Understanding Muslim Countries’ Support for China’s Actions in Xinjiang: A Qualitative-Comparative Analysis

Entendendo o apoio dos países muçulmanos às ações da China em Xinjiang: uma análise qualitativa e comparativa

Abstract

This study examines why 23 Muslim-majority countries supported China at the United Nations Human Rights Council (UN/HRC) in 2019, despite allegations of human rights abuses against the Uyghurs in Xinjiang. Using a fuzzy-set qualitative-comparative analysis (fsQCA), we compared the factors that led Muslim-majority and non-Muslim countries to support China. Our analysis found that Political Regime Affinity (PRA) was a necessary but not a sufficient condition for Muslim-majority countries to support China, while China’s Foreign Aid (ODA) was a necessary but not sufficient condition for non-Muslim countries. These findings suggest that ideological factors, related to the autocratic political regime (PRA), played a significant role in Muslim-majority countries’ decision to support China in the UN/HRC in 2019. However, it is important to note that other factors may have also been involved. These findings have important implications for understanding the complexities of international relations and the factors that shape states behaviour.

Keywords
China; diplomacy; fsQCA; UN Human Rights Council; Xinjiang

Resumo

Este estudo examina por que 23 países de maioria muçulmana apoiaram a China no Conselho de Direitos Humanos das Nações Unidas (ONU/HRC) em 2019, apesar das alegações de abusos de direitos humanos contra os uigures em Xinjiang. Usando uma análise comparativa qualitativa de conjunto difuso (fsQCA), comparamos os fatores que levaram os países de maioria muçulmana e os de maioria não muçulmana a apoiar a China. Nossa análise constatou que a afinidade com o regime político (PRA) era uma condição necessária, mas não suficiente, para que os países de maioria muçulmana apoiassem a China, enquanto a ajuda externa da China (ODA) era uma condição necessária, mas não suficiente, para os países não muçulmanos. Essas descobertas sugerem que fatores ideológicos, relacionados ao regime político autocrático (PRA), desempenharam um papel significativo na decisão dos países de maioria muçulmana de apoiar a China na ONU/HRC em 2019. No entanto, é importante observar que outros fatores também podem estar envolvidos. Essas descobertas têm implicações importantes para a compreensão das complexidades das relações internacionais e dos fatores que moldam o comportamento dos Estados.

Palavras-chave
China; diplomacia; fsQCA; Conselho de Direitos Humanos da ONU; Xinjiang

Introduction

China is home to around 25 million Muslims, who belong to various ethnic groups (Song 2022). Out of the 56 officially recognised ethnicities in China, 10 are Muslim, including the Hui, Uyghur, Kazak, Dongxiang, Kirghiz, Salar, Tajik, Uzbek, Bonan, and Tatar (BBC 2021). While Muslims can be found throughout China, they are concentrated in regions such as Xinjiang, Gansu, Tibet, and Ningxia (Armijo 2008). In Xinjiang’s Autonomous Region, about 50% of the population are Muslim, mostly belonging to the Uyghur ethnic group, which has Turkmen origins and cultural influences from Central Asia (Song 2022). The Han, who make up more than 90% of China’s population, are the dominant ethnic group in the region and represent the country’s traditional civilisation (BBC 2009).

Xinjiang is a strategic region for the People’s Republic of China (PRC) due to its large territorial area, which serves as a contact point for Central Asian countries, and its abundant natural resources, including oil, coal, natural gas, and precious metals (Armijo 2008). Uyghur separatist groups have long claimed that Xinjiang is not a legal part of the PRC, but that it was annexed in 1949. In the 1980s and 1990s radical Islamic movements gained traction in China, particularly in the Xinjiang region, in the context of the Iranian Revolution, the Soviet Union’s defeat in Afghanistan, and the Chinese government’s crackdown on illegal religious practices, including the construction of mosques and unauthorised Islamic schools. The Islamic Movement of East Turkestan was a significant force in the region and sought to establish the Republic of East Turkestan, directly challenging the PRC’s territorial integrity (Vieira 2020).

The intensification of separatist movements in Xinjiang, which continues to this day, has been met with increasing force by the Chinese government. ‘In 1996 and 2001, the government responded with national campaigns to “heavily fight” against crime, resulting in arrests, trials, and convictions of rebels’ (Vieira 2020: 283). The government has also associated Uyghur separatist groups with terrorist groups in Central Asia, such as the Taliban, using its support for the War on Terror of the United States (USA) as justification (Armijo 2008). According to separatist groups, the PRC has also promoted Han Chinese migration to dilute the influence of the Uyghurs in the region. Since then, the international community has been closely monitoring the Chinese government’s repression against Muslims in Xinjiang, with the UN accusing China of committing genocide in detention camps (BBC 2022) and violating human rights (BBC 2009).

The issue of separatist movements in Xinjiang and China’s action to repress it are not under analysis in this work. There are many conflicting narratives at play and access to reliable information is biased. On the one hand, there is Beijing’s official discourse and its restrictions on the circulation of information. On the other hand, there is the discourse of Western powers that denounce alleged human rights violations in China and use this and other supposedly humanitarian agendas to weaken regimes and states that do not align with the US-centric world order. To avoid any bias or subjective interpretations, this study focuses solely on the motivations that led Muslim-majority countries to align with China in the UN/HRC in 2019, notwithstanding the allegations of human rights violations against the Uyghur community in Xinjiang.

In 2019, a coalition of 50 countries¹, including 23 Muslim-majority countries², endorsed Beijing’s actions in Xinjiang at the United Nations Human Rights Council (HRC/UN). We aim to examine the motivations behind this endorsement. The letter sent to the UN/HRC stated that the co-signatories believed China had been transparent in its actions, protected the Muslim population, combated religious extremism, and implemented anti-terrorist measures to ensure the region’s security (UN 2019). This statement of support was released on 26 July 2019, shortly after a group of 22 western and/or developed countries³ issued a statement denouncing Chinese policies towards the Uyghur community and demanding that the PRC respect human rights in Xinjiang and the country as a whole (HRW 2019). The map below shows the division of countries into pro-China and anti-China coalitions in the UN/HRC regarding the Xinjiang issue. The blue-coloured countries represent the 22 western and/or developed nations that issued a statement of repudiation and condemnation towards China’s policies in Xinjiang. The black-coloured countries are non-Muslim nations that have aligned with China (27). Finally, the green-coloured countries are Muslim-majority states that supported China’s stance on Xinjiang (23).

Figure 1
Coalitions of pro and anti-China countries in the UN/HRC regarding the Xinjiang issue

This article aims to investigate why 23 Muslim-majority countries supported China’s actions in Xinjiang despite accusations of human rights violations against Muslim Uyghurs. The research questions are: (1) How did the PRC influence the foreign policy decisions of these countries in its favour? (2) What factors played a critical role in this process, and how did they differ between Muslim-majority and non-Muslim countries that also sided with China? (3) Which of these factors were necessary, sufficient, INUS, and/or SUIN in explaining the countries’ alignment with China?

To answer these questions, we utilised a fuzzy-set qualitative-comparative analysis (fsQCA) approach applied to both Muslim-majority and non-Muslim countries that supported China in the UN/HRC in 2019. Through this study, we investigated the role of individual conditions (whether they were necessary and/or sufficient) in shaping foreign policy behaviours and how they combined (whether they are INUS or SUIN) to generate the outcome of aligning with China regarding Xinjiang, specifically in the case of the Muslim-majority countries. By utilising this approach, we were able to analyse the motivations behind the support of the PRC, as well as the foreign policy instruments used by China to garner this support.

