The Overall Development of the Belt and Road Countries: Measurement and Assessment

Abstract

Based on a multidimensional assessment indicators system and a two-layer assessment model, this article measures and evaluates the overall development of China and the 148 countries that signed cooperation agreements with China to jointly build the Belt and Road (we refer to this group of countries as the Belt and Road Countries or simply BRCs henceforth) from 2015 to 2019 using panel normalization method. This research shows that the overall development of BRCs is generally on track, and most countries are showing an upward development trend, mainly Asian and African countries. Second, some features of the overall development of BRCs are clearly evident, such as (a) the economic development is not high enough but relatively stable; (b) the resource endowments are not excellent, but rich fossil fuels are concentrated in a few countries of BRCs; (c) the environment protection of the countries is reasonably well managed; (d) social development indicators of these countries are slightly lagging behind the world average. Third, certain challenges face this group of countries, for example, good governance, economic structural transformation, and industrialization need improvement. Finally, there is enormous potential for strengthening cooperation between BRCs, particularly in energy, manufacturing, green development, social development linking with the people’s livelihood improvement, and so on.

Keywords

Belt and Road Countries (BRCs)Belt and Road Initiative (BRI)overall development assessment indicators system measurement and assessment

Introduction

Since the global financial crisis in 2008, the world economic development pattern has undergone unprecedented changes. On the one hand, the economies of Western countries have witnessed a general recession due to the global financial crisis; on the other hand, a group of emerging market countries and developing countries, represented by China, have seen rapid economic recovery and development, making them a vital engine driving global economic growth and playing an increasingly important role in global governance (Hu, 2018; Hu et al., 2018). At the same time, the growing unbalanced and unfair world development has encouraged the populist, nationalist, and protectionist forces in some regions, posing significant challenges to global development and globalization. Facing the domestic and international economic situation, Chinese President Xi Jinping put forward the Belt and Road Initiative (BRI) in the autumn of 2013. This was an effort to provide new growth momentum to the global economy, calling for a joint effort to build a Silk Road Economic Belt and a twenty-first Century Maritime Silk Road. Establishing closer global connectivity and promoting effective allocation of economic resources at the regional and even international levels was expected to achieve interconnected regional and global economic growth. Furthermore, this would boost the transition and upgrading of globalization and realize mutual benefits and common development (Hu, 2020).

The BRI has received an enormous response from the international community. According to the Belt and Road Portal, by February 8, 2022, China has signed more than 200 cooperation agreements with 148 countries and 32 international organizations to build the Belt and Road jointly, including 51 African, 38 Asian, 27 European, 21 Latin American, and 11 Oceanian countries.¹ The data from the World Bank show that the total population, land area, and GDP of these 148 countries in 2020 totaled 3.56 billion, 74.92 million km², and US$20.12 trillion (at constant 2010 price), accounting for 45.92%, 57.65%, and 24.5% of the world’s total, respectively. Including China, the total population, land area, and GDP in 2020 reached 4.96 billion, 84.34 million km², and US$31.90 trillion (at constant 2010 price), and their shares in the world were 64.01, 64.90, and 38.95%, respectively. As the initiator of the BRI, China should be included in the Belt and Road Countries (BRCs), so the total country number has reached 149 so far. These countries account for more than 60% of the world’s population and land area, and their GDP accounts for nearly 40% of the world’s total. Therefore, on the one hand, the BRCs play a pivotal role in the world, but on the other hand, they are relatively behind in economic development.

According to the World Bank’s income classification standard in 2020,² of the 149 countries, 35 were high-income countries, accounting for 23.49%; 91 were middle-income countries, accounting for 61.11% (including 42 upper-middle- and 49 lower-middle-income countries); and 23 were low-income countries, accounting for 15.44%. The average per capita GDP of the BRCs was US$ 9,032.47 (at the current price), reaching the upper-middle-income level but still slightly lower than the world average of US$ 10,925.73 (at the current price). In terms of urban-rural structure, more than half of BRCs’ population lived in urban areas, with an average urbanization rate of 56.25%, slightly higher than the world average (56.15%) in 2020.

It has been over 9 years since the BRI was put forward. Several pragmatic and cooperative construction projects have already been implemented, some successfully completed. Significant milestones have been achieved in the BRI cooperation. However, existing research on the BRI tends to focus only on 65 countries along the Belt and Road. Though such research is of high significance, it may not satisfy the current practical needs and even lead to conclusions that do not align with the overall Belt and Road development. This is not conducive to China and related countries for making realistic assessments and decisions to promote high-quality projects in BRI. Hence, from the perspective of the 149 BRCs³, a qualitative and quantitative study was conducted on the comprehensive development level of each country in an effort to accurately understand their development status, basic characteristics, and main issues at the macroscopic and fundamental levels. This will lay the foundation for further research on relevant issues related to the Belt and Road development.

Measurement and Assessment Indicators System

National development is a comprehensive concept that cannot be fully reflected by economic growth alone. Since 1990, the United Nations Development Program (UNDP) has been publishing the Human Development Index (HDI) report annually, making efforts to shift the idea of development from the “economic-growth-centered view” to a “human-development-centered view.” Based on three variables; per capita national income, life expectancy, and mean years of schooling, a comprehensive index (HDI) was developed to measure the development level of each country. However, with the changes in times, the HDI could not reflect the rich connotation of today’s development. For example, when the HDI was designed, the central concept was mainly the development of society and the economy, while sustainable development of the environment was not fully considered. Based on our previous research, besides the improvement of economic strength and the development of social undertakings, other aspects, such as the improvement of governance, the efficient utilization of resources, environmental protection, the upgrading of economic structures, and the potential of development, must be included in national development evaluation.

Constructing the Indicators System

To measure the overall development of the BRCs, this article considers the performance of a country from eight aspects: Economic development, governance, resource endowments, environmental protection, social development, business environment, structural transformation, and size effect. It selects 23 secondary indicators (Figure 1) based on the principles of representativeness, comprehensiveness, comparability, and data availability⁴.

Figure 1.

Assessment Indicators System of the Overall Development of the BRCs.

Economic development. Economic development is the foundation of a country’s overall development. This article selects three indicators: the aggregate GDP (at constant 2010 price), GDP per capita (at constant 2010 price), and GDP growth rate. These can comprehensively reflect economic development from three aspects of scale, efficiency, and growth to measure the economic development levels of the BRCs.

Governance. International development practices and academic studies indicate that good governance is the foundation of the institutional system, and the improvement of institutional quality is essential for national development. The World Bank’s Worldwide Governance Indicators (WGI) have been widely used to measure national governance levels and institutional quality. Therefore, this article adopts the World Bank’s WGI to measure the governance levels of the BRCs. WGI includes six governance dimensions: voice and accountability, political stability and absence of violence/terrorism, regulatory quality, government effectiveness, the rule of law, and control of corruption.

Resource endowments. Resource endowments are the basis of national development and profoundly impact the potential, direction, breadth, and depth of a country’s development. Compared with countries with fewer resource endowments, countries with rich or diversified resources can usually produce more goods, so they tend to be wealthier and more likely to achieve high-level comprehensive development. Cultivated land resources, freshwater resources, and fossil energy are three major strategic natural resources directly related to the national economy and people’s livelihood. Additionally, they are of high significance to industrial cooperation among countries. Hence, we selected three indicators, namely, per capita cultivated land area, per capita freshwater resource, and per capita fossil energy output (including the output of coal, natural gas, and oil), to reflect the resource endowments of the BRCs.

Environment protection. Environment protection has important implications for sustainable development and is a core component of the high-quality development of the Belt and Road (Hu, 2020). As countries worldwide are working together to address climate change, its practical significance cannot be overstated. For the environment protection dimension, we selected per capita carbon dioxide emission, a representative indicator widely used to measure environmental pollution, and the share of natural resource rents in GDP as a supplementary indicator to reflect a country’s extent of exploitation of natural resources in economic development.

