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The benefits of sharing, rent cars on demand by the hour or day to go where you want, when you want..

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What is Carsharing – AKA “Airbnb for Cars”?

8 best carsharing services, the car share rental process, frequently asked questions, take control behind the wheel, airbnb for cars: 8 best carsharing services [& how they work] in 2024.

Since 2012, Brett Helling has built expertise in the rideshare and delivery sectors, working with major platforms like Uber, Lyft, and DoorDash.

He acquired Ridester.com in 2014, the first ridesharing marketplace, leveraging his direct experience to enhance the site. His insights at Ridester are recognized by Forbes, Vice, and CNBC.

Expanding his reach, Brett founded Gigworker.com and authored “ Gigworker: Independent Work and the State of the Gig Economy “, demonstrating his comprehensive knowledge of the gig economy.

More about Brett | How we publish content

Is Instacart Plus Worth It? I Used The Service To Find Out

A full list & breakdown of uber services [& how they work], how uber taxi works for riders: pricing, features & more.

Key Takeaways

• Carsharing offers on-demand vehicle rentals, similar to Airbnb’s lodging service.
• Unlike traditional rentals, carsharing provides flexibility with app-based, self-service booking.
• Turo, Zipcar, Getaround, Maven, Enterprise CarShare, SHARE NOW, GIG Car Share, and Evo Car Share are popular services.
• Carsharing process includes booking through an app, making payments, picking up the car, and driving.

Carsharing, often referred to as the “Airbnb for Cars,” is a service that allows individuals to rent vehicles on-demand through app-based, self-serve systems.

It provides a convenient way to rent cars by the hour, minute, or mile, with vehicles listed by local owners aiming to earn passive income.

This service caters to a broad audience, including urban residents, young travelers, and those seeking sustainable transportation options.

Unlike traditional car rentals, carsharing offers unparalleled flexibility, allowing users to rent a vehicle at any time without being restricted by business hours or fixed rental periods, paying only for the time or distance they use.

Below, we’ll list eight of the top car sharing services in the United States.

• Enterprise CarShare
• GIG Car Share
• Evo Car Share

Double-check your insurance before renting!

Many insurance providers exclude carsharing services as part of your standard auto policy.

Before you rent a vehicle with a carsharing service, make sure that you understand what is covered – and not covered – with your standard insurance policy.

If you’re in doubt, get the extra coverage from the carsharing company.

• Peer-to-peer car rental platform
• Offers a wide range of vehicles
• Insurance and unlimited mileage included

Often dubbed the “Airbnb for cars”, Turo is a peer-to-peer car rental platform that bridges car owners with individuals seeking to rent vehicles for short periods.

Unlike traditional car rental services, Turo offers a diverse range of vehicles from everyday models to high-end luxury cars and vintage classics. It’s available in various cities across the U.S., Canada, the UK, and other select international locations.

Pricing is set by individual car owners, and the cost typically includes insurance and  unlimited mileage . An added benefit of Turo is the ability to deliver the car to a chosen location in some instances.

Booking a vehicle on Turo is simple. Browse the range of available cars, select your desired pick-up and drop-off dates, and message the car owner to arrange the details.

Of all the carsharing services, Turo is my personal favorite.

Turo is my personal favorite among all of the carsharing services on this list. The prices are affordable, and the cars are incredibly high quality.

For example, I recently took a trip to Waco, Texas to visit a friend. I rented a 2018 Mercedes S-class on Turo – for the same price that a 2022 Kia Forte would have been at enterprise. Talk about an upgrade!

For those looking to make some extra income by  renting out their car , Turo offers an avenue to list their vehicle for rent when it’s not in use.

Download Turo on  iPhone  or  Android .

• Known car share service, global availability
• Includes gas, insurance, and roadside assistance
• Subscription-based with hourly/daily rates

Zipcar  is perhaps the best-known car share service.

Available in cities and universities across the United States and around the world, the company allows users to book nearby cars 24/7 straight from its website or mobile app. You only need a reservation and a “Zipcard” that allows you to unlock your selected car.

On the Zipcar platform, you can find all sorts of cars to fit your needs, including standard sedans and luxury SUVs.

Rental rates  for these vehicles vary by city (they usually start at about $9-$10 per hour) but always include gas, insurance, and roadside assistance at all hours. You’ll get 180 miles included in your reservation each day and only pay 45 cents per additional mile.

Before you use Zipcar’s services, you need to subscribe to the service for $7 per month or $70 per year (students at partnered universities may get cheaper rates). However, this gives you the flexibility to book any time and from anywhere Zipcars are available — which includes over 400 cities in the U.S. alone.

Download ZipCar on  iPhone  or  Android .

3. Getaround

• Peer-to-peer car share with competitive rates
• No membership card needed; unlock with smartphone
• Includes insurance and roadside maintenance

If you’re looking for competitive rates or a wider variety of cars,  Getaround  may be the service for you.

As a peer-to-peer car share, this mobile app can connect you to privately owned cars in your area, which means you’ll have far more choices when it comes to rates and vehicle models. For example, you may find anything from sedans and convertibles to pick-up trucks and cargo vans.

With Getaround, you don’t need a membership card. You can use your smartphone to locate and unlock any Getaround vehicle in your area and start a trip. Your hourly rental rates will vary widely based on the exact listing you select, and you can always expect to pay a booking fee (at least $2 and usually under 10% of your trip cost).

An “under-25” fee will apply to each trip younger drivers book. Twenty miles per hour (or 200 miles per day) are included in the initial booking cost, after which you’ll be charged 50 cents per mile or $5 per mile for specialty cars.

You need to fill up on gas at the end of your trip, but insurance and roadside maintenance are included.

Getaround allows users to book on-demand in seven countries with no annual fees. The company offers car share services in 12 U.S. states and Washington, D.C.

Download Getaround on  iPhone  or  Android .

• GM-owned, offers gas, hybrid, and electric vehicles
• Operates in select U.S. cities
• No monthly or annual fees

Owned by General Motors,  Maven  is a safe and secure car-sharing service that allows you to rent gas, hybrid, and electric vehicles.

Much like Getaround, it enables you to find and unlock privately-owned vehicles with just your smartphone. Additionally, every sedan or SUV you drive goes through a proper safety inspection before it’s listed.

Maven has scaled back its operations quite a bit in the past year, so it’s only available in parts of Michigan (Ann Arbor and Detroit) and California (Los Angeles and San Francisco).

Drivers in these states can access vehicles on-demand with rates starting at $8 per hour with no monthly or annual membership fee.

Download Maven on  iPhone  or  Android .

5. Enterprise CarShare

• Enterprise's car share option with a wide fleet range
• Membership card access; includes gas and insurance
• Rates vary widely by location

Enterprise is best known as a traditional car rental company but recently joined the car share space as well.  Enterprise CarShare  connects drivers to its trusted fleet of newer, well-maintained cars, which range from electric sedans and fuel-efficient SUVs to cargo vans and luxury cars.

Much like Zipcar members, Enterprise CarShare users both unlock their vehicles and end trips by scanning their membership cards.

Enterprise CarShare rates vary extremely widely by location. Monthly membership fees in the United States range from $5-$50, and hourly rates can start at $5-$10. Mileage rates can be 25-45 cents per mile, and you may get 200 miles free.

Generally speaking, these fees are fairly economical compared to other apps, especially considering they include gas, 24/7 customer service, and even physical damage protection.

This car share brand is most prominent in the United Kingdom, but it is available in nine cities across eight U.S. states as well as at plenty of universities and businesses in these markets and beyond. It’s also available in select Canadian locations.

Download Enterprise CarShare on  iPhone  or  Android .

6. SHARE NOW

• Merger of Car2Go and DriveNow
• Free-floating model, drop off at on-street parking
• Minute, hourly, and daily rates; includes insurance

The result of a large merger between Daimler AG’s  Car2Go  and BMW Group’s DriveNow,  SHARE NOW  is a car sharing network that spans eight European countries and select U.S. cities.

SHARE NOW functions much like a scooter-share service with free-floating vehicles that can be dropped off at legal on-street parking spots in the operating area.

They list slightly different parking rules for each country (such as when parking is and isn’t at your own cost), so  check  for the restrictions in the country you’d like to use SHARE NOW.

Dropping the car off at streetside parking or parking garages makes it perfect for both one-way and roundtrip commutes.

Another unique feature of SHARE NOW is its assortment of cars. You can access models from reliable and highly coveted brands like Mercedes-Benz, BMW, MINI, and Smart at any time of the day. Some European cities currently have all-electric fleets.

With SHARE NOW, you can even rent by the minute. Rates start at 24 cents per minute and $15.99 per hour, though you can book by the day at a $69.99 flat rate.

Mileage does affect your price, but you will receive free parking, gas, and insurance with your purchase. SHARE NOW is completely free to join and currently offers a $10 credit for signing up.

In the United States, SHARE NOW is located in New York, Seattle, and Washington, D.C.

Download SHARE NOW on  iPhone  or  Android .

7. GIG Car Share

• AAA-powered, focuses on hybrid and electric vehicles
• Free-floating model with included gas, insurance, parking
• Rates set at per minute or hourly basis

Another option we can’t miss is  GIG Car Share , an environmentally friendly car sharing service powered by the American Automobile Association (AAA).

