The impact of COVID-19 related regulations and restrictions on mobility and potential for sustained climate mitigation across the Netherlands, Sweden and the UK: A data-based commentary.

This article identifies behavioral changes during the COVID-19 pandemic and maps potential strategies to capitalize on these changes in the context of climate change mitigation policies. As a habit-breaking event, this pandemic is certainly an opportunity to achieve positive behavioral change in this context, and the authors make an effort to map these opportunities based on the COM-B model of behavior.

In general, I agree with the arguments presented by the authors but raise three points I believe will make the discussion more comprehensive and help provide some more evidence to back some of the claims made in the paper.

The first point concerns the potential of long-term changes in urban structure and land uses , that have not been considered in the paper. While I agree that work-from-home can considerably reduce commuting carbon emission under the present urban structures, it is not clear what the long-term effect of this behavioral change will be in residential location patterns. Commuting time is a key factor for households when deciding where to live and given that in most cases employment is located in central areas, this is a key factor determining how far to live from the city center. In the cases of cities well served by transit, this incentivizes moving to locations with good transit access.  Released from this constraint, households are now free to choose locations even farther away from the city center and might not prioritize transit access as much, fueling urban sprawl, which would have the negative effect of increasing car dependency and reducing the viability of transit services.  Combined with the trends reported by the authors of reductions in transit use and increases in private vehicle use, there is a non-negligible possibility that current behavioral trends result in increased emission in the long term, and this should be considered in your analysis.

There is also the issue of the city center decline as a result of less people visiting, which also fuels urban sprawl as central locations might not be as profitable for firms and retailers as before, and these might opt for non-central locations with lower land prices. In such a context, it would seem difficult to “increase opportunities to use public transport through improved infrastructure,” since its viability might actually be reduced. The same can be said for any other type of transit-oriented development and/or compact city strategies which rely on certain levels of density and land use mixes to yield benefits.

Of course, the levels of uncertainty regarding any future predictions are very high, but in the same spirit of this paper that seeks to map potential outcomes, long-term changes in urban structure and land uses should be considered, including potential negative effects.

The second point concerns the issue of the durability of behavioral interventions . In the context of transportation planning, travel demand management strategies have been used to nudge individuals into more socially or environmentally desirable behaviors, but my concern with this kind of approach is that the evidence on the durability of behavioral changes is scarce. That is, how long do these nudges really last for is not clear, to the best of my knowledge, in the literature. As such, if the authors could show some evidence regarding the durability of these strategies, it would certainly strengthen the conclusions presented.

In the particular context of the COVID-19 pandemic, there is already some evidence of mobility patterns slowly returning to some extent to pre-pandemic levels. This is the case for Japan, which is the context I am most familiar with, but I would expect similar trends elsewhere as well. As such, it would be useful to add a temporal dimension to the changes in transport use you report in Chapter 2 in order to evaluate which effects are lasting, and which are not, at least up to the most recent data point available. Ideally you would add some plots summarizing key findings graphically. Data from the Google Mobility Panel for example would be useful in this regard ( https://www.google.com/covid19/mobility/ )

The third point concerns the validity of the model presented . This is important since you are making policy recommendations. Although I understand this is a commentary paper, I still think it necessary to add some discussion on the validity of the model, and the uncertainty associated with predictions of future behavioral trends.

15 ^th July 2021

Title :  The impact of COVID-19 related regulations and restrictions on mobility and potential for sustained climate mitigation across the Netherlands, Sweden and the UK: A data-based commentary.

Authors : Elizabeth Corker, Kaloyan Mitev, Astrid Nilsson, Milan Tamis, Thijs Bouman, Stefan Holmlid, Fiona Lambe, Susan Michie, Matthew Osborne, Reint Jan Renes, Linda Steg, Lorraine Whitmash

Journal : UCL Open: Environment

Level of importance: 4/5

Level of validity: 3/5

Level of completeness: 4/5

Level of comprehensibility: 3/5

Competing interests: None

Dear Authors and Editor,

This is an interesting paper highlighting behaviour changes across the Netherlands, Sweden and the UK during 2020 in response to COVID- 19 restrictions and regulations. It aims to understand these changes for potential use in mitigating climate change.

