class: center, middle, inverse, title-slide .title[ # ] --- <style> .tab { display: inline-block; margin-left: 8em; } .title-slide .remark-slide-number { display: none; } .title-slide { background-image: url("images/title.png"); background-size: cover; } </style> <center> <img src="images/globalLinePlot.png" width=82%> </center> --- background-color: #fff class: center ## China is driving the EV revolution <center> <img src="images/world-ev-sales-line.png" width=80%> </center> --- background-color: #fff <center> <img src="images/annual-sales-china.png" width=84%> </center> --- background-color: #fff <center> <img src="images/annual-sales-comparison.png" width=100%> </center> --- class: center ### China offers more affordable BEVs across all range categories <center> <img src="images/range-price-us-china-class-2024-us.png" width=100%> </center> Data scraped from autocango.com (China) and carsheet.io (USA) Interactive version at https://jhelvy.github.io/science-2025/ --- class: center ### China offers more affordable BEVs across all range categories <center> <img src="images/range-price-us-china-class-2024-both.png" width=100%> </center> Data scraped from autocango.com (China) and carsheet.io (USA) Interactive version at https://jhelvy.github.io/science-2025/ --- background-color: #fff ## .center[China dominates mineral processing supply chain] <center> <img src="images/supply-chains.png" width=100%> </center> .font60[Source: https://thegraduatepress.org/2022/04/01/how-china-came-to-dominate-the-supply-chain-of-rare-earths-and-critical-minerals/] --- class: inverse, middle, center # How did this happen? -- # .orange[*It's the subsidies, right?!*] --- ### .center[Estimated $230B in subsidies (2009 - 2023)] <center> <img src="images/china-subsidy-table.png" width=80%> </center> .font60[https://www.csis.org/blogs/trustee-china-hand/chinese-ev-dilemma-subsidized-yet-striking] --- ### .center[Estimated $230B in subsidies (2009 - 2023)] <center> <img src="images/china-subsidy-table2.png" width=80%> </center> .font60[https://www.csis.org/blogs/trustee-china-hand/chinese-ev-dilemma-subsidized-yet-striking] --- class: inverse, middle, center # Institutions Matter --- background-image: url("images/institutions-1.png") background-size: cover --- background-image: url("images/institutions-2.png") background-size: cover --- background-image: url("images/institutions-3.png") background-size: cover --- class: inverse, middle, center # Why are _Chinese_ firms making the best EVs?<br>(and not multinationals) --- class: center # The Chinese Joint Venture System ## 1980s: 以市场换技术 = “Exchange market for technology” -- <center> <img src="images/jv-system.png" width=70%> </center> ??? Past research suggests system has largely failed to transfer technology (Brandt & Thun, 2010; Feng, 2010; Howell, 2016; Huang, 2003; Lazonick & Li, 2012; Nam, 2011) --- class: inverse, middle, center ### “这就像吸食鸦片一样,一旦你沾染上了就永远也无法戒掉。” 何光远, 中国前机械工业部部长 <br> ### “It's like opium. Once you've had it you will be addicted forever.” Guangyuan He, Former Minister of Machinery and Industry (Reuters, 2012) --- # .center[Who's going to make the EVs?] -- <br> ### .font80[❌] Multinationals don't want to share cutting-edge technology -- <br> ### .font80[❌] Chinese JV partners lack incentives to innovate -- <br> ### ✅ Indigenous non-JV firms --- class: center .leftcol55[ <center> <img src="images/jv-ev-sales-1.png" width=100%> </center> ] .rightcol45[ <br> ### Multinational OEMs dominate traditional auto sector through large JVs <br> ### Indigenous non-JV firms dominate emerging EV sector <br><br><br> .font70[.left[Helveston, J. P., Y. Wang, V. Karplus, E. Fuchs (2018) “Institutional Complementarities: The Origins of Experimentation in China’s Plug-in Electric Vehicle Industry.” _Research Policy_. 48(1), pg. 206-222]] ] --- class: center .leftcol55[ <center> <img src="images/jv-ev-sales-2.png" width=100%> </center> ] .rightcol45[ <br> ### Multinational OEMs dominate traditional auto sector through large JVs <br> ### Indigenous non-JV firms dominate emerging EV sector <br><br><br> .font70[.left[Helveston, J. P., Y. Wang, V. Karplus, E. Fuchs (2018) “Institutional Complementarities: The Origins of Experimentation in China’s Plug-in Electric Vehicle Industry.” _Research Policy_. 