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Related Experiment Video

Updated: Jan 18, 2026

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Forecasting Nitrous Oxide emissions based on grey system models.

Huaping Sun1, Jingjing Jiang2, Muhammad Mohsin3,4

  • 1School of Finance and Economics, Jiangsu University, Zhenjiang, 212013, People's Republic of China. shp@ujs.edu.cn.

Environmental Geochemistry and Health
|September 29, 2019
PubMed
Summary
This summary is machine-generated.

Accurate forecasting of Nitrous Oxide (N2O) emissions is crucial for environmental policy. The Non-homogeneous Grey Model effectively predicted future N2O levels for major emitting countries.

Keywords:
Discrete Grey ModelEven Grey ModelNitrous Oxide forecastingNitrous Oxide policyNon-homogeneous Grey Model

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Area of Science:

  • Environmental Science
  • Atmospheric Chemistry
  • Mathematical Modeling

Background:

  • Accurate forecasting of national energy performance is essential for effective environmental policies.
  • Monitoring and reducing Nitrous Oxide (N2O) and other harmful emissions requires reliable predictive models.
  • Six major emitting countries (China, India, Indonesia, Japan, Russia, USA) account for 61% of global N2O emissions.

Purpose of the Study:

  • To predict Nitrous Oxide (N2O) emissions for the year 2030.
  • To evaluate the effectiveness of three advanced mathematical grey prediction models.
  • To identify the most accurate model for forecasting N2O emissions based on historical data.

Main Methods:

  • Utilized historical N2O emission data from 2000 to 2016 for six key countries.
  • Employed three grey prediction models: Even Grey Model (1, 1), Discrete Grey Model (1, 1), and Non-homogeneous Discrete Grey Model.
  • Assessed model performance using mean absolute percentage error (MAPE).

Main Results:

  • The Non-homogeneous Discrete Grey Model demonstrated superior accuracy with the lowest MAPE (1.9%).
  • The Discrete Grey Model (1, 1) had a MAPE of 2.16%, and the Even Grey Model (1, 1) had a MAPE of 2.4%.
  • China recorded the highest N2O emissions during the study period, followed by the USA, India, Indonesia, Russia, and Japan.

Conclusions:

  • The Non-homogeneous Discrete Grey Model is highly effective for forecasting N2O emissions, even with limited data.
  • Findings provide valuable insights for policymakers and researchers focused on N2O emission reduction strategies.
  • Accurate N2O emission forecasts are critical for developing targeted environmental policies.