Social development. Social progress is an integral part of national development, and its main aspects include health, education, employment, and gender equality. Therefore, indicators are selected in these four aspects to measure the social development level of the BRCs, including life expectancy, mean years of schooling, unemployment rate, and the share of the female labor force. Among these, the health and education of the population are essential embodiments of a country’s human capital level; a high employment rate is often associated with high social stability; gender equality is a significant aspect of social progress.

Business environment. The business environment directly and significantly impacts the production and operation activities of enterprises in a country. It profoundly affects the investment decisions of multinational enterprises and, thus, the country’s economic vigor and overall development. The World Bank’s Doing Business indicator assesses the business environment of countries around the world from two aspects, the effectiveness of government services to enterprises and the degrees of institutional and legal protection for enterprises. It is employed to measure the business environment of the BRCs.

Structural transformation. In the field of development economics, structural transformation usually refers to the process in which production factors gradually shift from agriculture to industrial or service sectors or from traditional economy to modern economy, indicating that there are at least two aspects in structural transformation, namely economic structural transformation and urban-rural structural transformation. Therefore, the urbanization rate and the share of manufacturing value added to GDP are selected to measure the structural transformation of the BRCs.

Scale effect. The scale and size are critical initial conditions and factor endowments of a country, which play a fundamental role in national development. Countries of different scales tend to adopt different development strategies and paths that produce varying development effects. A larger scale often leads to cost reduction of the internal resource flow of a country, which is conducive to forming scale advantages, deepening labor division and cooperation, manifesting the internal stability of its domestic economic system, and thus boosting the comprehensive development of the country. This article selects two indicators, the total population and land area, to assess the scales of the BRCs. The population is vital to national development. Generally, a larger population means a bigger market scale and higher development potential. Land area is the geographical space for national development. A vast territory typically has richer natural resources, which can better meet the needs of national development.

Data Collection and Processing

This research focuses on the overall development level of the 149 BRCs. Because of the varying impacts of the COVID-19 pandemic on different countries since 2020, we selected a period from 2015 to 2019 when measuring and assessing the overall development level of each country to obtain a consistent result. In the long run, the COVID-19 pandemic and its impact on the overall development of all countries need to be taken into account. However, it will take some time after the pandemic to get relatively stable results.

Most data used in this article are from the WDI database of the World Bank (2021). However, we turned to other international organizations’ databases to supplement in case of insufficient or missing data. For example, relevant data on governance indicators were from the WGI database of the World Bank (2020b); the data on per capita fossil energy from the BP Statistical Review of World Energy (2020); the data on mean years of schooling from the Human Development Reports (HDR) of the UNDP (2020); the data on business environment indicators from the Doing Business database of the World Bank (2020a); and the data on the shares of manufacturing value added in GDP from the database of the United Nations Industrial Development Organization (UNIDO) (2021).

To ensure the integrity of samples, four methods were adopted to deal with the missing data of individual countries. First, information from the statistical departments of relevant countries was used. For instance, data on the Cook Islands and Niue are not available in the World Bank database, so we turned to official data released by their national statistical departments (Ministry of Finance and Economic Management [MFEM], 2020; Niue Statistics Office [NSO], 2020). Second, in some instances, research literature from relevant sectors was used. For example, the data on per capita freshwater resources of Pacific Island countries were collected from research reports produced by the United Nations Environment Program (UNEP) (2012). Third, we used the linear interpolation method, including two- or three-year average and moving average methods. Fourth, we used the regional average method as a last resort. When the previous three methods did not work, we used the average data of other countries in the same region of the target country as a supplement. For example, the business environment data of Cuba, the Cook Islands, and Niue are not available in the World Bank, so we used the average of Caribbean countries to represent the data of Cuba and the average of Pacific Island countries to represent the data of the Cook Islands and Niue. This method was also employed to obtain the mean years of schooling and the shares of natural resource rents in GDP for the Cook Islands and Niue. It is worth noting that the above situations are exceptional. The data of most countries are complete and available. Only data from a few countries must be obtained by data processing, most of which are tiny countries, especially island countries. Generally, the data obtained by these methods will not compromise the overall analysis and assessment results.

Measurement and Assessment Model

To calculate the overall development index of the BRCs, we first constructed an assessment model with a two-tier structure. The model design includes first-level indicators which relate to the abovementioned eight aspects of economic development, governance, resource endowment, environment protection, social development, business environment, structural transformation, and size effect. Each first-level indicator is divided into several secondary indicators; these eight aspects have 23 secondary indicators. Second, we used a low-to-high, layer-by-layer weighted arithmetic average quantitative calculation method. More specifically, the overall development index was obtained by the equal weighting of the eight first-level indicators, and the value of each first-level index was determined by all the secondary indicators subordinate to the first-level indicators through equal weighting. The value of the second indicator was derived from its actual statistical data.

According to the different meanings of the indicators, there are two cases of the evaluation of the secondary indicators: One is that the “unemployment rate,” “carbon dioxide emissions per capita,” and “share of natural resource rents in GDP” in the indicator system are three indicators given lower assessment scores when the number is bigger, that is, the country with the biggest number is rated as zero on the indicator, and the country with the smallest number is rated as a full score. The second case is that the bigger the original value of the secondary indicator, the higher the score of the assessment; a country with the biggest number is given a full score on the corresponding indicator, and in turn, a country with the smallest number is rated as zero. This case is more common.

The evaluation model is calculated from the data matrix (DATA), score matrix (SCORE), and rank matrix (RANK). The calculation process is divided into three steps.

Step 1. Constructing DATA Matrix

The matrix has a total of 149 rows and 23 columns, and its elements are $X_{i j}$ , and $X_{i j}$ indicate the original data of the $i$ th country $(i = 1, \dots, 149)$ corresponding to the $j$ th secondary indicator $(j = 1, \dots, 23)$ . Each column of the DATA matrix corresponds to the original data of all countries under a specific indicator, and the units of the different columns are different. Each line corresponds to the original data of a country under all indicators. Thus, the DATA matrix can be written as:

D A T A = {[\begin{matrix} \begin{matrix} X_{11} & X_{12} \\ X_{21} & X_{23} \end{matrix} & \begin{matrix} \dots & X_{1 23} \\ \dots & X_{2 23} \end{matrix} \\ \begin{matrix} ⋮ & ⋮ \\ X_{149 1} & X_{149 2} \end{matrix} & \begin{matrix} ⋮ & ⋮ \\ \dots & X_{149 23} \end{matrix} \end{matrix}]}_{149 \times 23}

Step 2. Constructing SCORE Matrix

First, according to the matrix above, construct a secondary index score matrix ( ${S C O R E}^{(2)}$ ). The ${S C O R E}^{(2)}$ matrix has a total of 149 rows and 23 columns, and its element is $S_{i j}^{(2)}$ . $S_{i j}^{(2)}$ indicates the results of the $i$ th country $(i = 1, \dots, 149)$ corresponding to score of the secondary indicator $(j = 1, \dots, 23)$ . Each column of the ${S c o r e}^{(2)}$ matrix corresponds to the score of all countries under a specific secondary indicator. Each row of the ${S c o r e}^{(2)}$ matrix corresponds to a country’s score under all secondary indicators.

For case one: the bigger the number, the lower the score. Set the original data to take the minimum value $\min_{i = 1, \dots, n} \{X_{i j}\}$ of the national score as the full score, the original data to the maximum value $\max_{i = 1, \dots, 149} \{X_{i j}\}$ of the national score as zero. The formula of $S_{i j}^{(2)}$ is, $S_{i j}^{(2)} = \frac{\max_{i = 1, \dots, n} \{X_{i j}\} - X_{i j}}{\max_{i = 1, \dots, n} \{X_{i j}\} - \min_{i = 1, \dots, n} \{X_{i j}\}} \times 100$ and $0 ≦ S_{i j}^{(2)} ≦ 100$ . This formula is used for indicators of the unemployment rate, carbon dioxide emissions per capita, and share of natural resource rents in GDP.

