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Air pollution and urban road transport: evidence from the world’s largest low-emission zone in London

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Abstract

Low-emission zones (LEZs) have been implemented widely in Europe to tackle air pollution sourced from vehicular emissions. We quantify the effectiveness of the world's largest LEZ—London's LEZ—in reducing its target pollutant, PM10. Using a difference-in-difference (DID) framework, we find that the least stringent phase I of London's LEZ induced a short-term increase in the roadside PM10 within the zone by about 14.8%, whereas the longer and more restrictive phase II significantly drove down the PM10 by 5.5%. We explore the underlying reasons behind the disparity in policy effect across stages. We show that upon the introduction of phase I, the traffic volume of targeted heavy goods vehicles (HGVs) and temporarily exempted light goods vehicles (LGVs) has substantially increased, outweighing the environmental effect of a higher proportion of greener vehicles. We provide possible behavioral explanations for this phenomenon.

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Fig. 1

source: Traffic for London). Note: Blue boundary—LEZ; Red boundary: London local authority boundary

Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

source is transport for London. b Number of Phase I and Phase II subject vehicles in London. Note: Data source is transport for London

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Notes

  1. For instance, congestion charge schemes were introduced in major cities like Stockholm and London, aiming at shifting the peak-hour demand for roads. In the United Kingdom, legislation tightening the standards of diesel and petrol fuels used for road transport was updated frequently, while the purchase of green vehicles was among the most heavily subsidized areas. In the year 2018, the low-emission vehicles grant in the UK provides discounted prices to consumers purchasing eligible vehicles through subsidizing dealership by £1,500 to £8,000 based on vehicle type.

  2. Ultra-low sulfur diesel (with a maximum of 15 ppm of sulfur) has been available in the UK since 1999. The sulfur-free requirement further pushes down the standard to 10 ppm.

  3. We explored the impact of the LEZ on an alternative air pollutant, NO2, in London using 33 air quality stations. The results largely conform to those on PM10 and are available upon request.

  4. For complete demonstrations of equivalence of ambient air monitoring methods, see https://ec.europa.eu/environment/air/quality/legislation/pdf/equivalence.pdf

  5. A typical roadside station resides within 1 m from a representative highway or truck road and monitors air quality 2–3 m from the grounTo investigate changesd. According to the LAQN, the reported observations of each station should be representative of air quality in similar geographical locations within a reasonable scope.

  6. We exclude stations with more than 20% missing data on daily PM10 concentrations during our sample period. We also drop stations located along or within 10 miles outside of the LEZ border, as the policy treatment effect inside London is contaminated with potential spillover effect in these areas.

  7. The LOWESS curves reserve both long-run trends and short-term waves, and are robust to extreme outliers (Cleveland 1979).

  8. An alternative way to construct this model is to nest it in Eq. (1) with separate identifiers for inside and nearby stations, respectively. Estimation results using the nested model are highly consistent with those using separate models. Although not showing here due to space constraints, nested model results are available upon request.

  9. This observation period for the estimation of the LEZ effects on vehicle compliance behaviors is shorter than the observation period in our main estimation for two reasons. First, the TfL does not monitor the compliance behaviors of vehicles before the announcement of the LEZ policy. Second, the compliance rate approached above 95% for both phases several months before the end of phase II. Thus, our compliance regressions and figures are produced based on a 2.5-year time window rather than the full length of 6 years in the main specification.

  10. We use linearly interpolated data for two reasons. First, we have a limited number of observations in the early announcement period of the LEZ. However, the earliest stage is expected to carry the most dramatic change in the compliance rate. From Fig. 6a, we also see that approaching the end of the observation window, the compliance rates in both phases grow very slowly. The marginal change over time is minimal, since the compliance rate is capped at 100%. Therefore, we use the interpolated data to extract more variation from the traffic sector to explain the air quality fluctuations at the day level.

  11. For instance, all the weather covariates are now the average of daily level weather conditions throughout the week. We replace the non-workday dummy by the proportion of holidays within a week and replace the no-rain-for-5-days dummy by the proportion of such days within a week. Similarly, we replace all the wind direction indicators by the proportion of days with a certain direction of wind during a week.

  12. Normally, the issue of small sample size can be overcome using a bootstrap method. However, the potential correlation between observations within and across stations may challenge the validity of bootstrap in our setting. With the assumption of no across-cluster (i.e., across-station) dependence of data, we performed 200 bootstrap replications with resampling based on stations and obtained bootstrapped estimation results that are highly consistent with our main findings. We are glad to provide these results upon request.

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Authors and Affiliations

  1. Department of Finance and Economics, Texas State University, McCoy Hall 504, 601 University Dr, San Marcos, TX, 78666, USA

    Muxin Zhai

  2. Department of Economics, Simon Fraser University, Burnaby, Canada

    Hendrik Wolff

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  1. Muxin Zhai
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Correspondence to Muxin Zhai.

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The authors would like to express sincere appreciations to Robert Halvorsen, Jim LeSage, Neil Bruce, Judy Thornton, David Layton, Jing Tao, Yoram Barzel, Aseem Prakash, and Jorge Rojas for their helpful comments on the paper. All remaining errors are our own.

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Zhai, M., Wolff, H. Air pollution and urban road transport: evidence from the world’s largest low-emission zone in London. Environ Econ Policy Stud 23, 721–748 (2021). https://doi.org/10.1007/s10018-021-00307-9

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