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

Zheng Fang (), Jianying Xie, Ruiming Peng and Sheng Wang
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Jianying Xie: Department of Econometrics and Business Statistics, Monash University, Clayton, VIC 3800, Australia
Ruiming Peng: Department of Econometrics and Business Statistics, Monash University, Clayton, VIC 3800, Australia
Sheng Wang: Department of Econometrics and Business Statistics, Monash University, Clayton, VIC 3800, Australia

Econometrics, 2021, vol. 9, issue 4, 1-15

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.

Keywords: climate finance; air pollution; ConvLSTM2D; stock price; finance market; deep neural network; time series; regression analysis (search for similar items in EconPapers)
JEL-codes: B23 C C00 C01 C1 C2 C3 C4 C5 C8 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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