A bi-level fuzzy programming (BFLP) method was developed for energy systems planning (ESP) and carbon dioxide (CO₂) mitigation under uncertainty. BFLP could handle fuzzy information and leader-follower problem in decision-making processes. It could also address the tradeoffs among different decision makers in two decision-making levels through prioritizing the most important goal. Then, a BFLP-ESP model was formulated for planning energy system of Beijing, in which the upper-level objective is to minimize CO₂ emission and the lower-level objective is to minimize the system cost. Results provided a range of decision alternatives that corresponded to a tradeoff between system optimality and reliability under uncertainty. Compared to the single-level model with a target to minimize system cost, the amounts of pollutant/CO₂ emissions from BFLP-ESP were reduced since the study system would prefer more clean energies (i.e. natural gas, LPG and electricity) to replace coal fuel. Decision alternatives from BFLP were more beneficial for supporting Beijing to adjust its energy mix and enact its emission-abatement policy. Results also revealed that the low-carbon policy for power plants (e.g., shutting down all coal-fired power plants) could lead to a potentially increment of imported energy for Beijing, which would increase the risk of energy shortage. The findings could help decision makers analyze the interactions between different stakeholders in ESP and provide useful information for policy design under uncertainty.