Economic Growth: Advances in Analysis Methodologies and Technologies (SpringerBriefs in Applied Sciences and Technology) 3031383621, 9783031383625

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SpringerBriefs in Applied Sciences and Technology Vitor Joao Pereira Domingues Martinho

Economic Growth: Advances in Analysis Methodologies and Technologies

SpringerBriefs in Applied Sciences and Technology

SpringerBriefs present concise summaries of cutting-edge research and practical applications across a wide spectrum of fields. Featuring compact volumes of 50 to 125 pages, the series covers a range of content from professional to academic. Typical publications can be: • A timely report of state-of-the art methods • An introduction to or a manual for the application of mathematical or computer techniques • A bridge between new research results, as published in journal articles • A snapshot of a hot or emerging topic • An in-depth case study • A presentation of core concepts that students must understand in order to make independent contributions SpringerBriefs are characterized by fast, global electronic dissemination, standard publishing contracts, standardized manuscript preparation and formatting guidelines, and expedited production schedules. On the one hand, SpringerBriefs in Applied Sciences and Technology are devoted to the publication of fundamentals and applications within the different classical engineering disciplines as well as in interdisciplinary fields that recently emerged between these areas. On the other hand, as the boundary separating fundamental research and applied technology is more and more dissolving, this series is particularly open to trans-disciplinary topics between fundamental science and engineering. Indexed by EI-Compendex, SCOPUS and Springerlink.

Vitor Joao Pereira Domingues Martinho

Economic Growth: Advances in Analysis Methodologies and Technologies

Vitor Joao Pereira Domingues Martinho Agricultural School (ESAV) CERNAS-IPV Research Centre Polytechnic Institute of Viseu (IPV) Viseu, Portugal

ISSN 2191-530X ISSN 2191-5318 (electronic) SpringerBriefs in Applied Sciences and Technology ISBN 978-3-031-38362-5 ISBN 978-3-031-38363-2 (eBook) https://doi.org/10.1007/978-3-031-38363-2 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Acknowledgements

This work is funded by National Funds through the FCT—Foundation for Science and Technology, I.P., within the scope of the project Refa UIDB/00681/2020. Furthermore, we would like to thank the CERNAS Research Centre and the Polytechnic Institute of Viseu for their support. I would also like to thank all those who have contributed in some way to this work. A special thanks to my wife Lúcia Domingues Martinho and my two daughters Inês Domingues Martinho e Isabel Domingues Martinho.

v

About This Book

This book analyses the economic growth frameworks worldwide and assesses interrelationships between economic evolution, life cycle sustainability assessment approaches and new technologies in the framework of the digital transition. In other words, it intends to bring more insights into the world’s economic dynamics and sustainability dimensions. Additionally, it aims to highlight how life cycle methodologies and artificial intelligence can better support different actors for more sustainable economic growth. Readers of the book benefit, for example, from diverse perspectives on the contributions of evaluation methodologies and digital technologies to more sustainable economic growth. This is important, namely for the students, policymakers and public institutions. Economic growth will be analysed using the concepts of sigma and beta convergence from Neoclassical Theory and the Verdoorn– Kaldor law of Keynesian developments. For sustainability assessments, the methodologies associated with social life cycle assessment and life cycle cost analysis will be considered. In the context of digital technologies, special emphasis will be given to artificial intelligence approaches.

vii

Contents

1

2

3

4

Economic Growth: Sigma and Beta Convergence Processes Worldwide . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Data Analysis and Sigma Convergence . . . . . . . . . . . . . . . . . . . . . . 1.3 Results for the Beta Convergence . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4 Discussion and Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1 1 2 11 17 18

Clubs of Convergence: Insights from the Main Groups of Countries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Sigma Convergence and Catch-Up Rates . . . . . . . . . . . . . . . . . . . . 2.3 Findings for Cluster Analysis and Beta Convergence . . . . . . . . . . 2.4 Discussion and Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

21 21 22 29 34 35

World Trends: Differences and Similitudes Between Absolute and Conditional Convergence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Sigma Convergence and Data Analysis . . . . . . . . . . . . . . . . . . . . . . 3.3 Results for Conditional Beta Convergence with Panel Data . . . . . 3.4 Discussion and Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

37 37 38 46 46 50

Constant, Decreasing or Increasing Returns to Scale: Evidence from the Verdoorn and Kaldor Laws . . . . . . . . . . . . . . . . . . . 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Data Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 Results for the Kaldor Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4 Discussion and Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

53 53 54 63 63 67

ix

x

5

6

Contents

Circular and Cumulative Processes in Economic Growth: The Importance of the External Demand . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Data Assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3 Findings Considering the Developments of Thirlwall and Those of the New Economic Geography . . . . . . . . . . . . . . . . . 5.4 Discussion and Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Interrelationships Between Economic Growth and Sustainability: Highlights from the Literature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2 Bibliometric Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3 Insights from the Literature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4 Discussion and Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

69 69 70 76 76 79

81 81 83 89 89 91

7

Sustainable Development: Contributions from Life Cycle Cost Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 7.2 Bibliographic Data Assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94 7.3 Contributions from the Literature for the Topics Analysed . . . . . . 102 7.4 Discussion and Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104

8

Social Life Cycle Assessment: Relationships with the Economic Growth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2 Co-occurrence, Bibliographic Coupling and Co-citation Links Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.3 A Literature Review Based on Bibliographic Data . . . . . . . . . . . . . 8.4 Discussion and Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

9

Machine and Deep Learning: Their Roles in the Context of the Economic Growth Processes and Sustainability Assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.2 Keywords, Countries, Organisations, Cited Authors, Documents and Cited Sources as Items . . . . . . . . . . . . . . . . . . . . . . 9.3 Literature Review Based on Bibliometric Information . . . . . . . . . 9.4 Discussion and Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

107 107 108 115 115 117

119 119 120 128 129 130

Contents

10 Economic Growth, Sustainability Assessment and Artificial Intelligence: Combinations Among These Three Dimensions . . . . . . 10.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.2 Metrics from the Scopus Database . . . . . . . . . . . . . . . . . . . . . . . . . . 10.3 Predict European Farming Costs with Machine Learning Approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.4 Discussion and Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

xi

133 133 134 140 141 143

About the Author

Vitor Joao Pereira Domingues Martinho is Coordinator Professor with Habilitation at the Polytechnic Institute of Viseu, Portugal, and holds a Ph.D. in Economics from the University of Coimbra, Portugal. He was President of the Scientific Council, President of the Directive Council and President of the Agricultural Polytechnic School of Viseu, Portugal, from 2006 to 2012. He participated in various technical and scientific events nationally and internationally, has published several technical and scientific papers, is referee of some scientific and technical journals and participates in the evaluation of national and international projects. Occasionally, he is Guest Professor at Portuguese Higher Education Institutions and participates in several technical and scientific projects.

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Chapter 1

Economic Growth: Sigma and Beta Convergence Processes Worldwide

Abstract The economic growth process worldwide has been subject to several disturbances with different origins. These disruptions have consequences on the economic dynamics of the countries with impacts on human living conditions and the efforts for more sustainable development. These old and new challenges call for contributions that allow for a better understanding of the current contexts, namely in terms of economic evolution and how the interactions between diverse nations have been affected. In this framework, this chapter intends to assess the convergence process worldwide, considering statistical information from the World Bank for the gross domestic product (GDP) per capita and the last decades. To achieve these objectives, the developments from the Neoclassical Theory were considered, specifically those related to the sigma and beta convergence approaches. To better support the methodologies taken into account, structural breaks were also tested. The findings obtained highlight the differences in the dynamics of economic growth in distinct sub-periods over the last decades. Keywords World bank · Structural breaks · Panel data · Neoclassical theory

1.1 Introduction Economic growth worldwide depends on several factors, some of them are already clearly identified in the scientific literature, such as trade. Trade openness may contribute, in certain conditions, to reduce poverty [1]. A concern of the scientific community is to identify patterns for this economic growth and try to understand if these frameworks present dynamics of convergence, or divergence, between the regions and countries. Sigma and beta convergence concepts are among the approaches considered to assess these contexts. The GPD per capita is the variable often considered to analyse the trends of convergence. Nonetheless, these sigma and beta concepts have been also used to assess the convergence in other variables [2] and indicators [3]. Sometimes these approaches are combined with different models and methodologies [4], © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 V. J. P. D. Martinho, Economic Growth: Advances in Analysis Methodologies and Technologies, SpringerBriefs in Applied Sciences and Technology, https://doi.org/10.1007/978-3-031-38363-2_1

1

2

1 Economic Growth: Sigma and Beta Convergence Processes Worldwide

including spatial autocorrelation methods [5], welfare indices [6] new techniques [7] and environmental efficiency [8]. This convergence analysis has been carried out in some cases for the whole economy and in other circumstances by sectors [9], branches [10] and activities [11]. In these phenomena of convergence, the policies designed by the governments and the international organisations have their impacts. This is particularly relevant in economic unions, such as the European Union, where European policies have great influence on the socioeconomic dynamics of the member-states and the respective processes of convergence [12]. This is visible, for example, in the frameworks of European higher education [13]. But, the economic policies and the respective funds’ transfer of the governments worldwide are also crucial to improve the development in regions with greater difficulties [14]. External shocks, such as the financial crisis of 2008/2009, also have their impacts on the economic growth dynamics and, consequently, on the processes of convergence, or divergence, between regions and countries [15]. Another issue in these economic growth dynamics is the discussion about whether convergence is absolute or conditioned to specific particularities of regions and countries, namely in terms of human capital [16]. The signs of convergence, or divergence, depend too on the periods of assessment considered [17] and on the approaches taken into account [18]. Considering the frameworks described before, this research aims to analyse the process of convergence worldwide, considering the Neoclassical Theory developments [19] and the sigma and beta convergence concepts [20, 21]. To achieve these objectives, statistical information was considered for the GDP per capita from the World Bank database [22]. Some countries were removed because of the lack of data. The speed of convergence was calculated following the study of Tondl [23]. For the regressions and statistical analyses carried out, panel data approaches were taken into account [24–26], as well as the procedures proposed by the Stata software [27–29]. For a better understanding of the contexts analysed, unknown breaks were tested through the Quandt likelihood ratio (QLR) test [30–32]. For the literature review, the Web of Science Core Collection database was consulted [33].

1.2 Data Analysis and Sigma Convergence In general, the GDP per capita increased continuously worldwide over the period 2000–2021, with the exception of the years associated with the world shocks related to the global financial crisis and the pandemic COVID-19 (Fig. 1.1). Figure 1.2 reveals signs of structural breaks (QLR test) for this variable in 2018. These findings, for this time series, need to be analysed deeper in future research.

1.2 Data Analysis and Sigma Convergence 18000 16000 14000 12000 10000 8000 6000 4000 2000 0

Fig. 1.1 Evolution of the world GDP per capita, over the period 2000–2021

Fig. 1.2 Testing for structural breaks in the world GDP per capita, over the period 2000–2021

3

4

1 Economic Growth: Sigma and Beta Convergence Processes Worldwide

The sigma convergence for the world GDP per capita, assessed through the coefficient of variation, shows trends of convergence until 2009 and after until 2020 (Table 1.1). These outcomes highlight the disruptions promoted by the external shocks associated with the financial crisis and COVID-19 in the convergence tendencies verified over the period 2000–2021 for this variable. Figure 1.3 presents evidence of structural breaks for the coefficient of variation of the world GDP per capita in the year 2016. Luxembourg, Macao SAR (China), Qatar, Bermuda, Singapore, United Arab Emirates, San Marino, Brunei Darussalam, Switzerland, Ireland and Norway are some of the countries with the highest averages, across the period 2000–2021, for the world GDP per capita (Table 1.2). Romania, Argentina, Chile, Malaysia, Kazakhstan, Antigua and Barbuda, Uruguay, Mexico, Bulgaria and Mauritius of some of the nations with intermediate averages for the GDP per capita in the period considered (Table 1.3). The following countries are some of those with the lowest averages (Table 1.4): Eswatini; Vietnam; Bolivia; Angola; Philippines; Morocco; Samoa; Cabo Verde; and Tonga. Table 1.1 Sigma convergence (coefficient of variation) for the world GDP per capita, over the period 2000–2021

Time

Coefficient of variation

2000

1.171

2001

1.168

2002

1.162

2003

1.157

2004

1.159

2005

1.143

2006

1.134

2007

1.118

2008

1.092

2009

1.077

2010

1.090

2011

1.104

2012

1.097

2013

1.104

2014

1.085

2015

1.043

2016

1.035

2017

1.033

2018

1.031

2019

1.021

2020

1.010

2021

1.027

1.2 Data Analysis and Sigma Convergence

5

Fig. 1.3 Testing for structural breaks in the coefficient of variation of the world GDP per capita over the period 2000–2021 Table 1.2 Highest averages, across the period 2000–2021, for the world GDP per capita

Country name

Average GDP per capita

Luxembourg

111,617.664

Macao SAR, China

98,170.132

Qatar

93,934.835

Bermuda

85,359.243

Singapore

78,565.758

United Arab Emirates

75,330.792

San Marino

69,579.896

Brunei Darussalam

65,979.936

Switzerland

65,749.360

Ireland

63,773.555

Norway

62,000.141

Kuwait

60,035.325

USA

56,085.468

Denmark

52,463.470

Netherlands

52,037.626

Austria

51,627.298 (continued)

6 Table 1.2 (continued)

1 Economic Growth: Sigma and Beta Convergence Processes Worldwide

Country name

Average GDP per capita

Hong Kong SAR, China

50,332.500

Iceland

49,777.222

Germany

48,268.091

Sweden

47,840.682

Belgium

47,837.581

Bahrain

47,498.101

Canada

45,643.907

Finland

45,547.704

Australia

45,045.684

UK

43,262.273

Saudi Arabia

43,183.394

France

42,606.178

Italy

42,529.247

Japan

38,909.262

New Zealand

38,720.641

Spain

37,513.140

Aruba

37,363.348

Cyprus

37,265.072

Oman

36,819.316

Bahamas, The

35,143.348

Israel

34,717.055

Malta

34,687.616

Korea, Rep

34,220.048

Czechia

33,567.262

Puerto Rico

33,415.863

Slovenia

33,289.052

Portugal

31,642.689

Greece

31,313.650

Estonia

28,611.815

Curacao

26,499.493

Lithuania

26,487.836

Hungary

25,991.403

Libya

25,821.945

St. Kitts and Nevis

24,945.797

Trinidad and Tobago

24,941.329 (continued)

1.2 Data Analysis and Sigma Convergence Table 1.2 (continued)

Table 1.3 Intermediate averages, across the period 2000–2021, for the world GDP per capita

Country name

7

Average GDP per capita

Slovak Republic

24,852.335

Croatia

24,819.473

Poland

24,303.782

Equatorial Guinea

23,856.948

Latvia

23,464.871

Russian Federation

23,100.674

Seychelles

23,078.993

Panama

22,658.165

Turkiye

21,788.406

Country name

Average GDP per capita

Romania

21,418.236

Argentina

21,355.184

Chile

21,247.325

Malaysia

21,224.837

Kazakhstan

20,351.568

Antigua and Barbuda

19,853.073

Uruguay

18,804.526

Mexico

18,603.244

Bulgaria

17,833.496

Mauritius

17,552.297

Costa Rica

16,898.748

Suriname

16,749.352

Montenegro

16,685.437

Maldives

16,533.940

Lebanon

15,980.462

Barbados

15,797.152

Palau

15,780.378

Belarus

15,521.140

Gabon

14,576.129

Serbia

14,367.630

Thailand

14,174.396

St. Lucia

14,008.129

Iran, Islamic Rep

13,936.516 (continued)

8 Table 1.3 (continued)

1 Economic Growth: Sigma and Beta Convergence Processes Worldwide

Country name

Average GDP per capita

Brazil

13,840.412

North Macedonia

13,376.690

Botswana

13,308.222

Dominican Republic

13,132.279

Grenada

13,060.749

South Africa

12,970.939

Colombia

12,123.288

St. Vincent and the Grenadines

11,872.993

Ukraine

11,639.821

Azerbaijan

11,360.428

Dominica

11,348.761

Paraguay

11,181.649

Bosnia and Herzegovina

10,837.181

Algeria

10,798.603

Guyana

10,792.768

Fiji

10,702.146

Ecuador

10,389.241

Jordan

10,363.122

Albania

10,339.423

Georgia

10,224.804

Sri Lanka

9953.539

Jamaica

9901.550

Peru

9844.463

Tunisia

9809.547

China

9597.875

Armenia

9576.679

Egypt, Arab Rep.

9391.740

Belize

9109.654

Moldova

9073.832

Namibia

8947.628

Turkmenistan

8820.440

Indonesia

8597.434

Iraq

8503.629

Mongolia

8468.287

Bhutan

7913.419

El Salvador

7674.463

Guatemala

7532.328

1.2 Data Analysis and Sigma Convergence Table 1.4 Lowest averages, across the period 2000–2021, for the world GDP per capita

9

Country name

Average GDP per capita

Eswatini

7210.458

Vietnam

6791.206

Bolivia

6764.556

Angola

6762.031

Philippines

6253.417

Morocco

6180.713

Samoa

5530.274

Cabo Verde

5487.302

Tonga

5473.843

West Bank and Gaza

5292.356

Lao PDR

5186.467

Uzbekistan

5160.212

Honduras

4854.734

Mauritania

4851.732

Nicaragua

4845.330

Sudan

4540.024

Nigeria

4517.567

Congo, Rep.

4451.420

India

4445.906

Pakistan

4227.881

Kyrgyz Republic

4204.969

Marshall Islands

4069.346

Cote d’Ivoire

4019.905

Ghana

3981.348

Kenya

3844.331

Tuvalu

3804.400

Bangladesh

3701.081

Micronesia, Fed. Sts

3427.823

Cameroon

3386.998

Sao Tome and Principe

3380.537

Papua New Guinea

3321.136

Comoros

3055.499

Haiti

3016.282

Timor-Leste

2986.536

Senegal

2933.490

Cambodia

2923.932 (continued)

10 Table 1.4 (continued)

1 Economic Growth: Sigma and Beta Convergence Processes Worldwide

Country name

Average GDP per capita

Zambia

2900.240

Myanmar

2884.235

Vanuatu

2880.322

Nepal

2820.853

Benin

2752.084

Tajikistan

2501.292

Solomon Islands

2322.261

Lesotho

2258.526

Zimbabwe

2115.046

Gambia, The

2025.691

Guinea

2024.442

Tanzania

2013.191

Mali

1973.348

Kiribati

1956.277

Uganda

1830.118

Afghanistan

1790.624

Togo

1754.049

Guinea-Bissau

1720.061

Burkina Faso

1700.136

Madagascar

1523.739

Chad

1521.816

Rwanda

1520.877

Liberia

1466.341

Sierra Leone

1432.730

Ethiopia

1373.745

Malawi

1297.930

Niger

1048.328

Mozambique

1037.332

Central African Republic

959.173

Congo, Dem. Rep.

879.806

Burundi

792.734

There are signs of structural breaks for the world GDP per capita growth rates, over 2000–2021, in 2016 (Fig. 1.4). These results for the structural breaks with the QLR test need further assessment in future research.

1.3 Results for the Beta Convergence

11

Fig. 1.4 Testing for structural breaks in the world GDP per capita growth rate over the period 2000–2021

1.3 Results for the Beta Convergence Considering the results obtained in the previous section for the years 2009 and 2020 (with the data analysis and the sigma convergence) and for the years 2016 and 2018 (with the QLR test), a regression with the convergence model was carried out taking into account dummies variables for these years. These findings for the model with panel data are those presented in Table 1.5. The results found confirm disturbances in 2009 and 2020, but not in 2016 and 2018. On the other hand, there is no beta convergence worldwide for the period 2000–2021, considering that the coefficient of convergence has no statistical significance. There is not also evidence of beta convergence for the period 2000–2008 (Table 1.6) and for the period 2009–2019 (Table 1.7). The signs of beta convergence appear in the period 2009–2020 (Table 1.8) and 2009–2021 (Table 1.9). In these models, the dependent variable is the GDP per capita growth rate.

0.006 0.006

0.000

− 0.081*

Dummy2018

Dummy2020

Note *Statistically significant at 1%

0.016

0.006

− 0.004

Dummy2016

0.048*

0.006

− 0.041*

Dummy2009

Constant

0.002

− 0.003

Logarithm of GDPpc lagged

Prais–Winsten regression, correlated panels corrected standard errors (PCSEs)

Standard error

Coefficient

Independent variable

Approach

3.000

0.003

0.000

0.934

− 13.990

0.516

− 0.080

0.000

0.139

P > |z|

− 0.650

− 7.150

− 1.480

Z

Beta convergence (%) 138.340*

Hausman test 52.501*

Pesaran’s test of cross-sectional independence

Table 1.5 Results of the beta convergence for the world GDP per capita with panel data, over the period 2000–2021

28,025.920*

13.621*

Modified Wald test Wooldridge test for for groupwise autocorrelation heteroskedasticity

12 1 Economic Growth: Sigma and Beta Convergence Processes Worldwide

Constant

Logarithm of GDPpc lagged

0.003

0.044** 0.022

− 0.002

2.000

− 0.680

Independent Coefficient Standard Z variable error

Note *Statistically significant at 1%; **Statistically significant at 5%

Prais–Winsten regression, correlated panels corrected standard errors (PCSEs)

Approach

0.045

0.497

23.370*

65.550*

61,948.510*

10.337*

P > |z| Beta Hausman Pesaran’s test of Modified Wald Wooldridge convergence test cross-sectional test for groupwise test for (%) independence heteroskedasticity autocorrelation

Table 1.6 Results of the beta convergence for the world GDP per capita with panel data, over the period 2000–2008

1.3 Results for the Beta Convergence 13

Constant

Logarithm of GDPpc lagged

0.002

0.036** 0.014

− 0.002

2.550

− 1.090

Independent Coefficient Standard Z variable error

0.011

0.275

138.160* 28.481*

140,000.000*

3.715***

P > |z| Beta Hausman Pesaran’s test of Modified Wald Wooldridge convergence test cross-sectional test for groupwise test for (%) independence heteroskedasticity autocorrelation

Note *Statistically significant at 1%; **Statistically significant at 5%; ***Statistically significant at 10%

Prais–Winsten regression, correlated panels corrected standard errors (PCSEs)

Approach

Table 1.7 Results of the beta convergence for the world GDP per capita with panel data, over the period 2009–2019

14 1 Economic Growth: Sigma and Beta Convergence Processes Worldwide

Constant

Logarithm of GDPpc lagged

0.062*

− 0.006*

0.015

0.002

4.060

− 3.030

Independent Coefficient Standard Z variable error

Note *Statistically significant at 1%; **Statistically significant at 5%

Prais–Winsten regression, correlated panels corrected standard errors (PCSEs)

Approach

0.000

0.002

0.577

205.260* 142.204*

71,213.190*

4.642**

P > |z| Beta Hausman Pesaran’s test of Modified Wald Wooldridge convergence test cross-sectional test for groupwise test for (%) independence heteroskedasticity autocorrelation

Table 1.8 Results of the beta convergence for the world GDP per capita with panel data, over the period 2009–2020

1.3 Results for the Beta Convergence 15

Constant

Logarithm of GDPpc lagged

0.049*

− 0.004*

0.009

0.001

5.500

− 3.600

Independent Coefficient Standard Z variable error

Note *Statistically significant at 1%

Prais–Winsten regression, correlated panels corrected standard errors (PCSEs)

Approach

0.000

0.000

0.359

204.480* 147.280*

35,765.880*

14.069*

P > |z| Beta Hausman Pesaran’s test of Modified Wald Wooldridge convergence test cross-sectional test for groupwise test for (%) independence heteroskedasticity autocorrelation

Table 1.9 Results of the beta convergence for the world GDP per capita with panel data, over the period 2009–2021

16 1 Economic Growth: Sigma and Beta Convergence Processes Worldwide

1.4 Discussion and Conclusions

17

1.4 Discussion and Conclusions This research aimed to analyse the economic growth dynamics worldwide. For that, the convergence tendencies of the world GDP per capita over the period 2000–2021 were assessed, considering data from the World Bank. This statistical information was analysed through the models of the Neoclassical Theory and the respective concepts of sigma and beta convergence. The possibility of existing structural breaks in the time series was also tested with the QLR test. In the regressions, the methodologies associated with the panel data were taken into account. One of the focuses of the scientific literature about economic growth is to understand if the economic growth worldwide follows convergence/divergence trends among regions inside the countries, nations and parts of the world. Sigma and beta convergence are concepts often present in the studies related to the convergence assessments and a relevant part of the literature includes in the research the GDP per capita. Nonetheless, other variables and methodologies have been considered. The policy instruments, the respective support programmes and the international organisations play here a fundamental role in territorial cohesion worldwide. The external shocks, particularly those that occurred with the global financial crisis and the COVID-19 pandemic, had their impacts on the dynamics of economic growth. The impacts from the external shocks verified in the last decades are visible in the data analysis and the sigma convergence assessment for the entire sample considered. In any case, the impacts on the reduction of the GDP per capita seem to be greater with the pandemic than with the global financial crisis; however, the consequent signs of divergence look proportionally lower in the COVID-19 context. Of course, more data about the consequences of the pandemic are needed, but from this analysis, it appears that the implications of the global financial crisis had proportionally a greater impact on the divergence process worldwide. To test the potential existence of structural breaks in the times series, a previous regression with variable dummies and panel data was carried out that confirmed the presence of breaks in 2009 and 2020. Taking into account, these findings several regressions were performed for different sub-periods that revealed the existence of convergence tendencies in 2009–2020 and 2009–2021. In terms of practical implications, more statistical information is needed, particularly for more recent years, to better assess the impacts of the COVID-19 pandemic on the economic growth dynamics, but seems that the disruptions promoted by the global financial crisis were proportionally greater than by the pandemic. In any case and despite the need for more assessments to confirm these findings, these two external shocks had impacts on the evolution of the GDP per capita worldwide and consequently on the dynamics of economic growth. These results suggest the need for adjusted policies to deal with the respective consequences on the population’s well-being. For future research, it could be interesting to investigate the possibility of the existence of convergence clubs between countries.

18

1 Economic Growth: Sigma and Beta Convergence Processes Worldwide

Acknowledgements This work is funded by National Funds through the FCT—Foundation for Science and Technology, I.P., within the scope of the project Refa UIDB/00681/2020. Furthermore we would like to thank the CERNAS Research Centre and the Polytechnic Institute of Viseu for their support.