The study examines two analytical dimensions of power archetypes proposed by Chen and Chang (2013), namely soft power and hard-soft power, to understand the motivations that led Muslim-majority countries to align with China regarding the Xinjiang issue. Soft power dimension comprises two conditions: Bilateral Partnerships (BP) and Political Regime Affinity (PRA). Hard-soft power dimension comprises five conditions, including China’s Ranking in Country’s Exports (EXP), Chinese Foreign Direct Investment (FDI), China’s Outward Direct Aid (ODA), Participation in the Belt and Road Initiative plus Level of Income (BRI), and Military Diplomacy (MD). The main findings point to the following: (i) for Muslim-majority countries, Political Regime Affinity was a necessary condition when combined with Bilateral Partnership and Military Diplomacy; (ii) for non-Muslim countries, China’s Direct Foreign Aid (ODA) was the only necessary condition.

This work comprises two main sections in addition to the introduction (1) and conclusions (4). Section 2 discusses the methodology and includes theoretical and conceptual aspects, a description of the qualitative-comparative analysis (QCA) and process tracing, as well as the definition of calibration metrics of the analytical conditions tested in the fsQCA software for both Muslim-majority countries and non-Muslim countries. Section 3 presents the results obtained from testing the conditions in the software. Finally, Section 4 presents the conclusions of the study.

Methodology

On the archetypes of power in international relations

Joseph Nye’s classification of power has been widely accepted and used in the international relations field. According to the author, hard power is the manifestation of power that involves tangible resources, such as military force and money, and is implemented by states through the use of force (pushing), payments and agenda setting. In turn, soft power involves intangible resources such as culture, political values and foreign policy, and is manifested through positive attraction and persuasion (pulling) (Nye 2011). There is also smart power, which is the combination of hard power (coercion and payment) and soft power (persuasion and attraction) and refers to the pursuit of optimal strategies that involve the necessary capacities to achieve the foreign policy objectives. Nevertheless, the term smart power remains quite generic and lacks methodological refinement beyond the binomial hard and soft.

Inspired by Chen and Chang (2013), we propose an expansion of this classification according to resources and to applications of power. Resources are subdivided into tangible (economic and military) and intangible (foreign policy/diplomacy, institutions and culture). The applications are subdivided into coercive/hard (sanctions, antagonism and containment) and persuasion/soft (reciprocity, benefits, legitimacy and unilateral favours). Table 1 shows this classification.

Table 1
Archetypes of power resources and power application

		Application of power
		Attraction	Coercion
Power resources	Intangible	Soft	Soft-hard
	Tangible	Hard-soft	Hard

• Source: Chen and Chang (2013), authors’ elaboration.

It is important to note that the four archetypes of power proposed by Chen and Chang (2013) can encompass various foreign policy events. Soft power involves using intangible resources to attract and generate consensus, while soft-hard power (the hard use of soft power) uses these same resources but aims for material returns, either through diplomatic sanctions, diplomatic blockade, regime change or campaigns to demonise opponents or colour revolutions. On the other hand, hard-soft power (the soft use of hard power) utilises material resources such as military and economic aid to attract and generate consensus, while hard power uses these same resources for coercion and the creation of asymmetry. Figure 2 illustrates how different foreign policy facts can be classified under these four archetypes of power.

Figure 2
Categories of power (and foreign policy facts)

Our research aims to identify the crucial factors that influenced the diplomatic alignment of Muslim-majority and non-Muslim countries regarding the Xinjiang issue in 2019. We hypothesise that China’s favourable position was a result of the effective utilisation of both tangible (hard) and intangible (soft) resources of power, which created a perception of legitimacy (soft application of power in a diplomatic arena: the UN/HRC). Thus, our challenge is to determine which specific conditions played the most significant role in shaping the alignment of Muslim-majority countries vis-à-vis non-Muslim countries.

The method: process tracing and Qualitative-Comparative Analysis (QCA)

Process tracing is a method used to explain the causes that impact the occurrence or non-occurrence of a given event. It involves historical explanations that may not be generalizable (Mahoney, Kimball and Koivu 2009). The method analyses necessary and sufficient causes, with necessary conditions being those that are always present when the result occurs and without which the result does not occur. These conditions enable but do not guarantee the result. On the other hand, sufficient conditions are those that, when present, produce the result, but their presence is not mandatory to obtain a certain outcome. However, ‘their presence guarantees the achievement or consummation of the result that one wishes to explain’ (Amorim Neto and Rodriguez 2016: 1008).

In addition to these, process tracing also considers INUS and SUIN conditions. INUS condition is ‘insufficient but necessary part of a condition that is itself not necessary but sufficient for the result’ (Mahoney, Kimball and Koivu cited inAmorim Neto and Rodriguez 2016: 1008). It is a factor that, alone, is neither necessary nor sufficient to the result, but which belongs to a set of conditions that, ensembled, become a sufficient condition. SUIN condition is ‘a cause that is a sufficient but not necessary part of a factor that is insufficient but necessary for a result’ (Mahoney cited inAmorim Neto and Rodriguez 2016: 1009). This type of factor typically has indirect effects, because, with other conditions, it conforms a condition that is necessary for the result.

To understand how the conditions at play impacted the outcome, we utilised qualitative-comparative analysis (QCA) as our analytical method. Developed by Charles Ragin (1987), QCA focuses on complex causality, examining how different combinations of conditions can contribute to a particular event (Parente and Federo 2019; Rihoux and Ragin 2008). Rather than aiming for broad analytical generalisations, QCA seeks limited generalisations that apply specifically to the cases and period under investigation (Freitas and Neto 2015).

The qualitative-comparative analysis (QCA) method uses Boolean algebra to assess the presence or absence of combinations of conditions (called configurations) that lead to a certain outcome. This approach allows for both the qualitative depth of analysis and quantitative generalisation across a larger number of cases (Pappas and Woodside 2021). QCA uses a truth-table to analyse the various combinations of conditions and their corresponding empirical cases. This enables the identification of necessary and sufficient conditions for the result to occur (Freitas and Neto 2015: 108).

Qualitative-comparative analysis can be conducted in different ways, including Crisp-Set QCA and Multi-Value QCA. For our research, we employed Fuzzy-Set QCA (fsQCA), which assigns scores between 0 and 1 to the analysed conditions (through analytical calibration) to represent varying degrees of their presence or absence and their impact on the result, which also takes these values (Ragin 2007). A score of zero (0) indicates complete absence, while a score of one (1) denotes full presence. By utilising fsQCA, we aimed to provide a more realistic approach to our subject matter, as it acknowledges the varying degrees of relevance of the conditions to the outcome in each case (Pappas and Woodside 2021). We tested the conditions that influence the outcome (support for China) for the 50 countries in the support coalition using the fsQCA software⁴.

To verify the properties of necessity and consistency of the conditions, the software provides the measures of consistency and coverage of each one and of every configuration. The consistency measure evaluates the degree to which cases with a given condition or combination of conditions agree with the result, while the coverage measure evaluates the proportion of cases that contain a specific condition or combination of conditions in the set of all cases that present the result (Junior and Ferreira 2018: 53). A condition can be considered necessary for the result if its consistency measure is close to 0.9 and coverage close to 0.5 (Legewie 2013). Conditions or combinations of conditions can be considered sufficient if they present a consistency measure of 0.75 or more for the result (Kent 2008).

