Influencing Factors of Coal Price and Its Future Price Forecast

Qinxuan Que

1, a

and Siwei Li

2, b*

Glorious Sun School of Business and Management, Donghua University, Shanghai, China

School of Statistics and Mathematics, Zhongnan University of Economics and Law, Wuhan, Hubei, China

Keywords: Analytic Hierarchy Process, Fourier Fitting, Nonlinear Least Squares.

Abstract: This article used the analytic hierarchy process to analyze the impact of nine factors on coal prices. These

factors include nine factors such as the supervision of relevant national departments, national policies,

energy consumption, transportation costs, climate change, travel mode, domestic coal market, international

coal market, and coal production. In the end, it is concluded that transportation costs and national policies

and departmental supervision have the greatest impact on coal prices, and the three factors that have the

least impact on coal prices are: the domestic coal market, the international coal market, and the mode of

travel. Specifically, the weight of the influence of transportation costs on coal prices is 0.32; the weight of

the influence of national policies and departmental supervision on coal prices is 0.22; the weight of the

influence of relevant departmental supervision on coal prices is 0.14. According to the forecast model, the

coal price will be declining in the next 31 days. In the next 36 months, the coal price will not change much

and will continue to fall. After a period of continuous decline, the coal price will usher in an increase.

Finally, this article put forward policy recommendations on controlling coal prices.

1 INTRODUCTION

As an upstream industry in the basic industries of the

national economy such as electric power and

building materials, the status of coal resources and

price levels will have a direct impact on the national

economy, and the further exploitation and use of

coal resources has made its importance increasingly

prominent. Looking for the influencing factors of

coal prices is to have a deeper understanding of the

changes in coal prices. Effective forecasting of coal

prices in China is to provide an effective basis for

industry construction and scientific decision-making

by related departments. Zhang Jianying (2015) uses

the VAR model to find that the factors affecting coal

prices include commodity prices, macroeconomic

prosperity index and coal production in addition to

changes in their own prices; Wang Wen, Li Guodong

(2016) analyze the influence factors of coal prices

from four levels: micro, macro, industry and

international market.

Based on this, this paper determines the ten basic

influencing factors that affect the price of thermal

coal in Qinhuangdao, and uses the analytic hierarchy

process to build a comprehensive price forecast

model on the basis of element selection to realize a

rational judgment on the trend of coal prices.

2 MATERIALS AND METHODS

2.1 Materials

This article used the thermal coal price data of

Qinhuangdao Port from July 3, 2006 to April 30,

2020 to conduct a case study.

2.2 Methods

2.2.1 Hierarchical Analysis

Combining the knowledge learned in economics:

value determines price, supply and demand affects

the value law of price, this article first defines the

first-level indicator that affects coal prices as

production cost (C1) and supply and demand (C2),

that is, the criterion level. Through reading a large

number of documents, this article has summarized 9

secondary indicators that affect coal prices, namely:

energy consumption (P1), climate change (P2),

regulation by relevant national authorities (P3),

national policies (P4), transport costs (P5), mode of

Que, Q. and Li, S.

Inﬂuencing Factors of Coal Price and Its Future Price Forecast.

DOI: 10.5220/0011733100003607

In Proceedings of the 1st International Conference on Public Management, Digital Economy and Internet Technology (ICPDI 2022), pages 203-207

ISBN: 978-989-758-620-0

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

203

Figure 1: Hierarchical analysis of coal price influencers.

Table 1: Weights that affect coal price factors.

Transport

costs(P5)

National

policies(P4)

regulation by

relevant national

authorities (P3)

Coal

production

(P9)

Energy

consumption

(P1)

Climate

change(P2)

Domestic

coal market

(P7)

International

coal market

(P8)

mode of

travel

(P6)

0.32 0.22 0.14 0.09 0.06 0.06 0.05 0.05 0.01

travel (P6), domestic coal market (P7), international

coal market (P8), coal production (P9), that is, the

plan level. It is worth mentioning that the primary

index is the basic factor that affects the price of coal,

and the nine secondary indicators selected affect the

price of coal by affecting the primary index. (Zhang,

2005).

2.2.2

Prediction Model Combining

Nonlinear Least Squares Method and

Fourier Approximation

The nonlinear least squares method is a parameter

estimation method that estimates the parameters of

the nonlinear static model based on the minimum

sum of squares of the error. Where y is the output of

the system, x is the input, and θ is the parameter

(Wang, 2020). When the fitted function is a linear

function to the parameters, this article uses the

goodness of fit R-square to evaluate the quality of

the fit. It can be seen from the formula that the

normal range of the goodness of fit is [0,1]. The

closer to 1, the stronger the explanatory power of the

variables of the equation to y, and the better the

model fits to the data.

First, this paper simplifies the time of the data,

assuming that the data for the week represented by

each day is assumed to be weekly data, resulting in

671 weeks of data. This article then imports the data

into matlab and fits the processed data through the

cftool toolbox.

