Key regions: China, Germany, Thailand, Saudi Arabia, India
The E-Scooter-sharing market in EMEA is experiencing significant growth and development.
Customer preferences: Customers in EMEA are increasingly opting for E-Scooter-sharing services due to their convenience, affordability, and environmental benefits. The younger generation, in particular, is drawn to the flexibility and ease of use that E-Scooter-sharing provides. Additionally, the rise of urbanization and congestion in many cities in the region has further fueled the demand for alternative transportation options like E-Scooter-sharing.
Trends in the market: One of the key trends in the E-Scooter-sharing market in EMEA is the expansion of services to new cities and countries. E-Scooter-sharing companies are constantly seeking opportunities to enter new markets and establish a presence in different regions. This trend is driven by the increasing popularity of E-Scooter-sharing and the potential for high demand in untapped markets. Another trend in the market is the integration of E-Scooter-sharing with existing transportation infrastructure. Many cities in EMEA are exploring ways to incorporate E-Scooter-sharing into their public transportation systems, allowing customers to seamlessly switch between different modes of transportation. This integration not only enhances the overall transportation experience for customers but also helps to reduce congestion and promote sustainability.
Local special circumstances: The E-Scooter-sharing market in EMEA is influenced by various local special circumstances. For example, in densely populated cities with limited parking space, E-Scooter-sharing offers a convenient solution for short-distance travel. Additionally, the presence of bike lanes and well-developed cycling infrastructure in certain cities makes E-Scooter-sharing even more attractive to customers. Furthermore, the regulatory environment in each country and city within EMEA can have a significant impact on the E-Scooter-sharing market. Some cities have implemented strict regulations and permit requirements for E-Scooter-sharing companies, while others have taken a more relaxed approach. The level of government support and cooperation also plays a role in shaping the market dynamics.
Underlying macroeconomic factors: Several underlying macroeconomic factors contribute to the development of the E-Scooter-sharing market in EMEA. The region's strong economic growth, increasing disposable income, and changing consumer preferences towards sustainable and eco-friendly transportation options have all contributed to the rising demand for E-Scooter-sharing services. Moreover, technological advancements and the increasing availability of smartphones have made it easier for customers to access and use E-Scooter-sharing services. The convenience of booking and unlocking E-Scooters through mobile apps has further fueled the market growth. In conclusion, the E-Scooter-sharing market in EMEA is experiencing significant growth and development driven by customer preferences for convenience and sustainability, as well as the integration of E-Scooter-sharing with existing transportation infrastructure. Local special circumstances and underlying macroeconomic factors also play a role in shaping the market dynamics in different countries and cities within the region.
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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