The use of the fsQCA software allows for the automatic generation of consistency and coverage measures based on the analytical calibration of conditions and results, both of which are assigned fuzzy scores between 0 and 1. Then, we can make qualitative inferences. It is important to note that the calibration process is not arbitrary, as it is based on theoretical and conceptual considerations relevant to the research topic. To perform our analysis, we first describe the cases, conditions, and their respective analytical calibrations on a fuzzy scale (between 0 and 1). Afterwards, we explain the results. Our objective is to analyse the support for the People’s Republic of China (PRC) in the United Nations Human Rights Council (UN/HRC) in 2019 by Muslim-majority countries and non-Muslim countries, using the analytical dimensions of soft power and hard-soft power. Figure 3 shows the relationship between causes and consequences for this research.

Figure 3
Causal relations

Cases, conditions and analytical calibrations

Calibration of cases

In this study, using a fuzzy-set qualitative-comparative analysis (fsQCA), we aim to evaluate the hypothesis that the support for China in the UN/HRC in 2019 was influenced by specific conditions. Our focus was on the 23 Muslim-majority countries as we want to identify which conditions were most decisive in their alignment with China vis-à-vis the alignment of non-Muslim countries. Thus, we calibrate the outcome variable as follows: Muslim-majority countries that supported China were assigned a score of 1, while non-Muslim countries that aligned with the PRC received a score of 0. These scores were assigned based on the categorization of countries as Muslim-majority (1) and non-Muslim (0). Table 2 displays the analytical calibration of the outcome variable for all cases.

Table 2
Calibration for cases

Country	Calibration	Country	Calibration
Angola	0	Kuwait	1
Saudi Arabia	1	Laos	0
Algeria	1	Myanmar	0
Bangladesh	1	Mozambique	0
Bahrain	1	Nepal	0
Belarus	0	Nigeria	1
Bolivia	0	Oman	1
Burkina Faso	1	Palestine	1
Burundi	0	Pakistan	1
Cameron	0	Democratic Republic of Congo	0
Cambodia	0	Russia	0
Comoros	1	Serbia	0
Congo	0	Syria	1
Cuba	0	Somalia	1
North Korea	0	Sri Lanka	0
Djibouti	1	Sudan	1
Egypt	1	Southern Sudan	0
United Arab Emirates	1	Tajikistan	1
Eritrea	0	Togo	0
Philippines	0	Turkmenistan	1
Gabon	0	Uganda	0
Equatorial Guinea	0	Uzbekistan	1
Yemen	1	Venezuela	0
Iran	1	Zambia	0
Iraq	1	Zimbabwe	0

• Source: authors’ elaboration.

Let us now move on to the explanation of the calibration metrics for the conditions evaluated in this study.

Calibration of explanatory conditions

Seven explanatory conditions were selected based on China’s foreign policy vectors identified in the literature, including diplomatic, economic, and political-military factors. It is worth noting that although other factors such as rivalries of Muslim countries with other major powers like the USA, Russia, or the European Union may have contributed to the outcome (specially in relation to small countries), this study focuses solely on China’s projection abroad.

Bilateral Partnership (BP)

China has pursued a non-alliance strategy since 1982, which prioritises the establishment of strategic partnerships in its foreign policy (Strüver 2017). This strategy has grown due to the country’s economic opening process and its efforts to break out of the western siege that followed the Tiananmen Square incidents in 1989 (Feng and Huang 2014). Unlike traditional alliances, which are often formed in response to perceived threats, strategic partnerships are typically goal-oriented and seek to promote mutual interests. These partnerships are not necessarily influenced by ideological factors, and they tend to involve pragmatic actions that are tailored to the specific needs of the parties involved.

Developing countries often find strategic partnerships attractive as they require less surrender of sovereignty. Such partnerships allow for domestic issues that may cause tension between countries to be ignored or softened. This approach seems to be effective for China, as its foreign policy, at least rhetorically, tends to be more aligned with the developing countries. According to surveys, these countries tend to align themselves more with China in UN human rights resolutions after signing partnerships, unlike OECD member countries, which tend to limit partnerships with China to economic issues only (Strüver 2017).

China’s bilateral partnerships are categorised based on their level of cooperation and there are five levels of partnership in ascending order of importance for China. The first level is a friendly cooperative partnership, which focuses on specific issues such as trade. The second level is a cooperative partnership, which emphasises mutual respect and benefits. The third level is a comprehensive cooperative partnership, which aims to strengthen the relationship through high-level bilateral visits, improve contacts, and outline common interests. The fourth level is a strategic partnership, which involves close coordination on matters of interest to the region and the international system, including military issues. The fifth level is a comprehensive strategic partnership, which seeks full cooperation and development in regional and international affairs. For Chinese leaders, a comprehensive strategic partnership must meet three conditions: strong political trust, broad economic ties, and good relations in various areas, including culture (Fulton [n.d]).

We define the analytical calibrations for each case based on the most recent status of bilateral partnership developed with China until 2019⁵, the year of the coalition in support of the PRC in the UN/HRC. We propose the following scores: 1 for countries that have a comprehensive strategic partnership with China, 0.66 for those who have a strategic partnership, 0.50 for comprehensive cooperative partnership, 0.33 for cooperative partnership and friendly cooperative partnership, and 0 for those who do not have any type of bilateral partnership. The hierarchy of relevance of China’s bilateral partnerships is an indication that these partnerships are an essential tool for China’s foreign policy, so we hypothesise that the higher the level of cooperation between a country and PRC the greater the probability that China will use its foreign policy to shape the behaviour of that country in its favour.

Political Regime Affinity (PRA)

One of the foundations of China’s foreign policy is the Five Principles of Peaceful Coexistence, which prioritise respect for sovereignty, non-aggression, and non-interference in other countries’ affairs. According to the Chinese diplomatic corps, the country’s relations are guided by pragmatism and are not based on social systems or ideologies (China’s Embassy [2022]). In this condition, we seek to identify the extent to which the political regimes of Muslim-majority countries influenced their support for China in 2019. To define these regimes and calibrate the cases, we used data from 2019 primarily from the Our World in Data (OWID) database.

The OWID analysis is based on data from the V-DEM Institute (Varieties of Democracy) and on the classification proposed by Anna Lührmann, Marcus Tannenberg, and Staffan I. Lindberg (2018). The authors’ classification deals with four types of political regimes: (1) closed autocracies; (2) electoral autocracies; (3) electoral democracies; and (4) liberal democracies. To investigate the existing spectrum between autocracy and democracy, political scientists used the institutional guarantees proposed by Robert Dahl in his theory of polyarchy, which include elections of public officials, free and fair elections, freedom of expression, alternative sources of information, associative autonomy, and inclusive citizenship. Figure 4 provides a summary of the classification of the four political regimes.

Figure 4
Classification of political regimes

According to OWID, China’s regime was classified as closed autocratic in 2019. Our assumption is that the closer a country’s political regime is to China’s on the political spectrum, the greater the likelihood of support for PRC’s endeavours in Xinjiang. Therefore, countries with liberal democratic or electoral regimes are less likely to support China than autocratic countries. The fuzzy analytical calibrations for this condition for the 50 cases were defined as follows: score 1 for countries with a closed autocratic regime (like China); 0.66 for those with an autocratic electoral regime; 0.5 for those with no data available in OWID; 0.33 for countries with an electoral democratic regime; and 0 for those with a liberal democratic regime.

