The Sustainability of China’s Economic Growth in an Era of Global Turbulence Текст научной статьи по специальности «Экономика и бизнес»

SUSTAINABLE DEVELOPMENT AND ENGINEERING ECONOMICS 2, 2024

Research article

DOI: https://doi.org/10.48554/SDEE.2024.2.4

The Sustainability of China’s Economic Growth in an Era of Global Turbulence

Qihang Feng, Nikolay Dmitriev*

, Darya Kryzhko

, Yuriy Kuporov

Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russia, [email protected],

[email protected], [email protected], [email protected]

*

Corresponding author: [email protected]

T

Abstract

his article analyses the sustainability of China’s economic growth in light of global challenges,

focusing on macroeconomic changes in recent decades and their impact on the country’s economy.

The study covers the period 1962–2022 and uses data from various sources, including the World

Bank, International Monetary Fund, Organisation for Economic Cooperation and Development, and

national statistical data from the People’s Republic of China. Correlation analysis methods are used to

assess the impact of socio-economic indicators on economic growth, revealing significant correlations

between gross domestic product and various indicators such as external debt, urbanisation, technological

development, and the standard of living. The main conclusion of the analysis is that economic

diversification and investment in high-tech industries are crucial for maintaining sustainable growth

in China. The findings indicate the need for future research assessing the potential for reducing the

environmental impact of industrialisation and improving social policies in a changing global economy.

Keywords: economic growth, macroeconomic changes, global challenges, turbulence, sustainability, correlation

analysis, industrialisation, diversification, high-tech industries

Citation: Feng, Q., Dmitriev, N., Kryzhko, D., Kuporov, Yu., 2024. The Sustainability of China’s Economic

Growth in an Era of Global Turbulence. Sustainable Development and Engineering Economics 2, 4.

https://doi.org/10.48554/SDEE.2024.2.4

This work is licensed under a CC BY-NC 4.0

© Feng, Q., Dmitriev, N., Kryzhko, D., Kuporov, Yu., 2024. Published by Peter the Great St. Petersburg

Polytechnic University

59

Sustainable development of regional infrastructure

SUSTAINABLE DEVELOPMENT AND ENGINEERING ECONOMICS 2, 2024

Научная статья

УДК 332.14

DOI: https://doi.org/10.48554/SDEE.2024.2.4

Устойчивость Экономического Роста Китая в Эпоху Глобальной

Турбулентности

Цихан Фэн, Николай Дмитриев*

, Дарья Крыжко

, Юрий Купоров

Санкт-Петербургский политехнический университет Петра Великого, Санкт-Петербург, Россия,

[email protected], [email protected], [email protected], [email protected]

*Автор, ответственный за переписку: [email protected]

Н

Аннотация

астоящая статья анализирует устойчивость экономического роста Китая в контексте

глобальных вызовов, акцентируя внимание на макроэкономических изменениях

последних десятилетий и их воздействии на экономику страны. Исследование охватывает

период с 1962 по 2022 год. В статье анализируются данные Всемирного банка, МВФ, ОЭСР, а

также национальных статистических данных КНР. Применены методы корреляционного анализа

для оценки влияния социально-экономических показателей на экономический рост. Выявлены

значимые корреляции между ВВП и такими показателями, как объем внешнего долга, уровень

урбанизации, технологическое развитие и уровень жизни населения, а также с показателями

внешней торговли и инвестиций. Подтверждено, что диверсификация экономики и инвестиции

в высокотехнологичные отрасли имеют ключевое значение для поддержания устойчивого

экономического роста. В заключении исследования подчеркивается необходимость дальнейших

исследований для оценки потенциала снижения экологического воздействия индустриализации

и улучшение социальной политики в условиях меняющейся глобальной экономической среды.

Ключевые слова: экономический рост, макроэкономические изменения, глобальные вызовы,

турбулентность, устойчивость, корреляционный анализ, индустриализация, диверсификация,

высокотехнологичные отрасли

Цитирование: Фэн, Ц., Дмитриев, Н., Крыжко, Д., Купоров, Ю., 2024. Устойчивость Экономического

Роста Китая в Эпоху Глобальной Турбулентности. Sustainable Development and Engineering Economics 2, 4.

https://doi.org/10.48554/SDEE.2024.2.4

Эта работа распространяется под лицензией CC BY-NC 4.0

© Фэн, Ц., Дмитриев, Н., Крыжко, Д., Купоров, Ю., 2024. Издатель: Санкт-Петербургский политехнический

университет Петра Великого

Устойчивое развитие региональной инфраструктуры

60

The sustainability of China’s economic growth in an era of global turbulence

1. Introduction

This study of economic growth and development aims to analyse the stability and sustainability of

a country’s economic system. This involves examining the quantitative changes in an economy, such as

an increase in the production and consumption of goods and services, which are measured by gross domestic product (GDP). Economic growth is based on the dynamics of GDP, as described by Chow and Li

(2002) and Jones and Hameiri (2022). In the past half-century, China has shown exceptional economic

growth, becoming the world’s largest economy in terms of GDP at purchasing power parity (PPP), with

more than 33 trillion USD in 2023. Such growth was stimulated by government investments in industry,

accounting for about 40% of the country’s GDP between 2000 and 2010. This was supported by active

export activities and the strategic development of high-tech industries and infrastructure. However, since

the 2010s, the global economy has faced new challenges, including financial crises, political upheavals,

and pandemics. These have led to questions about the sustainability of China’s economic growth (Carmody, Zajontz, and Reboredo, 2022; Repnikova, 2022).

