Climate Finance: Mapping Air Pollution and Finance Market in Time Series
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<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" d...
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| Format: | Article |
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MDPI AG
2021-12-01
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| Series: | Econometrics |
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| Online Access: | https://www.mdpi.com/2225-1146/9/4/43 |
| _version_ | 1797505306253590528 |
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| author | Zheng Fang Jianying Xie Ruiming Peng Sheng Wang |
| author_facet | Zheng Fang Jianying Xie Ruiming Peng Sheng Wang |
| author_sort | Zheng Fang |
| collection | DOAJ |
| description | 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<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mrow></mrow><mrow><mn>2.5</mn></mrow></msub></semantics></math></inline-formula>, PM<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mrow></mrow><mn>10</mn></msub></semantics></math></inline-formula>, SO<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mrow></mrow><mn>2</mn></msub></semantics></math></inline-formula>, NO<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mrow></mrow><mn>2</mn></msub></semantics></math></inline-formula>, CO, and O<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mrow></mrow><mn>3</mn></msub></semantics></math></inline-formula> 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. |
| first_indexed | 2024-03-10T04:16:44Z |
| format | Article |
| id | doaj.art-24d810308d384934be0e1adc88adea03 |
| institution | Directory Open Access Journal |
| issn | 2225-1146 |
| language | English |
| last_indexed | 2024-03-10T04:16:44Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Econometrics |
| spelling | doaj.art-24d810308d384934be0e1adc88adea032023-11-23T07:58:20ZengMDPI AGEconometrics2225-11462021-12-01944310.3390/econometrics9040043Climate Finance: Mapping Air Pollution and Finance Market in Time SeriesZheng Fang0Jianying Xie1Ruiming Peng2Sheng Wang3Department of Econometrics and Business Statistics, Monash University, Clayton, VIC 3800, AustraliaDepartment of Econometrics and Business Statistics, Monash University, Clayton, VIC 3800, AustraliaDepartment of Econometrics and Business Statistics, Monash University, Clayton, VIC 3800, AustraliaDepartment of Econometrics and Business Statistics, Monash University, Clayton, VIC 3800, AustraliaClimate 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<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mrow></mrow><mrow><mn>2.5</mn></mrow></msub></semantics></math></inline-formula>, PM<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mrow></mrow><mn>10</mn></msub></semantics></math></inline-formula>, SO<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mrow></mrow><mn>2</mn></msub></semantics></math></inline-formula>, NO<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mrow></mrow><mn>2</mn></msub></semantics></math></inline-formula>, CO, and O<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mrow></mrow><mn>3</mn></msub></semantics></math></inline-formula> 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.https://www.mdpi.com/2225-1146/9/4/43climate financeair pollutionConvLSTM2Dstock pricefinance marketdeep neural network |
| spellingShingle | Zheng Fang Jianying Xie Ruiming Peng Sheng Wang Climate Finance: Mapping Air Pollution and Finance Market in Time Series Econometrics climate finance air pollution ConvLSTM2D stock price finance market deep neural network |
| title | Climate Finance: Mapping Air Pollution and Finance Market in Time Series |
| title_full | Climate Finance: Mapping Air Pollution and Finance Market in Time Series |
| title_fullStr | Climate Finance: Mapping Air Pollution and Finance Market in Time Series |
| title_full_unstemmed | Climate Finance: Mapping Air Pollution and Finance Market in Time Series |
| title_short | Climate Finance: Mapping Air Pollution and Finance Market in Time Series |
| title_sort | climate finance mapping air pollution and finance market in time series |
| topic | climate finance air pollution ConvLSTM2D stock price finance market deep neural network |
| url | https://www.mdpi.com/2225-1146/9/4/43 |
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