Explanatory conditions for the dimension of Chinese hard-soft power

China’s Ranking in Country’s Exports (EXP)

Since the 1990s, the Chinese government has orchestrated one of the largest economic expansions in history, achieving an average growth rate of 9% per year and lifting millions of people out of poverty. During this process, China recognized the need to expand its investments and consumer markets to other nations, while also opening up more export opportunities to underdeveloped countries (Chen 2011). Therefore, in this scenario, we anticipate that China’s position in the exports of the countries under analysis will play a critical role in the alignment of the UN/HRC in 2019, since it indicates the weight of China in the income generation of these countries. For instance, China was the primary trading partner for Angola, Saudi Arabia, Cameroon, Congo, North Korea, Myanmar, Russia, South Sudan, Equatorial Guinea, Iran, Turkmenistan, and Uzbekistan, leading their rankings of exports (Trade Map 2018).

Data were obtained from the Trade Map portal (Trade Map 2022), which provides information on exports from the countries under analysis. The standard year of analysis was 2018, which is the year prior to the publication of the letter in the UN/HRC. For countries without data available for this year, the data from 2015 or the closest available year were used. Countries with data prior to 2015 were not considered in the analysis (such as Djibouti in 2009, Gabon in 2009, Iraq in 2014, Syria in 2010, and Venezuela in 2013). Most countries had data available for 2018, with the exception of Algeria (2017), Bangladesh (2015), Yemen (2015), and Sri Lanka (2017). China’s position in national exports was defined based on comparisons with the value exported to other countries in the same year.

China’s most significant rank as an export destination of the 50 countries was the 1st and the least was 36th. The average position of the PRC in the exports of these states was 6.2. To calibrate this condition, we assigned a score of 1 if China was the top destination for exports, 0.66 if it was ranked between the 2nd and 5th main destination, 0.5 if it was ranked between 6th and 10th, 0.33 if it was ranked between 11th and 15th, and 0 if China was ranked above the 15th position or if there was no export data for the country under analysis between 2015-2018. We sought to determine whether a higher dependence on exports to China increased the likelihood of diplomatic support.

Chinese Foreign Direct Investment (FDI)

Over the last 30 years, China has experienced remarkable economic growth and attracted capital through its bringing in policy. In addition, the country has increasingly exported capital to other states via foreign direct investment (FDI), which is part of the going out policy that was initiated during the 10th Five Year Plan (2001-2005). According to Li (2010), the majority of Chinese investments abroad come from medium and large companies with state participation, which are classified into four categories: financial, commercial, industrial, and technological. However, private companies are also growing in their involvement in FDI (Li 2008). It is important to note that China’s foreign investments might have political and market goals and are focused on various objectives, including internationalising companies, expanding consumer markets, acquiring raw materials, upgrading technical, technological, and scientific capabilities, reducing production costs, increasing external projection, and, more recently, promoting the internationalisation of the yuan (Goldsmith and Wagner [n.d.]).

China’s foreign direct investments (FDI) are not limited to developed countries, being significant to underdeveloped countries as well. To secure investment deals, China provides infrastructure, lower-interest loans, and financial grants. Such investments create employment opportunities, stimulate integration into global markets, improve infrastructure, and stimulate industry, among other benefits (Goldsmith and Wagner [n.d]). So, we seek to evaluate the importance of these investments in gaining support from countries, particularly Muslim-majority countries, for China’s stance in the UN/HRC in 2019.

We obtained data on Chinese foreign investments in the 50 countries from the International Monetary Fund website for the year 2018 (the year before the publication of the letter in the UN/HRC) (IMF 2018). We also collected the values of total world investments for these countries to analyse China’s weight in the total foreign direct investment (FDI) received. We calculated the ratio between Chinese investment and world investment for each country in 2018. For instance, in the case of Zambia, Chinese investment was US$2.611m, and world investment was US$6.311m. Thus, China’s investments accounted for 41% of world investments in Zambia.

The world’s FDI for the 50 countries under analysis represented only 5% of the total FDI for the rest of the world, amounting to US$1 982.270m out of US$36 330.600m. On the other hand, the average Chinese investment in these countries in 2018 represented a significant 21% of the world’s total investments. Given the importance of Chinese investments in these countries, we calibrated the condition as follows: a score of 1 is assigned if Chinese investments accounted for more than 40% of the total world investments in the country; a score of 0.66 if they accounted for between 30% and 40%; a score of 0.50 if they accounted for between 20% and 30%; a score of 0.33 if they accounted for between 10% and 20%; and a score of 0 if Chinese investments represented less than 10% of world investments in the country or if there is no data available.

China’s Outward Direct Aid (ODA)

China’s direct foreign aid (ODA) has grown with its economic expansion and diplomatic projection, especially since the 2000s, similarly to other economic conditions. Unlike FDI, which generally involves trade facilitation and openness mechanisms, ODA is defined by its objective of promoting development, well-being and reducing poverty in the host country through concessional financial mechanisms such as donations and interest-free or low-interest loans.

China has emerged as a significant official lender, providing more than US$350b in official funding to developing countries between 2000 and 2014, rivalling traditional Western donors (Dreher et al. cited in Mandon and Woldemichael 2022). Despite this growing role, China’s foreign aid activities lack detailed public reports, and the country does not participate in global reporting systems such as the OECD’s Creditor Reporting System (CRS) and the International Aid Transparency Initiative (IATI). To address this lack of transparency, a network of researchers from various universities has developed the Tracking Underreported Financial Flows (TUFF) tool, which is available on the Aid Data Portal. TUFF aims to collect information on donors and recipients who lack comprehensive information on foreign aid (Aid Data 2021).

The Aid Data Portal provides data on development projects financed by China between 2000 and 2017. It captures information on the known universe of projects with development, commercial, or representational intent, that have received financial or in-kind commitments (or pledges) from China (Aid Data 2021). According to the portal, there were 13 157 projects financed by China in the world during that period, spanning 165 countries. In the 50 countries under analysis, China financed 5498 projects. These countries are particularly relevant to China, as they received over 40% of China’s total foreign aid between 2000 and 2017. On average, China funded 110 projects in the 50 analysed countries, compared to 80 projects worldwide.

The aim of our assessment is to determine whether Chinese foreign aid for development played a significant role in the support of Muslim-majority countries towards Xinjiang policies. To achieve this, we have calibrated the condition as follows: a score of 1 if the country under analysis had 110 or more development projects financed by China, a score of 0.66 if there were between 80 and 109 projects, a score of 0.50 if there were between 50 and 79 projects, a score of 0.33 if there were between 30 and 49 projects, and a score of 0 if there were less than 30 Chinese-funded development projects or if there is no data available on PRC aid for the case under review.

Participation in the Belt and Road Initiative plus Level of Income (BRI)

The Belt and Road Initiative (BRI) was announced in 2013 by Chinese President Xi Jinping, with the aim of strengthening China’s ties with countries around the world through large investments in infrastructure and trade. Its objectives are arranged in five axes: (1) policy coordination; (2) facilities connectivity; (3) lower trade barriers; (4) financial integration; and (5) promote interpersonal ties, such as cultural and academic ones (BRI [2022]). This initiative is in line with China’s new foreign policy direction, which is guided by the slogan striving for achievement, in which China seeks greater activism and assertiveness in the international system (Wei 2020).

