Skip to main content

Advertisement

SpringerLink
Log in

The correlation between green finance and carbon emissions based on improved neural network

  • S.I. : Machine Learning based semantic representation and analytics for multimedia application
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

The development of green finance and the quantitative evaluation of its impact on the ecological environment provide empirical evidence for the construction of the carbon trading accounting system. Among them, carbon trading is an important part of green finance, and the accounting of businesses related to carbon emission rights has promoted the development of regional green finance. In order to explore the relationship between green finance and carbon emissions, this paper builds an analysis model of the relationship between green finance and carbon emissions based on big data and machine learning based on big data technology and machine learning technology. Moreover, this paper conducts simulation tests through the system and compares the output results with the actual situation after system simulation to verify the effectiveness of the model in this paper. From the experimental research results, it can be seen that the correlation analysis model of green finance and carbon emissions based on big data and machine learning constructed in this paper has a good performance in the correlation analysis of green finance and carbon emissions. Moreover, it is not difficult to see through the model of this paper that there is a clear correlation between green finance and carbon emissions.

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
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

References

  1. Cueva C, Roberts RE, Spencer T et al (2015) Cortisol and testosterone increase financial risk taking and may destabilize markets[J]. Sci Rep 5(1):1–16

    Article  Google Scholar 

  2. Lee CJ, Andrade EB (2015) Fear, excitement, and financial risk-taking[J]. Cogn Emot 29(1):178–187

    Article  Google Scholar 

  3. Bartram SM, Brown GW, Waller W (2015) How important is financial risk?[J]. J Financ Quant Anal 50(4):801–824

    Article  Google Scholar 

  4. Cooper WW, Kingyens AT, Paradi JC (2014) Two-stage financial risk tolerance assessment using data envelopment analysis[J]. Eur J Oper Res 233(1):273–280

    Article  MathSciNet  Google Scholar 

  5. Montford W, Goldsmith RE (2016) How gender and financial self-efficacy influence investment risk taking[J]. Int J Consum Stud 40(1):101–106

    Article  Google Scholar 

  6. Wanjohi SM, Wanjohi JG, Ndambiri JM (2017) The effect of financial risk management on the financial performance of commercial banks in Kenya[J]. Int J Finance Bank Res 3(5):70–81

    Article  Google Scholar 

  7. Magendans J, Gutteling JM, Zebel S (2017) Psychological determinants of financial buffer saving: the influence of financial risk tolerance and regulatory focus[J]. J Risk Res 20(8):1076–1093

    Article  Google Scholar 

  8. Cole ES, Walker D, Mora A et al (2014) Identifying hospitals that may be at most financial risk from Medicaid disproportionate-share hospital payment cuts[J]. Health Aff 33(11):2025–2033

    Article  Google Scholar 

  9. Zalik A (2015) Resource sterilization: reserve replacement, financial risk, and environmental review in Canada’s tar sands[J]. Environ Plan A 47(12):2446–2464

    Article  Google Scholar 

  10. Lucarelli C, Uberti P, Brighetti G (2015) Misclassifications in financial risk tolerance[J]. J Risk Res 18(4):467–482

    Article  Google Scholar 

  11. Cong W (2020) Study of financial warning ensemble model for listed companies based on unbalanced classification perspective. Int J Intell Inf Technol 16(1):32–48

    Article  Google Scholar 

  12. West T, Worthington AC (2014) Macroeconomic conditions and Australian financial risk attitudes, 2001–2010[J]. J Fam Econ Issues 35(2):263–277

    Article  Google Scholar 

  13. Tanimura T, Jaramillo E, Weil D et al (2014) Financial burden for tuberculosis patients in low-and middle-income countries: a systematic review[J]. Eur Respir J 43(6):1763–1775

    Article  Google Scholar 

  14. Scharding TK (2015) Imprudence and immorality: a Kantian approach to the ethics of financial risk[J]. Bus Ethics Q 25(2):243–265

    Article  Google Scholar 

  15. Chen AS, Leung MT, Pan S et al (2020) Financial hedging in energy market by cross-learning machines. Neural Comput Appl 32:10321–10335

    Article  Google Scholar 

  16. Wang GWY, Woo SH, Mileski J (2014) The relative efficiency and financial risk assessment of shipping companies[J]. Marit Policy Manag 41(7):651–666

    Article  Google Scholar 

  17. Li S, Shi W, Wang J, Zhou H (2021) A deep learning-based approach to constructing a domain sentiment lexicon: a case study in financial distress prediction. Inf Process Manag 58(5):1673

    Google Scholar 

  18. Allen P, Petsoulas C (2016) Pricing in the English NHS quasi market: a national study of the allocation of financial risk through contracts[J]. Public Money Manag 36(5):341–348

    Article  Google Scholar 

  19. Thomä J, Chenet H (2017) Transition risks and market failure: a theoretical discourse on why financial models and economic agents may misprice risk related to the transition to a low-carbon economy[J]. J Sustain Finance Invest 7(1):82–98

    Article  Google Scholar 

  20. Sosnovska O, Zhytar M (2018) Financial architecture as the base of the financial safety of the enterprise[J]. Balt J Econ Stud 4(4):334–340

    Article  Google Scholar 

  21. Acemoglu D, Ozdaglar A, Tahbaz-Salehi A (2015) Systemic risk and stability in financial networks[J]. Am Econ Rev 105(2):564–608

    Article  Google Scholar 

  22. Castellano R, Cerqueti R, Rotundo G (2020) Exploring the financial risk of a temperature index: a fractional integrated approach[J]. Ann Oper Res 284(1):225–242

    Article  MathSciNet  Google Scholar 

  23. Cohn A, Engelmann J, Fehr E et al (2015) Evidence for countercyclical risk aversion: an experiment with financial professionals[J]. Am Econ Rev 105(2):860–885

    Article  Google Scholar 

  24. Maggiori M (2017) Financial intermediation, international risk sharing, and reserve currencies[J]. Am Econ Rev 107(10):3038–3071

    Article  Google Scholar 

  25. Pinelis I (2014) An optimal three-way stable and monotonic spectrum of bounds on quantiles: a spectrum of coherent measures of financial risk and economic inequality[J]. Risks 2(3):349–392

    Article  Google Scholar 

Download references

Acknowledgements

The research is supported by: The Sailing Research Project of Shandong Management University, (NO. QH2020R06)

Author information

Authors and Affiliations

  1. School of Economics and Trade, Shandong Management University, Jinan, 250357, Shandong, China

    Chenghao Sun

Authors
  1. Chenghao Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chenghao Sun.

Ethics declarations

Conflict of interest

The authors declare no conflict of interest.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sun, C. The correlation between green finance and carbon emissions based on improved neural network. Neural Comput & Applic 34, 12399–12413 (2022). https://doi.org/10.1007/s00521-021-06514-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-021-06514-5

Keywords

Search

Navigation