References 1. Y. Li, J. Wang, K. Oh, Effects of globalization on the convergence of poverty levels among Asian countries. Int. Econ. J. 36, 193 (2022) 2. M. Abdollahian, Z. Yang, Towards trade equalisation: a network perspective on trade and income convergence across the twentieth century. New Polit. Econ. 19, 601 (2014) 3. N. Wang, X. Fu, S. Wang, H. Yang, Z. Li, Convergence characteristics and distribution patterns of residential electricity consumption in China: an urban-rural gap perspective. Energy 254 (2022) 4. A. Abdullah, H. Doucouliagos, E. Manning, Are regional incomes in Malaysia converging? Pap. Reg. Sci. 94, S69 (2015) 5. V. Balash, O. Balash, A. Faizliev, E. Chistopolskaya, Economic growth patterns: spatial econometric analysis for Russian regions. Information 11 (2020) 6. C. Mendez, Lack of global convergence and the formation of multiple welfare clubs across countries: an unsupervised machine learning approach. Economies 7 (2019) 7. M. Gonzalez, M. Navarro, Patterns of convergence in Spanish regions: an application of Phillips-Sul’s methodology. Revista de Estudios Regionales 165 (2017) 8. J. Yang, R. Zou, J. Cheng, Z. Geng, Q. Li, Environmental technical efficiency and its dynamic evolution in China? Industry: a resource endowment perspective. Resour. Policy 82 (2023) 9. G. Butnaru, M. Mironiuc, C. Huian, A. Haller, Analysis of economic growth in tourism under the impact of terrorism and of the waves of refugees. Amfiteatru Econ. 20, 885 (2018) 10. E. Felice, The roots of a dual equilibrium: GDP, productivity, and structural change in the Italian regions in the long run (1871–2011). Eur. Rev. Econ. Hist. 23, 499 (2019) 11. A. Shingal, The services sector in India’s States: a tale of growth convergence and trade. World Econ. 37, 1773 (2014) 12. A. Iancu, Economic convergence applications—second part. Romanian J. Econ. Forecast. 8, 24 (2007) 13. T. Agasisti, C. Perez-Esparrells, G. Catalano, S. Morales, Is expenditure on higher education per student converging across EU-15 Countries? Stud. High. Educ. 37, 235 (2012) 14. R. Ginanjar, V. Zahara, S. Suci, I. Suhendra, Human development convergence and the impact of funds transfer to regions: a dynamic panel data approach. J. Asian Fin. Econ. Bus. 7, 593 (2020) 15. S. Nagy, D. Siljak, Is the European union still a convergence machine? Acta Oeconomica 72, 47 (2022) 16. M. Herbst, P. Wojcik, Economic growth and income divergence in the polish subregions: some determinants and spatial patterns. Ekonomista 175 (2012) 17. J. Martinez-Carrion, R. Maria-Dolores, Regional inequality and convergence in Southern Europe. Evidence from height in Italy and Spain, 1850–2000. Revista de Economia Aplicada 25, 75 (2017) 18. Y. Turganbayev, Regional convergence in Kazakhstan. Post-Communist Econ. 28, 314 (2016) 19. R.M. Solow, A contribution to the theory of economic growth. Q. J. Econ. 70, 65 (1956) 20. R.J. Barro, X. Sala-I-Martin, Convergence across states and regions. Brook. Pap. Econ. Act. 1991, 107 (1991) 21. X.X. Sala-i-Martin, Regional cohesion: evidence and theories of regional growth and convergence. Eur. Econ. Rev. 40, 1325 (1996)

References

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22. World Bank, World Bank Open Data. https://data.worldbank.org 23. G. Tondl, The Changing Pattern of Regional Convergence in Europe. Robert Schuman Centre (EUI), RSC No. 97/53, 130688 24. N. Islam, Growth empirics: a panel data approach. Q. J. Econ. 110, 1127 (1995) 25. D. Hoechle, Robust standard errors for panel regressions with cross-sectional dependence. Stand. Genomic Sci. 7, 281 (2007) 26. O. Torres-Reyna, Panel Data Analysis Fixed and Random Effects Using Stata (v. 6.0) (2007) 27. StataCorp, Stata 15 Base Reference Manual (2017) 28. StataCorp, Stata Statistical Software: Release 15 (2017) 29. Stata, Statistical Software for Data Science|Stata. https://www.stata.com/ 30. R.E. Quandt, Tests of the hypothesis that a linear regression system obeys two separate regimes. J. Am. Stat. Assoc. 55, 324 (1960) 31. H.-J. Kim, D. Siegmund, The likelihood ratio test for a change-point in simple linear regression. Biometrika 76, 409 (1989) 32. D.W.K. Andrews, Tests for parameter instability and structural change with unknown change point. Econometrica 61, 821 (1993) 33. Web of Science, Web of Science Core Collection. https://www.webofscience.com/wos/woscc/ basic-search

Chapter 2

Clubs of Convergence: Insights from the Main Groups of Countries

Abstract The dynamics of economic growth present differences in several sub-periods of the last decades and specific world regions. This evidence for groups of countries seems to show signs of clubs of convergence where the economic growth in some groups follow a trend that is distinct from the tendencies presented in other contexts. A deeper interpretation of these scenarios may bring relevant contributions to identify weaknesses and strengths, particularly for economies with greater development difficulties. In this context, it is aimed in this chapter to highlight the dynamics of convergence in the world regions and a specific sub-period over the last decades. For that, statistical information from the World Bank was considered. These data were analysed through approaches related to sigma and beta convergence. Panel data methodologies were considered for the regressions carried out and, for a better understanding of these processes, catch-up rates were also calculated. The results found show constraints verified in some world regions that may provide relevant insights for future research and policy design. Keywords Gross domestic product (GDP) per capita · World regions · Sigma and beta convergence · Catching-up processes

2.1 Introduction International trade plays a fundamental role in the performance of the economic growth of countries and regions, including in the processes of convergence, or divergence, between countries, particularly in the European Union [1]. These processes of convergence worldwide involve phenomena of catching-up among poor and rich countries or regions. Sometimes economies converge to the same steady state and, in other contexts, different economies with different characteristics converge to different steady states, which reveals the existence of convergence clubs. In these circumstances, some economies converge to the same steady state which is different from those to which other economies eventually converge. This presence of convergence clubs was found, © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 V. J. P. D. Martinho, Economic Growth: Advances in Analysis Methodologies and Technologies, SpringerBriefs in Applied Sciences and Technology, https://doi.org/10.1007/978-3-031-38363-2_2

21

22

2 Clubs of Convergence: Insights from the Main Groups of Countries

for example, in the African framework [2] and nations [3]. These clubs were too found for the human capital in China [4] and in the contexts of East Asia [5]. The assessments performed to show if the economies converge, or divergence, have been carried out at national and regional levels [6], and for the different world contexts, including between members of regional economic integration organisations, such as the Association of Southeast Asian Nations (ASEAN) [7]. These analyses also have considered the new approaches developed by the scientific community [8]. The spatial models are among the recent developments to extend the original methodologies considered for the economic growth investigation [9]. The consideration of spatial dependence may be particularly important in the convergence analysis [10]. These dynamics of economic growth and the associated processes of convergence, or divergence, are impacted by the world events, such as the global financial crisis, favouring some economies and harming others [11]. Considering these contexts, this chapter intends to analyse the processes of convergence worldwide, considering the developments from the theory [12], the sigma and beta approaches [13, 14] and the contributions of Islam [15] for the models with panel data. The possibility of the existence of convergence clubs [16, 17] was assessed, testing the sigma and beta convergence in groups of countries created through cluster analysis. For a deeper analysis of the processes of convergence, catch-up rates were calculated [18], as well as the speed of convergence [19]. For the panel data regressions, the Hoechle [20], Torres-Reyna [21] and Stata software [22–24] procedures were taken into account. For the literature survey, the Web of Science [25] database was used. The data were obtained from the World Bank [26]. Some nations were excluded due to the availability of statistical information.

2.2 Sigma Convergence and Catch-Up Rates The results for the sigma convergence of the world GDP per capita, over the period 2009–2020, show signs of divergence among 2009 and 2011, some convergence in 2012, divergence in 2013 and after convergence trends until 2020 (Table 2.1). In any case, there is evidence of a strong contraction of the GDP per capita in 2020, as a consequence of the contexts associated with the COVID-19 pandemic (Fig. 2.1). The years 2015 and 2020 present the lowest values for catch-up rate means, revealing that, on average, the gap between each country and the world-weighted average decreased in these years (Fig. 2.2). Some of the countries with the highest catch-up rate averages, across the period taken into account (2009–2020), for the world GDP per capita are the following (Table 2.2): France; Sint Maarten (Dutch part); Australia; Ireland; Libya; Equatorial Guinea; Sweden; Germany; Canada; UK; Greece; and Italy. Azerbaijan, Afghanistan, Brazil, Luxembourg, Malawi, West Bank and Gaza, Mozambique, Congo (Dem. Rep.), Niger, Solomon Islands, Sierra Leone and Comoros are some of the nations with intermediate averages for the catch-up rates (Table 2.3). The lowest catch-up

2.2 Sigma Convergence and Catch-Up Rates Table 2.1 Sigma convergence (coefficient of variation) for the world GDP per capita, over the period 2009–2020

Fig. 2.1 GDP per capita averages, across world countries, over the period 2009–2020

23

Time

CV

2009

1.076

2010

1.085

2011

1.098

2012

1.091

2013

1.096

2014

1.077

2015

1.036

2016

1.028

2017

1.027

2018

1.025

2019

1.020

2020

1.009

22500 22000 21500 21000 20500 20000 19500

19000 18500 18000

rate averages appear, for example, in the following countries (Table 2.4): Bhutan; St. Kitts and Nevis; Indonesia; Croatia; Brunei Darussalam; Belarus; Vietnam; Bosnia and Herzegovina; Thailand; Moldova; and Chile. The catch-up rate averages are negative for the countries with the intermediate and lowest values, showing that the gap in the GDP per capita between the respective countries and the world-weighted average decreased in the period considered.

24

2 Clubs of Convergence: Insights from the Main Groups of Countries

Fig. 2.2 Catch-up rate averages, across world countries, for the GDP per capita, over the period 2009–2020

15 10 5 0 -5 -10

-15 -20 -25 -30 -35 -40 Table 2.2 Highest average catch-up rates, across the period 2009–2020, for the world GDP per capita

Country name

Average catch-up rates

France

91.076

Sint Maarten (Dutch part)

51.948

Australia

19.377

Ireland

18.628

Libya

16.001

Equatorial Guinea

14.307

Sweden

12.014

Germany

11.913

Canada

11.372

UK

10.582

Greece

10.031

Italy

8.750

Bahrain

8.689

The Bahamas

8.190

Hong Kong SAR, China

7.526

Iceland

6.667

Singapore

6.271

United Arab Emirates

5.832

Aruba

5.710

Denmark

5.561 (continued)

2.2 Sigma Convergence and Catch-Up Rates Table 2.2 (continued)

Country name

25

Average catch-up rates

Belgium

5.416

USA

4.507

Spain

4.454

Curacao

4.376

Netherlands

3.117

Turkmenistan

2.382

Trinidad and Tobago

2.103

Switzerland

1.562

Lebanon

1.492

Austria

1.276

Norway

1.130

Antigua and Barbuda

1.075

Barbados

0.946

Maldives

0.910

Cyprus

0.882

Puerto Rico

0.872

St. Lucia

0.836

Jordan

0.808

Argentina

0.795

Suriname

0.779

Dominica

0.608

Angola

0.438

Belize

0.403

Congo, Rep.

0.347

Sudan

0.226

Jamaica

0.214

South Africa

0.152

Iran, Islamic Rep.

0.097

Tunisia

0.088

Central African Republic

0.080

Haiti

0.068

Vanuatu

0.061

Madagascar

0.046

Chad

0.044

Burundi

0.041

The Gambia,

0.041 (continued)

26

2 Clubs of Convergence: Insights from the Main Groups of Countries

Table 2.2 (continued)

Country name

0.027

Algeria

0.025

Liberia

0.019

Micronesia, Fed. Sts.

0.015

Kiribati

0.008

Lesotho

Table 2.3 Intermediate average catch-up rates, across the period 2009–2020, for the world GDP per capita

Average catch-up rates

Cabo Verde

0.002

Guinea-Bissau

− 0.010

Mali

− 0.010

Country name

Average catch-up rates

Azerbaijan

− 0.012

Afghanistan

− 0.012

Brazil

− 0.013

Luxembourg

− 0.018

Malawi

− 0.019

West Bank and Gaza

− 0.026

Mozambique

− 0.028

Congo, Dem. Rep.

− 0.030

Niger

− 0.030

Solomon Islands

− 0.031

Sierra Leone

− 0.032

Comoros

− 0.034

Nigeria

− 0.035

Ecuador

− 0.039

Zambia

− 0.047

Honduras

− 0.060

Cameroon

− 0.061

Namibia

− 0.066

Uganda

− 0.071

Zimbabwe

− 0.095

Burkina Faso

− 0.095

Togo

− 0.098

Benin

− 0.098

Mauritania

− 0.104

Kyrgyz Republic

− 0.108

Senegal

− 0.109 (continued)

2.2 Sigma Convergence and Catch-Up Rates Table 2.3 (continued)

27

Country name

Average catch-up rates

Samoa

− 0.112

Tanzania

− 0.126

Rwanda

− 0.130

Papua New Guinea

− 0.134

Grenada

− 0.144

Nicaragua

− 0.151

Guinea

− 0.157

Gabon

− 0.179

Sao Tome and Principe

− 0.181

Pakistan

− 0.198

Ukraine

− 0.202

Kenya

− 0.202

Ethiopia

− 0.231

El Salvador

− 0.244

Iraq

− 0.254

Nepal

− 0.259

Qatar

− 0.269

Guatemala

− 0.271

Tajikistan

− 0.300

Tuvalu

− 0.302

Eswatini

− 0.317

Tonga

− 0.318

Bolivia

− 0.332

Mexico

− 0.332

Fiji

− 0.344

Cote d’Ivoire

− 0.355

Cambodia

− 0.378

St. Vincent and the Grenadines

− 0.387

Portugal

− 0.396

Ghana

− 0.402

Palau

− 0.427

Morocco

− 0.452

Timor-Leste

− 0.459

Egypt, Arab Rep.

− 0.489

Marshall Islands

− 0.497

India

− 0.519

Myanmar

− 0.537 (continued)

28

2 Clubs of Convergence: Insights from the Main Groups of Countries

Table 2.3 (continued)

Table 2.4 Lowest average catch-up rates, across the period 2009–2020, for the world GDP per capita

Country name

Average catch-up rates

Peru

− 0.539

Bangladesh

− 0.539

Philippines

− 0.556

Colombia

− 0.564

Botswana

− 0.572

Uzbekistan

− 0.632

Montenegro

− 0.710

Lao PDR

− 0.808

North Macedonia

− 0.853

Albania

− 0.857

Paraguay

− 0.887

Kosovo

− 0.910

Country name

Average catch-up rates

Bhutan

− 0.916

St. Kitts and Nevis

− 1.000

Indonesia

− 1.013

Croatia

− 1.096

Brunei Darussalam

− 1.136

Belarus

− 1.155

Vietnam

− 1.161

Bosnia and Herzegovina

− 1.189

Thailand

− 1.191

Moldova

− 1.199

Chile

− 1.214

Sri Lanka

− 1.236

Mongolia

− 1.250

Armenia

− 1.322

Serbia

− 1.325

Mauritius

− 1.400

Costa Rica

− 1.404

Georgia

− 1.448

Uruguay

− 1.574

Nauru

− 1.598

Dominican Republic

− 1.599

Russian Federation

− 1.868

Cayman Islands

− 1.890 (continued)

2.3 Findings for Cluster Analysis and Beta Convergence Table 2.4 (continued)

29

Country name

Average catch-up rates

Bulgaria

− 1.897

Panama

− 2.144

Macao SAR, China

− 2.246

China

− 2.464

Guyana

− 2.546

Kazakhstan

− 2.659

Bermuda

− 2.816

Seychelles

− 3.098

Malaysia

− 3.128

Romania

− 4.151

Slovak Republic

− 4.188

Hungary

− 4.391

Turkiye

− 4.781

Slovenia

− 4.863

Latvia

− 4.929

Poland

− 6.042

San Marino

− 6.151

Japan

− 6.550

Oman

− 7.501

Czechia

− 8.407

Estonia

− 8.422

Israel

− 10.208

Lithuania

− 11.219

New Zealand

− 12.947

Kuwait

− 16.690

Korea, Rep.

− 22.416

Saudi Arabia

− 28.256

Malta

− 96.461

Finland

− 541.985

2.3 Findings for Cluster Analysis and Beta Convergence To analyse the existence of convergence clubs among the investigated countries, four clusters were identified through cluster analysis for the GDP per capita. On average, the GDP per capita increases from clusters 1 to 4. The results of the convergence model with panel data (the dependent variable is the GDP per capita growth rate) for clusters 1, 2, 3 and 4 are those presented, respectively, in Tables 2.5, 2.6, 2.7 and 2.8. The speeds of convergence are, respectively, the following: 15%; 31%; 13%;

Logarithm of GDPpc lagged

Regression with Driscoll–Kraay standard errors, fixed effects

Note *Statistically significant at 1%

Constant

Independent variable

Approach

0.309

0.036

− 0.137*

1.189*

Standard error

Coefficient

3.840

− 3.740

Z

0.003

0.004

P > |z|

14.695

Beta convergence (%) 139.720*

Hausman test 67.775*

Pesaran’s test of cross-sectional independence

10,553.000*

Modified Wald test for groupwise heteroskedasticity

48.614*

Wooldridge test for autocorrelation

Table 2.5 Results for the beta convergence with panel data for the GDP per capita, across world countries and over the period 2009–2020 (cluster 1)

30 2 Clubs of Convergence: Insights from the Main Groups of Countries

Constant

Logarithm of GDPpc lagged

2.704**

− 0.266**

0.909

0.090

2.970 0.014

30.870

66.060*

46.014*

20,322.720*

5.733**

P > |z| Beta Hausman Pesaran’s test of Modified Wald Wooldridge convergence test cross-sectional test for groupwise test for (%) independence heteroskedasticity autocorrelation

− 2.950 0.014

Independent Coefficient Standard Z variable error

Note *Statistically significant at 1%; **Statistically significant at 5%

Regression with Driscoll–Kraay standard errors, fixed effects

Approach

Table 2.6 Results for the beta convergence with panel data for the GDP per capita, across world countries and over the period 2009–2020 (cluster 2)

2.3 Findings for Cluster Analysis and Beta Convergence 31

Constant

Logarithm of GDPpc lagged

1.310*** 0.588

2.230 0.050

15.710*

25.4780*

3111.070*

36.107*

P > |z| Beta Hausman Pesaran’s test of Modified Wald Wooldridge convergence test cross-sectional test for groupwise test for (%) independence heteroskedasticity autocorrelation

− 2.210 0.051 12.841

Standard Z error

− 0.121*** 0.054

Independent Coefficient variable

Note *Statistically significant at 1%; ***Statistically significant at 10%

Regression with Driscoll–Kraay standard errors, fixed effects

Approach

Table 2.7 Results for the beta convergence with panel data for the GDP per capita, across world countries and over the period 2009–2020 (cluster 3)

32 2 Clubs of Convergence: Insights from the Main Groups of Countries

Constant

Logarithm of GDPpc lagged

4.412**

− 0.387**

1.920

0.168

2.300 0.044

48.885

8.080**

5.273*

21,289.180*

45.023*

P > |z| Beta Hausman Pesaran’s test of Modified Wald Wooldridge convergence test cross-sectional test for groupwise test for (%) independence heteroskedasticity autocorrelation

− 2.300 0.044

Independent Coefficient Standard Z variable error

Note *Statistically significant at 1%; **Statistically significant at 5%

Regression with Driscoll–Kraay standard errors, fixed effects

Approach

Table 2.8 Results for the beta convergence with panel data for the GDP per capita, across world countries and over the period 2009–2020 (cluster 4)

2.3 Findings for Cluster Analysis and Beta Convergence 33

34

2 Clubs of Convergence: Insights from the Main Groups of Countries

and 49%. The convergence is stronger between the countries with higher GDP per capita averages. The poorer countries continue to deserve special attention from governments and international organisations.

2.4 Discussion and Conclusions This chapter proposed to analyse the existence of convergence clubs worldwide, over the period 2009–2020, considering statistical information from the World Bank for the GDP per capita. These data were analysed with the concepts of sigma and beta convergence and approaches (catch-up rates between each country and the weighted average for the countries considered) to assess the presence of catching-up processes. Panel data methodologies were taken into account for the results obtained with the regressions. In the processes of convergence, it is expected that occur phenomena of catchingup between the poor and rich countries/regions. At the beginning of the convergence theory, the idea was that all countries would tend to converge for the same steady state and later appeared the hypothesis of the existence of convergence clubs, where it was admitted that some countries may converge to a different steady state from other countries with distinct characteristics and conditions. The findings for the sigma convergence reveal that, in general, there are signs of convergence until 2020 and the data analysis shows a strong contraction of the GDP per capita with the COVID-19 pandemic. Additionally, the catch-up rates averages were negative in 2020, showing that the gap between each country and the world decreased. In fact, the beginning of the world crises seems to be characterised by periods of convergence, the problem appears to occur when the countries internalise the consequences of the shocks and in the recovery processes. The regressions carried out for the different world clusters of countries (obtained considering the values of GDP per capita with cluster analysis methodologies), over the period 2009–2020, show that the speed of convergence is greater for clusters 2 and 4 (the GDP per capita averages increase from cluster 1 to cluster 4). This means that the poorer countries deserve special attention from the national and international communities. In terms of practical implications, the results highlight the impacts of the COVID19 pandemic on the economic growth dynamics. On the other hand, the findings found for the catching-up process and the speed of convergence call for special attention, particularly with the poorer countries. In terms of policy recommendations, it is suggested to design more programmes of financial assistance for countries with greater economic difficulties to support them to overcome the consequences of external shocks. For future research, it could be relevant to try understanding the effects of other variables, particularly those associated with human capital, on these convergence patterns.

References

35

Acknowledgements This work is funded by National Funds through the FCT—Foundation for Science and Technology, I.P., within the scope of the project Refa UIDB/00681/2020. Furthermore we would like to thank the CERNAS Research Centre and the Polytechnic Institute of Viseu for their support.

References 1. L. De Benedictis, L. Tajoli, Openness, similarity in export composition, and income dynamics. J. Int. Trade Econ. Dev. 16, 93 (2007) 2. A. Ibourk, Z. Elouaourti, Regional convergence and catching up process in Africa: a tale of three clubs. Reg. Sci. Policy Pract. 1 (2023) 3. K. You, S. Dal Bianco, Z. Lin, J. Arnankwah-Arnoan, Bridging technology divide to improve business environment: insights from African nations. J. Bus. Res. 97, 268 (2019) 4. O.M. Valerio Mendoza, M. Tamas Borsi, F. Comim, Human capital dynamics in China: evidence from a club convergence approach. J. Asian Econ. 79, 101441 (2022) 5. Z.Y. Zhang, Can the rest of East Asia catch up with Japan: some empirical evidence. Jpn. World Econ. 15, 91 (2003) 6. I.S. Kano, I. Lengyel, Convergence clubs of nuts3 regions of the V4 group. E M Ekon. Manage. 24, 22 (2021) 7. L.K. Lim, M. McAleer, Convergence and catching up in ASEAN: a comparative analysis. Appl. Econ. 36, 137 (2004) 8. T. Misiak, Is the division of western and Eastern Poland still valid? The evolution of regional convergence in Poland. Econ. Bus. Rev. 8, 145 (2022) 9. M. Pietrzykowski, Convergence in GDP per capita across the EU regions-spatial effects. Econ. Bus. Rev. 5, 64 (2019) 10. K. Tipayalai, C. Mendez, Regional convergence and spatial dependence in Thailand: global and local assessments. J. Asia. Pac. Econ. (2022) 11. M. Iwanicz-Drozdowska, P. Smaga, B. Witkowski, Financial development. Have postcommunist countries converged? Transform. Bus. Econ. 15, 389 (2016) 12. R.M. Solow, A contribution to the theory of economic growth. Q. J. Econ. 70, 65 (1956) 13. R.J. Barro, X. Sala-I-Martin, Convergence across states and regions. Brook. Pap. Econ. Act. 1991, 107 (1991) 14. X.X. Sala-i-Martin, Regional cohesion: evidence and theories of regional growth and convergence. Eur. Econ. Rev. 40, 1325 (1996) 15. N. Islam, Growth empirics: a panel data approach. Q. J. Econ. 110, 1127 (1995) 16. W.J. Baumol, Productivity growth, convergence, and welfare: what the long-run data show. Am. Econ. Rev. 76, 1072 (1986) 17. M. Chatterji, Convergence clubs and endogenous growth. Oxf. Rev. Econ. Policy 8, 57 (1992) 18. European Union, Catching-Up, Growth and Convergence of the New Member States 19. G. Tondl, The Changing Pattern of Regional Convergence in Europe. Robert Schuman Centre (EUI), RSC No. 97/53, 130688 20. D. Hoechle, Robust standard errors for panel regressions with cross-sectional dependence. Stand. Genomic Sci. 7, 281 (2007) 21. O. Torres-Reyna, Panel Data Analysis Fixed and Random Effects Using Stata (v. 6.0) (2007) 22. StataCorp, Stata 15 Base Reference Manual (2017) 23. StataCorp, Stata Statistical Software: Release 15 (2017) 24. Stata, Statistical Software for Data Science|Stata. https://www.stata.com/ 25. Web of Science, Web of Science Core Collection. https://www.webofscience.com/wos/woscc/ basic-search 26. World Bank, World Bank Open Data. https://data.worldbank.org

Chapter 3

World Trends: Differences and Similitudes Between Absolute and Conditional Convergence

Abstract The world economic growth and the respective trends of convergence/ divergence are influenced by several factors which make these processes different worldwide in the function of the specific conditions of each context. For example, the Endogenous Growth Theory suggests that the processes of convergence are not unconditional and there is not only one steady state, but there are several steady states and the convergence trends are conditional and depend on the influence of certain variables, specifically those related to human capital. Considering these perspectives, this study aims to assess the processes of conditional convergence worldwide, taking into account data associated with the gross domestic product (GDP) per capita and variables related to human capital. This statistical information was assessed through approaches associated with the sigma and beta convergence and panel data. The insights obtained reveal that, in fact, the indicators related to human capital may play a determinant role in the convergence tendencies in some circumstances and over the period considered. Keywords Econometric models · Neoclassical theory · Endogenous growth theory

3.1 Introduction A usual discussion in the scientific literature, particularly in the economic one, about the processes of convergence is if these frameworks have patterns of absolute or conditional convergence. In other words, the question is if the economies converge for the same steady state independently of their characteristics, or if the convergence is conditioned to some specific particularities, specifically the human capital. These were some of the aspects, between others, taken into account by Cermeno [1] for the Mexican states. The absolute and conditional convergences were also tested for the following contexts: British regions [2]; renewable energy use in European countries [3]; cities and counties in China [4]; total factor productivity in developing and developed countries [5]; Western Balkan countries [6]; groups of countries with and without fossil fuel endowments [7]; between the former © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 V. J. P. D. Martinho, Economic Growth: Advances in Analysis Methodologies and Technologies, SpringerBriefs in Applied Sciences and Technology, https://doi.org/10.1007/978-3-031-38363-2_3

37

38

3 World Trends: Differences and Similitudes Between Absolute …

socialist countries [8]; Eastern Partnership Countries and the European Union tendencies [9]; Indian states [10]; European Union countries [11]; and North and South of Cyprus [12]. Another not consensual discussion is about the adequacy of the methodology considered to test the hypothesis of absolute and conditional convergence [13]. This divergence in the opinions about the patterns of economic growth and the methodologies used is old and, in some cases, is based, for example, on different perspectives about the returns to scale. The idea of absolute convergence is based on the developments of the Neoclassical Theory [14], considering assumptions of decreasing returns to scale and that the poorer economies have economic growth rates higher than the richer ones and in this way, countries and regions converge for the same steady state. Nonetheless, the Endogenous Growth Theory demonstrated that economic growth is dependent on the characteristics of each economy and in this way is conditioned by the human capital stock and by technological change and innovation [15, 16]. In these frameworks, it is expected to find increasing returns to scale and endogenous technological change [17, 18]. From this perspective, this study aims to analyse the conditional convergence of the GDP per capita, considering the sigma and beta concepts [19, 20], panel data methodologies [21–26] and statistical information from the World Bank [27]. Some countries were not considered because data for some years of this period were missing. The data considered are those associated with the GDP per capita and the following proxies for the human capital stock and skills: school enrolment, secondary (% gross); school enrolment, tertiary (% gross). The Web of Science [28] database was considered for the literature analysis.