RESULTS AND DISSCUSSION

3.1

The Weighted Results of The

Analytic Hierarchy Process

According to the weight vectors of w1, w2, and w3

obtained by the analytic hierarchy process, this

article constructed the weight table of P1-P9, and

calculated the composite weight of each layer

element to the system target through EXCEL, and

finally this article got the main factors that affect the

coal price:

From the above table, it can be seen that

transportation costs, national policies and

departmental supervision have the greatest impact

on coal prices, and the three factors that have the

least impact on coal prices are: domestic coal

market, international coal market, and travel mode.

Transportation costs, national policies and

departmental supervision have the greatest impact

on coal prices. Transportation costs (0.32):

transportation costs are a major factor affecting coal

prices in China. China's coal transportation mainly

has railway transport, road transport, waterway

transportation and so on. Among them, railway

traffic accounts for more than half of the total traffic,

railway traffic will have a greater impact on coal

prices. Especially in the case of large demand for

coal, the shortage of railway transportation will

increase the cost of coal transportation, thus driving

up the price of coal (Zhang, 2016). In addition,

changes in waterway transport prices can also have

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an impact on coal prices (Wang, 2016).

National policy (0.33): the relevant policies

issued by the state is an important factor affecting

coal prices. Supervision by relevant state

departments (0.14): As China's relevant regulatory

authorities strengthen supervision of the coal

industry, China's coal enterprises to safety systems

and safety technology standards are higher, thereby

increasing the cost of coal production safety

prevention and control.

The three factors that have the least impact on

coal prices are: domestic coal market, international

coal market, and travel mode. Domestic coal market

(0.05): China's domestic coal market directly reflects

the supply and demand of coal, will inevitably affect

coal prices. International coal market (0.05): With

the further development of economic globalization,

the change of international coal price is bound to

have an impact on domestic coal price. The path of

action can be to influence the price of coal by

affecting the amount of coal imported and exported.

Mode of travel (0.01): People's choice of travel

mode includes car, public transport, walking, etc.,

which will affect the social demand of the

downstream coal industry, thus affecting the demand

for coal.

3.2 Prediction Model Combining

Nonlinear Least Squares Method

and Fourier Approximation

Figure 2 shows that the curve approaching Fourier

can roughly fit the data of coal prices over the years.

R-square is 0.8234, close to 1, and the fit is good.

3.3 Forecast Trends in Coal Prices

Over the Next 31 Days, The Next 35

Weeks and The Next 36 Months

According to the forecast model, this paper obtains

the coal price in the next 31 days, the coal price in the

next 35 weeks and the trend of coal price change in

the next 36 months, as shown in Figures 3, 4 and 5.

Figure 2: Fourier approximates the prediction model.

Figure 3: Coal prices for the next 31 days.

Inﬂuencing Factors of Coal Price and Its Future Price Forecast

205

Figure 4: Changes in coal prices over the next 35 weeks.

Figure 5: Changes in coal prices over the next 36 months.

According to the forecast model, coal prices are

falling over the next 31 days. As the images show,

coal prices are falling over time over time, and at a

slower rate over the next 35 weeks. Coal prices will

not change much in the next 36 months, ushering in

growth after a period of sustained decline.

4 CONCLUSIONS

This article used hierarchical analysis to analyze the

impact of nine factors on coal prices. These factors

include the supervision of relevant state

departments, national policies, energy consumption,

transportation costs, climate change, mode of travel,

domestic coal market, international coal market, coal

production nine factors. Finally, it is concluded that

transportation costs, national policies and sectoral

regulation have the greatest impact on coal prices,

while the three factors with the least impact on coal

prices are: domestic coal market, international coal

market, and mode of travel. Specifically, the impact

of transportation costs on coal prices is weighted at

0.32; the impact of national policies and sectoral

regulation on coal prices is weighted at 0.22; and the

impact of relevant departments on coal prices is

weighted at 0.14. According to the forecast model,

coal prices in the next 31 days are falling, coal prices

in the coming year is not much change and continue

to decline, coal prices after a period of sustained

decline, will usher in growth.

Finally, this article put forward policy proposals

to control coal prices. First of all, the government

adopts a strict control of the import of coal policy, so

as to effectively control the impact of the

international market on domestic coal prices.

Second, the government strengthens supply-side

management and strictly controls illegal coal

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production. Third, the government adopted policies

to stimulate domestic coal demand, thereby

promoting a balance between supply and demand.

Finally, the government adopts reasonable charges

for coal transportation to increase coal transportation

capacity, thus stimulating coal consumption.

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Wang You, nonlinear minimum second multiplication

https://wenku.baidu.com/view/322a624079563c1ec5da

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Wang Wen, Li Guodong. Based on the study of the

influencing factors of coal price in China, based on the

layer analysis method, management and management,

2016 (12): 87-90.

Zhang Tonggong Lei Zhongmin, Analysis, Research and

Discussion of The Factors of Coal Price Volatility,

Volume 27, No. 12: 2005, 16-19

Zhang Jianying. Var Model Analysis of The Influencing

Factors of Coal Price In China, 2016 (01): 108-112.

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