For case two: the bigger the number, the higher the score. Set the original data to take the maximum value $\max_{i = 1, \dots, n} \{X_{i j}\}$ of the national score as the full score, the original data to the minimum value $\min_{i = 1, \dots, 149} \{X_{i j}\}$ of the national score as zero. The formula of $S_{i j}^{(2)}$ is $S_{i j}^{(2)} = \frac{X_{i j} - \min_{i = 1, \dots, n} \{X_{i j}\}}{\max_{i = 1, \dots, n} \{X_{i j}\} - \min_{i = 1, \dots, n} \{X_{i j}\}} \times 100$ , and $0 ≦ S_{i j}^{(2)} ≦ 100$ . This formula is used for other secondary indicators.

The following matrix of ${S C O R E}^{(2)}$ is obtained by calculating each element of the ${S C O R E}^{(2)}$ matrix from the above formula:

{S C O R E}^{(2)} = {[\begin{matrix} \begin{matrix} S_{11}^{(2)} & S_{12}^{(2)} \\ S_{21}^{(2)} & S_{23}^{(2)} \end{matrix} & \begin{matrix} \dots & S_{1 23}^{(2)} \\ \dots & S_{2 23}^{(2)} \end{matrix} \\ \begin{matrix} ⋮ & ⋮ \\ S_{149 1}^{(2)} & S_{149 2}^{(2)} \end{matrix} & \begin{matrix} ⋮ & ⋮ \\ \dots & S_{149 23}^{(2)} \end{matrix} \end{matrix}]}_{149 \times 23}

Then, according to the ${S C O R E}^{(2)}$ matrix, construct a primary index score matrix ( ${S C O R E}^{(1)}$ ). The ${S C O R E}^{(1)}$ matrix has a total of 149 rows and 8 columns, whose element is $S_{i j}^{(1)}$ . $S_{i j}^{(1)}$ indicates the results of the ith country $(i = 1, \dots, 149)$ corresponding to the score result of the jth first level indicators $(j = 1, \dots, 8)$ . Each column of the ${S C O R E}^{(1)}$ matrix corresponds to the score of all countries under a certain level. Each row of the ${S C O R E}^{(1)}$ matrix corresponds to a country’s score under all the first-level indicators. Assuming that the primary indicators of the jth $(j = 1, \dots, 8)$ first-level indicators are the sth column to the tth column in the ${S c o r e}^{(2)}$ matrix, the weight of the pth $(s ≦ p ≦ t)$ column is $w_{p}^{(2)} ≧ 0$ . Then, $S_{i j}^{(1)}$ equals to the weighted average of all secondary indicators, the formula is $S_{i j}^{(1)} = \sum_{p = s}^{t} (S_{i p}^{(2)} ∙ w_{p}^{(2)}) / \sum_{p = s}^{t} w_{p}^{(2)}$ , and $0 ≦ S_{i j}^{(1)} ≦ 100$ . This report selects the weighted average as 1, and each element of the ${S c o r e}^{(1)}$ matrix is calculated. Thus, the following ${S C O R E}^{(1)}$ matrix is obtained:

{S C O R E}^{(1)} = {[\begin{matrix} \begin{matrix} S_{11}^{(1)} & S_{12}^{(1)} \\ S_{21}^{(1)} & S_{23}^{(1)} \end{matrix} & \begin{matrix} \dots & S_{18}^{(1)} \\ \dots & S_{28}^{(1)} \end{matrix} \\ \begin{matrix} ⋮ & ⋮ \\ S_{149 1}^{(1)} & S_{149 2}^{(1)} \end{matrix} & \begin{matrix} ⋮ & ⋮ \\ \dots & S_{149 8}^{(1)} \end{matrix} \end{matrix}]}_{149 \times 8}

Finally, according to the ${S C O R E}^{(1)}$ matrix, the development index score matrix ( $S C O R E$ ) is constructed. The $S C O R E$ matrix has a total of 149 rows and 1 column, and its element is $S_{i}$ . $S_{i}$ indicates the results of the ith $(i = 1, \dots, 149)$ country corresponding to the scoring results of the development index. Assuming that the weight of the jth $(j = 1, \dots, 8)$ level indicator is $w_{j}^{(1)} ≧ 0$ . Then, $S_{i}$ equal to the weighted average of the eight first-level indicators, the formula is $S_{i} = \sum_{j = 1}^{8} (S_{i j}^{(1)} ∙ w_{j}^{(1)}) / \sum_{j = 1}^{8} w_{j}^{(1)}$ , and $0 ≦ S_{i} ≦ 100$ . This report selects the weighted average as 1, and each element of the $S C O R E$ matrix is calculated. Thus, the following matrix is obtained:

S C O R E = {[\begin{matrix} \begin{matrix} S_{1} \\ S_{2} \end{matrix} \\ \begin{matrix} ⋮ \\ S_{149} \end{matrix} \end{matrix}]}_{149 \times 1}

Step 3. Constructing RANK Matrix

First, according to the ${S C O R E}^{(2)}$ matrix, we build a secondary indicator rank matrix (RANK⁽²⁾). The RANK⁽²⁾ matrix has a total of 149 rows and 23 columns, and its element is $R_{i j}^{(2)}$ . $R_{i j}^{(2)}$ indicates the sorting results for the ith $(i = 1, \dots, 149)$ corresponding to the ranking result of the jth $(j = 1, \dots, 23)$ secondary indicators. Each column of the ${R A N K}^{(2)}$ matrix corresponds to the ranking result for all countries under a certain secondary indicator. Each row of the ${R A N K}^{(2)}$ matrix corresponds to the sorting result of a country under all secondary indicators. Rank the ${S C O R E}^{(2)}$ matrix elements in the order from high to low, then the order of $R_{i j}^{(2)}$ equals to the order of $S_{i j}^{(2)}$ , and the formula is $R_{i j}^{(2)} = R a n k \{S_{i j}^{(2)}| {[S_{i j}^{(2)}]}_{i = 1, \dots, 149}\}$ , and $1 ≦ R_{i j}^{(2)} ≦ 149$ . $R_{i j}^{(2)} = 1$ indicates that the country ranks highest in the 149 countries, ranking first; $R_{i j}^{(2)} = 149$ indicates the country gets the lowest score in the 149 countries, ranking last. The formula is then calculated for each element of the ${R A N K}^{(2)}$ matrix, resulting in the following ${R A N K}^{(2)}$ matrix:

{R A N K}^{(2)} = {[\begin{matrix} \begin{matrix} R_{11}^{(2)} & R_{12}^{(2)} \\ R_{21}^{(2)} & R_{23}^{(2)} \end{matrix} & \begin{matrix} \dots & R_{1 23}^{(2)} \\ \dots & R_{2 23}^{(2)} \end{matrix} \\ \begin{matrix} ⋮ & ⋮ \\ R_{149 1}^{(2)} & R_{149 2}^{(2)} \end{matrix} & \begin{matrix} ⋮ & ⋮ \\ \dots & R_{149 23}^{(2)} \end{matrix} \end{matrix}]}_{149 \times 8}

Then, according to the ${S C O R E}^{(2)}$ matrix, we build a secondary indicator rank matrix (RANK⁽¹⁾). The RANK⁽¹⁾ matrix has a total of 149 rows and 8 columns, and its elements are $R_{i j}^{(1)}$ . $R_{i j}^{(1)}$ indicates the sorting results for the ith $(i = 1, \dots, 149)$ corresponding to the ranking result of the jth $(j = 1, \dots, 8)$ secondary indicators. Each column of the ${R A N K}^{(1)}$ matrix corresponds to the ranking result for all countries under a certain secondary indicator. Each row of the ${R A N K}^{(1)}$ matrix corresponds to the sorting result of a country under all secondary indicators. Rank the ${S C O R E}^{(1)}$ matrix elements in the order from high to low, then the order of $R_{i j}^{(1)}$ equals to the order of $S_{i j}^{(1)}$ , the formula is $R_{i j}^{(1)} = R a n k \{S_{i j}^{(1)}| {[S_{i j}^{(1)}]}_{i = 1, \dots, 149}\}$ , and $1 ≦ R_{i j}^{(1)} ≦ 149$ . $R_{i j}^{(1)} = 1$ indicates the country ranks highest in the 149 countries, ranking the first; $R_{i j}^{(1)} = 149$ indicates the country gets the lowest score in the 149 countries, ranking last. The formula is then calculated for each element of the ${R A N K}^{(1)}$ matrix, resulting in the following ${R A N K}^{(1)}$ matrix:

{R A N K}^{(1)} = {[\begin{matrix} \begin{matrix} R_{11}^{(1)} & R_{12}^{(1)} \\ R_{21}^{(1)} & R_{23}^{(1)} \end{matrix} & \begin{matrix} \dots & R_{1 23}^{(1)} \\ \dots & R_{2 23}^{(1)} \end{matrix} \\ \begin{matrix} ⋮ & ⋮ \\ R_{149 1}^{(1)} & R_{149 2}^{(1)} \end{matrix} & \begin{matrix} ⋮ & ⋮ \\ \dots & R_{149 23}^{(1)} \end{matrix} \end{matrix}]}_{149 \times 8}

Finally, according to the SCORE matrix, we construct a second indicator rank index matrix (RANK). The RANK matrix has a total of 149 rows and 1 column, and its elements are $R_{i}$ . $R_{i}$ represents the ranking result of the ith $(i = 1, \dots, 149)$ national development index, then the column elements of the $S C O R E$ matrix are arranged from high to low, the rank of $R_{i}$ is equal to that of $S_{i} .$ The formula is $R_{i} = R a n k \{S_{i}| {[S_{i}]}_{i = 1, \dots, 149}\}$ , and $1 ≦ R_{i} ≦ 149$ . $R_{i} = 1$ indicates the country ranked highest in the 149 countries; $R_{i} = 149$ means the country gets the lowest score in the 149 countries in terms of this indicator, ranking last. Thus, the RANK matrix can be written as:

R A N K = {[\begin{matrix} \begin{matrix} R_{1} \\ R_{2} \end{matrix} \\ \begin{matrix} ⋮ \\ R_{149} \end{matrix} \end{matrix}]}_{149 \times 1}

Measurement Results and Analysis

Results

Based on the above-mentioned data and model, we obtained the scores and ranks of the eight subcategories of development as well as the overall development scores and ranks. The detailed results are provided in Table 1 (the results are ranked from high to low according to the five-year average scores of the overall development).

Table 1.

Scores and Rankings of the Overall Development Level of the BRCs.

