Key regions: China, Germany, Thailand, Saudi Arabia, India
The E-Scooter-sharing market in Central Asia has experienced significant growth in recent years, driven by changing customer preferences, emerging trends, local special circumstances, and underlying macroeconomic factors. Customer preferences in the E-Scooter-sharing market have shifted towards more sustainable and convenient transportation options. With increasing concerns about environmental pollution and traffic congestion, consumers in Central Asia are looking for alternative modes of transportation that are eco-friendly and efficient. E-Scooter-sharing services provide a solution by offering electric scooters that produce zero emissions and can navigate through congested city streets more easily than cars or motorcycles. Trends in the E-Scooter-sharing market in Central Asia have been shaped by the unique characteristics of the region. One trend is the adoption of dockless E-Scooter-sharing systems, where users can pick up and drop off scooters anywhere within a designated service area. This flexibility has made E-Scooter-sharing more convenient for users, as they are not limited to specific docking stations. Additionally, the use of smartphone apps for booking and unlocking scooters has made the process more seamless and user-friendly. Local special circumstances have also contributed to the growth of the E-Scooter-sharing market in Central Asia. The region is home to several densely populated cities with limited public transportation options. E-Scooter-sharing services fill this gap by providing a convenient and affordable mode of transportation for short distances. Moreover, the relatively low cost of purchasing and maintaining electric scooters compared to cars or motorcycles makes them an attractive option for both consumers and service providers. Underlying macroeconomic factors have played a role in the development of the E-Scooter-sharing market in Central Asia. Economic growth in the region has led to an increase in disposable incomes, allowing more people to afford the cost of using E-Scooter-sharing services. Additionally, government initiatives to promote sustainable transportation and reduce air pollution have created a favorable regulatory environment for E-Scooter-sharing companies to operate. In conclusion, the E-Scooter-sharing market in Central Asia is experiencing growth due to changing customer preferences, emerging trends, local special circumstances, and underlying macroeconomic factors. As consumers in the region seek more sustainable and convenient transportation options, E-Scooter-sharing services provide a viable solution. The adoption of dockless systems and smartphone apps has made E-Scooter-sharing more convenient for users. The region's densely populated cities and limited public transportation options have created a demand for E-Scooter-sharing services. Economic growth and government initiatives have also contributed to the development of the market. Overall, the E-Scooter-sharing market in Central Asia is expected to continue growing in the coming years.
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.
Mon - Fri, 9am - 6pm (EST)
Mon - Fri, 9am - 5pm (SGT)
Mon - Fri, 10:00am - 6:00pm (JST)
Mon - Fri, 9:30am - 5pm (GMT)
Mon - Fri, 9am - 6pm (EST)
