AI Export and Digital Silk Road: A Comparative Analysis of China’s Influences on Digital Economies and Geopolitics across Southeast Asia

Hung, Jason Article — Accepted Manuscript (Postprint) AI Export and Digital Silk Road: A Comparative Analysis of China’s Influences on Digital Economies and Geopolitics across Southeast Asia Frontiers in Political Science Suggested Citation: Hung, Jason (2025) : AI Export and Digital Silk Road: A Comparative Analysis of China’s Influences on Digital Economies and Geopolitics across Southeast Asia, Frontiers in Political Science, ISSN 2673-3145, Frontiers Media, Lausanne, https://www.frontiersin.org/journals/political-science/articles/10.3389/fpos.2025.1685231/ This Version is available at: https://hdl.handle.net/10419/333717 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ (This version is accepted for publication at Frontiers in Political Science.) 1 AI Export and Digital Silk Road: A Comparative Analysis of China’s Influences on Digital 1 Economies and Geopolitics across Southeast Asia 2 3 4 Jason Hung1 5 6 1Independent researcher, formerly the University of Cambridge, UK 7 8 Email: [email protected] 9 10 11 Abstract 12 13 There is a lack of original research investigating the relationships between China’s artificial 14 intelligence (AI) exports and their possible impacts on strengthening Digital Silk Road (DSR) co-15 operation. This paper first studies the distribution of China’s AI export projects to Southeast16 Asian countries (2006–2017) and then examines whether larger Southeast Asian AI importers 17 were more active DSR partners (2018–2020). The analysis, drawing on data from the China’s AI18 Exports Database (CAIED), the IISS China Connects, and the World Bank Open Data , employs 19 a robust panel data methodology incorporating lagged explanatory variables and clustered20 standard errors to mitigate issues of heteroscedasticity and auto-correlation. Findings suggest21 that Southeast Asian countries importing AI technologies from China between 2006 and 2017 22 had not been statistically more DSR active between 2018 and 2020. Instead, the paper finds a23 positive impact that country-level economic freedom is a highly significant contributing factor to 24 being an active DSR partner. This study enhances transparency in the dynamics of China’s25 digital geopolitics by highlighting economic competitiveness as the primary driver of DSR 26 partnerships, rather than prior AI import history.27 28 Keywords 29 30 Belt-and-Road Initiative; Digital Silk Road Initiative; China; digital economy; digital geopolitics 31 32 Overview33 34 China's Belt and Road Initiative (BRI), launched in 2013, aims to foster development and35 investment partnerships across Asia, Europe, Africa, Oceania, and Latin America. This global 36 initiative has facilitated China’s expansion of economic and political influence (McBride et al.,37 2023). In Southeast Asia, China has emerged as a dominant investor, signing infrastructure 38 deals worth billions annually (Dayant & Stanhope, 2024). A core dimension for China to build39 global connectivity is through trade. China’s supply chain connectivity has risen across BRI40 regions, with Southeast Asian countries (both richer and poorer economies) remaining the most 41 connected (LSE Ideas, 2018). Within the region, Singapore, Vietnam, Thailand, Malaysia,42 Cambodia, and Myanmar are ranked among the world’s most connected economies to China 43 via trade (LSE Ideas, 2018).44 45 Since BRI partnerships between the two parties are extensively researched and addressed in 46 existing scholarship, this paper will instead examine the Digital Silk Road (DSR) relationships47 between China and Southeast Asia. The DSR, first officially mentioned in China’s 13th Five48 Year Plan (2016-20) , has become a key pillar of the BRI. In recent years, given the intensified 49 United States-China technological and artificial intelligence (AI) rivalry, expanding the DSR’s50 influence on Southeast Asian markets has become crucial for China (Zheng, 2024). China's 51 DSR prioritisation as a national strategy is shaped by two factors. The push factor is the rapid52 (This version is accepted for publication at Frontiers in Political Science.) 