Against　the　backdrop　of　China’s＇Dual　Carbon＇　strategy,　the　identification　of　regional　carbon　emission　drivers　and　the　projection　of　future　scenarios　have　emerged　as　pivotal　components　in　promoting　high-quality　development　and　green　transformation.　This　study　focuses　on　Shandong　Province,　a　representative　high-energy-consumption　region　located　in　eastern　coastal　China,　and　employs　an　extended　STIRPAT　modeling　framework.　A　dataset　encompassing　thirteen　variables　across　demographic,　economic,　energy,　structural,　and　openness　dimensions　is　constructed　to　systematically　characterize　the　mechanisms　underlying　regional　carbon　emissions　and　their　evolutionary　dynamics.　To　address　the　issue　of　multicollinearity　inherent　in　multivariable　modeling,　this　study　introduces　the　Minimax　Concave　Penalty　(MCP)　regression　approach　for　robust　estimation　of　the　extended　STIRPAT　model.　The　results　demonstrate　that　MCP　regression　not　only　enhances　model　fitting　accuracy　but　also　effectively　isolates　key　explanatory　variables,　mitigating　the　over-shrinkage　problem　associated　with　Lasso.　The　principal　drivers　of　carbon　emissions　in　Shandong　Province　are　identified　as　the　level　of　economic　development,　energy　intensity,　population　size,　educational　attainment,　and　degree　of　openness　to　external　economies.　The　integration　of　MCP　regression　with　the　extended　STIRPAT　model　offers　a　feasible　and　efficient　approach　for　elucidating　the　mechanisms　of　regional　carbon　emissions　and　conducting　scenario-based　projections.　©　2025　Copyright　held　by　the　owner/author(s).