Forecasting China's wastewater discharge using dynamic factors and mixed-frequency data. (December 2019)

• Record Type: Journal Article
• Authors: Ding, Lili; Lv, Zhanlei; Han, Meng; Zhao, Xin; Wang, Wei
• Is Part Of: Environmental pollution. Volume 255(2019)Part 1
• Publication Date: 2019-12
• Subjects: Wastewater discharge -- MIDAS regression -- Forecast combination; Pollution -- Periodicals; Pollution -- Environmental aspects -- Periodicals; Environmental Pollution -- Periodicals; Pollution -- Périodiques; Pollution -- Aspect de l'environnement -- Périodiques; Pollution -- Effets physiologiques -- Périodiques; Pollution; Pollution -- Environmental aspects; Periodicals; Electronic journals; 363.73
• DOI: 10.1016/j.envpol.2019.113148
• Languages: English
• ISSNs: 0269-7491

Abstract

Abstract: Forecasting wastewater discharge is the basis for wastewater treatment and policy formulation. This paper proposes a novel mixed-data sampling regression model, i.e., combination-MIDAS model to forecast quarterly wastewater emissions in China based on dynamic factors at different frequencies. The results show that a significant auto-correlation for wastewater emissions exists and that water consumption per ten thousand gross domestic product is the best predictor of wastewater emissions. The forecast performances of the combination-MIDAS models are robust and better than those of the benchmark models. Therefore, the combination-MIDAS models can better capture the characteristics of wastewater emissions, suggesting that the proposed method is a good method to deal with model misspecification and uncertainty for the control and management of wastewater discharge in China. Graphical abstract: Image 1 Highlights: Chinese quarterly total amount of wastewater discharge are forecasted. Mixed frequency data are useful to forecast wastewater discharge. Combination-MIDAS models provide better performance. Abstract : Main findings: WC/TTGDP is the best predictor of wastewater emissions and the forecast performance of the combination-MIDAS models are robust and better than those of the benchmark models.