3.2 Sigma Convergence and Data Analysis There is a clear trend of sigma convergence over the period considered (2013–2020) for the world gross domestic product per capita, that were more accentuated between the years 2013 and 2015 (Table 3.1). There are also signs of convergence for the world school enrolment, secondary (% gross), particularly after 2016 (Fig. 3.1). The world school enrolment, tertiary (% gross) presents different tendencies of convergence. In any case, it occurs for this variable, with disturbances in 2015 and 2028 (Fig. 3.2). Greece, Australia, Turkiye, Grenada, Korea (Rep.), Puerto Rico, Finland, Belarus, Spain, USA, Singapore, Argentina and Chile are between the countries with the highest averages for school enrolment in tertiary (% gross) (Table 3.2). Some of the nations with intermediate averages for school enrolment in tertiary (% gross) are the following (Table 3.3): Kazakhstan; Fiji; Georgia; United Arab Emirates; Brazil; Hungary; Armenia; Romania; San Marino; Slovak Republic; Bahrain; and China. Ghana, Aruba, Samoa, Cameroon, St. Vincent and the Grenadines, Bhutan,

3.2 Sigma Convergence and Data Analysis

39

Table 3.1 Sigma convergence (coefficient of variation) for the world GDP per capita, over the period 2013–2020 Time

Coefficient of variation

2013

1.107

2014

1.087

2015

1.046

2016

1.038

2017

1.037

2018

1.035

2019

1.025

2020

1.015

0.400 0.350 0.300 0.250 0.200 0.150 0.100 0.050 0.000

2013

2014

2015

2016

2017

2018

2019

2020

Fig. 3.1 Sigma convergence (coefficient of variation) for the world school enrolment, secondary (% gross), over the period 2013–2020

Seychelles, Nepal, São Rome and Principe, Benin and Togo are some examples of countries with the lowest averages for school enrolment in tertiary (% gross) averages (Table 3.4).

40

3 World Trends: Differences and Similitudes Between Absolute …

0.700 0.680 0.660 0.640 0.620 0.600 0.580 0.560 0.540

2013

2014

2015

2016

2017

2018

2019

2020

Fig. 3.2 Sigma convergence (coefficient of variation) for the world school enrolment, tertiary (% gross), over the period 2013–2020 Table 3.2 Averages, across the period 2013–2020, for the world school enrolment, secondary (% gross), and world school enrolment, tertiary (% gross) [countries with the highest values for the school enrolment, tertiary (% gross)] Country name

School enrolment, secondary (% gross)—average

School enrolment, tertiary (% gross)—average

Greece

104.527

135.597

Australia

144.727

115.112

Turkiye

103.491

102.540

Grenada

114.092

99.199

98.809

96.245

Korea, Rep. Puerto Rico

86.500

94.700

Finland

148.903

90.125

Belarus

104.090

89.129

Spain

125.085

88.764

USA

98.542

88.375

Singapore

105.839

88.369

Argentina

107.653

88.326

Chile

101.918

87.424

St. Kitts and Nevis

109.591

85.861

Netherlands

114.971

85.005

Austria

100.141

83.553 (continued)

3.2 Sigma Convergence and Data Analysis

41

Table 3.2 (continued) Country name

School enrolment, secondary (% gross)—average

Latvia

110.697

83.167

97.917

82.704

Denmark

129.701

81.453

Ukraine

96.261

81.413

Russian Federation

101.690

81.397

Norway

116.093

80.654

New Zealand

116.563

80.351

Slovenia

112.862

79.892

Iceland

117.359

77.239

Belgium

158.793

76.894

Ireland

Macao SAR, China

School enrolment, tertiary (% gross)—average

129.184

75.653

Hong Kong SAR, China 104.695

74.410

Estonia

113.340

72.061

Lithuania

108.262

71.717

Peru

103.211

70.935

99.936

70.825

Bulgaria

97.865

70.580

Canada

112.347

69.553

Sweden

142.910

69.065

Cyprus

Germany

98.601

68.915

Poland

109.944

68.795

Croatia

99.447

67.176

Portugal

119.179

65.570

Czechia

103.306

65.054

France

103.870

64.990

Iran, Islamic Rep.

86.255

64.839

Saudi Arabia

111.827

64.774

Israel

104.396

63.825

Japan

101.833

63.489

Italy

101.667

63.452

94.869

63.055

Uruguay

116.608

62.834

Mongolia

94.776

62.071

122.420

60.640

95.889

60.092

101.841

59.855

Serbia

UK Albania Switzerland

(continued)

42

3 World Trends: Differences and Similitudes Between Absolute …

Table 3.2 (continued) Country name

School enrolment, secondary (% gross)—average

School enrolment, tertiary (% gross)—average

Montenegro

90.587

56.643

Kuwait

95.595

55.661

Palau

129.993

54.689

Moldova

107.246

54.557

Malta

101.119

54.229

80.032

54.198

Costa Rica

128.531

53.920

Colombia

97.088

53.737

Dominican Republic

Table 3.3 Averages, across the period 2013–2020, for the world school enrolment, secondary (% gross), and world school enrolment, tertiary (% gross) [countries with the intermediate values for the school enrolment, tertiary (% gross)] Country name

School enrolment, secondary (% gross)—average

School enrolment, tertiary (% gross)—average

Kazakhstan

109.805

53.600

94.046

53.493

Georgia

101.670

53.282

United Arab Emirates

101.456

53.162

Brazil

103.268

51.728

Hungary

103.546

51.561

Armenia

85.777

49.943

Romania

91.330

49.241

San Marino

60.776

48.984

Slovak Republic

90.945

48.949

Bahrain

99.935

48.036

Fiji

China

47.598

Thailand

107.604

47.144

Ecuador

102.440

46.669

Panama

76.862

45.996

Bosnia and Herzegovina

45.738

Kyrgyz Republic

93.522

44.749

West Bank and Gaza

87.672

44.260

Algeria

43.989 (continued)

3.2 Sigma Convergence and Data Analysis

43

Table 3.3 (continued) Country name

School enrolment, secondary (% gross)—average

School enrolment, tertiary (% gross)—average

Malaysia

83.814

43.176

North Macedonia

80.587

41.141

Mauritius

96.970

40.785

Oman

105.285

38.734

Mexico

101.235

37.630

Philippines

87.891

34.672

Jordan

66.492

34.065

Egypt, Arab Rep.

85.062

34.024

Indonesia

85.394

33.901

Tunisia

89.708

33.350

Morocco

80.933

32.157

Brunei Darussalam

97.013

31.888

El Salvador

77.032

28.570

Vietnam Azerbaijan

28.433 94.450

Jamaica

27.197 27.169

India

73.592

27.043

Tajikistan

88.500

26.925

Botswana

26.807

Maldives

83.303

26.143

Cayman Islands

80.334

25.888

Marshall Islands

64.353

25.823

Belize

83.568

24.429

Bermuda

76.915

24.110

Cabo Verde

92.858

23.667

Namibia

23.321

Honduras South Africa

22.407 105.223

22.149

Gabon

73.063

21.066

Guatemala

52.574

20.425

Sri Lanka

98.870

19.759

Nicaragua Luxembourg

19.324 103.908

19.118

Curacao

99.287

18.942

Myanmar

61.206

18.816 (continued)

44

3 World Trends: Differences and Similitudes Between Absolute …

Table 3.3 (continued) Country name

School enrolment, secondary (% gross)—average

School enrolment, tertiary (% gross)—average

Tonga

95.071

18.400

Bangladesh

69.498

18.218

Sudan

45.117

17.166

Lao PDR

63.158

16.433

St. Lucia

89.049

16.366

Qatar

16.270

Table 3.4 Averages, across the period 2013–2020, for the world school enrolment, secondary (% gross), and world school enrolment, tertiary (% gross) [countries with the lowest values for the school enrolment, tertiary (% gross)] Country name Ghana

School enrolment, secondary (% gross)—average 70.165

Aruba

School enrolment, tertiary (% gross)—average 16.011 15.868

Samoa

93.721

14.890

Cameroon

52.026

14.673

St. Vincent and the Grenadines

106.800

14.570

Bhutan

84.276

14.425

Seychelles

80.140

14.132

Nepal

73.256

13.905

Sao Tome and Principe

78.919

13.737

Benin

55.519

13.180

Togo

56.327

12.857

Cambodia

54.830

12.364

Nigeria

46.825

11.980

Senegal

48.424

11.771

Turks and Caicos Islands

73.366

11.549

Congo, Rep.

66.444

11.420

Lesotho

61.615

10.557

Kenya

10.540

Uzbekistan

93.372

10.080

Pakistan

40.261

9.740

Ethiopia

34.236

9.558

Afghanistan

53.092

9.493

Angola

50.671

9.257 (continued)

3.2 Sigma Convergence and Data Analysis

45

Table 3.4 (continued) Country name

School enrolment, secondary (% gross)—average

School enrolment, tertiary (% gross)—average

Cote d’Ivoire

46.635

9.071

Guinea

37.341

8.909

Comoros

59.013

8.876

Zimbabwe

52.406

8.497

Rwanda

40.004

7.202

Congo, Dem. Rep.

44.859

6.846

Eswatini

75.828

6.748

Mozambique

32.673

6.692

Burkina Faso

36.116

5.991

Mauritania

33.988

5.431

Mali

41.485

5.146

Madagascar

37.200

4.987

Uganda

26.635

4.842

Tanzania

29.091

4.588

Burundi

44.385

4.251

Niger

20.386

3.536

Chad

22.019

3.343

Paraguay Malawi Antigua and Barbuda Bahamas, The Barbados

1.417 39.408

1.127

108.904 76.895 105.560

Bolivia

90.411

Central African Republic

16.087

Djibouti

48.981

Dominica

96.905

Equatorial Guinea Liberia

38.725

Micronesia, Fed. Sts. Nauru

88.016

Papua New Guinea

46.974

Sierra Leone

40.301

Suriname

77.237 (continued)

46

3 World Trends: Differences and Similitudes Between Absolute …

Table 3.4 (continued) Country name Timor-Leste

School enrolment, secondary (% gross)—average

School enrolment, tertiary (% gross)—average

77.711

Trinidad and Tobago Tuvalu

71.322

Vanuatu

52.924

3.3 Results for Conditional Beta Convergence with Panel Data Table 3.5 reveals the existence of absolute beta convergence (speed of convergence of about 29%) for the world GDP per capita (the dependent variable in the model considered is the GDP per capita growth rate), over the period considered (2013– 2020). These findings are confirmed in Table 3.6 for a model considering independent variables as proxies for human capital. Nonetheless, the results presented in this table exclude the hypothesis of convergence conditioned by the variables considered for human capital. The evidence of convergence conditioned by the school enrolment in tertiary (% gross) growth rate appears in Table 3.7, when the model allows for more lags in the regression, showing that the effects of human capital education on the economy are delayed in time.

3.4 Discussion and Conclusions This study intends to investigate the hypothesis of conditional convergence worldwide, particularly analysing the impact of variables related to human capital on the economic growth dynamics between world countries. For that, data from the World Bank (for the period 2013–2020) were considered for the GDP per capita and the following variables as proxies for the human capital: school enrolment, secondary (% gross); school enrolment, tertiary (% gross). This statistical information was analysed with panel data methodologies and taking into account the concepts of sigma and beta convergence. A usual discussion in the scientific literature is about if the regions and countries converge for the same steady state, following patterns of absolute convergence, or if the convergence tendencies are conditioned by specific characteristics of the economies. Another not-unanimous discussion is about the methodologies taken into account to assess the processes of economic growth. Particularly, the Endogenous Growth Theory brought new developments to support the understanding of the convergence trends found between different economies. The sigma convergence highlights trends of convergence, for the GDP per capita, over the period considered, specifically between 2013 and 2015. Signs of convergence

Constant

Logarithm of GDPpc lagged

2.345** 0.769

− 0.249** 0.083

3.050

− 3.020

Independent Coefficient Standard Z variable error

Note *Statistically significant at 1%; **Statistically significant at 5%

Regression with Driscoll–Kraay standard errors, fixed effects

Approach

0.023

0.023

28.661

141.080* 163.486*

80,149.280**

54.906*

P > |z| Beta Hausman Pesaran’s test of Modified Wald Wooldridge convergence test cross-sectional test for groupwise test for (%) independence heteroskedasticity autocorrelation

Table 3.5 Results for the beta convergence of the world GDP per capita, considering panel data over the period 2013–2020

3.4 Discussion and Conclusions 47

2.464** 1.016

0.006

Constant

0.027

0.017

− 0.251** 0.105

TER growth − 0.010

SEC growth

Logarithm of GDPpc lagged

2.420

− 1.730

0.620

− 2.400

Independent Coefficient Standard Z variable error

0.052

0.134

0.556

0.053

28.893

80.800*

430,000.000*

33.023*

P > |z| Beta Hausman Pesaran’s test of Modified Wald Wooldridge convergence test cross-sectional test for groupwise test for (%) independence heteroskedasticity autocorrelation

Note *Statistically significant at 1%; **Statistically significant at 10%; SEC growth, School enrolment, secondary (% gross) growth rate; TER growth, School enrolment, tertiary (% gross) growth rate

Regression with Driscoll–Kraay standard errors, fixed effects

Approach

Table 3.6 Results for the beta convergence of the world GDP per capita, considering panel data over the period 2013–2020 and the model extended with variables as proxies to the human capital

48 3 World Trends: Differences and Similitudes Between Absolute …

Constant

2.464**

0.721

3.420

0.750 − 2.370

− 3.390

0.023

0.017

SEC growth

0.074

Standard Z error

TER growth − 0.010*** 0.004

− 0.251**

Logarithm of GDPpc lagged

Independent Coefficient variable

0.014

0.055

0.484

80.800*

430,000.000*

33.023*

Beta Hausman Pesaran’s test of Modified Wald Wooldridge convergence test cross-sectional test for groupwise test for (%) independence heteroskedasticity autocorrelation

0.015 28.893

P> |z|

Note *Statistically significant at 1%; **Statistically significant at 5%; ***Statistically significant at 10%; SEC growth, School enrolment, secondary (% gross) growth rate; TER growth, School enrolment, tertiary (% gross) growth rate

Regression with Driscoll–Kraay standard errors, fixed effects

Approach

Table 3.7 Results for the beta convergence of the world GDP per capita, considering panel data over the period 2013–2020, the model extended with variables as proxies to the human capital and allowing more lags in the regression

3.4 Discussion and Conclusions 49

50

3 World Trends: Differences and Similitudes Between Absolute …

were also found for the world school enrolment, secondary (% gross), and the world school enrolment, tertiary (% gross). For example, among the countries with the highest averages for school enrolment in tertiary (% gross) are the following: Greece, Australia, Turkiye, Grenada, Korea (Rep.), Puerto Rico, Finland, Belarus, Spain, USA, Singapore, Argentina and Chile. The results obtained from the regressions carried out reveal that the world GDP per capita follows a pattern of absolute convergence, for the period 2013–2020, the sample used and the approaches taken into account. The variables considered as proxies for the human capital have a residual impact and the respective coefficient of regression has statistical significance only when more lags were considered for the regressions (showing some time delay in the implications). In terms of practical implications, the economic processes of convergence worldwide are, namely, present evidence of absolute convergence over the period 2013– 2020, for the sample considered and the methodologies used. It is suggested more studies with other variables to test the conditional convergence in the sample considered and to test other approaches. For future research, it could be important to compare these results with those obtained with the models associated with the divergence theory. Acknowledgements This work is funded by National Funds through the FCT—Foundation for Science and Technology, I.P., within the scope of the project Refa UIDB/00681/2020. Furthermore we would like to thank the CERNAS Research Centre and the Polytechnic Institute of Viseu for their support.

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11. O. Stoica, A. Roman, D. Diaconasu, Real convergence and European integration with focus on the new member states. Sci. Ann. Econ. Bus. 66, 215 (2019) 12. V. Yorucu, O. Mehmet, Absolute and conditional convergence in both zones of Cyprus: statistical convergence and institutional divergence. World Econ. 37, 1315 (2014) 13. R. Chumacero, On the power of absolute convergence tests. Stud. Nonlin. Dyn. Econometr. 10 (2006) 14. R.M. Solow, A contribution to the theory of economic growth. Q. J. Econ. 70, 65 (1956) 15. R.E. Lucas, On the mechanics of economic development. J. Monet. Econ. 22, 3 (1988) 16. R.J. Barro, Economic growth in a cross section of countries. Q. J. Econ. 106, 407 (1991) 17. P.M. Romer, Increasing returns and long-run growth. J. Polit. Econ. 94, 1002 (1986) 18. P.M. Romer, Endogenous technological change. J. Polit. Econ. 98, S71 (1990) 19. R.J. Barro, X. Sala-I-Martin, Convergence across states and regions. Brook. Pap. Econ. Act. 1991, 107 (1991) 20. X.X. Sala-i-Martin, Regional cohesion: evidence and theories of regional growth and convergence. Eur. Econ. Rev. 40, 1325 (1996) 21. N. Islam, Growth empirics: a panel data approach. Q. J. Econ. 110, 1127 (1995) 22. D. Hoechle, Robust standard errors for panel regressions with cross-sectional dependence. Stand. Genomic Sci. 7, 281 (2007) 23. O. Torres-Reyna, Panel Data Analysis Fixed and Random Effects Using Stata (v. 6.0) (2007) 24. StataCorp, Stata 15 Base Reference Manual (2017) 25. StataCorp, Stata Statistical Software: Release 15 (2017) 26. Stata, Statistical Software for Data Science|Stata. https://www.stata.com/ 27. World Bank, World Bank Open Data. https://data.worldbank.org 28. Web of Science, Web of Science Core Collection. https://www.webofscience.com/wos/woscc/ basic-search

Chapter 4

Constant, Decreasing or Increasing Returns to Scale: Evidence from the Verdoorn and Kaldor Laws

Abstract The discussions in the scientific literature about economic growth trends are vast and not unanimous. The economic theory has different opinions about economic growth that disagree, sometimes, if the economies tend to converge or diverge over time. In fact, the Neoclassical Theory argues that the economies tend to converge for the same steady state and the Keynesian Theory defends that the processes of economic growth promote divergence between countries/regions through increasing returns to scale. In the processes of divergence, the richer economies improve their performance and the poorer countries become worse. Taking into account these contexts, this research intends to test the existence of increasing returns to scale, using data for output and employment growth. For these analyses, the approaches related to the Verdoorn and Kaldor laws were considered. The results obtained highlight the existence of increasing returns to scale in some frameworks and reveal the importance of national and international policies implementation to promote a more balanced and sustainable development. Keywords Circular and cumulative processes · Economic growth · Keynesian theory · External demand

4.1 Introduction The presence of decreasing, constant and increasing returns to scale has generated not unanimous debate in the economic literature. Particularly the Keynesian Theory argues that the economic growth trend follows processes with learning by doing and spillover effects in the presence of increasing returns to scale and promote self-reinforced circular and cumulative phenomena, where richer economies become richer and the poorer ones become worse. These contexts were explained, for example, by Kaldor [1, 2] through their laws, where the manufacturing sector is the engine of the economy and the productivity growth is endogenous and dependent on the output growth (Verdoorn law [3]). These laws were reconsidered by, for example, McCombie [4], Parikh [5] and Stoneman [6]. The importance of the manufacturing © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 V. J. P. D. Martinho, Economic Growth: Advances in Analysis Methodologies and Technologies, SpringerBriefs in Applied Sciences and Technology, https://doi.org/10.1007/978-3-031-38363-2_4

53

54

4 Constant, Decreasing or Increasing Returns to Scale: Evidence …

sector for the economy is also associated with its capacity to produce tradable goods, appearing here the manufacturing exports as a determinant variable [7]. In addition, some extended models have been considered [8]. The Verdoorn–Kaldor laws have been taken into account for studies related to the following issues worldwide: European regions [9]; Turkish contexts [10]; open economy Stock-Flow Consistent models [11]; Chinese framework [12]; Peruvian manufacturing sector [13]; Portuguese agroforestry activities [14]; and Australian economy [15]. The systematic review of Barreto et al. [16] highlighted some gaps in the literature about the Kaldor developments in the Pacific Alliance and the Association of Southeast Asia Nations, namely those related to the lack of research that comparatively assess the determinants of Kaldor and the scarcity of studies about convergence, or divergence, among nations and trade unions. This literature survey shows that the Kaldor laws were the target of criticism and diverse studies reconsidered these laws, proposing, in some cases, alternative models and approaches trying to make compatible the different points of view. In any case, the developments of Kaldor have been taken into account by diverse studies as methodologies to assess the presence of increasing returns to scale. Kaldor proposed that in the Verdoorn law productivity growth should be replaced by employment growth to avoid spurious relationships. In this alternative approach, the presence of increasing returns to scale is confirmed with statistically significant and positive coefficients close to zero. In this way, this chapter proposes to estimate the relationship between the employment growth (using as proxies the following variables: total labour force; employment in industry (% of total employment); and employment to population ratio, 15+, total (%) and the output growth (gross domestic product (GDP) per capita) with panel data [17–21]. For the literature assessment, the Web of Science information was taken into account [22]. The statistical information was obtained from the World Bank [23] (some countries were excluded because of the lack of data).

4.2 Data Analysis Over the period 2014–2020, the world GDP per capita averages increased until 2019 and decreased drastically in 2020 because of the COVID-19 pandemic (Fig. 4.1). A similar pattern was found for the world labour force (total) averages (Fig. 4.2) and for the world employment to population ratio [15+, total (%)] averages (Fig. 4.4). A different trend was found for the world employment in industry (% of total employment) averages (Fig. 4.3). Qatar, Oman, Czechia, Slovak Republic, Bahrain, Lesotho, Tunisia, Slovenia, Iran (Islamic Rep.), Turkmenistan, United Arab Emirates, Belarus, Poland and Hungary are countries between those with the highest averages for employment in industry (% of total employment) (Table 4.1). The intermediate averages appear in countries such as Indonesia, Honduras, Lebanon, Brazil, Argentina, Belgium, Benin, Iraq,

4.2 Data Analysis

55

22000

21500

21000

20500

20000

19500

19000

2014

2015

2016

2017

2018

2019

2020

Fig. 4.1 World GDP per capita averages, over the period 2014–2020

19600000 19400000 19200000

19000000 18800000 18600000 18400000 18200000 18000000 17800000

2014

2015

2016

2017

2018

2019

2020

Fig. 4.2 World labour force (total) averages, over the period 2014–2020

Bangladesh, Cabo Verde, Switzerland, New Zealand, Kazakhstan, France and South Africa (Table 4.2). The lowest averages for employment in industry (% of total employment) occur, for example, in the following nations (Table 4.3): Armenia; Belize; Netherlands; Moldova; Singapore; St. Lucia; Jamaica; Samoa; Botswana; Nepal; Greece; Cameroon; Sudan; Azerbaijan; Kenya; and Fiji.

56

4 Constant, Decreasing or Increasing Returns to Scale: Evidence …

20 20 20 20 20

20 19 19

2014

2015

2016

2017

2018

2019

2020

Fig. 4.3 World employment in industry (% of total employment) averages, over the period 2014– 2020

57 57 56 56 55 55 54

54

2014

2015

2016

2017

2018

2019

2020

Fig. 4.4 World employment to population ratio [15+, total (%)] averages, over the period 2014– 2020

4.2 Data Analysis

57

Table 4.1 Averages, across the period 2014–2020, for the world GDP per capita (PPP, constant 2017 international $), the labour force (total), employment in industry (% of total employment) and employment to population ratio [15+, total (%)] [countries with the highest values for the employment in industry (% of total employment)] Country name

GDP per capita, PPP Labour force, (constant 2017 total international $)

Employment in industry (% of total employment)

Employment to population ratio, 15+, total (%)

Qatar

95,627.880

1,956,237.000

54.022

87.265

Oman

34,399.010

2,346,927.000

42.859

67.250

Czechia

37,995.140

5,367,420.000

37.741

57.825

Slovak Republic

29,921.790

2,739,405.000

36.335

54.410

Bahrain

48,791.710

815,379.900

35.155

70.577

Lesotho

2546.647

918,518.100

34.211

53.732

Tunisia

10,762.210

4,154,056.000

33.371

39.535

Slovenia

36,054.590

1,019,096.000

32.980

53.875

Iran, Islamic Rep.

14,546.700

27,183,863.000

32.830

37.559

Turkmenistan

13,438.870

1,937,513.000

32.189

44.975

United Arab Emirates

69,405.530

6,310,136.000

32.087

79.478

Belarus

18,742.970

5,007,011.000

31.508

60.276

Poland

30,008.140

18,355,950.000

31.404

53.883

Hungary

29,533.030

4,661,290.000

31.315

52.861

Algeria

11,601.230

11,921,481.000

31.142

36.860

Bosnia and Herzegovina

13,334.940

1,357,076.000

31.067

36.432

North Macedonia

15,670.610

953,386.300

30.746

42.808

6117.560

1,156,871.000

30.024

32.679

Bulgaria

21,336.240

3,337,607.000

30.022

51.093

Estonia

33,482.900

692,787.400

29.713

58.768

Romania

26,645.820

9,023,435.000

29.603

51.814

China

14,216.420

775,000,000.000

28.392

64.921

Malaysia

25,516.530

15,688,240.000

27.574

62.714

Germany

52,300.280

43,482,178.000

27.500

58.527

Croatia

26,679.550

1,828,699.000

27.201

45.674

Sri Lanka

13,060.970

8,541,381.000

27.151

50.496

West Bank and Gaza

(continued)

58

4 Constant, Decreasing or Increasing Returns to Scale: Evidence …

Table 4.1 (continued) Country name

GDP per capita, PPP Labour force, (constant 2017 total international $)

Employment in industry (% of total employment)

Employment to population ratio, 15+, total (%)

Trinidad and Tobago

26,901.150

696,797.100

27.015

56.817

Russian Federation

26,208.170

74,500,975.000

26.994

59.185

Turkiye

26,936.600

31,971,493.000

26.660

45.827

Egypt, Arab Rep.

