Skip to main content
SpringerLink
Log in

Study on carbon emission reduction countermeasures based on carbon emission influencing factors and trends

  • Research Article
  • Published:
Environmental Science and Pollution Research Aims and scope Submit manuscript

Abstract

The carbon mitigation response encompasses a variety of strategies aimed at mitigating greenhouse gas emissions resulting from human activities. These measures are crafted to address the challenges posed by climate change and facilitate the transition of businesses towards a low-carbon paradigm. Leveraging the analytical outcomes of the extended STIRPAT model and the PSO-BP prediction model, this paper suggests countermeasures for reducing carbon emissions in China’s metal smelting industry. The overarching objective is to contribute to China’s attainment of the “dual carbon objectives.” The study identifies key factors influencing carbon emissions in the metal smelting industry, ranked in descending order of sensitivity: population, coal consumption, urbanization rate, total metal production, carbon intensity, proportion of secondary industry, and GDP per capita. Results from three established scenarios—namely, low carbon, standard, and high carbon—indicate a consistent decline in carbon emissions from China’s metal smelting industry over the next 15 years. This research not only enhances the findings of existing studies on carbon emissions in the metal smelting sector but also introduces an innovative approach to carbon emission reduction within China’s metal smelting industry.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Data availability

The original contributions presented in the study are included in the article/Supplementary Materials; further inquiries can be directed to the corresponding author.

References

  • Adebayo TS, Oladipupo SD, Rjoub H et al (2023) Asymmetric effect of structural change and renewable energy consumption on carbon emissions: designing an SDG framework for Turkey. Environ Dev Sustain 25:528–556. https://doi.org/10.1007/s10668-021-02065-w

    Article  PubMed  Google Scholar 

  • Ali HS, Sahoo M, Alam MM et al (2023) Structural transformations and conventional energy-based power utilization on carbon emissions: empirical evidence from Pakistan. Environ Dev Sustain 25:2419–2442. https://doi.org/10.1007/s10668-022-02133-9

    Article  Google Scholar 

  • Chen J, Chen Y, Mao B, Wang X, Peng L (2022) Key mitigation regions and strategies for CO 2 emission reduction in China based on STIRPAT and ARIMA models. Environ Sci Pollut Res Int 29(34):51537–51553

    Article  PubMed  Google Scholar 

  • Chen Y, Pan Y, Wang M, Ding T, Zhou Z, Wang K (2023) How do industrial sectors contribute to carbon peaking and carbon neutrality goals? A heterogeneous energy efficiency analysis for Beijing. Struct Change Econ Dyn 66:67–80

    Article  Google Scholar 

  • Cheng A, Han X, Jiang G (2023) Decomposition and scenario analysis of factors influencing carbon emissions: a case study of Jiangsu Province, China. Sustainability 15(6718):6718

    Article  CAS  Google Scholar 

  • Dai D, Li K, Zhao S, Zhou B (2022) Research on prediction and realization path of carbon peak of construction industry based on EGM-BP model. Front Energy Res 10. https://doi.org/10.3389/fenrg.2022.981097

  • Guangyue X, Wang Y, Rehman H (2023) The future trajectory of carbon emissions in the process of carbon neutrality in South Korea. J Environ Manag 345:118588

    Article  Google Scholar 

  • Guo F, Zhang L, Wang Z, Ji S (2022) Research on determining the critical influencing factors of carbon emission integrating GRA with an improved STIRPAT model: taking the Yangtze River Delta as an example. Int J Environ Res Public Health 19(14):8791

    Article  PubMed  PubMed Central  Google Scholar 

  • Huang Y, Liu J, Shi M (2023) Analysis of influencing factors and prediction of carbon emissions of typical urban agglomerations in China: a case study of Beijing-Tianjin-Hebei region. Environ Sci Pollut Res 30:52658–52678. https://doi.org/10.1007/s11356-023-26036-y

    Article  CAS  Google Scholar 

  • Huo Z, Zha X, Mengyao L, Ma T, Zhichao L (2023) Prediction of carbon emission of the transportation sector in Jiangsu Province-regression prediction model based on GA-SVM. Sustainability 15(3631):3631

