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Climate Finance: Mapping Air Pollution and Finance Market in Time Series

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  • Zheng Fang

    (Department of Econometrics and Business Statistics, Monash University, Clayton, VIC 3800, Australia
    Current address: Department of Data Science and Artificial Intelligence, Monash University, Clayton, VIC 3800, Australia.
    These authors contributed equally to this work.)

  • Jianying Xie

    (Department of Econometrics and Business Statistics, Monash University, Clayton, VIC 3800, Australia
    These authors contributed equally to this work.)

  • Ruiming Peng

    (Department of Econometrics and Business Statistics, Monash University, Clayton, VIC 3800, Australia
    These authors contributed equally to this work.)

  • Sheng Wang

    (Department of Econometrics and Business Statistics, Monash University, Clayton, VIC 3800, Australia
    These authors contributed equally to this work.)

Abstract

Climate finance is growing popular in addressing challenges of climate change because it controls the funding and resources to emission entities and promotes green manufacturing. In this study, we determined that PM 2.5 , PM 10 , SO 2 , NO 2 , CO, and O 3 are the target pollutant in the atmosphere and we use a deep neural network to enhance the regression analysis in order to investigate the relationship between air pollution and stock prices of the targeted manufacturer. We also conduct time series analysis based on air pollution and heavy industry manufacturing in China, as the country is facing serious air pollution problems. Our study uses Convolutional-Long Short Term Memory in 2 Dimension (ConvLSTM2D) to extract the features from air pollution and enhance the time series regression in the financial market. The main contribution in our paper is discovering a feature term that impacts the stock price in the financial market, particularly for the companies that are highly impacted by the local environment. We offer a higher accurate model than the traditional time series in the stock price prediction by considering the environmental factor. The experimental results suggest that there is a negative linear relationship between air pollution and the stock market, which demonstrates that air pollution has a negative effect on the financial market. It promotes the manufacturer’s improving their emission recycling and encourages them to invest in green manufacture—otherwise, the drop in stock price will impact the company funding process.

Suggested Citation

  • Zheng Fang & Jianying Xie & Ruiming Peng & Sheng Wang, 2021. "Climate Finance: Mapping Air Pollution and Finance Market in Time Series," Econometrics, MDPI, vol. 9(4), pages 1-15, December.
    • Handle: RePEc:gam:jecnmx:v:9:y:2021:i:4:p:43-:d:694969
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    References listed on IDEAS

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    1. Sidra Mehtab & Jaydip Sen, 2020. "A Time Series Analysis-Based Stock Price Prediction Using Machine Learning and Deep Learning Models," Papers 2004.11697, arXiv.org, revised May 2021.
    2. Ayodele Ariyo Adebiyi & Aderemi Oluyinka Adewumi & Charles Korede Ayo, 2014. "Comparison of ARIMA and Artificial Neural Networks Models for Stock Price Prediction," Journal of Applied Mathematics, Hindawi, vol. 2014, pages 1-7, March.
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    Cited by:

    1. Bushra Mushtaq & Anthony Halog & Shahzad Hussain, 2025. "Exploring the Synergies Between Industrial Green TFP and Circular Economy: A Systematic Review," Circular Economy and Sustainability, Springer, vol. 5(8), pages 6991-7022, December.
    2. Goshu Desalegn & Anita Tangl, 2022. "Developing Countries in the Lead: A Bibliometric Approach to Green Finance," Energies, MDPI, vol. 15(12), pages 1-19, June.
    3. Goshu Desalegn & Anita Tangl, 2022. "Enhancing Green Finance for Inclusive Green Growth: A Systematic Approach," Sustainability, MDPI, vol. 14(12), pages 1-13, June.
    4. Chiang, Thomas C., 2025. "The effect of climate policy uncertainty and induced risks on US aggregate and sectoral stock returns," Research in International Business and Finance, Elsevier, vol. 76(C).
    5. Gu, Leilei & Peng, Yuchao & Vigne, Samuel A. & Wang, Yizhi, 2023. "Hidden costs of non-green performance? The impact of air pollution awareness on loan rates for Chinese firms," Journal of Economic Behavior & Organization, Elsevier, vol. 213(C), pages 233-250.
    6. Aya Salama Abdelhady & Nadia Dahmani & Lobna M AbouEl-Magd & Ashraf Darwish & Aboul Ella Hassanien, 2024. "Green finance growth prediction model based on time-series conditional generative adversarial networks," PLOS ONE, Public Library of Science, vol. 19(7), pages 1-21, July.
    7. Zheng Fang & Jae-Young Han, 2025. "Realized GARCH Model in Volatility Forecasting and Option Pricing," Computational Economics, Springer;Society for Computational Economics, vol. 66(5), pages 3637-3657, November.

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