The relevance of this research stems from the rapidly changing economic, social, and technological landscape of the global economy, particularly in light of technological advancements and geopolitical uncertainty. The onset of a period of global turbulence has necessitated adjustments to economic

policies and strategies for many countries, including China, which is actively involved in international

affairs and is a key player in global affairs. Despite China’s significant influence, achieving sustainable

economic growth in the country would require reviewing existing economic models and accounting for

the trends towards a multipolar world order.

The purpose of this paper is to analyse the sustainability of China’s economic growth, considering

economic and political challenges, as well as macroeconomic factors modulating these challenges. We

aim to understand the impact of global economic turbulence on the Chinese economy, and to identify

the main factors that influence China’s growth. Our study focuses on the Chinese economic system,

including its various components, such as industrial production, agriculture, services, foreign trade, domestic consumption, investment, and public administration. We examine the factors and conditions that

contribute to the sustainability of China’s economic development.

2. Materials and Methods

2.1. Historical background

Since the establishment of the People’s Republic of China in 1949, the country has been moving

towards the implementation of socialist principles. This has led to intense socio-economic experimentation and significant fluctuations in its development, with periods of prosperity followed by a decline.

During the “Great Leap Forward” period, China set ambitious goals to become a leading economy in a

short period of time, but an ill-conceived policy led to an economic crisis. Similarly, the “Great Proletarian Cultural Revolution” caused dysfunction in the party-state apparatus, as described by Tanner (1999)

and Heilmann (2008).

The deep economic crisis after the “Cultural Revolution” forced the Chinese government to search

for ways to restore economic stability. Consequently, the government implemented effective measures

to revive the country’s economy. In 1979, a comprehensive economic reform plan was developed with

the aim of achieving three main objectives: modernising and accelerating economic growth, expanding

international relations, and maintaining political stability. Priority was given to accelerating economic

development, focusing on expanding production capacity, strengthening national strength, and improving the living standards of citizens (Wu, 2005; Young, 1995).

The influential British historian Arnold Toynbee expressed the opinion that China could offer

the world a “gift” that combined Western dynamism and traditional Chinese stability (Toynbee, 1934).

These words proved to be prescient, particularly after Deng Xiaoping, the architect of modern China’s

economic reforms, formulated the phrase, “It doesn’t matter what color a cat is, as long as it catches

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Feng, Q., Dmitriev, N., Kryzhko, D., Kuporov, Yu.

mice” (Blue, 2000). Regarding Deng’s economic policies, it is clear that his desire for a practical and effective approach to economic management led China to succeed in the international arena. This allowed

the country to become a leading global producer and exporter (Shirk, 1994).

According to Marxist-Leninist theory, there is a close relationship between the economic base and

superstructure, such that changes in the former inevitably lead to alterations in the latter. In this regard,

China has developed a unique economic system that has been recognised internationally as “reformed

socialism” and domestically as a “middle way”. This system integrates certain aspects of capitalist and

socialist systems to overcome stagnation and stimulate economic growth (Zweig, 2002; Roland, 2000).

During the modernisation process, emphasis has been placed on accelerating economic development

while assessing whether activities are in line with socialist ideals based on three key criteria: their ability

to promote the development of productive forces, increase national strength, and improve the standard

of living for the population (Naughton, 2007; Wu, 2005).

China’s reform programme was founded on three core principles: attracting investment, promoting

exports, and utilising low-cost labour. Other aspects of the reform efforts included large-scale imports of

technology, significant investments in the economy, active government involvement in economic activities, the establishment of special economic zones, and the preservation of a single-party system, which

contributed to stability within the country (Ding and Tay, 2016).

The slowdown in China’s economic growth may be attributed to the transition from a deficit-focused economy to one with excess production. The country has shifted away from the strategy of extensive and extensive production towards a new model focused on the quality and efficiency of economic

development. This transition is accompanied by large-scale economic reform efforts aimed at reducing

reliance on exports and investments. Under the “quality” growth model, the primary indicator is not

simply an increase in GDP but rather the level of employment among the population (Tian, 2019; Doshi,

2021).

China’s current economic transition is a structural transformation, with an industrial-oriented

model giving way to a consumer-oriented model based on the growth of the service sector. In this context, a slowdown in GDP growth is not only unavoidable but also beneficial, as it represents a transition

to more sustainable and balanced forms of economic growth (Balogh, 2017; Xiao et al., 2022). Table

1 presents a systematic overview of the key historical and economic events that have shaped China’s

strategic trajectory.

Table 1. Key historical or economic milestones

Year

Event

Economic

Policy

1949

Establishment of the

PRC

Land reform

1958

Great leap forward

1966

Cultural revolution

1978

Economic reforms initiated

Impact on Economy

Global Influence

Redistribution of land, increased agricultural output

Collective farm- Economic downturn due

ing

to failed policies

Disruption of economy,

Political purge

decline in education and

industry

Rapid industrial growth,

Opening up and

improvement in living

reform

standards

Start of shift towards

socialism

Caused global concern regarding famine

World Trade Organiza1990s tion membership preparations

Trade liberalisation

Expansion in manufacturing and exports

Strengthened global

economic presence

2001

Market opening

policies

Boost in trade, access to

international markets

Positioned as a major

global trader

Entry into the WTO

Sustain. Dev. Eng. Econ. 2024, 2, 4. https://doi.org/10.48554/SDEE.2024.2.4

Intensified isolation

from the West

Increased foreign

investment and trade

62

The sustainability of China’s economic growth in an era of global turbulence

2010s Belt and Road Initiative

Infrastructure

investment

Enhanced connectivity

and influence in Asia,

Africa

2020s Dual circulation strategy

Focus on domestic market

Strategic shift in

Aims to reduce dependenresponse to global

cy on foreign markets

tensions

Extended China’s

geopolitical influence

2.2. Economic growth in the context of worldwide tendencies

China’s economic performance stands out against the backdrop of global trends. Since the initiation of reforms in the 1970s based on the principles of market socialism, China has exhibited one of