2 growth of China’s digital economy in recent years. The pull factor is China’s perception of the 53 digital gap in many developing countries, including Southeast Asian countries, which restricts 54 their digital economy growth (Zheng, 2024). Since the 2008 global financial crisis, China has55 actively intensified its efforts to build its technological dominance, especially in the development 56 and application of AI technology (Chung, 2023).57 58 Although the popularisation of AI technology has surged since the launch of ChatGPT in 2022, 59 China's AI Exports Database (CAIED) indicates that China has strategically been delivering AI60 export projects globally since decades ago. Existing scholarship covers the analysis of China’s 61 DSR partnerships with Southeast Asian countries. However, there is a lack, if not an absence,62 of original research investigating whether China’s global strategy of AI exports has impacted 63 DSR partnerships across Southeast Asia. If not, it is scholarly important to identify the key64 driver(s) that have impacted DSR partnerships with Southeast Asian countries. This paper will65 assess whether larger AI importers from China are more active DSR players. If not, it will 66 explore what factor(s) determine how active Southeast Asian countries are as DSR partners.67 Such an investigation helps enrich the global dialogue and enhance transparency in the 68 dynamics of China’s AI exports and DSR cooperation.69 70 Literature Review 71 72 Despite the ‘debt trap’ conspiracy criticisms (McBride et al., 2023), many Southeast Asian 73 countries, such as Indonesia and Thailand, have welcomed BRI, including DSR, partnership74 opportunities (Busbarat et al., 2023; Yu, 2017). From an economic standpoint, Chinese 75 investments have the potential to bolster Southeast Asia’s economic growth and regional76 connectivity. However, there have been concerns across Southeast Asia that countries heavily77 dependent on Chinese investment may face long-term risks to their own economies due to 78 rising debts and to their societies owing to geopolitical interventions (Busbarat et al., 2023).79 Additionally, racial tensions and safety issues have compounded the criticisms against 80 Southeast Asian governments that are overly economically dependent on China (Busbarat et al.,81 2023; Dayant & Stanhope, 2024). Therefore, this paper would like to study today’s dynamics of82 DSR partnerships between China and Southeast Asian countries, in order to present which 83 Southeast Asian countries are more or less willing to form digital trade ties with China.84 85 Existing scholarship suggests that the Philippines is a Southeast Asian country who maintains86 negative BRI relationships with China, as of writing this paper. During the administration of 87 President Rodrigo Duterte, the Philippines was more willing to form BRI cooperation with China.88 However, when President Bongbong Marcos took office in 2022, several early-stage89 infrastructure development projects funded by China were cancelled (Busbarat et al., 2023). 90 Philippine elite and public sentiment have hardened towards the Chinese Government in recent91 years. Other Southeast Asian countries are more ambiguous, or even divided, in their stance 92 towards the Chinese Government. Southeast Asian elites from countries such as Singapore and93 Indonesia prefer maintaining robust economic and security ties with major international players94 to offset China’s dominance in the region (Feigenbaum et al., 2024). As this paper focuses on 95 evaluating DSR instead of BRI relationships between China and Southeast Asia, it would be96 interesting to investigate whether the Philippines has cut DSR ties with China. Also, it would be 97 interesting to understand the attitudes of other leading regional players, such as Singapore and98 Indonesia, regarding the formation of DSR partnerships with China. 99 100 The pandemic boosted digital economies worldwide, facilitating Southeast Asia to offer101 investment opportunities in e-commerce logistics and digital financial services to circumvent102 restrictions on personal mobility and in-person meetings between 2020 and early 2023. Here, 103 (This version is accepted for publication at Frontiers in Political Science.) 3 regionally advanced economies such as Singapore, Malaysia, Thailand, and Indonesia have 104 developed more competitive technological adoption environments (Chung, 2023). In such 105 circumstances, Southeast Asian countries will continue to build and expand their digital106 economies in the long term. Investigating China-Southeast Asia relations through DSR 107 partnerships is a timely and academically important topic. Understanding how active a108 Southeast Asian country is as a DSR player indicates its digital capacity, connectivity, and109 competitiveness. Such digital competence, to some degree, reflects the country’s economic 110 strength. This paper will discuss the nuances in digital geopolitics and economics to add111 transparency to the discourse on the strategic plans of China’s technological and AI investment. 