	Overall Development		Score of Sub-indices
Country	Ranking	Score	Economic Development	National Governance	Resource Endowment	Environment Protection	Social Development	Business Environment	Structure Transformation	Scale effect
Singapore	1	60.64	37.00	93.87	0.01	87.20	85.23	99.43	82.13	0.20
New Zealand	2	59.45	30.80	99.34	8.99	88.77	85.94	100.0	60.81	0.97
China	3	58.64	52.83	43.80	1.92	88.13	71.62	57.08	75.45	78.33
South Korea	4	56.84	31.02	74.13	0.52	81.39	83.42	97.36	84.76	2.13
Austria	5	55.29	34.17	89.60	2.77	88.71	82.44	88.68	55.40	0.57
Czech	6	54.47	26.89	77.18	4.11	84.89	81.86	83.11	77.11	0.61
Lithuania	7	54.30	24.32	76.81	10.89	93.71	76.74	90.66	60.97	0.29
Luxembourg	8	53.66	52.18	96.00	1.63	76.33	81.06	69.53	52.49	0.03
Estonia	9	53.45	25.26	83.60	8.21	80.76	80.79	93.21	55.64	0.18
Russia	10	53.01	26.05	36.51	18.19	76.33	74.25	81.04	56.53	55.16
Latvia	11	52.16	23.68	73.51	9.97	93.64	74.91	91.51	49.80	0.26
Malaysia	12	51.97	24.56	62.81	3.54	84.58	73.90	90.28	73.95	2.12
Slovenia	13	51.87	27.04	76.71	2.19	89.55	79.32	80.85	59.15	0.13
Chile	14	51.42	23.47	78.34	6.08	90.33	73.50	74.72	61.99	2.93
Poland	15	51.40	26.23	71.16	4.74	87.22	77.16	85.38	56.98	2.29
Portugal	16	50.74	26.26	80.02	1.69	92.41	70.57	83.77	50.52	0.65
Hungary	17	50.64	24.77	65.06	6.20	92.73	75.15	73.77	66.80	0.63
Slovakia	18	50.33	25.27	70.93	3.66	90.71	74.98	80.28	56.46	0.34
Malta	19	49.84	29.40	78.89	0.28	95.03	78.35	57.92	58.80	0.02
Italy	20	49.76	34.25	66.22	1.92	91.57	68.30	74.91	57.82	3.06
Thailand	21	49.29	22.18	46.35	4.03	93.11	74.81	82.83	67.01	4.03
United Arab Emirates	22	49.04	32.13	69.71	8.52	58.34	77.11	88.58	57.38	0.56
Uruguay	23	49.02	21.97	75.11	11.12	96.00	68.26	50.57	68.49	0.66
Romania	24	48.62	23.77	58.78	6.67	93.75	71.63	76.04	56.90	1.40
Georgia	25	48.36	20.93	63.82	2.90	95.84	68.10	94.34	40.57	0.34
Peru	26	48.11	21.42	50.63	7.32	94.68	78.51	67.83	59.48	5.03
Bulgaria	27	48.00	21.89	58.87	7.40	90.38	71.79	74.72	58.39	0.58
Belarus	28	47.95	19.21	39.35	8.65	89.95	77.16	73.87	74.47	0.96
Cyprus	29	47.86	29.08	74.94	1.17	90.68	75.50	72.92	38.54	0.07
Katar	30	47.67	38.16	63.93	31.81	38.50	81.37	63.30	64.16	0.13
Costa Rica	31	47.46	22.08	68.70	3.16	96.88	66.17	65.66	56.73	0.33
Croatia	32	46.84	23.63	64.53	3.75	93.32	68.19	73.87	47.08	0.32
Kazakhstan	33	46.37	22.70	43.98	26.48	71.87	76.92	76.89	43.22	8.89
Indonesia	34	46.18	24.48	48.55	2.68	94.97	65.30	59.06	59.26	15.16
Turkey	35	45.67	27.39	42.75	3.83	92.40	56.49	73.40	63.81	5.25
Brunei	36	45.43	26.96	67.74	19.46	63.83	68.06	54.43	62.91	0.03
Argentian	37	45.35	21.93	51.09	12.47	92.76	69.06	37.55	68.00	9.93
Panama	38	45.14	23.37	56.82	5.48	96.02	75.20	62.74	41.10	0.37
Bahrain	39	44.70	25.38	49.30	3.12	65.62	72.64	68.40	73.12	0.06
North Macedonia	40	44.50	20.33	51.94	3.03	93.89	51.25	90.38	45.01	0.15
Serbia	41	44.37	21.09	52.86	5.61	89.20	63.27	72.83	49.60	0.52
Greece	42	44.19	25.02	59.67	3.59	90.44	57.86	64.62	51.53	0.78
Moldova	43	43.57	20.06	44.28	8.62	95.29	69.14	73.87	37.08	0.20
Mongolia	44	43.27	20.86	53.20	9.35	81.38	67.26	63.30	45.95	4.87
Botswana	45	43.21	21.01	68.72	1.75	94.23	54.90	62.08	41.18	1.81
Montenegro	46	43.20	21.86	55.88	0.22	93.72	62.41	74.34	37.12	0.06
Armenia	47	43.11	21.20	47.91	2.34	96.71	58.56	71.42	46.53	0.19
Cuba	48	43.04	20.30	42.13	3.94	95.99	77.65	43.56	60.00	0.72
Vietnam	49	43.03	22.20	44.49	1.63	93.90	77.70	60.94	39.07	4.33
Oman	50	42.95	23.28	57.51	6.24	61.34	69.44	66.79	57.88	1.11
Salvador	51	42.84	19.60	46.54	1.68	97.90	62.06	53.87	60.82	0.29
Ukraine	52	42.83	18.47	35.70	10.65	92.09	66.26	63.49	52.62	3.37
Jamaica	53	42.81	19.23	58.19	0.96	95.30	65.33	65.57	37.80	0.14
Morocco	54	42.66	20.23	46.32	3.00	96.22	49.56	68.30	55.01	2.63
Trinidad and Tobago	55	42.51	20.74	55.79	11.94	74.76	73.58	52.92	50.26	0.06
Dominican Republic	56	42.43	23.13	48.01	1.39	95.99	63.69	44.34	62.38	0.52
Philippines	57	42.40	22.85	45.46	1.22	97.65	67.99	45.66	53.72	4.67
Fiji	58	42.36	20.31	56.33	6.07	96.30	63.82	53.68	42.29	0.09
Seychelles	59	41.94	23.27	62.38	0.02	90.82	75.84	48.30	34.86	0.00
Tunisia	60	41.69	19.44	47.64	3.30	94.59	47.83	62.45	57.39	0.88
Niue	61	41.39	24.46	43.66	15.62	91.84	67.39	40.29	47.85	0.00
Albania	62	41.29	20.41	52.97	3.94	96.25	63.90	55.94	36.73	0.19
Rwanda	63	41.00	21.77	52.99	1.42	96.79	68.35	71.60	14.60	0.50
South Africa	64	40.93	20.96	57.50	4.03	86.22	39.06	63.40	50.53	5.74
Barbados	65	40.79	22.44	76.01	0.37	93.11	73.04	41.98	19.35	0.01
Saudi Arabia	66	40.62	26.26	46.92	8.84	59.61	60.48	52.74	62.36	7.74
Ecuador	67	40.51	19.13	41.19	4.32	92.94	71.96	39.43	53.77	1.36
Ghana	68	40.37	20.88	54.19	2.35	93.29	60.11	46.13	44.27	1.74
Sri Lanka	69	39.97	20.67	50.39	1.12	98.40	69.29	48.11	30.83	0.95
Dominica	70	39.94	18.91	66.89	1.45	96.18	48.14	50.57	37.35	0.00
Samoa	71	39.88	19.96	68.80	3.02	97.49	61.44	52.92	15.35	0.01
Kuwait	72	39.52	27.75	49.62	15.64	42.09	67.57	49.81	63.49	0.20
Tonga	73	39.43	19.78	57.46	3.12	97.59	68.58	53.11	15.83	0.01
Namibia	74	39.30	19.35	61.12	4.89	95.91	39.70	49.06	41.77	2.60
Cook Islands	75	39.26	26.46	43.03	1.28	91.63	72.43	40.29	38.95	0.00
Azerbaijan	76	39.18	19.19	36.03	5.42	82.08	73.64	63.49	32.93	0.60
Cape Verde	77	38.87	20.50	66.35	1.35	98.04	54.08	30.19	40.44	0.03
Kyrgyzstan	78	38.81	19.94	36.09	3.73	94.60	66.39	50.75	38.18	0.81
Uzbekistan	79	38.72	21.48	27.52	2.46	88.56	70.64	51.60	45.00	2.50
Antigua and Barbuda	80	38.55	24.11	65.49	0.64	91.84	70.28	45.09	10.91	0.00
Bosnia and Herzegovina	81	38.51	20.77	44.89	5.36	89.33	50.61	56.32	40.55	0.28
Vanuatu	82	38.49	19.98	55.04	4.74	98.68	67.36	49.72	12.32	0.05
Bolivia	83	38.31	20.24	38.00	8.87	94.28	69.80	21.98	49.64	3.71
Kenya	84	37.91	20.95	39.23	1.65	98.18	65.50	51.42	22.86	3.53
Surinam	85	37.91	19.40	49.26	20.13	84.75	60.41	17.64	51.18	0.50
Nicaragua	86	37.84	18.45	35.02	5.59	97.69	63.79	31.23	50.41	0.60
Guyana	87	37.57	20.92	47.36	40.74	89.57	51.77	33.40	16.17	0.63
Zambia	88	37.29	19.50	44.48	3.65	92.87	54.37	50.57	30.01	2.87
Lebanon	89	37.01	18.27	32.66	0.34	93.64	61.26	33.96	55.69	0.27
Solomon Islands	90	36.62	19.70	47.62	8.00	87.75	74.29	34.62	20.89	0.11
Senegal	91	36.30	21.25	51.74	3.07	97.26	44.15	23.87	47.91	1.14
Cambodia	92	36.21	21.72	34.45	4.19	98.16	68.81	27.92	33.29	1.11
Iran	93	36.06	20.81	29.52	4.32	77.26	55.18	37.08	56.47	7.86
Tanzania	94	35.92	21.21	40.97	3.60	97.07	66.55	27.92	25.35	4.66
Nepal	95	35.70	20.68	36.79	1.82	98.99	68.04	45.28	12.58	1.43
Egypt	96	35.48	21.55	31.72	0.76	93.20	48.18	37.64	44.30	6.49
Cote D’Ivoire	97	35.16	22.05	39.54	2.33	97.44	47.07	27.08	43.92	1.84
Benin	98	35.09	20.37	45.36	3.56	96.92	55.36	22.26	36.13	0.74
Grenada	99	34.93	22.17	62.39	0.57	96.06	49.02	30.19	19.06	0.00
Kiribati	100	34.57	20.09	59.96	0.26	98.97	50.87	16.98	29.43	0.01
Uganda	101	34.40	20.65	38.68	2.44	93.73	61.10	36.13	20.40	2.09
Maldives	102	33.99	23.24	43.63	0.12	94.58	63.71	28.30	18.29	0.02
Myanmar	103	33.93	21.07	29.56	4.95	95.57	57.61	9.34	49.46	3.90
Cameroon	104	33.80	20.19	27.80	4.71	95.75	58.22	11.42	50.00	2.32
Bangladesh	105	33.79	22.38	32.94	0.79	98.91	56.00	8.68	44.54	6.11
Tajikistan	106	33.79	21.61	24.19	1.88	96.83	61.08	26.98	37.03	0.74
Laos	107	33.75	21.56	34.99	6.14	93.89	67.78	19.06	25.59	0.95
Pakistan	108	33.62	20.80	28.52	2.15	97.95	47.15	27.74	34.84	9.79
Togo	109	33.52	20.40	34.71	4.95	94.17	52.55	22.08	38.84	0.44
Gabon	110	33.40	21.22	35.58	12.95	85.57	41.77	13.87	55.40	0.86
Madagascar	111	33.29	19.46	35.05	3.05	95.74	69.83	12.26	28.23	2.69
Lesotho	112	33.07	17.64	46.07	2.68	95.15	27.80	38.02	37.03	0.17
Mali	113	33.06	20.55	31.69	5.17	95.57	41.86	24.91	40.37	4.39
Venezuela	114	33.05	22.40	13.06	6.47	82.93	65.62	2.36	67.79	3.74
Sao Tome and Principe	115	32.92	19.47	47.05	1.37	97.39	46.76	6.73	44.57	0.01
Djibouti	116	32.72	22.29	32.07	0.06	98.95	45.04	21.70	41.51	0.10
Mozambique	117	32.62	19.67	34.33	3.12	90.55	55.01	28.30	26.53	3.43
Micronesia	118	32.52	19.00	60.56	2.20	97.68	54.42	20.19	6.13	0.01
Papua New Guinea	119	32.42	20.37	38.57	10.69	91.40	52.73	41.13	2.78	1.68
Algeria	120	32.26	20.25	31.96	3.87	85.13	51.25	15.57	41.31	8.75
Nigeria	121	32.15	19.81	27.36	2.91	94.32	44.22	22.55	36.40	9.61
Gambia	122	32.12	20.18	39.73	2.91	96.77	42.69	18.49	36.10	0.11
Zimbabwe	123	31.95	17.75	23.20	4.04	96.27	64.48	17.36	30.78	1.69
Burkina Faso	124	31.87	20.76	42.46	4.03	93.72	44.36	21.42	26.66	1.52
Guinea	125	31.75	21.80	30.80	5.59	90.99	49.44	14.72	39.51	1.18
Central Africa	126	31.68	19.61	14.43	8.21	92.77	65.44	2.26	48.70	2.07
The Niger	127	31.31	20.64	35.43	11.32	94.92	51.90	18.40	13.27	4.64
Equatorial Guinea	128	30.85	16.11	19.53	8.09	77.87	46.77	8.21	70.11	0.13
Guinea-Bissau	129	30.65	20.13	24.56	3.02	90.91	50.51	8.87	47.04	0.15
Angola	130	30.54	17.78	29.42	3.98	87.16	54.83	5.19	41.07	4.87
Liberia	131	30.52	17.15	34.64	6.05	88.72	59.71	8.40	29.06	0.46
Ethiopia	132	30.35	22.77	31.16	2.21	94.50	58.78	13.21	12.89	7.25
Mauritania	133	30.32	19.76	34.54	1.31	94.23	39.33	14.62	35.49	3.30
Democratic Republic of the Congo	134	30.31	20.06	13.02	3.26	88.46	56.66	3.58	47.57	9.83
Republic of Congo	135	29.22	14.99	25.96	7.29	78.99	54.96	5.57	44.79	1.22
Sierra Leone	136	29.16	17.00	37.65	5.23	92.33	43.95	17.55	19.07	0.49
Comorin	137	28.87	19.09	32.50	1.28	98.38	43.72	15.66	20.30	0.03
Burundi	138	27.74	17.01	18.65	1.64	91.54	58.78	15.00	18.86	0.47
Sudan	139	26.55	19.54	13.65	6.76	95.84	30.64	15.28	23.61	7.11
Chad	140	25.93	17.19	19.74	5.08	88.50	46.28	3.68	22.59	4.38
Iraq	141	25.68	21.75	16.23	3.57	70.11	42.64	12.17	36.31	2.67
East Timor	142	25.50	19.70	40.37	2.44	64.11	58.57	6.79	11.94	0.09
Eritrea	143	25.43	16.43	13.06	2.63	90.85	55.72	1.13	23.18	0.43
Libya	144	24.78	23.22	6.31	7.41	65.76	44.66	2.55	42.70	5.61
South Sudan	145	24.64	15.37	2.29	33.03	89.82	44.43	2.74	7.12	2.32
Afghanistan	146	24.29	18.63	14.50	3.10	99.28	34.49	6.79	14.26	3.29
Syria	147	24.14	16.80	4.42	3.76	82.63	43.22	9.53	31.58	1.18
Yemen	148	23.45	11.62	6.52	0.81	97.62	28.67	8.02	31.72	2.61
Somalia	149	20.93	18.74	0.43	1.09	93.01	29.77	0.47	21.49	2.44