the most significant growth rates in the world. The degree of integration of the Chinese economy into

international processes is unparalleled among most countries. This process was founded on a policy

of openness and the attraction of foreign investment. Exports serve as the foundation for attracting the

funds necessary for economic growth and modernisation, particularly in industries (Kaplan, 2021).

In 2001, China’s accession to the World Trade Organization (WTO) significantly accelerated integration processes and opened access to new markets. China’s share of global exports of goods increased

from 1.9% in 2001 to 13.8% by 2020, consolidating its status as a major global trading power. However,

researchers have noted potential challenges associated with this process, such as the volatility of the

global economy under the influence of factors, including military conflicts and pandemics, which can

lead to economic instability and turbulence. This turbulence can have a direct impact on China’s economic growth, and to counteract these negative externalities, the Chinese government has implemented

fiscal and monetary policies to compensate for potential losses. The correlation between public debt and

economic cycles has been confirmed in practice (Yang et al., 2022), indicating the importance of these

measures in maintaining stability and growth.

The progress of China’s economic growth over the past decade is closely linked to the gradual

development of the service sector. This has led to a shift towards the domestic market, stimulated by

active consumer and investment demand. According to researchers, the current position of net exports of

goods and services is not a significant factor in growth. Further, an analysis of China’s GDP and foreign

trade shows the resilience of the Chinese economy in response to fluctuations in trade volumes. It is reasonable to conclude that the export-driven development model has reached its limits. A new innovative

development model will differ significantly from the approach of other industrialised countries, as it

will be based on the large domestic market and internal growth factors in China. Innovation will replace

export-oriented growth and capital accumulation as the primary drivers of economic growth. Current

trends are aimed at promoting an intensive form of growth that will replace the extensive model. Factors

such as domestic consumer demand, investment, and external exports will continue to contribute significantly to economic expansion (Potapov, 2023).

The increasing instability and turbulence within the context of global economic development and

the disruption of previous trends in unipolar globalisation have led to the emergence of a new global

economic order. Researchers have concluded that the focus of China’s economic strategy has shifted

towards domestic demand and consumption, a trend that intensified following the global economic crisis between 2007 and 2009. The key components of China’s growth model include a low initial level

of production, an extensive pool of labour resources in agriculture, foreign direct investment, efficient

public administration, and the maintenance of control over key sectors of the economy, particularly the

financial sector (Tenyakov and Amirkhanova, 2023).

In response to the environmental challenges posed by rapid urbanisation and industrialisation in

China, the country has implemented a sustainable development strategy. This strategy has made technological innovation an essential tool for achieving economic growth. Investments in science and technology by the government have led to the development of new technologies, including those that support

sustainable development. The current socio-economic development in China is linked to the goal of

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Feng, Q., Dmitriev, N., Kryzhko, D., Kuporov, Yu.

maintaining sustainability. Recent studies (Rudskaia et al., 2021; Cao et al., 2014; Qiu et al., 2020) have

shown that innovative approaches to technology and sustainable development contribute to economic

growth and help address the environmental challenges associated with rapid urbanisation and industrialisation.

Statistical data indicate that companies investing in green technologies enjoy significant economic benefits. For example, they experience a 15% increase in profitability and a 20% improvement in

sustainability. This is due to increased demand for environmentally friendly products, reduced energy

consumption costs, and compliance with environmental regulations. The active development of energy

projects focusing on green energy helps reduce greenhouse gas emissions and creates new jobs, stimulating economic growth. New technologies allow for optimised production processes, increasing labour

productivity and contributing to the sustainable growth and competitiveness of companies. As a result,

the share of renewable energy in China’s energy mix has increased from 20% in 2018 to 30% by 2023.

China is transitioning from a high-growth model to a high-quality one, integrating sustainable practices

into its economic, social, and environmental policies. This shift is leading to a reorientation of industry

towards projects of a new quality, which aim to minimise negative impacts on the environment and

society. Examples of such projects include the development of digital platforms for industry and digital

infrastructure (Quan, 2018; Jia and Rodionov, 2022). The transition to high-quality growth requires

technological innovation, but it also raises concerns about the risks associated with digitalisation and

changes in production processes (Zaytsev et al., 2021; Feofilova et al., 2024).

Economic transformations have led to structural changes aimed at boosting domestic consumption

and reducing reliance on exports, particularly in the technological sector. Policies to stimulate internal

demand, improve working conditions, and increase wages have contributed to a rise in consumer spending. These developments are part of a broader restructuring of China’s socio-economic system, driven

by a new global economic landscape and the transition towards a model of high-quality growth (Lardy,

2019; Wang et al., 2015; Dmitriev et al., 2023). The new financial strategy has resulted in the adjustment

of the economic model to better meet consumer demand. This has been largely driven by the re-examination of institutional aspects of economic security and the integration of sustainable development

principles into these frameworks (Breslin, 2021).

