Nordic environmental resilience: balancing air quality and energy efficiency by applying artificial neural network

Abul Ala Noman, Faheem Ur Rehman, Irfanullah Khan, Mehran Ullah*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Downloads (Pure)

Abstract

Maintaining public health and environmental safety in the Nordic nations calls for a strict plan to define exact benchmarks on air quality and energy efficiency. This study investigates the complicated interaction of decentralized energy production (DEP) with energy efficiency, and air quality index in the Nordic nations from 1990 to 2022 using System GMM and Artificial Neural Network (ANN) approach. Our research explored positive role of decentralized energy production and technological advancement to propel notable increases in energy efficiency, hence lowering pollution expressed as PM2.5 level. Our research indicates, however, that although international trade, GDP and urbanization assist to enhance energy efficiency, they also contribute to pollution by raising PM2.5 Level by higher energy usage. Furthermore damaging to environmental quality is the persistent link shown by economic disparity and the energy price index with increased degrees of pollution and less energy efficiency. Policy frameworks must devised sustainable development policy (decentralized energy production) to significantly improve energy efficiency and lower the amount of pollution. This calls for proper urban planning and a close observation of the possible drawbacks of growing GDP, trade, economic disparity, and energy expenses.
Original languageEnglish
Article number1429058
Number of pages15
JournalFrontiers in Public Health
Volume12
DOIs
Publication statusPublished - 5 Nov 2024

Keywords

  • environmental management
  • air quality
  • energy efficiency
  • artificial neural network
  • Nordic countries

Fingerprint

Dive into the research topics of 'Nordic environmental resilience: balancing air quality and energy efficiency by applying artificial neural network'. Together they form a unique fingerprint.

Cite this