A pathway design framework for rational low-carbon policies based on model predictive control

Climate change presents a profound global challenge, urging nations to transition toward low-carbon development. However, despite increasing attention, existing research lacks an integrated and dynamically adaptive framework to support rational policy design across complex low-carbon economy (LCE) systems. This study develops a comprehensive pathway design framework for LCEs, capable of identifying key drivers of economic growth and carbon emissions, and formulating optimized development trajectories under varying carbon reduction targets and policy constraints. We construct an LCE model that incorporates economic, environmental, social, energy, and policy dimensions, using hierarchical regression and an autoregressive with exogenous inputs (ARX) model to capture the multifaceted drivers of growth and emissions. To optimize development pathways, we employ Economic Model Predictive Control (EMPC) and Tracking Model Predictive Control (TMPC). Drawing on historical data from China, we establish and validate an integrated framework for low-carbon policy design. Our findings demonstrate that targeted stimulation of low-carbon consumption patterns and the strategic promotion of green technologies, such as new energy vehicles, can effectively decouple economic growth from carbon emissions—offering viable pathways to meet climate goals without compromising development objectives. The proposed framework significantly enhances our understanding of causal relationships within the LCE system, captures system feedback, supports dynamic optimization, and accommodates a wide range of policy instruments. It enables policymakers to explore alternative low-carbon policy pathways under varying constraints, facilitating more rational and adaptive resource allocation. The framework’s dynamic optimization mechanism ensures continued relevance and effectiveness in the face of evolving climate targets.