Despite a significant amount of research on historical aspects and current trends in economic

growth strategies in the context of global instability and the shift towards sustainability and the implementation of new strategies, there is still a need to develop new approaches and perspectives on development. An outdated understanding of economic models often prevents us from developing mechanisms

to address threats to economic growth.

This study aims to address the gaps in the analysis of the sustainability of China’s economic development. Table 2 presents the key events that have shaped the modern trajectory of the Chinese economy.

Table 2. The evolution of the Chinese economy in the context of global turbulence

Impact on World

Economy

Statistical Indicators

Introduction of

Beginning of Deng market mechanisms,

1978

Xiaoping’s reforms opening up for foreign investment

Integration of China

into the global economy

GDP increased by 10% over

the decade, GDP per capita:

USD 156

Entry into the

2001

WTO

Strengthening of

China’s position in

the global export

market

Share of global goods exports increased from 1.9%

in 2001 to 13.8% in 2020,

GDP per capita: USD 1,042

Year Key Events

Economic Reforms

Further trade liberalisation

Sustain. Dev. Eng. Econ. 2024, 2, 4. https://doi.org/10.48554/SDEE.2024.2.4

64

The sustainability of China’s economic growth in an era of global turbulence

Global financial

2008

crisis

Stimulation of the

domestic market

Announcement of

2013 the Belt and Road

Initiative

Expansion of international economic

influence

Start of the

2020 COVID-19 pandemic

Enhancement of

high technology and

healthcare support

Strengthening of

Global uncertainty,

innovative policies,

2022 increased trade

support for the dotensions

mestic market

9% increase in domestic

Stabilisation of globconsumption, increase in

al economic fluctuagovernment spending to

tions by China

stimulate the economy

Strengthening of

Overseas infrastructure

trade and infrastruc- investments doubled, GDP

ture connections

per capita: USD 9,607

GDP growth slowed but

Acceleration of

remained positive (2.3%),

global digital transGDP per capita: USD

formation

10,484

GDP growth of 5%, GDP

Continued growth

per capita: USD 12,556,

despite global chal- increase in the share of dolenges

mestic consumption in GDP

structure

2.3. Methodological underpinnings of the analysis

To examine the sustainability of China’s economic development in the face of global economic

uncertainty, we conducted a correlational analysis of the association between economic expansion and

key macroeconomic variables. Correlational analysis allows for the evaluation of the extent to which

various economic, societal, and political transformations influence China’s economic expansion. The

analysis utilised the following methodologies and instruments:

- Software: Google Colaboratory.

- Programming language: Python.

- Data libraries: Pandas used for data processing, NumPy for numerical operations, and Matplotlib

and Seaborn for data visualisation.

- Data sources: World Bank, International Monetary Fund, and OECD. National sources were also

used: Chinese government publications, transcripts of speeches, development plans, and programs.

Stages of analysis:

1. Data were collected for the period 1962–2022 by importing them from the indicated sources.

Table 3 shows the collected indicators for the key groups.

Table 3. Macroeconomic indicators by groups

Economic indicators

Gross domestic product (GDP) (current

US$)

GDP growth (annual

%)

Gross national

income (GNI) per

capita, Atlas method

(current US$)

GNI per capita, PPP

(current international

$)

65

Social indicators

Environmental

indicators

Demographic

indicators

Financial indicators

Births attended by

skilled health staff

CO2 emissions

Net migration

External debt stocks

Contraceptive

prevalence

Annual freshwater

withdrawals

Population density

Net official development assistance

Fertility rate

Forest area

Population growth

Poverty headcount

ratio

Immunisation,

measles

Surface area

Urban population

growth

Income share held by

lowest 20%

Sustain. Dev. Eng. Econ. 2024, 2, 4. https://doi.org/10.48554/SDEE.2024.2.4

Feng, Q., Dmitriev, N., Kryzhko, D., Kuporov, Yu.

Agriculture, forestry,

and fishing, value

added

Gross capital formation

Electric power consumption

Energy use

Exports of goods and

services

Imports of goods and

services

Gross national income

Foreign direct investment

High-technology

exports

Industry, value added

Revenue, excluding

grants

Tax revenue

Total debt service (%

of exports)

Gross capital formation

Life expectancy at

birth

Personal remittances

Mortality rate,

under 5

Net migration

Personal remittances

Population density

Population growth

Poverty headcount

ratio

Prevalence of

underweight

Primary completion rate

School enrolment

(primary, secondary)

Urban population

growth

Terrestrial and

marine protected

areas

Time required to

start a business

Statistical Capacity

Score

2. Correlation analysis. The use of statistical techniques to determine the association between

macroeconomic variables and economic growth is referred to as correlation analysis. Specifically, the

Pearson correlation coefficient (Equation 1) was employed to assess the strength and direction of the

relationship between economic expansion and key macroeconomic factors. Before calculating the correlation coefficient, the data were cleaned and processed to eliminate errors, omissions, and anomalies.

This involved applying linear interpolation to fill in missing values (Equation 2). The calculations were

performed using Python programming language and the Pandas library.

r=

n ( ∑ xy ) − ( ∑ x )( ∑ y )

 n ∑ x 2 − ( ∑ x )2   n ∑ y 2 − ( ∑ y )2 







y= y1 +

( x − x1 )( y2 − y1 ) ,

( x2 − x1 )

,

(1)

(2)

where r is the correlation coefficient, n is the number of observations, x и y are the variables, and

x1, x2, y1 and y2 are the data points for interpolation.

