Car-sharing - Moldova

  • Moldova
  • Revenue in Moldova's Car-sharing market is forecasted to reach US$0.50m in 2024.
  • The revenue is anticipated to demonstrate an annual growth rate (CAGR 2024-2029) of 10.13%, leading to a projected market volume of US$0.81m by 2029.
  • By 2029, the number of users in Moldova's Car-sharing market is expected to reach 10.05k users.
  • The user penetration is projected to be 0.2% in 2024 and 0.3% by 2029.
  • The average revenue per user (ARPU) in Moldova is expected to be US$71.57.
  • By 2029, 100% of the total revenue in Moldova's Car-sharing market will be generated through online sales.
  • In a global context, the United States is set to generate the most revenue (US$2,986m in 2024).
  • The Car-sharing market in Moldova is rapidly expanding, driven by urbanization and a growing preference for sustainable transportation solutions.

Key regions: Europe, Germany, India, United States, Malaysia

 
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Analyst Opinion

The Car-sharing market in Moldova has been steadily growing in recent years, reflecting a global trend towards shared mobility solutions.

Customer preferences:
Customers in Moldova are increasingly valuing convenience, cost-effectiveness, and environmental sustainability when it comes to transportation options. This has led to a rise in the popularity of car-sharing services as an alternative to traditional car ownership.

Trends in the market:
One noticeable trend in the Moldovan car-sharing market is the diversification of services offered by providers. Companies are introducing innovative features such as flexible pricing plans, a wider selection of vehicle models, and enhanced mobile applications to attract and retain customers. Additionally, partnerships with local businesses and integration with public transportation systems are becoming more common, further expanding the reach of car-sharing services.

Local special circumstances:
Moldova's relatively small size and population density make it an ideal environment for the growth of car-sharing services. The compact urban areas and well-connected transportation infrastructure create a conducive setting for convenient and efficient shared mobility solutions. Furthermore, the increasing urbanization and changing lifestyles in Moldova are driving the demand for flexible and on-demand transportation options, positioning car-sharing as a favorable choice for many residents.

Underlying macroeconomic factors:
The economic landscape of Moldova, with a rising middle class and increasing disposable incomes, plays a significant role in the development of the car-sharing market. As more people have the financial means to access transportation services beyond traditional car ownership, the demand for convenient and affordable mobility solutions like car-sharing is expected to continue growing. Additionally, government initiatives to promote sustainable transportation and reduce traffic congestion further support the expansion of the car-sharing market in Moldova.

Methodology

Data coverage:

The data encompasses B2C enterprises. Figures are based on bookings, revenues, and online shares of car-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.

Overview

  • Revenue
  • Sales Channels
  • Analyst Opinion
  • Users
  • Global Comparison
  • Methodology
  • Key Market Indicators
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