Key regions: India, Thailand, Malaysia, China, South America
The E-Scooter-sharing market in EU-27 has experienced significant growth in recent years, driven by changing customer preferences, emerging trends in the market, and local special circumstances.
Customer preferences: Customers in the EU-27 have shown a growing preference for convenient and eco-friendly transportation options. E-Scooter-sharing services provide a flexible and sustainable alternative to traditional modes of transportation, such as cars or public transport. The ease of use, affordability, and ability to avoid traffic congestion make e-scooters an attractive option for short-distance travel in urban areas.
Trends in the market: One of the key trends in the EU-27 E-Scooter-sharing market is the increasing number of market players. Several companies have entered the market, offering their own e-scooter-sharing services to meet the growing demand. This competition has led to innovation in terms of technology, pricing models, and service quality, benefiting the customers. Another trend in the market is the integration of e-scooter-sharing services into existing transportation networks. Many cities in the EU-27 have embraced e-scooters as a part of their sustainable mobility strategies, integrating them with public transportation systems. This integration allows customers to seamlessly switch between different modes of transport, enhancing the overall convenience and accessibility of e-scooter-sharing services.
Local special circumstances: The EU-27 consists of diverse countries with varying urban landscapes, regulations, and infrastructure. These local special circumstances have influenced the development of the E-Scooter-sharing market in different ways. For example, cities with well-developed cycling infrastructure and a strong cycling culture have seen a higher adoption rate of e-scooters. On the other hand, cities with limited infrastructure and stricter regulations have faced challenges in the implementation and operation of e-scooter-sharing services.
Underlying macroeconomic factors: The growth of the E-Scooter-sharing market in the EU-27 can also be attributed to underlying macroeconomic factors. The increasing urbanization, rising population density, and the need for sustainable transportation solutions have created a favorable environment for the development of e-scooter-sharing services. Additionally, advancements in technology and the availability of affordable e-scooter models have made it easier for companies to enter the market and expand their operations. In conclusion, the E-Scooter-sharing market in the EU-27 is developing rapidly due to changing customer preferences, emerging market trends, local special circumstances, and underlying macroeconomic factors. The convenience, affordability, and eco-friendliness of e-scooters have made them a popular choice for short-distance travel in urban areas. As the market continues to evolve, it is expected that further innovations and integration with existing transportation networks will drive its growth.
Data coverage:
The data encompasses B2C enterprises. Figures are based on bookings and revenues of e-scooter-sharing services.Modeling approach:
Market sizes are determined through a bottom-up approach, building on a specific rationale for each market. As a basis for evaluating markets, we use financial reports, third-party studies and reports, federal statistical offices, industry associations, and price data. To estimate the number of users and bookings, we furthermore use data from the Statista Consumer Insigths Global survey. In addition, we use relevant key market indicators and data from country-specific associations, such as demographic data, GDP, consumer spending, internet penetration, and device usage. This data helps us estimate the market size for each country individually.Forecasts:
In our forecasts, we apply diverse forecasting techniques. The selection of forecasting techniques is based on the behavior of the relevant market. For example, ARIMA, which allows time series forecasts, accounting for stationarity of data and enabling short-term estimates. Additionally, simple linear regression, Holt-Winters forecast, the S-curve function and exponential trend smoothing methods are applied.Additional notes:
The data is modeled using current exchange rates. The market is updated twice a year in case market dynamics change.
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