Currently located in Sacramento and soon expanding to Seattle, the company tries to help you reduce your carbon footprint by connecting you to hybrid and electric vehicles as well as making a largely car-free lifestyle possible.

GIG Car Share works a lot like SHARE NOW, with free-floating vehicles that can be picked up and dropped off just about anywhere in your market.

Cars are unlocked via smartphone, but you can also request a membership card if you want back-up. That way, you won’t be stuck if you have spotty service).

Gas, insurance, and parking are all included in the company’s rates, which are currently set at 15 cents per minute (up to $15) and $15 per hour for both hybrids and electrics. AAA members can get discounts on rides.

Download GIG Car Share on  iPhone  or  Android .

8. Evo Car Share

Based out of Vancouver, Canada,  Evo Car Share  offers a fleet of hybrid Toyota Prius C vehicles for environmentally conscious urban commuters.

Evo stands out with its free-floating model, allowing users to pick up and drop off vehicles anywhere within a designated home zone. This makes it especially handy for those spontaneous trips or one-way commutes.

Each Evo vehicle is equipped with bike and ski racks, appealing to the active lifestyle of many Vancouver residents. Rates are competitive, and pricing is transparent with no hidden fees. The service includes fuel, insurance, and parking in designated areas.

Evo’s focus on sustainability and convenience has made it a preferred choice for many Vancouverites looking for a green and flexible transportation option.

• Based in Vancouver with a fleet of hybrid Toyota Prius
• Free-floating model, vehicles equipped with bike/ski racks
• Includes fuel, insurance, and designated parking

While some of the details may differ depending on the different companies you choose, all carsharing companies operate along the same broad lines. Here is how the process generally works:

One of the primary appeals of carsharing is its seamless booking system. Users can easily browse through a variety of vehicles, selecting their preferred car, duration, and time. This is made even more straightforward through dedicated apps, allowing for hassle-free reservations at the touch of a button.

Costs will vary based on the service provider, the type of vehicle chosen, and the rental duration. Payments are typically made upfront using a credit card. It’s essential to be aware that additional fees might be incurred, such as cleaning charges for any spills or messes.

A standout feature of carsharing is the ease of picking up your chosen vehicle. Unlike traditional rentals, carsharing platforms enable users to quickly locate and unlock vehicles in their vicinity through the app. Detailed instructions guide users through the unlocking process, ensuring a smooth start to their journey.

Here’s where the adventure begins! Enjoy the freedom of driving around, whether it’s for a quick errand or a leisurely drive.

While most carsharing experiences are straightforward, it’s crucial to be aware of specific rules some companies might have – for instance, Getaround’s 200-mile limit .

Ensure you’re familiar with these to avoid any unexpected charges. Once you’re done, simply return the car to the designated drop-off point.

Read our answers to the FAQs below to learn more about car shares and how they work.

1. Can car shares be used to drive for Uber or Lyft?

Car shares are typically for personal use, making them unsuitable for rideshare drivers, especially with company logos violating Uber and Lyft standards. However, Maven Gig and Getaround’s partnership with Uber offer rideshare-approved rental options for drivers.

2. How old do I need to be to use car share services?

Car share services have varying age requirements, like Zipcar’s 21-year minimum (18 for affiliated students). Always review terms before signing up to avoid issues.

3. Is car sharing always the cheapest option?

Car shares are cost-effective for infrequent users, but regular or long-distance drivers might find long-term rentals or ownership more economical.

When you don’t have access to your own car, it may seem like taxis, rideshare apps, and public transportation are your only options. However, car shares continue to expand, providing an alternative to daily car rentals and letting you control when you stop and go — no need to wait for a driver.

With car sharing, you’ll only pay for the time you really need without taking on major recurring costs like auto insurance and car payments. If you do find the need or budget for a car, take a look at our guide to the nine best car-buying apps so you can shop wisely for your permanent ride.

1 thought on “Airbnb For Cars: 8 Best Carsharing Services [& How They Work] In 2024”

Brett; nice summary here. I am wondering why you don’t mention Turo? I thought they were the largest peer-to-peer car-sharing platform?

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Rent the perfect vehicle with Getaround car rental services There are a thousand reasons why you may need a rental car. Maybe you’re moving and need a cargo van to move your furniture across town. Perhaps you’re going on a romantic getaway with your partner and want to rent a convertible to cruise around in. Or maybe you’re going on a business trip and need a luxury rental car to impress your clients! No matter what your needs are, Getaround is the perfect car rental option.

Getaround car sharing is an environmentally friendly , convenient, and affordable alternative to traditional car rental. When you rent a car with Getaround, you’re actively choosing to share an individual owner’s car when they wouldn’t be driving it anyway. That helps keep new cars off the road, giving the environment a tiny break it desperately needs.

Getaround cars are conveniently located across communities, at airports, and in cities around the world. That means car-sharing guests benefit from having affordable and convenient access to nearby rental cars. But there’s more! Getaround car rental is super flexible. You can rent a car by the hour or by the day. And, if your plans change, no big deal! You can cancel within 1 hour of booking or more than 24 hours before the start of your trip for free.

• Download the free Getaround app to your smartphone. Search for the vehicle you’d like to rent based on location and duration (by the hour or day…), and book the vehicle that best meets your needs.
• Start driving! The Getaround app turns your smartphone into a car key. You can unlock the car directly from the app, meaning you have full flexibility to get the car any time after your rental pickup time.

Getaround offers a huge array of fleet styles including trucks, vans, cars, convertibles, SUVs, and so much more. The variety of sizes, styles, colors, models, and makes is as large as the community or area where you’re renting a car. When you search for a car to book on the Getaround app or website, you can filter by your needs.

In our category filter, you can choose the type of vehicle you need: SUV/Jeep? Coupe/Sedan? Minivan? Pickup truck? The car rental options are endless. In our class filter, you can choose between economy, luxury, and performance. And guess what? You can even filter by transmission (automatic or manual) and by drivetrain (all-wheel drive, anyone?). But it doesn’t end there! You can even filter by special features. If you’re going on a ski trip and need a roof rack, you can search specifically for that. Pet-friendly cars, cars with bike racks, and cars with Bluetooth… You can request whatever you need from your Getaround rental car.

Whether you’re /going on a road trip with friends or taking a weekend getaway, Getaround car rental meets all your vehicle rental needs.

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Do you live in a city? Or maybe you’re in college? Whatever the reason, you might not own a car and you’d like to rent one on occasion. Maybe traditional rental car companies just aren’t doing it for you, and you’ve already had great success with gig economy companies such as Uber and Airbnb .

Or, on the flip side, you’ve got your own car but you don’t need it all that often, especially now that you work from home . It’d be great to make a little extra money without having to put in a lot of effort. Why let your vehicle sit around and collect dust when it could be put to work for you?

Wouldn’t it be nice if there was an Airbnb for cars?

Let’s take a look at three different car-sharing companies that act like Airbnb for cars, plus one app worthy of an honorable mention. All of these platforms offer the ability to rent cars, whether you’re an owner or someone looking for a ride.

Apps most like Airbnb for cars

Probably the most well-known name on this list, Turo does exactly as is advertised. As a renter, you simply input your location and the dates during which you’ll need the car.

Turo will then come back with a list of results from which to choose. You can filter by price, the type of car you’d like to rent and delivery fee.

Be aware that there may be quite a few fees associated with renting from Turo, including an added protection plan . This can add a significant charge to your rental.

Here’s a look at a one-day rental for a Hyundai Sonata in San Diego. Although you’re technically renting the car for just $36 for one day, Turo also charges a $15 trip fee. Even with the early bird discount, your total amounts to $49.20.

At this rate, however, you’ll need to travel to Oceanside to pick up the car. You can opt to have it delivered to the airport, but that adds an extra $120.

There’s also a 200-mile maximum for the car, which won’t even allow you to get to Los Angeles and back. Still, if you’re able to find a car that’s near enough to your location and has unlimited miles (or you’re not planning on traveling far), Turo may be a good alternative to a traditional rental car .

Turo is available in three countries, including the U.S., Canada and the United Kingdom.

» Learn more: Does your credit card cover car-sharing insurance?

2. Getaround

Getaround is another car-sharing platform that works similarly to Airbnb. Like Turo, you’ll input your search requirements and will be presented with a variety of car offerings.

The company has some pretty widespread coverage, with cars available in 46 metropolitan areas. Pricing will vary according to where you are, but you may find more expensive pricing than Turo.

Here’s a look at the same one-day rental search we conducted for Turo above. Right off the bat, the cheapest rental car price started at $49.19.

After adding in a mandatory booking fee (for all bookings) and license fee (for first-time bookings using a new or changed license), the rental shot up to $86.62 for the day, well above the price Turo was charging.

3. Car Shair

Are you feeling fancy? Do you live in Orange County, Los Angeles or San Diego, in California, Las Vegas, Dallas or Atlanta? Consider Car Shair , another available app to rent people’s cars. This one, however, allows you to book ultra-high-end cars.

Although it has a relatively small geographic footprint, you can also rent ebikes from this platform, with prices as low as $4 per hour or even less for daily rates.

For Car Shair, we conducted the same search as we did above with Turo and Getaround, looking for a one-day rental in San Diego at the end of August. Initial results returned a few different options, including an Audi A3 E-tron with a starting rate of $51 for the day.

