The Impact of Digital Transformation on the Global Value Chain Position of the Guangdong–Hong Kong–Macao Greater Bay Area

Li, Xiumin; Tan, Minshan

doi:10.3390/systems12060223

Open AccessArticle

The Impact of Digital Transformation on the Global Value Chain Position of the Guangdong–Hong Kong–Macao Greater Bay Area

by

Xiumin Li

^* and

Minshan Tan

School of Economics, Guangdong University of Technology, Guangzhou 510520, China

^*

Author to whom correspondence should be addressed.

Systems 2024, 12(6), 223; https://doi.org/10.3390/systems12060223

Submission received: 22 May 2024 / Revised: 8 June 2024 / Accepted: 17 June 2024 / Published: 20 June 2024

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Abstract

:

When considering the economic growth of the Greater Bay Area, digital transformation stands out as a crucial catalyst. Given its significance, it is imperative to delve into both the theoretical and empirical aspects of how digital transformation affects the region’s position in the global value chain. Theoretical hypotheses are put forward regarding the impact and mechanisms of digital transformation on the global value chain position of the Greater Bay Area based on a global value chain perspective. A three-tier nested input–output table that incorporates the Greater Bay Area is constructed, and trade decomposition and global value chain position measurement models specific to the area are developed for analyzing its current state in terms of global value chain position. Empirical testing was conducted to examine how digital transformation impacts this position. Digital transformation will significantly enhance the position of the Greater Bay Area in the division of global value chains, mainly through enhancing technological innovation transformation capabilities and value added by exports from the Greater Bay Area. Digital transformation within the Greater Bay Area has had a more substantial positive impact than the digital transformation in other provinces in China and digital transformation from foreign sources. In terms of city heterogeneity, it is evident that the global value chain from Hong Kong and Macao has experienced a more significant impact from digital transformation compared to cities in the Pearl River Delta. Therefore, the Greater Bay Area should increase the use of both digital transformation and differentiated use of digital transformation. PRD cities should actively learn from Hong Kong and Macao’s forms of digital construction and promote the Greater Bay Area’s global value chain status.

Keywords:

Guangdong–Hong Kong–Macao Greater Bay Area; digital transformation; global value chain position; technological innovation transformation capabilities; value added by exports from the greater bay area

1. Introduction

As China’s integration into the global trading landscape has deepened, it has emerged as a pivotal trading nation. According to data from 2000 to 2016, the domestic value-added proportion of China’s exports witnessed a substantial increase, rising from 65% to 83% [1]. However, despite the magnitude of China’s exports, a significant chunk of this domestic value addition is derived from the backend of the global value chain, highlighting a high reliance on foreign technology and markets, primarily in the realm of processing and assembly. In the midst of the rapid evolution of new economies worldwide, China is poised to face a critical juncture. The labor dividend, once a cornerstone of China’s economic growth, is being gradually eroded by the advent of the new economy, transforming the demographic dividend into a challenge. Prolonged reliance on processing and assembly work for value-added gains threatens to limit China’s profit margins. Furthermore, China has encountered the formidable challenge of “neck strangulation” in core technologies, severely impeding the progress of implementing emerging technologies and confining them to the lower rungs of the global value chain. Consequently, the pressing challenge of broadening the scope of value-added acquisition and breaking free from these low-end constraints has emerged as a pivotal priority for China. It is imperative for China to expand its profit margins in international trade, transcend these limitations, and ascend to a more prominent position in the global value chain as a fundamental strategic imperative.

Currently, the digital economy is exerting increasingly profound effects on the global value chain [2,3]. The diverse products and services fostered by the digital economy are injecting significant momentum into China’s participation in the global value chain. The digital economy is influencing the global value chain in numerous ways, including the digitization of supply chains [4], intelligent manufacturing and Industry 4.0 [5], sophisticated forms of digital innovation [6], and digital platforms [7,8,9], all of which are driving the transformation and upgrading China’s position in the global value chain. However, the key to promoting China’s integration into the global value chain is to attach importance to the construction of the global value chain in the Guangdong–Hong Kong–Macao Greater Bay Area.