According to Table 1, the top 10 BRCs with the highest level of overall development scores are Singapore, New Zealand, China, South Korea, Austria, the Czech Republic, Lithuania, Luxembourg, Estonia, and Russia. Except for China’s scale dimension and Russia’s resource dimension, the rest of the top ten countries are relatively poor in resource endowments and small in scale but good in governance, environmental protection, social development, business environment, and structural transformation, making them among the best in terms of comprehensive development level. Somalia, Yemen, Syria, Afghanistan, South Sudan, Libya, Eritrea, East Timor, Iraq, and Chad are the last ten countries. Although they score well in the environmental dimension, their performances in other aspects are poor, so they rank at the bottom.

Regionally, the overall development level of European countries is generally high, owing mainly to their outstanding performance in governance, environmental protection, social development, and business environment. Among the top 20 countries, 14 are European, accounting for 70%. Except for Bosnia and Herzegovina, which ranks 80th, other European countries all rank in the top 70s. Apart from the environmental dimension, African countries perform poorly in other aspects, so their overall development levels are generally low. Fifteen of the last 20 countries are African, accounting for 75%. Moreover, no African country ranks in the top 40. Botswana, Morocco, Seychelles, Tunisia, Rwanda, South Africa, and Ghana rank in the top 70s, while the other 39 countries rank below 70. The ranks of Asian countries vary widely in comprehensive development level, leading to a scattered ranking distribution. The top three countries are Singapore, China, and South Korea, and the bottom three are Yemen, Syria, and Afghanistan. Americas are similar to Asian countries. For example, there are 100 places between Chile, which ranks 14th, and Venezuela, which ranks the 114th.

According to the dynamic change trend from 2015 to 2019 (Figure 2), the overall development scores of most countries (96 countries, accounting for 64.43%) were on the rise, and 52 countries, most of which are in Africa and Asia, witnessed increases of more than one point, indicating an upward trend in the overall development of the BRCs. Among them, the overall development scores of Kenya and Uzbekistan increased the most, both by over five points. In addition, Guinea, Kazakhstan, Timor-Leste, Belarus, Ukraine, Côte d’Ivoire, Brunei, Tajikistan, and Serbia all registered increases of over three points in comprehensive development level. At the same time, 53 countries (accounting for 35.57%) experienced declines in their overall development scores, and 27 of them saw declines of over one point. The scores of Barbados and Lebanon dropped by more than three points, while Yemen, Trinidad and Tobago, Congo, South Africa, Grenada, Nicaragua, and Panama decreased by more than two points.

Figure 2.

Dynamic Changes of Overall Development Scores of the BRCs.

Main Features

1. The overall performance of economic development of the BRCs could be better, but their economic growth is relatively stable. According to the scores of the economic development dimension, except China and Luxemburg, with scores of more than 50, other countries all scored below 40, and only nine countries scored above 30; the scores of most countries (97 countries) were between 20 and 30, and those of 43 countries were lower than 20, indicating that the overall economic development performance of the BRCs is not prominent. As mentioned earlier, in 2020, the GDP of the 148 countries (excluding China) was US$20.12 trillion in total, accounting for only 24.56% of the world’s total; even with China included, the share was only 38.95%, indicating that the BRCs have not taken up a superior position in global economic development.

Most countries ranking high in the economic dimension are Asian and European, featuring larger economic scales (like China) or higher GDP per capita (like Luxembourg) and stable economic growth. In contrast, those ranking at the bottom is mainly African, featuring a smaller economic scale, lower GDP per capita, and volatile economic growth. In 2020, among the BRCs, Asian countries registered a total GDP of US$20.77 trillion (at constant 2010 price), accounting for 65.11% of the BRCs’ total, followed by European countries, with a total GDP of US$6.65 trillion (at constant 2010 price), accounting for 20.85%; the GDP of African countries totaled US$2.50 trillion (at constant 2010 price), accounting for 7.82%; and the total GDP of countries in South America, North America, and Oceania was US$1.99 trillion, accounting for only 6.22%. China’s GDP accounted for 36.94% of the total GDP of the BRCs. Except for China, five countries with large economic scales were Italy, Russia, South Korea, Turkey, and Indonesia, whose total GDP accounted for 23.88% of the BRCs. China included, the total share of the six countries exceeded 60%.

From 2015 to 2019, the average annual GDP growth rate of the BRCs stabilized at about 3%, which was slightly higher than the annual growth rate of world GDP in the same period, significantly higher than that of high-income countries, and lower than the low- and middle-income countries. The five-year average annual GDP growth rates of 130 countries were favorable, suggesting relatively stable economic growth. According to the regional comparison of the average annual growth rates (see Table 2), Asian countries registered the highest GDP growth rate among the BRCs, with a five-year average of 3.45%. In contrast, South American countries saw the lowest, with a five-year average of only 1.49%. At the country level, different countries in Africa and Asia demonstrated significantly varying economic growth rates, especially in Africa. Ethiopia achieved the highest economic growth rate, with a five-year average annual GDP growth rate of 8.91%, while Yemen experienced a large decline, with a five-year average annual growth rate of −9.25%.

Table 2.

The Top and Bottom 15 BRCs in Terms of Economic Growth from 2015 to 2019.