10,541.820

30,282,457.000

26.504

39.901

Italy

40,916.500

25,796,876.000

26.281

43.934

Tonga Suriname Vietnam

6068.929

36,456.140

26.260

52.901

18,004.360

235,997.600

26.176

51.189

9095.433

54,831,232.000

25.902

74.646

Serbia

16,802.980

3,222,882.000

25.875

46.082

Mexico

19,602.700

53,916,412.000

25.582

57.617

Austria

53,762.520

4,581,889.000

25.406

57.773

Lithuania

33,727.040

1,473,155.000

25.271

55.936

Kuwait

50,436.510

2,358,619.000

25.045

70.726

Korea, Rep.

40,669.930

28,212,462.000

25.013

60.918

Cambodia

3948.049

8,101,282.000

24.905

73.267

Mauritius

21,542.960

607,207.700

24.870

54.759

India

5934.782

483,000,000.000

24.703

45.369

Japan

40,812.340

67,473,751.000

24.654

58.912

Saudi Arabia

45,781.080

14,331,142.000

24.633

53.759

Portugal

32,493.340

5,217,548.000

24.535

53.153

Ukraine

12,078.350

21,287,194.000

24.481

51.020

Eswatini

8217.111

372,089.100

24.377

39.101

Pakistan

4838.578

69,152,900.000

24.108

49.299

18,221.780

218,777.100

24.047

56.445

6822.323

13,040,428.000

23.792

53.524

Latvia

28,556.190

999,722.900

23.705

55.433

Guyana

12,930.380

289,088.100

23.597

45.816

Kyrgyz Republic

4943.431

2,508,554.000

23.441

58.383

Thailand

16,764.460

40,077,312.000

23.177

67.573

Maldives Uzbekistan

(continued)

4.2 Data Analysis

59

Table 4.1 (continued) Country name

GDP per capita, PPP Labour force, (constant 2017 total international $)

Employment in industry (% of total employment)

Employment to population ratio, 15+, total (%)

Chile

24,387.790

8,791,460.000

22.839

54.787

Congo, Rep.

4233.233

2,094,090.000

22.510

53.712

Morocco

7791.333

11,805,201.000

22.194

41.575

8505.250

2,663,315.000

22.017

56.090

46,843.160

2,722,990.000

21.977

54.177

El Salvador Finland

Table 4.2 Averages, across the period 2014–2020, for the world GDP per capita (PPP, constant 2017 international $), the labour force (total), employment in industry (% of total employment) and employment to population ratio [15+, total (%)] [countries with the intermediate values for the employment in industry (% of total employment)] Country name

GDP per capita, PPP (constant 2017 international $)

Labour force, total

Employment in Employment to industry (% of total population ratio, employment) 15+, total (%)

Indonesia

10,873.290

130,000,000.000

21.897

64.305

Honduras

5313.987

4,130,448.000

21.452

59.639

Lebanon

16,779.390

2,116,086.000

21.437

42.367

Brazil

14,702.040

103,000,000.000

21.256

56.118

Argentina

22,682.120

19,625,894.000

21.049

53.727

Belgium

50,078.120

5,079,672.000

21.022

49.998

Benin

2987.148

4,266,069.000

20.881

63.447

Iraq

9737.329

10,127,626.000

20.689

36.828

Bangladesh

4865.680

66,168,886.000

20.680

54.886

Cabo Verde

6220.334

233,556.700

20.667

50.554

Switzerland

69,140.920

4,893,004.000

20.604

64.989

New Zealand 41,832.400

2,702,366.000

20.525

66.635

Kazakhstan

24,996.810

9,105,877.000

20.493

66.687

France

44,007.330

30,526,419.000

20.298

50.363

South Africa 13,761.950

23,306,143.000

20.237

43.643

Mongolia

11,558.620

1,295,922.000

20.151

56.719

St. Vincent and the Grenadines

13,409.550

52,573.430

20.110

52.245

8051.635

5,375,556.000

20.052

66.797

38,332.510

22,987,144.000

20.044

47.176

Bolivia Spain

(continued)

60

4 Constant, Decreasing or Increasing Returns to Scale: Evidence …

Table 4.2 (continued) Country name Jordan

GDP per capita, PPP (constant 2017 international $)

Labour force, total

Employment in Employment to industry (% of total population ratio, employment) 15+, total (%)

9731.331

2,573,506.000

19.832

32.526

Colombia

14,182.470

25,260,152.000

19.803

61.282

Australia

48,356.740

13,013,555.000

19.761

61.436

Norway

63,543.820

2,781,467.000

19.684

61.560

Uruguay

22,733.350

1,734,930.000

19.642

58.807

Canada

47,865.680

20,127,037.000

19.600

60.996

USA

59,803.620

164,000,000.000

19.539

58.937

Guatemala

8303.476

6,258,970.000

19.532

58.415

Sao Tome and Principe

3899.538

65,760.710

19.504

46.579

Malta

41,680.550

238,083.900

19.472

55.505

Senegal

3211.931

4,205,124.000

19.417

46.023

Albania

12,678.900

1,355,232.000

19.407

49.025

Togo

1969.377

2,684,919.000

19.300

56.124

Comoros

3223.957

204,328.400

19.300

40.571

Afghanistan

2079.949

9,113,680.000

19.280

41.695

Paraguay

13,280.850

3,120,653.000

19.172

66.069

Denmark

54,713.430

2,959,586.000

19.018

58.139

Dominican Republic

16,342.220

4,738,589.000

18.917

58.146

Panama

29,152.860

1,904,438.000

18.668

60.065

Brunei Darussalam

61,161.920

210,387.600

18.558

58.975

3254.030

2,362,844.000

18.512

38.694

Tajikistan Ireland

77,213.720

2,357,904.000

18.458

57.207

UK

45,338.160

34,108,358.000

18.395

59.963

Ecuador

11,514.980

7,874,375.000

18.345

62.600

Barbados

15,621.510

144,810.300

18.328

57.481

Sweden

51,428.780

5,314,277.000

18.309

59.561

Libya

20,948.570

2,093,503.000

18.090

38.096

Montenegro

19,271.140

277,406.700

18.046

45.468

Costa Rica

19,757.760

2,403,290.000

18.044

55.596

Ghana

4938.831

12,865,424.000

17.926

66.098

Nicaragua

5537.941

2,888,327.000

17.911

61.931

Mauritania

5242.803

978,814.700

17.882

37.115 (continued)

4.2 Data Analysis

61

Table 4.2 (continued) Country name Philippines

GDP per capita, PPP (constant 2017 international $)

Labour force, total

Employment in Employment to industry (% of total population ratio, employment) 15+, total (%)

7805.927

43,263,531.000

17.763

58.014

Iceland

54,116.010

211,787.600

17.632

73.406

Israel

38,848.120

3,981,697.000

17.362

60.727

Cyprus

37,740.910

632,863.900

17.350

55.466

Somalia

1064.974

2,657,734.000

16.939

27.631

Myanmar

4308.901

24,196,284.000

16.820

62.006

Namibia

10,236.380

898,361.400

16.761

47.244

Peru

12,165.930

17,311,215.000

16.667

72.561

Table 4.3 Averages, across the period 2014–2020, for the world GDP per capita (PPP, constant 2017 international $), the labour force (total), employment in industry (% of total employment) and employment to population ratio [15+, total (%)] [countries with the lowest values for the employment in industry (% of total employment)] Country name

Armenia

GDP per capita, PPP Labour force, (constant 2017 total international $)

Employment in industry (% of total employment)

Employment to population ratio, 15+, total (%)

12,515.470

1,411,089.000

16.594

54.183

8896.615

167,101.700

16.446

59.019

Netherlands

54,459.650

9,246,983.000

16.273

61.029

Moldova

11,550.900

965,509.900

16.254

41.456

Singapore

93,479.960

3,463,092.000

15.863

67.852

St. Lucia

Belize

14,533.880

98,674.000

15.651

56.046

Jamaica

9862.851

1,424,277.000

15.629

60.628

Samoa

6065.488

69,406.430

15.499

49.105

14,429.150

996,255.300

15.354

50.256

3507.960

7,660,761.000

15.319

35.531

28,457.350

4,748,039.000

15.166

40.504

Botswana Nepal Greece Cameroon

3665.425

9,987,295.000

14.595

69.265

Sudan

4457.092

11,533,622.000

14.587

39.909

14,350.370

4,830,308.000

14.529

61.535

Azerbaijan Kenya

4334.459

21,545,497.000

14.382

71.231

Fiji

12,262.170

371,223.000

14.341

55.100

Equatorial Guinea

20,427.330

492,210.400

14.248

50.894

Gabon

14,510.630

651,952.600

14.215

38.161 (continued)

62

4 Constant, Decreasing or Increasing Returns to Scale: Evidence …

Table 4.3 (continued) Country name

GDP per capita, PPP Labour force, (constant 2017 total international $)

Employment in industry (% of total employment)

Employment to population ratio, 15+, total (%)

Hong Kong SAR, China

58,292.270

3,955,264.000

13.854

58.661

Puerto Rico

34,098.220

1,137,679.000

13.084

35.962

Rwanda

1961.772

4,217,316.000

12.923

50.940

Georgia

13,517.460

1,963,011.000

12.753

56.400

115,972.800

385,644.100

12.338

68.348

Nigeria

5173.477

62,427,848.000

12.175

54.746

Haiti

3120.975

4,752,428.000

11.981

56.614

Timor-Leste

3502.362

518,072.400

11.668

63.960

33,260.480

226,996.700

11.661

64.573

Cote d’Ivoire

4710.963

9,162,952.000

11.620

62.866

Luxembourg

114,032.000

298,535.300

11.321

56.323

Zambia

3357.575

5,662,793.000

10.593

54.955

Solomon Islands

2577.935

323,433.300

10.044

83.287

Guinea-Bissau

1809.683

609,679.100

10.031

54.178

10,625.550

352,437.700

9.987

60.808

Macao SAR, China

Bahamas, The

Bhutan Lao PDR

7122.395

2,826,999.000

9.780

57.970

Madagascar

1523.572

13,320,977.000

9.620

84.153

Congo, Dem. Rep.

1032.674

29,819,359.000

9.477

62.908

Zimbabwe

2263.225

5,616,112.000

9.410

61.633

Ethiopia

1962.096

51,063,976.000

9.331

78.265

Papua New Guinea

3846.963

2,773,866.000

8.981

46.300

Sierra Leone

1639.803

2,486,455.000

8.956

52.806

Uganda

2159.953

14,855,970.000

8.418

67.696

Liberia

1516.427

2,128,708.000

8.415

74.493

Chad

1656.131

4,750,541.000

8.384

59.907

Mozambique

1268.406

12,513,764.000

8.198

76.133

Mali

2122.422

6,957,598.000

8.070

67.972

Angola

7195.468

12,693,828.000

7.692

69.703

Malawi

1465.600

6,729,274.000

7.678

65.050

Vanuatu

2954.590

121,184.900

7.474

67.978 (continued)

4.4 Discussion and Conclusions

63

Table 4.3 (continued) Country name

GDP per capita, PPP Labour force, (constant 2017 total international $)

Employment in industry (% of total employment)

Employment to population ratio, 15+, total (%)

Niger

1166.949

8,148,303.000

7.248

73.016

Gambia, The

1963.666

793,322.600

6.798

56.986

Tanzania

2433.187

26,025,626.000

6.622

81.526

826.067

1,825,545.000

6.455

66.484

Burkina Faso

1979.918

7,118,726.000

6.415

62.410

Guinea

2329.042

3,798,061.000

6.397

51.554

Djibouti

4397.465

224,267.400

5.634

23.580

Burundi

758.494

4,726,887.000

3.476

78.329

Central African Republic

4.3 Results for the Kaldor Model Taking into account the world labour force (total) growth rates as the dependent variable, Table 4.4 shows that there is evidence of strong increasing returns to scale (the coefficient of the independent variable is statistically significant and close to zero). These signs disappear when the employment in industry (% of total employment) growth rates is considered as the dependent variable (Table 4.5). Considering the importance of the manufacturing sector for the Kaldor model, these findings need deeper assessment in future research. The signs of increasing returns to scale reappeared when the employment to population ratio [15+, total (%)] growth rates were taken into account as the dependent variable (Table 4.6).

4.4 Discussion and Conclusions This chapter aims to test for the existence of increasing returns in the economic growth processes worldwide and to find signs that support the perspectives of divergence trends between the world countries defended, for example, by the literature associated with the Keynesian Theory. For that, the developments related to the Verdoorn and Kaldor laws were taken into account for regression with panel data. The statistical information was obtained from the World Bank for the period 2014–2020. As proxies for employment, the following variables were considered: total labour force; employment in industry (% of total employment); and employment to population ratio, 15+, total (%). Some of the disagreements at the basis of the discussions about if economic growth follows trends of convergence or divergence are related to the perspectives about scale economies. In fact, the authors associated with the Keynesian Theory

0.012*

Constant

Note *Statistically significant at 1%

0.089*

GDPpc growth rate

Prais–Winsten regression, correlated panels corrected standard errors (PCSEs), without autocorrelation

Coefficient

Independent variable

Approach

0.001

0.027

Standard error

8.500

3.340

Z

0.000

0.001

P > |z|

37.330*

Hausman test 25.986*

Pesaran’s test of cross-sectional independence

27,000,000.000*

Modified Wald test for groupwise heteroskedasticity

0.795

Wooldridge test for autocorrelation

Table 4.4 Results for the Kaldor model, with panel data over the period 2014–2020, considering the labour force (total) growth rates as the dependent variable

64 4 Constant, Decreasing or Increasing Returns to Scale: Evidence …

GDPpc growth rate

Regression with robust fixed effects 0.000

0.070

− 0.028

0.006*

Standard error

Coefficient

Note *Statistically significant at 1%

Constant

Independent variable

Approach

14.180

− 0.400

Z

0.000

0.693

P > |z|

22.500*

Hausman test 1.370

Pesaran’s test of cross-sectional independence

5,600,000.000*

Modified Wald test for groupwise heteroskedasticity

0.000

Wooldridge test for autocorrelation

Table 4.5 Results for the Kaldor model, with panel data over the period 2014–2020, considering the employment in industry (% of total employment) growth rates as the dependent variable

4.4 Discussion and Conclusions 65

GDPpc growth rate

Prais–Winsten regression, correlated panels corrected standard errors (PCSEs), without autocorrelation

0.030 0.002

− 0.004**

Standard error

0.193*

Coefficient

6.450 − 2.540

Z

Note *Statistically significant at 1%; **Statistically significant at 5%

Constant

Independent variable

Approach

0.011

0.000

P > |z|

30.080*

Hausman test 49.789*

Pesaran’s test of cross-sectional independence

36,000,000.000*

0.291

Modified Wald test Wooldridge test for groupwise for heteroskedasticity autocorrelation

Table 4.6 Results for the Kaldor model, with panel data over the period 2014–2020, considering the employment to population ratio [15+, total (%)] growth rates as the dependent variable

66 4 Constant, Decreasing or Increasing Returns to Scale: Evidence …

References

67

argue that economic growth worldwide follows patterns in the presence of increasing returns to scale, where the richer economies become richer and poorer ones become poorer. In these processes of divergence, the manufacturing sector plays a crucial role, considering its capacity to produce tradable goods, highlighting in this way the importance of international trade for the economic dynamics and the differences among countries. The COVID-19 pandemic had a relevant impact on the GDP per capita worldwide, as well as on the labour force and world employment. In any case, world employment in the industry seems to follow a different pattern that may be better explored in future research. Among the countries with the highest averages for employment in industry (% of total employment) are, for example, the following: Qatar, Oman, Czechia, Slovak Republic, Bahrain, Lesotho, Tunisia, Slovenia, Iran (Islamic Rep.), Turkmenistan, United Arab Emirates, Belarus, Poland and Hungary. The results obtained for the Kaldor model (relation between the employment growth and the output growth, where the employment is endogenous) present that there are strong signs of increasing returns to scale and this may compromise the processes of convergence in the medium and long terms. On the other hand, there are no signs of increasing returns to scale, when employment in the industry is considered. In terms of practical implications, there is evidence of increasing returns to scale in some circumstances and this may compromise the trends of convergence. In any case, more studies ate suggested to bring more contributions about these tendencies. More policies and programmes are needed to better support the countries with more economic weaknesses. For future research, it is suggested to analyse the contributions of other approaches and theories for these frameworks. Acknowledgements This work is funded by National Funds through the FCT—Foundation for Science and Technology, I.P., within the scope of the project Refa UIDB/00681/2020. Furthermore we would like to thank the CERNAS Research Centre and the Polytechnic Institute of Viseu for their support.

References 1. N. Kaldor, Causes of the Slow Rate of Economic Growth of the United Kingdom: An Inaugural Lecture (Cambridge University Press, London, 1966) 2. N. Kaldor, Strategic Factors in Economic Development (NewYork State School of Industrial and Labor Relations, Cornell University, Ithaca, NY, 1967) 3. P.J. Verdoorn, Fattori the Regolano Lo Sviluppo Della Produttività Del Lavoro. L’Industria 1, 3 (1949) 4. J. Mccombie, Economic-growth, Kaldor laws and the static dynamic Verdoorn law paradox. Appl. Econ. 14, 279 (1982) 5. A. Parikh, Differences in growth-rates and Kaldors laws. Economica 45, 83 (1978) 6. P. Stoneman, Kaldors law and British economic-growth—1800–1970. Appl. Econ. 11, 309 (1979)

68

4 Constant, Decreasing or Increasing Returns to Scale: Evidence …

7. N. Marconi, C. Reis, E. de Araujo, Manufacturing and economic development: the actuality of Kaldor’s first and second laws. Struct. Change Econ. Dyn. 37, 75 (2016) 8. P. Tridico, R. Pariboni, Inequality, financialization, and economic decline. J. Post Keynesian Econ. 41, 236 (2018) 9. A. Angeriz, J. Mccombie, M. Roberts, New estimates of returns to scale and spatial spillovers for EU regional manufacturing, 1986–2002. Int. Reg. Sci. Rev. 31, 62 (2008) 10. E. Bairam, Economic-growth and Kaldor law—the case of Turkey, 1925–78. Appl. Econ. 23, 1277 (1991) 11. E. Carnevali, Price mechanism and endogenous productivity in an open economy stock-flow consistent model. Metroeconomica 72, 22 (2021) 12. J. Hansen, J. Zhang, A Kaldorian approach to regional economic growth in China. Appl. Econ. 28, 679 (1996) 13. F. Jimenez, Growth and premature deindustrialization in Peru 1950–2015: a Kaldorian analysis. Revista Economia 40, 155 (2017) 14. V. Martinho, Socioeconomic impacts of forest fires upon Portugal: an analysis for the agricultural and forestry sectors. Sustainability 11 (2019) 15. P. Plummer, M. Taylor, Enterprise and competitive advantage in the Australian context: a spatial econometric perspective. Spat. Econ. Anal. 6, 311 (2011) 16. D. Barreto, C. Arenas, J. Sanchez, C. Varon, J. Duque, P. Rubio, Determinants of Economic Growth in Founder Countries of the a Pacific and Athelliance ASA and the Association of Southeast Asia Nations: Approach from a Systematic Literature Review (Equidad & Desarrollo, 2022) 17. D. Hoechle, Robust standard errors for panel regressions with cross-sectional dependence. Stand. Genomic Sci. 7, 281 (2007) 18. O. Torres-Reyna, Panel Data Analysis Fixed and Random Effects Using Stata (v. 6.0) (2007) 19. StataCorp, Stata 15 Base Reference Manual (2017) 20. StataCorp, Stata Statistical Software: Release 15 (2017) 21. Stata, Statistical Software for Data Science|Stata. https://www.stata.com/ 22. Web of Science, Web of Science Core Collection. https://www.webofscience.com/wos/woscc/ basic-search 23. World Bank, World Bank Open Data. https://data.worldbank.org

Chapter 5

Circular and Cumulative Processes in Economic Growth: The Importance of the External Demand

Abstract The Keynesian Theory and the New Economic Geography defend that the economic growth processes follow trends of divergence, through self-reinforced circular and cumulative phenomena, promoted by increasing returns to scale. The Keynesian Theory explains these processes highlighting the importance of the external demand and based on the Verdoorn law, where the productivity growth is endogenous and dependent on the output growth. The New Economic Geography developments are based on the real wage differences between economies and on transport costs. Following these contexts and the current conjuncture worldwide, this chapter proposes to test the impacts of external demand and real wages (as engines of circular and cumulative phenomena) on output growth. For that, statistical information associated with output growth, exports growth and wages was considered. To analyse these data, the Thirlwall law and the developments of the New Economic Geography were taken into account. The findings identified to reveal the importance of exports and employment compensation for the economic growth patterns worldwide and the relevance of dealing with these differences to promote more sustainable development. Keywords Keynesian theory · New economic geography · Output growth · Real wages

5.1 Introduction Tradable goods and international trade appear as important variables in the Keynesian models [1], namely with the contributions of Thirlwall [2] for the Keynesian Theory through developments that later became known as the Thirlwall law. The idea is that the output growth is dependent on the export growth [3]. Thirlwall followed, for example, the research of Verdoorn [4] and Kaldor [5, 6]. These developments of Thirlwall were considered and discussed by several studies [7, 8] and generated debate [9, 10]. In any case of highlighting the following frameworks where these approaches were taken into account: Brazilian context [11]; © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 V. J. P. D. Martinho, Economic Growth: Advances in Analysis Methodologies and Technologies, SpringerBriefs in Applied Sciences and Technology, https://doi.org/10.1007/978-3-031-38363-2_5

69

70

5 Circular and Cumulative Processes in Economic Growth

Spanish economy [12]; Portuguese and Spanish economic dynamics [13]; the economic context in Argentina [14]; government policies and exports in the growth processes [15]; the presence of structural heterogeneity [16]; Indian economy [17]; Kazakhstan context [18]. On the other hand, the New Economic Geography reinforced the idea of increasing returns to scale and the existence of circular and cumulative processes, where real wages and transport costs are some of the most important variables. In summary, the economies with higher real wages attract more population and economic activity and this is promoted by the intention of being closer (population and economic sectors) to save transport and communication costs [19, 20]. This creates self-reinforced processes of agglomeration, where the economic activity and population tend to concentrate in poles. Generally, two poles are generated, one stronger and a second weaker. This explains why the economic sectors and the population are agglomerated in big cities and regions inside the countries and worldwide expressions such as core-peripheral countries are found. The only forces of anti-agglomeration come from sectors dependent on immobile production factors, such as agriculture (dependent on immobile land). Considering these perspectives from the literature [21], this chapter intends to bring more contributions about the interrelationships between the output (gross domestic product (GDP) per capita) growth, the external demand [exports of goods and services (annual % growth)] and the wages [compensation of employees (% of expense)]. The statistical information for these variables was obtained from the World Bank [22] (some nations were removed, because statistical information for some years was missing), and these data were analysed through panel data approaches, following the procedures proposed by Hoechle [23], Torres-Reyna [24] and the Stata software [25–27].

5.2 Data Assessment The COVID-19 pandemic promoted several disruptions worldwide in different domains, including in the world GDP per capita (Fig. 5.1). These impacts are also visible in the world compensation of employees (% of expense) averages (Fig. 5.2) and world exports of goods and services (annual % growth) averages (Fig. 5.3). Nonetheless, there were some signs of a downward trend in these variables in previous years. Some of the nations with the highest averages for the exports of goods and services (annual % growth) are the following (Table 5.1): Ireland; Mongolia; Madagascar; Serbia; Cambodia; North Macedonia; Romania; Cyprus; Kiribati; Philippines ; Armenia; Poland; Lithuania; Moldova; Malta; Australia; Hungary; and Morocco.

5.2 Data Assessment

71

29500 29000 28500 28000 27500 27000 26500 26000 25500 25000

24500

2013

2014

2015

2016

2017

2018

2019

2020

2019

2020

Fig. 5.1 World GDP per capita averages, over the period 2013–2020

25

25

24

24

23

23 2013

2014

2015

2016

2017

2018

Fig. 5.2 World compensation of employees (% of expense) averages, over the period 2013–2020

Some of the countries with the lowest averages are, for example, Austria, Germany, Brazil, Guatemala, Japan, Canada, Dominican Republic, Cote d’Ivoire, Marshall Islands, Ghana, Indonesia, UK, Spain, Malaysia and Norway (Table 5.2).

72

5 Circular and Cumulative Processes in Economic Growth

10

5

0 2013

2014

2015

2016

2017

2018

2019

2020

-5

-10

-15

-20 Fig. 5.3 World exports of goods and services (annual % growth) averages, over the period 2013– 2020

Table 5.1 Averages, across the period 2013–2020, for the world GDP per capita (PPP, constant 2017 international $), compensation of employees (% of expense) and exports of goods and services (annual % growth) [countries with the highest values for the exports of goods and services (annual % growth)] Country name

GDP per capita, PPP (constant Compensation Exports of goods and services 2017 international $) of employees (annual % growth) (% of expense)

Ireland

74,287.033

25.461

12.976

Mongolia

11,426.893

16.975

12.265

1519.510

49.430

8.805

16,627.580

15.421

7.853

Madagascar Serbia Cambodia

3854.235

44.149

7.824

North Macedonia

15,475.400

14.949

7.408

Romania

26,076.893

18.520

7.376

Cyprus

37,206.528

29.417

6.588

Kiribati

1972.898

37.682

6.538

Philippines

7656.543

36.135

6.124

Armenia

12,285.697

20.992

6.000

Poland

29,443.409

13.836

5.779

Lithuania

33,084.788

16.082

5.449

Moldova

11,333.831

11.879

5.321 (continued)

5.2 Data Assessment

73

Table 5.1 (continued) Country name

GDP per capita, PPP (constant Compensation Exports of goods and services 2017 international $) of employees (annual % growth) (% of expense)

Malta

40,887.155

30.634

4.797

Australia

48,155.224

10.576

4.563

Hungary

29,008.597

18.480

4.401

Morocco

7611.470

45.925

4.383

Bosnia and Herzegovina

13,103.361

26.852

4.364

Slovenia

35,577.021

17.762

4.136

Luxembourg 113,909.349

20.196

3.926

Singapore

92,470.053

24.850

3.720

Bulgaria

21,033.103

18.626

3.614

Latvia

28,076.049

17.563

3.302

Costa Rica

19,543.662

43.613

3.219

2098.386

38.789

3.186

Slovak Republic

29,512.860

14.839

3.078

Turkiye

26,558.619

23.550

3.074

Netherlands

54,107.201

7.695

3.071

Georgia

13,295.290

18.588

3.026

Czechia

Mali

37,453.429

12.653

2.963

Congo, Rep.