Currently, over 130 countries participate in the Belt and Road Initiative (BRI), accounting for more than 60% of the world’s population, over a third of the global GDP, and around three quarters of energy resources (Nedopil 2022). However, some Western observers express concern that China may be using ‘debt trap diplomacy’ to sway its BRI partners towards supporting its domestic and international objectives. This concept suggests that China’s significant creditor capacity could allow it to exert influence over high-risk countries in particular (Kuo and Kommenda 2018).

In this context, we seek to investigate whether the support of Muslim-majority countries for China was influenced by their participation in the BRI and their respective income levels. We anticipate that low-income countries involved in the New Silk Road will be more reliant on China’s economy and investments, making them more likely to align diplomatically with China on Xinjiang. To rank the 50 countries by income (2019), we obtained data from the World Bank Portal (World Bank 2019), while information on participation in the Belt and Road Initiative was sourced from the Green Finance & Development Centre (Green Fdc 2022) and the Council on Foreign Relations (2021) (SACKS 2021).

We will consider the information valid only for those countries that had joined the BRI until 2018 (the year prior to the publication of the letter of support for the PRC). To analyse whether the participation of Muslim-majority countries in the BRI and their respective income levels influenced their support for China, we will use the following analytical calibration: a score of 1 will be assigned if the country under analysis participates in the BRI and has a low income; a score of 0.66 will be given if the country participates in the BRI and has lower-middle income; a score of 0.50 if it participates in the BRI and has upper-middle income; a score of 0.33 if it participates in the BRI and has high income; and a score of 0 if it did not join the BRI until 2018 or if there are no official documents confirming its participation in the BRI.

Military Diplomacy (MD)

Military diplomacy refers to a range of activities undertaken by a state to achieve its foreign policy goals in the areas of defence and security. These activities include visits by military officials, exchange of information and equipment, cooperation and communication between the armed forces of two or more countries (Pajtinka 2016). In China, military partnerships have become more important during Xi Jinping’s government. However, the Chinese government maintains the rhetoric of being closer to developing countries by not seeking, at least in official discourse, the same level of interference exercised by the USA and European countries over their allies (Elmahly and Sun 2018). Among the measures taken by China in the area of military diplomacy are the improvement of strategies for evacuating nationals in conflict zones, providing humanitarian aid, conducting search and rescue operations, protecting expatriates, and participating in peacekeeping missions (Degang 2018). Currently, China funds more than 10% of peacekeeping missions (Elmahly and Sun 2018).

This condition is based on a study by Allen, Saunders and Chen (2017), which evaluates Chinese military diplomacy using joint military exercises, naval port calls, and high-level meetings. From 2003 to 2016, they identified 2875 interactions in these areas with 152 countries, averaging 18 interactions per country. The average for the 50 countries supporting China in 2019 was also 18 interactions. However, as there is no data available on the military interactions of each of the 23 Muslim countries with the rest of the world, this indicator cannot measure China’s weight compared to other states in these cases. Nevertheless, it can show the weight of China-aligned Muslim countries in the UN/HRC in Beijing’s broader strategy for all countries. We can thus assess the extent of China’s military diplomacy efforts for each country that aligned with Beijing.

Given that the average number of China’s military interactions with the world and with the 50 countries supporting China was 18, we will use this number to calibrate our analysis. Countries that have had more than 18 military interactions with China will be assigned a score of 1, those with between 14 and 17 interactions a score of 0.66, those with between 10 and 13 interactions a score of 0.5, those with between 5 and 9 interactions a score of 0.33, and those with less than 5 interactions a score of 0. This indicator will help us assess the extent of China’s military diplomacy efforts towards each country that has aligned itself with China.

Testing the conditions in fsQCA: results and analysis

We created a data matrix based on our analytical calibration metrics for each condition, as shown in Table 3. The first column lists the 50 cases, with Muslim-majority countries highlighted in bold. The second column displays the scores for our main focus of study, Muslim-majority countries that supported China in the UN/HRC in 2019, which received a score of 1, while non-Muslim countries received a score of 0. The remaining columns represent the calibrated conditions for each case: Bilateral Partnerships (BP), Political Regime Affinity (PRA), China’s Ranking in Country’s Exports (EXP), Chinese Foreign Direct Investment (FDI), China’s Outward Direct Aid (ODA), Participation in the Belt and Road Initiative plus Level of Income (BRI), and Military Diplomacy (MD).

Table 3
Data matrix

Cases	M	BP	RPA	EXP	FDI	ODA	BRI	MD
Angola	0	0.66	0.66	1	0	1	0.66	0.5
Saudi Arabia	1	1	1	1	0	0	0.33	0.66
Algeria	1	1	0.66	0.33	0	0.33	0.5	0.33
Bangladesh	1	1	0.66	0.33	0.5	1	0.66	1
Bahrain	1	0	1	0.5	0	0	0.33	0
Belarus	0	1	0.66	0.5	0	1	0.5	1
Bolivia	0	0.66	0.33	0.5	0	0.66	0.66	0.5
Burkina Faso	1	0	0.33	0	0	0	0	0
Burundi	0	0	0.66	0.5	0	1	1	0
Cameroon	0	0	0.66	1	0	1	0.66	0.33
Cambodia	0	1	0.66	0.66	0.66	0	0.66	1
Comoros	1	0	0.66	0	0.33	0.5	0	0
Congo	0	1	0.66	1	0	1	0	0.33
North Korea	0	0.33	1	1	1	1	0	1
Cuba	0	0	1	0.66	0	1	0.5	1
Djibouti	1	0.66	0.66	0	1	0.66	0.66	1
Egypt	1	1	0.66	0.5	0	0.5	0.66	1
United Arab Emirates	1	1	1	0.5	0	0	0.33	0.33
Eritrea	0	0	1	0	0.66	0.5	0	0
Philippines	0	1	0.66	0.66	0	0.66	0.66	1
Gabon	0	0	0.66	0	0	0.66	0.5	0.33
Equatorial Guinea	0	0.5	0.66	1	0.5	1	0.5	0
Yemen	1	0	1	0.33	0	0.5	1	0.66
Iran	1	1	0.66	1	0.5	0.5	0.5	0.66
Iraq	1	0.66	0.66	0	0	0	0.5	0
Kuwait	1	0.66	0.66	0.66	0	0	0.33	0
Laos	0	1	0.66	0.66	1	1	0.66	1
Myanmar	0	1	0.66	1	0.5	1	0.66	1
Mozambique	0	1	0.66	0.66	0.33	1	1	0.5
Nepal	0	1	0.33	0.5	0.66	1	1	1
Nigeria	1	0.66	0.33	0	0	0.66	0.66	0.33
Oman	1	0.66	1	0.66	0	0	0.33	1
Palestine	1	0	0.5	0	0	0	0	0
Pakistan	1	1	0.66	0.66	0.66	1	0	1
Dem. Rep. of Congo	0	0	0.66	0.66	1	1	1	0.33
Russia	0	1	0.66	1	0	1	0	1
Serbia	0	1	0.5	0	0	0.5	0.5	1
Syria	1	0	1	0	0	0.5	0	0.33
Somalia	1	0	1	0.66	0	0.5	1	0
Sri Lanka	0	0.66	0.33	0.5	0	1	0.66	1
Sudan	1	0.66	1	0.66	1	1	0.66	0.66
Southern Sudan	0	0	1	1	1	1	1	0
Tajikistan	1	1	0.66	0.66	1	1	1	1
Togo	0	0	0.66	0	0.33	1	1	0.33
Turkmenistan	1	0.66	0.66	1	0.66	0.33	0.66	0.33
Uganda	0	0	0.66	0	0.33	1	1	0.33
Uzbekistan	1	1	0.66	1	1	1	0.66	0.66
Venezuela	0	1	0.66	0	0	0.66	0.5	0.5
Zambia	0	0	0.5	0.66	1	1	0.66	0.66
Zimbabwe	0	0	0.66	0.5	0.5	1	1	0.66

• Source: authors’ elaboration.