48(1), pg. 206-222]] ] --- class: inverse, middle # .center[EV Challenges in the US] -- <br> .leftcol30[ ] .rightcol70[ ## 1) Policy environment ## 2) Consumer preferences ## 3) Affordability crisis ] --- # .center[**Decades of Policy Whiplash**] <br> -- .cols4[ ### Obama<br>(2009–2016) - $7,500 EV tax credit - Goal: 1M EVs by 2015 - DOE battery R&D investment ] -- .cols4[ ### Trump I<br>(2017–2020) - Proposed eliminating EV tax credits - Rolled back fuel economy standards - Withdrew from Paris Agreement ] -- .cols4[ ### Biden<br>(2021–2024) - IRA: $369B in clean energy investment - Expanded EV credits, domestic content rules - 100% EV federal fleet goal ] -- .cols4[ ### Trump II<br>(2025–Now) - Gutted IRA - Rolled back fuel economy standards ] --- class: inverse, middle # .center[EV Challenges in the US] <br> .leftcol30[ ] .rightcol70[ ## 1) Policy environment ## .red[2) Consumer preferences] ## 3) Affordability crisis ] --- ## .center[US buyers are more opposed to EVs than Chinese buyers] <center> <img src="images/pev-wtp.png" width=80%> </center> .font80[Helveston et al. (2015) "Will subsidies drive electric vehicle adoption? Measuring consumer preferences in the U.S. and China" _Transportation Research Part A: Policy and Practice_. 73, 96–112. DOI: [10.1016/j.tra.2015.01.002](https://www.sciencedirect.com/science/article/abs/pii/S0965856415000038)] --- class: center, middle ### US buyers demand far more driving range than Chinese buyers <center> <img src="images/range.png" width=80%> </center> --- class: inverse, middle # .center[EV Challenges in the US] <br> .leftcol30[ ] .rightcol70[ ## 1) Policy environment ## 2) Consumer preferences ## .red[3) Affordability crisis] ] --- background-image: url("images/affordable_cars.png") background-size: cover https://www.nytimes.com/interactive/2026/04/13/opinion/affordable-car-cost.html --- background-color: #fff class: center # The EV "Deserts" of America <center> <img src="images/map_usa_new.png" width=80%> </center> .font90[.left[Data: **128M vehicle listings** from 60k dealerships (2016 - 2025), marketcheck.com]] --- background-color: #fff class: center # The EV "Deserts" of America <center> <img src="images/map_usa.png" width=100%> </center> .font90[.left[Data: **128M vehicle listings** from 60k dealerships (2016 - 2025), marketcheck.com]] --- class: inverse, middle, center # How should the US respond to Chinese EVs? --- ## .center[Bipartisan Response (So Far): **Ban Chinese tech**] .leftcol[ <center> <img src="images/lutnick.png" width=100%> <i/center> ] <br> .rightcol[ ### **Block imports**: Steep tariffs on imported Chinese EVs, batteries (First Biden, continued w/Trump) <br> ### **Block investment**: Stricter Foreign Entities of Concern (FEOC) rules ] --- <br><br><br> .leftcol[ ## .center[Option A] ## .center[**Exclude Chinese EVs**] ### - China "cheated" its way to the top with subsidies ### - Chinese EVs are a security threat ] -- .rightcol[ ## .center[Option B] ## .center[**Engage strategically**] ### - China built real competitive advantages in manufacturing and supply chains ### - Chinese EVs are winning markets, US OEMs are losing markets ] --- .leftcol[ ## .center[The Tesla Example:<br>Transparent Cybersecurity] <br> ### China allowed Tesla in via<br>targeted compliance: - Store vehicle data **within China** - Restrict operations near **sensitive areas** - Use only **government-approved** mapping systems (Baidu) ] .rightcol[ <br> <center> <img src="images/tesla.png" width=70%> </center> ] --- ## .center[The European Model:<br>Block Imports, Welcome Chinese FDI] <br> .center[Access to cutting-edge battery tech] .center[Create local jobs] .center[Increase supply chain resilience] -- .leftcol[ ### .center[**CATL**] $7.6B battery plant in Debrecen<br> 100 GWh/year for Mercedes-Benz, BMW, Volkswagen ] .rightcol[ ### .center[**BYD**] First European EV manufacturing facility<br> Producing BEVs and PHEVs for the European market ] --- # .center[US Opportunities] .leftcol[ ## .center[Technology Licensing Agreements] ### **Ford-CATL**: Licensing battery technology in a Michigan plant ### **Challenge**: CATL