Driven　by　advancements　in　information　technology　and　the　modernization　of　the　manufacturing　industry,　shared　manufacturing　has　emerged　as　a　novel　industrial　organization　mode　that　enables　manufacturing　enterprises　to　mutually　utilize　external　idle　resources.　Concurrently,　to　meet　the　requirements　of　sustainable　development,　manufacturing　enterprises　need　to　balance　economic　efficiency　with　energy　consumption　in　their　production　practices.　This　study　investigates　a　new　energy-aware　parallel　machine　scheduling　problem　shared　manufacturing　environments,　where　production　time,　cost,　and　energy　consumption　vary　across　different　jobs　and　machines.　The　objective　is　to　minimize　the　weighted　sum　of　makespan,　total　energy　consumption,　and　overall　sharing　costs.　We　first　formulate　the　problem　as　a　mixed-integer　linear　programming　(MILP)　model.　Then　the　problem　is　further　refined　into　an　improved　MILP　model　with　fewer　integer　variables.　Given　the　strong　NP-hard　nature　of　the　problem,　an　effective　tailored　heuristic　(ETH)　is　developed　based　on　analyzed　problem　properties　to　solve　large-scale　instances.　It　includes　three　main　phases:　pre-processing,　job　assignment,　and　local　search.　Extensive　experimental　results　demonstrate　that　the　improved　MILP　model　significantly　outperforms　the　original　one,　and　the　ETH　can　solve　large-sized　instances　with　up　to　400　machines　and　20000　jobs　within　one　second,　with　gaps　of　less　than　1%.　The　proposed　MILP　models　and　ETH　can　assist　manufacturing　enterprises　in　making　more　informed　decisions　and　scheduling　resource　allocation　while　considering　economic　and　environmental　factors.