After adding in insurance and taxes, the total rose to $72.33 for the day.

This is significantly cheaper than the older Toyota Corolla offered on Getaround, and for this price, you’re renting a plug-in Audi hatchback.

Long story short, if you’re in a market where Car Shair operates, it’s well worth a look for a vehicle.

» Learn more: How to rent cars for cheap

Our honorable mention here goes to Zipcar , which is a multinational car-sharing platform. It has a presence in the U.S., Canada, Costa Rica, Iceland, Taiwan, Turkey and the United Kingdom, so it’s likely you’ve seen one or two in your travels.

Zipcar, though, isn’t a peer-to-peer car-sharing platform. Instead, all the cars available for rent are owned by Zipcar itself. As a Zipcar member, you simply head over to an available car, unlock it via the app and begin driving. There’s no denying it’s convenient.

However, becoming a member also incurs a monthly fee. Unlike these other applications, you’ll need to either pay $9 per month or $90 per year in order to access car reservations. In return, you’ll get free gas on all your trips and a maximum trip length of 180 miles included, with an additional cost per mile after that.

The types of cars you can rent differ, but rates start at $10 per hour and $83 per day.

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If you’re looking for a car-sharing platform

Although peer-to-peer car rental sharing hasn’t blown up like Airbnb, there are still some decent options for those looking for short-term rentals. Turo is the best known with the largest presence, and you’ll find its platform across a few different countries. Car Shair, meanwhile, offers a variety of rental cars in specific locations — and you may even find a deal or two on a high-class vehicle.

Otherwise, if you’re a car owner, you could consider one of these sites to earn some extra money on the side.

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Car Sharing for Travel

I have updated my spreadsheet of sharing economy transportation resources and this is a summary of my finding so far. While I’m a big fan of public transportation, some places in the world it’s better, easier, and nicer to explore with a car. And fortunately for travelers, there are a wealth of new options for getting around using sharing economy resources. These fall into four distinct categories:

• Peer to peer car rentals
• Membership based car rentals
• Peer to peer taxi services
• Peer to peer ride sharing

Peer to peer car rentals expand car rental options beyond the traditional rental companies, and generally have at least slightly better pricing with more diversity of vehicles and pick up locations. If you are a car owner this is a fine way to earn some extra cash when you’re not using the car. If you are in need of a car for a day or more, either in your home town or on the road, this is a good option to consider, especially if you find rental car companies are too expensive in your location. For now this is only available in cities, but services are expanding quickly in the U.S. and Europe. For one example, see my review of Flightcar .

Membership based car rentals are generally more appropriate for people to use in their home city, but they have the added bonus of being usable on the road if the company you join has services in a city you are visiting. So if you are a member of Zipcar in San Francisco you could also use this service on a trip to Boston. These services are most cost effective when you need a car for a few hours. If you want to use a car for multiple days and/or drive more than a few miles, you’re probably better off renting a car from a commercial rental agency or from a peer to peer rental service.

Peer to peer taxi services are smart phone driven alternatives to commercial taxis, currently found in major U.S. cities. They are particularly useful when you are somewhere that doesn’t have taxis driving by. According to several recent analyses of cost across various cities, the prices of these services aren’t actually very different from using a regular taxi, especially during rush hour. Though Sidecar comes out consistently about 20% cheaper than a cab.

Peer to peer ride sharing is the option I’m most excited about for travel. This is catching on in Europe, Australia, and the U.S. Drivers join a website where they post upcoming trips they are taking and how many open seats they have, along with a price per seat. Then riders can sign up to join those trips. Drivers and riders get reviewed, and in some cases you can select traveling companions based on features like how talkative they are. I’m looking forward to trying out Blablacar or Carpooling on my upcoming trip to Spain as a way to visit areas outside of the cities where I’ll be staying. It appears that at least for some places this is cheaper than taking the train or bus. And it sounds like a pleasant way to meet locals and travel in comfort without all the unnecessary stops inherent to public transportation.

Not included in the categories above but another interesting development, there is now a ride sharing of taxi service in NYC so if you find yourself there you might want to check out Bandwagon .

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• Published: 27 October 2022

Impacts of shared mobility on vehicle lifetimes and on the carbon footprint of electric vehicles

• Johannes Morfeldt   ORCID: orcid.org/0000-0002-3618-1259 1 &
• Daniel J. A. Johansson   ORCID: orcid.org/0000-0002-9138-1384 1  

Nature Communications volume  13 , Article number:  6400 ( 2022 ) Cite this article

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• Environmental impact

Shared cars will likely have larger annual vehicle driving distances than individually owned cars. This may accelerate passenger car retirement. Here we develop a semi-empirical lifetime-driving intensity model using statistics on Swedish vehicle retirement. This semi-empirical model is integrated with a carbon footprint model, which considers future decarbonization pathways. In this work, we show that the carbon footprint depends on the cumulative driving distance, which depends on both driving intensity and calendar aging. Higher driving intensities generally result in lower carbon footprints due to increased cumulative driving distance over the vehicle’s lifetime. Shared cars could decrease the carbon footprint by about 41% in 2050, if one shared vehicle replaces ten individually owned vehicles. However, potential empty travel by autonomous shared vehicles—the additional distance traveled to pick up passengers—may cause carbon footprints to increase. Hence, vehicle durability and empty travel should be considered when designing low-carbon car sharing systems.

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Introduction.

Decarbonizing road transportation is an important step in achieving the Paris Agreement 1 , with battery electric vehicles (BEVs) being one of the main strategies considered 2 , 3 . Transitioning towards a fully electrified passenger car fleet effectively eliminates tailpipe carbon dioxide (CO 2 ) emissions and has the potential to significantly reduce lifecycle CO 2 emissions 4 . Nevertheless, social and environmental sustainability concerns have been raised related to battery manufacturing and the mining of raw materials 5 .

Pathways with low resource exploitation and high energy efficiency are beneficial for decarbonization since they reduce the overall energy demand and material requirements. Options for passenger cars include various on-demand mobility schemes (including ride sourcing and ride sharing) that could replace individual passenger car ownership 6 , 7 , 8 . Implementing such schemes on a large scale would probably depend on self-driving (autonomous) vehicles 9 , 10 . Autonomous vehicles could decrease costs and increase the convenience of such schemes thus rendering it preferable over individually owned cars (including other arrangements where the car is primarily used by one household) 9 .

Car sharing and ride sharing could increase resource efficiency and reduce the environmental load of the system by replacing on the order of ten individually owned cars per shared car 11 . At the same time, the shared cars will likely be used more intensively during their lifetimes as compared to individually owned cars 12 . Moreover, shared autonomous vehicles may travel around without passengers for a large extent of their cumulative lifetime distance (so called “empty travel” or “deadhead travel”), which could lead to faster vehicle fleet turnover and increased manufacturing-phase emissions 13 . Simulation studies of shared autonomous vehicles have found that empty travel could increase the total vehicle travel distance by 10 to 100% in urban areas compared to the intended travel distance (i.e., the distance traveled by the car to transport a passenger or a group of passenger from one point to another) 11 , 14 . Empirical studies show a level of around 60% for taxi rides 15 and 40% for ride-sourcing services 16 , 17 on top of the intended (or served) travel distance.

The cumulative driving distance over the vehicle’s lifetime is an important assumption when estimating the carbon footprint of passenger car travel but varies significantly among studies 18 and has been shown to have large impacts on the results 19 . This assumption is even more uncertain for future mobility schemes, including systems based on car sharing or ride sharing 20 , 21 . Nevertheless, carbon footprint studies tend to assume that shared autonomous BEVs would travel at least as far as current taxis over the course of their lifetimes 12 , 13 , 22 , 23 . Hence, considering a relationship between driving intensity and vehicle lifetime is critical when assessing the carbon footprint of shared autonomous BEVs.

Studies using survival analysis 24 , 25 have determined that both calendar age and cumulative driving distance are important for the decision to retire a vehicle. Studies using statistical analyses of historical data have also shown that changes in driving intensity over the lifetime of the vehicle can have impacts on CO 2 emissions 26 and that vehicle lifetime extensions can result in lower carbon footprints 27 . However, to our knowledge, no study has yet attempted to establish a relationship between driving intensity and vehicle lifetime, and the implications of such a relationship on carbon footprints. Moreover, the carbon footprint-related consequences of changes in driving intensity in response to shared autonomous BEVs and plausible levels of empty travel have not yet been analyzed in situations where energy systems are decarbonized over time. To meet the goals of the Paris Agreement, shifts towards low-carbon manufacturing processes and electricity mixes used for charging needs to happen over the course of the next 30 years 2 , 4 . Thus, the vehicle’s lifetime, its annual driving intensity, and its interaction with decarbonizing energy systems will play important roles for the carbon footprint of passenger car travel over the coming decades.

