Invest Green to Drive Green: Investigating Requirements and Investment Needs for Road Transportation Electrification in the United States, and Policy Measures to Consider
Notice bibliographique
Résumé
Electrification is widely regarded as the most promising and primary pathway to the long-term decarbonization of road transportation, making it an important strategy for decreasing transportation-related greenhouse gas (GHG) emissions. Previous studies have investigated various EV-related topics, ranging from battery technology to the total cost of owning an EV. All studies acknowledge that the upfront investment needed for an EV transition will be significant. Since then, there have been studies and modeling methods designed to understand cost requirements to meet fuel efficiency targets and GHG emission reduction goals in the transportation sector. Most of these models target a specific topic or scope, such as the cost associated with vehicle electrification versus that with hydrogen fuel cell electric vehicles, or the cost associated with light-duty (LD) electrification versus that for medium- and heavy-duty (M/HD) vehicles. However, very few studies have looked into both the production capacity and associated upfront, capital investment need (which is different than cost) for EV and related battery production in the U.S. at the national level, in relation to different scenarios with different EV penetration levels. Furthermore, an investigation of comparing such investment to investment needs for a national EV charging network has received far less attention in scholarly research. The second chapter in this dissertation estimates and compares the planned downstream battery production capacity and vehicle assembly capacity for LDs to the needed ones in the U.S. under four different scenarios by 2030 and 2050. This is done through a unique way of collecting data and projecting future production capacity needs: publicly available original equipment manufacturer (OEM) and battery suppliers’ investment announcements and plans. Though this is done manually, it assures the accuracy and timeliness of data needed specifically for this study. For example, announcements for R&D are not included in this study. In the second chapter, it is found that additional investments are needed soon to scale up the EV production and a doubling of the announced investment would be needed through 2035 to sustain ambitious EV sales scenarios. To date, more investments have been announced for downstream EV battery production than for vehicle assembly, possibly indicating that OEMs may still be waiting to see how fast EV sales can ramp up in different areas, depending on consumer demand and the readiness of the local EV recharging network. To keep production plans on track, consistent long-term commitment to the electrification transition and a less-disrupted EV supply chain may help.It is clear that EV production scale-up and EV charging infrastructure buildout need to happen fast at the same time. It becomes evident to me that it would be necessary to understand the level of production capacities and relevant investment requirements for charging infrastructure, to provide insights for both the industry and policymakers to better allocate their limited funding and budget timely. The third and fourth chapter of this dissertation investigate and estimate the future charging infrastructure requirements and investment needs for LDs and M/HDs, respectively. In both chapters, the main method is rooted in the traditional systematic literature review and meta-analysis. In the fourth chapter which focuses on charging infrastructure requirements for M/HDs, several major fleets were interviewed due to the lack of literature and to ensure the assumptions used in this study are not far away from things happening on the ground. In the meta-analyses, this dissertation estimates the charger-to-vehicle ratio and the charging investment per vehicle, and applies these values to the existing U.S. Transportation Transition Model where vehicle sales and stock value are already available on a yearly basis in all scenarios. This makes a direct connection between vehicle stock and charging needs and shows various avenues for future research. The third and fourth chapter find that a significant amount of investment needs, either from the public or the private sector, is needed to scale up charging infrastructure for both LDs and M/HDs in the U.S. While the amount of investment needed for battery production and vehicle assembly is considerable (e.g., $56 billion and $70 billion through 2030 for LD downstream battery production and LD vehicle assembly, respectively, for the most ambitious scenario considered in this dissertation), that for charging infrastructure is even higher, especially for medium- and heavy-duty vehicle charging in the U.S. (e.g., $72 billion and $99 billion for LD and M/HD charging infrastructure buildout through 2030, respectively, for the most ambitious scenario considered in this dissertation). In terms of the timing of the investment needs, this dissertation indicates that the annual investment needs for charging infrastructure usually peak around 5 years after the investment for battery and vehicle production peak. Nevertheless, there are many uncertainties and challenges in estimating charging needs due to, for example, charging technologies, charging patterns, and electrical upgrades on the utility side; future research should investigate how these may affect charging infrastructure needs in detail. Regulatory policies and financial incentives usually play an important role in helping meet different requirements and investment needs in the road transportation electrification transition. In the last chapter of this dissertation, the role of EV trade is explored, to support six countries’ ambitions for meeting their EV sales targets by 2030. The method used in the second chapter is used in chapter five as well, to estimate and project the planned and needed EV production capacity in the U.S., Canada, Mexico, Europe, Japan, and South Korea. This dissertation considers all six countries and regions as a combined market where EV trade can happen freely. It is found that, in general, considering EV trade could help narrow shortfalls in capacity in one country by up to 20% in some cases. Going forward, it may be helpful to strengthen domestic EV production capacity and consider diversifying EV sources from other markets like China and India. Importantly, clear and strong policies and incentives are needed to ensure that planned capacities will be realized and the investment to support additional capacity will be spurred.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».