This data matrix was used in the fsQCA software to test the relations of necessity and sufficiency of the conditions for the result (support of Muslim-majority countries for Beijing). As previously noted, Nicolas Legewie (2013) suggests that a condition can be considered necessary for the result if it presents a consistency measure close to 0.90 and a coverage measure close to 0.50. However, in our study, none of the conditions reached these parameters. Nevertheless, the condition referring to the Political Regime Affinity (PRA) showed the highest consistency and coverage measures, with 0.74 and 0.48 respectively, making it the best placed in the ranking of possible necessary conditions for the support from Muslim-majority countries to the PRC. The Bilateral Partnerships (BP) condition followed with a consistency of 0.59 and a coverage of 0.49. On the other hand, the other conditions showed consistency levels below 0.50, and are therefore deemed irrelevant to the result when tested as necessary conditions.

When testing the necessity of conditions for the result (M=1) using the OR (+) expression, several relationships were identified. The first is the Bilateral Partnership or Political Regime Affinity (PB+PRA), which had a consistency of 0.86 and a coverage of 0.46. The second relationship is between Political Regime Affinity or Military Diplomacy (PRA+MD), which had a consistency of 0.81 and a coverage of 0.45. The third relationship is between Political Regime Affinity or China’s Outward Direct Aid (PRA+ODA), with a consistency of 0.81 and a coverage of 0.43. The fourth relationship is between Political Regime Affinity or China’s Ranking in Country’s Exports (EXP) (PRA+EXP), with a consistency of 0.78 and a coverage of 0.46. The fifth relationship is between Political Regime Affinity or Chinese Foreign Direct Investment (PRA+FDI), with a consistency of 0.78 and a coverage of 0.48. Finally, the sixth relationship is between Political Regime Affinity or Participation in the Belt and Road Initiative plus Level of Income (PRA+BRI), with a consistency of 0.77 and a coverage of 0.45. The remaining dyadic combinations had less than 0.75 consistency, and therefore are less relevant to the result when tested as necessary conditions.

The results showed that all conditions combined with the OR expression included Political Regime Affinity (PRA) with a consistency greater than 0.75. This suggests that there is a high likelihood that this condition was necessary for Muslim countries to support China in the UN/HRC in 2019, as most Muslim supporters are either closed or electoral autocracies. On the other hand, economic conditions such as EXP, ODA, FDI, and BRI appeared in four out of the six combinations, but always accompanied by Political Regime Affinity (PRA). These economic conditions did not appear as necessary conditions individually for the support of the Muslim-majority countries (M=1), which contradicts the initial conjecture that hard-soft power dimensions would assume a greater weight in this alignment with the PRC. The results suggest that Political Regime Affinity (PRA), a strictly soft power condition, is the central factor that differentiates Muslim and non-Muslim countries’ support for China.

To test for sufficiency, we used our data matrix (Table 3) to generate a truth table (Fuzzy Truth Table Algorithm) with 128 possible configurations of conditions. These configurations were automatically generated by the Quine-McCluskey algorithm of the fsQCA software, based on the calibration scores for each condition and each case. The number of configurations is determined by the 2k formula, where k represents the number of conditions to be tested (in our case, 7). The sufficiency test enables us to examine the complex causality phenomenon, in which different combinations of conditions can contribute to the outcome. We analysed the conditions and/or combinations of conditions that were sufficient for Muslim-majority countries to align with China (M=1) by excluding lines that had no empirical cases, as recommended by Ragin (2017), and assigning a score of 1 to configurations with a consistency level of at least 0.75, as suggested by Kent (2008).

Table 4
Truth Table (M=1)

BP	PRA	EXP	FDI	ODA	BRI	MD	No. Cases	M	Cases	Cons.
1	1	1	1	0	1	0	1	1	Turkmenistan	1
1	1	1	0	0	0	0	1	1	Kuwait	0.88
0	0	0	0	0	0	0	1	1	Burkina Faso	0.78
1	1	1	0	0	0	1	2	1	Saudi Arabia, Oman	0.75
1	1	1	1	1	0	1	1	0	Pakistan	0.71
1	1	0	1	1	1	1	1	0	Djibouti	0.64
1	1	1	1	1	1	1	4	0	Laos, Sudan, Tajikistan, Uzbekistan	0.63
1	1	1	1	0	1	1	1	0	Cambodia	0.55
1	0	0	0	1	1	0	1	0	Nigeria	0.45
1	1	1	0	1	0	1	1	0	Russia	0.40
0	1	1	1	1	0	1	1	0	North Korea	0.39
1	1	1	0	1	0	0	1	0	Congo	0.35
1	1	1	0	1	1	1	1	0	Philippines	0.35
0	1	1	0	1	1	0	1	0	Cameroon	0.30
0	1	0	0	1	1	0	2	0	Togo, Uganda	0.25
0	1	1	1	1	1	0	2	0	Dem. Rep. of Congo, South Sudan	0.19

• Source: fsQCA software, authors’ elaboration.

In the truth table, the first seven columns represent the conditions, and the rows represent their respective combinations. The eighth column shows the number of cases included in each configuration (lines), while the ninth column shows the outcome (M=1). The tenth column displays the cases covered by each configuration. Finally, the eleventh column shows the consistency of the condition combinations for the outcome and the cases. The numbers 1 and 0 for the conditions represent their presence or absence, respectively, for the outcome and cases. In the ninth column, M indicates whether the combination of conditions met the minimum consistency of 0.75 for sufficiency (1) or not (0). Our truth table generated 16 logically possible combinations, which cover 22 out of the 50 countries that supported China, of which 11 are Muslim-majority countries.

After analysing the truth table, we found that the first four configurations were sufficient for five Muslim countries to support China, with a consistency measure of 0.75 or more. The first configuration had the highest consistency of 1 for the result and was met by Turkmenistan. It consisted of the combination of five conditions: Bilateral Partnerships (BP), Political Regime Affinity (PRA), China’s Ranking in Country’s Exports (EXP), Chinese Foreign Direct Investment (FDI), and Participation in the Belt and Road Initiative plus Level of Income (BRI). The second configuration had a consistency of 0.88 and was met by Kuwait, with a combination of three conditions: Bilateral Partnerships (BP), Political Regime Affinity (PRA), and China’s Ranking in Country’s Exports (EXP). The third configuration had a consistency of 0.78 and was met by Burkina Faso, but none of the evaluated conditions were present. The fourth configuration had a consistency of 0.75 and was met by Saudi Arabia and Oman, with a combination of four conditions: Bilateral Partnerships (BP), Political Regime Affinity (PRA), China’s Ranking in Country’s Exports (EXP), and Military Diplomacy (MD).