    Article  CAS  Google Scholar 

  • Islam S, Rahaman SH (2023) The asymmetric effect of ICT on CO2 emissions in the context of an EKC framework in GCC countries: the role of energy consumption, energy intensity, trade, and financial development. Environ Sci Pollut Res Int 30(31):77742

    Article  PubMed  Google Scholar 

  • Khajavi H, Rastgoo A (2023) Predicting the carbon dioxide emission caused by road transport using a random forest (RF) model combined by meta-heuristic algorithms. Sustain Cities Soc 93:104503

    Article  Google Scholar 

  • Li C, Li Y, Xing J (2023a) Multivariate grey prediction model application in civil aviation carbon emission based on fractional order accumulation and background value optimization. Sustainability 15(9127):9127

    Article  Google Scholar 

  • Li R, Wang Q, Liu Y, Jiang R (2021) Per-capita carbon emissions in 147 countries: the effect of economic, energy, social, and trade structural changes. Sustain Prod Consump 27:1149–1164

    Article  Google Scholar 

  • Li Y, Zhou L, Gao P, Yang B, Han Y, Lian C (2022a) Short-term power generation forecasting of a photovoltaic plant based on PSO-BP and GA-BP neural networks. Front Energy Res 2022:9. https://doi.org/10.3389/fenrg.2021.824691

    Article  Google Scholar 

  • Li Y, Dai J, Zhang S, Cui H (2023c) Dynamic prediction and driving factors of carbon emission in Beijing, China, under carbon neutrality targets. Atmosphere 14(798):798

    Article  ADS  Google Scholar 

  • Lv T, Hu H, Xie H et al (2023) An empirical relationship between urbanization and carbon emissions in an ecological civilization demonstration area of China based on the STIRPAT model. Environ Dev Sustain 25:2465–2486. https://doi.org/10.1007/s10668-022-02144-6

    Article  Google Scholar 

  • Mutascu M (2022) CO2 emissions in the USA: new insights based on ANN approach. Environ Sci Pollut Res 29:68332–68356. https://doi.org/10.1007/s11356-022-20615-1

    Article  Google Scholar 

  • Nie W, Ao O, Duan H (2023) A novel grey prediction model with a feedforward neural network based on a carbon emission dynamic evolution system and its application. Environ Sci Pollut Res 30:20704–20720. https://doi.org/10.1007/s11356-022-23541-4

    Article  CAS  Google Scholar 

  • Peng D, Liu H (2023) Measurement and driving factors of carbon emissions from coal consumption in China based on the Kaya-LMDI model. Energies 16(1):439

    Article  CAS  Google Scholar 

  • Pu X, Yao J, Zheng R (2022) Forecast of energy consumption and carbon emissions in China’s building sector to 2060. Energies 15(14):4950. https://doi.org/10.3390/en15144950

    Article  CAS  Google Scholar 

  • Qader MR, Khan S, Kamal M et al (2022) Forecasting carbon emissions due to electricity power generation in Bahrain. Environ Sci Pollut Res 29:17346–17357. https://doi.org/10.1007/s11356-021-16960-2

    Article  Google Scholar 

  • Sixuan W, Zeng X, Li C, Cang H, Tan Q et al (2023) CO2 emission forecasting based on nonlinear grey Bernoulli and BP neural network combined model. Soft Comput 27(21):15509–15521

    Article  Google Scholar 

  • Sun J, Guo X, Wang Y et al (2022) Nexus among energy consumption structure, energy intensity, population density, urbanization, and carbon intensity: a heterogeneous panel evidence considering differences in electrification rates. Environ Sci Pollut Res 29:19224–19243. https://doi.org/10.1007/s11356-021-17165-3

    Article  Google Scholar 

  • Sun Y, Huang Z, Chi F (2023) Analysis of systemic factors affecting carbon reduction in Chinese energy-intensive industries: a dual-driven DEMATEL model. Energy 285:129319

    Article  Google Scholar 

  • Verbič M, Satrovic E, Mujtaba A (2022) Assessing the driving factors of carbon dioxide and total greenhouse gas emissions to maintain environmental sustainability in Southeastern Europe. Int J Environ Res 16:105. https://doi.org/10.1007/s41742-022-00486-7

