Summarize Paper logo SummarizePaper.com

AI summary · arXiv: 2204.03094v1 · econ.EM

Super-linear Scaling Behavior for Electric Vehicle Chargers and Road Map to Addressing the Infrastructure Gap

Alexius Wadell, Matthew Guttenberg, Christopher P. Kempes, Venkatasubramanian Viswanathan
Original paper Submitted to arXiv on 06 Apr. 2022 3 pages, 3 figures, 1 table License: CC BY-SA 4.0

How to read this summary? Illustrated guide

The essentials

  • Expanding charging infrastructure is crucial for widespread adoption of electric vehicles (EVs)
  • Researchers used scaling analysis to estimate EV infrastructure needs at a county-level resolution
  • Current deployment of EV charging stations scales super-linearly with population, unlike gasoline stations and other infrastructure
  • Abundance of EV stations is still in its early stages compared to more mature transportation infrastructures
  • Power delivery capabilities of existing gasoline stations and EV efficiencies were considered in estimating the infrastructure gap at the county level
  • Scaling analysis provides a unique forecast and practical way to assess infrastructure needs on a large scale
  • Super-linear scaling exponent in current build-out of EV charging infrastructure indicates interesting differences from established sub-linear scaling behavior in other infrastructures
  • Emerging infrastructures like EV charging stations may initially be driven by social phenomena before transitioning to standard infrastructural constraints as cities increase in size and densify
  • Scaling analysis opens up possibilities for further research in estimating future infrastructure requirements for emerging technologies like EVs.

Key points written by AI: they may contain errors, check them against the paper.

AI-generated keywords: scaling analysis electric vehicles infrastructure needs super-linear scaling social interactions

Read further

  • Comprehensive Summary
  • Layman's Summary
  • Blog article
  • Original abstract
Enabling widespread adoption of electric vehicles (EVs) requires a significant expansion of charging infrastructure. In order to estimate the infrastructure needs for EVs in the United States at a county-level resolution, researchers formulated the problem as a scaling analysis. Surprisingly, they found that the current deployment of EV charging stations scales super-linearly with population, which is different from the sub-linear scaling observed in gasoline stations and other infrastructure. This suggests that the abundance of EV stations is still in its early stages compared to more mature transportation infrastructures. By considering the power delivery capabilities of existing gasoline stations and taking into account EV efficiencies, the researchers were able to estimate the gap in EV infrastructure at the county level. This estimation provides a roadmap for future expansion of EV infrastructure. The use of scaling analysis allowed for a unique forecast in this domain. The researchers highlight that coarse-grained scaling analysis offers a practical way to assess infrastructure needs on a large scale. Scaling relationships reveal how features change with system size and can provide insights into dominant trade-offs and mechanisms governing a system. While sub-linear scaling behavior is well-established for infrastructure assets, such as gas stations, the super-linear exponent observed in the current build-out of EV charging infrastructure indicates interesting differences. The appearance of super-linear exponents in human systems has been observed in quantities that rely on social interactions, suggesting that emerging infrastructures may initially be driven by social phenomena before transitioning to standard infrastructural constraints. The researchers argue that as cities increase in size and densify, social interactions per person increase, leading to greater demand for emerging infrastructures like EV charging stations. To estimate future infrastructure requirements for emerging technologies like EVs, scaling analysis has not been widely used. Therefore, this application opens up possibilities for further research in scaling theory. In summary, this study demonstrates that expanding and refining charging infrastructure is crucial for widespread adoption of electric vehicles. By using a scaling analysis framework and considering population size, the researchers estimated the infrastructure needs for EVs at a county-level resolution. The super-linear scaling of current EV charging infrastructure suggests that it is still in its early stages compared to other mature transportation infrastructures. The estimation of the EV infrastructure gap provides a roadmap for future expansion, and the use of scaling analysis offers unique insights into forecasting future infrastructure requirements for emerging technologies.
Created on 01 Aug. 2023

Was this synthesis useful?

Score: 1

Why do we need votes?

Votes are used to determine whether we need to re-run our summarizing tools. If the count reaches -10, our tools can be restarted.

