S. Hu, H. Qi, Z. Wang et al.
Environmental Science and Ecotechnology 30 (2026) 100682
We further quantified the overall CER potential of China’s PPI (Fig. 8b). Across the four scenarios, projected emissions reductions amounted to 16.9, 8.4, 5.6, and 4.2 million tons of CO 2 , corre- sponding to 10.3%, 5.2%, 3.4%, and 2.6% of total PPI emissions, respectively. Across plant types, PFPPs exhibited the highest CER potential in all scenarios, accounting for more than 40% of the total reduction. These results indicate that prioritizing the deployment of PV systems across PFPPs could deliver the largest sectoral-level carbon mitigation benefits. In addition, although individual HPPMPs exhibited relatively low CER potential, their large number and extensive aggregate land areas mean that their cumulative CER potential cannot be ignored. In contrast, SPPMPs and LPPMPs tended to show relatively high CER potential at the individual plant level. These plants typically operate at lower emission levels and occupy smaller site areas, resulting in comparatively limited cumulative CER potential at the sectoral scale.
curation, Validation. Xiaoyu Wu: Data curation. Yulin Han: Writing – review editing, Funding acquisition. Yi Man: Super- vision, Conceptualization, Funding acquisition, Writing – review editing.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgements
This work was supported by the National Natural Science Foundation of China (No. 22478141) and the Open Fund Project of the State Key Laboratory of Advanced Papermaking and Paper- based Materials (202419, 2025ZD04).
5. Conclusion
Appendix A. Supplementary data
Under China’s “dual carbon” strategy, which targets carbon emission peaking by 2030 and carbon neutrality by 2060, carbon emissions management in key industries has been increasingly strengthened. However, researchers have primarily relied on pro- vincial- or sector-level data, paying limited attention to plant-level analyses and mitigation pathways. To address this gap, we devel- oped a multimodal data fusion framework for PPPs that integrates remote-sensing imagery, textual, and numerical data to improve plant-level carbon accounting and identify the key functional zones that generate emissions. In addition, we systematically evaluated the CER potential of rooftop PV systems to provide quantitative support for the low-carbon transition in China’s PPI. The main conclusions of this study are summarized as follows: (1) In this study, we developed a multimodal data fusion framework for China’s PPI by integrating remote-sensing imagery and plant textual data. Model validation across five plant types demonstrated robust performance, with R 2 values ranging from 0.75 to 0.96 and MAPE values ranging from 8.10% to 19.49%. (2) Based on multimodal data fusion framework, 720 PPPs across China generated an estimated 163.6 million tons of CO 2 in 2022, with more than 60% emitted from nine eastern coastal provinces. Significant differences were also observed across plant types and individual plants. PFPPs, RFPPs, and HPPMPs accounted for 90.2% of total carbon emissions, with the top 10% of high-emission plants contributing nearly half of sectoral carbon emissions. These findings provide a quantitative basis for differentiated region-specific CER strategies. (3) Building on the emissions assessment, we developed a methodological framework to evaluate rooftop PV potential for PPPs using meteorological data and GSA. The scenario results revealed that a PV panel length of 0.5 m yielded the greatest emissions mitigation, with an annual reduction of approximately 16.9 million tons of CO 2 , corresponding to 10.3% of total sectoral emissions. This highlights rooftop PV deployment as a promising and scalable decarbonization strategy for the PPI.
Supplementary data to this article can be found online at https://doi.org/10.1016/j.ese.2026.100682.
