Main

Transport is the largest emitting sector of greenhouse gases, accounting for a quarter to a third of all emissions in developed countries1,2 with serious consequences for both climate and health3,4,5. Air pollutants such as nitrogen oxides (NOx), which are effectively reduced when standards are enforced, cause millions of deaths each year4,5. These impacts fall unequally on lower–middle-income countries (LMICs)6, which suffer more overall and per capita pulmonary deaths from air pollution4,7 and stand to suffer the greatest impacts from climate change8.

The source of vehicles in LMICs is dominated by unregulated trade1,2,3,9,10. As of 2020, 100 countries receiving used vehicles had no vehicle emissions standards9 and only 11 had ‘very good’9 emissions regulations. However, the United States, European Union, Japan and United Kingdom collectively supply 90% of used vehicles exported to (non-EU) LMICs9. The potential for rapid regulation is therefore incumbent on just four jurisdictions, all of which already maintain high vehicle emissions standards.

Using comprehensive government databases11, we quantify per kilometre rates at which vehicles generate carbon and pollution for every vehicle (N = 6,921,292) legally exported from Great Britain between January 2005 and December 2021. We compare these vehicle emissions to every private vehicle driven in Great Britain during the same period and those that would have been driven if they had not been scrapped.

These data reveal substantially higher rates of CO2 and pollution generation in exported vehicles, even under optimistic ‘functioning-as-new’ emissions intensity estimates that assume no vehicle modifications or vehicle degradation with age (Fig. 1 and Extended Data Fig. 1). Exported cars generate at least 23 g (13%) more CO2 per kilometre than cars scrapped in the same period (Fig. 1a and Supplementary Code) and at least 29 g (17%) more CO2 per kilometre than the contemporary on-road used car fleet (Fig. 1a; mean 197.0, 174.4 and 168.6 g km−1 CO2 for exported, scrapped and on-road fleets, respectively; interquartile ranges (IQRs) 170.1–225.3, 147.5–189.1 and 134.2–188.9 g km−1).

Fig. 1: Pollution generated per kilometre by used vehicle fleets.
figure 1

a–c, Exported cars (orange) generate at least 13–24% more CO2 per kilometre (a), similar average but (through overdispersion) higher median fine particulate matter pollution (b) and at least 53% more nitrogen oxides per kilometre than scrapped (green) or on-road (blue) vehicle fleets (c). d–f, Both exported car and light goods vehicle (LGV) fleets also have larger observed engine capacities (d) and worse fuel efficiency (e) despite being similar ages to on-road fleets and much younger than contemporary scrapped vehicles (f). Boxes show IQRs, whiskers are 1.5× IQR, sample size (millions) shown on upper x axes, summary data supplied in a Figshare repository25.

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Emissions figures were even more striking for other pollutants. Exported cars emit similar amounts of hydrocarbon particulates (Fig. 1b) but 48 mg km−1 more NOx (53% higher; Fig. 1c) than scrapped cars. Likewise, observed engine capacities were larger (Fig. 1d) and fuel efficiency at least 9% worse, by 3.3 miles per gallon (MPG, mean 38.5, 41.8 and 44.4 mpg for exported, scrapped and on-road used fleets respectively; IQR 33–45 mpg exported, 37–47 mpg scrapped and 37–49 mpg on-road fleets).

A substantial fraction (42%) of exported diesel vehicles were predicted to fail the current EURO-4 emissions standards12 that form the legal roadworthy minimum for all vehicles registered after 2000. A surprising 83% were predicted to fail the EURO-6 diesel12 CO2 emissions standards, and 98% failed the EURO-6 carbon monoxide and NOx standards. These differences are not the result of overdispersion, where a few high-emitting exports12 dragged up the average; similar or even larger gaps in pollution rates were observed for the median pollution rates of exported, scrapped and contemporary on-road used vehicles (Fig. 1).

Daily-resolution data for six million exported cars reveals that the gap between exported, on-road and scrapped fleets is consistent over time (Fig. 2a), apart from a narrowing and then rapid expansion of this gap over 2020–2021 alongside distortions of trade patterns, used car prices and vehicle testing regulations during the COVID-19 pandemic (Fig. 2b). That is, Great Britain persistently scraps lower-emissions vehicles while exporting higher-emission vehicles (Fig. 2b). Whereas geographical disparities due to uneven concentrations of upmarket export vehicles or differing ‘on-road’ usage were anticipated, this export gap was remarkably uniform. Almost every British postcode region (95%), representing a full cross section of society, export higher-polluting vehicles than those they drive or scrap (Fig. 2c).

