The RISC-V Vector Extension (RVV) offers flexibility and scalability for the growing demand for data-parallel workloads in embedded systems. However, RVV configurability introduces a large design space, the implications of which for resource-constrained processors remain insufficiently quantified. Understanding how parameters such as vector length (VLEN), maximum element size (ELEN), lane count, LMUL, and SEW affect system-level metrics enables efficient acceleration while preserving software portability and reducing verification effort. This work explores the RVV design space for embedded processors using a Zve32x subset and evaluates the impact of architectural parameters on performance, area, power, and energy efficiency. Annotated post-synthesis simulations across representative benchmarks enable quantitative trade-off analysis over multiple RVV configurations. Results show that larger VLENs and aggressive LMUL settings can yield substantial speedups but often increase area and power, whereas moderate configurations provide improved energy efficiency while meeting performance targets. The paper derives practical design guidelines for selecting RVV parameters in power- and areaconstrained embedded systems. The RISC-V core used in this work is publicly available athttps://github.com/gaph-pucrs/RS5.