In 2022, the Guangdong–Hong Kong–Macao Greater Bay Area boasted a land area of 55,914 square kilometers and a population of 86.44 million, contributing to a gross domestic product of 13 trillion yuan. With less than 0.58% of China’s land area and 6.1% of its population, the Greater Bay Area generated 10.8% of the country’s total economic output, ranking 12th among the world’s economies, exhibiting remarkable economic growth and serving as a crucial growth pole for both the nation and the world. Concurrently, the Greater Bay Area stands as a leading digital economy cluster in China. In 2022, the added value of Guangdong’s digital economy reached 6.4 trillion yuan, accounting for 12.8% of the national total and contributing 49.7% of the Greater Bay Area’s GDP. The digital economy has become a significant driver for high-quality economic development of the Greater Bay Area. Furthermore, the Greater Bay Area achieved impressive trade in goods in 2022, totaling 16.19 trillion yuan. This accounted for a significant proportion (38.5%) of China’s overall trade, positioning the region not only as a vital growth pole and a forerunning digital economy cluster, but also as a pivotal center for China’s foreign trade activities. This underscores the indispensable role the Greater Bay Area plays in the global economic landscape. However, there is a scarcity of literature examining the Greater Bay Area from the perspective of international trade, with most studies focusing on urban spatial networks [10,11]. Given the Greater Bay Area’s status as a major hub for China’s foreign trade and its close international ties, an international trade-oriented approach offers more valuable insights for China and the world. Therefore, this paper aims to investigate the impacts and mechanisms of digital transformation on the Greater Bay Area’s position in the GVC. This research not only contributes to the development of the Greater Bay Area, but also facilitates the long-term growth of China’s economy, serving as a significant model for the world.

Studies on the impact of digital transformation on the global value chain are extensive, and a large number of studies have shown that digital transformation is conducive to promoting the development of the global value chain [11,12,13,14,15,16,17,18,19,20]. Most existing studies are based on data at the national, industry, or firm levels. For instance, Gniniguè M et al. [18] focused on the national level, examining the positive impact of information and communication technologies (ICT) on the participation of developing countries in the global value chain. Kan D et al. [19] conducted research at the industry level, analyzing the impact of the digital economy on global value chain positions within the service industry, as well as its underlying mechanisms. From a firm-level perspective, Szalavetz A [20], based on interviews with ten Hungarian digital automotive technology suppliers, argued that digital transformation can, in principle, create opportunities for the firm economy to drive the entrepreneurial integration of economic players in the global automotive value chain. During the comprehensive literature review, a lack of research was identified in studies that distinctly categorize their subjects as specific geographical regions. Given the paramount importance of the Greater Bay Area as a vibrant growth hub for China and the global economy, it is imperative to scrutinize the ramifications of the digital economy on the region’s integration into global value chains.

As for the impact mechanism of the digital economy on the global value chain, currently, scholars mainly use trade cost [21], domestic export added value [21,22], export scale of high-tech products [22], productivity [23], resource allocation efficiency [24], technological innovation capability [24], knowledge spillover [17] and other mechanisms. In this study, we examine the intermediary mechanisms through which the digital economy influences global value chains.

In assessing the global value chain position, researchers employ six key methods: the vertical specialization index [25], export product price index [26], export technological complexity [27,28], global value chain position index [29], upstream and downstream indices [30,31,32], and global value chain average production stage number and production length index [33]. Among these methods, the global value chain status index is preferred due to its seamless integration with trade decomposition models and its ability to offer a detailed breakdown of sources of value addition. However, the successful implementation of this approach hinges on the availability of well-constructed trade decomposition models. In academia, two prominent methodologies for trade decomposition modeling stand out: the KPWW method, introduced by Koopman et al. [29], and the WWZ method proposed by Wang et al. [34], with the latter representing an innovative enhancement of the former. While these methods excel in international contexts, they fall short in addressing domestic regions comprehensively. Therefore, this paper builds a trade decomposition model applicable to the Guangdong–Hong Kong–Macao Greater Bay Area on the basis of WWZ and uses Koopman’s idea to build a global value chain position measurement model applicable to the Greater Bay Area.

