Manufacturing agglomeration and carbon emissions: an ensemble learning approach with evidence from South Korea

This study calculates two key manufacturing agglomeration indices, namely the specialization index and the diversification index, across different regions of South Korea. By leveraging random forest and gradient boosting decision tree models, it addresses a critical gap in understanding how the interplay between manufacturing agglomeration patterns affects carbon emissions. The findings indicate that specialized agglomeration significantly increases carbon emissions, whereas diversified agglomeration has a clear emission reduction effect, particularly in its early stages. Notably, a trade-off exists between the two agglomeration patterns, exerting a complex and evolving influence on carbon emissions. Regions with high specialization and low diversification tend to exhibit higher carbon emissions, while regions with high diversification and low specialization are associated with lower emissions. The study further reveals that introducing a specialized agglomeration development model in regions already characterized by diversified agglomeration leads to a significant increase in carbon emissions, especially during the early stages of diversification. Conversely, introducing a diversified agglomeration model in regions dominated by specialization reduces carbon emissions; however, as specialization intensifies, the emission reduction effects of diversification diminish considerably. Additionally, the influence of the interaction between these two agglomeration patterns on carbon emissions gradually weakens over time. This research provides new insights into the relationship between manufacturing agglomeration and carbon emissions, offering a valuable framework for evaluating policies and guiding sustainable industrial planning.