112 113 Methodology 114 115 As highlighted, this paper aims to study whether larger Southeast Asian AI importers from China116 are more active players in the DSR. If not, this paper aims to explore the major factor(s) that 117 determine how active Southeast Asian countries are as DSR partners. To satisfy these research118 aims, I need access to databases covering details about China’s AI exports and DSR projects. I 119 therefore analyse secondary data from multiple databases: the China’s AI Exports Database120 (CAIED) and the IISS China Connects. I further collect data from the World Bank Open Data.121 122 The CAIED database is run by the RAND Corporation, while the IISS China Connects database123 is operated by the International Institute for Strategic Studies. The CAIED tracks Chinese 124 government-supported development finance projects that utilised or enabled AI technology in125 the Global South between 2000 and 2017. Using the latest data mining tools, the CAIED 126 identified a total of 155 AI export projects from China. The CAIED records all AI or AI enabling127 projects imported across the globe from China, between 2000 and 2017. However, according to128 the CAIED’s tracked data, Chinese government-supported development finance projects that 129 utilised or enabled AI technology in Southeast Asia had been implemented between 2006 and130 2017. Here AI projects included all technology-facilitated trades that required AI processing or 131 were AI-powered. AI-enabling projects were, however, much broader. They referred to projects132 where AI acted as a catalyst to transform different sectors, such as building and delivering133 technological services (e.g. setting up CCTV network) to create a continuous stream of visual 134 data essential for training AI algorithms—laying the groundwork for future AI integration.135 136 Alternatively, the IISS China Connects database is a virtual interactive tool that visualises137 independently researched and verified datasets on China’s BRI and DSR projects planned, 138 implemented, or completed between 2000 and 2020/2021 (2021 for BRI projects and 2020 for139 DSR projects). I will use the CAIED to summarise how many AI projects had been imported by140 each Southeast Asian country from China until 2017. In doing so, I can analyse how active each 141 Southeast Asian country had been as an AI importer from China. Then, I will use the IISS China142 Connects to summarise how many DSR projects had been planned, implemented, or completed 143 between 2018 and 2020 by each Southeast Asian country. Through this approach, I am able to144 make data-driven evaluation on whether Southeast Asian countries importing more AI145 technologies from China until 2017 had formed tighter DSR partnerships with China between 146 2018 and 2020.147 148 Moreover, since this paper focuses on examining the DSR relationships between China and149 Southeast Asia, it is scholarly relevant to evaluate the digital capacity of all Southeast Asian 150 countries. The broader digital capacity a Southeast Asian country has, the better digital adoption 151 environment that country can offer and possibly the more DSR projects the country can form.152 Here, I measure digital capacity based on the World Bank Open Data of (1) mobile cellular153 subscriptions (per 100 people) and (2) individuals using the Internet (% of the population) using 154 (This version is accepted for publication at Frontiers in Political Science.) 4 2018-2020 data. I partly decide to measure digital connectivity by the proxy of mobile cellular 155 subscriptions (per 100 people) because mobile phones instead of landline phones and 156 computers have become more important as daily necessities. Measuring mobile cellular157 subscriptions in any given country per 100 people is a preferrable methodological design 158 because Southeast Asian countries have drastic differences in population sizes, so measuring159 and comparing the actual annual numbers of mobile cellular subscriptions between countries is160 not informative and indicative. 