Country	Mean Annual Growth Rate of GDP (%)	County	Mean Annual Growth Rate of GDP (%)
Ethiopia	8.91	Yemen	−9.25
Bangladesh	7.39	Equatorial Guinea	−7.16
Guinea	7.39	Republic of Gongo	−4.65
Rwanda	7.37	South Sudan	−3.10
Cote d’Ivoire	7.30	Republic of Trinidad and Tobago	−1.66
Djibouti	7.19	Eritrea	−1.28
Cambodia	7.08	Lebanon	−1.21
Tajikistan	7.00	Angola	−0.88
Vietnam	6.76	Syria	−0.86
China	6.71	Venezuela	−0.29
Laos	6.58	Sierra Leone	−0.26
Philippines	6.58	Dominica	−0.24
Libya	6.54	Chad	−0.17
Malta	6.51	Surinam	−0.14
Maldives	6.31	Burundi	−0.11
Continent	Mean Annual Growth Rate of GDP (%)	Continent	Mean Annual Growth Rate of GDP (%)
Asia	3.45	Europe	3.05
Oceania	3.24	North America	2.37
Africa	3.11	South America	1.49

Source: WDI database of the World Bank (2021).

2. Although resource endowment is not wealth as a whole, the BRCs have rich fossil energy resources concentrated in a few countries. According to our results, the resource endowment scores of the BRCs are generally low. Guyana, South Sudan, and Qatar had the highest scores of 40.74, 33.03, and 31.81, respectively, benefiting from their abundant resources of renewable freshwater, cultivated land, and fossil energy. Other countries all registered scores below 30. Among them, 132 countries scored below 10, and 98 countries scored below 5. Thus, the overall resource endowment of the BRCs is not high.

However, the BRCs include some of the most energy-producing countries in the world, which are rich in fossil energy resources. In 2019, the BRCs produced 7.40 billion tons of oil equivalent to fossil energy in total, accounting for 64.64% of the world’s total in the same period. Figure 3 shows that the share of BRCs’ fossil energy output in the world’s total output has been stable at about 65% for years, suggesting they are the most important force in global energy production. The share of natural gas is stable at about 60%, and that of oil is higher, remaining at over 60% despite the decline in recent years. Coal records the highest share, about 70% in 2019, mainly because China is still the largest coal producer in the world. Excluding China, the coal output of the BRCs only accounted for 22.97% of the world’s total in 2019.

Figure 3.

Source: BP statistical review of world energy (2020).

In 2019, 13 out of the 149 countries had a fossil energy output of more than 100 million tons of oil equivalent, including China, Russia, Saudi Arabia, Iran, Algeria, United Arab Emirates, Kazakhstan, Qatar, Kuwait, South Africa, Nigeria, Iraq, and Indonesia.⁵ Their total output accounted for 84.31% of the BRCs’ total and 54.50% of the world’s total. Most of them are net producers of fossil energy; hence their output is more than their consumption, creating favorable conditions for energy cooperation between China and relevant countries.

3. The BRCs have an excellent environmental foundation, which is constructive for the green development of the Belt and Road. According to the results, the environment protection dimension scores of the BRCs are generally high, with an average of 89.92. Except for Kuwait and Qatar, all countries score above 55, and 102 countries score over 90. The latest data from the World Bank show that the per capita carbon dioxide emission of the BRCs is about 4 metric tons, lower than the world average of 4.5 metric tons, indicating that the BRCs have a relatively good environmental foundation. Kuwait and Qatar are two exceptions. In 2019, Kuwait and Qatar’s per capita carbon dioxide emissions exceeded 32 metric tons and 21 metric tons, respectively, the highest among the BRCs and also worldwide. Therefore, the two countries rank at the bottom among the BRCs in environmental protection.

Figure 4 suggests that, since the start of the twenty-first century, BRCs’ share of carbon emissions in the world has been gradually rising, and the growth rate began to level off in 2013. In 2019, the BRCs contributed 58.78% to global carbon emissions, or 30.86%, excluding China. Although the share of about 30% is significantly higher than that of North America and Europe with downward trends, this number is not high for a group with more than 45% of the world population. A good environmental foundation may be due to two reasons. In underdeveloped countries, such as Afghanistan, Nepal, and Bangladesh, environmental damage is not serious, because their resource development intensity is constrained by their economic development capacity, while in developed countries, like Portugal and Italy, environment-friendly development models have been adopted after they experienced a high economic growth accompanying high consumption and pollution. The green development of the Belt and Road is one of the core objectives of the high-quality development of the BRI. The good environmental foundation of the BRCs not only provides opportunities for relevant countries to build a green Belt and Road but also makes lasting contributions to the global response to climate change.

Figure 4.

Source: Our world in data⁶.

4. The overall social development of the BRCs is slightly lower than the world average. The BRCs earned mediocre scores in terms of social development. Out of the 149 countries, 108 scored less than 70, and 62 scored less than 60. In 2019, the average life expectancy of the BRCs was 71.22 years, basically at the same level as the lower-middle-income countries’ 71.21 years and significantly lower than the world average level of 72.74 years; the mean years of schooling was 8.25 years, far behind the 12 years of high-income countries represented by the OECD, and lower than the world average of 8.5 years; and the unemployment rate was 6.92%, significantly higher than the world average of 5.37%. The overall social development of the BRCs is slightly lower than the world average level, so it is of profound practical significance to strive to achieve the goal of benefiting people’s livelihood through the high-quality development of the Belt and Road.

Main Challenges

1. The overall governance level of the BRCs is low, and their institutional environment is poor. According to the WGI database of the World Bank, from 2015 to 2019, the average value of governance indicators of the 149 BRCs was −0.27,⁷ lower than the world average in the same period. Among the BRCs, 99 countries had five-year averages below 0, mostly in Asia and Africa, and 22 countries had averaged less than −1, a signal of poor governance, with Somalia (−2.04) and South Sudan (−2.11) being the worst. There were 50 countries with averages above 0, mainly in Europe, and seven of them had averages greater than 1, namely New Zealand, Luxembourg, Singapore, Austria, Estonia, Portugal, and Malta.

According to the latest World Bank’s Doing Business report, in 2020, the BRCs’ business environment for enterprises ranked relatively low as a whole, with 90 countries ranking behind the 100th among the 213 economies around the world, and 13 countries even behind the 200th, reflecting that the overall institutional guarantee of the BRCs for enterprises to carry out economic activities is insufficient. Regionally, the business environment of European and Asian countries of the BRCs is relatively better than the African countries. Therefore, under a poor institutional environment, mediocre governance levels, and unfavorable business environment, promoting high-quality Belt and Road development is a challenge that cannot be ignored.

2. The size of BRCs is large overall, while individual countries are small. On the whole, the 149 BRCs account for more than 70% of the total number of countries in the world, with total population and land area exceeding 60% of the world’s total and a labor force of about 2.2 billion, accounting for about 65% of the world’s total. So, the overall scale of the BRCs is relatively large. However, in terms of average level, the average population, labor force, and land area of the 149 countries are only about 34 million, 15.6 million, and 570,000 km², respectively. Excluding China, the three averages of the 148 countries are even smaller, with only about 24 million, 10 million, and 500,000 km². Based on our measurement, among the 149 BRCs, only China and Russia score above 50, with 78.33 and 55.16, respectively, and Indonesia above 10 (15.16) in the scale dimension. Other countries all score below 10, and as many as 130 countries score below 5. Since most countries are relatively small in scale, all countries need to work closely to release a scale effect, and policy communication and coordination should play a vital role in the development of the BRI.

3. The BRCs face heavy economic transformation and development tasks due to their weak industrialization foundation. In 2019, the average value added of the manufacturing industry in the BRCs accounted for only 11.27% of GDP, lower than the world average of 16.53%, indicating a weak industrialization foundation. Excluding China, only 11 out of the 148 countries registered manufacturing value-added shares in GDP of more than 20% (including South Korea, Thailand, Czech Republic, Myanmar, Guinea, Belarus, Malaysia, Slovenia, Equatorial Guinea, Indonesia, and Bangladesh), and 67 countries had shares of less than 10%. The countries ranking at the bottom were South Sudan, Sierra Leone, Niue, East Timor, and Papua New Guinea, with less than 2% shares.