In this work, we aim to bridge this research gap by estimating the impact of vehicle lifetime and annual driving intensity on the carbon footprints of passenger cars used for sharing. We design a semi-empirical lifetime-intensity model for assessing the lifetime of passenger cars with increasing annual driving intensity. The model is used together with prospective lifecycle assessment using vehicle fleet turnover simulations to assess the carbon footprint impacts of shared autonomous BEVs and potential levels of empty travel. The effects of climate change mitigation in global vehicle manufacturing and electricity generation are considered in the assessment. These energy and industrial systems are assumed to decarbonize in line with the Paris Agreement’s goals for the results presented in the main article, while results for an alternative pathway in line with currently stated policies are presented in the Supplementary Information. We show that the carbon footprint depends on the cumulative driving distance, which depends on both driving intensity and calendar aging. Higher driving intensities generally result in lower carbon footprints due to increased cumulative driving distance over the vehicle’s lifetime. Shared cars could decrease the carbon footprint by about 41% in 2050, if one shared vehicle replaces ten individually owned vehicles. However, potential empty travel by autonomous shared vehicles—the additional distance traveled to pick up passengers—may cause carbon footprints to increase. Hence, vehicles should be designed for durability, and empty travel should be kept low, to enhance the carbon footprint benefits of sharing.

Vehicle lifetimes decrease with increased driving intensity

Statistics on vehicle retirement can provide insights into how vehicle lifetimes vary with driving intensity. Most Swedish vehicles retired in 2014–2018 had a lifetime between 7 and 26 years and lifetime driving distances between 43 and 390 thousand kilometers (km) (95% interval). Calculating the average annual driving intensity for these vehicles results in a range between 0.5 and 28 thousand km per year (95% interval). All vehicles analyzed are internal combustion engine vehicles (ICEVs) since we are interested in capturing the behavior of mature vehicle technologies; very few BEVs have been retired so far.

The statistics show an average lifetime of 16.3 years, average lifetime driving distance of 216 thousand km, and an average annual driving intensity of 14.2 thousand km per year. Note that while a normal distribution can approximate vehicle lifetimes well, lifetime distances may be better approximated by a Weibull distribution, see Supplementary Figs.  2 – 4 , confirming previous research 14 . Since the sample is unevenly distributed over driving intensities with a bias towards the mean, stratification is used as a starting point for characterizing how the vehicle lifetimes vary with average annual driving intensity, see Fig.  1 and details on the stratified samples in Supplementary Table  4 .

a Vehicle lifetime and cumulative driving distance. b Vehicle lifetime and average driving intensity. c Cumulative driving distance and average driving intensity. Results are shown for stratified samples based on average annual driving intensity classes of Swedish ICEVs retired between 2014 and 2018. The color indicates the driving intensity class of the data point.

The stratification is made for individual average annual driving intensity classes, varying from 0 to 100,000 km per year in steps of 10,000 km per year. For each individual driving intensity class, a close to linear relationship exists between vehicle lifetime and cumulative driving distance. The linear slope becomes steeper with each higher driving intensity class, see Fig.  1a . This suggests that the calendar age of a vehicle becomes generally shorter with increasing annual driving intensity. Further, the cumulative driving distances are distributed across a wide range for higher driving intensity classes, see Fig.  1c , while the distribution is narrower for lower driving intensities. Hence, the probability distribution of retirement becomes wider as the annual driving intensity increases, which means that the probability of a retirement decision at a specific cumulative driving distance becomes smaller. Finally, the distribution of vehicle lifetimes becomes narrower and shifts towards lower vehicle lifetimes as the average driving intensity increases, see Fig.  1b . Hence, we focus the following analysis on empirically describing the relationship between driving intensity and vehicle lifetime in order to capture the impact of vehicle use on retirement age. The data presented here does not corroborate the assumption of a fixed cumulative driving distance, which is assumed in many lifecycle assessments of vehicles 13 , 18 .

The average vehicle lifetime decreases with each higher driving intensity class, from 19 years for average driving intensities of 0–10,000 km per year to 3.9 years for average driving intensities of 90,001–100,000 km per year, see Fig.  1b . The standard deviation of the distributions also indicates that the range of probable lifetimes becomes narrower with increasing annual driving intensity (although the standard deviation increases in relative terms). The standard deviation decreases from 5.0 years for driving intensities of 0–10,000 km per year to 1.9 years for driving intensities of 90,001–100,000 km per year. Results for a categorization in four vehicle sizes (mini, medium, large, and luxury size cars, see Supplementary Fig.  5 ) suggest that cars with low annual driving intensity are mainly represented by small size cars, while large to luxury size cars mainly have higher annual driving intensities. Medium size cars cover the full spectrum of annual driving intensities.

Currently, battery degradation is often raised as a constrain to the cumulative driving distance and lifetime of BEVs 28 , 29 , 30 , but the BEV is a relatively new technology on the market and, hence, statistics on battery lifetimes from real-world driving are scarce. The number of electric vehicles on the world’s roads were in the thousands in 2010 and grew rapidly to reach about 2 million by 2016 and over 10 million by 2020 31 , 32 . Hence, if enough retirement statistics for electric vehicles were available to make thorough statistical analyses, most vehicles would be much less than 10 years old. However, the limited data currently available on cars with batteries in Swedish vehicle retirement statistics show similar distributions as the stratified data presented above, see  Supplementary Notes   1 – 3 and Supplementary Figs.  11 , 12 . The data show shorter lifetimes on average (due to the limited historic data on electrified vehicles) and with a bias towards hybrid electric vehicles (HEVs) due to very few BEVs and plug-in hybrid electric vehicles (PHEVs) having been retired during the analyzed period.

Many BEV manufacturers already have warranties for their batteries of about seven to eight years or about 150,000 to 240,000 km, whichever comes first 33 , 34 , 35 , 36 , 37 . Future battery chemistries may further reduce degradation. Some studies suggest that future batteries may have significantly longer lifetimes than today. This is expected in response to altered battery chemistries 38 , changes in charging and use behavior 39 , and/or changed battery design 40 . Those changes could potentially yield a cumulative driving distance of more than three million kilometers—effectively outliving the rest of the vehicle. These improvements, if they materialize, would likely improve the cycling of the batteries. However, other factors could still limit the vehicle’s lifetime 25 , such as accidents, aging of other vehicle parts (e.g., structural elements of chassis and body), economic reasons, and consumer trends. Further, the durability of the vehicle is significantly dependent on the vehicle design, material selection, and business models 41 .

In summary, the results suggest that the annual driving intensity indeed has a strong influence on vehicle lifetimes. The relationship between driving intensity and vehicle lifetime may differ between BEVs and ICEVs, but not enough data is yet available to make such an analysis thoroughly. As a consequence, the remainder of this article explores how changes in annual driving intensity may influence the carbon footprint of passenger car travel, assuming that the relationship shown for ICEVs is applicable as a proxy for individually owned and shared autonomous BEVs. We capture the uncertainty in future vehicle lifetimes of (shared and autonomous) BEVs by highlighting extreme values for the relationship between annual driving intensity and vehicles lifetime as well as the empirically estimated relationship based on ICEV retirement data.

Impact of driving intensity on fleet-wide carbon footprints

This section presents carbon footprint estimates for BEVs at different average annual driving intensities based on the developed semi-empirical lifetime-intensity model, see Methods section for full description and discussion of the design. The model estimates the expected lifetime of a vehicle given a certain assumed average annual driving intensity. As we discussed in the previous section and in  Supplementary Notes   1 – 3 , it is assumed that the lifetime-intensity model is representative for BEVs despite being calibrated on data for ICEVs. Note also that we assume that current average vehicles in terms of weight are representative for future systems 4 .

To capture the relationship between driving intensity and vehicle lifetime, we use the elasticity design of the lifetime-intensity model with Weibull distribution, see Eqs. ( 2 ), ( 3 ), ( 6 ) in Methods section, and elasticities ( ε ≈ −0.65 and β ≈ 0.51) in the simulations. The lifetime-intensity model is trained with empirical data (i.e., Swedish vehicle retirement statistics described in the previous section) using maximum likelihood estimation, see Supplementary Tables  6 , 7 . A lifetime-intensity elasticity of −0.65 implies that the scale of the lifetime, is reduced by about −0.65% if annual driving intensity is increased by 1%. The scale parameter can be seen as the characteristic lifetime and is close to the average lifetime. Consequently, the average cumulative lifetime driving distance increases by approximately 0.35% on average if annual driving intensity is increased by 1%.

Carbon footprints are also estimated for two extreme cases, ε = 0 and ε = −1, representing no influence of driving intensity on lifetime and full influence of driving intensity, respectively. The two extreme cases show the sensitivity of the model design to the assumed elasticity. The range represents possible cases if the model was trained on different retirement data. ε = 0 is a relevant extreme case if future individually owned and/or shared autonomous BEVs would age in a way where driving intensity has no importance in the decision to retire vehicles. This could be the case if the vehicle and battery degradation are only influenced by calendar age. ε = −1 represents a case where vehicle aging, including aging of the battery, is only dependent on the distance driven (e.g., if battery aging only depends on the number of charging cycles). This latter approach is used in many lifecycle assessments 13 , 18 , where fixed cumulative vehicle distances are assumed. Note though that β is based on the empirical data ( β ≈ 0.51) also for the extreme cases.

