House at dusk with lights at the wondows. Photo by Valentina Locatelli on Unsplash

Peer-to-peer energy: what can we learn from more than a decade of Airbnb?

22 June, 2021

Michael Fell

Reading time: 5 minutes

CREDS researcher Mike Fell discusses the background to and key findings of his recently published research that set out to discover what we can learn from Airbnb about the potential impacts of peer-to-peer energy trading.

The first time I stayed in an Airbnb was in Leith, Edinburgh, when I was there for the Fringe. I paid £26 per night for a nice room in flat over a pub. That’s a fraction of what I would have paid in a hotel – if I’d been able to find any vacancies. (Plus – pub.) I’ve stayed in many Airbnb’s since then, and appreciate the prices, great locations, and personal contact.

There are plenty of services aspiring to be the Airbnb of energy. These peer-to-peer (P2P) schemes let people buy and sell electricity directly between each other. For example, if I had a solar panel and was in the same scheme as you, you could buy my excess generation. Similar drivers exist in energy as for accommodation, with the promise of savings for buyers, better income for sellers, and potentially a more personal experience.

But while disruptive new services like Airbnb or its equivalents in energy hold many benefits, they also pose challenges. In the case of Airbnb, we’ve probably all heard about its potential to drive up rents and displace local people.

But the world has had more than a decade, and millions of stays, to learn about the impacts of Airbnb. In energy, all we have is a relative handful of small scale trials, often with small numbers of participants. This means that policymakers, regulators, and service designers are potentially flying blind when it comes to anticipating the impacts of the emergence of P2P-like arrangements in energy.

Drawing on experience in other sectors

What if that comparatively large amount of evidence on the impacts of Airbnb could be usefully drawn on to provide some insight here? You might argue that the situations and services are just too different. Take the example of increasing rents I mention above. That wouldn’t be expected to happen in energy – if anything, as more locally generated power is traded locally, prices are likely to fall or at least remain lower than they otherwise would be.

My solution was to attempt to bring a “realist” approach. Instead of just looking at evidence on the distributional impacts (i.e. different impacts on different population segments) of Airbnb, I also focused on the mechanisms by which these impacts came about, and the contexts in which they happened.

For example, one of my findings is that there is quite consistent evidence of the potential for discriminatory practices to emerge in Airbnb, such as preferentially accepting guests on the basis of race, gender, or sexuality. I suggest the mechanism here is the introduction of more personal information and choice, into a context of prejudices we know exist to some degree in society. I argue that this could plausibly be the case in energy too, and recommend monitoring for this if/when P2P emerges more broadly.

Possible lessons from Airbnb

Based on screening a large number of research papers on Airbnb, I identified 28 with evidence on distributional impacts. Consideration of the mechanisms and contexts underlying any outcomes led me to infer the following for similar services in energy:

  • The benefits of selling services in P2P energy trading schemes would be expected to accrue disproportionately to those living in areas with grid management challenges (similar to Airbnbs in desirable areas), who are younger and more highly educated…
  • …but households already in possession of (or able to acquire) generating technology such as PV panels are strongly positioned to benefit.
  • Less affluent households may be expected to participate and benefit more in areas with network constraints than those without.
  • However, this will probably be to a lesser degree on average than the more affluent, since they are more likely only be able to trade in less valuable services (such as provision of flexibility, compared to supply of electricity) – similar to letting out a room or bed on Airbnb, compared to an entire place.
  • See also the finding on potential from discrimination mentioned in the previous section.

While some of this may sound predictable, I argue that the existence of consistent evidence for these impacts from another sector, along with reasoning for why the mechanisms might apply, should lead stakeholders to give these concerns more weight. I suggest that ways to pre-emptively address them while allowing for much-needed innovation could include: requirements to monitor impacts in the above dimensions, incentivising diversity, considering some reasonable limits on trading choices, diversifying what can be traded, and better informed targeting.

In a time of climate emergency, disruption of the kind brought by Airbnb to the accommodation sector is both inevitable and necessary. Regulation and fears of adverse effects should not stand in its way. But that doesn’t mean that we shouldn’t do everything we can to anticipate the ways that benefits for all could be maximised, or harm caused, and then design and monitor carefully, stepping in where necessary. I think learning from other sectors with deeper experience of innovation of similar kinds is a vital part of this anticipatory work.

Link to full paper: Anticipating distributional impacts of peer-to-peer energy trading: Inference from a realist review of evidence on Airbnb

Banner photo credit: Valentina Locatelli on Unsplash