In summary, existing research has produced significant findings regarding the impact of the digital economy on the global value chain and the measurement of global value chain position. However, academic research in this area remains broad, with a lack of in-depth studies on specific regions. Thus, this paper aims to compile a three-tier nested city–province–world input–output table that encompasses 11 cities in the Greater Bay Area. It will establish trade decomposition models and global value chain position models tailored for studying the Greater Bay Area to analyze the actual state of the global value chain position in the region. Building on this analysis, this paper will delve into the impact and mechanisms of digital transformation on the global value chain position in the Greater Bay Area.

The main innovations of this study are as follows: (1) Theoretical Innovation: This paper delves into the mechanisms underlying how digital transformation affects the global value chain position of the Greater Bay Area. It examines the interplay between digital transformation, technological innovation transformation capability, value added by exports from the Greater Bay Area, and the region’s position in the global value chain. This theoretical framework offers a more practical path to improvement. (2) Data Innovation: This study utilizes multiple input–output databases, including the EORA, ADB, and CEADs databases, to compile a comprehensive input–output table that covers cities, provinces, and the world, specifically including the Greater Bay Area. This approach addresses the issue of missing data for Hong Kong and Macao, providing a solid data foundation for future research. (3) Framework Innovation: Based on previous work, this study establishes a trade decomposition model tailored for the Guangdong–Hong Kong–Macao Greater Bay Area. This model integrates the region into a unified trade framework, offering a novel analytical framework for subsequent studies. (4) Empirical Innovation: In contrast to most existing empirical studies that focus narrowly on Pearl River Delta cities or specific urban functions, this paper takes a broader perspective by examining the overall situation of the Greater Bay Area from the global value chain angle, incorporating the digital economy and considering Hong Kong and Macao. The conclusions drawn are more aligned with the overall development strategy of the Greater Bay Area, timely, and possess strong practical relevance. (5) It is of great significance at home and abroad. This paper uses a multi-regional input–output table to analyze the impact of digital transformation on the global value chain position from the city level, and provides a reference for similar studies both at home and abroad when analyzing the impact mechanisms of urban digital transformation on the global value chain position and the decomposition method of the urban global value chain.

Restricted by data availability, the input–output table compiled in this paper is only delineated into 19 distinct sectors. Consequently, when analyzing the impact of digital transformation on the global value chain position, we just took these 19 sectors of the 11 cities in the Greater Bay Area as an example.

The remaining sections of this paper are organized as follows: Section 2 presents the theoretical mechanisms and assumptions. Section 3 covers data preparation, establishment of the calculation model, and application. Section 4 outlines the empirical model setting, variable selection, and data description. Section 5 analyzes the empirical test results and verification of mechanisms. Finally, Section 6 provides the conclusion and recommendations.

2. Theoretical Mechanism

2.1. The Impact of Digital Transformation on the Global Value Chain Position of the Guangdong–Hong Kong–Macao Greater Bay Area

Digital transformation provides a crucial pathway for economies and regions that are catching up to enhance their capacity to benefit from the global value chain and break free from the low-end lock. Similarly, digital transformation provides a crucial pathway for the Guangdong–Hong Kong–Macao Greater Bay Area to enhance its capacity to benefit from the global value chain and break free from the low-end lock. By leveraging digital transformation, the Greater Bay Area can elevate its profit potential, overcome challenges associated with being confined to the low end, and improve its position within the global value chain. The benefits of digital transformation include transcending time, geography, and space constraints, accelerating information dissemination, and fostering connectivity between the Greater Bay Area and international markets. Through the use of digital technologies such as the internet, big data, and artificial intelligence, the Greater Bay Area can better align its products with market demand, thereby enhancing product competitiveness. Furthermore, digital transformation facilitates closer collaboration between cities, fosters inter-industry connections, promotes regional integration within the Guangdong–Hong Kong–Macao Greater Bay Area, encourages cross-city cooperation, extends the value chain of the region, enhances value addition capabilities, and advances the positions of various cities in the global value chain. Additionally, active involvement in digital transformation enables the Greater Bay Area to seize technological leadership, achieve independent and controllable digital technologies, reduce dependence on imported goods, and effectively enhance its position in the global value chain. These aspects underscore the positive impact of digital transformation on elevating the global value chain position of the Guangdong–Hong Kong–Macao Greater Bay Area. Building upon this, this paper proposes Hypothesis 1:

Hypothesis 1:

Digital transformation enhances the global value chain position of the Guangdong–Hong Kong–Macao Greater Bay Area.

2.2. The Mechanism of Digital Transformation on the Global Value Chain Position of the Guangdong–Hong Kong–Macao Greater Bay Area

2.2.1. Technology Innovation Transformation Capability

Digital transformation has the potential to significantly bolster technological innovation transformation capabilities and elevate a country or region’s position in the global value chain by enhancing its innovative prowess and fostering the emergence of new digital technology industries. The Guangdong–Hong Kong–Macao Greater Bay Area stands to benefit from this transformation in a similar fashion. Digital transformation in the Greater Bay Area will enhance the innovation capability of its cities and foster the development of digital technology industries by strengthening technological innovation transformation capabilities there. For a more detailed analysis, firstly, digital transformation enhances the innovation capability of cities in the Greater Bay Area. Digital transformation can promote collaborative innovation within the Greater Bay Area, and through digital technology platforms and data sharing, different cities can more easily collaborate on innovative activities. Such collaborative innovation can lead to more opportunities for technological exchange and cooperation and enhance the innovation capabilities and statuses of Greater Bay Area cities in the global value chain. Moreover, digital transformation transforms marketing methods by improving technological innovation transformation capabilities, improving data analysis and recommendation personalization, and enhancing the understanding of market demands and consumer behaviors, enabling Greater Bay Area enterprises to accurately position their target markets, provide products and services that meet market demands, and win a greater market share in the global value chain.

Secondly, digital transformation catalyzes the emergence of new digital technology industries and propels the commercialization of these sectors. The advent of digital transformation has spurred the development of emerging fields like data analytics, artificial intelligence, and the Internet of Things, fostering new digital technology industries and introducing fresh growth engines for the Guangdong–Hong Kong–Macao Greater Bay Area. This development has led to the proliferation of higher value-added industries, ultimately boosting the Greater Bay Area’s position in the global value chain. The commercial application of digital technology industries accelerates the digital upgrade of the global value chain, promotes the global circulation of digital products and services, and contributes to the growth of global trade. Additionally, the Greater Bay Area’s cities have strengthened their cooperation regarding the R&D and innovation of digital technology with both domestic and international partners, further enhancing the competitiveness of these cities. However, in the digital transformation of the Greater Bay Area, it is imperative to address the digital divides between cities within the region, the Greater Bay Area, and other countries and regions globally. To narrow or mitigate this information gap, various regions should adopt tailored digital transformation solutions [35] or strengthen local government intervention or supervision [36]. This approach will facilitate the progress of digital transformation and ensure equitable benefits across the Greater Bay Area. Therefore, this article proposes Hypothesis 2:

Hypothesis 2:

Digital transformation enhances the global value chain position of the Guangdong–Hong Kong–Macao Greater Bay Area by improving technology innovation transformation capabilities.

