AccScience Publishing / AJWEP / Online First / DOI: 10.36922/AJWEP025160117
ORIGINAL RESEARCH ARTICLE

Analyzing emission and carbon reduction support policies using latent Dirichlet allocation and a Sankey-bubble chart

Na Li1* Xiaoming Wu1
Show Less
1 School of Economics and Management, Southwest Petroleum University, Chengdu, Sichuan, China
Received: 14 April 2025 | Revised: 17 June 2025 | Accepted: 27 June 2025 | Published online: 22 July 2025
© 2025 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
Abstract

This study presents an in-depth analysis of China’s emission and carbon reduction support policies from 2016 to 2023 using text mining techniques. The main objective is to examine the evolution, thematic focus, and implementation outcomes of these policies across different stages, thereby providing insights into their development patterns and potential future direction. Based on the latent Dirichlet allocation model implemented in Python 3.7, the study identified and refined 14 initial topic terms spanning three policy phases, which were subsequently integrated and interpreted. Through topic clustering and visualization using the Sankey-bubble chart, the research simulated the evolution of policy themes over time. The results reveal a clear shift in policy focus – from market-driven mechanisms to green development and technology-led approaches. In the later stages, policies exhibit more comprehensive and systematic characteristics. In conclusion, the study contributes to a deeper understanding of the development trajectory, orientation, and implementation effectiveness of China’s carbon reduction policies, offering valuable insights for future policy development.

Keywords
Emission and carbon reduction support policies
Evolutionary trends
Latent Dirichlet allocation
Sankey-bubble chart
Funding
This study was financially supported by National Social Science Fund of China Major Project Research on the high-quality development path of natural gas industry driven by the energy revolution’(No.22&ZD105) and the Sichuan Key Research Base of Humanities and Social Sciences (Project Approval No. ZYZX-YB-2411).
Conflict of interest
The authors declare that they have no competing interests.
References
  1. Wang X. Analysis of the effectiveness of energy-use rights trading on emission reduction and carbon reduction. Econ Issues. 2023;(4):79-85. doi:10.16011/j.cnki.jjwt.2023.04.013

 

  1. Binjie G, Haixia Z, Xinliao L, Tianyuan Z, Jinding F. Research progress and prospects of collaborative reduction of pollution and carbon dioxide based on bibliometrics. J Environ Eng Technol. 2023;13(1):85-95. doi: 10.12153/j.issn.1674-991X.20210780

 

  1. Gong X, Sun T. Study on the impact mechanism of energy-saving and emission reduction policy tools to reduce carbon intensity: A qualitative comparative analysis of fuzzy sets based on data from 30 provinces (cities and autonomous regions). Urban Issues. 2021;(7):23-32. doi: 10.13239/j.bjsshkxy.cswt.210703

 

  1. Li Y, Zhao Q, Xue Z. Development path of carbon dioxide capture, utilisation and storage technology and industrialisation under the ‘double carbon’ target. Pet Drilling Tech. 2023;45(6):655-660. doi: 10.13639/j.odpt.202201052

 

  1. Sun Y, Fan J, Jia WG. A study on the stages and effective paths to achieve carbon peaking - based on the perspective of green gdp accounting. Ind Technol Econ. 2023;42(12):95-104.

 

  1. Kan L. Study on China’s 2060 carbon neutrality target and its implementation path. Ecol Econ. 2021;37(11):6.

 

  1. Liu Z, Zhu K, Yan J, et al. Analysis of carbon emission reduction potential of the power sector under industrial structure optimisation. J Ind Eng Eng Manage. 2014;28(2):87-92+86. doi: 10.13587/j.cnki.jieem.2014.02.004

 

  1. Al-Amin AQ, Rasiah R, Chenayah S. Prioritizing climate change mitigation: An assessment using Malaysia to reduce carbon emissions in future. Environ Sci Policy. 2015;50:24-33. doi: 10.1016/j.envsci.2015.02.002

 

