We　present　novel　multi-period　portfolio　selection　models　that　incorporate　Environmental,　Social,　and　Governance　(ESG)　considerations　into　investment　decisions.　Although　ESG　investing　has　gained　significant　attention,　most　existing　approaches　rely　on　static　models,　which　inadequately　capture　the　dynamic　nature　of　markets　and　evolving　investment　conditions.　To　address　this　limitation,　we　develop　an　ESG-adjusted　multi-period　mean-risk　(ESG-MMR)　portfolio　optimization　framework,　specifically　developing　two　variants:　an　ESG-adjusted　multi-period　mean-variance　(ESG-MMV)　model　and　a　hybrid　ESG-MMV-CVaR　model　that　incorporates　Conditional　Value　at　Risk　(CVaR)　as　a　supplementary　risk　measure.　For　ESG-MMV　model,　we　leverage　the　state　separation　theorem　to　derive　a　semi-analytical　optimal　portfolio　policy,　showing　that　the　optimal　strategy　follows　a　piecewise　affine　function　of　wealth　which　can　be　efficiently　computed　by　solving　coupled　equations　offline.　For　ESG-MMV-CVaR　model,　we　develop　two　Progressive　Hedging　Algorithm　(PHA)　variants-one　guaranteeing　theoretical　convergence　and　the　other　prioritizing　practical　efficiency.　Through　numerical　experiments,　we　demonstrate　that　our　dynamic　approaches　significantly　outperform　static　ESG　portfolio　models.　Our　framework　is　flexible　and　can　accommodate　various　risk　measures　beyond　variance　and　CVaR,　offering　practitioners　an　efficient　toolkit　for　implementing　ESG　considerations　in　modern　portfolio　management.