4342.225

28.733

2.778

Nepal

3442.251

21.320

2.751

Croatia

26,359.574

15.171

2.658

Sweden

51,057.956

9.245

2.545

Portugal

32,187.031

21.143

2.523

Cameroon

3637.522

38.486

2.430

Belgium

49,843.926

6.178

2.423

Russian Federation

26,223.698

14.080

2.389

Nicaragua

5497.515

38.033

2.387

Denmark

54,309.159

11.023

2.350

Israel

38,546.649

23.464

2.248

Paraguay

13,139.618

52.539

2.167

Estonia

32,988.181

16.472

2.166

United Arab Emirates

68,524.188

34.951

2.138 (continued)

74

5 Circular and Cumulative Processes in Economic Growth

Table 5.1 (continued) Country name

GDP per capita, PPP (constant Compensation Exports of goods and services 2017 international $) of employees (annual % growth) (% of expense)

West Bank and Gaza

6117.647

54.554

2.044

Switzerland

68,883.347

6.927

2.035

Greece

28,376.543

20.850

1.954

Korea, Rep.

40,213.834

10.094

1.689

Finland

46,653.841

7.809

1.644

Table 5.2 Averages, across the period 2013–2020, for the world GDP per capita (PPP, constant 2017 international $), compensation of employees (% of expense) and exports of goods and services (annual % growth) [countries with the lowest values for the exports of goods and services (annual % growth)] Country name

GDP per capita, PPP (constant 2017 international $)

Compensation of employees (% of expense)

Exports of goods and services (annual % growth)

Austria

53,666.923

10.564

1.624

Germany

52,007.021

5.756

1.603

Brazil

14,833.217

11.440

1.563

8232.307

35.188

1.553

Japan

40,657.003

5.905

1.513

Canada

47,720.565

10.495

1.416

Dominican Republic

15,993.882

37.401

1.309

Cote d’Ivoire

4608.907

36.481

1.133

Marshall Islands

4693.180

37.686

1.094

Guatemala

Ghana

4897.731

36.305

1.055

Indonesia

10,689.394

15.628

1.032

UK

45,069.898

14.141

1.021

Spain

37,961.386

6.666

1.013

Malaysia

25,120.830

32.470

0.985

Norway

63,335.716

15.379

0.928

Albania

12,514.199

24.055

0.887

Botswana

14,393.567

44.825

0.790

Peru

12,097.770

18.894

0.740

Sri Lanka

12,855.122

27.621

0.702

France

43,858.153

18.170

0.692 (continued)

5.2 Data Assessment

75

Table 5.2 (continued) Country name

El Salvador Italy Bangladesh

GDP per capita, PPP (constant 2017 international $)

Compensation of employees (% of expense)

Exports of goods and services (annual % growth)

8441.548

41.212

0.686

40,835.448

13.323

0.595

4749.059

23.438

0.405

Iceland

53,622.591

23.989

0.352

Chile

24,340.762

21.168

0.255

USA

59,383.032

9.316

0.230

Belarus

18,750.779

12.812

0.228

New Zealand

41,543.481

24.031

0.136

9844.823

38.928

0.028

Kazakhstan

24,837.310

6.362

− 0.013

South Africa

13,793.882

14.054

− 0.249

Saudi Arabia

45,767.731

54.943

− 0.518

Bhutan

10,413.489

36.887

− 0.584

Uruguay

22,621.604

25.478

− 0.805

Namibia

10,222.098

44.882

− 0.909

Thailand

16,599.198

27.556

− 0.912

Jamaica

2547.941

42.784

− 1.093

14,092.794

10.674

− 1.321

Ethiopia

1906.602

11.145

− 1.796

Solomon Islands

2579.669

34.530

− 3.194

Mauritius

21,243.551

34.795

− 3.432

Vanuatu

2947.456

46.346

− 3.601

Ukraine

12,196.135

13.560

− 4.471

Lesotho Colombia

9957.701

48.001

− 4.702

Bahamas, The

33,331.213

34.451

− 5.305

Equatorial Guinea

21,516.793

20.201

− 6.293

16,954.511

24.238

− 6.323

121,176.470

30.430

− 7.596

Jordan

Lebanon Macao SAR, China

76

5 Circular and Cumulative Processes in Economic Growth

5.3 Findings Considering the Developments of Thirlwall and Those of the New Economic Geography The developments of Thirlwall suggest that the external demand (exports) of tradable goods is the engine of economic growth. These developments become known in the economic theory as the Thirlwall law. The subjacent idea is that output growth is dependent on international trade. Following these perspectives, a regression was carried out considering the world GDP per capita growth rate as the dependent variable and the exports of goods and services (annual growth rate) as the independent variable (Table 5.3). The results confirm the positive and statistically significant relationship between these two variables. The New Economic Geography emphasise the importance of wages to promote circular and cumulative processes of agglomeration of the population and economic activities. The findings presented in Table 5.4 show that, in fact, the GDP per capita growth rate positively impacts the compensation of employees (% of expense) growth rate. For the world GDP per capita growth rate as dependent variables, the results were not statistically significant, showing that the impacts are from the GDP per capita to the compensations of employees.

5.4 Discussion and Conclusions This research aims to investigate the relationships between international trade and the wages of the workers with the output of the countries. To achieve these proposals, developments associated with the Keynesian Theory (namely those related to the Thirlwall law) and the New Economic Geography were taken into account. These theories predict increasing returns to scale in the dynamics of economic growth and consequently tendencies of divergence among regions and countries, through selfreinforced processes of circular and cumulative phenomena. Data from the World Bank were considered for the period 2013–2020 and the following variables: GDP per capita; exports of goods and services (annual % growth), as proxies for the external demand; and compensation of employees (% of expense), as proxies for the wages. From the theory, it is expected that the external demand (the exports) promotes positive externalities on the output (some of the explanations for these processes become known in the theory as the Thirlwall law). In practice, these assumptions argue that output growth is endogenous and dependent on international trade growth and subsequently through the Verdoorn–Kaldor laws this has implications on the competitiveness of the economies. On the other hand, the New Economic Geography defends that the population and economic activity tend to agglomerate to save transport and communication costs.

0.005

Constant

0.003

0.026

Standard error

1.330

11.070

Z

Note *Statistically significant at 1%; **Statistically significant at 5%

0.289*

Exports of goods and services (annual growth rate)

Prais–Winsten regression, correlated panels corrected standard errors (PCSEs), without autocorrelation

Coefficient

Independent variable

Approach

0.184

0.000

P > |z|

6.040**

Hausman test 39.194*

Pesaran’s test of cross-sectional independence

35,693.590*

Modified Wald test for groupwise heteroskedasticity

1.336

Wooldridge test for autocorrelation

Table 5.3 Results for the model with the GDP per capita growth rates as the dependent variable, considering panel data over the period 2013–2020

5.4 Discussion and Conclusions 77

GDPpc growth rate

Prais–Winsten regression, correlated panels corrected standard errors (PCSEs), without autocorrelation

Note *Statistically significant at 1%

Constant

Independent variable

Approach

0.075 0.005

− 0.006

Standard error

0.221*

Coefficient

2.920 − 1.170

Z

0.244

0.003

P > |z|

15.970*

Hausman test 12.317*

Pesaran’s test of cross-sectional independence

140,000.000*

2.682

Modified Wald test Wooldridge test for groupwise for heteroskedasticity autocorrelation

Table 5.4 Results for the model with the compensation of employees (% of expense) growth rates as the dependent variable, considering panel data over the period 2013–2020

78 5 Circular and Cumulative Processes in Economic Growth

References

79

The pandemic impacted the world compensation of employees (% of expense) averages (Fig. 5.2) and world exports of goods and services (annual % growth) averages. These trends, however, seem to come already from previous years. Ireland, Mongolia, Madagascar, Serbia, Cambodia, North Macedonia, Romania, Cyprus, Kiribati, Philippines, Armenia, Poland, Lithuania, Moldova, Malta, Australia, Hungary and Morocco are some of the countries with the highest averages for exports of goods and services (annual % growth). The regressions carried out with panel data, to try to test the assumptions related to the Thirlwall law, show that, in fact, the GDP per capita growth rate is endogenous and dependent on the exports of goods and services (annual growth rate). On the other hand, considering the New Economic Geography, the findings reveal that the compensation of employees (% of expense) growth rate is dependent on the world GDP per capita. In terms of practical implications, international trade is determinant for the processes of economic growth worldwide and these dynamics impact employment conditions, namely the compensation of employees dynamics. It is recommended special attention, by the policy and decision-makers, about the trade dynamics and employment conditions in the design of policy instruments and measures. For future research, it could be interesting to bring more insights into economic growth and sustainability. Acknowledgements This work is funded by National Funds through the FCT—Foundation for Science and Technology, I.P., within the scope of the project Refa UIDB/00681/2020. Furthermore we would like to thank the CERNAS Research Centre and the Polytechnic Institute of Viseu for their support.

References 1. D. Ciriaci, D. Palma, The role of knowledge-based supply specialisation for competitiveness: a spatial econometric approach. Pap. Reg. Sci. 87, 453 (2008) 2. A.P. Thirlwall, The balance of payments constraint as an explanation of international growth rate differences. BNL Q. Rev. 32, 45 (1979) 3. D. Alencar, F. Jayme, G. Britto, Growth, distribution, and external constraints: a post-Kaleckian model applied to Brazil. Rev. Polit. Econ. 33, 44 (2021) 4. P.J. Verdoorn, Fattori the Regolano Lo Sviluppo Della Produttività Del Lavoro. L’Industria 1, 3 (1949) 5. N. Kaldor, Causes of the Slow Rate of Economic Growth of the United Kingdom: An Inaugural Lecture (Cambridge University Press, London, 1966) 6. N. Kaldor, Strategic Factors in Economic Development (NewYork State School of Industrial and Labor Relations, Cornell University, Ithaca, NY, 1967) 7. R. Blecker, New advances and controversies in the framework of balance-of-paymentsconstrained growth. J. Econ. Surv. 36, 429 (2022) 8. M. Davila-Fernandez, J. Oreiro, M. Davila, Endogenizing non-price competitiveness in a BoPC growth model with capital accumulation. Struct. Change Econ. Dyn. 44, 77 (2018) 9. P. McGregor, J. Swales, Thirlwall law and balance of payments constrained growth—further comment on the debate. Appl. Econ. 23, 9 (1991)

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10. J. McCombie, Thirlwalls law and balance of payments constrained growth—more on the debate. Appl. Econ. 24, 493 (1992) 11. D. Alencar, E. Strachman, Balance-of-payments-constrained growth in Brazil: 1951–2008. J. Post Keynesian Econ. 36, 673 (2014) 12. J. Alonso, Growth and the external constraint: lessons from the Spanish case. Appl. Econ. 31, 245 (1999) 13. C. Garcimartin, V. Kvedaras, L. Rivas, Business cycles in a balance-of-payments constrained growth framework. Econ. Model. 57, 120 (2016) 14. J. Massot, R. Merga, A balance-of-payments-constrained growth model for a small commodity exporting country: Argentina between 1971 and 2016. Int. Rev. Appl. Econ. 36, 562 (2022) 15. G. Morlin, Growth led by government expenditure and exports: public and external debt stability in a supermultiplier model. Struct. Change Econ. Dyn. 62, 586 (2022) 16. H. Nishi, A multi-sectoral balance-of-payments-constrained growth model with sectoral heterogeneity. Struct. Change Econ. Dyn. 39, 31 (2016) 17. A. Razmi, Balance-of-payments-constrained growth model: the case of India. J. Post Keynesian Econ. 27, 655 (2005) 18. Z. Ybrayev, Balance-of-payments-constrained growth model: an application to the Kazakhstan’s economy. Eurasian Econ. Rev. 12, 745 (2022) 19. P. Krugman, Increasing returns and economic geography. J. Polit. Econ. 99, 483 (1991) 20. M. Fujita, P. Krugman, A.J. Venables, The Spatial Economy: Cities, Regions, and International Trade (The MIT Press, 1999) 21. Web of Science, Web of Science Core Collection. https://www.webofscience.com/wos/woscc/ basic-search 22. World Bank, World Bank Open Data. https://data.worldbank.org 23. D. Hoechle, Robust standard errors for panel regressions with cross-sectional dependence. Stand. Genomic Sci. 7, 281 (2007) 24. O. Torres-Reyna, Panel Data Analysis Fixed and Random Effects Using Stata (v. 6.0) (2007) 25. StataCorp, Stata 15 Base Reference Manual (2017) 26. StataCorp, Stata Statistical Software: Release 15 (2017) 27. Stata, Statistical Software for Data Science|Stata. https://www.stata.com/

Chapter 6

Interrelationships Between Economic Growth and Sustainability: Highlights from the Literature

Abstract The great discussions currently are about how to make compatible economic growth and sustainability. In other words, the big challenge for the next years is to identify strategies that promote economic growth and human well-being without compromising the environment and the future of the planet. This is, in fact, a hard task for the current and next generations. The needs of humanity for more food and employment are in an increasing trend and these frameworks call for continuous economic growth worldwide, but these tendencies are supported often compromising the availability of resources for the next generations. The objective of this research is to bring more insights from the scientific literature about the interrelationships between economic growth and sustainability. For that, several documents, obtained from the Scopus database and related to the issues here addressed, were considered. These documents were analysed through bibliometric approaches and following the VOSviewer software procedures. With this research, several contributions for more sustainable economic growth were presented. Keywords Bibliometric analysis · Systematic review · VOSviewer · Scopus database

6.1 Introduction The manufacturing sector plays a fundamental role in economic growth, but, in some circumstances and sectors, the industry has relevant impacts on climate change and, consequently, on sustainability. These contexts call for a deeper understanding of the supply chains to better identify approaches for more sustainable development [1]. The sustainability of the tourism sector also has aroused the curiosity of the scientific community [2], including its relationship with water quality [3] and environmental degradation [4].

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 V. J. P. D. Martinho, Economic Growth: Advances in Analysis Methodologies and Technologies, SpringerBriefs in Applied Sciences and Technology, https://doi.org/10.1007/978-3-031-38363-2_6

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Public policies are crucial to make compatible the different dimensions of sustainability. This is true for the Mexican frameworks [5] as it is for other frameworks worldwide. Renewable energy policies, for instance, may be interesting instruments to promote economic growth without compromising the environmental dimensions [6]. The needs for continuous, and ever-increasing, economic growth create additional pressures on the available resources, specifically on the water [7], and bring new concerns about greenhouse gas emissions. This is particularly worrying in the agricultural sector, where the concerns with food security may compromise, in certain cases, the goals to mitigate the carbon emissions. The understanding of the relationships between food security and carbon emissions is crucial to control global warming [8]. Land tenure is another dimension to take into account in the discussions about food security [9]. If economic growth creates difficulties to deal with climate change, global warming also impacts economic asymmetries [10]. Another current concern for the governments is the sustainability of urban infrastructures and the associated changes verified in the last decades. In fact, currently, there is a continuous migration of the population from the rural areas to the cities [11], creating problems of desertification in some regions and difficulties of overpopulation in the cities [12]. Of course, there are differences in these phenomena worldwide, but this is a real trend verified in many nations. The new technologies may bring new perspectives for a greener economy [13]. Considering this literature survey, this chapter aims to highlight insights from the literature about the relationships between economic growth, sustainability and climate change worldwide. For that, a bibliometric analysis and systematic review [14] were carried out based on 959 documents obtained on a search performed on 17 May 2023 in the Scopus [15] database for the following topics: “economic growth”; and “sustainability”; and “climate change”. For the bibliometric analysis, the VOSviewer software procedures were followed [16–18]. For the bibliometric analysis, bibliographic data were considered, as well as the following links: cooccurrence; bibliographic coupling; and co-citation. The metrics were obtained with full counting and the next conditions: 1 as the minimum number of occurrences of a keyword; 1 as the minimum number of documents of a country; 1 as the minimum number of documents of an organisation; 1 as the minimum number of citations of an author; 0 as the minimum number of citations of a document; and 1 as the minimum number of citations of a source. To identify the top items (keywords, countries, organisations, authors, documents and sources), the total link strength metric was taken into account [19].

6.2 Bibliometric Analysis

83

6.2 Bibliometric Analysis The keywords with the highest values for the total link strength metric (related to the number of documents where they appear together) are the following (Table 6.1): climate change; sustainable development; sustainability; economic growth; economics; and economic development. These keywords highlight a clear concern with the impacts of carbon dioxide on the relationships between economic growth, sustainability and climate change. On the other hand, this group of keywords shows that, sometimes, different expressions are used to express the same context. The keywords with higher relatedness present also a relevant number of occurrences (Fig. 6.1) and average citations. China, Turkey, Pakistan, UK, Malaysia, USA, Nigeria, Australia, India and Spain are the countries of affiliation of the authors with the biggest total link strength (Table 6.2). The top 10 countries of affiliation have also an important number of documents (Fig. 6.2) and citations. Between the organisations with the greatest values for the total link strength appear the following (Table 6.3): Faculty of Economics Administrative and Social Sciences, Istanbul Gelisim University; Institute of Climate Change, Universiti Kebangsaan Malaysia; School of Management and Economics, Beijing Institute of Technology; Australian National University’s, Fenner School of Environment and Society; and Australian Griffith University. Some of the top 10 organisations have also a relevant number of documents (Fig. 6.3) and citations. Table 6.1 Top 10 all keywords items, with the highest total link strength, for co-occurrence links and the following topics: “economic growth”; and “sustainability”; and “climate change” All keywords

Total link strength

Occurrences

Average publication year

Average citations

Climate change

6740

510

2018

30

Sustainable development

5301

355

2018

33

Sustainability

4799

406

2018

29

Economic growth

4580

344

2019

28

Economics

3061

187

2017

36

Economic development

2764

142

2019

39

Carbon dioxide

2573

136

2019

43

Environmental economics

1902

112

2018

45

Environmental sustainability

1851

98

2019

46

Economic and social effects

1723

87

2019

39

84

6 Interrelationships Between Economic Growth and Sustainability …

Fig. 6.1 All keywords items for co-occurrence links and the following topics: “economic growth”; and “sustainability”; and “climate change” Table 6.2 Top 10 countries items, with the highest total link strength, for bibliographic coupling links and the following topics: “economic growth”; and “sustainability”; and “climate change” Countries

Total link strength

Documents

Citations

Average publication year

China

39,151

135

3516

2020

Turkey

32,217

62

3046

2021

Pakistan

20,783

44

914

2022

UK

18,704

100

3512

2017

Malaysia

18,088

55

1470

2021

USA

16,110

150

3823

2016

Nigeria

12,320

21

850

2021

Australia

11,450

75

2577

2016

India

11,109

72

736

2019

Spain

10,260

46

1005

2020

6.2 Bibliometric Analysis

85

Fig. 6.2 Countries items for bibliographic coupling links and the following topics: “economic growth”; and “sustainability”; and “climate change” Table 6.3 Top 10 organisations items, with the highest total link strength, for bibliographic coupling links and the following topics: “economic growth”; and “sustainability”; and “climate change” Organisations

Total link strength

Documents

Citations

Average publication year

Faculty of Economics 13,828 Administrative and Social Sciences, Istanbul Gelisim University, Istanbul, Turkey

13

408

2021

Institute of Climate Change, Universiti Kebangsaan Malaysia, Bangi, 43600, Malaysia

12,935

10

180

2022

School of Management and Economics, Beijing Institute of Technology, Beijing, 100081, China

9476

9

397

2022

Australian National University’s, Fenner School of Environment and Society, Australia

6463

1

47

2010

Griffith University, Australia

6463

1

47

2010 (continued)

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6 Interrelationships Between Economic Growth and Sustainability …

Table 6.3 (continued) Organisations

Total link strength

Documents

Citations

Average publication year

Faculty of Social Sciences and Humanities, Universiti Kebangsaan Malaysia, Bangi, 43600, Malaysia

6453

5

97

2022

Department of Finance & Accounting, Akfa University, 1st Deadlock, 10th Kukcha Darvoza Street, Tashkent, Uzbekistan

6070

4

171

2022

Faculty of Engineering And Built Environment, Universiti Kebangsaan Malaysia, Bangi, 43600, Malaysia

5812

4

60

2022

Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi, 43,600, Malaysia

5812

4

60

2022

Economic and Finance Application and Research Center, ˙IStanbul Ticaret University, Istanbul, Turkey

4417

3

6

2022

The authors with the highest total link strength (related to the number of times they are cited together) and the respective citations are those presented in Table 6.4 and Fig. 6.4. The top 10 documents are those exhibited in Table 6.5. Energy policy, Energy, Environmental Science and Pollution Research, Sustainability and Science are some of the sources with the biggest total link strength (Table 6.6), and some of these items (sources) have a significant number of citations (Fig. 6.5).

6.2 Bibliometric Analysis

87

Fig. 6.3 Organisations items for bibliographic coupling links and the following topics: “economic growth”; and “sustainability”; and “climate change”

Table 6.4 Top 10 cited authors items, with the highest total link strength, for co-citation links and the following topics: “economic growth”; and “sustainability”; and “climate change” Cited authors

Total link strength

Citations

Shahbaz, M.

51,977

475

Hargroves, K.

49,988

65

Adebayo, T.S.

45,783

359

Ozturk, I.

43,154

398

Smith, M.

41,824

62

Desha, C.

40,229

54

Bekun, F.V.

38,566

334

Stern, N.

35,702

125

Kirikkaleli, D.

32,200

256

Stasinopoulos, P.

32,099

42

88

6 Interrelationships Between Economic Growth and Sustainability …

Fig. 6.4 Cited authors items for co-citation links and the following topics: “economic growth”; and “sustainability”; and “climate change” Table 6.5 Top 10 documents items, with the highest total link strength, for bibliographic coupling links and the following topics: “economic growth”; and “sustainability”; and “climate change” Documents

URL

Total link strength

Citations

Raihan [20]

https://doi.org/10.1007/s10 669-022-09848-0

606

23

Raihan [21]

https://doi.org/10.1016/j. nexus.2022.100144

597

8

Smith [22]

https://doi.org/10.4324/978 1849776370

561

47

Raihan [23]

https://doi.org/10.1016/j. nexus.2022.100067

556

24

Raihan [24]

https://doi.org/10.1007/s41 685-023-00278-7

547

3

Raihan [25]

https://doi.org/10.1007/s40 710-022-00590-y

543

16

Shujah-Ur-Rahman [26]

https://doi.org/10.1007/s11 356-019-06343-z

536

77

Raihan [27]

https://doi.org/10.1016/j.rcr adv.2022.200096

515

37

Amin [28]

https://doi.org/10.1080/135 04509.2023.2166142

495

0

Raihan [29]

https://doi.org/10.1016/j. nexus.2022.100148

494

4

6.4 Discussion and Conclusions

89

Table 6.6 Top 10 cited sources items, with the highest total link strength, for co-citation links and the following topics: “economic growth”; and “sustainability”; and “climate change” Cited sources

Total link strength

Citations

Energy Policy

54,331

1141

The Stern Review: The Economics of Climate Change

38,919

39

Plan B 3.0: Mobilizing to Save Civilization

36,792

36

Oecd Environmental Outlook to 2030

30,845

31

Energy

26,272

556

Environ Sci Pollut Res

23,381

588

Sustainability

22,237

633

Factor Five: Transforming the Global Economy Through 80% Improvements in Resource Productivity

21,806

22

Science

20,723

438

Global Environment Outlook: Environment for Development (Geo-4) Report

19,741

19

Fig. 6.5 Cited sources items for co-citation links and the following topics: “economic growth”; and “sustainability”; and “climate change”

6.3 Insights from the Literature Renewable energy use, technological innovation, digital transition, sustainable cities and economic sectors, human capital and adjusted policies are some of the dimensions highlighted by the literature (Table 6.7) to make compatible economic growth and sustainability in times of concern with consequences of the climate change.

6.4 Discussion and Conclusions This chapter intends to highlight insights into the relationships among economic growth and sustainability. To achieve these objectives, a bibliometric analysis and a literature review, based on this bibliometric assessment, were carried out. A search on the Scopus database was performed on 17 May 2023 for the topics: “economic growth”; and “sustainability”; and “climate change”. This bibliometric analysis followed the procedures proposed by the VOSviewer software. In this assessment co-occurrence, bibliographic coupling and co-citation links were considered.

90

6 Interrelationships Between Economic Growth and Sustainability …

Table 6.7 Some highlights from the literature Documents

URL

Insights

Raihan [20]

https://doi.org/10.1007/ s10669-022-09848-0

Using more renewable energy and technological innovation may mitigate Malaysia’s emissions, however economic growth reduce the environmental quality

Raihan [21]

https://doi.org/10.1016/j. nexus.2022.100144

Variables that contribute to reduce the environmental quality in Bangladesh

Smith [22]

https://doi.org/10.4324/ 9781849776370

Separate economic growth from environment

Raihan [23]

https://doi.org/10.1016/j. nexus.2022.100067

Renewable energy and smart farming to improve the environmental sustainability

Raihan [24]

https://doi.org/10.1007/ s41685-023-00278-7

Potentialities from agriculture to mitigate emissions in Vietnam

Raihan [25]

https://doi.org/10.1007/ s40710-022-00590-y

Renewable energy, sustainable cities and tourism to promote environmental sustainability

Shujah-Ur-Rahman [26]

https://doi.org/10.1007/ s11356-019-06343-z

Renewable energy and human capital to achieve sustainability

Raihan [27]

https://doi.org/10.1016/j. rcradv.2022.200096

Renewable energy use and sustainable forest planning to better environmental sustainability

Amin [28]

https://doi.org/10.1080/ 13504509.2023.2166142

Practical signs and policy suggestions for sustainability

Raihan [29]

https://doi.org/10.1016/j. nexus.2022.100148

Practices to increase the environmental sustainability

Some economic sectors and activities, such as manufacturing, are fundamental for economic growth, but bring also, in some circumstances, negative impacts on climate change and environmental sustainability. The national and international policies and institutions have here a crucial role to make more compatible economic growth with sustainable development. The main challenge for the stakeholders is to make compatible the needs of economic growth, because of the employment (for example), and the preservation of the planet’s natural resources. Climate change, sustainable development, sustainability, economic growth, economics and economic development are some of the keywords found in the documents analysed, revealing concerns, for example, with the impacts of carbon dioxide emissions. Some of the relevant countries of affiliation of the authors for the network assessed (taking into account the approaches followed) are the following: China; Turkey; Pakistan; UK; Malaysia; USA; Nigeria; Australia; India; and Spain. The relevant sources are, for example, the following: Energy policy; Energy; Environmental Science and Pollution Research; Sustainability; and Science.

