We present a computationally-efficient optimization model that finds the least-cost generation unit expansion, commitment, and dispatch plan to serve hourly electricity demand and ancillary service requirements. We apply the model to a case study based on data from the electricity market in Texas (ERCOT) to analyze the market and investment impacts of several incentive mechanisms that support variable renewable energy (VRE) investments and carbon emission reductions. In contrast to many previous studies, the model determines least-cost VRE investments under different cost and incentive assumptions rather than analyzing scenarios where VRE expansion is pre-determined. We find that electricity prices can vary significantly under different incentive mechanisms, even when comparable generation portfolios result. Therefore, the preferred incentive mechanism depends on stakeholder objectives as well as the prevailing electricity market framework. Our results indicate that a carbon tax is more system cost-efficient for reducing emissions, while production and investment tax credits are more system cost-efficient for increasing VRE investments. Similarly, incentive mechanisms that reduce electricity prices may increase the need for separate revenue sufficiency mechanisms (e.g. a capacity market) more than a policy that increases electricity prices. Moreover, the impacts on consumer payments are not always aligned with changes in system costs. Overall, the analysis illustrates the importance of considering electricity market impacts in assessing the economic efficiency of VRE and carbon incentive mechanisms.