    Article  CAS  Google Scholar 

  • Wang H, Li B, Khan MQ (2022d) Prediction of Shanghai Electric power carbon emissions based on improved STIRPAT model. Sustainability 14(20):13068. https://doi.org/10.3390/su142013068

    Article  CAS  Google Scholar 

  • Wang Q, Zhang F, Li R (2023c) Free trade and carbon emissions revisited: the asymmetric impacts of trade diversification and trade openness. Sustain Dev:1–26. https://doi.org/10.1002/sd.2703

  • Wang Q, Ren F, Li R (2023a) Exploring the impact of geopolitics on the environmental Kuznets curve research[J]. Sustain Dev. https://doi.org/10.1002/sd.2743

  • Wang Q, Sun J, Pata UK, Li R, Kartal MT (2023b) Digital economy and carbon dioxide emissions: examining the role of threshold variables[J]. Geosci Front.https://doi.org/10.1016/j.gsf.2023.101644

  • Wang Q, Wang X, RongrongLi. (2022c) Does urbanization redefine the environmental Kuznets curve? An empirical analysis of 134 countries. Sustain Cities Soc 76:103382

    Article  Google Scholar 

  • Wang Q, Zhang F, Li R (2022b) Revisiting the environmental kuznets curve hypothesis in 208 counties: the roles of trade openness, human capital, renewable energy and natural resource rent. Environ Res 216(3):114637

    PubMed  Google Scholar 

  • Wang X, Zhou Y, Bi Q, Cao Z, Wang B (2022e) Research on the low-carbon development path and policy options of China’s transportation under the background of dual carbon goals. Science 10. https://doi.org/10.3389/fenvs.2022.905037

  • Wen T, Liu Y, Yun He B, Liu H (2023) Modeling and forecasting CO2 emissions in China and its regions using a novel ARIMA-LSTM model. Heliyon 9(11):e21241

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Xiao M, Xingzhi P (2023) Decomposition of carbon emission influencing factors and research on emission reduction performance of energy consumption in China. Front Environ Sci 10. https://doi.org/10.3389/fenvs.2022.1096650

  • Xinfa T, Yifei S, Chenhui Z, Lihong W, Yan L (2023) Research on the promotion of digital teaching and learning toward achieving China’s dual-carbon strategy. Front Environ Sci 11. https://doi.org/10.3389/fenvs.2023.983526

  • Zhang C, Zou X, Lin C (2023) Carbon footprint prediction of thermal power industry under the dual-carbon target: a case study of Zhejiang Province, China. Sustainability 15:3280. https://doi.org/10.3390/su15043280

    Article  CAS  Google Scholar 

Download references

Funding

This work was financially supported by the Science and Technology Research Project of Jiangxi Education Department (Project number: GJJ2201327).

Author information

Authors and Affiliations

  1. School of Economic Management and Law, Jiangxi Science and Technology Normal University, Nanchang, 330013, Jiangxi, China

    Tang Xinfa, Liu Shuai & Wan Youwei

  2. State Grid Jiangxi Electric Power CO., LTD, Beijing, China

    Wang Yonghua

Authors
  1. Tang Xinfa
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Liu Shuai
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wang Yonghua
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Wan Youwei
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Xinfa Tang: conceptualization and writing—review and editing, and supervision. LiuShuai: conceptualization, methodology, writing—original draft preparation, writing—review, and editing, and resources. Wang Yonghua: conceptualization, methodology, formal analysis, investigation, writing—original draft preparation, and writing—review and editing. Wan Youwei: methodology, writing—original draft preparation, resources, and resources.

Corresponding author

Correspondence to Tang Xinfa.

Ethics declarations

Ethical approval

Not applicable.

Consent to participate

All the participants in this study were recruited with available informed consent.

Consent for publication

All authors are informed and agree to the study.

Competing interests

The authors declare no competing interests.

Additional information

Responsible Editor: V.V.S.S. Sarma

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tang, X., Liu, S., Wang, Y. et al. Study on carbon emission reduction countermeasures based on carbon emission influencing factors and trends. Environ Sci Pollut Res 31, 14003–14022 (2024). https://doi.org/10.1007/s11356-024-31962-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11356-024-31962-6

Keywords

Search

Navigation