References

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The literature review based on the bibliometric analysis highlight, to make compatible economic growth with sustainability (mitigating climate change) the importance of the use of renewable energy, promote innovation, consider the artificial intelligence approaches, promote urban sustainability and design adjusted policies and programs. In terms of practical implications, it seems that there is a field to be explored about these topics, particularly including other domains from the science and extending the network to other countries and organisations. In terms of policy recommendations, it is suggested to create conditions and programs to promote more studies on topics less explored in countries with fewer research dynamics and generate conditions to make more visible some work already realised and not published. For future research, it could be relevant to analyse the relationships between sustainability and the life cycle tools. Acknowledgements This work is funded by National Funds through the FCT—Foundation for Science and Technology, I.P., within the scope of the project Refª UIDB/00681/2020. Furthermore we would like to thank the CERNAS Research Centre and the Polytechnic Institute of Viseu for their support.

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12. A.L.C. Ferrer, A.M.T. Thomé, A.J. Scavarda, Sustainable urban infrastructure: a review. Resour. Conserv. Recycl. 128, 360 (2018) 13. P. Tamasiga, H. Onyeaka, E.H. Ouassou, Unlocking the green economy in African countries: an integrated framework of FinTech as an enabler of the transition to sustainability. Energies 15(22), 8658 (2022) 14. D. Moher, A. Liberati, J. Tetzlaff, D.G. Altman, Preferred reporting items for systematic reviews and meta-analyses: the prisma statement. BMJ 339, b2535 (2009) 15. Scopus, Scopus database, https://www.scopus.com/search/form.uri?display=basic#basic 16. N.J. van Eck, L. Waltman, Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics 84, 523 (2010) 17. N.J. van Eck, L. Waltman, Manual for VOSviewer version 1.6.19 (2023). https://www.vosvie wer.com/documentation/Manual_VOSviewer_1.6.19.pdf 18. VOSviewer, VOSviewer—Visualizing scientific landscapes, https://www.vosviewer.com// 19. V.J.P.D. Martinho, Impacts of the COVID-19 pandemic and the Russia-Ukraine conflict on land use across the world. Land 11, 10 (2022) 20. A. Raihan, R.A. Begum, M.N.M. Said, J.J. Pereira, Relationship between economic growth, renewable energy use, technological innovation, and carbon emission toward achieving Malaysia’s Paris agreement. Environ. Syst. Decis. 42, 586 (2022) 21. A. Raihan, D.A. Muhtasim, S. Farhana, M.A.U. Hasan, M.I. Pavel, O. Faruk, M. Rahman, A. Mahmood, Nexus between economic growth, energy use, urbanization, agricultural productivity, and carbon dioxide emissions: new insights from Bangladesh. Energy Nexus 8, 100144 (2022) 22. C.D. Smith Charlie Hargroves, M. Harrison, Cents and Sustainability: Securing Our Common Future by Decoupling Economic Growth from Environmental Pressures (Routledge, London, 2011) 23. A. Raihan, A. Tuspekova, The nexus between economic growth, renewable energy use, agricultural land expansion, and carbon emissions: new insights from Peru. Energy Nexus 6, 100067 (2022) 24. A. Raihan, An econometric evaluation of the effects of economic growth, energy use, and agricultural value added on carbon dioxide emissions in Vietnam. Asia-Pac. J. Reg. Sci. (2023). https://doi.org/10.1007/s41685-023-00278-7 25. A. Raihan, D.A. Muhtasim, M.I. Pavel, O. Faruk, M. Rahman, Dynamic impacts of economic growth, renewable energy use, urbanization, and tourism on carbon dioxide emissions in Argentina. Environ. Process. 9, 38 (2022) 26. Shujah-ur-Rahman, S. Chen, S. Saud, N. Saleem, M.W. Bari, Nexus between financial development, energy consumption, income level, and ecological footprint in CEE countries: do human capital and biocapacity matter? Environ. Sci. Pollut. Res 26, 31856 (2019) 27. A. Raihan, A. Tuspekova, Toward a sustainable environment: nexus between economic growth, renewable energy use, forested area, and carbon emissions in Malaysia. Resour. Conserv. Recycl. Adv. 15, 200096 (2022) 28. N. Amin, H. Song, M.S. Shabbir, M.U. Farrukh, I. Haq, Moving towards a sustainable environment: do disaggregated energy consumption, natural resources, financial development and economic globalization really matter? Int. J. Sustain. Dev. World Ecol. 30(5), 515–532 (2023). https://doi.org/10.1080/13504509.2023.2166142 29. A. Raihan, A. Tuspekova, Towards sustainability: dynamic nexus between carbon emission and its determining factors in Mexico. Energy Nexus 8, 100148 (2022)

Chapter 7

Sustainable Development: Contributions from Life Cycle Cost Analysis

Abstract To promote more sustainable developments, several approaches have been developed to support more adjusted assessments and the processes of policy design by governments and international organisations. The life cycle assessment and the life cycle cost analysis are examples of methodologies used to better understand the impacts of products and investments on sustainability. Nonetheless, the scientific literature shows that there is a field to be explored about the contributions of the life cycle cost analysis for more sustainable development. Considering these motivations, this research proposes to highlight contributions from the literature about the interrelationships among sustainability and life cycle cost analysis. To achieve these objectives, various documents from the Scopus database were considered for topics associated with these issues. These documents were assessed through co-citation links and considering authors, sources and documents as items. The top documents identified through this bibliometric analysis were taken into account for a survey of the literature. The results obtained provide relevant insights on the relationships between sustainable development and the life cycle cost analysis. Keywords Co-citation links · Authors, sources and documents items · Highlights from the literature

7.1 Introduction The life cycle assessment and life cycle cost(ing) analysis/assessment are among the approaches considered in the sustainable development investigation, as parts of the life cycle sustainability assessment [1]. The life cycle cost analysis is an interesting methodology to assess the economic dimensions of products and processes, supporting the stakeholders to follow more sustainable developments [2]. The use of life cycle methodologies in the agricultural sector is increasing [3], but there is still some work to do, namely because the life cycle cost analysis is not yet harmonised for agri-food productions [4].

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 V. J. P. D. Martinho, Economic Growth: Advances in Analysis Methodologies and Technologies, SpringerBriefs in Applied Sciences and Technology, https://doi.org/10.1007/978-3-031-38363-2_7

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The scientific literature has analysed the consideration of the life cycle cost analysis in the following topics: pavement networks [5]; construction industry [6, 7]; wastewater treatment [8]; building life cycle evaluation [9]; gold mining activities [10]; solid oxide fuel cells [11]; and green roofs [12]. The scientific literature has brought relevant insights about the relevance of bibliometric analysis as support for systematic reviews. In fact, the bibliometric approaches provide adjusted information to more objectively identify the documents to be surveyed through systematic review [13, 14]. There are still, however, some gaps in the literature about the interlinkages between sustainable development and life cycle cost analysis, showing that there is a field to be explored about the contributions of the life cycle costing assessment for sustainability. Considering these motivations, this research proposes to bring more insights into the contributions of the life cycle cost analysis for sustainable development worldwide and the different sectors. For that, 1489 documents were considered. These documents were obtained from the Scopus [15] database, in a search that was carried out on 17 May 2023, for the following topics: “sustainable development”; and “life cycle cost*”. These studies were analysed through bibliometric analysis, following the VOSviewer procedures [16–18], which was taken into account as support for a literature survey [19]. To identify the top items analysed (all keywords, countries, organisations, authors, documents and sources), it was followed the approaches considered by Martinho [20], for example. In the figures presented in the remaining structure of this chapter, the distance between items represents the relatedness. On the other hand, the dimensions of the circles and labels represent the number of occurrences for the co-occurrence links, the number of documents for the bibliographic coupling links and the number of citations for the co-citation links [17].

7.2 Bibliographic Data Assessment Figure 7.1 and Table 7.1 highlight the keywords with the highest occurrences and total link strength, respectively. Table 7.1 also shows that keywords with the biggest values for the total link strength metric have a too important number of occurrences and average citations. These keywords reveal the relevance of the life cycle analysis tools to better support decision making and promote more sustainable development. USA, China, Germany, Italy, UK, Spain, Australia and Portugal are the countries with more relatedness with other countries (the relatedness for bibliographic coupling links is based on the number of references they share) and with a relevant number of documents and citations (Fig. 7.2 and Table 7.2). The top 10 organisations with the greatest values for the total link strength are those presented in Table 7.3. These organisations of affiliation of the authors are, for example, from Spain, Brazil, Germany and Hungary. The dimensions of the circles and labels presented in Fig. 7.3 are related to the number of documents of each item (organisation) and the proximity of the items is associated with the relatedness.

7.2 Bibliographic Data Assessment

95

Fig. 7.1 All keywords as items and co-occurrence links for the topics: “sustainable development”; and “life cycle cost*”

Figure 7.4 and Table 7.4 exhibit the metrics for the cited authors considering cocitation links. The authors with the highest values for the total link strength are, for example, the following: Heijungs, R.; Frangopol, D.M.; Finkbeiner, M.; Hunkeler, D.; Azapagic, A.; Traverso, M.; Huppes, G.; Kucukvar, M. For the co-citation links the relatedness is based on the number of times the items are cited together. The dimension of the circles and labels in Fig. 7.4 symbolises the number of citations and the proximity represents the relatedness. Table 7.5 shows the top 10 documents for bibliographic coupling links (relatedness based on the number of references the items share and in this way, it is expected that they are related to the same issue). The sources with the highest total link strength and relevant citations (Fig. 7.5 and Table 7.6) are related to energy, sustainability, life cycle assessment and cleaner production. The majority of the top 10 sources are associated with energy, such as: Energy; Energy Build.; Appl. Energy; Energy Policy; Renew. Sustain. Energy Rev.; and Energy Build. These findings reveal that there is a field to be explored in other domains.

96

7 Sustainable Development: Contributions from Life Cycle Cost Analysis

Table 7.1 Top 10 all keywords as items, with the biggest total link strength, and co-occurrence links for the topics: “sustainable development”; and “life cycle cost*” All keywords

Total link strength

Occurrences

Average publication year

Average citations

Sustainable development

18,774

1382

2016

20

Life cycle

15,618

1094

2017

20

Costs

10,288

686

2017

17

Cost–benefit analysis

6460

398

2017

17

Environmental impact

6434

390

2017

25

Sustainability

5756

358

2017

30

Life cycle assessment

5635

305

2019

31

Life cycle assessment (lca)

5389

311

2017

27

Life cycle analysis

4740

239

2018

32

Decision making

4602

289

2017

25

Fig. 7.2 Countries as items and bibliographic coupling links for the topics: “sustainable development”; and “life cycle cost*”

7.2 Bibliographic Data Assessment

97

Table 7.2 Top 10 countries as items, with the biggest total link strength, and bibliographic coupling links for the topics: “sustainable development”; and “life cycle cost*” Countries

Total link strength

Documents

Citations

Average publication year

USA

20,020

346

6711

2015

China

15,059

140

2963

2019

Germany

13,037

120

2959

2015

Italy

12,827

125

3634

2019

UK

11,189

101

2621

2016

Spain

10,032

61

1146

2019

Australia

9439

75

2390

2016

Portugal

6956

40

1058

2018

Netherlands

5943

42

2724

2017

France

5902

33

1162

2018

Table 7.3 Top 10 organisations as items, with the biggest total link strength, and bibliographic coupling links for the topics: “sustainable development”; and “life cycle cost*” Organisations

Total link strength

Documents

Citations

Average publication year

Departament 2466 D’enginyeria Mecanica, Urv, Universitat Rovira I Virgili, Av. Paisos Catalans, 26, Tarragona, 43007, Spain

1

35

2019

Escola de Engenharia, Programa de Pós-Graduação em Engenharia Civil, UFF, Universidade Federal Fluminense, Rua Passo Da Pátria, 156, 3° Andar—Bloco D, São Domingos, Cep 24210-240 Niteróirj, Brazil

2466

1

35

2019

Escola Politécnica, 2466 Departamento de Construção Civil, Ufrj, Universidade Federal do Rio de Janeiro, Av. Athos da Silveira Ramos, 149, Ct, Bloco D, Sala 207, Ilha do Fundão, Rio de Janeiro, Rj Cep 21941-590, Brazil

1

35

2019

(continued)

98

7 Sustainable Development: Contributions from Life Cycle Cost Analysis

Table 7.3 (continued) Organisations

Total link strength

Documents

Grea Research Group, Inspires Research Centre, Universitat de Lleida, Pere de Cabrera S/N, Lleida, 25001, Spain

2466

1

35

2019

Materials Science & 2466 Physical Chemistry, Department of Materials Science & Physical Chemistry, Universitat de Barcelona, Martí I Franqués, 1, Barcelona, 08028, Spain

1

35

2019

Institute of Environmental Science and Technology (ICTA)

1976

2

111

2018

Sostenipra (ICTA-IRTA-Inèdit)

1976

2

111

2018

Institute of Sustainability in Civil Engineering, Rwth Aachen University, Aachen, Germany

1618

2

47

2021

Faculty of Civil 1595 Engineering, Technische Universität Dresden, Dresden, 01069, Germany

2

46

2021

John Von Neumann Faculty of Informatics, Obuda University, Budapest, 1034, Hungary

2

46

2021

1595

Citations

Average publication year

7.2 Bibliographic Data Assessment

99

Fig. 7.3 Organisations as items and bibliographic coupling links for the topics: “sustainable development”; and “life cycle cost*”

Fig. 7.4 Cited authors as items and co-citation links for the topics: “sustainable development”; and “life cycle cost*”

100

7 Sustainable Development: Contributions from Life Cycle Cost Analysis

Table 7.4 Top 10 cited authors as items, with the biggest total link strength, and co-citation links for the topics: “sustainable development”; and “life cycle cost*” Cited authors

Total link strength

Citations

Heijungs, R.

21,963

299

Frangopol, D.M.

17,908

274

Finkbeiner, M.

17,324

192

Hunkeler, D.

15,814

263

Azapagic, A.

14,266

227

Traverso, M.

14,205

159

Huppes, G.

13,984

199

Kucukvar, M.

13,245

138

Ciroth, A.

12,857

193

Zamagni, A.

12,232

126

Table 7.5 Top 10 documents as items, with the biggest total link strength, and bibliographic coupling links for the topics: “sustainable development”; and “life cycle cost*” Documents

URL

Total link strength

Naves [21]

https://doi.org/10.1016/j.solener.2018. 04.011

660

35

Costa [1]

https://doi.org/10.1016/j.scitotenv.2019. 05.435

543

95

Fauzi [22]

https://doi.org/10.3390/su11030636

532

69

Miah [23]

https://doi.org/10.1016/j.jclepro.2017. 08.187

487

79

Neugebauer [24]

https://doi.org/10.1016/j.jclepro.2015. 04.053

450

67

Hoogmartens [25]

https://doi.org/10.1016/j.eiar.2014. 05.001

407

208

399

6

Hu [26]

Citations

Zimek [27]

https://doi.org/10.3390/su11123283

381

14

Degieter [4]

https://doi.org/10.1016/j.scitotenv.2022. 158012

364

4

De Luca [28]

https://doi.org/10.1016/j.jclepro.2017. 10.119

340

93

7.2 Bibliographic Data Assessment

101

Fig. 7.5 Cited sources as items and co-citation links for the topics: “sustainable development”; and “life cycle cost*” Table 7.6 Top 10 cited sources as items, with the biggest total link strength, and co-citation links for the topics: “sustainable development”; and “life cycle cost*”

Cited sources

Total link strength

Citations

J. Clean. Prod.

53,127

1463

Int. J. Life Cycle Assess.

34,048

851

Energy

31,962

688

Energy Build.

26,156

624

Sustainability

25,849

635

Appl. Energy

21,469

388

Build. Environ.

18,907

455

Energy Policy

17,449

405

Renew. Sustain. Energy Rev.

16,385

356

Energy Build

16,291

267

102

7 Sustainable Development: Contributions from Life Cycle Cost Analysis

7.3 Contributions from the Literature for the Topics Analysed The contributions presented in Table 7.7 highlight the importance of the different life cycle tools (life cycle analysis, life cycle cost analysis and social life cycle analysis) for the sustainability assessment. However, these tools are still under development, particularly because of difficulties associated with the lack of data (namely for the economic and social dimensions). On the other hand, a standardisation is missing for these approaches and respective indicators, which brings additional constraints when it is intended to promote some comparability. More research about these topics may bring added value, specifically to integrate the different life cycle tools in a comparable framework. Table 7.7 Insights from the literature Documents

URL

Highlights

Naves [21]

https://doi.org/10.1016/j.solener. 2018.04.011

Life cycle cost analysis as an important base for the life cycle sustainability assessment

Costa [1]

https://doi.org/10.1016/j.scitotenv. 2019.05.435

Life cycle costing is part of the life cycle sustainability assessment

Fauzi [22]

https://doi.org/10.3390/su11030636

Potentialities of life cycle sustainability assessment

Miah [23]

https://doi.org/10.1016/j.jclepro. 2017.08.187

Joining environmental and economic assessments

Neugebauer [24]

https://doi.org/10.1016/j.jclepro. 2015.04.053

Lack of data and different indicators provided by distinct institutions hamper the implementation of assessment tools

Hoogmartens [25]

https://doi.org/10.1016/j.eiar.2014. 05.001

Relationships between life cycle analysis, life cycle costing and cost–benefit analysis

Hu [26]

Life cycle costing is an important tool for the sustainability assessment

Zimek [27]

https://doi.org/10.3390/su11123283

Practical and methodological constraints limit the implementation of life cycle tools

Degieter [4]

https://doi.org/10.1016/j.scitotenv. 2022.158012

Harmonising the life cycle cost analysis is recommended to guarantee comparability

De Luca [28]

https://doi.org/10.1016/j.jclepro. 2017.10.119

Life cycle sustainability assessment is still under development

7.4 Discussion and Conclusions

103

7.4 Discussion and Conclusions This study intends to bring more insights from the literature about the contributions of the life cycle cost analysis for sustainability. In this way, a search on 17 May 2023 was carried out in the Scopus database for the topics: “sustainable development”; and “life cycle cost*”. From this search, 1489 documents were identified that were assessed through bibliography data analysis and following the VOSviewer software procedures. In this assessment were analysed keywords, countries, organisations, cited authors, documents and cited sources items from different links. The life cycle cost analysis appears in the literature as part of the life cycle sustainability assessment and has been considered to analyse the economic dimensions of products and processes. This approach has been taken into account in assessments related to the construction and related activities, for example, but there is still some work to do in other sectors and activities, particularly those associated with agricultural practices and products. The bibliometric analysis highlights some relevant (considering the approaches here considered based, specifically on the total link strength) keywords in the documents identified, such as the following: sustainable development; life cycle; costs; cost–benefit analysis; environmental impact; and sustainability. These findings highlight the importance of life cycle tools to support decision-makers. The USA, China, Germany, Italy, the UK, Spain, Australia and Portugal are some of the relevant countries. Finally, a relevant part of the top 10 sources (with the highest total link strength) is focused on the energy fields. The systematic review highlights the relevance of the life cycle tools; however, there is still some work to do, specifically to: bring more insights for the development of these tools; mitigate difficulties associated with the lack of data; standardise the indicators and approaches; create comparable frameworks among the different life cycle tools. In terms of practical implication, the bibliometric analysis and the literature review highlight the need for more research to better develop the life cycle tools, the importance of these methodologies for the sustainable development assessment and more standardisation of the associated indicators and approaches. For policy recommendations, it could be pertinent to create regulations and guidelines for the implementation of these tools in the different economic sectors and activities. For future research, it is suggested to investigate the tools to assess the social dimensions in the economic growth processes. Acknowledgements This work is funded by National Funds through the FCT—Foundation for Science and Technology, I.P., within the scope of the project Refa UIDB/00681/2020. Furthermore, we would like to thank the CERNAS Research Centre and the Polytechnic Institute of Viseu for their support. This work is also co-financed by the PRR—Plano de Recuperação e Resiliência (República Portuguesa) and the European NextGeneration EU Funds (recuperarportugal.gov.pt) through application PRR-C05-i03-I-000030—“Carbo2Soil—Reforçar a Complementaridade entre agricultura e pecuária para aumentar a fertilidade dos solos e a sua capacidade de sequestro de carbono”.

104

7 Sustainable Development: Contributions from Life Cycle Cost Analysis

References 1. D. Costa, P. Quinteiro, A.C. Dias, A systematic review of life cycle sustainability assessment: current state, methodological challenges, and implementation issues. Sci. Total Environ. 686, 774 (2019) 2. T. Ahmad, M.J. Thaheem, Economic sustainability assessment of residential buildings: a dedicated assessment framework and implications for BIM. Sustain. Cities Soc. 38, 476 (2018) 3. A.I. De Luca, N. Iofrida, P. Leskinen, T. Stillitano, G. Falcone, A. Strano, G. Gulisano, Life cycle tools combined with multi-criteria and participatory methods for agricultural sustainability: insights from a systematic and critical review. Sci. Total Environ. 595, 352 (2017) 4. M. Degieter, X. Gellynck, S. Goyal, D. Ott, H. De Steur, Life cycle cost analysis of agri-food products: a systematic review. Sci. Total Environ. 850, 158012 (2022) 5. W.S. Alaloul, M. Altaf, M.A. Musarat, M.F. Javed, A. Mosavi, Systematic review of life cycle assessment and life cycle cost analysis for pavement and a case study. Sustainability 13(8), 4377 (2021) 6. M. Ershadi, F. Goodarzi, Core capabilities for achieving sustainable construction project management. Sustain. Prod. Consum. 28, 1396 (2021) 7. J.V. Neto, J.R. De Farias Filho, Sustainability in the civil construction industry: an exploratory study of life cycle analysis methods. Int. J. Environ. Technol. Manage. 16, 420 (2013) 8. M. Furness, R. Bello-Mendoza, J. Dassonvalle, R. Chamy-Maggi, Building the ‘bio-factory’: a bibliometric analysis of circular economies and life cycle sustainability assessment in wastewater treatment. J. Clean. Prod. 323, 129127 (2021) 9. S. Geng, Y. Wang, J. Zuo, Z. Zhou, H. Du, G. Mao, Building life cycle assessment research: a review by bibliometric analysis. Renew. Sustain. Energy Rev. 76, 176 (2017) 10. Z.M. Konaré, D.D. Ajayi, S. Ba, A.K. Aremu, Application of life cycle sustainability assessment (LCSA) in the gold mining sector: a systematic review. Int. J. Life Cycle Assess. 28, 684–703 (2023) https://doi.org/10.1007/s11367-023-02160-2 11. K.M.A. Salim, R. Maelah, H. Hishamuddin, A.M. Amir, M.N. Ab Rahman, Two decades of life cycle sustainability assessment of solid oxide fuel cells (SOFCs): a review. Sustainability 14(19), 12380 (2022) 12. T.P. Scolaro, E. Ghisi, Life cycle assessment of green roofs: a literature review of layers materials and purposes. Sci. Total Environ. 829, 154650 (2022) 13. V.J.P.D. Martinho, Agri-food contexts in Mediterranean Regions: contributions to better resources management. Sustainability 13, 12 (2021) 14. V.J.P.D. Martinho, Bibliographic coupling links: alternative approaches to carrying out systematic reviews about renewable and sustainable energy. Environments 9, 2 (2022) 15. Scopus, Scopus database, https://www.scopus.com/search/form.uri?display=basic#basic 16. N.J. van Eck, L. Waltman, Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics 84, 523 (2010) 17. N.J. van Eck, L. Waltman, Manual for VOSviewer version 1.6.19 (2023) https://www.vosvie wer.com/documentation/Manual_VOSviewer_1.6.19.pdf 18. VOSviewer, VOSviewer—visualizing scientific landscapes, https://www.vosviewer.com// 19. D. Moher, A. Liberati, J. Tetzlaff, D.G. Altman, Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. BMJ 339, b2535 (2009) 20. V.J.P.D. Martinho, Agricultural Policy: A Driver for Structural and Technological Change (Springer International Publishing, Cham, 2022) 21. A.X. Naves, C. Barreneche, A.I. Fernández, L.F. Cabeza, A.N. Haddad, D. Boer, Life cycle costing as a bottom line for the life cycle sustainability assessment in the solar energy sector: a review. Sol. Energy 192, 238 (2019) 22. R.T. Fauzi, P. Lavoie, L. Sorelli, M.D. Heidari, B. Amor, Exploring the current challenges and opportunities of life cycle sustainability assessment. Sustainability 11, 3 (2019) 23. J.H. Miah, S.C.L. Koh, D. Stone, A hybridised framework combining integrated methods for environmental life cycle assessment and life cycle costing. J. Clean. Prod. 168, 846 (2017)

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24. S. Neugebauer, J. Martinez-Blanco, R. Scheumann, M. Finkbeiner, Enhancing the practical implementation of life cycle sustainability assessment—proposal of a tiered approach. J. Clean. Prod. 102, 165 (2015) 25. R. Hoogmartens, S. Van Passel, K. Van Acker, M. Dubois, Bridging the gap between LCA, LCC and CBA as sustainability assessment tools. Environ. Impact Assess. Rev. 48, 27 (2014) 26. M.-M. Hu, C.-B. Zhang, L. Dong, P.-C. Xiang, Q. Zhang, Economical pillar of sustainability assessment on resource circulation-development and application of life cycle costing approach. Zhongguo Huanjing Kexue/China Environ Sci 38, 4788 (2018) 27. M. Zimek, A. Schober, C. Mair, R.J. Baumgartner, T. Stern, M. Füllsack, The third wave of LCA as the “decade of consolidation.” Sustainability 11, 12 (2019) 28. A.I. De Luca, G. Falcone, T. Stillitano, N. Iofrida, A. Strano, G. Gulisano, Evaluation of sustainable innovations in olive growing systems: a life cycle sustainability assessment case study in southern Italy. J. Clean. Prod. 171, 1187 (2018)

Chapter 8

Social Life Cycle Assessment: Relationships with the Economic Growth

Abstract The social impacts of the economic growth processes are often underestimated, sometimes because of the difficulties in effectively assessing these implications and other times due to a scale of priorities where the economic dimensions appear with special relevance. The social life cycle assessment appears as an approach to highlight the impacts of the life cycle of the products on the social human conditions. Another question is about the various interrelationships between the social life cycle assessment and economic growth. It seems interesting to investigate how the social life cycle assessment influences economic growth and how the economic trends deal with these social analyses. In this way, this chapter investigates the several interrelationships among the social life cycle assessment and the economic growth highlighted by the scientific literature. From this perspective, several scientific documents were considered for these subjects. These documents were first explored through bibliometric analysis and after considering a literature survey for the top documents. This chapter highlights how the social life cycle assessment may contribute to a more sustainable economic development. Keywords Bibliographic data · Life cycle costs analysis · VOSviewer · Systematic review

8.1 Introduction The social dimensions are one of the areas of sustainability that have been gaining relevance in scientific research related to sustainable development. An example of this importance is the increasing consideration in the literature of tools such as social life cycle assessment. This approach supports stakeholders in assessments related to the social impacts of products throughout their life cycle [1]. In the construction sector, for example, generally, the focus of the social life cycle assessment is on the production stage [2], ignoring other phases of the supply chain from the extraction of raw materials to the end of life of products, where the social impacts are also real and need to be taken into account. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 V. J. P. D. Martinho, Economic Growth: Advances in Analysis Methodologies and Technologies, SpringerBriefs in Applied Sciences and Technology, https://doi.org/10.1007/978-3-031-38363-2_8

107

108

8 Social Life Cycle Assessment: Relationships with the Economic Growth

The social life cycle assessment is part of the life cycle sustainability analysis. These approaches need standardisation, identification of boundaries, construction of adjusted databases, development of categories to better compare studies and improve the assessment methodologies [3]. Specifically, the social life cycle assessment is a tool under development and can be considered at different levels (products, economic sectors, regions and countries) [4]. This methodology appears among the topics associated with sustainable processes, value creation and competitive advantage [5]. Four phases may be identified for this approach, particularly the following: beginning; uncertainty; development; and search for harmonisation [6]. The literature highlights the consideration of the social life cycle assessment concepts and guidelines in studies that include the following fields: production of feedstocks for biodiesel in Brazil [7]; food supply chain [8]; and circular economy [9, 10]; industry 4.0 [11]; building sector [12, 13]; municipal solid waste [14]. In summary, the social life cycle analysis may provide interesting insights for the sustainability assessments, where the new technologies associated with the digital transition may bring relevant added value. Nonetheless, more studies are needed to develop more databases and categories and improve the standardisation of the methodologies applied. In this scenario, this research proposes to bring more contributions from the literature about the relationships among the economic dimensions and the social life cycle assessment. To achieve these objectives, 347 documents were taken into account from the Scopus [15] database, on a search carried out on 17 May 2023, for the following topics: “economic”; and “social life cycle”. These studies were assessed through bibliometric analysis (following, for example, the research of Martinho [16]) and systematic review [17]. For the bibliometric analysis, the procedures proposed by the VOSviewer software were considered [18–20].

