Rated 3.5 of 5.
Level of importance:
Rated 3 of 5.
Level of validity:
Rated 2 of 5.
Level of completeness:
Rated 4 of 5.
Level of comprehensibility:
Rated 4 of 5.
|COM-B, Moment of change, Climate change, COVID-19, People and their environment, Behaviour change
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.