# Plant-level carbon accounting of China's pulp and paper industry via multimodal fusion

## TL;DR

This study uses advanced data methods to estimate carbon emissions from China's pulp and paper industry at the plant level, revealing significant regional and operational disparities.

## Contribution

A novel multimodal fusion framework combining remote sensing and textual data enables precise plant-level carbon accounting.

## Key findings

- China's 720 pulp and paper plants emitted 163.6 million tonnes of CO2 in 2022, with over 60% from eastern coastal provinces.
- A multimodal fusion model achieved R2 values up to 0.96 in predicting plant-level emissions.
- Rooftop photovoltaic deployment could reduce annual emissions by up to 10.3% in the sector.

## Abstract

Plant-scale industrial carbon accounting is critical for developing targeted emission-reduction policies. However, most assessments of carbon-intensive sectors rely on aggregate statistics, which obscure significant heterogeneity among individual plants. China's pulp and paper industry (PPI), the largest globally, encompasses diverse production processes, raw material inputs, and emission sources. Existing accounting frameworks rely on statistical data and average emission factors within poorly defined system boundaries, which prevents differentiation at the individual plant level. Here, we propose a multimodal data fusion framework that integrates high-resolution remote-sensing imagery with plant textual data to capture structural and operational characteristics undetectable by any single data modality. Applied to 720 pulping and papermaking plants across China, the framework achieves R2 values of up to 0.96 across five plant types and estimates total sectoral carbon emissions at 163.6 million tonnes of CO2 in 2022, with pronounced regional disparities concentrated in eastern coastal provinces. Analysis of functional-zone contributions further reveals that wastewater treatment areas are a consistent cross-category emission driver, and that just 5% of high-emission plants account for approximately 43% of sectoral emissions—a skewed structure that demands differentiated regulatory intervention. Incorporating regional solar radiation data, rooftop photovoltaic deployment is projected to reduce annual PPI emissions by up to 10.3%, with primary-fiber pulp plants offering the greatest mitigation leverage. Beyond China's PPI, this scalable, data-driven approach provides a transferable blueprint for granular, plant-level carbon accounting in other heterogeneous heavy industries.

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•China's 720 pulp and papermaking plants emitted ∼163.6 Mt CO2 in 2022, with >60% from eastern coastal provinces.•A multimodal DeepLabv3+/BERT fusion framework predicts carbon emissions with R2 values reaching 0.96.•Just 5% of high-emission plants account for ∼43% of total sectoral emissions, revealing extreme emission skewness.•Rooftop photovoltaic deployment could cut annual PPI emissions by up to 16.9 Mt CO2 (10.3% of the total).

China's 720 pulp and papermaking plants emitted ∼163.6 Mt CO2 in 2022, with >60% from eastern coastal provinces.

A multimodal DeepLabv3+/BERT fusion framework predicts carbon emissions with R2 values reaching 0.96.

Just 5% of high-emission plants account for ∼43% of total sectoral emissions, revealing extreme emission skewness.

Rooftop photovoltaic deployment could cut annual PPI emissions by up to 16.9 Mt CO2 (10.3% of the total).