2.2.2. Value Addition of Exports from the Greater Bay Area

The metric of value added by exports is crucial for gauging the economic benefits a region or country derives from participating in international trade. Digital transformation, by leveraging data assets and fostering a “digital + industry” value chain, plays a significant role in enhancing the added value of exports and, consequently, elevating a region’s position in the global value chain. The same is true for the Guangdong–Hong Kong–Macao Greater Bay Area. Amid the ongoing process of digital transformation, cities within the Greater Bay Area can harness advanced digital technologies such as big data, artificial intelligence, and 5G to aggregate and transform vast quantities of data into valuable assets. This infusion of digital capabilities injects new vitality into urban industrial production, fosters the creation of greater value, propels upgrades in industrial structures, and augments the value added to the region’s exports. Ultimately, these efforts contribute to enhancing the global value chain position of the Guangdong–Hong Kong–Macao Greater Bay Area. These measures present diverse opportunities for augmenting the value added by the Greater Bay Area and create an expanded scope for the region to elevate its value addition within the global value chain. In doing so, the Greater Bay Area can proactively engage in the global value chain, ultimately bolstering its position within this critical economic framework. In light of these insights, this article proposes Hypothesis 3:

Hypothesis 3:

Digital transformation enhances the global value chain position of the Guangdong–Hong Kong–Macao Greater Bay Area by increasing the value added by exports from the Greater Bay Area.

The theoretical diagram of the direct and indirect impacts of digital transformation on the global value position of the Guangdong-Hong Kong-Macao Greater Bay Area is shown in Figure 1.

3. Data Preparation, Calculation Model Establishment, and Application

3.1. Data Preparation: Compilation of Three-Layer Nested Input–Output Tables for Guangdong–Hong Kong–Macao Greater Bay Area

Input–output tables serve as the foundational framework for assessing the global value chain position. However, current domestic and international input–output tables do not encompass the 11 cities within the Guangdong–Hong Kong–Macao Greater Bay Area, hindering their direct applicability in evaluating the region’s global value chain position. To address this gap, this article integrates insights from the EORA, ADB, and CEADs databases to construct a comprehensive three-tier nested input–output table. This table incorporates the nine cities in the Pearl River Delta of China, Hong Kong, Macao, other cities in Guangdong Province, 30 additional provinces in Mainland China, Taiwan Province, 23 foreign countries and regions1, and the rest of the world (ROW). At the first tier, a global multi-regional input–output table was established, encompassing China (segmented into Mainland China, Hong Kong, Macao, and Taiwan) along with China’s 23 primary trading partner countries and other global regions. The second tier comprises the multi-regional input–output table for China, integrating the 31 provinces of Mainland China within the global input–output structure. The third tier represents the city input–output table, where Guangdong Province, within the second tier of the China multi-regional input–output table, is disaggregated into the Pearl River Delta and other cities in Guangdong Province. Countries, regions, and cities were further delineated into 19 distinct sectors2 within this comprehensive nested input–output framework.

The specific compilation method outlined for constructing the input–output table involved a detailed process: (1) Standardization and Sector Convergence: Initially, the units were standardized to ten thousand US dollars. Subsequently, we cross-referenced and amalgamated various sectors. These sectors originated from multiple sources, including the world input–output tables from the EORA and ADB databases, as well as the China inter-provincial and urban input–output tables from the CEADs database. Through this process, we consolidated them into 19 distinct sectors. Furthermore, the final demand components were consolidated into categories such as household consumption, government consumption, gross fixed capital formation, and changes in inventories. (2) Segmentation of Input–Output Data for China: The input–output data for China were segmented by leveraging the direct consumption coefficients of Mainland China and Macao from the EORA input–output table. This process involved dividing the China data within the ADB input–output table into Mainland China and Macao segments to generate a new input–output table inclusive of Macao. The balanced table was achieved using the RAS method. (3) Embed China’s Inter-Provincial Input–Output Table into a New Global Framework: The inter-provincial input–output table of China from the CEADs database was integrated into a fresh world input–output table incorporating Macao. Following this integration, the table was balanced utilizing the RAS method to yield a comprehensive provincial-world input–output table. (4) Embed of the Nine Cities in the Pearl River Delta: The adjustment involved incorporating the nine cities of the Pearl River Delta and other cities in Guangdong Province into the urban input–output table sourced from the CEADs database. This process required merging the outflows from cities in Guangdong Province to other mainland cities and the inflows from other mainland cities to cities in Guangdong Province into total outflows and inflows, respectively. By utilizing connectivity coefficients between Guangdong Province and Chinese other provinces and the world, the nine cities of the Pearl River Delta and other cities in Guangdong Province were embedded into the provincial-world input–output table encompassing 31 provinces in Mainland China. The final balancing was achieved through the RAS method, resulting in a three-tier nested urban-provincial-world input–output table encompassing the eleven cities of the Guangdong–Hong Kong–Macao Greater Bay Area. The specific structure of the newly compiled three-tier nested input–output table is shown in Table 1. The detailed compilation process of the three-layer nested input–output table is shown in the Appendix A. This table will provide the data foundation for calculating the digital transformation and the global value chain position of the Guangdong–Hong Kong–Macao Greater Bay Area.