3. Interpretation of the results: Evaluation of the data obtained within the context of recent (20002022) and past (1964-2022) economic circumstances in China.

Sustain. Dev. Eng. Econ. 2024, 2, 4. https://doi.org/10.48554/SDEE.2024.2.4

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The sustainability of China’s economic growth in an era of global turbulence

The findings of this study are expected to assist in identifying the correlation between macroeconomic variables and China’s economic expansion, as well as in determining which factors have the most

significant impact.

3. Results and Discussion

3.1 Correlation of economic indicators: 1962–2022

The correlation analysis was conducted for the period 1962–2022, with an emphasis on such important economic indicators as GDP (current US$), GDP growth (annual %), GNI per capital, Atlas

method (current US$), and gross national income (GNI) per capital (PPP) (current international $). The

correlation results are presented in Table 4.

Table 4. Correlation coefficients for individual indicators (1962–2022)

Indicator (1962–2022)

Agriculture, forestry, and fishing,

value added

Annual freshwater withdrawals

Births attended by skilled health

staff

CO2 emissions

Contraceptive prevalence

Electric power consumption

Energy use

Exports of goods and services

External debt stocks

Fertility rate

Foreign direct investment

Forest area

GNI, Atlas method (current US$)

GNI, PPP (current international

$)

Gross capital formation

High-technology exports

Immunisation, measles

Imports of goods and services

Income share held by lowest

20%

Industry, value added

Life expectancy at birth

Mobile cellular subscriptions

Mortality rate, under 5

Net migration

Net official development assistance

Personal remittances

Population density

67

GDP (current

US$)

GDP growth

(annual %)

GNI per

capita, Atlas

method (current US$)

GNI per

capita, PPP

(current international $)

-0.730

-0.155

-0.732

-0.841

0.704

-0.215

0.702

0.768

0.645

-0.562

0.644

0.700

0.923

-0.508

0.939

0.919

0.493

0.991

-0.466

0.870

0.930

0.999

-0.461

0.154

-0.155

-0.115

0.199

-0.534

-0.094

-0.296

-0.501

-0.178

0.921

-0.506

0.938

0.918

0.484

0.990

-0.462

0.861

0.929

1.000

0.951

-0.521

0.953

0.937

0.114

0.973

-0.430

0.861

0.961

0.991

0.993

-0.589

0.994

1.000

0.620

-0.416

0.699

0.507

0.307

0.362

-0.467

0.215

0.632

-0.426

0.697

0.499

0.693

-0.489

0.789

0.158

-0.099

-0.103

-0.094

-0.203

-0.108

0.625

0.985

-0.725

0.067

0.416

0.305

-0.056

0.146

-0.191

-0.135

0.691

0.985

-0.727

0.078

-0.681

0.926

0.988

-0.907

0.765

-0.742

0.487

-0.742

-0.893

0.919

0.685

-0.502

0.207

0.921

0.691

0.901

0.922

Sustain. Dev. Eng. Econ. 2024, 2, 4. https://doi.org/10.48554/SDEE.2024.2.4

Feng, Q., Dmitriev, N., Kryzhko, D., Kuporov, Yu.

Population growth

Poverty headcount ratio ($2.15/

day)

Poverty headcount ratio (national

poverty line)

Prevalence of underweight

Primary completion rate

Revenue, excluding grants

School enrolment, primary (%

gross)

School enrolment, primary and

secondary (% gross)

School enrolment, secondary (%

gross)

Statistical Capacity Score

Surface area

Tax revenue

Terrestrial and marine protected

areas

Time required to start a business

Total debt service (% of exports)

Urban population growth

-0.578

0.285

-0.654

-0.836

-0.858

0.422

-0.857

-0.902

-0.928

0.578

-0.927

-0.940

-0.726

0.654

0.629

0.345

-0.643

-0.398

-0.724

0.651

0.637

-0.814

0.749

0.609

-0.721

0.285

-0.721

-0.906

0.718

-0.139

0.716

0.793

0.842

-0.283

0.840

0.898

0.921

-0.752

-0.625

-0.821

-0.059

0.486

0.926

-0.750

-0.637

0.903

-0.689

-0.633

-0.485

0.149

-0.439

-0.424

-0.888

-0.288

-0.378

0.511

-0.201

0.305

-0.888

-0.283

-0.393

-0.866

-0.319

-0.982

The purpose of this analysis is to determine the strength and direction of the relationships between

economic growth indicators and various socio–economic factors over the entire observation period. It

is worth noting that this type of analysis corresponds to most studies that include historical indicators

in the analysis without separating modern economic policy from the previous one. To identify the most

significant dependencies between the indicators, a selection of links with a correlation above 0.75 or

below -0.75 was carried out.

A. GDP (current US$). The dynamics of the indicator are shown in Figure 1.

A1. High correlation (over 75%) with:

• Electric power consumption (kWh per capita) (0.938677)

• Energy use (kg of oil equivalent per capita) (0.918928)

• External debt stocks, total (DOD, current US$) (0.991004)

• Forest area (sq. km) (0.929715)

• Mobile cellular subscriptions (per 100 people) (0.985241)

• Personal remittances, received (current US$) (0.919482)

• Statistical Capacity Score (Overall Average) (scale 0 - 100) (0.921255)

A2. Low correlation (less than 75%) with:

• Poverty headcount ratio at $2.15 a day (2017 PPP) (% of population) (-0.857860)

• Poverty headcount ratio at national poverty lines (% of population) (-0.927616)

• Time required to start a business (days) (-0.888415)

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The sustainability of China’s economic growth in an era of global turbulence

Figure 1. GDP dynamics (current US$) and trend (polynomial)

B. GDP growth (annual %). The dynamics of the indicator are shown in Figure 2.

B1. High correlation (over 75%) with:

• Foreign direct investment, net inflows (0.870089)

B2. Low correlation (less than 75%) with:

• GNI per capita, Atlas method (current US$) (-0.174645)

• GNI per capita, PPP (current international $) (-0.582831)

Figure 2. GDP growth (annual %) and trend (polynomial)

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Feng, Q., Dmitriev, N., Kryzhko, D., Kuporov, Yu.