8.2 Co-occurrence, Bibliographic Coupling and Co-citation Links Analysis In general, the social life cycle assessment as a tool to analyse and promote sustainable economic growth is interrelated with the other tools for the life cycle sustainability assessment, and this is visible in the metrics presented for the top keywords in Fig. 8.1 and Table 8.1. The top 10 keywords appear in documents published between 2017 and 2019. The keyword “life cycle” is the one with the highest total link strength and seems that with the biggest number of occurrences. Germany, Italy, the USA, Spain, Brazil, France, Netherlands, China, Canada and Hong Kong are the top 10 countries with the highest total link strength (Table 8.2). These countries exhibit also important values for the number of documents and citations (Fig. 8.2 and Table 8.2). The average publication year for the documents published by the authors of these countries ranges between 2017 and 2020.

8.2 Co-occurrence, Bibliographic Coupling and Co-citation Links Analysis

109

Fig. 8.1 Co-occurrence links and all keywords items for the topics: “economic”; and “social life cycle”

The top 10 organisations with the highest relatedness with other organisations are presented in Table 8.3 and between them are, for example, the following: Centre for Environmental and Marine Studies (CESAM), Department of Environment and Planning, University of Aveiro; European Commission-Joint Research Centre, Institute for Environment and Sustainability—Sustainability Assessment Unit; BWI Center for Industrial Management, Department of Management, Technology, Economics, Eth Zurich; Civil and Environmental Engineering, University of California, Berkeley; and Environmental and Ecological Engineering, Purdue University. Some of these organisations have also a relevant number of documents (Fig. 8.3 and Table 8.3). The top 10 cited authors (with the highest total link strength) are those exhibited in Table 8.4, and the top 10 documents are presented in Table 8.5. Figure 8.4 shows the cited authors for co-citation links. In this figure, the dimension of the circle and labels represents the number of citations and the proximity symbolises the relatedness (number of times they were cited together). Figure 8.5 and Table 8.6 present some metrics for co-citation links and cited sources items. The cited sources with the biggest values for the total link strength are, namely the International Journal of Life Cycle Assessment and the Journal of Cleaner Production in the different versions for their designations.

110

8 Social Life Cycle Assessment: Relationships with the Economic Growth

Table 8.1 Co-occurrence links and top 10 all keywords items, with the greatest total link strength, for the topics: “economic”; and “social life cycle” All keywords

Total link strength

Occurrences

Average publication year

Average citations

Life cycle

2960

187

2019

23

Life cycle assessment

2404

129

2018

45

Sustainable development

2373

149

2019

29

Social life cycle 2153 assessment

144

2019

25

Social life

2080

120

2019

23

Economic and social effects

1748

115

2019

23

Sustainability

1649

98

2019

37

Life cycle analysis

1602

76

2019

24

Article

1535

65

2018

43

Environmental impact

1271

67

2017

39

Table 8.2 Bibliographic coupling links and top 10 countries items, with the greatest total link strength, for the topics: “economic”; and “social life cycle” Countries

Total link strength

Documents

Citations

Average publication year

Germany

21,013

58

2981

2017

Italy

20,025

54

1311

2019

USA

13,620

39

1419

2018

Spain

12,516

38

800

2020

Brazil

11,150

23

791

2019

France

9868

19

993

2018

Netherlands

8399

15

325

2018

China

7742

18

422

2019

Canada

7551

17

606

2018

Hong Kong

6523

10

179

2019

8.2 Co-occurrence, Bibliographic Coupling and Co-citation Links Analysis

111

Fig. 8.2 Bibliographic coupling links and countries items for the topics: “economic”; and “social life cycle” Table 8.3 Bibliographic coupling links and top 10 organisations items, with the greatest total link strength, for the topics: “economic”; and “social life cycle” Organisations

Total link strength

Documents

Citations

Average publication year

Centre for Environmental and Marine Studies (CESAM), Department of Environment and Planning, University of Aveiro, Campus Universitário de Santiago, Aveiro, 3810–193, Portugal

4158

5

118

2021

CIRAD, UPR GECO, Boulevard 3939 de la Lironde, Montpellier Cedex 5, 34398, France

1

59

2018

(continued)

112

8 Social Life Cycle Assessment: Relationships with the Economic Growth

Table 8.3 (continued) Organisations

Total link strength

Documents

Citations

Average publication year

Department of Economic Studies, University «G. D’annunzio», Viale Pindaro 42, Pescara, 65127, Italy

3939

1

59

2018

European Commission-Joint Research Centre, Institute for Environment and Sustainability—Sustainability Assessment Unit, Via Enrico Fermi 2749, T.P. 270, Ispra, Va 21027, Italy

3939

1

59

2018

Irstea, UMR ITAP—ELSA, 361 Rue Jean-François Breton, Montpellier Cedex 5, 34,196, France

3939

1

59

2018

Program of Industrial Engineering (EPII), Faculty of Production and Services Engineering (FIPS), Universidad Nacional de San Agustín de Arequipa (UNSA), Av. Independencia S/N—Pabellón Nicholson, Arequipa, Arequipa, Peru

3925

1

66

2020

Sustainable Production Systems 3925 Laboratory (LESP), Postgraduate Program of Production Engineering (PPGEP), Department of Production Engineering (DAENP), Universidade Tecnológica Federal Do Paraná (UTFPR), Av. Monteiro Lobato S/N, Km. 04, Ponta Grossa, Paraná, Brazil

1

66

2020

BWI Center for Industrial Management, Department of Management, Technology, Economics, Eth Zurich, Switzerland

3911

1

106

2016

Civil and Environmental Engineering, University of California, Berkeley, Ca, USA

3911

1

106

2016

Environmental and Ecological Engineering, Purdue University, West Lafayette, In, USA

3911

1

106

2016

8.2 Co-occurrence, Bibliographic Coupling and Co-citation Links Analysis

113

Fig. 8.3 Bibliographic coupling links and organisations items for the topics: “economic”; and “social life cycle” Table 8.4 Co-citation links and top 10 cited authors items, with the greatest total link strength, for the topics: “economic”; and “social life cycle” Cited authors

Total link strength

Citations

Traverso, M.

50,606

404

Finkbeiner, M.

49,628

390

Ciroth, A.

39,321

357

Hauschild, M.Z.

28,329

244

Jørgensen, A.

27,360

255

Lehmann, A.

25,306

202

Zamagni, A.

25,050

196

Heijungs, R.

21,824

185

Macombe, C.

21,040

152

Valdivia, S.

20,352

180

Table 8.5 Bibliographic coupling links and top 10 documents items, with the greatest total link strength, for the topics: “economic”; and “social life cycle” Documents

URL

Total link strength Citations

Ramos Huarachi [6]

https://doi.org/10.1016/j.jclepro.2020. 121506

1725

66

Di Cesare [4]

https://doi.org/10.1007/s11367-016-1169-7

1554

59

Tsalis [21]

https://doi.org/10.1016/j.jclepro.2017.07.003 1378

Sutherland [22]

https://doi.org/10.1016/j.cirp.2016.05.003

1115

106

Hossain [23]

https://doi.org/10.1007/s11367-017-1373-0

1113

54

Subramanian [24]

https://doi.org/10.1016/j.jclepro.2017.10.006 1113

Chhipi-shrestha [25]

https://doi.org/10.1007/s10098-014-0841-5

979

104

Dubois-iorgulescu [26] https://doi.org/10.1007/s11367-016-1181-y

30

20

975

25

Yang [27]

https://doi.org/10.1016/b978-0-12-818355-7. 922 00005-1

19

Gulisano [28]

https://doi.org/10.1016/b978-0-12-811935-8. 917 00004-4

5

114

8 Social Life Cycle Assessment: Relationships with the Economic Growth

Fig. 8.4 Co-citation links and cited authors items for the topics: “economic”; and “social life cycle”

Fig. 8.5 Co-citation links and cited sources items for the topics: “economic”; and “social life cycle”

8.4 Discussion and Conclusions

115

Table 8.6 Co-citation links and top 10 cited sources items, with the greatest total link strength, for the topics: “economic”; and “social life cycle” Cited sources

Total link strength

Citations

Int. J. Life Cycle Assess.

32,752

1036

Int J Life Cycle Assess.

24,493

896

Sustainability

21,814

516

J. Clean. Prod.

21,621

536

J Clean Prod.

14,500

322

Journal of Cleaner Production

12,270

276

International Journal of Life Cycle Assessment

12,251

241

Int. J. Life Cycle Assess

11,531

367

J. Clean. Prod.

9760

232

The International Journal of Life Cycle Assessment

6506

198

8.3 A Literature Review Based on Bibliographic Data Table 8.7 shows some insights from the literature about the relationships between the following topics: “economic”; and “social life cycle”. The top 10 documents highlight the need for a better reorganisation of definitions and indicators, more studies related to these dimensions and more adjusted support for the system boundaries. Bioeconony, manufacturing and corporate social profile of companies appear as the focus of some research associated with the social life cycle analysis. On the other hand, sustainability is more than the three indices for the environmental, economic and social dimensions. Finally, the UNEP/SETAC Guidelines are presented as relevant support for the social life cycle assessment.

8.4 Discussion and Conclusions This chapter proposes to analyse interrelationships between the economic growth and the social life cycle assessment, bringing more highlights from the scientific literature published in international databases. For that, 347 documents were surveyed through bibliometric analysis with co-occurrence, bibliographic coupling and cocitation links. These documents were obtained from the Scopus database on a search carried out on 17 May 2023 for the topics: “economic”; and “social life cycle”. In this bibliometric assessment, the procedures proposed by the VOSviewer software were followed. The social dimensions have gained importance in the scientific literature associated with the sustainability domains, with increases in the consideration by the researchers of the social life cycle assessment tools. These tools are part of the life

116

8 Social Life Cycle Assessment: Relationships with the Economic Growth

Table 8.7 Insights from a review based on bibliographic data Documents

URL

Ramos Huarachi [6]

https://doi.org/10.1016/j.jcl Bioeconomy is an important focus of the studies related to the social life cycle epro.2020.121506 assessment

Insights

Di Cesare [4]

https://doi.org/10.1007/s11 367-016-1169-7

Tsalis [21]

https://doi.org/10.1016/j.jcl Social life cycle assessment and the corporate social profile of companies epro.2017.07.003

Sutherland [22]

https://doi.org/10.1016/j. cirp.2016.05.003

Manufacturing and the social dimensions of sustainability

Hossain [23]

https://doi.org/10.1007/s11 367-017-1373-0

Life cycle methods integration, sensitivity analysis and more studies are needed

Subramanian [24]

https://doi.org/10.1016/j.jcl Sustainability is more than a sum of three indices epro.2017.10.006

Reorganization definitions and indicators

Chhipi-Shrestha [25] https://doi.org/10.1007/s10 098-014-0841-5

UNEP/SETAC Guidelines for social life cycle assessment

Dubois-Iorgulescu [26]

https://doi.org/10.1007/s11 367-016-1181-y

Better support for the system boundaries

Yang [27]

https://doi.org/10.1016/ b978-0-12-818355-7.000 05-1

Life cycle sustainability assessment dimensions

Gulisano [28]

https://doi.org/10.1016/ b978-0-12-811935-8.000 04-4

Social life cycle assessment is part of the life cycle tools

Note UNEP/SETAC, United Nations Environment Programme/Society of Environmental Toxicology and Chemistry

cycle sustainability assessment and still need standardisation, the definition of boundaries, construction of databases and development of more guidelines. The social life cycle assessment has been considered in domains related to, for example, the feedstock for biodiesel, supply chains, bioeconomy, industry, construction sector and municipal waste. The bibliometric assessment highlights that some of the relevant keywords (with the highest relatedness) are the following: life cycle; life cycle assessment; sustainable development; social life cycle assessment; social life; economic and social effects; sustainability; life cycle analysis; and environmental impact. These keywords show the interrelationships of the social life cycle assessment with the economic dynamics and sustainability. Some of the relevant countries are the following: Germany; Italy; the USA; Spain; Brazil; France; Netherlands; China; Canada; and Hong Kong. The relevant sources are namely the International Journal of Life Cycle Assessment and the Journal of Cleaner Production. The systematic literature review suggests a better definition of indicators for the social life cycle assessment, more research to support the development of these tools,

References

117

the sustainability of more than a sum of indices, and the UNEP/SETAC Guidelines have been taken into account for the assessments based on this life cycle tool. In terms of practical implications, the social life cycle assessment is under development, and more research is needed to support more standardisation and overcome some of the restrictions identified. For policy recommendations, it is suggested to promote scientific contributions based on the already designed UNEP/SETAC Guidelines. For future research, it could be important to understand the interlinkages between economic growth, sustainability and digital transition. Acknowledgements This work is funded by National Funds through the FCT—Foundation for Science and Technology, I.P., within the scope of the project Refa UIDB/00681/2020. Furthermore, we would like to thank the CERNAS Research Centre and the Polytechnic Institute of Viseu for their support. This work is also co-financed by the PRR—Plano de Recuperação e Resiliência (República Portuguesa) and the European NextGeneration EU Funds (recuperarportugal.gov.pt) through application PRR-C05-i03-I-000030—“Carbo2Soil—Reforçar a Complementaridade entre agricultura e pecuária para aumentar a fertilidade dos solos e a sua capacidade de sequestro de carbono”.

References 1. S. Sala, A. Vasta, L. Mancini, J. Dewulf, E. Rosenbaum, Social Life Cycle Assessment: State of the Art and Challenges for Supporting Product Policies. https://doi.org/10.2788/53485 2. J.G. Backes, M. Traverso, Application of life cycle sustainability assessment in the construction sector: a systematic literature review. Process 9(7), 1248 (2021) 3. D. Costa, P. Quinteiro, A.C. Dias, A systematic review of life cycle sustainability assessment: current state, methodological challenges, and implementation issues. Sci. Total Environ. 686, 774 (2019) 4. S. Di Cesare, F. Silveri, S. Sala, L. Petti, Positive impacts in social life cycle assessment: state of the art and the way forward. Int. J. Life Cycle Assess. 23, 406 (2018) 5. N. Marulanda Grisales, in Proc. Int. Bus. Inf. Manag. Assoc. Conf., IBIMA: Educ. Excell. Innov. Manag. Vis., ed. by K.S. Soliman. Sustainable Production as an Alternative for Creating Value and Competitive Advantage: Analysis of Global Research (International Business Information Management Association, IBIMA, 2019), pp. 5357–5369, https://ibima.org/accepted-paper/ sustainable-production-as-an-alternative-for-creating-value-and-competitiveadvantage- analysis-of-global-research/ 6. D.A. Ramos Huarachi, C.M. Piekarski, F.N. Puglieri, A.C. de Francisco, Past and future of social life cycle assessment: historical evolution and research trends. J. Clean. Prod. 264, 121506 (2020) 7. M.W. Costa, A.A.M. Oliveira, Social life cycle assessment of feedstocks for biodiesel production in Brazil. Renew. Sustain. Energy Rev. 159, 112166 (2022) 8. E. Desiderio, L. García-Herrero, D. Hall, A. Segrè, M. Vittuari, Social sustainability tools and indicators for the food supply chain: a systematic literature review. Sustain. Prod. Consum. 30, 527 (2022) 9. C. Mesa Alvarez, T. Ligthart, A social panorama within the life cycle thinking and the circular economy: a literature review. Int. J. Life Cycle Assess. 26, 2278 (2021) 10. T. Stillitano, E. Spada, N. Iofrida, G. Falcone, A.I. De Luca, Sustainable agri-food processes and circular economy pathways in a life cycle perspective: state of the art of applicative research. Sustainability 13, 1 (2021)

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11. F.E. Garcia-Muiña, R. González-Sánchez, A.M. Ferrari, D. Settembre-Blundo, The paradigms of industry 4.0 and circular economy as enabling drivers for the competitiveness of businesses and territories: the case of an Italian ceramic tiles manufacturing company. Soc. Sci. 7(12), 255 (2018) 12. S. Y. Janjua, P. K. Sarker, and W. K. Biswas, A Review of Residential Buildings’ Sustainability Performance Using a Life Cycle Assessment Approach, J. Sustain. Res 1, (2019). 13. S. Liu, S. Qian, Towards sustainability-oriented decision making: model development and its validation via a comparative case study on building construction methods. Sustain. Dev. 27, 860 (2019) 14. A. Mirdar Harijani, S. Mansour, Municipal solid waste recycling network with sustainability and supply uncertainty considerations, Sustain. Cities Soc. 81, 103857 (2022) 15. Scopus, Scopus database, https://www.scopus.com/search/form.uri?display=basic#basic 16. V.J.P.D. Martinho, Impacts of the COVID-19 pandemic and the Russia-Ukraine conflict on land use across the world. Land 11, 10 (2022) 17. D. Moher, A. Liberati, J. Tetzlaff, D.G. Altman, Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. BMJ 339, b2535 (2009) 18. N.J. van Eck, L. Waltman, Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics 84, 523 (2010) 19. N. J. van Eck, L. Waltman, Manual for VOSviewer version 1.6.19 (2023), https://www.vosvie wer.com/documentation/Manual_VOSviewer_1.6.19.pdf 20. VOSviewer, VOSviewer—visualizing scientific landscapes, https://www.vosviewer.com// 21. T. Tsalis, A. Avramidou, I.E. Nikolaou, A social LCA framework to assess the corporate social profile of companies: insights from a case study. J. Clean. Prod. 164, 1665 (2017) 22. J.W. Sutherland et al., The role of manufacturing in affecting the social dimension of sustainability. CIRP Ann. 65, 689 (2016) 23. Md.U. Hossain, C.S. Poon, Y.H. Dong, I.M.C. Lo, J.C.P. Cheng, Development of social sustainability assessment method and a comparative case study on assessing recycled construction materials. Int. J. Life Cycle Assess. 23, 1654 (2018) 24. K. Subramanian, C.K. Chau, W.K.C. Yung, Relevance and feasibility of the existing social LCA methods and case studies from a decision-making perspective. J. Clean. Prod. 171, 690 (2018) 25. G.K. Chhipi-Shrestha, K. Hewage, R. Sadiq, ‘Socializing’ sustainability: a critical review on current development status of social life cycle impact assessment method. Clean Tech. Environ. Policy 17, 579 (2015) 26. A.-M. Dubois-Iorgulescu, A.K.E.B. Saraiva, R. Valle, L.M. Rodrigues, How to define the system in social life cycle assessments? A critical review of the state of the art and identification of needed developments. Int. J. Life Cycle Assess. 23, 507 (2018) 27. S. Yang, K. Ma, Z. Liu, J. Ren, Y. Man, in Life Cycle Sustainability Assessment for DecisionMaking, ed. by J. Ren, S. Toniolo. Chapter 5—Development and Applicability of Life Cycle Impact Assessment Methodologies (Elsevier, 2020), pp. 95–124 28. G. Gulisano, A. Strano, A.I. De Luca, G. Falcone, N. Iofrida, T. Stillitano, in Sustainable Food Systems from Agriculture to Industry, ed. by C.M. Galanakis. 4—Evaluating the Environmental, Economic, and Social Sustainability of Agro-Food Systems Through Life Cycle Approaches (Academic Press, 2018), pp. 123–152

Chapter 9

Machine and Deep Learning: Their Roles in the Context of the Economic Growth Processes and Sustainability Assessment

Abstract Sustainability assessments are crucial for more balanced economic growth, and in these contexts, the new technologies, namely those associated with the digital transition, play a fundamental role. Indeed, the technologies related to the Era 4.0 are a hope for governments and international organisations, for example, to deal with the need of promoting economic growth and, at the same time, reduce the carbon footprint. Nonetheless, these digital approaches require expertise and, sometimes, the stakeholders are not prepared to use these new methodologies, or the infrastructures available are not adjusted to the new requirements. In this framework, this study proposes to analyse the contributions of machine and deep learning for economic growth and sustainability assessments. For that, bibliometric analysis and systematic review were carried out, considering documents related to topics associated with the issues here addressed. The insights obtained highlight the potentialities of the machine and deep learning methodologies for sustainability assessments and economic growth. Keywords Agriculture 4.0 · Life cycle cost analysis · Bibliometric analysis · Literature insights

9.1 Introduction The new technologies from the Era 4.0 may bring interesting contributions to the interlinkages between sustainability and economic growth, specifically improving the efficiency in the use of resources, with added value for economic profitability and carbon footprint mitigation [1]. Of course, there are also weaknesses and threats related to the digital technologies implementation, particularly those associated with the availability of infrastructures and the needed skills of the stakeholders. In any case, there is a way to run with interesting perspectives. Machine learning technologies appear, in some cases, as support for methodologies of assessment of the economic growth performance and the respective variables

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 V. J. P. D. Martinho, Economic Growth: Advances in Analysis Methodologies and Technologies, SpringerBriefs in Applied Sciences and Technology, https://doi.org/10.1007/978-3-031-38363-2_9

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9 Machine and Deep Learning: Their Roles in the Context …

that may influence the associated dynamics [2]. These approaches are also considered for predictive analysis. These machine learning methodologies were taken into account, for example, to assess relationships among renewable energies and environmental pollution indicators, for the BRICS, G7 and European Union (EU) countries [3]. Another application of the machine learning tools was in the literature review about the circular economy, green economy and bioeconomy [4]. These digital technologies have been taken into account in the following subjects: causes of environmental sustainability [5]; smart cities (may contribute to make compatible economic growth, adjusted transports, sustainability and adjusted planning for the cities) [6]; energy use, economic performance and environmental impacts [7]; relations among human capital and green growth [8]; impacts of industry 4.0 on environmental, social and governance investment in the energy sector [9]; forecasting the acceptance of reopening a nuclear power plant [10]; precision agriculture [11]; hybrid desalination [12]; and interlinkages among total natural resources, economic growth, human capital and cities changes with carbon footprint [13]. Artificial intelligence opens, indeed, new perspectives for the economic sectors’ performance, with relevant potentialities to improve the transparency in the chains, traceability and sustainability, including in the autonomous vehicles supply circuits [14]. These technologies may support analysis with multi-sector and multi-actor to identify efficient policy designs [15]. Considering these frameworks, this chapter aims to bring more insights into the machine learning contributions to the relationships among economic growth and sustainability, considering bibliometric analysis and literature survey based on the metrics from bibliographic data, considering the PRISMA approach [16]. The documents taken into account were obtained from Scopus [17] database, on a search carried out on 17 May 2023. From this search, 38 documents were identified for the following topics: “economic growth”; and “sustainability”; and “machine learning”. To identify the top items, the total link strength metric was considered [18], and for the bibliometric assessment, the procedures proposed by the VOSviewer software were followed [19–21].

9.2 Keywords, Countries, Organisations, Cited Authors, Documents and Cited Sources as Items Figure 9.1 presents the number of occurrences (dimension of the circles and labels) for the keywords identified and the relatedness (proximity) among the items. Table 9.1 exhibits the total link strength, number of occurrences, average publication year and average citations. The keywords with the highest total link strength are closely related to the topics analysed: economic growth; sustainability; and machine learning. The countries with the greatest total link strength are the following (Table 9.2): UK; China; France; Saudi Arabia; Australia; Tunisia; Pakistan; Ecuador; Taiwan; and Italy. Some of these countries have also a relevant number of documents and

9.2 Keywords, Countries, Organisations, Cited Authors, Documents …

121

Fig. 9.1 Bibliographic data, co-occurrence links and all keywords as items, for the following topics: “economic growth”; and “sustainability”; and “machine learning”

citations. The average publication year for the documents published by the authors affiliated with these countries ranges among 2021 and 2022. Figure 9.2 highlights the number of documents (dimensions of the circles and labels) for each item and the respective relatedness. The top 10 organisations for the highest total link strength presented in Table 9.3 are from Italy, Romania, Saudi Arabia and Tunisia. Figure 9.3 shows the number of documents for each organisation and the relatedness (proximity). Some of these top 10 organisations have also an important number of citations. Figure 9.4 and Table 9.4 exhibit the main metrics for the cited authors and cocitation links for the topics: “economic growth”; and “sustainability”; and “machine learning”. The relatedness of the co-citation links is associated with the number of times the items (authors in this case) are cited together. For the bibliographic coupling links, the relatedness is related to the number of references the respective items share, and for the co-occurrence links, the relatedness is based on the number of documents where the items appear together. Table 9.5 presents the top 10 documents.