recently added to DOD's list of "Chinese military companies" ] -- .rightcol[ ## .center[Chinese FDI<br>into U.S.] ### **Gotion batteries**: Multi-billion dollar investments in Illinois and Michigan ### **Challenge**: Uncertainty around Foreign Entities of Concern (FEOC) status ] --- class: inverse, middle, center # Consumers aren't waiting for Washington --- background-image: url("images/public-opinion.png") background-size: cover --- <center> <img src="images/ford-xiaomi.png" width=80%> </center> --- class: inverse, middle, center ## The U.S. risks becoming an **"island of tailpipes"** --- # .center[Strategic Responses] ### 🇨🇦🇲🇽 **North American Integration**: Build sufficient scale through Canada and Mexico partnerships -- ### 📋 **Transparent Security Standards**: Develop compliance frameworks all connected vehicles must meet — *regardless of origin* -- ### 🔋 **Battery Manufacturing Focus**: Prioritize domestic production as platform for commercializing next-gen U.S. technologies -- ### 🤝 **Strategic Chinese Partnerships**: Pursue licensing agreements and FDI with targeted safeguards — not wholesale restrictions -- ### 📈 **Sustained Policy Support**: Restore and maintain IRA incentives long enough for investments to mature --- class: inverse background-image: url("images/blue.jpg") background-size: cover <br> # Thanks! <br> ### Slides available at <span class="white-text">jhelvy.com/slides</span> <style> .white-text a { color: white !important; } </style> .footer-large[.white[.right[ @jhelvy
<br> jhelvy.com
<br> jph@gwu.edu
<br> @jhelvy.bsky.social
]]] --- class: inverse, middle, center # Extra Slides --- class: center background-color: #FFF ## China's EV subsidies are on par with typical subsidies needed to start an industry <center> <img src="images/industrial_policy_subsidies_h.png" width=100%> </center> --- class: center ### China offers more affordable BEVs across all range categories <center> <img src="images/range-price-us-china-class-2024-labels-edit.png" width=100%> </center> Data scraped from autocango.com (China) and carsheet.io (USA) Interactive version at https://jhelvy.github.io/science-2025/ --- class: inverse, middle, center # How many dealerships are carrying EVs? --- class: center ## **4/5** dealers have _new_ BEV; **2/5** dealers have _used_ BEV <center> <img src="images/pev-percent-dealers.png" width=95%> </center> --- .leftcol70[ <center> <img src="images/bev_percent_listings_total.png" width=100%> </center> ] .rightcol30[ ## Most dealers carry very few EVs ] --- ## .center[EV percentage of vehicle listings for $40-50k price bin ] <center> <img src="images/bev_percent_listings_price_bin_subset.png" width=40%> </center> --- class: center <br> ## **Majority of BEV listings in high-price segments** <center> <img src="images/bev_percent_listings_price_bin.png" width=100%> </center> --- class: inverse, middle, center # How hard is it to get to a PEV dealer? --- background-color: #fff ### **Vehicle accessibility metric**:<br>Road travel time from census tract centroid to<br>nearest dealership with a target vehicle <center> <img src="images/pev-access-diagram.png" width=100%> </center> *Road travel times obtained using Open Source Routing Machine (OSRM) --- class: middle .leftcol65[ <center> <img src="images/travel_time_pev_cv_cfp.png" width=100%> </center> ] .rightcol35[ ## PEV travel times are converging towards conventional vehicle times 80% of pop: - CV in ~12 min - PEV in ~22 min (2024) - PEV in ~60 min (2016) ] --- class: middle .leftcol65[ <center> <img src="images/cfp_grid.png" width=100%> </center> ] .rightcol35[ ### Additional travel times (PEV - CV) by demographic blocks shows disparities Places with worse PEV access: - Lower income areas - Rural areas - Republican strongholds ] --- ### .center[**Local vehicle market**: Vehicles in 90 minute driving isochrone] .leftcol40[ Combine ML algorithm of predicted household vehicle budget (using Census data) with local market characteristics ] .rightcol60[ <center> <img src="images/local_market.png" width=100%> </center> ] --- background-image: url("images/top-four-1.png") background-size: cover --- background-image: url("images/top-four-2.png") background-size: cover