关键词: 电子废弃物, 碳排放量, 季节调整, GRNN, 预测

Abstract: As one of the largest producers of Waste Electrical and Electronic Equipment (WEEE) in the world, China’s theoretical waste volume of WEEE reached 7.674 million tons in 2021, of which more than 75.762% came from five kinds of WEEE, including the waste television, refrigerator, washing machine, air conditioner (AC) and microcomputer. Of the five kinds of WEEE, the waste AC has the largest carbon emission reduction potential. Carbon emissions from 22.06 tons can be avoided if one ton of waste air conditioner is collected and treated, and the reduction of carbon emissions is mainly attributed to the recovery of refrigerants. Predicting the carbon reduction potential of discarded air conditioning refrigerants can provide data support for the formulation of national carbon emission reduction policies and optimization of recycling and dismantling enterprises carbon emission reduction schemes. To accurately predict carbon emissions, precisely forecasting the waste volume is of great importance. However, there are several points that need to be improved in existing models for predicting the waste volume. First, some methods that rely on empirical data tend to introduce subjectivity and uncertainty into the prediction outcomes. Second, existing researches mainly use annual data for forecast and analysis, which hinders the government and enterprises from formulating more targeted policies and plans. Third, few studies consider socio-economic factors like GDP(Gross Domestic Product) and average temperature in predicting WEEE carbon emissions.
This paper proposes an X12-GRNN framework for carbon emission prediction of waste AC. The first step is to forecast the quarterly sales of AC. Nine socio-economic factors that may affect the sales of AC are identified through market research and expert advice. Then the Pearson correlation coefficients between nine factors series X_i(i=1,2,…,9) and AC sales Y are calculated to choose the important factor series. After that, we use the X12 method to decompose the factor series with seasonal characteristic and sales into trend components (TC), seasonal factors (SF), and irregular components (IR). Then, we develop three Generalized Regression Neural Network (GRNN) models for TC, SF and IR, and the results of prediction are TC(Y^), SF(Y^) and IR(Y^), respectively. By integrating the predicted TC(Y^), SF(Y^) and IR(Y^), a quarterly forecast series of AC sales Y^ are obtained. The second step is to estimate the waste volume of AC. Combining the parameters of the AC quarterly sales Y with those of their life cycle, we predict the waste volume of AC with the market supply A model. Then, the calculated average AC weight of 44.251kg is applied to convert quantity into weight of waste AC. The third step is to estimate the carbon emissions of waste AC refrigerants. The market shares of R22, R32, and R410a refrigerants between 2011 and 2025 are predicted by Holt-Winters-No Seasonal (HWN) method. Then, based on existing researches, parameters such as refrigerant filling volume of household AC, the annual leakage rate and recycling rate of refrigerant in waste AC, and the carbon emissions of other materials in the recovery progress of waste AC are set. Finally, the carbon emissions of waste AC refrigerants are calculated.
The main findings can be summarized in three points. Firstly, the carbon emissions from discarded household AC are enormous. From the beginning of discarded household AC in 2005 to the end of the calculation period in 2052, a cumulative emission of 1.103×10⁹t CO₂-eq is expected without the recovery of refrigerants. Secondly, the standardized recovery of discarded household AC has a high carbon reduction potential. If the recovery rate of refrigerants from discarded AC increases from 5.000% to 10.000% during the calculation period, it will reduce emissions by 5.161×10⁷t CO₂-eq. Thirdly, the use of environmentally friendly refrigerants is an effective measure to reduce carbon emissions from discarded household AC. During the calculation period, the use of R32 refrigerant AC will result in much smaller carbon emissions compared to R22 and R410a refrigerant AC, accounting for only one-fifth of R22 and one-third of R410a. However, there are still several issues such as the integration of other prediction models into the X12-GRNN framework, the inclusion of imported AC quantity into the research scope, and the setting of carbon emission parameters that require further discussion and research in the future.

Key words: e-waste, carbon emissions, seasonal adjustment, GRNN, prediction