The continuous increase in waste generation warrants global management of waste to reduce the adverse economic, social, and environmental impact of waste while achieving goals for sustainability. The complexity of waste management systems due to different waste management practices renders such systems difficult to analyze. System dynamics (SD) approach aids in conceptualizing and analyzing the structure, interactions, and mode of behavior of the complex systems. The impact of the underlying components can therefore be assessed in an integrated way while the impact of possible policies on the system can be studied to implement appropriate decisions. This review summarizes various applications of SD pertinent to the waste management practices in different countries. Practices may include waste generation, reduction, reuse/recovery, recycling, and disposal. Each study supports regional-demanding targets in environmental, social, and economic scopes such as expanding landfill life span, implementing proper disposal fee, global warming mitigation, energy generation/saving, etc. The interacting variables in the WMS are specifically determined based on the defined problem, ultimate goal, and the type of waste. Generally, population and gross domestic product can increase the waste generation. An increase in waste reduction, source separation, and recycling rate could decrease the environmental impact, but it is not necessarily profitable from an economic perspective. Incentives to separate waste and knowledge about waste management are variables that always have a positive impact on the entire system.
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● Established a quantification method of pollutant emission standard.● Predicted the SO2 emission intensity of single coking enterprises in China. ● Evaluated the influence of pollutant discharge standard on prediction accuracy.● Analyzed the SO2 emissions of Chinese provincial and municipal coking enterprises. Industrial emissions are the main source of atmospheric pollutants in China. Accurate and reasonable prediction of the emission of atmospheric pollutants from single enterprise can determine the exact source of atmospheric pollutants and control atmospheric pollution precisely. Based on China’s coking enterprises in 2020, we proposed a quantitative method for pollutant emission standards and introduced the quantification results of pollutant emission standards (QRPES) into the construction of support vector regression (SVR) and random forest regression (RFR) prediction methods for SO2 emission of coking enterprises in China. The results show that, affected by the types of coke ovens and regions, China’s current coking enterprises have implemented a total of 21 emission standards, with marked differences. After adding QRPES, it was found that the root mean squared error (RMSE) of SVR and RFR decreased from 0.055 kt/a and 0.059 kt/a to 0.045 kt/a and 0.039 kt/a, and theR2 increased from 0.890 and 0.881 to 0.926 and 0.945, respectively. This shows that the QRPES can greatly improve the prediction accuracy, and the SO2 emissions of each enterprise are highly correlated with the strictness of standards. The predicted result shows that 45% of SO2 emissions from Chinese coking enterprises are concentrated in Shanxi, Shaanxi and Hebei provinces in central China. The method created in this paper fills in the blank of forecasting method of air pollutant emission intensity of single enterprise and is of great help to the accurate control of air pollutants. 相似文献