The impact of driving intensity on the carbon footprint of BEVs is estimated using a vehicle fleet turnover simulation set to meet a certain annual travel demand. Hence, fewer cars are needed to meet the travel demand if the average annual driving intensity per vehicle increases, see details in Methods. The carbon footprint is presented as emissions per vehicle-kilometer, based on the average annual emissions for a given year, including emissions from electricity used for charging and vehicle manufacturing, divided by the travel demand of that year. Figure  2 shows the results for BEVs assuming global electricity technology mix and that global manufacturing and electricity generation follow a climate change mitigation pathway in line with the goals of the Paris Agreement. The assumed pathways for carbon intensities of electricity generation used for charging are shown in Supplementary Fig.  1 .

Results show the impact on annual average carbon footprints for meeting a certain travel demand: total carbon footprint ( a , d ), manufacturing-phase emissions ( b , e ), and use-phase emissions ( c , f ) for 2030 ( a – c ) and for 2050 ( d – f ), depending on the elasticity of the semi-empirical lifetime-intensity model (solid: empirical elasticity, ε ≈ −0.65, dotted: full influence of driving intensity, ε = −1, and dashed: no influence of driving intensity, ε = 0). The results assume that global manufacturing and electricity generation decarbonize in line with the Paris Agreement’s goals.

Emissions per vehicle-kilometer related to vehicle manufacturing decrease with increasing driving intensity in all cases, see Fig.  2b, e . The reason is that increased average annual driving intensity results in that fewer cars are needed to meet the travel demand. Emissions per vehicle-kilometer in the use-phase are constant for all cases since total use-phase emissions are proportionate to the travel demand, see Fig.  2c, f . Intuitively, average use-phase emissions depend on the vehicle-specific energy use and the carbon intensity of the electricity mix used for charging in each specific year, which can be seen when comparing the level in Fig.  2c, f . Hence, the total emissions per vehicle-kilometer varies with manufacturing emissions when increasing the driving intensity, see Fig.  2a, d .

As expected, manufacturing-phase emissions decrease rapidly and approach zero with increasing driving intensity when vehicle retirement is unaffected by driving intensity, i.e., ε = 0, displayed as dashed lines in Fig.  2 . This is due to the retirement decision solely depending on the calendar age when ε = 0, which is based on the empirical calendar age distribution in the model. This can be compared to when vehicle retirement is largely affected by the driving intensity, i.e., ε = −1, displayed as dotted lines in Fig.  2 . In this case, the driving distance over the whole lifetime of each vehicle is close to independent of the driving intensity. Hence, the reduction in the number of cars needed to meet the travel demand when the annual driving intensity increases would be counteracted by the number of retired vehicles that reach their maximum cumulative driving distance. This results in an inflow of new vehicles needed to replace the retired ones, which is close to independent of the driving intensity. The reasons that the number of vehicles slightly drop with increasing intensity when ε = −1 are the characteristics of the Weibull distribution, how it shifts as the intensity increases, and that the annual driving intensity for each individual car is assumed to drop by 4.4% per year. The significance of the drop in the annual driving intensity is tested in Supplementary Fig.  9 , showing that manufacturing emissions decrease less when the driving intensity is assumed to be constant over the lifetime of each vehicle.

A lifetime-intensity elasticity based on empirical evidence, i.e., ε  = −0.65, results in a development in-between the two extremes, displayed as solid lines in Fig.  2 . A sensitivity analysis shows that the shape of the curves for total carbon footprint are scaled but similar in relative terms when assuming average Swedish or European Union (EU) electricity for charging, see Supplementary Fig.  9 . Further, the general pattern of the relationship between average annual driving intensity and the carbon footprint is similar also if energy systems and global manufacturing do not decarbonize in line with the Paris Agreement and instead develops according to stated policies, see Supplementary Fig.  9 .

To summarize, our results show that measures intended to increase annual driving intensity of individual cars to meet a given travel demand would result in carbon footprint reductions. Such measures include car sharing services, e.g., existing ride sourcing systems and future systems using shared autonomous BEVs. Such services could replace individual car ownership, but they may also increase driving distances because of empty travel to pick up passengers. This risk is evident for current taxis and ride sourcing services 15 , 16 , 17 as well as in simulations of future transport systems using shared autonomous vehicles 11 , 14 . In the next section, we explore how empty travel could impact the carbon footprint when simultaneously considering the possible influence that increased driving intensity might have on the lifetime of vehicles.

Empty travel by autonomous vehicles may increase emissions

The risk of empty travel when using on-demand mobility services, including those provided by autonomous vehicles, could reduce the resource and environmental efficiency of sharing. The lifetime-intensity model shows that the lifetime of the vehicle is likely to decrease with increased annual driving intensity. Vehicles may need to be replaced more often if a large part of that annual driving intensity is made up by empty travel, with increasing emissions in vehicle and battery manufacturing as a result 13 . Here, we explore how the carbon footprints of individually owned BEVs, individually owned autonomous BEVs and shared autonomous BEVs depend on the elasticity of the lifetime-intensity model and the share of empty travel. Further, we estimate the breakeven level of empty travel, i.e., the point where the carbon footprints of a fleet of shared autonomous BEVs and one of individually owned BEVs (without any empty travel) are equal.

The impact of empty travel on the carbon footprint for a fleet of shared autonomous BEVs is analyzed using the vehicle fleet turnover simulation, see details in Methods section. Simulations are made for assumptions on additional empty travel on top of the intended travel—ranging from 0 to 100%, and for assumptions on how many individually owned BEVs a shared autonomous BEV replaces—five or ten, while still meeting the given level of annual travel demand. Specific energy use per km is assumed to be the same irrespective of if the car is autonomous or not. Note though that the combination of a shared autonomous BEV replacing ten individually owned BEVs and assuming 100% empty travel results in high annual driving intensity of ca 280,000 km, which may not be possible to achieve for one car. Hence, such extreme combinations are included only for illustrative purposes. We also analyze one case with individually owned autonomous BEVs that are not shared but still may travel empty. This can occur, for example, when autonomously parking and/or charging at remote spots.

In the case where an individually owned autonomous BEV causes empty travel, the breakeven level occurs at 0% as expected, see Fig.  3a, d . This means that any empty travel caused by using the autonomous BEV results in an increase in the average carbon footprint, as compared to using a regular BEV to meet the same travel demand. In the case where individually owned BEVs are replaced by shared autonomous BEVs, we first note that a system with shared autonomous BEVs in 2030 reduces the carbon footprint per intended km traveled if no empty travel is assumed. The carbon footprint decreases from 96 g CO 2 per km for individually owned BEVs to 74 and 69 g CO 2 per km if one shared autonomous BEV replaces five or ten individually owned BEVs, respectively, assuming empirical elasticity for the lifetime-intensity model. The corresponding numbers for 2050 are 32 g CO 2 per km for individually owned BEVs, and 22 and 19 g CO 2 per km if one shared autonomous BEV replaces five or ten individually owned BEVs, respectively.

Estimated based on the average carbon footprint of individually owned autonomous BEVs that replace one individually owned BEV ( a , d ) and shared autonomous BEVs replacing five ( b , e ) or ten ( c , f ) individually owned BEVs depending on the elasticity of the semi-empirical lifetime-intensity model (solid: shared autonomous BEV – empirical elasticity, ε ≈ −0.65, dotted: shared autonomous BEV – full influence of driving intensity, ε = −1, dashed: shared autonomous BEV – no influence of driving intensity, ε = 0, and dot-dashed: individually owned BEV – current driving intensity). a – c Show results for 2030 and d – f show results for 2050. The results assume that global manufacturing and electricity generation decarbonize in line with the Paris Agreement’s goals.

The breakeven level of empty travel for a fleet in 2030 occurs at 34 and 44% for shared autonomous BEVs that replace five and ten individually owned BEVs, respectively, see intersection between solid and dot-dashed lines in Fig.  3b, c . As global manufacturing and electricity generation decarbonize further, additional levels of empty travel are possible before breakeven with the carbon footprint of individually owned BEVs is reached. Hence, for a fleet in 2050, the breakeven level of empty travel increases to 64% and 87% for shared autonomous BEVs that replace five and ten individually owned BEVs, respectively, Fig.  3e, f .

As discussed in the previous section, only manufacturing-phase emissions are affected by the lifetime-intensity model. A larger negative elasticity implies a larger inflow and outflow of shared autonomous BEVs in each year, implying a larger average carbon footprint, see Supplementary Fig.  8 . The elasticity representing no influence of driving intensity on vehicle lifetime ( ε  = 0) results in higher breakeven levels as compared to the elasticity based on empirical evidence. In this case, for 2030, the breakeven level is 59 and 66% for shared autonomous BEVs that replace five and ten BEVs, respectively, see dashed lines in Fig.  3b, c , and over 100% in 2050, see dashed lines in Fig.  3e, f . Conversely, the elasticity representing full influence of driving intensity on vehicle lifetime ( ε = −1) results in lower breakeven levels. In this case for 2030, 14 and 18% for shared autonomous BEVs that replace five and ten individually owned BEVs, respectively, see dotted lines in Fig.  3b, c , and 22 and 29% for 2050, respectively, see dotted lines in Fig.  3e, f .