There are two reasons for the low shares of the manufacturing industry in these countries. First, their shares of agriculture in the national economy are high. In 2019, the average share of value added of agriculture in the GDP of the 149 countries was around 12%, higher than the middle-income countries’ level of 8.09% and much greater than the world average of 3.55%. In Sierra Leone, the value added of agriculture to GDP was over 50% (54.34%). In Chad, Liberia, Niger, Mali, Kenya, Central Africa, Ethiopia, Comoros, and Guinea-Bissau, the shares all exceeded 30%. These are typical agricultural countries. In addition, more than 20 countries had 20 ~ 30% shares, and more than 25 countries had 10 ~ 20%. The economic transformation pressure faced by such countries is mainly because of transitioning from an agricultural economy to an industrialized economy. Second, many countries rely heavily on natural resources for economic development. In 2019, the average share of natural resource rents in the GDP of the 149 countries was 6.47%, more than three times the world average of 2.02%. 72 out of the 149 countries had shares exceeding the world average level. Among them, about 30 countries had shared about five times the world average, and about 15 countries had shared around 10 times the world average, including Iran, Congo, East Timor, Libya, Kuwait, Iraq, Equatorial Guinea, Oman, Angola, Azerbaijan, Saudi Arabia, Brunei, Chad, Gabon, and Qatar. The pressure of economic transformation faced by such countries mainly comes from their unsustainable economic development model relying too much on energy.

Conclusion and Prospect

The comprehensive measurement and overall assessment of the development of the BRCs will benefit the BRI in three aspects. First, the governments of the BRCs will better understand the critical issues discussed above and formulate targeted policies to improve their overall development effectively. Second, by their comparative advantages, the BRCs can further enhance the level and effect of cooperation through the BRI platform. Third, more efforts will be targeted to form and improve the theoretical system for the high-quality development of the Belt and Road. This article measures and assesses the overall development levels of the 149 BRCs from 2015 to 2019 from eight aspects: economic development, governance, resource endowments, environmental protection, social development, business environment, structural transformation, and scale effect, and draws the following conclusions.

First, in terms of comprehensive development level, the top 10 BRCs are Singapore, New Zealand, China, South Korea, Austria, Czech Republic, Lithuania, Luxembourg, Estonia, and Russia, and the bottom 10 BRCs are Somalia, Yemen, Syria, Afghanistan, South Sudan, Libya, Eritrea, East Timor, Iraq, and Chad. Regionally, European countries generally have high overall development levels due to their outstanding performance in governance, environmental protection, social development, and business environment; however, African countries lag in all dimensions, so their overall development levels are generally low, and Asian countries vary widely in comprehensive development level, leading to a scattered ranking distribution. From the perspective of dynamic trends, the BRCs show a promising development trend.

Second, there are several remarkable features of the BRCs’ development. Their overall economic development performance is not outstanding, and they have not earned a dominant position in global economic development. Still, their economic growth rates are relatively stable, with an average five-year GDP growth rate of about 3%. Their overall resource endowments are not high, but most energy-producing countries worldwide are among the BRCs. Their total output of fossil energy accounts for about 65% of the world’s total for years, and fossil energy production is concentrated in a few countries, which provides opportunities for energy cooperation between China and relevant countries. The BRCs have an excellent foundation for environmental protection, which not only offer opportunities for the green development of the Belt and Road but also significantly contribute to the global response to climate change. Their overall performance in social development is slightly lower than the world average.

Third, there are also some challenges in the comprehensive development of the BRCs. Their overall governance level and capacity are mediocre, lower than the world average. At the same time, the relatively poor business environment suggests insufficient institutional guarantee for enterprises to carry out economic activities in relevant countries, which may pose some challenges to the high-quality Belt and Road development. In general, the scale pattern characterized by the “large overall, but small in individual countries” may form a market segmentation that is not conducive to giving full play to the scale advantage in economic development. The BRCs are facing a weak industrialization foundation and high pressure of economic transformation issues because their shares of agriculture in the national economy remain high, and their national economy heavily depends on the consumption of natural resources.

Based on the analysis of this article, under the framework of the BRI, China and partner countries have broad prospects for cooperation, at least in the following aspects.

First, energy cooperation. China is the leading net consumer of energy in the world. Although in recent years, it has shown commitment to increasing the consumption of non-fossil energy to achieve its goal of carbon peaking and carbon neutrality, fossil energy remains the main energy source for China today. Take oil as an example. According to the data of BP Statistical Review of World Energy 2021, China produced 195 million tons of oil in 2020, accounting for 4.68% of the world’s total. Yet, in the same year, its oil consumption reached 669 million tons, accounting for 16.7% of the world’s total. The BRCs include major net producers of fossil energy in the world, which is a favorable condition for China and relevant countries to strengthen energy cooperation and achieve win–win development with complementary supply and demand. Therefore, under the BRI framework, the energy cooperation between China and these partner countries is promising.

Second, industrial cooperation. After more than 40 years of reform and opening up, China has successfully transformed from an agricultural to an industrial economy. The developing countries among the BRCs can learn from China’s experience in economic transformation and development. Moreover, by jointly building industrial parks in some countries, China’s capital, technology, equipment, market, and other elements can promote the industrialization of relevant countries. China has set up more than 100 major overseas industrial parks or economic cooperation zones, which are important platforms for industrial cooperation between China and relevant countries.

Third, green development cooperation. High standards, sustainability, and benefiting people’s livelihood are the main goals of the high-quality development of the Belt and Road, and the realization of the sustainability goal depends immensely on the green Belt and Road development. Furthermore, China is committed to playing a more critical role in responding to global climate change, in which Belt and Road construction can be an essential channel. This research finds that the BRCs either have an excellent environmental foundation or have rich experience in environmental protection, which opens up a broad prospect for cooperation between China and other BRCs in this regard.

Fourth, cooperation in people’s livelihood development. This article finds that the BRCs are below the world average level in social development, so the development of people’s livelihoods is the realistic demand of relevant countries. The BRI emphasizes the principle of “people-centered” development, and promoting people’s livelihood is one of the main goals for eradicating poverty and improving their living standards, which will create social development opportunities for the BRCs.

Fifth, exploring new institutional arrangements for deepening cooperation. The international cooperation platform of the BRI should be fully utilized to explore new institutional arrangements to strengthen global collaboration that can facilitate a faster and more effective flow of economic factors and reduce various institutional costs in transportation, customs clearance, trade, and investment, among the BRCs. At present, the overall scale of the BRCs is relatively large. In contrast, the scales of most individual countries are relatively small, so efforts in this area are of particular significance in releasing the scale effect of development.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The research is funded by the Major Project of the National Social Science Fund (Grant No. 19ZDA100); the Special Research Project of the National Social Science Fund on “the Belt and Road” construction (Grant No. 19VDL012); the Start-up Research Project of Introducing Talents of Beijing Normal University at Zhuhai (Grant No. 111032101).

Notes

References

British Petroleum (BP). (2020). Statistical review of world energy 2020 . British Petroleum.

(2018). The belt and road: Initiative, implementation, and prospects. Chinese Journal of Population Science , 2018(1), 2–18.

(2020). Promoting the high-quality development of the Belt and Road—Xi Jinping’s ideas on the high-quality development of the Belt and Road. Study & Exploration , 2020(10), 102–119.

, & Pan

(2017). Overall development of countries along the belt and road: Measurement, rank, and assessment. Global Journal of Emerging Market Economies , 9(1–3), 12–27.

, Tang

, Yin

, & Liu

(2018). Comprehensive measurement and development prospects of emerging market countries. Social Sciences in China , 274(10), 59–85.

Ministry of Finance and Economic Management (MFEM). (2020). Cook Islands statistics . https://www.mfem.gov.ck/statistics

Niue Statistics Office (NSO). (2020). Statistics Niue . https://niuestatistics.nu/

United Nations Development Programme (UNDP). (2020). Human development report 2020 . UNDP.

United Nations Environment Program (UNEP). (2012). Freshwater under threat—Pacific Islands . UNEP.

10.

United Nations Industrial Development Organization (UNIDO). (2021). MVA manufacturing value added database . http://stat.unido.org

11.

World Bank. (2021). World development indicators database . http://data.worldbank.org

12.

World Bank. (2020a). Doing business 2020 . World Bank.

13.

World Bank. (2020b). Worldwide governance indicator . www.govindicators.org