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Forecasting China's wastewater discharge using dynamic factors and mixed-frequency data
- Source :
- Environmental Pollution, 255(1):113148. ELSEVIER SCI LTD
- Publication Year :
- 2019
-
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. (C) 2019 Elsevier Ltd. All rights reserved.
- Subjects :
- China
Mixed frequency
010504 meteorology & atmospheric sciences
IMPROVE
Health, Toxicology and Mutagenesis
010501 environmental sciences
Wastewater
MIDAS regression
Toxicology
01 natural sciences
Gross domestic product
SUSTAINABILITY
REGRESSION-MODELS
POLLUTION
Benchmark (surveying)
Forecast combination
Wastewater discharge
EMISSIONS
0105 earth and related environmental sciences
ECONOMIC-GROWTH
Environmental engineering
Sampling (statistics)
Regression analysis
General Medicine
Models, Theoretical
PERFORMANCE
NETWORKS
SCARCITY
Environmental science
Sewage treatment
VOLATILITY
Forecasting
Subjects
Details
- Language :
- English
- ISSN :
- 02697491
- Database :
- OpenAIRE
- Journal :
- Environmental Pollution, 255(1):113148. ELSEVIER SCI LTD
- Accession number :
- edsair.doi.dedup.....2e87e75c799a101813adc9680121f2b2