Economic activities with mass production and consumption have exacerbated global warming and resource depletion. At the 29th Conference of the Parties (COP 29) to the United Nations Framework Convention on Climate Change, The Breakthrough Agenda announced that governments from 61 supporting countries, representing 80% of global Greenhouse Gas (GHG) emissions, have agreed on priority actions to cut GHG emissions. COP 29 discussions highlighted the need to consider the global supply chain in reducing GHG. Global supply chains generate GHG emissions through manufacturing processes across multiple countries. Conversely, reverse supply chains recover End-of-Life (EOL) products, enabling resource recovery and GHG emission reduction through recycling. Manufacturing Decision Makers (DMs) face the challenge of designing environmentally friendly and economical Global and Closed-Loop Supply Chain (GCLSC) networks, where GHG reduction, increased recycling rates, and cost decrease often conflict. This study designs a GCLSC network considering cost, material-based GHG emissions and the entire recycling rate using Linear Physical Programming (LPP) for multi-criteria decision-making. First, the GCLSC network is modeled and described using integer programming. Next, objective functions are formulated using LPP to minimize cost and GHG emissions, and to maximize the entire recycling rate. Finally, numerical experiments are conducted to illustrate a design example of GCLSC network.