  1. Ahmed M, Shuai C, Ahmed M. Influencing factors of carbon emissions and their trends in china and india: A machine learning method. Environ Sci Pollut Res. 2022;29(32):48424-48437. doi: 10.1007/s11356-022-18711-3

 

  1. Han W, Hui L, Han W, Shulan W, Wenjie Z. Study on the difference between pollution reduction and carbon reduction and regional economic development level in China. J Environ Eng Technol. 2022;12(5):1584-1592. doi: 10.12153/j.issn.1674-991X.20210268

 

  1. Zhang Z, Zhang T, Feng D. Regional differences, dynamic evolution and convergence of carbon emission intensity in China. J Quant Technol Econ. 2022;39(4):67-87. doi: 10.13653/j.cnki.jqte.2022.04.001

 

  1. Farahani HR, Rassafi AA, Zhang K, Nie YM. A multi-hop control scheme for traffic management. Transp Res Part C Emerg Technol. 2021;130:103278. doi: 10.1016/j.trc.2021.103278

 

  1. Zhang S, Li M, Wang C. Provincial carbon emission trends and differentiated peak pathways in China. China Popul Resour Environ. 2021;31(9):45-54.

 

  1. Song P, Zhang HM, Mao XQ. Carbon emission reduction pathways in Chongqing towards peak carbon targets. China Environ Sci. 2022;42(3):1446-1455. doi: 10.19674/j.cnki.issn1000-6923.20210923.006

 

  1. Neha S, Sharma RL, Kundan Y. Sustainable development by carbon emission reduction and its quantification: An overview of current methods and best practices. Asian J Civil Eng Build Hous. 2023;24(8):3797-3822. doi: 10.1007/s42107-023-00732-z

 

  1. Halsns K, Some S, Pathak M. Beyond synergies: Understanding sdg trade-offs, equity and implementation challenges of sectoral climate change mitigation options. Sustain Sci. 2024;19(1):35-49. doi: 10.1007/s11625-023-01322-3

 

  1. Xiao T, Shu Y, Li H, et al. Assessment of CO2 synergistic benefits of air pollution control policies in Taiyuan City’s 14th five-year plan. Environ Sci. 2024;45(3):1265-1273. doi: 10.13227/j.hjkx.202304046

 

  1. Ashina S, Fujino J, Masui T, Ehara T, Hibino G. A roadmap towards a low-carbon society in japan using backcasting methodology: Feasible pathways for achieving an 80% reduction in Co2 emissions by 2050. Energy Policy. 2012;41:584-598. doi: 10.1016/j.enpol.2011.11.020

 

  1. Marsden G, Mullen C, Bache I, Bartle I, Flinders M. Carbon reduction and travel behaviour: Discourses, disputes and contradictions in governance. Transp Policy. 2014;35:71-78. doi: 10.1016/j.tranpol.2014.05.012

 

  1. Dutta V, Dasgupta P, Hultman N, Gadag G. Evaluating expert opinion on India’s climate policy: Opportunities and barriers to low-carbon inclusive growth. Clim Dev. 2016;8(4):336-350. doi: 10.1080/17565529.2015.1067181

 

  1. He J, Lu L, Wang HL. Win-win path analysis of economic growth and CO2 emission reduction. China Popul Resour Environ. 2018;28(10):9.

 

  1. Jiang Y, Tang X, Ren KP, Ding I. A study on the drivers of pollution and carbon reduction in China based on double nested sda. Syst Eng Theor Pract. 2022;42(12):11.

 

  1. Xu W. Improving tax policies in support of green development: Top-level design and policy synergy. Int Taxation China. 2023;(4):9-14. doi: 10.19376/j.cnki.cn10-1142/f.2023.04.007

 

  1. Ma H, Zhang B. A characterisation of China’s open public data policy supply based on the lda model. Mod Intell. 2023;43(8):35-44.

 

  1. Jiang T, Xiao W, Zhang C, Ge B. A time series text visualisation method based on Sankey diagram. Comput Appl Res. 2016;33(9):6.

