Urban Stock-Demography Approach to Uncover Electric Vehicle Battery and Embedded Lithium Stock across 366 Cities in China
Bibliographic record
Abstract
Limited primary lithium resources has urged China to expand the secondary sources through recycling lithium-ion batteries (LIBs) to support its electric passenger vehicles (EPVs) ambitions. However, the lack of detailed geographic data complicates lithium stock measurement and hinders efficient recycling efforts. This study integrates urban demography approach into in-use stock analysis to map the spatial–temporal dynamics of lithium stock in EPVs across 366 Chinese cities from 2010 to 2020. The findings show LIB capacity increased from nearly 0 to 125 GWh in 2020 and in-use lithium stock reached 26.6 kilotons—2.4% of China’s total lithium reserves. Regional disparities persist: around 21 very large and super-large cities hold over 58% of total lithium in-use stock, with eastern regions dominating distribution; smaller cities, despite faster growth, maintain a smaller share. Population size, economic activity, and policy incentives emerge as key drivers, with a U-shaped relationship between per-capita lithium stock and newly introduced stock reflecting market saturation in some areas and policy-driven growth in others. Such high-resolution data can help governments and companies design recycling networks tailored to regional needs, promoting operational efficiency and sustainable lithium supply chains.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".