In this paper we explore the impact of several sources of uncertainties on the assessment of energy and climate policies when one uses in an harmonized way stochastic programming (SP) in a large scale bottom-up (BU) model and Monte-Carlo simulation (MC) in a large scale top-down (TD) model. The BU model we use is the Times Integrated Assessment Model (TIAM), which is run in a stochastic programming version to provide a hedging emission policy to cope with the uncertainty characterizing climate sensitivity. The TD model we use is the computable general equilibrium model GEMINI-E3. Through Monte-Carlo simulations of randomly generated uncertain parameter values one provides a stochastic micro- and macro-economic analysis. Through statistical analysis of the simulation results we analyze the impact of the uncertainties on the policy assessment.