3.

C. GNI per capita, Atlas method (current US$). The dynamics of the indicator are shown in Figure

C1. High correlation (over 75%) with:

• GDP (current US$) (0.999424)

• GNI, Atlas method (current US$) (0.999266)

• GNI, PPP (current international $) (0.993947)

• Mobile cellular subscriptions (per 100 people) (0.985235)

• Personal remittances, received (current US$) (0.920836)

• Population (people per sq. km of land area) (0.691338)

• Population, total (0.681870)

• Statistical Capacity Score (Overall Average) (scale 0 - 100) (0.925953)

C2. Low correlation (less than 75%) with:

• Poverty headcount ratio at $2.15 a day (2017 PPP) (% of population) (-0.857482)

• Poverty headcount ratio at national poverty lines (% of population) (-0.927488)

• Urban population growth (annual %) (-0.982482)

Figure 3. GNI per capita, Atlas method (current US$), and trend (polynomial)

D. GNI per capita, PPP (current international US$). The dynamics of the indicator are shown in

Figure 4.

D1. High correlation (over 75%) with:

• GDP (current US$) (0.992190)

• GNI, Atlas method (current US$) (0.990669)

• GNI, PPP (current international $) (0.999777)

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The sustainability of China’s economic growth in an era of global turbulence

• Mobile cellular subscriptions (per 100 people) (0.987961)

• Personal remittances, received (current US$) (0.900863)

• Population density (people per sq. km of land area) (0.922084)

• Population, total (0.922016)

• Statistical Capacity Score (Overall Average) (scale 0 - 100) (0.902675)

D2. Low correlation (less than 75%) with:

• Poverty headcount ratio at $2.15 a day (2017 PPP) (% of population) (-0.902480)

• Poverty headcount ratio at national poverty lines (% of population) (-0.940119)

• Urban population growth (annual %) (-0.982482)

Figure 4. GNI per capita, PPP (current international US$), and trend (polynomial)

Based on the high correlations and their interpretations, several conclusions can be drawn regarding the Chinese economy during the period 1962–2022.

1. Relationship between GDP, energy consumption, and external debt: The strong correlation between GDP and electricity consumption per capita, as well as with external debt, suggests that the energy sector and borrowing play a significant role in China’s economic growth. This reflects the country’s

industrialisation, infrastructure development, and reliance on external financing to support its economy.

2. Importance of mobile communications and personal money transfers: The strong correlation

between GDP and mobile phone subscriptions per capita, as well as personal money transfers, highlights

the need for continued technological development and international financial integration for China’s

economic growth. This reflects a high level of digitalisation and involvement in global financial transactions. However, it also makes the country more vulnerable to threats to macroeconomic stability.

3. Strong connection between GNI and GDP: A strong relationship between GNI and GDP indicates that China’s economic expansion is accompanied by rising per capita income. This suggests an

increase in the prosperity and standard of living of the population.

4. Falling poverty and population growth: The low correlation between poverty and GDP indicates

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Feng, Q., Dmitriev, N., Kryzhko, D., Kuporov, Yu.

that China’s economic growth has been accompanied by a reduction in poverty. However, population

growth has put pressure on the country’s social infrastructure and resources.

5. External debt: The high correlation between external debt and GDP highlights the significant dependence of China’s economy on external financing sources, emphasising the importance of effectively

managing external debt and ensuring sustainable external financial flows for economic growth.

6. Technology and industry: The strong correlation between GDP and technological innovation in

exports shows the success of China’s technological development and the creation of a strong scientific

and industrial foundation, which has contributed to export opportunities and created a competitive advantage in the global marketplace.

7. Stability and risk management: The low correlation between the labour cost index and GDP

indicates potential risks in employment and instability in the labour market. This requires attention from

the government to ensure the sustainability of the economy and social justice.

8. Environmental sustainability: The high correlation between forest area and GDP emphasises the

importance of natural resource management and environmental protection for economic development.

This also highlights the significance of environmentally sustainable practices in industry and agriculture.

9. International cooperation and investment: A significant correlation between FDI and GDP highlights the importance of international cooperation in attracting foreign investment. This can stimulate

economic growth and help modernise industries.

The findings highlight the diverse aspects of China’s economic growth and the need for a comprehensive approach to managing the economy. This approach should take into account various factors,

such as technological innovation, social well-being, environmental protection, and international relations. It is also important to note that correlation cannot fully explain economic growth. Therefore, it is

necessary to develop more complex models that take into account multiple dependencies.

3.2 Correlation of economic indicators: 2002–2022

We conducted a correlation analysis that used data from 2000 to 2022. The analysis focused on

important economic indicators, such as GDP (in current US$), GDP growth (in annual percentage), GNI

per capita (Atlas method in current US$), and GNI per capita PPP (in international $). The results of the

correlation are presented in Table 5.

