the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 05 Oct 2023
Early opportunity signals of a tipping point in the UK’s second-hand electric vehicle market
Abstract. The use of early warning signals to detect the movement of natural systems towards tipping points is well established. Here, we explore whether the same indicators can provide early opportunity signals (EOS) of a tipping point in a social dataset – views of online electric vehicle (EV) adverts from a UK car selling website (2018–2023). The daily share of EV adverts views (versus non-EV adverts) is small but increasing overall and responds to specific external events, including abrupt petrol/diesel price increases, by spiking upwards before returning to a quasi-equilibrium state. An increasing return time observed over time indicates a loss of resilience of the incumbent state dominated by ICEV advert views. View share also exhibits increases in lag-1 autocorrelation and variance consistent with hypothesised movement towards a tipping point to an EV-dominated market. Segregating the viewing data by price range and year, we find a change in viewing habits from 2023. Trends in EOS from EV advert views in low-mid price ranges provide evidence that these sectors of the market may have passed a tipping point, consistent with other evidence that second-hand EVs recently reached price parity with equivalent ICEV models. We provide a case study of how EOS can be used to predict the movement towards tipping in social systems using novel data.
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RC1: 'Comment on egusphere-2023-2234', Lennart Baumgärtner, 05 Mar 2024
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General comments
The authors apply statistical methods from the natural systems literature to a social system: the second-hand electric vehicle market in the UK. Based on statistical indicators, they identify “early opportunity signals” (EOS) of a “tipping point” in the overall EV resale market and different niche markets thereof.
They introduce a novel, interesting dataset of the second-hand EV market in the UK with high temporal resolution and details on different heterogeneous sub-markets (particularly different price ranges). They apply existing statistical methods to the new social context of this dataset that help identify non-linear dynamics in social systems. Clearly, these dynamics are very interesting to a number of research fields, as well as policymakers and the private sector. The work presents a meaningful contribution to the field, both in its methods and their application.
At the same time, the fact that the methods and concepts are novel in the field of social science means that they require precise definitions and detailed explanations. As discussed below, the authors should include more details to enable a comprehensive review of their method and make it both replicable and interpretable. I am also concerned about the statistical significance of some of the results.
Provided the authors provide these details and a second review of their method considers them adequate, the work is well worth publishing.
Specific comments
- The authors use a number of concepts without providing a clear definition thereof. First and foremost, it is not clear what constitutes a “tipping point”. While this concept may be commonly applied to natural systems, it is unclear what it means in the case of social systems. Particularly in light of S-curve diffusion of technologies, whereby new technologies grow exponentially over several decades (as described by Grübler and cited by the authors), it is unclear how a tipping-point can be interpreted. Depending on the definition, this may have implications for the interpretation of the results and the methods used. Other terms that are used without clear definitions in the social context include “critical slowing down”, or “early warning signals”.
- The authors make numerous statistical estimations based on their data. In order to make this work replicable to this and other data sets, it is essential to provide details of these methods. This is especially important in cases where the description of their method is somewhat ambiguous, such as the measurement of the “lag-1 autocorrelation (AR(1))” or the “detection algorithm called asdetect”. I highly recommend the authors include a technical appendix to this work that shows the estimators used and formulas for their stochastic models. This appendix could include:
- Data cleaning procedures (such as inflation adjustments)
- The AR(1) and variance estimators and models
- The kernel smoother used to de-trend the time-series data
- The estimator for the Kendall tau correlation
- Sensitivity analyses and significance tests
- The authors have made a substantial effort to test the statistical significance of their results. There are, however, a few open questions that should be addressed:
- Figure 1 g): shows the return time for the 5 different spikes investigated. Given the limited data and the fact that there are no error bars associated with each data point, it is unclear if the increase in return time is statistically significant.
- Figure 2: the authors use a variance and AR(1) estimation of the noise over time. Apart from the fact that it is not clear what they estimate in the AR(1) case, the variance estimation may be biased due to the fact that the noise could have heavy tails, as indicated by the spikes in the data. Also, note that most variance estimators assume i.i.d. normal residuals. This contradicts the earlier analysis of increasing return times.
- Figure 2: The fact that the time-series data is bounded from below (by zero) may pose an issue to the noise estimation, particularly after de-trending the time-series. It is unclear how this affects the noise during the early phases of the data
- Figure 2: The use of a 2-year moving average window to estimate the AR(1) and variance means that individual points in the resulting time-series are strongly correlated. A single event, such as a spike in the data, will impact the variance estimation for all windows that include that event. This may explain the large jumps in the time-series observed in Figure 2 c) and d), as well as Figure 4. The significance test performed by the authors (re-shuffeling the time-series) may not be sufficient to proof statistical significance in the presence of strong correlation since it discards the autocorrelation structure of the data.
- Figure 5: Given the estimation challenges described above, it is unclear how significant the results of Figure 5 are.
Technical corrections
- The caption of Figure 2 does not match what is shown.
- In section 3.2, the authors mention that EVs have not yet reached price parity with ICEVs. It would be good to confirm that with newer Chinese EVs entering the European market and the fact that policy-enabled price parity has already been reached (see section 1 and section 4)
- Figures 2 and 4 are unclear on the cost units used (Figure 2 shows no unit on fuel costs, Figure 4 does not indicate any inflation adjustments)
- The authors should comment on the nature of the second-hand vehicle market. The fact that second-hand vehicles are of different ages may distort their price.