3.2. Establishment of the Calculation Model

3.2.1. Prerequisite for Establishing the Calculation Model: Establishment of the Trade Decomposition Model

Building upon the structure of the three-tier nested input–output table encompassing the Guangdong–Hong Kong–Macao Greater Bay Area (as depicted in Table 1) established earlier, this paper, aligning with the outflow characteristics (exports) from cities within the Greater Bay Area, adopted the trade decomposition model put forth by Wang et al. [34] and decomposed the total outflows (exports)

E^{r S^{'}}

from city

r

within the Greater Bay Area as follows:

E^{r *} = E^{r s} + E^{r S} + E^{r S^{'}},

(1)

where

r

represents cities within the Greater Bay Area,

s

denotes cities within the Greater Bay Area excluding

r

,

S

stands for domestic provinces, and

S^{'}

represents other countries in the world.

E^{r s}

denotes the outflows from city

r

within the Greater Bay Area to city

s

(excluding city

r

),

E^{r S}

represents the outflows from city

r

within the Greater Bay Area to domestic province

S

, and

E^{r S^{'}}

indicates the exports from city

r

within the Greater Bay Area to country

S^{'}

.

This paper assumes that within the Guangdong–Hong Kong–Macao Greater Bay Area, there are cities

r

,

s

, and

t

. Additionally, cities within the Greater Bay Area engage in trade with province

S

, province

T

, country

S^{'}

, and country

T^{'}

. This paper decomposes the trade outflows (exports) of cities in the Greater Bay Area into outflows to cities within the Greater Bay Area, outflows to other domestic provinces, and exports to foreign countries.

The first trade decomposition model is for the outflows (exports) from cities in the Guangdong–Hong Kong–Macao Greater Bay Area to cities within the Greater Bay Area, denoted as

E^{r s}

:

\begin{matrix} E^{r s} = {(V^{r} B^{r r})}^{T} # {Y^{r s}}_{1} + {(V^{r} L^{r r})}^{T} # (A^{r s} B^{s s} {Y^{s s}}_{2}) + {(V^{r} L^{r r})}^{T} # (A^{r s} B^{s s} {Y^{s t}}_{3} + A^{r s} B^{s s} {Y^{s P}}_{4} + A^{r s} B^{s s} {Y^{s W}}_{5} + A^{r s} B^{s t} \sum_{i \neq r, i \in C} {Y^{t i}}_{6} \\ + A^{r s} B^{s t} {Y^{t P}}_{7} + A^{r s} B^{s t} {Y^{t W}}_{8} + A^{r s} B^{s S} \sum_{i \neq r, i \in C} {Y^{S i}}_{9} + A^{r s} B^{s S} {Y^{S P}}_{10} + A^{r s} B^{s S} {Y^{S W}}_{11} + A^{r s} B^{s S^{'}} \sum_{i \neq r, i \in C} {Y^{S^{'} i}}_{12} + A^{r s} B^{s S^{'}} {Y^{S^{'} P}}_{13} + A^{r s} B^{s S^{'}} {Y^{S^{'} W}}_{14}) \\ + {(V^{r} L^{r r})}^{T} # (A^{r s} B^{s s} {Y^{s r}}_{15} + A^{r s} B^{s t} {Y^{t r}}_{16} + A^{r s} B^{s S} {Y^{S r}}_{17} + A^{r s} B^{s S^{'}} {Y^{S^{'} r}}_{18} + A^{r s} B^{s r} {Y^{r r}}_{19}) \\ + {(V^{r} L^{r r})}^{T} # (A^{r s} B^{s r} \sum_{i \neq r, i \in C} {Y^{r i}}_{20} + A^{r s} B^{s r} {Y^{r P}}_{21} + A^{r s} B^{s r} {Y^{r W}}_{22}) + {(V^{r} B^{r r} - V^{r} L^{r r})}^{T} # {(A^{s r} X^{r})}_{23} \\ + {(\sum_{u \neq r, u \in C} V^{u} B^{u r})}^{T} # {Y^{r s}}_{24} + (\sum_{u \neq r, u \in C} V^{u} B^{u r})^{T} # (A^{r s} L^{s s} {Y^{s s}}_{25} + A^{r s} L^{s s} \sum_{i \neq s, i \in C} {E^{s i}}_{26} + A^{r s} L^{s s} {E^{s P}}_{27} + A^{r s} L^{s s} {E^{s W}}_{28}) \\ + {(\sum_{K \in P} V^{K} B^{K r})}^{T} # {Y^{r s}}_{29} + {(\sum_{K \in P} V^{K} B^{K r})}^{T} # (A^{r s} L^{s s} {Y^{s s}}_{30} + A^{r s} L^{s s} \sum_{i \neq s, i \in C} {E^{s i}}_{31} + A^{r s} L^{s s} {E^{s P}}_{32} + A^{r s} L^{s s} {E^{s W}}_{33}) \\ + {(\sum_{A \in W} V^{A} B^{A r})}^{T} # {Y^{r s}}_{34} + {(\sum_{A \in W} V^{A} B^{A r})}^{T} # (A^{r s} L^{s s} {Y^{s s}}_{35} + A^{r s} L^{s s} \sum_{i \neq s, i \in C} {E^{s i}}_{36} + A^{r s} L^{s s} {E^{s P}}_{37} + A^{r s} L^{s s} {E^{s W}}_{38}) \end{matrix}

(2)

By decomposing the outflows (exports)

E^{r s}

from city

r

to city

s

within the Greater Bay Area, a total of 38 components can be identified, among which:

The first category (items 1–2) refers to the value added achieved by the direct outflow of value added from city

r

to city

s

in the Greater Bay Area. The second category (items 3–14) refers to the city-added value indirectly realized by city

r

in the Greater Bay Area through city

s

in the form of intermediate products. Items 3–5 refer to the added value realized by the city

s

from the inflow of intermediate goods to the production of final goods and then outflow or export. Among them, item 5 represents the urban value added indirectly realized through international circulation within the Greater Bay Area. Items 6–14 represent the value added realized by city

s

through the processing of incoming intermediate products, which are then either outflowed or exported in the form of intermediate products. Among them, items 8 and 11–14 are city value additions achieved through international circulation. The sum of items 5, 8, and 11–14 is denoted as

i v w

. The third category (items 15–19) represents the portion of added value that is absorbed by city

s

within the Greater Bay Area that then flows back to city

r

. Item 18 represents the value addition returned through international circulation, denoted as

v r w

. The fourth category (items 20–23) is the double counting of added value. Item 22 represents the portion of double counting generated through participation in international circulation, denoted as

d c w

. The fifth category (items 24–38) is value added from outside the city. Items 24–28 represent value added from other cities in the Greater Bay Area. Among them, item 28 represents the portion of added value realized through international circulation. Items 29–33 represent value added from other provinces in the country. Among them, item 33 represents the portion of added value realized through participation in international circulation. Items 34–38 are the value added from other countries, which are feature value addition through participation in international circulation. The sum of items 28, 33, and 34–38 is denoted as

o v w

.

