China's electric vehicle and climate ambitions jeopardized by surging critical material prices

Affordable electric vehicles (EVs) are essential for sustainable transportation. However, a recent surge in prices of critical materials like lithium, cobalt, nickel, and manganese, used in EV batteries, raises concerns about EV competitiveness. The price volatility, exemplified by the extreme nickel price spike in early 2022, significantly impacts the EV market. The price of these critical minerals increased dramatically in 2021 (280%) and continued to rise in early 2022 (438% for lithium). This necessitates an assessment of how material price surges affect EV uptake and inform cost-effective deployment strategies. Existing literature focuses on material flow analysis and independent EV uptake, neglecting the cost comparison with alternatives. This research endogenizes EV uptake based on cost comparisons across all options, focusing on China, the world's largest EV market, and its ambitious carbon neutrality target requiring an 80% reduction in transportation CO2 emissions by 2050. China aims for EVs to become mainstream by 2035, emphasizing the importance of understanding material price impacts on its fleet electrification and global carbon neutrality goals. Most projections assume decreasing low-carbon technology costs, ignoring potential material price surges. This study uses the Global Change Assessment Model (GCAM) to assess the impact of varying critical material price surge scenarios on EV uptake and CO2 emissions, considering different battery types (NCM111, NCM622, NCM811, NCM9.5.5, NCA, LFP, LMO).

Existing research highlights persistent and deepening shortages of critical materials needed for EV batteries. The International Energy Agency (IEA) estimates a 30-fold increase in lithium, nickel, and other minerals will be needed by 2040 to meet global climate targets, exceeding committed mine production. This literature shows mineral shortages will constrain EV deployment. However, these studies primarily use a material-flow perspective, largely ignoring the cost comparison between EVs and alternatives. This study addresses this gap by endogenizing EV uptake based on cost considerations.

To assess the impacts of critical material price surges on EV uptake and CO2 emissions in China, the researchers extended the Global Change Assessment Model (GCAM v5.2). This integrated assessment model incorporates economic, energy, land, water, and climate systems. The model was enhanced to include changes in lithium, cobalt, nickel, and manganese prices under high, medium, and low surge scenarios (2020-2060). Different lithium-ion battery (LIB) types (NCM111, NCM622, NCM811, NCM9.5.5, NCA, LFP, LMO) were modeled. The model endogenously determines EV uptake based on cost comparisons with internal combustion engine vehicles (ICEVs), considering technological innovation and material price fluctuations. The model's transportation module includes passenger and freight sectors (LDV-4W, bus, truck), each with various technology options (ICEV, EV, FCEV, HEV, NGV). The model uses socioeconomic development scenarios (Shared Socioeconomic Pathways) to project future demand. The logit-choice formulation determines technology market share based on cost and feasibility. A conversion formula translates transportation service demand into vehicle numbers. The study analyzes EV cost evolution, ICEV cost evolution, EV penetration rate, ICEV penetration rate, and CO2 emission changes under different scenarios, including the impact of material recycling (RE scenario).

The study's key findings show that under the high material price surge scenario, cobalt prices would increase by 213% (2030) and 467% (2060), lithium by 380% (2035), and nickel by 164% (2060). This leads to significant EV cost increases; for example, using NCM622 batteries, light duty vehicle costs would be 7-18% higher in 2030 and 15-21% higher in 2060 than in the baseline scenario. EV penetration rates would decrease substantially: 35% (2030) and 51% (2060) under the high scenario versus 49% (2030) and 67% (2060) in the baseline. The high scenario leads to a 28% increase in cumulative CO2 emissions (2020-2060) from road transport. The increase in CO2 emissions is more pronounced for EVs with ternary LIBs (especially high-cobalt ones). Cobalt-free LIBs (LFP and LMO) show smaller increases in CO2 emissions. Material recycling significantly mitigates the impact of material price surges on EV costs and penetration rates, especially in the long term. In the high scenario with recycling, EV costs would decrease by 11-15% by 2060, and the EV penetration rate would increase to 59%. Recycling could reduce CO2 emissions from road transport by 36% in the high-recycling scenario by 2060.

The results demonstrate that the surge in critical material prices significantly undermines the projected EV adoption in China, jeopardizing the carbon neutrality target. The increased EV costs make ICEVs more competitive, leading to higher CO2 emissions. The study highlights the need to consider material price volatility in future EV adoption projections. Material recycling and battery chemistry innovation (e.g., cobalt-free batteries) are crucial for mitigating the risks, especially in the long term. The findings emphasize the importance of supply chain diversification and international cooperation to secure the supply of critical materials.

This research underscores the critical threat that surging prices of critical materials pose to China's EV development and its carbon neutrality goals. Underestimating this factor leads to overly optimistic projections. The study strongly advocates for material recycling, battery technology innovation (including cobalt-free options), and proactive policy interventions (supply chain resilience, investment in recycling, supportive regulations) to secure a sustainable path toward EV adoption and carbon reduction.

The study relies on model projections and assumptions about future material prices, technological advancements, and policy interventions. The accuracy of the results depends on the reliability of these inputs. Furthermore, the model might not fully capture all the complexities of the EV market and the interactions between different sectors.