Influencing Factors of Coal Price and Its Future Price Forecast
Qinxuan Que
1, a
and Siwei Li
2, b*
1
Glorious Sun School of Business and Management, Donghua University, Shanghai, China
2
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
Copyright
c
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.
3
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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Zhang Tonggong Lei Zhongmin, Analysis, Research and
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Volume 27, No. 12: 2005, 16-19
Zhang Jianying. Var Model Analysis of The Influencing
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