Characterizing end-of-life household vehicles’ generations in China: Spatial-temporal patterns and resource potentials

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Abstract

China is now boasting a huge number of end-of-life vehicles (ELVs) with a low recovery rate, resulting in a waste of resources and severe environmental pollution. Predicting the generation of ELVs in future helps to enhance recycling efficiency by providing a basis for the planning of recycling industry. However, previous research did not provide a detailed, mid-term forecast. Based on a stocks-driven model and the bottom-up extrapolation of in-use stocks, this study characterizes the generation of end-of-life household vehicles (HVs) in China by addressing the detailed spatial-temporal patterns and resource potentials during 2019–2050. The results show that the annual end-of-life HVs in China will continuously increase during 2019–2050, resulting an accumulated 1.48 billion units, among which urban areas will account for 86%, and internal combustion engine vehicles (ICEVs) will take up 80%. Regarding the spatial patterns, eastern region will possess the largest proportion, and wide variations are found among all provinces due to the difference in population size and economic development level, which have important implications for further planning of end-of-life HVs recycling industry. Before 2050, accumulated quantity of all types of metals in end-of-life HVs will approach domestic mine production (in 2019), or even approach the current global mine production, indicating they have a great potential as an indispensable source for domestic resource supply.

Introduction

China is boasting a huge number of end-of-life vehicles (ELVs) in recent years. ELVs contain tremendous metal and non-metal resources, and are identified as the core scope of urban mineral resources (together with WEEE and waste wire and cable) by the Chinese government, as well as in many industrial nations (Zeng et al., 2020). Unfortunately, the recycling rate of ELVs in China is estimated as only 16.5% in 2019 (MOC, 2019), which not only results a huge waste of resources, but also causes severe environmental pollution. Especially with the gradual obsoleting of new energy vehicles (NEVs), the timely and proper handling of traction batteries in NEVs will deserve great attention.

Previous research indicates the current recycling capacity is severely insufficient in contrast with theoretical generation scale of ELVs (Zhang et al., 2020b). With the continuous expansion of ELVs in the following decades, the gap between recycling demand and recycling capacity will be enlarged. Besides, the release of Detailed Rules for the Implementation of the Measures for the Recycling of End-of-Life Vehicles (MOC, 2020) will hopefully bring a rapid development of ELV recycling industry. Then predicting the amount of ELVs in future can provide a basis for the planning of recycling industry, including the layout and capacity of household vehicles (HVs) recycling centers, the layout and capacity of collecting network for NEVs battery, and the estimation of urban mining potentials, etc.

In recent years, there are quite a few studies on modeling in-use stocks and predicting the volume of ELVs. Econometric models have been adopted to modeling vehicle ownership. For key variables considered in the model, GDP (or per capita GDP) is the most frequently used one, sometimes acted as the only variable (Andersen et al., 2008; Singh et al., 2020), sometimes combined with others factors such as income, vehicle price, population, and urbanization (Hao et al., 2011a). The latter two variables were also used to model the worldwide vehicle saturation level (Dargay et al., 2007). Other models such as the Fuel Economy and Environmental Impacts model were used as well (Huo and Wang, 2012). Similar method was adopted to predict ELVs, for example, the potential correlation among European ELV flows and two key variables (GDP and population) was evaluated through a linear regression model (D'Adamo et al., 2020). Material flow analysis (MFA) is one of the widely used methods for ELVs modeling (Modaresi and Müller, 2012; Zhang et al., 2020b), sometimes combined with other methods such as life cycle assessment (Liu et al., 2020) and Input-Output Model (Kagawa et al., 2015; Ohno et al., 2014) to get further results. System dynamics model has also been used (Azmi and Tokai, 2017; Rosa and Terzi, 2018), which often considers more factors than MFA.

