Using a machine learning framework for natural language processing to create a high-resolution carbon emission map for urban manufacturing

Tianyu Wang, Fengying Yan, Jian Ma, Xiaoping Zhang and Liang Dong

Environment and Planning B, 2026, vol. 53, issue 2, 284-302

Abstract: Managing carbon emissions from the manufacturing sector is crucial for sustainable development, and effective identification of manufacturing land is key to achieving this goal. However, current methods for identifying urban manufacturing land remain inadequate. In this study, we employ a fine-tuned, pre-trained natural language processing model based on Bidirectional Encoder Representations from Transformers to classify points of interest data into manufacturing industry categories. This approach enables us to identify manufacturing land and allocate corresponding carbon emissions data to specific parcels. The global Moran’s Index and local Moran’s Index are applied to analyze the relationship between manufacturing concentration and carbon emission intensity. The results demonstrate that the fine-tuned model achieved an accuracy rate of 91.6% on the test set, successfully identifying 98.72% of the manufacturing land in the study area. The intensity of carbon emissions from manufacturing exhibits a significant positive spatial correlation, with urban areas characterized by high-high and low-low clustering of emissions. In rural areas, high-emission manufacturers tend to be co-located with low-emission enterprises. Within individual manufacturing sectors, most exhibit low-low clustering, suggesting a potential relationship between such clustering and lower carbon emissions. This study provides detailed spatial data for the management of carbon emissions in the manufacturing sector and addresses the gap in micro-scale research on the correlation between manufacturing concentration and carbon emissions.

Keywords: Carbon emission map; manufacturing concentration; Bidirectional Encoder Representations from Transformers; spatial heterogeneity (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:sae:envirb:v:53:y:2026:i:2:p:284-302

DOI: 10.1177/23998083241312948

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