Forecast and Analysis Energy Structure in Seven Regions of China

Jiemin Wang, Yueyu Li and Tian Li

Business School, Sichuan university, Chengdu, China

Keywords: Energy Structure, Markov Chain

Abstract: As the world's energy consuming country, the change of China's energy structure has greatly affected the global

energy emission. The prediction of its energy structure is of great significance to the energy policy guidance of the

whole world and the development of the global economy. Because of China's vast territory, it is also necessary to

forecast its energy structure by region. According to the characteristic that the energy consumption structure has

no Markov aftereffect, this paper establishes a forecast model of the energy supply structure with planning

constraints, and predicts the energy structure in the next 5 years by the model. The results show that the energy

structure of different regions in China has different characteristics and will not change much in the long term.

1 INTRODUCTION

With the acceleration of industrialization, China has

achieved tremendous economic development in the

past few decades, and now it has become the world's

second largest economy and occupies a pivotal

position in the international arena. However, behind

the great achievements, we have also paid a great

price. The rapid development of economy brings not

only the improvement of living standard, but also the

excessive consumption of primary energy, great

increase of carbon emissions and other environmental

problems. In 2005, China was the world's second

largest emitter of carbon, accounting for 18% of

global emissions. In 2006, China was already the

world's largest emitter of primary energy

consumption. In 2018, China's carbon emissions were

27.32% of the world's total, already more than the

United States and the European Union combined. Mi

et al. (2017) concluded that China's carbon emissions

have some time to grow and are expected to peak in

2026. China is already facing huge international

pressure to conserve energy and reduce emissions.

Therefore, it is extremely necessary to analyze and

predict the energy structure.

This paper is divided into four parts. The second

part, literature review, Part III, Data sources, the

introduction of the model and a brief proof. The

fourth part, the result display and analysis. The fifth

part, gives the plan and the suggestion.

2 LITERATURE REVIEW

With the increasingly serious environmental

problems, more and more scholars begin to pay

attention to the problem of carbon emissions. Most of

their research has focused on three points. First, study

the decoupling relationship between carbon

emissions and economic growth, the main method of

this problem is Tapio decoupling mode(Tapio,

2005) .A large number of scholars have used this

model to analyze the relationship between carbon

emissions and economic growth in various industries.

Q. Wang and Wang (2019) studied the decoupling

relationship between carbon emissions from the

transportation sector and economic growth in six

provinces of China. Zhao, Kuang, and Huang (2016)

analyzed the decoupling factors of carbon emissions

in the transportation industry of Guangdong Province.

Du, Zhou, Pan, Sun, and Wu (2019)[5] analyzed the

decoupling factors between carbon emissions and

economic growth in China's construction industry.

And Another scholars Q. Wang, Su, and Li (2018)

made a comparative analysis on the decoupling

relationship between carbon emissions and economic

growth in China and India.

The second is to study the driving factors of

carbon emission and carbon emission intensity with

main method LMDI model(Ang, 2003). W. Wang,

Liu, Zhang, and Li (2013) explored the influence of

carbon emission and carbon emission intensity

driving factors in Jiangsu Province. Zhang, Song, Su,

and Sun (2015) used LMDI method to analyze and

Wang, J., Li, Y. and Li, T.

Forecast and Analysis Energy Structure in Seven Regions of China.

DOI: 10.5220/0011103600003355

In Proceedings of the 1st International Joint Conference on Energy and Environmental Engineering (CoEEE 2021), pages 5-9

ISBN: 978-989-758-599-9

study the influencing factors of decoupling between

China's economic growth and energy consumption. In

order to find an effective way to reduce China's

carbon emission intensity. Other scholars have

focused on the drivers of carbon emissions in various

industries. Ren, Yin, and Chen (2014)[10] analyzed

the influencing factors of carbon emissions in China's

manufacturing industry. Some scholars also used

LMDI to explore the energy structure and energy

consumption. Xia and Wang (2020) analyzed the

driving factors of energy consumption structure and

built a hybrid prediction model.

3 DATA SOURCES AND

METHODS

3.1 Data Sources

This paper selects the data of primary energy

consumption and energy structure of each province in

China from 1997 to 2017. All of these come from

China statistical Yearbook[12] and National Bureau

of Statistics[13]

3.2 Markov Model

Markov model is built on the basic principle of

Markov chain. Markov chain means that in the

transformation process of things, the transformation

of state is only related to the previous state, so the

future state of Markov chain is only related to the

current state, and has nothing to do with the past

state. Therefore, this model has a relatively high

prediction accuracy for non-aftereffect sequences.

The markov model is established as follows:

The set of Markov processes is {S,T}, where S is

a discrete set of states. T = {t



,...



} is the time set.

S = {S



,









,...,S





}. S





represents the i state at

time t. Let S





= i be the state at time m. S





= j

be the state at time m+1, So the probability of going

from state i to state j is p



= {P(S





i) | P(S





= j}. The matrix P = p



is called the

one-step transfer matrix. The elements in the matrix

satisfy p



≥1, and

∑





 

= 1.