For China, forecasting on ELVs generation has received much academic attention as well. Specifically, Hu and Kurasaka (2013) projected the ELV population in province-level regions of China by 2020, mainly based on estimated vehicle scrap rates; Xin et al. (2018) forecasted the number of ELVs in China from 2016 to 2020 based on general regression neural network; based on a material flow analysis model, Zhang et al. (2020a) predicted the amount of ELVs (household vehicles) in China by 2025, and then assigned the national results to provincial level; Sun et al. (2017) estimated ELVs in China from 2017 to 2030; using the gray system model, Li and Wang (2016) forecasted ELVs in China during 2015–2024; by adopting a multiple linear regression method, Liu and Zhang (2016) predicted the generation of ELVs in China by 2020, and further estimated the resource potentials. It can be concluded that there are still several gaps in studies on predicting ELVs in China. First, the timeframe of predictions is usually set before 2030 (or even 2025), and mid-term scenario (by 2050) is missing. Second, detailed ELVs generation characteristics have not been fully reflected in previous predictions. To be specific, urban-rural disparity has not been considered in the modeling of vehicle in-use stocks and survival patterns, which would cause inaccurate predictions. Also, research aiming at depicting the whole picture of ELVs often overlooked the rapid development of NEVs, which will lead to deviations on the prediction results due to obvious difference in survival patterns and resources potentials of NEVs with internal combustion engine vehicles (ICEVs). Besides, most of predictions took China as a whole, where spatial distribution characteristics within China have not been manifested.

To fill in the above gaps, this research aims to address the questions of “what are the detailed generation patterns of ELVs and corresponding resources potentials in China till 2050?”. Therefore, we will conduct a thorough forecast on the generation patterns and resource potential of end-of-life household vehicles in China from 2019 to 2050. HV is focused as it is the largest subcategory of passenger vehicle in China, accounting for over 90% of total in recent years (MPS, 2020). To make a detailed estimation, the study will be carried out at provincial scale, in which HVs in urban and rural areas will be considered separately, and HV types (ICEVs and NEVs) will also be distinguished. In the following part, the methods and data will be firstly described. Then the trend of HVs in-use stocks and end-of-life HVs generation will characterized; and thirdly, the resources potentials of end-of-life HVs will be assessed. Results uncertainties and sensitivities will be finally discussed.

Section snippets

Stocks-driven model

In this study, MFA is employed to develop a stocks-driven model (Zhang et al., 2020), which can be illustrated in the following equations.StSt1=FtinFtoutFtout=k=1bFtkin*probtk(k)

In Eq (1),Stand St1 represents the in-use stocks of HVs in year t andt1, respectively. Ftin represents the quantity of HVs that are purchased by households in year t. Ftout is the number of end-of-life HVs in year t, which can be calculated by Eq. (2). b is the maximum lifetime of HV. probtk(k) is the

In-use stocks of HVs from 2019 to 2050

The total in-use stocks of HVs in China from 2019 to 2050 is shown in Fig. 2. In general, the total stocks of HVs in China will grow continuously in the next 25 years, reaching a peak of 512 million in 2044, and maintain relatively stable thereafter, ending up in 510 million in 2050, which is 2.6 times of stocks in 2019. Among total in-use stocks, the share in urban areas will account for 76%∼85%, slightly floating in years. Chiefly resulting from difference in population trend, the in-use

Comparisons of predict results with other studies

Fig. 7 shows the comparisons of predicted results with previous studies. As has been mentioned, most of research make predictions for period before/till 2030. It is clear that the results of our study are located within the interval set by these previous results. Further comparisons found that except for the research that did not provide details on method and data (Sun et al., 2017), and studies that applied completely different methods (Li and Wang, 2016; Xin et al., 2018), results of this

Conclusions

Based on a stocks-driven model, this study forecasts the generation of end-of-life HVs and corresponding resource potentials in China during 2019–2050. Compared with previous research, this study extrapolates in-use stocks of HVs at provincial level and considers urban-rural disparity, ICEV/NEV type has also been differentiated, so a detailed and updated spatial-temporal generation pattern has been addressed. Major results are as follows.

The annual end-of-life HVs in China will keep continuous

CRediT authorship contribution statement

Ling Zhang: Conceptualization, Methodology, Writing – original draft, Funding acquisition, Supervision, Validation. Qingqing Lu: Data curation, Investigation, Software, Formal analysis. Wei Yuan: Data curation, Investigation. Songyan Jiang: Investigation, Formal analysis. Huijun Wu: Investigation, Formal analysis.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

The research was financially supported by the National Natural Science Foundation of China (41971259 & 41901243), and the Humanities and Social Science Fund of the Ministry of Education in China (19YJCZH252).

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