This paper is on designs a green closed-loop supply chain network considering economic factors and environmental concerns and interconnected facilities. Over or under-achievement of forecasted supply, demand, and return, caused by the isolation of manufacturing and remanufacturing facilities (MRFs), can be compensated through lateral transshipment between MRFs. The decision variables are opening MRFs, environmental investment level for adopting clean technology in each MRF which is defined by linguistic variables, and the flow of products between nodes. Environmental investment and selection of transportation modes play important roles in the concerned objectives. A fuzzy multi-objective mathematical programming model is proposed for the problem under inherent uncertainty of supply, demand, returns, costs, capacities in the network, imprecise Co2 emission of transportation modes, and technology level. Solving two numerical examples shows the applicability of the model in the real world with the selected best technological level of MRFs and the reduction of costs and Co2 emission.This paper is on designs a green closed-loop supply chain network considering economic factors and environmental concerns and interconnected facilities. Over or under-achievement of forecasted supply, demand, and return, caused by the isolation of manufacturing and remanufacturing facilities (MRFs), can be compensated through lateral transshipment between MRFs. The decision variables are opening MRFs, environmental investment level for adopting clean technology in each MRF which is defined by linguistic variables, and the flow of products between nodes. Environmental investment and selection of transportation modes play important roles in the concerned objectives. A fuzzy multi-objective mathematical programming model is proposed for the problem under inherent uncertainty of supply, demand, returns, costs, capacities in the network, imprecise Co2 emission of transportation modes, and technology level. Solving two numerical examples shows the applicability of the model in the real world with the selected best technological level of MRFs and the reduction of costs and Co2 emission.