161 162 It is equally important to evaluate the economic capacity of all Southeast Asian countries. The 163 broader economic capacity a Southeast Asian country has, the more favourable a digital164 adoption environment it can offer, and the closer digital trade ties it can form. Here, I measure 165 economic capacity based on Southeast Asia’s economic competitiveness, assessed by (3) the166 real GDP per capita (constant 2015 US$) from the World Bank Open Data and (4) the Index of167 Economic Freedom scores using 2018-2020 data. I was considering whether I should choose 168 the IMD World Competitiveness Ranking scores, Global Innovation Index scores or Index of169 Economic Freedom scores as one of the proxies for economic competitiveness. Initially, I 170 believe the Global Innovation Index, which measures countries’ innovative capability in171 knowledge, technology and creative outputs would be ideal to help capture economic capacity.172 However, the Global Innovation Index has missing data for Laos and Myanmar in the 2018 and 173 2019 data. Alternatively, while the IMD World Competitiveness Ranking measures how174 competitive an economy is based on government efficiency, business efficiency, and 175 infrastructure, I find this metric calculates both institutional and economic capacity of any given176 country which may fail to solely reflect country-level economic competitiveness. Therefore, I 177 decide to use the Index of Economic Freedom, which measures policy environment for business178 such as trade freedom and fiscal health of any country. Compared to the IMD World179 Competitiveness Ranking, the Index of Economic Freedom metric is partially but relatively less 180 reliant on institutional aspects to measure economic competitiveness. Therefore, in this181 research paper, I use both real GDP per capita (constant 2015 US$) and the Index of Economic 182 Freedom scores as proxies to measure Southeast Asian countries’ economic competitiveness.183 184 In time-series or panel data, if the variables are non-stationary (i.e. contain trends), regressions 185 can sometimes give false relationships known as spurious regressions. However, since my186 panel data is short (T=3; years: 2018, 2019, 2020), I cannot meaningfully run robust panel unit 187 root tests because these methods require longer time spans. However, any potential spurious188 regression concerns should be minimised given my methodological design. First, I build 189 additional robustness check by re-running regressions using lagged explanatory values and190 clustered standard errors at the country level (in Table 6) to show that my empirical results are191 not an artifact of autocorrelation. Autocorrelation tells us that, in panel or time-series data, 192 values today are often correlated with values yesterday. Therefore, in Table 6, I regress DSR193 projects in 2019 and 2020 on digital connectivity and economic competitiveness proxies using 194 2018 and 2019 data respectively. Such an approach helps avoid running spurious regressions195 with false significance. Second, and more importantly, I strategically use proxies for digital196 connectivity and economic competitiveness that are plausibly stationary (or nearly stationary). 197 For example, I use real GDP per capita (constant 2015 US$) which is less trending than198 aggregate national GDP. Also, I use mobile cellular subscriptions (per 100 people) instead of 199 the nominal total subscriptions as an independent variable, use individuals using the Internet (%200 of population), and use index scores from the Index of Economic Freedom. These mean my 201 independent variables are either in percentages and index, which are far less problematic for 202 stationarity.203 204 (This version is accepted for publication at Frontiers in Political Science.) 5 It is acknowledged that the fixed-effects model, which relies only on within-country variation, 205 inherently cannot estimate the coefficient for the time-invariant AI_exportivariable. While 206 advanced techniques like the between estimator or a two-stage process could potentially207 recover this estimate, the random-effects model was preferred for both the main analysis (Table 208 5) and the robustness check (Table 6). This is because the random-effects model allows for the209 simultaneous estimation of all time-variant and time-invariant variables, directly addressing the210 core research question of this paper regarding the influence of AI_exportion DSR activity. 