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Integrating Environmental Concerns: Evaluation of GGDP Innovation Economy Based on Entropy Weight and Coefficient of Variation

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DOI: 10.23977/infse.2024.050207 | Downloads: 5 | Views: 136

Author(s)

Chenyi Qiu 1, Yihua Tu 2

Affiliation(s)

1 School of Economies, Shanghai University, Shanghai, 200444, China
2 School of Communication and Information Engineering, Shanghai University, Shanghai, 200444, China

Corresponding Author

Chenyi Qiu

ABSTRACT

This study responds to the environmental repercussions of emphasizing Gross Domestic Product (GDP) over ecological concerns, like excessive resource depletion. With a pressing need for a more sustainable economic measure, we advocate for "Green" GDP (GGDP) as a holistic substitute. Our focus lies in integrating natural resource preservation into economic evaluations and promoting global accord on sustainable economic progress. Strategically selecting key economies worldwide, we redefine resource depletion and environmental degradation costs within GGDP assessments. Using methods like Entropy Weight and Coefficient of Variation, we establish a robust GGDP model. We also evaluate climate mitigation indicators across various countries, showcasing GGDP's positive impact through methods like BP neural networks. Additionally, we assess GGDP's resilience to fluctuations using predictive models like LSTM, highlighting its effectiveness in climate mitigation compared to GDP. This transition gains support from cosine similarity analysis, emphasizing GGDP's alignment with environmental indices. Enhancing the GGDP model through various analyses and incorporating indicators like GNI and research expenditure, our proposed framework emerges as a comprehensive, stable alternative, supporting sustainable economic growth while curbing environmental impact.

KEYWORDS

Entropy Weight, Coefficient of Variation, BP neural network, LSTM

CITE THIS PAPER

Chenyi Qiu, Yihua Tu, Integrating Environmental Concerns: Evaluation of GGDP Innovation Economy Based on Entropy Weight and Coefficient of Variation. Information Systems and Economics (2024) Vol. 5: 51-57. DOI: http://dx.doi.org/10.23977/infse.2024.050207.

REFERENCES

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[2] Li, J. L. (2021). Green development accounting of Daihai watershed based on GGDP, GEP, and GEEP. Inner Mongolia University. https://doi.org/10.27224/d.cnki.gnmdu.2021.000742.
[3] Zhao, Z., Luo, Y., & Huang, J. (2019). Global warming and sea level rise. Advances in Climate Change Research, 15(06), 700-703.
[4] Chen, L.P., & Yang, Z. Z. (2005). Empirical analysis of green GDP accounting in China. Journal of Harbin Institute of Technology (Social Science Edition), (06), 64-68. https://doi.org/10.16822/j.cnki.hitskb.2005.06.015.

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