A sensitivity analysis shows lower breakeven levels if global manufacturing and electricity generation follows a pathway in line with stated policies rather than one that achieves the goals of the Paris Agreement, see Supplementary Fig.  10 . It also shows that the breakeven level is significantly higher, above 100% in several cases, if lower carbon intensities are assumed for electricity used for charging (i.e., Swedish or European average electricity). The sensitivity of the assumed driving intensity decrease rate of 4.4% is also tested, showing higher breakeven levels for the additional empty travel with higher driving intensity decrease rates (i.e., when a larger share of the travel for one vehicle is concentrated to early years in the vehicle’s lifetime), while the opposite holds if the driving intensity is constant over time. Nevertheless, the assumed elasticity in the lifetime-intensity model has a higher impact on the results than the assumed driving intensity decrease rate.

The significance of the elasticity in these results points to the importance of designing future shared autonomous BEVs for durability. The reason for this can be summarized: the smaller the reduction in lifetime when increasing driving intensity, the larger the potential carbon footprint benefits of car sharing.

The passenger transport systems are likely to go through several changes during the coming decades. The most prominent changes include increased use of electrified and autonomous vehicles as well as on-demand mobility schemes, including car sharing and ride sharing. These trends will affect the pathways towards decarbonization of passenger car travel, including changes in charging patterns 13 , cost structures 9 , and the value of travel time 42 , 43 , 44 , which may induce additional travel activity 45 and cause modal shifts 46 , 47 . These trends may also cause changes in vehicle design, including materials used in manufacturing 48 and changes to facilitate material recycling 49 , but many of these aspects are yet to materialize.

Our analysis shows that the relationship between vehicle lifetime and driving intensity is an important factor when estimating the carbon footprint of shared mobility. Some analysts argue that passenger cars in today’s fleets are not being used enough to compensate for material use and emissions during the manufacturing phase 49 , 50 . Therefore, increasing the driving intensity, for example through shared autonomous BEVs, may be an option for reducing lifecycle emissions from passenger car travel. However, if increasing driving intensity also results in shortened vehicle lifetimes, as suggested by the statistics, the carbon footprint would not drop as much as if a fixed lifetime were assumed.

The statistical analysis and the results from the designed semi-empirical lifetime-intensity model suggest that increased intensity of vehicle use tends to increase the cumulative lifetime distance. Hence, the results indicate that shared autonomous BEVs would reduce the carbon footprint if it results in higher driving intensity of each individual vehicle. For example, we find that a system with shared autonomous BEVs can decrease the carbon footprint per kilometer of intended travel by about 41% if one shared vehicle replaces 10 individually owned vehicles in 2050. However, this assumes a level of zero empty travel. We show that the potential carbon footprint benefit can be reduced—and even erased—if the level of empty travel becomes large. Further, besides avoiding excessive empty travel, the emissions reduction potential of shared mobility could be further improved if ride sharing is adopted, since each traveler sharing the ride in that case would bear part of the carbon footprint by effectively increasing the occupancy ratio. Note that induced travel by autonomous BEVs (both individually owned and shared) has not been assessed in this study. Nevertheless, this risk is important to consider since the use of autonomous vehicles may substantially increase the travel demand. Autonomous vehicles may effectively reduce the value of travel time and the generalized travel cost 45 since the driver does not need to be attentive and can instead use their time in the vehicle for whatever they find convenient. The potential increase in the travel demand that may follow from reduced value of travel time could further increase the total carbon footprint for the fleet as a whole.

Finally, our conclusions rely on the assumption that the relationship between driving intensity and vehicle lifetime established in the semi-empirical model will hold also for future regular and autonomous BEVs. In this article, we present preliminary evidence suggesting that cars with batteries follow similar trends as ICEVs, but the design and lifetimes of future batteries and vehicles are highly uncertain. Hence, the intention with the analysis presented in this paper is to highlight potential consequences based on currently available data and discuss them in relation to alternative assumptions. Those alternative assumptions highlight a range of plausible outcomes if the lifetime characteristics of future batteries and vehicles may deviate from those of current passenger cars. Nevertheless, the analysis shows that the carbon footprint may be substantially reduced if the relationship between average annual driving intensity and vehicle lifetime is weakened, pointing to the importance of designing future BEVs (both autonomous and regular) for durability.

Swedish vehicle retirement statistics

Statistics on Swedish passenger cars retired between 2014 and 2018 are used to understand how changes in annual average driving intensity could influence vehicle lifetimes. The statistics are collected from the Swedish registry for road transport vehicles, regulated by Swedish law 51 . The excerpt, provided by the Swedish government agency Transport Analysis 52 , includes information on the manufacturing year, date of registration, car manufacturer, engine type, mass in running order, cumulative distance traveled at last inspection, date of last inspection, and date of deregistration. The excerpt only includes vehicles that were indeed retired at the date of deregistration. Hence, vehicles that were deregistered for administrative reasons or exported are excluded.

The filtered dataset includes 442,395 observations. The filtering performed by the authors aims to reduce bias in the results and applies the following criteria: (i) age or distance traveled must not be missing, equal to zero, or equal to 999,999, (ii) time between last inspection and date of deregistration must not be longer than 14 months, (iii) time between first registration of the vehicle and the manufacturing year must not be longer than two years, (iv) average distance traveled must not be greater than 600 km per day, (v) average distance traveled must not be less than 1 km per day, (vi) mass in running order must not be greater than 3000 kg, and (vii) engine type is gasoline or diesel without hybridization, ethanol or natural gas/biogas. Details and rationale for these criteria are provided in Supplementary Tables  1 – 3 . Criterion (ii) filters many observations but including them does not significantly impact the results.

Stratified random sampling is used to create a new dataset for analyzing the influence of increasing driving intensity since only a small share of the dataset represents cars with high average annual driving intensity, such as taxis or other commercial vehicles. The strata and random sample size are set to maximize the amount of information about vehicles with high driving intensity while also ensuring high enough sample size to enable further statistical analysis. This results in strata for average annual driving intensity classes of 10,000 km/year increments from 0 km/year to 100,000 km/year. The random sample size in each stratum is 200 observations, except for the highest intensity class where the whole sample of 145 observations is used, see Supplementary Table  4 .

Semi-empirical lifetime-intensity model

The semi-empirical lifetime-intensity model enables estimations of vehicle lifetime probabilities for a given annual average driving intensity. The model can easily be updated with new parameters on average vehicle retirement lifetime, its standard deviation, and the average annual driving distance, as new statistics become available. The model can also easily be recalibrated based on new stratified random sampling datasets to enable use for other geographical regions. Two model designs are considered together with two assumptions on the probability distribution of the lifetime data as a result of these prerequisites.

If the data is assumed to follow a normal distribution, we assume that the probability of a vehicle manufactured at year t 0 , with average annual driving intensity D , being retired at year t is

In the elasticity design, we introduce a factor dependent on the quota between the annual driving intensity of the vehicle and the average annual driving intensity of current vehicle retirements, D 0 , as part of the mean,

that adjusts the expected vehicle lifetime of current retirements, \({{{\tau }}}_{0}\) , dependent on the elasticity, \({{\varepsilon }}\) , that decides the level of influence of the driving intensity. An elasticity of −1 implies that the vehicle lifetime is fully determined by the driving intensity (e.g., if driving intensity is doubled, lifetime is halved), 0 indicates no influence and the lifetime is only determined by calendar age, while an elasticity above 0 would imply that the vehicle lifetime increases with driving intensity. This design benefits from easy interpretation, but it only applies for driving intensities equal to or greater than the current average, see Fig.  4 .

a Results for the elasticity design. b Results for the logistic design. The contours show probability density levels and are provided for both Normal (dot-dashed) and Weibull distributions (solid). Stratified samples of Swedish vehicle retirement statistics for 2014–2018 are provided in the background for comparison. The color indicates the driving intensity class of the data point.

The standard deviation,

is designed in a similar way to the design for the mean, where the constant \(\alpha=\frac{{\sigma }_{0}}{{\tau }_{0}}\) is determined based on a fit of a normal distribution to current vehicle retirement statistics. An additional elasticity, \({{\beta }}\) , is introduced in the standard deviation to account for the distributions becoming increasingly narrow with higher driving intensity classes, see Fig.  1b .

In the logistic design, we instead assume that the distribution is governed by a function inspired by the logistic curve to better capture the form of the stratified random sampling. The logistic curve function is slightly altered to reduce the number of parameters to fit to the data. Hence, \({{\mu }}\left({{D}}\right)\) and \({{{{{\rm{\sigma }}}}}}\left({{D}}\right)\) are defined as follows in this design.

where L and L 0 are the parameters that would be calibrated based on the stratified random sampling. This design applies for all driving intensities greater than zero.

If the data are assumed to follow a Weibull distribution, we assume that the probability of a vehicle manufactured in year t 0 , with average annual driving intensity D , being retired at year t , is

where the scale, \({{{{{\rm{\lambda }}}}}}\left({{D}}\right)\) , is defined in the same way as the mean, \({{\mu }}\left({{D}}\right)\) , see Eqs. (2, 4) above, and shape, \({{k}}\left({{D}}\right)\) , is defined in the same way as the standard deviation, \({{\sigma }}\left({{D}}\right)\) , see Eqs. ( 3 , 5 ) above. Note that \({{{\tau }}}_{0}\) in this case represents the scale of current vehicle retirement statistics and that \({{\alpha }}=\frac{{{{k}}}_{0}}{{{{\tau }}}_{0}}\) is determined by fitting a Weibull distribution. The fact that the median is lower than the mean for higher driving intensity classes indicates that the distribution is more positively skewed for higher driving intensity classes. This suggests that a Weibull distribution with a longer tail towards higher vehicle lifetimes would be a better fit, confirming previous research 24 , 53 .