211 212 Moreover, as mentioned, the IISS China Connects database shares all BRI and DSR projects 213 planned, implemented, or completed between 2000 and 2020/2021. However, the BRI and DSR214 were introduced in 2013 and 2015 respectively. Some may question why the IISS China 215 Connects database contains details about BRI and DSR projects from the 2000s while these216 initiatives were launched in the 2010s. It is noteworthy that the IISS China Connects database217 consists of officially labelled BRI and DSR projects. These are infrastructure and digital 218 connectivity projects that the Chinese Government, ministerial agencies, or state media have219 recognised as falling under the broader BRI and DSR enterprises launched in 2013 and 2015 220 respectively. Yet, the database further includes infrastructure projects that were not officially221 labelled as part of the BRI or DSR. These include projects initiated in the 2000s or early 2010s222 that did not receive explicit endorsement from the Chinese Government. These are known as 223 “BRI-like” or “DSR-like” projects and are included in the IISS China Connects database (IISS224 China Connects, n.d.). 225 226 Findings 227 228 According to the CAIED, 155 China’s AI-export projects were identified in 64 countries,229 classified as either AI applications or AI infrastructure (i.e., critical infrastructure for AI 230 applications, or as a tool that enabled AI applications to be adapted in the future) (Bouey et al.,231 2023). Upon directly searching for country reports on the CAIED, as Table 1 summarises, three 232 Southeast Asian countries received Chinese government-supported development finance233 projects that utilised or enabled AI technology between 2006 and 2017. These countries were234 Cambodia, Indonesia, and Laos. In Cambodia, in 2014, the Chinese Government granted RMB 235 20 million (i.e., US$2.76 million) to Cambodia’s Ministry of Public Security for a closed-circuit236 television (CCTV) Surveillance Project. Also, in 2015, China delivered 200 ultrasound medical 237 equipment units to the Cambodian Government.238 239 In Indonesia, in 2011, the Chinese Government provided a US$5.3 million grant for the240 Establishment and Operation of the Indonesia Maritime Surveillance Satellite System. In 2014,241 China signed an agreement to send remote sensing data to Indonesian remote sensing satellite 242 ground stations to help the country update the stations and improve its maritime law243 enforcement and disaster prevention mitigation. In 2015, in partnership with China's Huawei, 244 Indonesia revealed the ‘Safe City’ model site in Bandung. The model site includes monitoring245 points, transmission networks, data platforms, resource management systems, broadband246 clusters, and emergency command centres. 247 248 In Laos, in 2006, the China-Laos preferential loan framework agreement for Phase 1 of the 249 National E-Government Project was signed. In the following year, China Eximbank and the Lao250 Government signed an RMB 280 million (i.e., US$38.7 million) government concessional loan 251 agreement for Phase 1 of the National E-Government Project. In 2012, Huawei donated a cloud 252 computing laboratory to the National University of Laos in Vientiane. During the same donation253 ceremony, China also donated an electric car to the university. Then, in 2017, the Chinese254 (This version is accepted for publication at Frontiers in Political Science.) 6 Government signed the National Seismic Monitoring Network Project and Earthquake Data 255 Centre Project. 256 257 Table 1: AI Import Countries in Southeast Asia 258 ( Had the countries been the beneficiaries of Chinese Government-supported development 259 finance projects that utilised or enabled AI technology between 2006 and 2017? )260 Countries: AI Projects/Descriptions Cambodia Donation of Ultrasound Medical Equipment On June 15, 2015, China delivered 200 ultrasound medical equipment to the Cambodian Government. CCTV Surveillance Project On August 13, 2014, the Chinese Government granted RMB 20 million to Cambodia's Ministry of Public Security for a CCTV Surveillance Project. Indonesia Safe City Model Site Project Partnered with China's Huawei, Indonesia's first ‘Safe City’ model site was deployed in the main venue and surrounding areas of the event. The model site includes monitoring points, transmission networks, data platforms, resource management systems, broadband clusters, and emergency command centres. On April 11, 