References
[1] China Paper Association, China Paper Industry Sustainable Development White Paper, China Paper Association, Beijing, 2019. http://www.chinappi. org/reps/20190226104141217819.html (in Chinese). [2] S. Zhang, K. Yu, Y. Yu, Industrial transformation for synergistic carbon and pollutant reduction in China: using environmentally extended multi-regional input-output model and multi-objective optimization, Energy 318 (2025) 134830, https://doi.org/10.1016/j.energy.2025.134830. [3] S. van Ewijk, J.A. Stegemann, P. Ekins, Limited climate benefits of global recycling of pulp and paper, Nat. Sustain. 4 (2) (2021) 180 – 187, https:// doi.org/10.1038/s41893-020-00624-z. [4] M. Dai, M. Sun, B. Chen, L. Shi, M. Jin, Y. Man, Z. Liang, C.M.V.B. de Almeida, J. Li, P. Zhang, A.S.F. Chiu, M. Xu, H. Yu, J. Meng, Y. Wang, Country-specific net-zero strategies of the pulp and paper industry, Nature 626 (7998) (2024) 327 – 334, https://doi.org/10.1038/s41586-023-06962-0. [5] Q.L. Han, H.L. Zhao, G.X. Wei, Y.W. Zhu, T. Li, M. Xu, X. Guo, H.Z. Shi, Y. Lian, H.Q. Liu, Sustainable papermaking in China: assessing provincial economic and environmental performance of pulping technologies, ACS Sustain. Chem. Eng. 12 (11) (2024) 4517 – 4529, https://doi.org/10.1021/acssuschemeng.3c0 7611. [6] T. Lei, D. Guan, Y. Shan, B. Zheng, X. Liang, J. Meng, Q. Zhang, S. Tao, Adaptive CO 2 emissions mitigation strategies of global oil refineries in all age groups, One Earth 4 (8) (2021) 1114 – 1126, https://doi.org/10.1016/j.oneear.2021.07. 009. [7] J. Stangl, A. Borsos, C. Diem, T. Reisch, S. Thurner, Firm-level supply chains to minimize unemployment and economic losses in rapid decarbonization scenarios, Nat. Sustain. 7 (5) (2024) 581 – 589, https://doi.org/10.1038/ s41893-024-01321-x. [8] Q. Hao, X. Kuang, A study on the implementation of corporate carbon emission accounting in the production process, Pol. J. Environ. Stud. 33 (3) (2024) 2941 – 2957, https://doi.org/10.15244/pjoes/175300. [9] Y. Man, Y. Yan, X. Wang, J. Ren, Q. Xiong, Z. He, Overestimated carbon emission of the pulp and paper industry in China, Energy 273 (2023) 127279, https://doi.org/10.1016/j.energy.2023.127279. [10] Z. He, M. Hong, H. Zheng, J. Wang, Q. Xiong, Y. Man, Towards low-carbon papermaking wastewater treatment process based on kriging surrogate predictive model, J. Clean. Prod. 425 (2023) 139039, https://doi.org/10.1016/ j.jclepro.2023.139039. [11] W. Wu, Q. Tang, W. Xue, X. Shi, D. Zhao, Z. Liu, X. Liu, C. Jiang, G. Yan, J. Wang, Quantifying china's iron and steel industry's CO 2 emissions and environ- mental health burdens: a pathway to sustainable transformation, Environ. Sci. Ecotechnol. 20 (2024) 100367, https://doi.org/10.1016/j.ese.2023. 100367. [12] H. Zhang, X. Li, L. Chen, M. Wang, Global primary aluminum smelters' CO 2 mitigation potential and targeted carbon-neutral pathways, J. Clean. Prod. 474 (2024) 143628, https://doi.org/10.1016/j.jclepro.2024.143628. [13] H. Qi, Z. Wang, S. Hu, Z. He, Y. Han, Y. Man, Multiscale life-cycle carbon emission analysis for papermaking industry of China: evolution triggered by the waste import ban, ACS Sustainable Chem. Eng. 13 (28) (2025) 11128 – 11142, https://doi.org/10.1021/acssuschemeng.5c00355. [14] Y. Man, J. Li, M. Hong, Y. Han, Energy transition for the low-carbon pulp and paper industry in China, Renew. Sustain. Energy Rev. 131 (2020) 109998, https://doi.org/10.1016/j.rser.2020.109998.
CRediT authorship contribution statement
Song Hu: Writing – original draft, Methodology, Formal anal- ysis. Huaqing Qi: Data curation, Software. Zifei Wang: Data
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