Fig. 2: Export of consistently higher-emitting vehicles from Great Britain.
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a, Exported vehicles (orange) are consistently more polluting than contemporary scrapped (green) and used on-road (blue) vehicles. b, Cleaner choices and improving standards have lowered exported emissions after considerable delay but the gap between exported and scrapped fleets persists and is growing post-pandemic. c, Scrapped vehicles are cleaner than exports across 5,874 (95%) of our 6,145 observed postcode regions (2005–2021 inclusive), covering every community in Britain. Grey regions have insufficient data or no inspection sites. Orange line in b shows locally weighted smoothed spline; note non-zero y axis in a to emphasize variation.

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Exported vehicles will probably generate more pollution per kilometre independent of their destinations and patterns of use for simple physical reasons: compared to their more-efficient scrapped alternatives, export vehicles have larger observed engine capacities (Fig. 1d) and lower operating efficiencies (Fig. 1e), despite a younger average age (Fig. 1f). These fixed factors also mean that degradation rates are probably rank conserved, especially for CO2, and high-polluting vehicles will remain the most polluting as vehicle fleets age.

Air quality outcomes are more nuanced than individual emissions categories, including how vehicles are driven, road conditions, engine age, climate, payload and maintenance schedules13,14. Vehicles also generate pollution, such as ozone or non-tailpipe emissions, for which testing data were not available. Observational data are urgently needed to fill this gap.

Adding to the challenges of measuring emissions, emissions testing data have long been manipulated—for example, during ‘Dieselgate’, where nine major manufacturers used ‘defeat devices’ to alter performance and intentionally deceive environmental agencies and regulators. In the Dieselgate aftermath, vehicle manufacturers are, incredibly, allowed to legally manipulate vehicles during new car emissions testing14,15 by, for example, removing wing mirrors and seats, taping up high-drag surfaces or hard-baking and over-inflating tyres. Manufacturers are also now allowed to ‘adjust’ emissions estimates15 by 4.5% and programme vehicles to turn off emissions-reduction devices16 when the weather becomes ‘too hot’ or ‘cold’16. Manufacturers define ‘hot’ and ‘cold’. For example, Renault told the French government their emissions control devices should shut off above 35 °C and below 17 °C (Paris is colder 83% of the time15) to ‘protect the engine’15, at the cost of protecting the climate and human health. As a result, real-world emissions increasingly overshoot (currently by ~50%) emissions measured during testing14,17,18. Some 13% of diesel cars in the European Union now emit NOx at over ten times the legal standard18, outnumbering the 10% that actually meet those standards.

The lack of emissions standards in most destination countries also results in the routine stripping of emissions-reduction devices for resale19 or melting down before export9,19. One study tested 160 vehicles destined for Africa from the European Union19. Of the vehicles that could start, 85–93% failed to meet the (roadworthy minimum) EURO-4 emissions standards20, 20% of petrol (gasoline) vehicles did not comply with any emissions standard at all19 and 10% had their catalytic converters cut out19, increasing NOx and carbon monoxide pollution tenfold.

Used vehicles were exported at an average 8.5 years of age (IQR 5.0–10.6 years), and both emissions13 and fuel efficiency14 degrade with age. Our models, therefore, probably underestimate vehicle pollution rates substantially by relying on new car testing data that do not account for increasing emissions generation from vehicle ageing or modifications. The non-stationarity and complexity of emissions degradation curves13,14 and vehicle modifications mean that direct measurements—such as those increasingly captured by annual vehicle emissions tests required in the United Kingdom—are needed to improve estimates of exported emissions. Actual emissions are probably far higher13,14,18, perhaps 150% higher for CO2 under ideal ‘European-style’ driving conditions18, but enormous and unnecessary gaps in our knowledge remain.