2015, the ‘Safe City’ model site in Bandung was unveiled. Maritime Surveillance Satellite System Project In 2011, the Chinese Government provided a US$5.3 million grant for the Establishment and Operation of the Indonesia Maritime Surveillance Satellite System. Remote Sensing Data for Maritime Security Project On 6 October 2014, China signed an agreement to send remote sensing data to Indonesia remote sensing satellite ground stations to help the country update the stations and improve its maritime law enforcement and disaster prevention mitigation. Laos Donation of Cloud Computing Laboratory On November 30, 2012, Huawei donated a cloud computing laboratory to the National University of Laos in Vientiane. During the same donation ceremony, China also donated an electric car to the university. Phase 1 of National E-Government Project On November 20, 2006, the Chinese Government and Lao Government signed a preferential loan framework agreement for (This version is accepted for publication at Frontiers in Political Science.) 7 Phase 1 of the National E-Government Project. Then, in 2007, China Eximbank and the Lao Government signed an RMB 280 million government concessional loan agreement for Phase 1 of the National E-Government Project. On September 18, 2008, the Chinese Government and Lao Government signed an RMB 546 million preferential loan framework agreement for Phase 2 of the National E-Government Project. However, it is unclear if and when an actual loan agreement for Phase 2 was finalised. This issue merits further investigation. National Seismic Monitoring Network Project and Earthquake Data Centre Project On Aug 11, 2017, the Chinese Government signed the National Seismic Monitoring Network Project and Earthquake Data Centre Project. After the project is completed, Laos will have the ability to report earthquakes of magnitude 3.0 or above in most parts of the country, effectively improve the seismic monitoring capacity of Laos, improve the level of public service of the Lao Government, and provide a strong guarantee for Laos' economic and social development. Source: RAND261 262 I extract relevant data from the CAIED, IISS China Connects and World Bank Open Data263 databases and from the Index of Economic Freedom ranking and transport them into a newly 264 created panel dataset on STATA 18.2. The panel dataset contains observations from the same265 units (i.e. countries) over multiple time periods (i.e. years). Table 2 shows the description of 266 variable definitions and sources. Here I aim to build linear regression models (without and with267 using lagged explanatory variables) for panel data to examine the average effect of (1) AI export268 from China, (2) mobile cellular subscriptions (per 100 people) and (3) individuals using the 269 Internet (% of the population), (4) real GDP per capita (constant 2015 US$), (5) the Index of270 Economic Freedom scores on DSR activities across all involved Southeast Asian countries. 271 Appendix 1 lists out all the relevant variables of the panel dataset. We can see that country and272 AI export from China are time-invariant variables. Here country refers to the 10 Southeast Asian273 countries. AI export from China (i.e. the variable AI_export i) means whether each country had 274 any AI import trade from China between 2006 and 2017 (1 = yes; 0 = no). The variable DSR i,t 275 refers to the number of planned, implemented, or completed DSR projects a Southeast Asian 276 country undertook in any given year, between 2018 and 2020. The variables mobile i,t , Internet i,t , 277 real_GDP_per_capita i,t and IEF i,t refer to mobile cellular subscriptions (per 100 people), 278 individuals using the Internet (% of the population), real GDP per capita (constant 2015 US$)279 and the Index of Economic Freedom scores respectively.280 281 Table 2: Description of Variable Definitions and Sources282 Variable Description Source Countryi Southeast Asian country name (Group Identifier) By author Yeart Year of data collection (Time Identifier) By author DSRi,t How many DSR projects were planned, IISS China Connects (This version is accepted for publication at Frontiers in Political Science.) 