RESUMEN
Recycling of e-waste is an effective means for e-waste management. It has made great contribution to improving environmental benefits. This paper evaluates the emission reduction benefits and efficiency of e-waste recycling in China, using the direction distance function of DEA. Calculations show that from 2013 to 2017, the total emission reduction benefits of 29 provinces in China e-waste was 6.34 billion yuan, with an average emission reduction efficiency of 0.88. The emission reduction benefits of CO2 was 390 million tons, and the average emission reduction efficiency was only 0.82. The wastewater emission reduction benefits was 570 million yuan, with an average efficiency of 0.9. The emission reduction benefits of solid waste and SO2 are 5.37 billion yuan and 400 million yuan respectively, with the same emission reduction efficiency of 0.89. E-waste recycling in China still has huge potential for emission reduction.
Asunto(s)
Residuos Electrónicos , Administración de Residuos , China , Reciclaje , Residuos SólidosRESUMEN
Particulate matter with aerodynamic diameter below 10 µm (PM10) forecasting is difficult because of the uncertainties in describing the emission and meteorological fields. This paper proposed a wavelet-ARMA/ARIMA model to forecast the short-term series of the PM10 concentrations. It was evaluated by experiments using a 10-year data set of daily PM10 concentrations from 4 stations located in Taiyuan, China. The results indicated the following: (1) PM10 concentrations of Taiyuan had a decreasing trend during 2005 to 2012 but increased in 2013. PM10 concentrations had an obvious seasonal fluctuation related to coal-fired heating in winter and early spring. (2) Spatial differences among the four stations showed that the PM10 concentrations in industrial and heavily trafficked areas were higher than those in residential and suburb areas. (3) Wavelet analysis revealed that the trend variation and the changes of the PM10 concentration of Taiyuan were complicated. (4) The proposed wavelet-ARIMA model could be efficiently and successfully applied to the PM10 forecasting field. Compared with the traditional ARMA/ARIMA methods, this wavelet-ARMA/ARIMA method could effectively reduce the forecasting error, improve the prediction accuracy, and realize multiple-time-scale prediction. IMPLICATIONS: Wavelet analysis can filter noisy signals and identify the variation trend and the fluctuation of the PM10 time-series data. Wavelet decomposition and reconstruction reduce the nonstationarity of the PM10 time-series data, and thus improve the accuracy of the prediction. This paper proposed a wavelet-ARMA/ARIMA model to forecast the PM10 time series. Compared with the traditional ARMA/ARIMA method, this wavelet-ARMA/ARIMA method could effectively reduce the forecasting error, improve the prediction accuracy, and realize multiple-time-scale prediction. The proposed model could be efficiently and successfully applied to the PM10 forecasting field.