As with carbon leakage from heavy industry and manufacturing6, rich countries appear to be offshoring the cost of replacing high-polluting vehicles. There are, however, some positive trends. Whereas many improved emissions are artefacts of manipulated testing16,17,21, better fuel efficiency and air quality standards in the United States, United Kingdom, European Union and Japan are slowly reducing the estimated pollution of exported vehicles over time (Fig. 2a,b for Great Britain). These four jurisdictions are the collective source of over 95% of light used vehicle exports worldwide and despite creating 40% of global transport emissions22, implement world-leading vehicle emissions standards inside their own borders.

Imparting the same standards on exported vehicles, preventing the removal of emissions-reduction devices and redirecting clean vehicles from the scrapyard to the export fleet would all positively impact global emissions. Export licences can be indexed to increase duties on dirty vehicles or subsidize clean vehicle exports. Export countries have very few major vehicle ports1 and thousands of mechanics qualified to evaluate legal roadworthy standards. Tasking mechanics to randomly spot check vehicles in port and issue penalties when vehicles fail emissions tests would be an extremely low-cost intervention to stem the dirty used vehicle trade.

Such measures would not necessitate increased vehicle prices, which can reduce access to the economic benefits of vehicle ownership. Supply shocks can be mitigated or avoided by using policy and incentives and by redirecting clean vehicles from scrapyards to export. Cleaner vehicles also have smaller and more fuel-efficient engines on average and lower ongoing costs23 over the life of the vehicle, reducing net economic burdens.

Potential short-term price increases imposed on individuals are also offset by long-term reduction in the societal and economic costs from pollution2,4,5 and climate change1,3,8. Most LMICs are placing this consideration above others, with widespread moves to ban the import of dirty used vehicles, regardless of price shocks9,24. These policies reflect a growing desire for clean air over cheap cars. However, such moves are struggling for traction due to a lack of policing and resources and the unstemmed flow of unregulated imports24.

Developed economies can aid these goals and reduce the damage from vehicular emissions by raising export standards to match their own internal legal minimum standards12,20. Such low-cost interventions are an immense opportunity for rich high-emitting countries to reduce global emissions and cut pollution in the developing world. To instead overlook this problem and allow the continued flow of high-emissions vehicles would be a devastating missed opportunity and an ethical failure.

Methods

Data were obtained from the Department for Transportation—a department of the government of the United Kingdom—for all 65 million privately registered used vehicles undergoing mandatory annual vehicle inspections. Traditionally termed ‘MOT’ tests, they were undertaken across the United Kingdom between 1 January 2005 and 31 December 2021. Used vehicles were defined as all vehicles that had undergone at least one prior inspection. These annual vehicle inspections are required by law in the United Kingdom to assess roadworthiness. They begin one year after the vehicle is first registered for motorcycles and scooters and three years after first registration for all other vehicles (in Northern Ireland, the equivalent requirement applies after four years). Alongside these data, we obtained linked vehicle-specific data on all used vehicles that had been scrapped or issued a certificate of destruction (N = 9,077,804) and all vehicles that had been flagged as exported (N = 6,922,292). Both export and scrappage certifications are supplied with exact dates.

Used vehicle summary statistics for predicted emissions, age, observed engine capacity and all other vehicle properties were calculated at a daily resolution from 1 January 2005 to 31 December 2021 inclusive, for a comprehensive sample of every on-road vehicle test (267.5 million tests; one random test per annum was selected for each vehicle) and for every scrapped (issued a vehicle scrappage certificate or certificate of destruction) or exported vehicle (Supplementary Code). We restricted our ‘on-road’ data to one randomly sampled roadworthy test per vehicle per year to avoid the oversampling of mechanically unreliable vehicles, which are re-tested each time they fail a roadworthy certificate (Supplementary Code). This resulted in predictions of emissions intensity across 261.1 million vehicle days, or 42,000 vehicles per day, for cars (or class 4 vehicles) known to be on road at the time of their roadworthy inspection.

All analyses were performed using R version 4.0.5 (Supplementary Code). Some 993 vehicles (0.015%) that were erroneously flagged as both exported and scrapped or destroyed were removed from analysis. As the date of first use and the date of each vehicle inspection, scrappage, destruction or export were reported, we calculated vehicle ages at each of these events. Some 60,642 (0.4%) of the reported dates were impossible and were excluded, as they were in potentially reverse order or contained typographic errors. Another 7,380 vehicles over the age of 110 years (0.05%) were excluded from analysis as they largely—but not entirely—constituted age-related coding errors.