The state transfer equation is:

𝑆



= 𝑆



∗ P (1)

P = 𝑝



= 

𝑝



𝑝



⋯𝑝



𝑝



𝑝



⋯𝑝



⋮⋮⋱⋮

𝑝



𝑝



⋯𝑝



 (2)

Above we have defined the one-step transfer

equation 𝑝



, then we define the n-step transfer

equation 𝑃





Since the energy consumption structure does not

fully conform to the non-after-effect characteristics,

one-step transfer proof cannot be completely and

accurately determined. Therefore, in order to estimate

the one-step transfer matrix more accurately, the

following model is established:

𝑚𝑖𝑛(𝑓(𝑃)) = ||𝑆(𝑡+ 1) − 𝑆(𝑡) ∗ 𝑃||





  

(3)

∑

𝑝





  

= 1 (4)

𝑝



≥ 0 (5)

In the formula, T refers to the number of moments

experienced by the system, S is the set of all states at

each moment. The model is solved by nonlinear

programming method.

4 RESULTS AND DISCUSSION

4.1 One-Step Transition Matrix

The one-step transfer matrix of one region is obtained

from formula 4.5-4.7 and the result is as figure 1, The

proportion of coal in other areas is 0.9605, 0.8964,

0.9328, 0.9986, 0.9587, 0.9779, respectively.

⎝

⎜

⎛

0.7963 0 0 0.0344 0 0 0 0 0.1692

0.0001 0.1998 0.4724 0.0020 0.0186 0.3070 0 0 0.0001

0 0.0725 0.7964 0 0.0001 0.1293 0.0001 0.0013 0.0002

0.2235 0.0001 0.0015 0.7739 0.0005 0.0004 0.0001 0.0001 0

0 0.0029 0.0034 0.0012 0.1845 0.0013 0.7525 0.0094 0.0447

0.0005 0 0.1441 0.0011 0.0055 0.4102 0.0268 0.3732 0.0385

0.0039 0.0039 0.9380 0.0026 0.0034 0.0045 0.0401 0.0034 0.0004

0.9815 0.0012 0.0006 0.0001 0.0047 0.0003 0.0042 0.0035 0.0037

0.0012 0.0552 0.0102 0 0.0471 0.0026 0 0.1331 0.7506

⎠

⎟

⎞

South China

Figure 1: One-step transition matrix for South China.

CoEEE 2021 - International Joint Conference on Energy and Environmental Engineering

4.2 Energy Structure Change and

Forecast in Each Region

According to Formula 4.4, the one-step transfer

matrix is used to predict the energy consumption of

each region in the past five years, and the prediction

results are shown in table 2.

Under the prediction of Markov chain, the average

relative error of prediction in south China, North

China, Central China, East China, northwest China,

southwest China, Northeast China is 0.076, 0.075,

0.072, 0.048, 0.091, 0.082, 0.066.

The forecast results show that the proportion of

various energy sources in south China is relatively

healthy, the proportion of coal is relatively small, the

proportion of clean energy is relatively high, and the

proportion of various energy sources will not change

significantly in the next few years. In North China,

the proportion of coal is relatively high and rising

gradually, while the proportion of clean energy and

natural gas is relatively small. The proportion of coal

in central China is also very low, only 0.39 in 2020,

with a trend of gradual decline. The proportion of

clean energy is very high and rising. The proportion

of oil in east China is far higher than that in other

regions, and the proportion of clean energy is

relatively low. The proportion of coal is decreasing

year by year, while the proportion of oil is increasing

year by year, while the change of clean energy and

natural gas is not obvious. The proportion of coal in

northwest China is extremely high, reaching 70% in

2020 and showing an increasing trend year by year.

Other sources of energy are relatively small,

accounting for less than 1% of clean energy and

natural gas, and will not change significantly in the

next few years. The proportion of coal in southwest

China will continue to decrease, while the proportion

of other energy sources will increase to different

degrees. The proportion of coal in northeast China

will continue to decline, while the proportion of

natural gas and clean energy will continue to rise.

Table 2: Energy structure forecast for each region.