The parameters for the different model designs are estimated using maximum likelihood estimation, see Supplementary Table  7 . A comparison of modeled vehicle lifetimes with the stratified random samples for different driving intensity classes is presented in Fig.  4 and Supplementary Fig.  7 . The contour lines in Fig.  4 , also known as isodensity lines 54 , show how the points of equal probability density for a given vehicle lifetime shift depending on the assumed driving intensity ( y -axis) and on the model design (panel and line type). The highest probability density level is shown around the mean of the distribution, and the distance indicates the rate of change. This implies that a larger distance between the lines indicates a more spread-out distribution, analogously to on a topographic map.

Figure  4a clearly shows that the elasticity design deviates from the statistics at the average current driving intensity of 14,200 km per year and approaches an infinite lifetime as driving intensities decrease. The proposed correction for this issue is to use the logistic design, as demonstrated in Fig.  4b . However, a limitation of the logistic design is that the distribution of vehicle lifetimes is assumed to be kept constant for driving intensities higher than the stratum with highest driving intensity (i.e., higher than 100,000 km per year in this study), see Supplementary Fig.  6 . The elasticity design instead results in vehicle lifetimes that approach zero for very high driving intensities.

Regarding the choice of distribution, the Weibull distribution benefits from better reflecting the skewness of the statistics. However, it overcompensates for higher driving intensities when applied with the logistic design, resulting in longer tails of vehicle lifetimes than the statistics indicate, see the greater distance between lines in Fig.  4b . This difference between Normal- and Weibull-based model designs is close to negligible for the elasticity design. Benefits and drawbacks for the choice of distribution and model design are summarized in Table  1 .

Prospective lifecycle assessment with vehicle fleet turnover

A vehicle fleet turnover simulation is designed to evaluate the impact of lifetime-intensity elasticities on the carbon footprint for individually owned BEVs, individually owned autonomous BEVs and shared autonomous BEVs. The simulations also test assumptions on how many individual owned BEVs that a shared autonomous BEV can replace, and different levels of implied empty travel of shared autonomous BEVs.

Average carbon footprints (reported in g CO 2 per vehicle-kilometer of intended travel) are estimated for the fleet using a prospective lifecycle assessment framework based on GREET® 2 - Version 2019 55 adapted for scenario analysis 4 . The framework enables estimations of future carbon footprints of passenger cars depending on climate change mitigation efforts in electricity generation and global manufacturing. Two pathways for this mitigation are analyzed: one in line with stated policies and one that achieves the goals of the Paris Agreement. The results presented in the main paper assumes a pathway that achieves the goals of the Paris Agreement, while the results for stated policies are presented in the Supplementary Information. The stated policies pathway is based on currently implemented and stated climate policies by 2019 and the pathway in line with the goals of the Paris Agreement is designed to limit global mean temperature increase to below 1.8 °C. The two pathways are based on the IEA 56 scenarios named Stated Policies and Sustainable Development.

The vehicle fleet turnover simulation is designed for the fleet to match a constant annual travel demand equal to N 0 = 1000 cars driving at average driving intensity, D 0 = 14,200 km per year. For each year, t , the number of new cars needed are estimated by solving Eqs. ( 7 ), ( 8 ):

where the number of new cars, N , is estimated as the difference between the annual travel demand and the annual travel range of the current fleet, \(\hat{S}\) , divided by the annual vehicle-kilometers, m , for a new car.

The travel range of the current fleet, \(\hat{S}\) , in each year, t , is given by

where the fleet of the previous year is an age distribution for age cohorts, \(\widetilde{{{t}}}\) , from age 1 to 40 years, in one-year steps. The initial age distribution for the first year is estimated by a Weibull distribution (shape 1.4 and scale 13). The distribution is informed by statistics on the age of the Swedish vehicle car fleet 57 and serves to initiate the simulation, which is run for 50 iterations (years) to give it time to stabilize at a steady state level. The annual range covered by a car of a given age, \(\widetilde{{{t}}}\) , is given by

where the annual driving range is assumed to decrease by b = 4.4% per year over its lifetime (estimated based on statistics on driving distances in Sweden 57 ), D 0 represents the average annual driving intensity and τ 0 represents the mean vehicle lifetime. The retirements for each age cohort in year, t , are given by the cumulative probability distribution for the semi-empirical lifetime-intensity model, assuming the elasticity design and Weibull distribution, as described in Eqs. ( 2 ), ( 3 ), ( 6 ), assuming that the driving intensity, \(D\) , is equal to \({D}_{i}\cdot \left(1+\theta \right)\) , where D i is the intended travel distance and θ represents the additional share of empty travel. The probability of retirement earlier than a lifetime of one year is added to the probability of retirement at the one-year mark. This is to avoid truncating the probabilities for retirement for cars with a lifetime of less than one year, which is a risk for the extreme case of ( ε  = −1).

The model returns annual sales, stock, and retirements. Carbon footprints per km, CF, associated with that steady state are estimated based on the total manufacturing- and use-phase emissions, E , for each year divided by the total intended traveling distance, \({N}_{0}\cdot {D}_{0}\) :

Manufacturing-phase CO 2 emissions, E Manufacturing , are estimated for car sales in each year based on manufacturing processes as implemented in GREET® for the Stated Policies Scenario, while new and innovative processes are phased in over time for the Sustainable Development Scenario based on a literature review 4 . Use-phase CO 2 emissions, E Use , are estimated annually based on total traveled distance (including potential empty travel), vehicle energy use, and appropriate carbon intensities described below. The specific energy use of the cars are assumed be 201 Wh per km 4 . Autonomous BEVs are assumed to have the same specific energy use per km as regular BEVs. BEVs are assumed to charge with electricity produced using average global, European, or Swedish technology mixes (results for European and Swedish technology mixes are presented in the Supplementary Information).

The carbon intensity of electricity is based on estimates of average direct emissions for future electricity mixes for each respective geographic area, see description of the data sources for the scenarios below. 2019 is used as a base year to avoid the influence of the Covid-pandemic on the carbon intensities. The carbon intensities used for electricity represent averages for each respective geographic area following the attributional nature of the chosen prospective lifecycle assessment framework 58 , 59 . Upstream emissions occurring in production of fuels and power stations are accounted for by adding a weighted factor for future electricity mixes based on estimates by Pehl et al. 60 . We assume that Pehl et al.’s estimates of upstream emissions for each electricity generation technology can be applied regardless of geographic area and that their baseline and climate policy scenarios resemble the Stated Policies and Sustainable Development scenarios used in this study. Note that emissions for the construction of water and nuclear power stations are assumed to be zero for Sweden and the European Union due to their long lifetime, the fact that they were mainly constructed several decades ago, and that few new stations are planned. Hence, we assume that the emissions from the construction of these stations are only attributed to electricity production prior to 2019. Continuing to account for these construction-related emissions in the carbon intensity of electricity after 2019 would not have any significant impact on the results.

For the global electricity mix used in manufacturing and for charging, future direct emissions and adjustments to account for transmission and distribution losses (based on the difference between estimated supply and demand) are based on estimates by the IEA 56 for the two decarbonization pathways, Stated Policies and Sustainable Development. For the European electricity mix used for charging, direct emissions and adjustments to account for transmission and distribution losses are based on scenarios by the European Commission 61 combined with the cap of the European Union emissions trading system reaching zero in 2058 62 for both decarbonization pathways. For the Swedish electricity mix used for charging, direct emissions for 2019 are calculated based on the total emissions for electricity generation divided by the end-use of electricity 63 , 64 . Direct emissions are assumed to decrease linearly to zero by 2045 for both decarbonization pathways, in line with the adopted net-zero emission target and the Swedish government’s intention 65 to reach zero emissions from electricity generation. Upstream emissions are based on estimates by Pehl et al. 60 combined with projections for the future electricity generation mix by the IEA 56 , European Commission 61 , and Swedish Energy Agency 66 .

Reporting summary

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

Data availability

Data for all figures and additional data used in the analyses are available from the corresponding author upon request. Note that the detailed data on vehicle retirement are treated as confidential since data that could be traced back to individuals or companies are protection under the Swedish Public Access to Information and Secrecy Act (SFS 2009:400). Hence, requests for access to these detailed data should be made directly to the Swedish governmental agency Transport Analysis ( https://www.trafa.se/vagtrafik/fordon/ - dataset “Fordon på väg”).

Code availability

The computer code used to generate the results reported in this study are available from the corresponding author upon request.

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Acknowledgements

We acknowledge the support for this research by Mistra Carbon Exit financed by Mistra, the Swedish Foundation for Strategic Environmental Research (J.M. and D.J.A.J.), and by the NAVIGATE project financed by the European Commission, H2020/2019-2023, grant agreement number 821124 (D.J.A.J.).

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Morfeldt, J., Johansson, D.J.A. Impacts of shared mobility on vehicle lifetimes and on the carbon footprint of electric vehicles. Nat Commun 13 , 6400 (2022). https://doi.org/10.1038/s41467-022-33666-2

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Getting Around Costa Rica

There are many ways to get around Costa Rica- which is good because despite being a small country, Costa Rica has big distances.