 

  1. Blei DM, Ng AY, Jordan MI. Latent dirichlet allo-cation. J Mach Learn Res. 2003;3:993-1022.

 

  1. Cheng L, Lu XY, Xiao LM, Bai Y, Li XY. Water supply, use, consumption, discharge and return processes and their Sankey diagrams. Hydropower Energy Sci. 2021;39(6):5.

 

  1. Zhang H, Zhang Y, Liu J, Chen T. Quantitative evaluation of China’s industrial chain policy under the objective of industrial chain modernisation. Stat Inform Forum. 2023;38(9):32-46.

 

  1. Sakshi, Kukreja V. Recent trends in mathematical expressions recognition: An lda-based analysis. Expert Syst Appl. 2023;213:119028. doi: 10.1016/j.eswa.2022.119028

 

  1. Song WL, Xiao RL. Evolutionary paths of policy instruments in China’s photovoltaic industry and their implications. J Inf. 2022;41(1):177-184. doi: 10.39674/j.cnki.issn1000-7490.2022.01.020

 

  1. Xue F, Zhou M, Liu J. Can industrial transformation and upgrading reduce carbon emissions? Evidence from national industrial transformation and upgrading demonstration zones. Ind Econ Res. 2023;(2):1-13. doi: 10.13269/j.cnki.ier.2023.02.002

 

  1. Bakary K, Mouhamadou D, Seydou T. Contribution based on neurons networks for the prediction of greenhouse gas emissions in a handling port. Asian J Water Environ Pollut. 2024;21(6):261-269.doi: 10.3233/AJW240094

 

  1. Li N, Liu YT, Chen Z. Unlocking insights: Integrated text mining and interpretive structural modeling for enhanced user review analysis. PeerJ Comput Sci. 2024;10:e2541. doi: 10.7717/peerj-cs.2541

 

  1. Huang W, Zhu S, Yao X. Destination image recognition and emotion analysis: Evidence from user-generated content of online travel communities. Comput J. 2020;64(3):296-304. doi: 10.1093/comjnl/bxaa064

 

  1. Li N, Li S. Research on the LDA-ECD based support policy for China’s agricultural cold chain logistics. Sustain Futur. 2025;9:100460. doi: 10.1016/j.sftr.2025.100460

 

  1. Tu Z, Mao J, Xiao X, Ma G. From conflict to coordination: A new framework to measure the synergy level of pollution-carbon-growth. J Asian Econ. 2025;99.

 

  1. Fetisov V, Gonopolsky AM, Davardoost H, Ghanbari AR, Mohammadi AH. Regulation and impact of VOC and CO2 emissions on low-carbon energy systems resilient to climate change: A case study on an environmental issue in the oil and gas industry. Energy Sci Eng. 2023;11:1516-1535. doi: 10.1002/ese3.1383

 

  1. Fetisov V, Gonopolsky AM, Zemenkova MY, et al. On the integration of CO2 capture technologies for an oil refinery. Energies. 2023;16;865. doi: 10.3390/en16020865

 

  1. Mork D, Delaney S, Dominici F. Policy-induced air pollution health disparities: Statistical and data science considerations. Science. 2024;385(6707):391-396. doi: 10.1126/science.adp1870

 

  1. Karimi Kisomi M, Seddighi S, Mohammadpour R, Rezaniakolaei A H. Enhancing air filtration efficiency with triboelectric nanogenerators in face masks and industrial filters. Nano Energy. 2023;112:108514. doi: 10.1016/j.nanoen.2023.108514

 

  1. Zhang S, Liu H, Tang N, Zhou S, Yu J, Ding B. Spider-web-inspired PM0.3 filters based on self-sustained electrostatic nanostructured networks. Adv Mater. 2020;32(29):e2002361. doi: 10.1002/adma.202002361
Share
Back to top
Asian Journal of Water, Environment and Pollution, Electronic ISSN: 1875-8568 Print ISSN: 0972-9860, Published by AccScience Publishing