Based on the analysis, we have identified four configurations with a consistency measure of 0.75 or more, which were sufficient for five Muslim-majority countries to support China. Interestingly, we found that Political Regime Affinity (PRA) is always present in the combinations of sufficient conditions, indicating its necessary character, as previously hypothesised. On the other hand, China’s Outward Direct Aid (ODA) appears to be irrelevant as it did not appear in any sufficient combination. Each configuration can be considered an INUS condition, as no single condition alone meets the sufficiency criteria. It is worth noting that the case of Burkina Faso is consistent with the result but does not match any of the conditions tested in this study. Further research is necessary to explore other potential factors that may have influenced its alignment with PRC.

To validate our analysis, we conducted necessity and sufficiency tests for non-Muslim countries that supported China (M=0). None of the individual conditions reached the consistency of 0.90 to be considered necessary for the result (Legewie 2013). However, the condition related to China’s Outward Direct Aid (ODA) was ranked highest among possible necessary conditions, with a consistency of 0.87 and a coverage of 0.70. This suggests that the PRC’s financing of development projects played a significant role in the alignment of these countries with China in 2019. Meanwhile, the condition of Political Regime Affinity (PRA) had a consistency of 0.66 and a coverage of 0.51. However, this condition is more relevant for Muslim countries, as we have demonstrated earlier. The remaining conditions had a consistency of less than 0.50 and are therefore not significant to the result when tested as necessary conditions.

In carrying out the necessity test from the OR (+) expression, we have the following relationships: (1) the Bilateral Partnership or China’s Outward Direct Aid (BP+ODA), with a consistency of 0.95 and a coverage of 0.61; (2) China’s Outward Direct Aid or Military Diplomacy (ODA+MD), with a consistency of 0.95 and a coverage of 0.65; (3) Political Regime Affinity or China’s Outward Direct Aid (PRA+ODA), with a consistency of 0.91 and a coverage of 0.56; (4) Chinese Foreign Direct Investment or China’s Outward Direct Aid (FDI+ODA), with a consistency of 0.90 and a coverage of 0.69; (5) China’s Outward Direct Aid or Participation in the Belt and Road Initiative plus Level of Income (ODA+BRI), with a consistency of 0.90 and a coverage of 0.63; (6) China’s Ranking in Country’s Exports or China’s Outward Direct Aid (EXP+ODA), with a consistency of 0.90 and a coverage of 0.62; (7) the Bilateral Partnership or Political Regime Affinity (BP+PRA), with a consistency of 0.84 and a coverage of 0.53; (8) Participation in the Belt and Road Initiative plus Level of Income or Military Diplomacy (BRI+MD), with a consistency of 0.83 and a coverage of 0.61; (9) the Bilateral Partnership or Participation in the Belt and Road Initiative plus Level of Income (BP+BRI), with a consistency of 0.81 and a coverage of 0.58; (10) Political Regime Affinity or Military Diplomacy (PRA+MD), with a consistency of 0.81 and a coverage of 0.54; (11) China’s Ranking in Country’s Exports or Participation in the Belt and Road Initiative plus Level of Income (EXP+BRI), with a consistency of 0.8 and a coverage of 0.6; (12) Political Regime Affinity or Participation in the Belt and Road Initiative plus Level of Income (PRA+BRI), with a consistency of 0.79 and a coverage of 0.54; (13) China’s Ranking in Country’s Exports or Military Diplomacy (EXP+MD), with a consistency of 0.78 and a coverage of 0.59; (14) Political Regime Affinity or China’s Ranking in Country’s Exports (PRA+EXP), with a consistency of 0.76 and a coverage of 0.53; (15) the Bilateral Partnership or China’s Ranking in Country’s Exports (BP+EXP), with a consistency of 0.75 and a coverage of 0.56. The other dyadic combinations reached less than 0.75 consistency.

To further clarify the results, it seems that economic conditions played a more important role in non-Muslim countries aligning with China. China’s Outward Direct Aid (ODA) was a significant factor in six out of the 15 likely combinations of necessary conditions for the outcome (M=0), while China’s Ranking in Country’s Exports (EXP), Participation in the Belt and Road Initiative plus Level of Income (BRI), and Political Regime Affinity (PRA) were each present in five. This result aligns with the individual analysis of conditions, China’s Outward Direct Aid (ODA) was found to be decisive. Thus, it appears that for non-Muslim countries, China’s hard-soft power dimension had a greater impact on alignment.

Table 5
Descending order of necessary conditions combinations (M=0)

	Combination	Consistency	Coverage
1	BP+ODA	0.95	0.61
2	ODA+MD	0.95	0.61
3	PRA+ODA	0.91	0.56
4	FDI+ODA	0.90	0.69
5	ODA+BRI	0.90	0.63
6	EXP+ODA	0.90	0.62
7	BP+PRA	0.84	0.53
8	BRI+MD	0.83	0.61
9	BP+BRI	0.81	0.58
10	PRA+MD	0.81	0.54
11	EXP+BRI	0.80	0.60
12	PRA+BRI	0.79	0.54
13	EXP+MD	0.78	0.59
14	PRA+EXP	0.76	0.53
15	BP+EXP	0.75	0.56

• Source: fs QCA software, authors’ elaboration.

Table 6
Truth Table (M=0)

BP	PRA	EXP	FDI	ODA	BRI	MD	No. Cases	~M	Cases	Cons.
0	1	1	1	1	1	0	2	1	Dem. Rep. of Congo, South Sudan	0.80
0	1	0	0	1	1	0	2	0	Togo, Uganda	0.74
0	1	1	0	1	1	0	1	0	Cameroon	0.69
1	1	1	0	1	1	1	1	0	Philippines	0.64
1	1	1	0	1	0	0	1	0	Congo	0.64
0	1	1	1	1	0	1	1	0	North Korea	0.60
1	1	1	0	1	0	1	1	0	Russia	0.59
1	0	0	0	1	1	0	1	0	Nigeria	0.54
1	1	1	1	0	1	1	1	0	Cambodia	0.44
1	1	1	1	1	1	1	4	0	Laos, Sudan, Tajikistan, Uzbekistan	0.36
1	1	0	1	1	1	1	1	0	Djibouti	0.35
1	1	1	1	1	0	1	1	0	Pakistan	0.28
1	1	1	0	0	0	1	2	0	Saudi Arabia, Oman	0.24
0	0	0	0	0	0	0	1	0	Burkina Faso	0.21
1	1	1	0	0	0	0	1	0	Kuwait	0.11
1	1	1	1	0	1	0	1	0	Turkmenistan	0

• Source: fs QCA software, authors’ elaboration.

Our truth table has 16 possible configurations, covering 22 out of 50 countries that have aligned with the PRC, of which 11 are non-Muslims. To summarise, the alignment of non-Muslim countries with China is influenced more by economic conditions, such as China’s Outward Direct Aid (ODA), China’s Ranking in Country’s Exports (EXP), Participation in the Belt and Road Initiative plus Level of Income (BRI), and Chinese Foreign Direct Investment (FDI), rather than Political Regime Affinity (PRA). The truth table analysis revealed that only the first configuration, consisting of Political Regime Affinity (PRA), China’s Ranking in Country’s Exports (EXP), Chinese Foreign Direct Investment (FDI), China’s Outward Direct Aid (ODA) Participation in the Belt and Road Initiative plus Level of Income (BRI), was sufficient for two non-Muslim countries to align with China (Democratic Republic of Congo and South Sudan), with a consistency measure of 0.80 that met the cases. The second configuration, which covered the cases of Togo and Uganda, is the configuration of Political Regime Affinity (PR), China’s Direct Foreign Aid (ODA), and Participation in the Belt and Road Initiative and Income (BRI), which almost met the sufficiency parameters with a consistency measure of 0.7475.

