	2.310	23.237	0.814	1.180
Scenario 5	0.348	2675.70	3.088	2.427	24.855	0.855	1.307

Table 13.

Toxic and Harmful Substances of the Power Seat Under Five Scenarios (kg)

The toxic and harmful substances	COD	Fly ash	Slag	Industrial wastes
Scenario 1	0.985	13.998	2.556	35.905
Scenario 2	1.299	14.796	2.599	41.170
Scenario 3	1.602	15.567	2.642	44.964
Scenario 4	1.673	16.078	2.734	54.335
Scenario 5	1.796	16.404	2.753	62.714

Table 14.

Various Environmental Impact Values of the Power Seat Under Five Scenarios (Dimensionless)

Types of environmental impact	GWP	AP	EP	POCP	HTP	SW
Scenario 1	7.6500E-12	1.7162E-12	1.1609E-13	4.3845E-12	4.7854E-14	6.9293E-13
Scenario 2	1.0044E-11	2.0743E-12	1.4651E-13	5.7542E-12	5.8787E-14	7.7359E-13
Scenario 3	1.2351E-11	2.4194E-12	1.7582E-13	7.0797E-12	6.9328E-14	8.3448E-13
Scenario 4	1.2943E-11	2.5098E-12	1.8354E-13	7.3277E-12	7.2069E-14	9.6621E-13
Scenario 5	1.3807E-11	2.6441E-12	1.9449E-13	7.9117E-12	7.6214E-14	1.0815E-12

Dynamic scenario simulation

Static simulation results can provide the variation tendency of environmental impact equivalents during the seat electromotive process. However, static simulation is actually a single-variable analysis of the seat in the five scenarios. In the actual evaluation process, there exist great uncertainties in both material regeneration rate and motor efficiency. Therefore, these two variables should be considered simultaneously, and three-dimensional scenario simulations should be conducted to investigate ecological benefit changes in the seat electromotive process. In other words, environmental impact equivalent changes not only with the set scenarios but also with the change of material regeneration rate and motor efficiency in the sensitivity analysis. The comprehensive knowledge obtained by simultaneously accounting for these factors can provide accurate and scientific guidance for product design. By substituting the change on material regeneration rate and five scenarios into the evaluation model, variation in the power seat environmental impact equivalent when the material regeneration rate changed was calculated.

The variation tendency of the environmental impact equivalent with material regeneration rate change in the five scenarios was plotted using the three-dimensional drawing function in MATLAB (Fig. 6).

FIG. 6.

Variation tendency of power seat environmental impact equivalent with material regeneration rate change in five scenarios.

As shown in Fig. 6, the calculated environment impact equivalent fit well with static scenario simulation results, and the variation curves became wider from scenario 1 to 5. In other words, the constant increase in material regeneration rate caused the environmental impact equivalent to gradually decrease from scenario 1 to 5, accompanied by the increased variation rate. Therefore, to enhance the material regeneration rate by the same values, the effects of the power seat with more motors on the environment should be smaller, suggesting the ecological benefit of the power seat with a higher electrification degree is that it is more sensitive to the material regeneration rate.

Similarly, by substituting the changing values of motor efficiency into the evaluation model in the five scenarios, the environmental impact equivalents were calculated (Fig. 7).

FIG. 7.

Variation tendency of power seat environmental impact equivalent with motor efficiency in five scenarios.

According to projection results in the y−z plane, environmental impact equivalents under different scenarios significantly decreased to different degrees with the improvement of motor efficiency. Moreover, x−z plane projection results showed an enhanced reduction of environmental impact equivalents with increased motor efficiency from scenario 1 to 5. The power seat with a higher degree of electrodynamics and more motors exhibited more significant reduction of environmental impact equivalents when the same proportion of motor efficiency was improved. Therefore, the higher the electrodynamic degree of the power seat, the more obvious the effect on the environment by improving fuel conversion and motor working efficiency.

Conclusions

LCA results of vehicle components, including scrap recovery, greatly differed from those without consideration of this phase. Most LCA studies of vehicle components focus on manufacturing and usage stages, with some using even scarcer data to represent the disposal stage. However, no previous study has divided the vehicle component disposal phase into four processes: remanufacturing, reusing, burning, and burying. To calculate accurately, this study developed a mathematical model for the entire life of the seat components, including disposal and logistics process phases. Herein, a power seat was used as an empirical research object, filling a gap in vehicle component research. Using LCA of the entire life of the vehicle, the total environmental impact of the power seat was mathematically analyzed, and the results showed that all kinds of power seat emissions in the vehicle usage stage exceeded those in other stages, with CO₂ emission being the largest, followed by CO. In addition, different amounts of gases and toxic/harmful substances were emitted in the different stages.

Based on the above analysis, two key factors with great uncertainty were captured: material regeneration rate and motor efficiency. These were solved using sensitivity analysis, which showed the regeneration rate of steel was most sensitive to the environmental impact equivalent. Furthermore, the environmental impact equivalent of fuel conversion efficiency was greater compared with motor work efficiency. Therefore, the key to reducing environmental emissions of the power seat is to improve the steel regeneration rate and fuel conversion efficiency.

Scenario simulation is a novel method for in-depth exploration of the environmental impact of the power seat during the electrification process by setting five realistic scenarios in the evolution of the seat. Scenario 3 was concluded to be the turning point in the increase of environmental impact equivalent, and the impact of adding an elevating motor increased solely. In addition, more gas emissions and toxic/harmful substances were produced as the number of motors increased. At the same time, environmental emissions from the power seat with more motors were more sensitive to the material regeneration rate and motor efficiency.

While this study conducted a comprehensive and in-depth LCA of various scenarios of power seats and their electrification process, future technological changes are not taken into account. Therefore, future research should take into account changes in power seat technology.

Data availability

All data used to support the findings of this study are available from the corresponding author upon request.

Footnotes

Acknowledgments

This research was supported by Natural Science Foundation of China (No. 71173072), Shaanxi Natural the Fundamental Research Funds for the Central Universities (No. 300102229101), MOE (Ministry of Education in China) Project of Humanities and Social Sciences (No. 16YJCZH008), Project of Basic Applied Research Program in Qinghai Province (No. 2019-ZJ-7032). The authors thank all anonymous referees for their very valuable comments.

Author Contributions

Y.C. and Z.D. established the mathematical evaluation model, sensitivity analysis, and scenario analysis. Z.D. contributed to drafting the article. J.L. conducted the literature review and edited the article. Y.C., Z.D., and J.L. were in charge of the final version of the article.

Author Disclosure Statement

No competing financial interests exist.

Funding Information

No funding was received for this article.

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