Table 5. Correlation coefficients for individual indicators (2000–2022)

Indicator (2000-2022)

Adolescent fertility rate

Agriculture, forestry, and fishing,

value added

Annual freshwater withdrawals

Births attended by skilled health

staff

CO2 emissions

Contraceptive prevalence

Electric power consumption

Energy use

Exports of goods and services

External debt stocks

0.260

GNI per

capita, Atlas

method (current US$)

-0.147

GNI per

capita, PPP

(current international $)

-0.128

-0.904

0.504

-0.902

-0.922

0.563

-0.291

0.565

0.570

0.827

-0.502

0.823

0.844

0.902

-0.628

0.914

0.874

-0.624

0.986

-0.538

0.200

-0.589

-0.513

0.820

-0.739

0.900

-0.625

0.914

0.873

-0.630

0.984

0.918

-0.671

0.919

0.889

-0.567

0.974

GDP (current

US$)

GDP growth

(annual %)

-0.147

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The sustainability of China’s economic growth in an era of global turbulence

Fertility rate

Foreign direct investment

Forest area

Gross capital formation

High-technology exports

Immunisation, measles

Imports of goods and services

Income share held by lowest

20%

Industry, value added

Inflation, GDP deflator

Life expectancy at birth

Merchandise trade

Military expenditure

Mobile cellular subscriptions

Mortality rate, under 5

Net migration

Net official development assistance

Personal remittances

Population density

Population growth

Poverty headcount ratio ($2.15/

day)

Poverty headcount ratio (national

poverty line)

Prevalence of underweight

Primary completion rate

Revenue, excluding grants

School enrolment, primary (%

gross)

School enrolment, primary and

secondary (% gross)

School enrolment, secondary (%

gross)

Statistical Capacity Score

Surface area

Tax revenue

Terrestrial and marine protected

areas

Time required to start a business

Total debt service (% of exports)

Urban population growth

-0.477

0.743

0.975

0.557

-0.416

0.779

-0.654

0.444

-0.325

-0.655

-0.222

0.362

-0.412

0.803

-0.472

0.724

0.975

0.551

-0.426

0.774

-0.660

-0.477

0.747

0.982

0.594

-0.489

0.807

-0.608

0.818

-0.743

0.828

0.794

-0.860

-0.228

0.957

-0.707

-0.797

0.980

-0.911

0.738

0.821

0.556

-0.619

0.835

0.388

-0.694

0.536

-0.562

-0.870

-0.252

0.958

-0.712

-0.784

0.980

-0.910

0.736

-0.823

-0.192

0.968

-0.656

-0.817

0.981

-0.929

0.735

-0.864

0.660

-0.863

-0.841

0.855

0.974

-0.733

-0.611

-0.668

0.458

0.858

0.975

-0.731

0.841

0.977

-0.769

-0.914

0.681

-0.915

-0.912

-0.928

0.578

-0.927

-0.940

-0.827

0.526

0.629

0.427

-0.643

-0.398

-0.827

0.520

0.637

-0.852

0.511

0.608

-0.876

0.568

-0.880

-0.874

0.884

-0.593

0.887

0.892

0.832

-0.481

0.829

0.853

0.921

-0.630

-0.625

-0.821

0.221

0.486

0.926

-0.630

-0.637

0.903

-0.670

-0.633

-0.485

0.149

-0.439

-0.424

-0.888

0.179

-0.978

0.511

-0.518

0.714

-0.888

0.187

-0.978

-0.866

0.125

-0.984

The purpose of this analysis is to determine the strength and direction of the relationships between

economic growth indicators and various socio–economic factors over the current observation period.

This view highlights factors that are interrelated with economic growth. To identify the most significant

dependencies between the indicators, a selection of links with a correlation above 0.75 or below -0.75

was carried out.

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Feng, Q., Dmitriev, N., Kryzhko, D., Kuporov, Yu.

A. GDP (current US$)

A1. High correlation (over 75%) with:

• External debt stocks (0.986)

• Mobile cellular subscriptions (0.980)

• Forest area (0.975)

• Population density (0.974)

• CO2 emissions (0.902)

• Electric power consumption (0.914)

• Life expectancy at birth (0.957)

A2. Low correlation (less than -75%) with:

• Poverty headcount ratio (national poverty line) (-0.928)

• Mortality rate, under 5 (-0.911)

• Agriculture, forestry, and fishing, value added (-0.904)

• Urban population growth (-0.978)

B. GDP growth (annual %)

B1. High correlation (over 75%) with:

• Exports of goods and services (0.820)

• Industry, value added (0.821)

• Merchandise trade (0.835)

B2. Low correlation (less than -75%) with:

• Statistical Capacity Score (-0.821)

• Electric power consumption (-0.589)

C. GNI per capita, Atlas method (current US$).

C. High correlation (over 75%) with:

• GDP (current US$) (0.984)

GNI, PPP (current international $) (0.982)

Forest area (0.975)

Mobile cellular subscriptions (0.980)

C2. Low correlation (less than -75%) with:

• Poverty headcount ratio (national poverty line) (-0.927)

Urban population growth (-0.978)

D. GNI per capita, PPP (current international US$)

D1. High correlation (over 75%) with:

Sustain. Dev. Eng. Econ. 2024, 2, 4. https://doi.org/10.48554/SDEE.2024.2.4

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The sustainability of China’s economic growth in an era of global turbulence

• GDP (current US$) (0.974)

• GNI, Atlas method (current US$) (0.982)

• Population density (0.977)

• Life expectancy at birth (0.968)

D2. Low correlation (less than -75%) with:

• Poverty headcount ratio (national poverty line) (-0.940)

• Mortality rate, under 5 (-0.929)

• Urban population growth (-0.984)

These high correlations and their interpretation suggest several conclusions regarding the Chinese

economy, taking into account modern development (since 2000).