Ask questions about this paper to our AI assistant

How to chat with this paper? Illustrated guide

Compare with the PDF

You can also chat with multiple papers at once here.

Figures and plots

Ask about a figure by number or use its question button to request visual analysis. Up to two figures and four image panels per question. Values read from plots are approximate; check the original data.
AI assistant instructions?

Tools for this paper

Explain it differently 3 free rewrites left today
Save to a collection Group papers in collections to compare them, chat with them and export them. Create a free account Log in
Export BibTeX · RIS (Zotero, Mendeley, EndNote) · Obsidian (Markdown) · Notion (Markdown)

Advanced tools Premium

For researchers who follow a field closely: citation alerts, journal-club slides, version comparison and Zotero export. Discover Premium

Zotero
Citation alerts Citation alerts guide
Who works on it? The people and institutions who publish on its topic
Journal club Premium+ Slides to present it Propose for the journal club
Versions What changed between versions
Talk about it in public Premium+ Press kit: release, questions, hype check Press kit guide

Papers around this one

See them on a map

Ranked by how close their title and abstract are to this paper’s. Select some to read them together.

Closest papers

  1. Related
    Deep Learning Macroeconomics
    Rafael R. S. Guimaraes · 2022 · Econometrics · AI summary available
    Abstract

    Limited datasets and complex nonlinear relationships are among the challenges that may emerge when applying econometrics to macroeconomic problems. This research proposes deep learning as an approach to transfer learning in the former case and to map relationships between variables in the latter case. Although macroeconomists already apply transfer learning when assuming a given a priori distribution in a Bayesian context, estimating a structural VAR with signal restriction and calibrating parameters based on results observed in other models, to name a few examples, advance in a more systematic transfer learning strategy in applied macroeconomics is the innovation we are introducing. We expl…

  2. Related
    A Descriptive Method of Firm Size Transition Dynamics Using Markov Chain
    Boyang You, Kerry Papps · 2022 · Econometrics · AI summary available
    Abstract

    Social employment, which is mostly carried by firms of different types, determines the prosperity and stability of a country. As time passing, the fluctuations of firm employment can reflect the process of creating or destroying jobs. Therefore, it is instructive to investigate the firm employment (size) dynamics. Drawing on the firm-level panel data extracted from the Chinese Industrial Enterprises Database 1998-2013, this paper proposes a Markov-chain-based descriptive approach to clearly demonstrate the firm size transfer dynamics between different size categories. With this method, any firm size transition path in a short time period can be intuitively demonstrated. Furthermore, by utili…

  3. Related
    Digital Divide: Empirical Study of CIUS 2020
    Joann Jasiak, Peter MacKenzie, Purevdorj Tuvaandorj · 2023 · Econometrics · AI summary available
    Abstract

    Canada and other major countries are investigating the implementation of ``digital money'' or Central Bank Digital Currencies, necessitating answers to key questions about how demographic and geographic factors influence the population's digital literacy. This paper uses the Canadian Internet Use Survey (CIUS) 2020 and survey versions of Lasso inference methods to assess the digital divide in Canada and determine the relevant factors that influence it. We find that a significant divide in the use of digital technologies, e.g., online banking and virtual wallet, continues to exist across different demographic and geographic categories. We also create a digital divide score that measures the s…

  4. Related
    A multi-cell experimental design to recover policy relevant treatment effects, with an application to online advertising
    Caio Waisman, Brett R. Gordon · 2023 · Econometrics · AI summary available
    Abstract

    Experiments are an important tool to measure the impacts of interventions. However, in experimental settings with one-sided noncompliance, extant empirical approaches may not produce the estimates a decision-maker needs to solve their problem. For example, these experimental designs are common in digital advertising settings, but they are uninformative of decisions regarding the intensive margin -- how much should be spent or how many consumers should be reached with a campaign. We propose a solution that combines a novel multi-cell experimental design with modern estimation techniques that enables decision-makers to recover enough information to solve problems with an intensive margin. Our …