Here are the main points:

• First part of introduction (Covid-19 related changes and impacts) could benefit from references to publications on the impacts of Covid-19 on air quality or the environment (reducing carbon emissions etc.). Give some examples and references.
• As referring to “global warming” as “climate change” will cause confusion while reading, it would be better to use only the word “climate change”.
• There is a clear change in transportation behaviours, however it would be good to look at total energy and fuel consumptions in these countries to make a good comparison and to clarify whether total consumption shifted from transportation to home usage or not.
• In the paragraph starting “In April 2020, COVID-19 associated restrictions…”, rephrase the sentence of “By the end of March 2020, global road transport activity was almost 50% below the 2019 average (International Energy Agency, 2020).” Because it does not accurately reflect the IEA report.
• As mentioned in the paper, there is an increase in electric car sales and a decrease in internal combustion engine car sales. As there is no concrete evidence to show Covid-19 related behavioural changes caused these increases and decreases, it would be good to mention other factors such as shutdowns of the auto industry and suppliers around the world during several weeks due to Covid-19 restrictions. There is a decrease in total car sales due to decline in car production and a lack of availability due to showroom closures. It is not just behaviours of people working from home or changes in transportation. In addition, there is a clear increasing trend in electric vehicle sales over the years. In Europe, sales (plug-in hybrid electric vehicles and battery electric vehicles) increased by over 40% in 2019 compared to 2018 (see the figure (IEA, Global electric car stock, 2010-2019, IEA, Paris https://www.iea.org/data-and-statistics/charts/global-electric-car-stock-2010-2019)).
• Even if there is limited data available, it would be good to define “electric vehicles” in one way. Is it just referring to electric cars, electric bikes, or all of them? There is an inconsistency in the statements:
  • In the Netherlands data: The sales of Electric Vehicles (Plug-In Hybrid Vehicles and Fully Electric Vehicles) increased in 2020 compared to 2019, with sales of Electric Bikes (speed pedelecs) and mopeds (electric and manual) doubling during summer 2020 compared to summer 2019.
  • In Swedish data: Of car sales made, Electric Vehicles accounted for 32.2% compared to 11.3% in 2019.
  • In the UK data: Sales of Electric Vehicles (Fuel Cell Electric Vehicles and Plug-in Hybrid Electric Vehicles) increased by 185.9% and 91.2% respectively.
• In the paragraph starting “ Data from Transport for London showed tube journeys decreased by 94% or 100 million… ”’ it is enough to give the changes in percentages only. Give details (changes in million) in another sentence.  Also, define the period of the changes in bus use (Transport of London and Citymapper). Compared to what?
• After giving all details in the section on countries’ data, it would be good to have a table showing differences and similarities in terms of changes (percentage of people working from home, the use of public transport, etc.) This will make it easy to understand for readers.
• In Figure 1, it is a bit hard to read at first glance, so increase fonts and image quality.
• In the paragraph starting “ We cannot predict with certainty how citizens will behave…”, it lacks balance, and it would be good to think about some of the what-ifs. For example, some people want to go back to their normal routines (maybe even worse in terms of consuming and travelling etc. as they were stuck at home for a long time) pre-Covid-19.
• As many people stay at home and work from home during the pandemic, online orders, and shopping (food, electronic, cosmetic, fashion etc.) increased and gave extra pressure on delivery and shipment (many motorbikes and delivery vehicles), It would be good to have a look at data and to think about how this affected the transportation (maybe increasing carbon emission).
• Finally, even if the conclusion section gives the main arguments, it would be good to make it stronger.

