EconPapers    
Economics at your fingertips  
 

Impact of the Rapid Expansion of Renewable Energy on Electricity Market Price: Using machine learning and shapley additive explanation

Chao Li and Shunsuke Managi

Discussion papers from Research Institute of Economy, Trade and Industry (RIETI)

Abstract: The positive effects of greenness in living environments on human well-being are known. As a widely used proxy, the nighttime light (NTL) indicates the regional socio-economic status and development level. Higher development levels and economic status are related to more opportunity and higher income, ultimately leading to greater human well-being. However, whether simple increases in greenness and NTL always produce positive results remains inconclusive. Here, we demonstrate the complex relationships between human well-being and greenness and NTL by employing the random forest method. The accuracy of this model is 81.83%, exceeding most previous studies. According to the analysis results, the recommended ranges of greenness and NTL in living environments are 10.91% - 32.99% and 0 – 17.92 nW/cm 2 ・sr , respectively. Moreover, the current average monetary values of greenness and NTL are 3351.96 USD/% and 658.11 USD/(nW/cm 2 ・sr) , respectively. The residential areas are far away from the abundant natural resources, which makes the main population desire more greenness in their living environments. Furthermore, high urban development density, represented by NTL, has caused adverse effects on human well-being in metropolitan areas. Therefore, retaining a moderate development intensity is an effective way to achieve a sustainable society and improve human well-being.

Pages: 35 pages
Date: 2022-09
New Economics Papers: this item is included in nep-big, nep-cmp, nep-ene and nep-env
References: Add references at CitEc
Citations: Track citations by RSS feed

Downloads: (external link)
https://www.rieti.go.jp/jp/publications/dp/22e093.pdf (application/pdf)

Related works:
Working Paper: Impact of the Rapid Expansion of Renewable Energy on Electricity Market Price: Using machine learning and shapley additive explanation (2022) Downloads
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eti:dpaper:22093

Access Statistics for this paper

More papers in Discussion papers from Research Institute of Economy, Trade and Industry (RIETI) Contact information at EDIRC.
Bibliographic data for series maintained by TANIMOTO, Toko ().

 
Page updated 2023-01-03
Handle: RePEc:eti:dpaper:22093