  5. Related
    Econometric Inference with Machine-Learned Proxies: Partial Identification via Data Combination
    Lixiong Li · 2026 · Econometrics · AI summary available
    Abstract

    Empirical researchers increasingly use upstream machine-learning (ML) methods to construct proxies for latent target variables from complex, unstructured data. A naive plug-in use of such proxies in downstream econometric models, however, can lead to biased estimation and invalid inference. This paper develops a framework for partial identification and inference in general moment models with ML-generated proxies. Our approach does not require restrictive assumptions on the upstream ML procedure, such as consistency or known convergence rates, nor does it require a complete validation sample containing all variables used in the downstream analysis. Instead, we assume access to two datasets: a…

  6. Related
    Machine Learning Estimation of Heterogeneous Treatment Effects with Instruments
    Vasilis Syrgkanis, Victor Lei, Miruna Oprescu et al. · 2019 · Econometrics · AI summary available
    Abstract

    We consider the estimation of heterogeneous treatment effects with arbitrary machine learning methods in the presence of unobserved confounders with the aid of a valid instrument. Such settings arise in A/B tests with an intent-to-treat structure, where the experimenter randomizes over which user will receive a recommendation to take an action, and we are interested in the effect of the downstream action. We develop a statistical learning approach to the estimation of heterogeneous effects, reducing the problem to the minimization of an appropriate loss function that depends on a set of auxiliary models (each corresponding to a separate prediction task). The reduction enables the use of all …

  7. Related
    Adaptive Pricing in Insurance: Generalized Linear Models and Gaussian Process Regression Approaches
    Yuqing Zhang, Neil Walton · 2019 · Econometrics · AI summary available
    Abstract

    We study the application of dynamic pricing to insurance. We view this as an online revenue management problem where the insurance company looks to set prices to optimize the long-run revenue from selling a new insurance product. We develop two pricing models: an adaptive Generalized Linear Model (GLM) and an adaptive Gaussian Process (GP) regression model. Both balance between exploration, where we choose prices in order to learn the distribution of demands & claims for the insurance product, and exploitation, where we myopically choose the best price from the information gathered so far. The performance of the pricing policies is measured in terms of regret: the expected revenue loss cause…

  8. Related
    Understanding Volatility Spillover Relationship Among G7 Nations And India During Covid-19
    Avik Das, Dr. Devanjali Nandi Das · 2022 · International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.10, Issue 6, pp.c261-c269, June 2022 · AI summary available
    Abstract

    Purpose: In the context of a COVID pandemic in 2020-21, this paper attempts to capture the interconnectedness and volatility transmission dynamics. The nature of change in volatility spillover effects and time-varying conditional correlation among the G7 countries and India is investigated. Methodology: To assess the volatility spillover effects, the bivariate BEKK and t- DCC (1,1) GARCH (1,1) models have been used. Our research shows how the dynamics of volatility spillover between India and the G7 countries shift before and during COVID-19. Findings: The findings reveal that the extent of volatility spillover has altered during COVID compared to the pre-COVID environment. During this pande…

Go further

How are these papers found?

We compare the title and abstract of this paper with those of every paper summarized on this site, and with the papers Semantic Scholar recommends, those it cites and those citing it (for a much-cited paper, its most recent citations and, from OpenAlex, the most cited ones). Among the prior work and the follow-ups, the papers that use each other in depth, a method it uses and the much-cited follow-ups come a little higher. The levels (very close, close, related) come from the similarity of the texts, not from a judgment of quality; * marks a paper without an abstract, compared by its title. The sentence under a paper is written by an AI from the two abstracts: check it in the papers. A paper marked not relevant by three readers leaves the list.

For now only among the papers summarized on this site: search again to add the prior work, the follow-ups and Semantic Scholar’s recommendations.

The previous summary was created more than a year ago and can be re-run (if necessary) by clicking on the Run button below.

Disclaimer: The AI-based summarization tool and virtual assistant provided on this website may not always provide accurate and complete summaries or responses. We encourage you to carefully review and evaluate the generated content to ensure its quality and relevance to your needs.