122

9 Machine and Deep Learning: Their Roles in the Context …

Table 9.1 Bibliographic data, co-occurrence links and top 10 all keywords as items (considering the metrics total link strength), for the following topics: “economic growth”; and “sustainability”; and “machine learning” All keywords

Total link strength

Occurrences

Average publication year

Average citations

Machine learning

517

25

2021

41

Economics

347

17

2021

52

Sustainable development

331

17

2021

46

Economic growth

299

14

2021

14

Economic growths

273

13

2021

19

Sustainability

232

12

2021

49

Economic and social effects

186

8

2022

15

Economic development

186

6

2022

25

Article

159

5

2022

26

Learning systems

126

6

2021

91

Table 9.2 Bibliographic data, bibliographic coupling links and top 10 countries as items (considering the metrics total link strength), for the following topics: “economic growth”; and “sustainability”; and “machine learning” Countries

Total link strength

Documents

Citations

Average publication year

UK

1350

7

534

2021

China

913

7

61

2021

France

857

4

73

2022

Saudi Arabia

723

4

33

2022

Australia

688

3

2

2022

Tunisia

397

2

5

2022

Pakistan

379

2

27

2021

Ecuador

378

1

21

2022

Taiwan

373

2

47

2021

Italy

343

3

285

2021

9.2 Keywords, Countries, Organisations, Cited Authors, Documents …

123

Fig. 9.2 Bibliographic data, bibliographic coupling links and countries as items, for the following topics: “economic growth”; and “sustainability”; and “machine learning”

The cited sources presented in Fig. 9.5 and Table 9.6 are related to cleaner production (Journal of Cleaner Production), sustainability, computers and electronics, bioresources, environment, water, energy and sensors. Directly associated with energy appear three journals among those with the highest total link strength.

124

9 Machine and Deep Learning: Their Roles in the Context …

Table 9.3 Bibliographic data, bibliographic coupling links and top 10 organisations as items (considering the metrics total link strength), for the following topics: “economic growth”; and “sustainability”; and “machine learning” Organisations

Total link strength

Documents

Citations

Average publication year

Roma Tre University, Italy

722

2

232

2021

University of Teramo, Italy

722

2

232

2021

Department of Administrative Sciences and Regional Studies, Faculty of Juridical, Social and Political Sciences, Dunarea De Jos University, Galati, Romania

615

1

0

2022

Department of Automatic Control and Electrical Engineering, Faculty of Automatic Control and System Engineering, “Dunarea De Jos” University of Galati, Galati, Romania

615

1

0

2022

Department of Business Administration, Faculty of Economics and Business Administration, “Dunarea De Jos” University of Galati, Galati, Romania

615

1

0

2022

Department of 615 Computers and Information Technologies, Faculty of Automatic Control and System Engineering, “Dun˘area De Jos” University of Galati, Galati, Romania

1

0

2022

(continued)

9.2 Keywords, Countries, Organisations, Cited Authors, Documents …

125

Table 9.3 (continued) Organisations

Total link strength

Documents

Citations

Average publication year

Department of Finance, 615 Accounting and Economic Theory, Faculty of Economic Sciences and Business Administration, Transylvania University, Brasov, Romania

1

0

2022

Department of Food Science, Food Engineering, Biotechnology and Aquaculture, Faculty of Food Science and Engineering, “Dunarea De Jos” University of Galati, Galati, Romania

615

1

0

2022

Department of Business Administration, College of Business and Economics, Qassim University, P.O. Box 6640, Buraydah, 51452, Saudi Arabia

612

1

1

2022

Department of Economics, Faculty of Economics and Management of Nabeul, University of Carthage, Tunisia

612

1

1

2022

Fig. 9.3 Bibliographic data, bibliographic coupling links and organisations as items, for the following topics: “economic growth”; and “sustainability”; and “machine learning”

126

9 Machine and Deep Learning: Their Roles in the Context …

Fig. 9.4 Bibliographic data, co-citation links and cited authors as items, for the following topics: “economic growth”; and “sustainability”; and “machine learning” Table 9.4 Bibliographic data, co-citation links and top 10 cited authors as items (considering the metrics total link strength), for the following topics: “economic growth”; and “sustainability”; and “machine learning” Cited authors

Total link strength

Citations

Zhang, J

6637

25

Liu, Y

6311

18

Zhang, Y

5025

16

Wang, X

4749

9

Li, H

4719

10

Wu, J

4535

7

Liu, H

4496

5

Long, F

4424

4

Phuntsho, S

4424

4

Li, J

3831

12

Table 9.5 Bibliographic data, bibliographic coupling links and top 10 documents as items (considering the metrics total link strength), for the following topics: “economic growth”; and “sustainability”; and “machine learning” Documents

URL

Total link strength

Citations

Magazzino [22]

https://doi.org/10.1016/j.renene.2020. 11.050

22

166

Kahia [7]

https://doi.org/10.1016/j.resourpol.2022. 103104

18

1

Magazzino [23]

https://doi.org/10.1016/j.scitotenv.2020. 142510

17

66

Zhou [13]

https://doi.org/10.1016/j.resourpol.2022. 102782

12

21

Cristea [3]

https://doi.org/10.3389/fenvs.2022.100 5806

7

0

Adedoyin [2]

https://doi.org/10.1016/j.techfore.2022. 122044

6

0 (continued)

9.2 Keywords, Countries, Organisations, Cited Authors, Documents …

127

Table 9.5 (continued) Documents

URL

Magazzino [24]

https://doi.org/10.1016/j.jenvman.2021. 112241

Total link strength 6

Citations 53

Wu [25]

https://doi.org/10.1016/j.cam.2019. 112660

6

32

Warsame [26]

https://doi.org/10.1016/j.jclepro.2023. 136856

5

0

Jabeur [5]

https://doi.org/10.1007/s10666-021-098 07-0

5

4

Fig. 9.5 Bibliographic data, co-citation links and cited sources as items, for the following topics: “economic growth”; and “sustainability”; and “machine learning” Table 9.6 Bibliographic data, co-citation links and top 10 cited sources as items (considering the metrics total link strength), for the following topics: “economic growth”; and “sustainability”; and “machine learning” Cited sources

Total link strength

Citations

J. Clean. Prod.

9302

58

Sustainability

6722

66

Comput. Electron. Agricult.

5112

36

Bioresour. Technol.

4848

16

Sci. Total Environ.

4828

37

Water Res.

3388

11

Energy

3358

38

Renew. Sustain. Energy Rev.

3313

26

Renew. Energy

2719

30

Sensors

2603

12

128

9 Machine and Deep Learning: Their Roles in the Context …

9.3 Literature Review Based on Bibliometric Information The new technologies associated with the digital transition have several applications in the different dimensions of sustainability. This is highlighted in Table 9.7, where the following domains were assessed through machine learning methodologies: energy use, economic dynamics, greenhouse gas emissions and environmental quality; municipal waste and social conditions; urbanisation and footprint; patents and economic performance; carbon emissions prediction; and globalisation and sustainable development.

Table 9.7 Highlights from the literature about “economic growth”; and “sustainability”; and “machine learning” Documents

URL

Highlights

Magazzino [22]

https://doi.org/10.1016/j.renene. 2020.11.050

Energy consumption, economic growth and carbon emissions

Kahia [7]

https://doi.org/10.1016/j.resourpol. 2022.103104

Relations among energy use, economic growth and environmental quality with machine learning

Magazzino [23]

https://doi.org/10.1016/j.scitotenv. 2020.142510

Municipal solid waste, wealth and emissions of the waste sector

Zhou [13]

https://doi.org/10.1016/j.resourpol. 2022.102782

Natural resources, economic growth, urbanisation and ecological footprint

Cristea [3]

https://doi.org/10.3389/fenvs.2022. 1005806

Machine learning and prediction approaches to identify relationships between renewable energy and environmental pollution

Adedoyin [2]

https://doi.org/10.1016/j.techfore. 2022.122044

patent intensity has relevant impacts on the South Africa economic growth

Magazzino [24]

https://doi.org/10.1016/j.jenvman. 2021.112241

Linkages among pandemic deaths, air pollution and economic growth with deep and machine learning

Wu [25]

https://doi.org/10.1016/j.cam.2019. 112660

Financial framework and economic growth

Warsame [26]

https://doi.org/10.1016/j.jclepro. 2023.136856

Conflicts, development, and globalisation on environmental quality

Jabeur [5]

https://doi.org/10.1007/s10666-021- Machine learning approaches to predict the tendencies of carbon 09807-0 emissions

9.4 Discussion and Conclusions

129

9.4 Discussion and Conclusions This research intends to assess the contributions of machine learning to sustainability and economic growth, considering insights from the literature. To achieve these objectives, a bibliometric analysis was carried out that was considered to support an oriented literature survey. For that, a search was performed on 17 May 2023 in the Scopus database for the topics: “economic growth”; and “sustainability”; and “machine learning”. For the bibliometric assessment, the VOSviewer procedures were followed, and the following items were taken into account for different links: all keywords; countries; organisations; cited authors; documents; and cited sources. The digital transition may contribute to improve the efficiency in the use of scarce resources, such as water, energy and soil, bringing interesting contributions to the relationships between economic growth and sustainability. This is important, because may mitigate the environmental impacts and increase the income of the economic operators in the different sectors. The lack of digital skills among the stakeholders and the absence of adjusted infrastructures are serious constraints that limit the implementation of the new technologies in some circumstances. In any case, these digital approaches are used as predictive methodologies and to promote sustainability in the supply chains. Considering co-occurrence links, the bibliometric analysis highlights as relevant (with the biggest values for the total link strength) keywords related to the topics here addressed (economic growth, sustainability and machine learning). UK, China, France, Saudi Arabia, Australia, Tunisia, Pakistan, Ecuador, Taiwan and Italy are the countries with greater relatedness. The most relevant cited sources are the following: J. Clean. Prod.; Sustainability; Comput. Electron. Agricult.; Bioresour. Technol.; Sci. Total Environ.; Water Res.; Energy; Renew. Sustain. Energy Rev.; Renew. Energy; and Sensors. It seems that sources focused on the specific dimensions of sustainability are missing here, as well as more sources associated with artificial intelligence. The literature survey based on the bibliometric assessment shows the importance of the digital transition for economic growth and sustainability. On the other hand, this oriented review of the literature reveals that machine learning techniques, for instance, have been taken into account for assessments in diverse contexts of society and science, since municipal waste management, until globalisation and sustainable development. In terms of practical implications, the findings obtained with this research highlight the importance of the new technologies for economic growth more sustainable. On the other hand, it could be relevant to promote the use of these methodologies in other fields of society and science. This narrow perspective of the use of machine learning in the analyses of the interrelationships between economic growth and sustainability is visible in the focus of the most relevant cited sources identified. For future research, it is suggested to analyse the interlinkages among economic growth, assessments and artificial intelligence.

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9 Machine and Deep Learning: Their Roles in the Context …

Acknowledgements This work is funded by National Funds through the FCT—Foundation for Science and Technology, I.P., within the scope of the project Refa UIDB/00681/2020. This research is also funded by the Enovo company. This study was carried out under the international project “Agriculture 4.0: Current reality, potentialities and policy proposals” (CERNAS-IPV/2022/008). Furthermore, we would like to thank the CERNAS Research Centre and the Polytechnic Institute of Viseu for their support. This work is too co-financed by the PRR—Plano de Recuperação e Resiliência (República Portuguesa) and the European NextGeneration EU Funds (recuperarportugal.gov.pt) through application PRR-C05-i03-I-000030—“Carbo2Soil—Reforçar a Complementaridade entre agricultura e pecuária para aumentar a fertilidade dos solos e a sua capacidade de sequestro de carbono”.

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Chapter 10

Economic Growth, Sustainability Assessment and Artificial Intelligence: Combinations Among These Three Dimensions

Abstract Sustainability assessment, economic growth and artificial intelligence are related and these interrelationships will be narrower. The question here is how to combine these three dimensions to create a new approach that may be called as “Triple Eco-Digital Assessment”, which promotes more inclusive analysis, and considers the different dimensions associated with sustainability evaluation methodologies, artificial intelligence technologies and economic growth dynamics. In these frameworks, this chapter brings highlights about the several interrelationships between sustainability assessment, economic growth and new technologies and how these linkages may be combined. To achieve these objectives, the top items from the scientific literature were considered (taking into account bibliographic data and co-occurrence, bibliographic coupling and co-citation links). Additionally, the artificial intelligence approaches were considered for an assessment carried out to predict the costs of the farms from the European Union agricultural regions. The main highlights identified bring relevant insights for a more interlinked framework among economic growth, sustainability dynamics and the contexts of artificial intelligence. Keywords Digital transition · Life cycle cost analysis · Sustainable development · Bibliographic data

10.1 Introduction The different economic sectors are faced with diverse challenges in the current contexts worldwide, where the prediction of some costs may be relevant for the planning and production units’ management. This is true for the agricultural sector [1] and for other economic activities. The digital transition and the respective methodologies play a fundamental role in these costs prediction, particularly those associated with the artificial neural network methods [2]. Nonetheless, artificial intelligence also opens new concerns, including ethics and related to human rights, data management and privacy, at the social level and for the workers in the workplace [3]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 V. J. P. D. Martinho, Economic Growth: Advances in Analysis Methodologies and Technologies, SpringerBriefs in Applied Sciences and Technology, https://doi.org/10.1007/978-3-031-38363-2_10

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10 Economic Growth, Sustainability Assessment and Artificial …

In any case, artificial intelligence has been referred to in studies related to the following domains: economic perspectives of coal-source-focused cities in conditions of low carbon strategies [4]; digital transition in the urban facilities and services [5]; urban development in contexts of cities expansion and transformation [6]; water resource planning and management [7]; consequences of social media on geopolitics and economic growth worldwide [8]; in assessment associated with the index of sustainable economic welfare in the Chinese framework [9]; automated examinations of tunnels [10]; forecasting carbon emissions and footprint [11]; river quality analysis [12]; and industry 4.0 and how the companies are involved in these new realities [13]. The interrelationships between artificial intelligence, economic growth and life cycle analysis were considered by Naveenkumar et al. [14] for the management of municipal solid waste; however, it seems that there is here a long way to run, particularly to highlight the potentialities of these interlinkages for the a more sustainable development. These gaps in the literature justify more research about the interactions among economic growth, sustainability assessment and artificial intelligence technologies and suggest that can be proposed a new approach which represents these interrelationships. This approach may be called as “Triple Eco-Digital Assessment” and may be more one contribution to alert the stakeholders to the need of putting together economic growth, sustainability and smart technologies. This may contribute to improvements in the efficiency of the processes and negative environmental impact mitigations. For that, a bibliometric analysis was considered [15, 16], as well as a focused review [17]. On a search carried out on 17 May 2023 on the Scopus [18] database, 69 documents were found for the following topics: “economic growth”; and “assess*”; and “artificial intelligence”. The top items were identified [19], and the procedures proposed by the VOSviewer [20–22] software were followed. An application of the artificial intelligence methodologies was presented at the end of this study for the European Union framework.

10.2 Metrics from the Scopus Database For the topics “economic growth”; and “assess*”; and “artificial intelligence”, Fig. 10.1 and Table 10.1 show that the assessment is important for sustainable development and decision-making. However, between the top 10 keywords (with the biggest total link strength), concepts related to the life cycle sustainability assessment are missing. These top 10 keywords have also a relevant number of occurrences and average citations. The average publication year ranges between 2017 and 2022. The USA, India, China, South Korea, Norway, United Arab Emirates, Lebanon, Australia, Portugal and Denmark are the top 10 countries with the highest total link strength and among these countries are those with an important number of documents and citations (Fig. 10.2 and Table 10.2).

10.2 Metrics from the Scopus Database

135

Fig. 10.1 Metrics for all keywords and co-occurrence links, considering the topics: “economic growth”; and “assess*”; and “artificial intelligence”

The top 10 organisations belong to countries such as Australia, Indonesia, Vietnam and Pakistan (Fig. 10.3 and Table 10.3). Some of these organisations have also a relevant number of citations. The average publication year ranges among 2019 and 2023, showing the topicality of the themes addressed. The main metrics for the most interlinked cited author, considering co-citation links (the relatedness is based on the number of times they are cited together), are presented in Fig. 10.4 and Table 10.4. The top 10 cited authors are the following: Zhang, Y.; Liu, Y.; Karimi, K.; Zhu, X.; Liu, C.; Li, W.; Fosso Wamba, S.; Wang, M.; Chen, G.; and Bhattacharya, S.S. The top 10 documents for bibliographic coupling

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10 Economic Growth, Sustainability Assessment and Artificial …

Table 10.1 Metrics for the top 10 all keywords (taking into account the total link strength) and co-occurrence links, considering the topics: “economic growth”; and “assess*”; and “artificial intelligence” All keywords

Total link strength

Occurrences

Average publication year

Average citations

Artificial intelligence

836

46

2020

17

Economics

654

29

2019

21

Economic growths

382

20

2019

5

Risk assessment

278

13

2021

5

Sustainable development

225

9

2019

6

Decision making

220

9

2020

11

Economic analysis

188

10

2018

10

Decision support systems

184

7

2017

13

Economic growth

162

11

2022

12

Article

140

3

2019

23

Fig. 10.2 Metrics for countries and bibliographic coupling links, considering the topics: “economic growth”; and “assess*”; and “artificial intelligence”

links (the relatedness is associated with the number of references they share) are exhibited in Table 10.5. The most relevant cited sources are the following (Fig. 10.5 and Table 10.6): Bioresour. Technol.; Ieee Commun. Surveys Tuts.; International Journal of Information Management; Technological Forecasting and Social Change; J. Clean. Prod.; Journal of Business Research; Renew. Sustain. Energy Rev.; Mis Quarterly; Waste Manag.; and Ieee Access. It seems that sources related to other domains of the science are missing here, particularly those related to the sustainability assessment.

10.2 Metrics from the Scopus Database

137

Table 10.2 Metrics for top 10 countries (taking into account the total link strength) and bibliographic coupling links, considering the topics: “economic growth”; and “assess*”; and “artificial intelligence” Countries

Total link strength

USA

1682

India

1120

China

Documents

Citations

Average publication year

9

561

2020

9

108

2020

1006

13

22

2020

South Korea

934

3

4

2022

Norway

904

2

2

2023

United Arab Emirates

886

2

6

2023

Lebanon

840

1

0

2023

Australia

389

5

86

2022

Portugal

261

2

55

2019

Denmark

244

2

42

2018

Fig. 10.3 Metrics for organisations and bibliographic coupling links, considering the topics: “economic growth”; and “assess*”; and “artificial intelligence” Table 10.3 Metrics for top 10 organisations (taking into account the total link strength) and bibliographic coupling links, considering the topics: “economic growth”; and “assess*”; and “artificial intelligence” Organisations

Total link strength Documents Citations Average publication year

Australian Industrial Transformation Institute, Flinders University, Adelaide, Australia

282

1

1

2023

Australian Institute for Machine Learning, University of Adelaide, Adelaide, Australia

282

1

1

2023

Centre for Work Health and Safety, Parramatta, Nsw, Australia

282

1

1

2023

(continued)

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10 Economic Growth, Sustainability Assessment and Artificial …

Table 10.3 (continued) Organisations

Total link strength Documents Citations Average publication year

Independent Consultant, University of Adelaide, Adelaide, Australia

282

1

1

2023

South Australian Centre for Economic Studies, University of Adelaide, Adelaide, Australia

282

1

1

2023

Bina Nusantara University, Jalan Hang Lekir I No. 6, Senayan, Indonesia

222

1

15

2019

Department for 222 Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Viet Nam

1

15

2019

Department of Economics, The Islamia University of Bahawalpur (Iub), Bahawalpur, Pakistan

222

1

15

2019

Faculty of Social Sciences and Humanities, Ton Duc Thang University, Ho Chi Minh City, Viet Nam

222

1

15

2019

School of Business, Bina Nusantara University, Jl. Kebon Jeruk Raya No. 27. Kebon Jeruk, Jakarta Barat, Indonesia

222

1

15

2019

Fig. 10.4 Metrics for cited authors and co-citation links, considering the topics: “economic growth”; and “assess*”; and “artificial intelligence”

10.2 Metrics from the Scopus Database

139

Table 10.4 Metrics for top 10 cited authors (taking into account the total link strength) and cocitation links, considering the topics: “economic growth”; and “assess*”; and “artificial intelligence” Cited authors

Total link strength

Citations

Zhang, Y.

4815

17

Liu, Y.

3540

17

Karimi, K.

3520

5

Zhu, X.

2866

5

Liu, C.

2536

7

Li, W.

2340

8

Fosso Wamba, S.

2227

11

Wang, M.

2213

6

Chen, G.

2126

4

Bhattacharya, S.S.

2118

3

Table 10.5 Metrics for documents and bibliographic coupling links, considering the topics: “economic growth”; and “assess*”; and “artificial intelligence” Documents

URL

Links

Citations

Cebulla [3]

https://doi.org/10.1007/s00146-022-01460-9

1

Alexopoulos [23]

https://doi.org/10.1109/istas52410.2021.9629125

1

0

Haseeb [24]

https://doi.org/10.2991/ijcis.d.191025.001

2

15

1

Fig. 10.5 Metrics for cited sources and co-citation links, considering the topics: “economic growth”; and “assess*”; and “artificial intelligence”

140

10 Economic Growth, Sustainability Assessment and Artificial …

Table 10.6 Metrics for top 10 cited sources (taking into account the total link strength) and cocitation links, considering the topics: “economic growth”; and “assess*”; and “artificial intelligence” Cited sources

Total link strength

Citations

Bioresour. Technol.

4253

47

Ieee Commun. Surveys Tuts.

4002

23

International Journal of Information Management

2985

26

Technological Forecasting and Social Change

2201

22

J. Clean. Prod.

1930

21

Journal of Business Research

1434

15

Renew. Sustain. Energy Rev.

1386

16

Mis Quarterly

1162

11

Waste Manag.

1161

9

Ieee Access

1035

9

10.3 Predict European Farming Costs with Machine Learning Approaches In this section, considering data from the Farm Accountancy Data Network (FADN) [25] for 2020 and following the IBM SPSS Modeler [26] software procedures for machine learning methodologies will be identified the models with better accuracy to predict farming costs (total inputs costs) in the European Union regions. As predictors, the other variables presented in this database were taken into account. In the FADN database for each region, statistical information for a representative farm is presented. The models with better accuracy (lower relative error) are those associated with the linear regressions, chi-squared automatic interaction detection (CHAID) and random forest, for example. The worse performances were found for the decision trees (Tree-AS) and support vector machine (SVM) (Table 10.7). On the other hand, the most important predictive variables are, respectively, the following (Table 10.8): farm net value added; total intermediate consumption; total labour input; depreciation; yield of wheat; cows’ milk & milk products; fertilisers; total liabilities; and subsidies on external factors. These findings show the importance of the competitiveness of the farms for the level of input costs, as well as the level of use of some inputs, such as fertilisers and labour. These contexts are also influenced by subsidies.

10.4 Discussion and Conclusions

141

Table 10.7 Results with machine learning approaches to predict total input costs in the European Union farms Model

Build Time

Correlation

Number of Fields

Relative Error

Regression

22

1.000

119

0.000

Linear

22

1.000

45

0.000

Linear-AS

22

0.998

176

0.004

CHAID

22

0.995

22

0.010

Random forest

22

0.994

176

0.013

Random trees

22

0.979

176

0.044

C&R tree

22

0.974

42

0.051

Neural net

22

0.968

167

0.063

Tree-AS

22

0.600

1

0.640

SVM

22

0.709

167

1.114

Note Regression, linear regression; linear, linear regression; Linear-AS, linear regression; CHAID, chi-squared automatic interaction detection; random forest, tree model as the base model; random trees, multiple decision trees; C&R tree, classification and regression tree; neural net, neural network; Tree-AS, decision trees; SVM, support vector machine

Table 10.8 Most important predictive variables in the machine learning models

Nodes

Importance

Subsidies on external factors (e)

0.010

Total liabilities (e)

0.010

Fertilisers (e)

0.011

Labour input (hrs)

0.015

Cows’ milk & milk products (e/farm)

0.016

Yield of wheat (q/ha)

0.020

Depreciation (e)

0.025

Total labour input (AWU)

0.026

Total intermediate consumption (e)

0.027

Farm net value added (e)

0.062

10.4 Discussion and Conclusions This research aimed to highlight the interrelationships between economic growth, sustainability assessment and artificial intelligence and show how these three dimensions may be combined. For that, 69 documents were assessed through bibliometric analysis. These documents were found on a search carried out on 17 May 2023 in the Scopus database for the topics: “economic growth”; and “assess*”; and “artificial intelligence”. For this bibliometric assessment, the procedures proposed by the VOSviewer software were followed. To highlight the importance of artificial intelligence for assessments related to sustainability and economic dimensions, some

142

10 Economic Growth, Sustainability Assessment and Artificial …

results were presented for the prediction of costs in the European Union farms (data from the FADN were considered for 2020). The artificial intelligence approaches may support the stakeholders in the different tasks related to the management and planning of the production units belonging to the diverse economic costs, including the prediction of costs in the agricultural sector. In any case, the implementation of artificial intelligence brings new challenges, specifically those associated with ethical dimensions and human rights. The literature survey highlights that the interlinkages among economic growth, sustainability assessment and artificial intelligence have not been explored enough by the scientific community. The metrics from the bibliographic data reveal that the most relevant keywords (considering the approaches here used) are the following: artificial intelligence; economics; economic growths; risk assessment; sustainable development; decision making; economic analysis; and decision support systems. Some important keywords for the topics here addressed are missing, particularly those related to the sustainability assessment. The most important countries of affiliation are the following: USA; India; China; South Korea; Norway; United Arab Emirates; Lebanon; Australia; Portugal; and Denmark. In the cited sources, again, it seems that the sustainability assessment dimensions may be deeper explored in future research. In the prediction of the total input costs for the farms of the European Union regions, the results show that the linear regressions, CHAID and random forest models are those with better accuracy. In turn, the most important predictors are the farm net value added, total intermediate consumption, total labour input, depreciation, yield of wheat, cows’ milk & milk products, fertilisers, total liabilities and subsidies on external factors. In terms of practical implications, it is suggested to create an approach, which may be designated as “Triple Eco-Digital Assessment”, to promote more research about the interlinkages among economic growth, sustainability assessment (with life cycle tools, for example) and artificial intelligence. For policy recommendation, it could be important to rethink the agricultural subsidies in the European Union based on studies related to the impacts of the respective instruments on the costs of the farms, on the farming competitiveness and the use of some resources (such as the fertilisers and the labour). Acknowledgements This work is funded by National Funds through the FCT—Foundation for Science and Technology, I.P., within the scope of the project Refa UIDB/00681/2020. This research is also funded by the Enovo company. This study was carried out under the international project “Agriculture 4.0: Current reality, potentialities and policy proposals” (CERNAS-IPV/2022/008). Furthermore, we would like to thank the CERNAS Research Centre and the Polytechnic Institute of Viseu for their support. This work is too co-financed by the PRR—Plano de Recuperação e Resiliência (República Portuguesa) and the European NextGeneration EU Funds (recuperarportugal.gov.pt) through application PRR-C05-i03-I-000030—“Carbo2Soil—Reforçar a Complementaridade entre agricultura e pecuária para aumentar a fertilidade dos solos e a sua capacidade de sequestro de carbon”.

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