A Bidirectional Coupling Study on Carbon Emission Forecasting and Energy Planning for Zero-Carbon Industrial Parks: A Case Study of a Chemical Park in Northern China

Industrial parks are a major source of carbon emissions in China, and their zero-carbon transition is critical for achieving the dual-carbon goals. However, existing carbon emission forecasting models of parks commonly suffer from a lack of interpretability, making it difficult to establish clear links between predictions and specific mitigation measures. This paper develops a top-down carbon emission forecasting model based on an extended Kaya identity, decomposing park carbon emissions into five macro-level parameters: park area, land use type, output value per unit area, energy intensity, and carbon intensity. A chemical park in Northern China is selected as an empirical case for validation. The results demonstrate that the proposed model exhibits strong interpretability and policy alignment, providing clear decision-making support for zero-carbon energy planning in industrial parks.