The NLS-Based Nonlinear Grey Multivariate Model for Forecasting Pollutant Emissions in China.

Area of Science:

• Environmental Economics
• Econometrics
• Mathematical Modeling

Background:

• The relationship between pollutant discharge and economic growth is a key area in environmental economics.
• Accurate estimation of nonlinear relationships is crucial for effective environmental policy.
• China's economic growth has significant environmental implications.

Purpose of the Study:

• To establish a transformed nonlinear grey multivariable (TNGM (1, N)) model for estimating China's pollutant discharge dynamics with economic growth.
• To analyze the nonlinear relationship between per capita GDP and per capita wastewater discharge (WDPC), SO₂ emissions, and dust emissions.
• To compare the forecasting accuracy of the TNGM (1, N) model with the traditional GM (1, N) model.

Main Methods:

• Development of a transformed nonlinear grey multivariable (TNGM (1, N)) model using the nonlinear least square (NLS) method.
• Application of the Gauss-Seidel iterative algorithm to solve model parameters and improve precision.
• Empirical analysis and forecasting using TNGM (1, N) and traditional GM (1, N) models with Chinese data (1996-2015).

Main Results:

• The NLS algorithm effectively identifies nonlinear relationships between pollutant discharge and economic growth.
• The NLS-based TNGM (1, N) model demonstrates superior forecasting precision for WDPC, SO₂ emissions, and dust emissions compared to the GM (1, N) model.
• Forecasts indicate that per capita wastewater discharge (WDPC) increases with GDP per capita, while per capita SO₂ and dust emissions decrease.

Conclusions:

• The TNGM (1, N) model, enhanced by the NLS method, provides a more accurate approach to modeling environmental-economic relationships.
• Economic growth in China is associated with rising wastewater discharge but declining SO₂ and dust emissions.
• Findings offer valuable insights for environmental policy formulation and sustainable development strategies.