So, the study found that China’s Outward Direct Aid (ODA) is a necessary condition for non-Muslim countries to align with China, as it is present in both combinations that were sufficient for alignment. Bilateral Partnership (PB) and Military Diplomacy (MD) were found to be irrelevant, as they did not appear in any sufficient combination. Each configuration is an INUS condition, meaning no single condition alone meets the sufficiency parameters. This suggests that for non-Muslim countries, China’s foreign policy is largely influenced by economic constraints and the hard-soft power dimension. This finding contradicts the initial hypothesis that economic conditions would be decisive for the support of Muslim countries.

Conclusions

What were the motivations that led 23 Muslim-majority countries to align themselves with China in the UN/HRC in 2019, despite alleged human rights violations against Uyghurs in Xinjiang? How has the PRC shaped the foreign policy behaviours of Muslim-majority countries in its favour? What conditions were decisive in this process and differentiated the alignment of Muslim-majority countries and non-Muslim countries? How did these conditions operate, i.e. which ones were necessary, sufficient, INUS, and/or SUIN? To answer these questions, we performed a fuzzy-set qualitative-comparative analysis (fsQCA) and tracked conditioning processes that may have influenced this result.

In this manuscript, our hypothesis was that the coalition supporting China’s policies in Xinjiang was a result of a combination of hard and soft power resources, ultimately leading to soft power (legitimacy). Our initial conjecture was that economic conditions played a more significant role in securing the support of Muslim-majority countries for China. However, during the analysis, we found that economic conditions alone were not decisive in the alignment process. In examining the soft power dimension of Chinese foreign policy, we verified two central conditions: Bilateral Partnerships (BP) and Political Regime Affinity (PRA). In contrast, the hard-soft power dimension involved five conditions: China’s Ranking in Country’s Exports (EXP), Chinese Foreign Direct Investment (FDI), China’s Outward Direct Aid (ODA), Participation in the Belt and Road Initiative plus Level of Income (BRI), and Military Diplomacy (MD). We utilised the fsQCA software to test these conditions and summarise the results in Table 7.

Table 7
Conditions for Muslim and non-Muslim countries to support China in Xinjiang issue

Condition	Configuration	Relation	Consist.	Cases
Necessary	PRA	M=1	0.74	-
INUS	BP*PRA*EXP*FDI*BRI	M=1	1	Turkmenistan
	BP*PRA*EXP		0.88	Kuwait
	~BP*~PRA*~EXP*~FDI*~ODA~*BRI*~MD		0.78	Burkina Faso
	BP*PRA*EXP*MD		0.75	Saudi Arabia and Oman
Necessary	ODA	M=0	0.87	-
INUS	PRA*EXP*FDI*ODA*BRI	M=0	0.80	Dem. Rep. of Congo and South Sudan
	PRA*ODA*BRI		0.7475	Togo and Uganda

• Source: fs QCA software, authors’ elaboration.

In the table, the symbols have the following meanings: [] indicates a necessity relation; [] indicates a sufficiency relation; [~] indicates the absence of a condition; [*] represents an and logical operator between conditions; [M=1] denotes the support of Muslim-majority countries for China; and [M=0] represents the support of non-Muslim countries for PRC. It was observed that for the support of Muslim-majority countries, the Political Regime Affinity (PRA) was the decisive condition, while for non-Muslim countries, it was China’s Outward Direct Aid (ODA). None of the conditions were both necessary and sufficient, nor was there a single condition that was sufficient. Instead, the occurrence of the outcome depended on combinations of different conditions (INUS) for both Muslim and non-Muslim countries. Furthermore, Bilateral Partnership (BP) and Military Diplomacy (MD) were irrelevant for non-Muslims as they did not appear in any combination sufficient for the result, and China’s Outward Direct Aid (ODA) did not contribute to the result for Muslims.

We found that for Muslim-majority countries, the decisive condition for aligning with China was Political Regime Affinity (PRA), which was often accompanied by another soft power condition, such as Bilateral Partnership (BP), as well as certain hard-soft power conditions, such as Military Diplomacy (MD), as shown in the third sufficient combination. This partially supports our hypothesis that economic conditions were determinant for Muslim countries’ support of China, as they formed combinations of conditions that produced the result, but none of them were necessary or sufficient in isolation. On the other hand, China’s Outward Direct Aid (ODA) was necessary only for non-Muslim countries’ support of China.

In this study, it became clear that while China values pragmatic cooperation with partner countries and minimises political and ideological aspects, the role of Muslim-majority countries in this dynamic cannot be overlooked, especially regarding their support for PRC’s endeavours in Xinjiang. The Political Regime Affinity (PRA) was found to be a necessary condition for Muslim-majority countries to align with Beijing, whereas economic conditions were not as significant. This sets the alignment of Muslim-majority countries apart from non-Muslim countries, who were more motivated to support China for economic gains. Overall, this highlights the importance of understanding the various factors and combinations of conditions that contribute to the foreign policy behaviours of different countries towards PRC.

Notes

• 1
  Signatory countries: Angola, Saudi Arabia, Algeria, Bangladesh, Bahrain, Belarus, Bolivia, Burkina Faso, Burundi, Cameroon, Cambodia, Comoros, Congo, Cuba, North Korea, Djibouti, Egypt, United Arab Emirates, Eritrea, Philippines, Gabon , Equatorial Guinea, Yemen, Iran, Iraq, Kuwait, Laos, Myanmar, Mozambique, Nepal, Nigeria, Oman, Palestine, Pakistan, Democratic Republic of Congo, Russia, Serbia, Syria, Somalia, Sri Lanka, Sudan, South Sudan, Tajikistan, Togo, Turkmenistan, Uganda, Uzbekistan, Venezuela, Zambia and Zimbabwe.
• 2
  These Muslim-majority countries have populations with 50% or more Muslim individuals, but do not necessarily apply Islamic law.
• 3
  Germany, Australia, Austria, Belgium, Canada, Denmark, Spain, Estonia, Finland, France, Netherlands, Ireland, Iceland, Japan, Latvia, Lithuania, Luxembourg, Norway, New Zealand, United Kingdom, Sweden and Switzerland.
• 4
  FsQCA for Windows: https://sites.socsci.uci.edu/~cragin/fsQCA/software.shtml.
• 5
  Information on China’s bilateral partnerships was obtained from the following data sources:
• 6
  https://gaodawei.wordpress.com/chinese-partnerships; https://www.fmprc.gov.cn/web/gjhdq_676201/gj_676203/yz_676205/; https://pomeps.org/friends-with-benefits-chinas-partnership-diplomacy-in-the-gulf; Strüver, Georg. 2017. ‘China’s Partnership Diplomacy: International Alignment Based on Interests or Ideology’. The Chinese Journal of International Politics, 10 [1]: 31-65.

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Publication Dates

• Publication in this collection
  05 Apr 2024
• Date of issue
  Jan/Abr 2024

History

• Received
  06 Jan 2023
• Accepted
  28 June 2023