Region Year The energy structure

South

China

coal coke

Crude

oil

gasoline kerosene diesel Fuel oil

Natural

gas

Clean

energy

2018

2019

2020

2021

2022

0.3271

0.3269

0.3265

0.3256

0.0348

0.0349

0.2028

0.2027

0.2026

0.2025

0.2026

0.0516

0.0519

0.0523

0.0528

0.0535

0.0152

0.0638

0.0639

0.0641

0.0644

0.0140

0.0137

0.0557

0.0558

0.0559

0.0560

0.2349

0.2347

0.2345

0.2343

0.2342

North

China

coal coke

Crude

oil

gasoline kerosene diesel Fuel oil

Natural

gas

Clean

energy

2018

2019

2020

2021

2022

0.6541

0.6521

0.6503

0.6488

0.6477

0.0931

0.0948

0.0966

0.0985

0.1007

0.0498

0.0506

0.0515

0.0524

0.0531

0.0208

0.0211

0.0214

0.0217

0.0220

0.0126

0.0124

0.0122

0.0120

0.0118

0.0208

0.0220

0.0232

0.0246

0.0260

0.0031

0.0030

0.0029

0.0562

0.0554

0.0545

0.0533

0.0519

0.0894

0.0884

0.0872

0.0859

0.0839

Central

China

coal coke

Crude

oil

gasoline kerosene diesel Fuel oil

Natural

gas

Clean

energy

2018

2019

2020

2021

2022

0.3812

0.3865

0.3929

0.4006

0.4101

0.0536

0.0545

0.0556

0.0569

0.0585

0.0521

0.0528

0.0535

0.0543

0.0549

0.0391

0.0393

0.0394

0.0392

0.0064

0.0063

0.0061

0.0060

0.0058

0.0502

0.0495

0.0487

0.0479

0.0471

0.0055

0.0358

0.0351

0.0343

0.0333

0.0321

0.3761

0.3706

0.3640

0.3561

0.3466

East China coal coke Crude

oil

gasoline kerosene diesel Fuel oil Natural

gas

Clean

energy

2018

2019

2020

2021

2022

0.4544

0.4612

0.4689

0.4780

0.4887

0.0808

0.0793

0.0775

0.0752

0.0724

0.2316

0.2293

0.2267

0.2237

0.2199

0.0513

0.0504

0.0494

0.0479

0.0459

0.0085

0.0086

0.0088

0.0498

0.0492

0.0486

0.0479

0.0473

0.0409

0.0411

0.0414

0.0419

0.0427

0.0607

0.0588

0.0567

0.0543

0.0515

0.0220

0.0221

0.0222

0.0225

0.0229

Forecast and Analysis Energy Structure in Seven Regions of China

Northwest coal coke Crude

oil

gasoline kerosene diesel Fuel oil Natural

gas

Clean

energy

2018

2019

2020

2021

2022

0.7176

0.7095

0.7010

0.6923

0.6832

0.0362

0.0378

0.0396

0.0415

0.0434

0.1144

0.1195

0.1249

0.1307

0.1368

0.0199

0.0201

0.0202

0.0203

0.0204

0.0021

0.0022

0.0023

0.0320

0.0332

0.0344

0.0357

0.0370

0.0022

0.0023

0.0730

0.0727

0.0724

0.0720

0.0716

0.0026

0.0027

0.0028

0.0029

0.0030

Southwest coal coke

Crude

oil

gasoline kerosene diesel Fuel oil

Natural

gas

Clean

energy

2018

2019

2020

2021

2022

0.4464

0.4635

0.4814

0.5003

0.5202

0.0571

0.0586

0.0602

0.0619

0.0639

0.0537

0.0510

0.0479

0.0442

0.0399

0.0892

0.0843

0.0793

0.0744

0.0695

0.0228

0.0218

0.0207

0.0197

0.0188

0.1038

0.0985

0.0935

0.0888

0.0845

0.0068

0.0065

0.0062

0.0059

0.0057

0.1215

0.1161

0.1103

0.1042

0.0978

0.0987

0.0999

0.1006

0.0998

Northeast coal coke

Crude

oil

gasoline kerosene diesel Fuel oil

Natural

gas

Clean

energy

; 2018

2019

2020

2021

2022

0.4955

0.4996

0.5038

0.5080

0.5125

0.0594

0.0602

0.0610

0.0618

0.0626

0.2472

0.2501

0.2533

0.2567

0.2604

0.0352

0.0351

0.0349

0.0347

0.0345

0.0032

0.0031

0.0347

0.0360

0.0374

0.0389

0.0405

0.0083

0.0084

0.0086

0.0090

0.0307

0.0305

0.0301

0.0295

0.0286

0.0858

0.0772

0.0681

0.0587

0.4880

5 CONCLUSIONS AND

RECOMMENDATIONS

The results show that the polarization of energy

structure is very serious, and its transformation is

worrying. Except for south and central China. The

rest of the region's clean energy share is very small,

less than 10 percent. Moreover, the transformation

trend is slow, and even negative in north China,

northeast China and other regions. The results of this

paper also show that most of China is still coal-based,

with only southern and central China out of the state.

For now, China's renewable energy, clean energy

growth less so for a long period of time does not

change this situation. Account for this situation, the

government should according to different regions to

develop a different method of policy for north China,

east China, should strengthen the high energy

consumption, high pollution enterprise to control,

reduce the one of primary energy consumption.

Northwest, southwest and northeast should

strengthen the research and development of

performance source technology, accelerate the

growth rate of new energy and clean energy through

market incentive mechanism and supporting new

energy enterprises, so as to increase the proportion of

clean energy. For south and central China, the

government should continue its investment in new

energy research and development, so as to effectively

increase the proportion of clean energy.

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Forecast and Analysis Energy Structure in Seven Regions of China