For example, you may want to go from San Jose to La Fortuna. Put it in Google Maps and it’s only about 80 miles. But what you don’t see are all the curvy two-lane roads, stops, and other road conditions that might arise while taking what turns out to be an almost 3-hour drive. Driving in Costa Rica can be its own beast.

That’s one reason why it is so essential to pick the right way to get around Costa Rica when you are visiting- especially if you are visiting Costa Rica with kids as we do.

In this post, I’ll walk you through all the different ways you can get around Costa Rica. My vote? If you are exploring Costa Rica with kids and need both flexibility and a good bang for your buck, I recommend car rental. If you have a larger budget or a large group, then definitely opt for a private shuttle with a tour guide.

Arriving in Costa Rica

Costa Rica is well connected both through its international airports and international bus systems from Nicaragua and Panama.

When you arrive at either the San Jose Airport or the Liberia International Airport , you’ll find flights coming in from around the world- just like you would at any major international airport. If you need to know which airport to fly in to, it depends on where you are going after you arrive.

Land crossing in Central America between Costa Rica and either Panama or Nicaragua is also common.

You’ll need a valid passport to enter the country, and tourists from most countries are allowed a 90-day tourist visa automatically upon entry.

The issue is- how do you get around once you arrive?

Domestic Flights

If you are heading to some of the more remote areas of Costa Rica, and have the budget, then taking a domestic flight is a great option. I usually recommend these flights for people going to popular destinations such as the Osa Peninsula (Corcovado National Park), Puerto Viejo or the Caribbean coast, or remote areas like Tortuguero National Park.

You can also consider a domestic flight from San Jose to Guanacaste, although do keep in mind there is now a bustling international airport in Liberia . The domestic flights are done with a pretty small plane, so if you are a nervous flyer these are probably not the best option.

Have a big group or a big budget? Consider chartering a private plane to your destination. You can also do private helicopters. I have to say I have never done these, so am not a good resource on how to do them or the costs. It is an option, though.

Renting A Car

Renting a car in Costa Rica is the best option in my opinion, and we recommend renting from Adobe Rental Car. This is because you’ll pay less overall, support a local company, and they have the best customer service of any rental car company around. (And after 20 years of renting cars in Costa Rica, I’ve tried them all!) You can also get a 10-20% discount on your rental car through Pura Vida Moms.

You have to keep in mind a few things when renting a car, however:

• Your credit card insurance won’t cover the mandatory insurance in Costa Rica, so make sure to get a quote that includes insurance
• The car seats provided are not great as they are old and only 3 point harnesses so I take our own
• Gas in Costa Rica is more expensive than in the States and is sold by the liter. The good thing is that gas prices are fixed across the country, so you don’t need to play the gas game as you do in the US.
• You’ll need a navigation app for Costa Rica- whether you purchase a GPS with your rental car or use your cell phone, it’s a must.

I’ve written extensively about driving in Costa Rica and renting cars in Costa Rica , so please refer to those posts for more info.

Direct Transfers

You can hire a direct transfer (Adobe Rental Cars even does them) from point A to Point B. There are two types of direct transfers, private and shared.

Shared Shuttle

A shared shuttle means that you and a few other people (usually no more than 15 total) will share a bus that goes from one place to another. The advantages are that these are much faster than a public bus, and worth the money especially if you are a solo traveler who wants to get somewhere quickly, safely, and without hassles.

Another advantage is if you are doing what is called a tour connection where you are rafting from Point A to Point B, for example. This is common for those who choose to take a boat across Arenal Lake from Monteverde to La Fortuna to drastically cut down on travel time.

The disadvantage to a shared shuttle lies in the advantage- you will go from Point A to Point B and that’s it. Comfort stops for the bathroom and snacks are kept to a minimum, and you are in the shuttle with people you don’t know.

We use shared shuttle buses when we are going ot a walkable destination where we won’t need to get around much farther than the town center. A great example of those would be in Tamarindo, San Ramon, and Grecia. La Fortuna would not be a great example as everything is spread out.

Some people who hire shared shuttles will then use Uber or taxis when they arrive at their destination. Of course, this does add cost to the overall trip, which is why many times renting a car is cheaper for small groups.

Join the Costa Rica With Kids Facebook Group. Ask all the Costa Rica Travel questions you want- we will answer them!

Private shuttle.

This is essentially where you hire a private driver to take you exactly where you want ot go when you want to go there. You have shuttle services on demand for your whole trip. You can also hire a private guide in this instance as well, and the guide and driver work together.

A private shuttle is the more expensive option of the two, but for large groups, the cost per person can actually be less when you add up all the transportation costs.

If I had an unlimited budget and could hire a private shuttle and guide for every trip I went on to Costa Rica, it’s definitely the option I would pick. It’s not always feasible, however.

Costa Rica By Bus

Many many travelers want to see Costa Rica by public transportation, and public buses are definitely the absolute cheapest option (other than hitchhiking which I don’t recommend!).

There are two different types of buses that interconnect Costa Rica: direct buses ( bus directo ) and indirect buses, or colectivos . Here’s a bit on each:

Direct Buses

You can get on a bus that will go directly to your destination with very few stops. These buses will generally make one restroom stop at a public place where food and drink are sold. They will generally do this every 4 hours. A great example of this is the bus from San Jose to Liberia that stops in Esparza.

Direct buses adhere to a strict schedule and are not late. If you miss the bus- you missed the bus.

These buses tend to get crowded as there are both seats and people who are able to stand in the aisles while the bus is in motion. When the bus stops people generally get off and come back to their same seat. You need to watch your hand luggage in this case, as thieves know to target these bus stops.

For families with kids, direct buses are the best way to get around if you absolutely want or need to ride the bus. Keep in mind there are no toilets or food on board, and there isn’t entertainment.

The colectivo busses generally go shorter distances. They stop at each bus stop and pick up or let off passengers as needed. You can just ring a bell and the bus will stop. If the bus driver sees people flagging him down on the side of the road to stop, he will do so.

It’s a very slow way to travel- it’s also cheap. It can be fun for a while to meet new people on the bus and see the types of people that come and go, but honestly, if you don’t have much time, this can be a real test of patience.

For example, the local buses that go between San Ramon and Palmares (a distance of 5 km) can take up to an hour.

If you are looking to plan a Costa Rica trip via bus, I highly recommend the Costa Rica by Bus Facebook group .

Taxis are everywhere in Costa Rica as there are so many people without cars who live there. There are several types of taxis you can take, and note that it can be nearly impossible to get a local taxi when it’s pouring rain.

Note that the airports have official taxis that are to take people to and from the airport- these taxis are orange.

There are red taxis all over town in Costa Rica. The red taxis mean that the government has insured and licensed the taxi drivers and that they have a set fee for transport between destinations.

These taxis are metered (by what we call la maría). You simply ask the driver to start the meter, and when you arrive the fee for the ride is displayed. Most local taxis take a credit card, but not all so it can be good to have some cash on hand when traveling via taxi.

Taxi Pirata

These “pirate taxis” are not officially bonded and insured by the government. Therefore, you must agree on a price for your destination before you enter the vehicle. When you take a pirate taxi, you will generally pay less than you will for a red official taxi.

You also run a bigger safety risk with these taxis as they are not overseen by a dispatch. Some are, and you will find collective taxis that are “pirate” but run by a dispatch.

these taxis can be hired out for a half day or a day, and this can be an economical way to get around a destination if you don’t rent a car.

In large metropolitan cities and urban areas in the Central Valley (think San José, Heredia, Cartago Grecia, San Ramon, Palmares) you can find an Uber pretty easily. uber is generally cheaper than taxis, and you have the advantage of not having to exchange money because you pay in the app.

I think some beach destinations like Jaco may also have Uber, but it’s definitely not something that you have everywhere in the country so if you take an Uber to a destination, make sure you can also get home via Uber from that dentitions. For example, when I was at the Starbucks Hacienda Alsacia recently a couple was dropped off by Uber with their luggage so they could take the tour. At the end of the tour, they tried to get an Uber and couldn’t, and it was a huge mess to get a taxi out there to them.

Boats and Ferries

Water transportation is of course popular in Costa Rica, and can be an economical way to get to some remote destinations.

There are boat rides that will take you to remote areas not widely accessible by car. Two examples of those would be to arrive at Drake Bay or at Tortuguero.

There is also a nice boat transfer between La Fortuna and Monteverde.

You’ll also find boats that take you to Isla del Coso for extremely diving, and boats that will take out offshore for fishing, snorkeling and diving expeditions.

Ferries leaving from Puntarenas can be a great way to get your car onto the Nicoya Peninsula without taking a circuitous route. this can connect you with destinations such as the Bioluminescent Bay , Montezuma, Santa Teresa, and more.

The ferry has a really fun atmosphere too- they sell ice cream and beer and some have fun music. You can enjoy a shortcut while you relax and have fun. I think the ferry is a really fun experience for families.

Hopefully, this article has given you some insight into getting around Costa Rica. If you would like to talk to me about a customized itinerary or specific Costa Rica travel advice for your family’s next Costa Rica vacation, (zero sales- just advice!) check out my “ Ask Christa ” page for more information on custom Costa Rica trip planning geared towards family

The post Getting Around Costa Rica appeared first on Pura Vida Moms .