8 implemented, or completed by any given Southeast Asian country in any given year? (Dependent Variable) AI_exporti How many AI or AI-enabling projects were imported from China by any given Southeast Asian country in any given year? (TimeInvariant Explanatory Variable) CAIED mobilei,t What was the number of mobile cellular subscriptions (per 100 people) in any given Southeast Asian country in any given year? World Bank Open Data Interneti,t How many individuals were using the Internet (as % of population) in any given Southeast Asian country in any given year? World Bank Open Data real_GDP_per_capitai,t What was the real GDP per capita (constant 2015 US$) in any given Southeast Asian country in any given year? World Bank Open Data IEFi,t What was the Index of Economic Freedom score in any given Southeast Asian country in any given year? Index of Economic Freedom 283 Before building linear regression models for panel data, I would need to examine whether there284 is any collinearity issue among the independent variables. Table 3 shows the variance inflation285 factor (VIF) values of all independent variables that are used for building linear regression 286 models for panel data. The VIF values in the panel dataset indicate that there is no significant287 multicollinearity among the independent variables, as all values are between 1 and 5. The VIF 288 value tells us how much the variance of an estimated regression coefficient is inflated due to its289 linear relationship with the other independent variables. A VIF value of 1 means there is no290 correlation between that variable and the others; and a VIF value that is lower than 5 poses no 291 concern.292 293 Table 3: Variance Inflation Factor (VIF)294 Variable VIF 1/VIF AI_exporti 2.11 0.47 mobilei,t 1.99 0.50 Interneti,t 2.35 0.43 real_GDP_per_capitai,t 3.68 0.27 IEFi,t 3.62 0.28 Mean VIF 2.75 295 Once I run the VIF test, there is no meaningful need to conduct the Spearman rank correlations.296 However, I decide to develop and present Table 4 as an additional source that indicates no297 concerning multicolliearnity issue is detected. Table 4 shows that correlations between AI export 298 from China and all other independent variables that are used for regression analysis (in Tables299 (This version is accepted for publication at Frontiers in Political Science.) 15 https://carnegieendowment.org/posts/2023/12/how-has-chinas-belt-and-road-initiative-impacted559 southeast-asian-countries?lang=en (Accessed February 15, 2025). 560 IISS China Connects (n.d.). China Connects: Methodology. Retrieved from561 https://chinaconnects.iiss.org/methodology.aspx (Accessed March 5, 2025). 562 Chung, C. (2023). China’s Digital Silk Road. East Asian Policy , 15(2), 123-37.563 Dayant, A. & Stanhope, G. (2024). Mind the Gap: Ambition Versus Delivery in China’s 564 BRI Megaprojects in Southeast Asia . Sydney: Lowy Institute. 565 Feigenbaum, E., Ian, C., & Noor, E. (2024). China through A Southeast Asian Lens.566 Carnegie Endowment for International Peace. 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Retrieved from https://www.iseas.edu.sg/wp591 content/uploads/2024/01/ISEAS_Perspective_2024_1.pdf (Accessed February 28, 2025).592 593 Appendix594 595 Appendix 1: List of Relevant Variables from the CAIED and IISS China Connects 596 Database597 Countryi Yeart DSRi,t AI_exporti mobilei,t Interneti,t real_GDP_per_capitai,t IEFi,t Brunei 2018 0 0 129.27 95.0 29200.20 64.2 Brunei 2019 1 0 129.85 95.0 29996.20 65.1 Brunei 2020 1 0 119.93 96.3 30016.00 66.6 Cambodia 2018 2 1 119.31 . 1884.28 58.7 Cambodia 2019 6 1 129.96 52.3 2008.32 57.8 Cambodia 2020 0 1 126.08 53.7 1908.63 57.3 Indonesia 2018 0 1 118.33 39.9 3701.32 64.2 Indonesia 2019 8 1 125.24 47.7 3850.90 65.8 Indonesia 2020 5 1 129.40 53.7 3739.45 67.2 Laos 2018 0 1 51.38 36.3 2459.98 53.6 (This version is accepted for publication at Frontiers in Political Science.) 16 Laos 2019 0 1 64.07 47.0 2554.96 57.4 Laos 2020 0 1 63.11 54.0 2529.75 55.5 Malaysia 2018 6 0 128.87 81.2 10610.20 74.5 Malaysia 2019 9 0 133.37 84.2 10903.00 74.0 Malaysia 2020 0 0 129.02 89.6 10171.50 74.7 Myanmar 2018 3 0 116.97 29.4 1361.57 53.9 Myanmar 2019 5 0 155.67 36.5 1440.99 53.6 Myanmar 2020 1 0 148.16 45.4 1301.31 54.0 Philippines 2018 5 0 122.96 44.1 3410.94 65.0 Philippines 2019 4 0 151.01 43.0 3575.88 63.8 Philippines 2020 1 0 133.46 53.8 3198.67 64.5 Singapore 2018 2 0 152.07 88.2 61250.40 88.8 Singapore 2019 9 0 159.35 88.9 61345.50 89.4 Singapore 2020 6 0 150.26 92.0 59189.70 89.4 Thailand 2018 5 0 175.27 56.8 6314.20 67.1 Thailand 2019 8 0 181.22 66.7 6434.54 68.3 Thailand 2020 5 0 162.33 77.8 6035.19 69.4 Vietnam 2018 2 0 146.14 69.8 3048.28 53.1 Vietnam 2019 2 0 140.19 68.7 3241.08 55.3 Vietnam 2020 4 0 141.66 70.3 3303.17 58.8 598