The main goal of the current chemical and energy industry is to define innovative solutions and methodologies to meet the UE target concerning CO2 net zero emissions for 2050 or even negative in a long term perspective. In order to achieve this goal, during the last years the combined heat power and chemical (HPC) generation in chemical processes proved to be rather effective. However, although the use of renewables and bio-based materials allows to considerably reduce the processes environmental impact, their nature is characterized by uncertainties both in terms of availability and properties over the years seasons. In case these upstream deviations become relevant, the energy required to mitigate the disturbances should still be drawn by the electricity grid. In most of the countries, the electricity immitted in the grid is seldom obtained by renewable energy sources since it has the purpose to be stable and constantly available within a certain demand range. As a consequence, the process, whose initial purpose was to consume CO2, is actually a positive emission system due to the energy consumption aimed at compensating perturbations. In the light of these premises, in this research work the analysis of a power-to-methanol process under uncertainty is carried out according to the flexibility indexes based methodology proposed by Di Pretoro et al. (2019). The system is composed by an electrolysis section aimed at the production of green hydrogen and a cogeneration power plant fed by biomasses that provides CO2 for the methanol synthesis. Based on the characterization of the uncertainty related to the variable biomass nature over the year seasons and to renewable energy availability, this particular approach allows to quantify the system performances under of external perturbation in terms of costs and emissions for a given methanol demand. The obtained results provide a quantitative analysis of the power-to-methanol process behaviour and permits to synthesize the system flexibility by means of a single index and to correlate it with investment and operating costs as well as with the Greenhouse Warming Potential. This methodology enables then the decision maker to have more conscious expectations about the designed plant and could be used in further studies to adapt the system design to the expected deviation in order to have a more flexible process.