In general, this is a useful commentary bringing together current mobility information and behaviour-change psychology to provide policy recommendations for transportation in the context of COVID-19 and climate change.  The evidence-gathering to inform the commentary is a strong feature of the article, but also a potential weakness in so far as the limits of evidence from trends are not always acknowledged in the arguments that follow from them.

Sections 2 and 5: EV trends and COVID-19. You acknowledged the limitations of inferences from trends alone in section 6.  A specific instance of this limitation is in section 2. Namely, trends in EV purchasing are difficult to link to the COVID 19 pandemic, a priori.  This requires you to present either empirical evidence or a plausible argument to support such a link.  If either (or both) of these are available, please present them to the reader alongside the information about EV trends in section 2. If neither are available, then please acknowledge a reason for including this information in the article other than a cause-effect or correlational link.  For instance, could EV trends be relevant in terms of a moment of change afforded for EV adoption (which would help to justify inclusion of section 5.3., which seems to have little to do with COVID-19)?

Page 4: paragraph beginning "In April 2020, COVID-19 associated restrictions [...]" is a bit awkward, because the reader is probably expecting treatments at the national level (given the previous text), so to return to the global level at this stage is confusing. I suggest indicating this discursion using language (e.g., beginning with a clause like "Globally, ..." or "In a global context..." or "Concerning global COVID 19 associated restrictions...")

Section 2.1.: the information presented in 2.1. is lacking the close numerical/detailed support that is available in sections 2.2. and 2.3.  For a 'data-based commentary', descriptions such as 'almost doubled', 'some', 'has decreased', 'an increased use of', etc., are not informative unless supported with clear and accurate evidence from the sources, because an 'increase' may be by a single unit and 'almost doubled' can be negligible if the original number was a single unit.  Similarly, for subjective descriptors such as 'low' (p.4.) it is especially important to present information demonstrating why (in your opinion, which you want the reader to share) such descriptors are being used, rather than more neutral ones.  I acknowledge that this may be due to the limitations of the evidence you have available (which seems to often be grey literature), in which case these limitations should be reported alongside this summary of 'data'.

Page 6: final paragraph of section 2.3.  There should be a clear statement of the time period for this statistic; as this is a single-sentence paragraph, it may also be an opportunity for comment on issues to do with linking EV trends and COVID 19, mentioned above?

Page 8: "Government restrictions on [...] transport."  It would be useful to have factual information on the effectiveness and or extent of these measures. I realise that Appendix 3 gives a useful overview on measures in general and their timing, but adding information on these measures specifically would support this argument about opportunities for public transport.  (If there is not strong evidence on efficacy or information on extent of measures and their consequences, then perhaps qualify these as 'good ideas' rather than as events that were known to provide such opportunities.)

Page 9: "for example with the use of low-traffic neighbourhoods, [...] motivation for using private transport." It would be useful to support this by citing evidence of the efficacy of such measures.

Page 10: "Employees working from home reduce their transport-related behaviours."  This may be so in practice, but it does not follow invariably, as commuting is not the only purpose of travel.  A slight re-phrasing (e.g., reduced commuting) would be prudent.

Section 5.1.: it would be worth acknowledging that not all work can be done from home, this text applying mostly to 'white collar' work.  This may be increasingly more relevant in the countries focused upon in this article, with ongoing shifts towards service and technology sector employment, but there remains a necessary and substantially subset of people for whom work is location-based or travel-based by necessity.  This may be worth a sentence to acknowledge, perhaps providing statistics about capabilities of reductions (i.e., how many people can work from home), if they are available.

Page 10: "Intentions to use private vehicles more, after restrictions end, were identified, and sales of Electric Vehicles increased in all three countries."  Given that this is a review of evidence, it would do no harm to remind the reader of the previously cited evidence for these points by citing it again.

Please provide a complete reference for van Hagen, M., & Ton, D. (2020). Corona’s impact on the behavior of train passengers.  This information is insufficient for identifying the source in question.