The vehicle inspection and scrappage data were matched to a public dataset11 of fuel efficiency and emissions data from the Vehicle Certification Agency, for 70,000 measurements tabulated by vehicle make, model, fuel type and year, measured across vehicles sold in the United Kingdom from 2000 onwards. Emissions data for carbon dioxide, carbon monoxide, nitrogen oxides, total hydrocarbon particulates and fuel efficiency of UK vehicles were also obtained from the Vehicle Certification Agency11.Given the near-complete lack of emissions testing data for motorcycles and other vehicle classes, emissions testing data were only captured for cars and vans and were curated, quality-controlled and matched to the Department for Transport data (Supplementary Code).

Matching these emissions testing data resulted in 1.3 million exactly matched CO2 measurements for cars in the exported or scrapped fleet (8.4% of all exported cars and 8.1% of all scrapped cars) but only 3,222 exact-matched CO2 measurements for LGVs (0.3% of all LGVs), a number largely restricted by the abundance of rarer or untested models and the difficulty in exact matching heterogeneous make and model descriptions provided by the Vehicle Certification Agency. Exported vehicles with exact matches for measured emissions were lower (just 678 or 0.16% of all exported LGVs), excluding the reliable imputation of emissions from LGVs.

These data highlighted the extensive need for better, more comprehensive emissions testing for both new, on-road and exported vehicles. The regulatory environment needs to be restructured to fill these gaps. However, while testing regimes and data were insufficient to impute on-road emissions in motorcycle or LGV fleets, accurate imputation of car emissions and pollution was possible under the assumption that new car testing data would remain rank conserved over time. This assumption generates a lowest-possible estimate of emissions rates, under the assumption that used vehicles are ‘functioning-as-new’ at the point of scrappage, export or testing. This overly optimistic assumption is a key limitation of the study, one that highlights the need for far better measurement and testing of real-world emissions across all vehicle fleets.

Imputation models were constructed to capture the emissions of all exported (N = 6,072,730), scrapped (N = 9,077,804) or on-road (N = 261.1 million tests) class 4 vehicles (cars and vans below 3 T) that passed quality controls. We used the reported model year, fuel type (for example, ‘Petrol/Gasoline’, ‘Diesel-Electric’) and engine capacity (in cubic centimetres) from the annual vehicle inspection data to construct a model for each pollution type and vehicle property. Pollution types and vehicle properties that were imputed include CO2 (g km−1), total nitrous oxides (NOx; mg km−1), total particulate hydrocarbons (mg km−1), carbon monoxide (mg km−1) and fuel efficiency in ‘miles per gallon’ (MPG). Imputation models were kept deliberately simple, as we predict values across the broadest possible range of vehicles using variables that were reported at almost every vehicle inspection. This avoided overfitting and, as rare vehicles share engines and emissions technology with common makes and models, achieved accurate (Supplementary Materials and Extended Data Fig. 1) imputation for a very large (>99%) fraction of cases.

We developed imputation models using recursively partitioned regression trees26,27, a foundational and interpretable machine learning heuristic suited to discrete effects and small variable sets (Extended Data Fig. 1). This was implemented using the ‘rpart’ package26. Models were trained on three input variables—engine size in cubic centimetres, the year of vehicle manufacture and fuel type—using tenfold random-sample cross validation, with the ‘cp’ model complexity parameter set to 0.001 and a minimum of 100 vehicle make models used for a parent node (that is, the ‘minbucket’ parameter) and a minimum of 25 vehicle make models used for a child or leaf nodes (the ‘minsplit’ parameter; Supplementary Code).

These models achieved high accuracy, approaching the test–retest accuracy of the Vehicle Certification Agency testing regime (Extended Data Fig. 1). For example, consecutive tests of the same vehicle for CO2 were correlated by r = 0.9, whereas our recursively partitioned regression model attained an accuracy of r = 0.94 when imputing CO2 for a random holdout sample (random 20% holdout sample; N = 6,708 unique vehicle makes and models; Extended Data Fig. 1a,b). We found that grid searching to further optimize model fit was therefore unnecessary, as our initial model parameters generated models that approached the highest achievable accuracy11.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.