- The authors should comment on how they obtained the specific events listed in section 3.1 (and why they have excluded others)
Citation: https://doi.org/10.5194/egusphere-2023-2234-RC1 -
RC2: 'Comment on egusphere-2023-2234', Gianluca Grimalda, 19 Apr 2024
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The paper aims to apply techniques of analysis from earth sciences - specifically, the identification of Early Warning Systems - to economic choices. The main idea is that the slowness with which a system comes back to the current steady state can be interpreted as a sign of “stress” that signals the future convergence to another steady state. This methodology applies, in particular, to systems that are characterized by tipping point dynamics. In this paper, the authors apply this technique to the UK second-hand electric car markets.
I think the methodology is fascinating and could provide valuable insights into social and economic dynamics. I think the paper could be improved if the authors tried to incorporate more the specificity of social and economic steady states into their analysis. I also think the authors should critically discuss the extent to which their analysis should really be indicative of a tipping point switch.
Major points:
- The authors do not analyse in detail the theoretical underpinnings of what they call a switch from a market dominated by Internal Combustion Engine Vehicles (ICEV) to one dominated by Electric Vehicles (EV). I think the reader may benefit from a general treatment of a market equilibrium. A market equilibrium is characterized by equality of supply of demands with respect to a price level. From an economics perspective, the modelling of such a steady state would take into account that the purchase of a car is an investment, i.e., an action carried out today that will bring out benefits over the future. The buyer will take into account factors such as (a) the current price; (b) the future price level (if s/he wanted to resell the car); (c) the expected energy costs associated with the use of the vehicle; (d) accessibility of infrastructure, etc. The buyer may also consider non-economic factors such as (e) the environmental impact of using the car; (f) its popularity with the public, etc. The buyer will typically compare all the above factors with respect to several types of vehicles. At its simplest level, the buyer will compare the above factors for EV and CEV. The producer’s decision will also involve a comparison between the profitability of investing into EVs or CEVs taking into account expected demand. One could think that this interaction is characterized not just by two equilibria but by an infinite collection of equilibria ranging from no use of EVs to universal use of EVs, all of which are characterised by equality in demand and supply at a certain price level. In order for a product to “dominate” a market, one would want to introduce some non-linearity in the system. I think the most obvious way would be to assume a model of technological change characterised by a logistic-like curve, in which technological progress is steep when the technology is “young”, and it reaches a plateau when it is “mature” (see references below). The mature technology can then be overtaken by a new young technology. In the case of ICEV, the switch from increasing returns to scale to decreasing returns to scale in technological progress may be determined by institutional or economic constraints in access to a certain natural resource - fossil fuels in this case. This model would create a situation of multiplicity of equilibria with a tipping point dynamics. In general terms, we could also model this situation as a “coordination game” with a representative consumer and a representative producer, in which the two equilibria are characterised by high consumer demand for EV and high producer investment in EV, or low consumer demand for EV and low producer investment in EV. A coordination game is characterised by a tipping point dynamic.
- The analysis provided by the author is fascinating and suggestive of possible tipping point dynamics. However, it would take a big leap of faith to believe that the system will now switch to an EV-dominated market on the basis of four episodes of critical slowing down in the advert market. In the lack of extensive analyses of similar social dynamics, it becomes impossible to ascertain whether this evidence is sufficient or not. Relatedly, I felt that the claim that “a tipping point has been crossed for some price ranges” (line 221) is a bit of an overstatement. I suppose the authors could slightly reposition their paper in saying that they provide pioneering analyses of early ‘opportunity’ signals (EOS) predicting tipping points in social systems, which will have to be complemented by re-examination and further research to be thoroughly evaluated.
- It could be that the authors are right and product adverts are indeed successful EOS predicting tipping points in socio-economic systems. However, I am left wondering whether other signals may be used in addition to product adverts. In the light of the above market analysis, one could think of supply-side indicators, such as stock exchange fluctuations, firms’ investment, firms’ sales. From the demand side, google searches may also be used. All these indicators may converge into a dashboard of EOS. To the very least, the authors should critically discuss why they opted for the specific indicator they used, as well as the pros and cons of using a dashboard of indicators rather than just one indicator.
- It may also be helpful to characterize other differences between social systems and physics systems. The authors state that “EWS generally assume a timescale separation; a long term, slow forcing towards tipping” (line 44). As the authors already hint at, in social systems the forcing could be instantaneous and still trigger a switch. Suppose that all world governments announced the ban of fossil fuels from tomorrow. We can be sure that the system would tip overnight. With social systems, the forcing may be given by expectations over future states, thus possibly changing instantaneously. This characterisation probably does not apply to the present case study, but it may be worth bearing it in mind.
Minor points:
- In the conclusions, I notice a more modest definition of what the authors mean by tipping point, which is inconsistent with what given in the introduction: “a tipping point in UK public interest in (mostly) second-hand EVs is being approached”.
- Maybe an “objective” criterion should be given to define what a price spike is? This would be good also for replication/extension of the methodology.
- Relatedly, maybe some other petrol price spikes did not lead to any appreciable change in advert views? Is there a way we can rule out the choice of spikes was somehow “biased”?
- In the analyses reported at lines 136 and ff, what is N?
- I think it would be good to show the evolution of EV cars in the UK market, for the reader to appreciate actual market evolution.
- Line 11: ICEV not previously defined.
References:
Dosi, G., Moneta, A., & Stepanova, E. (2019). Dynamic increasing returns and innovation diffusion: bringing Polya Urn processes to the empirical data. Industry and Innovation, 26(4), 461-478.
Kucharavy, D., & De Guio, R. (2011). Application of S-shaped curves. Procedia Engineering, 9, 559-572.
Silverberg, G., & Verspagen, B. (1994). Collective learning, innovation and growth in a boundedly rational, evolutionary world. Journal of Evolutionary Economics, 4, 207-226.
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