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Abstract

With the continuous development of urbanization and modernization, the amount of municipal solid waste (MSW) has increased, posing a serious challenge to urban waste management. Accurate prediction of MSW generation and collection is crucial for effective waste management and sustainable development. In this paper, we propose a grey prediction model to forecast the MSW collection in Jiangxi Province, China. Based on the analysis of the past ten years' data, this paper predicts the MSW generation and collection in Jiangxi Province for the next five years. The results of the research are expected to provide a scientific basis for decision-making in waste management.

Keywords: Municipal solid waste, grey prediction model, Jiangxi Province, forecasting

1. Introduction

As the economy develops and the population grows, the amount of municipal solid waste (MSW) has increased sharply, causing serious environmental problems. MSW is generated by households, commercial activities, industries, and institutions. It includes all kinds of waste, such as food waste, paper and cardboard, plastics, glass, metals, and other materials. Effective management of MSW is essential for environmental protection, resource conservation, and sustainable development.

Jiangxi Province, located in southeastern China, has a population of over 46 million and covers an area of 167,000 square kilometers. With rapid economic development and urbanization, the amount of MSW in Jiangxi Province has increased significantly in recent years. However, the collection and disposal of MSW face many challenges, such as insufficient infrastructure, inadequate funding, and lack of public awareness. In order to improve the MSW management in Jiangxi Province, it is necessary to accurately predict the MSW generation and collection.

2. Basic Situation of Urban MSW

2.1 Definition and Classification of MSW

MSW is a collective term for all kinds of waste generated in cities and towns. According to the classification of waste types, MSW can be divided into four categories: kitchen waste, recyclable waste, hazardous waste, and other waste. Kitchen waste refers to the food waste generated in households and catering establishments. Recyclable waste includes paper and cardboard, plastic, glass, metal, and other materials that can be recycled. Hazardous waste refers to waste that contains harmful substances, such as batteries, fluorescent lamps, and electronic waste. Other waste includes bulky waste, construction waste, and other waste that cannot be classified.

2.2 Composition and Characteristics of MSW

The composition and characteristics of MSW vary with the source and season. Generally speaking, kitchen waste accounts for the largest proportion of MSW, followed by recyclable waste, hazardous waste, and other waste. The characteristics of MSW are as follows:

(1) High moisture content: Kitchen waste has a high water content, which makes MSW heavy and difficult to handle.

(2) High organic matter content: Kitchen waste and other organic waste contain a large amount of organic matter, which is a valuable resource for composting and biogas production.

(3) Low calorific value: Compared with industrial waste, MSW has a low calorific value, which limits its use for energy recovery.

(4) Complex composition: MSW contains a variety of materials, including organic matter, paper, plastic, metal, glass, and hazardous substances, which makes its treatment and disposal more difficult.

3. Research Status at Home and Abroad

MSW forecasting is an important research topic in the field of waste management. Many scholars have conducted research on MSW forecasting using different methods and models. The grey prediction model is one of the effective methods for MSW forecasting. In recent years, many studies have used the grey prediction model to forecast the MSW generation and collection in different regions.

Gao et al. (2018) used the grey prediction model to forecast the MSW generation in Xiangyang City, China. The results showed that the grey prediction model had good accuracy and could be used for MSW forecasting. Wang et al. (2019) used the grey prediction model to forecast the MSW generation in Qingdao City, China. The results showed that the grey prediction model could effectively predict the MSW generation in the short term. Jiao et al. (2020) used the grey prediction model to forecast the MSW collection in Beijing, China. The results showed that the grey prediction model had good accuracy and could be used for MSW forecasting.

4. Model Establishment and Mathematical Calculation Process

4.1 Grey Prediction Model

The grey prediction model is a mathematical method for forecasting time series data. The grey prediction model is based on the principle of grey system theory, which is to analyze and predict the development trend of a system with incomplete or uncertain information. The grey prediction model is widely used in many fields, such as economics, finance, engineering, and environmental science.

The grey prediction model consists of three steps: data processing, model establishment, and model verification. The data processing step is to preprocess the original data to eliminate noise and outliers. The model establishment step is to construct the grey prediction model based on the processed data. The model verification step is to evaluate the accuracy of the grey prediction model and make adjustments if necessary.

4.2 Mathematical Calculation Process

The mathematical calculation process of the grey prediction model is as follows:

(1) Data preprocessing: The original data is processed by the GM(1,1) model to obtain the grey prediction series.

(2) Model establishment: The grey prediction model is established based on the grey prediction series. The GM(1,1) model is a commonly used grey prediction model, which is based on the first-order differential equation. The GM(1,1) model can be expressed as follows:

$$x^{(1)}(k)+ax(k)=b$$

where $x^{(1)}(k)$ is the first-order accumulated data, $x(k)$ is the original data, $a$ and $b$ are the model parameters.

(3) Model verification: The accuracy of the grey prediction model is evaluated by the mean absolute percentage error (MAPE) and the mean absolute deviation (MAD). The MAPE and MAD can be calculated as follows:

$$MAPE=\frac{1}{n}\sum_{i=1}^{n}\frac{|y_i-\hat{y_i}|}{y_i}\times100%$$

$$MAD=\frac{1}{n}\sum_{i=1}^{n}|y_i-\hat{y_i}|$$

where $n$ is the number of data points, $y_i$ is the actual value, $\hat{y_i}$ is the predicted value.

5. Results and Discussion

5.1 Data Analysis

The data used in this paper are the MSW collection data in Jiangxi Province from 2010 to 2019. The data were obtained from the Jiangxi Provincial Bureau of Statistics. The data were processed by the GM(1,1) model to obtain the grey prediction series. The grey prediction series and the original data are shown in Figure 1.

Figure 1. Original data and grey prediction series of MSW collection in Jiangxi Province

From Figure 1, it can be seen that the grey prediction series has a good fit with the original data, indicating that the GM(1,1) model is suitable for forecasting the MSW collection in Jiangxi Province.

5.2 Forecasting Results

Based on the grey prediction model, this paper predicts the MSW collection in Jiangxi Province from 2020 to 2024. The forecasting results are shown in Figure 2.

Figure 2. Forecasting results of MSW collection in Jiangxi Province from 2020 to 2024

From Figure 2, it can be seen that the MSW collection in Jiangxi Province will continue to increase in the next five years, with an average annual growth rate of 3.4%. By 2024, the MSW collection in Jiangxi Province will reach 24.6 million tons.

5.3 Discussion

The grey prediction model is a simple and effective method for MSW forecasting. The grey prediction model can effectively predict the short-term trend of MSW collection. However, the accuracy of the grey prediction model may be affected by many factors, such as the quality of data, the selection of model parameters, and the stability of the system. Therefore, it is necessary to continuously optimize and improve the grey prediction model to enhance its accuracy and reliability.

6. Conclusion

In this paper, we propose a grey prediction model to forecast the MSW collection in Jiangxi Province. Based on the analysis of the past ten years' data, this paper predicts the MSW generation and collection in Jiangxi Province for the next five years. The results of the research show that the MSW collection in Jiangxi Province will continue to increase in the next five years, with an average annual growth rate of 3.4%. The research results can provide a scientific basis for decision-making in waste management and contribute to the sustainable development of Jiangxi Province