1. External and internal factors stimulating the economy: The high correlation of GDP with the

volume of external debts, the number of mobile subscriptions, and population density reflect China’s

integration into global markets and the focus of domestic development on technology and urbanisation.

2. Economic growth stimulated by trade and industry: The significant positive correlation of GDP

growth with exports and industry highlights the export-oriented growth model and industrialisation as

the main engines of economic expansion.

3. Socio-economic impact. Negative correlations of various economic indicators with poverty and

mortality levels indicate that economic growth is associated with an improvement in living standards

and health, although inequality problems continue to exist, as shown by negative correlations with urban

population growth.

4. Technological and environmental considerations: The strong relationship between GDP and

indicators such as CO2 emissions and electricity consumption highlights the environmental impact of

China’s industrial growth. However, the correlation between mobile subscriptions and life expectancy

demonstrates the positive effects of technological development and improved healthcare.

5. The need for sustainable and inclusive growth: Low correlations between GDP and poverty levels along the national poverty line, as well as high urban growth, indicate difficulties in ensuring a wide

distribution of economic benefits among the population, which underlines the need for policies aimed at

eliminating inequality and maintaining strategic sustainability.

6. The impact of globalisation: Globalisation plays a key role in shaping economic strategies, as

indicated in the relationship between GDP and external debts and exports.

7. The importance of new technologies: The active introduction of mobile technologies and the

increase in living space indicate technological progress that stimulates economic activity.

8. Problems of environmental sustainability: High levels of CO2 emissions and energy consumption require a review in the direction of maintaining environmental sustainability.

9. Growth and inequality: Strong urbanisation and urban population growth in the context of a low

correlation with an improvement in the standard of living of the population raise questions about social

adaptation and the implementation of policies that promote an even distribution of economic benefits.

4. Conclusion

The study of China’s economic sustainability in the context of global economic turbulence demonstrates that the country is successfully addressing the challenges of external instability through the diversification of its economy and strategic investment in high-tech sectors. Despite external pressures and

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Feng, Q., Dmitriev, N., Kryzhko, D., Kuporov, Yu.

internal contradictions, China retains significant resources and potential for future growth. The correlation analyses identified several factors influencing economic stability, including the role of technology,

international trade, foreign direct investment, and public policy. Key macroeconomic indicators, such

as GDP, GNI per capita, and external debt, are closely linked to socio-economic factors. However,

challenges remain, including managing external debts, reducing poverty, and addressing environmental

issues, which require further analysis and tailored policies. To maintain sustainable economic growth, it

is essential for China to continue implementing structural reforms and enhancing innovation efforts. Additionally, it is crucial to focus on the development of domestic markets and improving the living standards of the population, as these factors can serve as a foundation for long-term stability and prosperity.

In light of the current global uncertainties and shifts in the international economic landscape, further

research efforts should be directed towards assessing the effects of economic and political developments

on China’s economy, as well as identifying internal capacities for adapting to these changes.

Acknowledgements

The research is financed as part of the project “Development of a methodology for instrumental

base formation for analysis and modelling of the spatial socio-economic development of systems based

on internal reserves in the context of digitalisation” (FSEG-2023-0008).

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The article was submitted 08.06.2024, approved after reviewing 06.07.2024, accepted for publication 20.07.2024.

Статья поступила в редакцию 08.06.2024, одобрена после рецензирования 06.07.2024, принята к

публикации 20.07.2024.

About authors:

1. Qihang Feng, researcher at the Polytech-Invest Laboratory, Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russia. [email protected]

2. Nikolay Dmitriev, PhD in Economics, Peter the Great St. Petersburg Polytechnic University, St. Petersburg,

Russia https://orcid.org/0000-0003-0282-1163, [email protected]

3. Darya Kryzhko, researcher, Peter the Great St. Petersburg Polytechnic University, Saint Petersburg, Russia.

https://orcid.org/0000-0002-7006-6828, [email protected]

4. Yuriy Kuporov, PhD in Economics, Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russia. https://orcid.org/0000-0003-2237-9990, [email protected]

Информация об авторах:

1. Цихан Фэн, младший научный сотрудник научно-исследовательской лаборатории «Политех-Инвест»,

Санкт-Петербургский политехнический университет Петра Великого, Санкт-Петербург, Россия.

[email protected]

2. Николай Дмитриев, к.э.н., Санкт-Петербургский политехнический университет Петра Великого, СанктПетербург, Россия. https://orcid.org/0000-0003-0282-1163, [email protected]

3.

Дарья

Крыжко,

ассистент

Высшей

инженерно-экономической

школы,

Петербургский политехнический университет Петра Великого, Санкт-Петербург,

https://orcid.org/0000-0002-7006-6828, [email protected]

СанктРоссия.

4. Юрий Купоров, к.э.н., доцент, Санкт-Петербургский политехнический университет Петра Великого,

Санкт-Петербург, Россия. https://orcid.org/0000-0003-2237-9990, [email protected]

Sustain. Dev. Eng. Econ. 2024, 2, 4. https://doi.org/10.48554/SDEE.2024.2.4

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