Similarly, the trade decomposition model of the outflow

E^{r S}

from Guangdong–Hong Kong–Macao Greater Bay Area cities to other provinces in China is obtained:

\begin{matrix} E^{r S} = {(V^{r} B^{r r})}^{T} # {Y^{r S}}_{39} + {(V^{r} L^{r r})}^{T} # (A^{r S} B^{S S} {Y^{S S}}_{40}) + {(V^{r} L^{r r})}^{T} # (A^{r S} B^{S S} {Y^{S T}}_{41} + A^{r S} B^{S S} \sum_{i \neq r, i \in C} {Y^{S i}}_{42} + A^{r S} B^{S S} {Y^{S W}}_{43} \\ + A^{r S} B^{S T} {Y^{S P}}_{44} + A^{r S} B^{S T} \sum_{i \neq r, i \in C} {Y^{T i}}_{45} + A^{r S} B^{S T} {Y^{T W}}_{46} + A^{r S} B^{S s} {Y^{s P}}_{47} + A^{r S} B^{S s} \sum_{i \neq r, i \in C} {Y^{s i}}_{48} + A^{r S} B^{S s} {Y^{s W}}_{49} + A^{r S} B^{S S^{'}} {Y^{S^{'} P}}_{50} \\ + A^{r S} B^{S S^{'}} \sum_{i \neq r, i \in C} {Y^{S^{'} i}}_{51} + A^{r S} B^{S S^{'}} {Y^{S^{'} W}}_{52}) + {(V^{r} L^{r r})}^{T} # (A^{r S} B^{S S} {Y^{S r}}_{53} + A^{r S} B^{S T} {Y^{T r}}_{54} + A^{r S} B^{S s} {Y^{s r}}_{55} + A^{r S} B^{S r} {Y^{r r}}_{56} + A^{r S} B^{S S^{'}} {Y^{S^{'} r}}_{57}) \\ + {(V^{r} L^{r r})}^{T} # (A^{r S} B^{S r} {Y^{r P}}_{58} + A^{r S} B^{S r} \sum_{i \neq r, i \in C} {Y^{r i}}_{59} + A^{r S} B^{S r} {Y^{r W}}_{60}) + {(V^{r} B^{r r} - V^{r} L^{r r})}^{T} # A^{r S} {X^{S}}_{61} \\ + {(\sum_{u \neq r, u \in C} V^{u} B^{u r})}^{T} # {Y^{r S}}_{62} + {(\sum_{u \neq r, u \in C} V^{u} B^{u r})}^{T} # {(A^{r S} L^{S S} {Y^{S S}}_{63} + A^{r S} L^{S S} {E^{S C}}_{64} + A^{r S} L^{S S} {E^{S T}}_{65} + A^{r S} L^{S S} E^{S W})}_{66} \\ + {(\sum_{K \in P} V^{K} B^{K r})}^{T} # {Y^{r S}}_{67} + {(\sum_{K \in P} V^{K} B^{K r})}^{T} # (A^{r S} L^{S S} {Y^{S S}}_{68} + A^{r S} L^{S S} {E^{S C}}_{69} + A^{r S} L^{S S} {E^{S T}}_{70} + A^{r S} L^{S S} {E^{S W}}_{71}) \\ + {(\sum_{A \in C} V^{A} B^{A r})}^{T} # {Y^{r S}}_{72} + {(\sum_{A \in C} V^{A} B^{A r})}^{T} # (A^{r S} L^{S S} {Y^{S S}}_{73} + A^{r S} L^{S S} {E^{S C}}_{74} + A^{r S} L^{S S} {E^{S T}}_{75} + A^{r S} L^{S S} {E^{S W}}_{76}) \end{matrix}

(3)

The decomposition of the outflow

E^{r S}

from city

r

in the Greater Bay Area to province

S

in China can be obtained as follows:

The first category (items 39–40) is the portion of added value directly absorbed by province

S

. The second category (items 41–52) represents the portion of urban added value indirectly realized by city

r

through the outflow of intermediate products to province

S

. Items 41–43 represent the added value portion where province

S

produces final products from the incoming intermediate products and then re-outflows or exports them, among which, item 43 represents the added value portion realized through participation in international circulation. Items 44–52 refer to the added value by the intermediate products that province

S

processes into intermediate products and then flows out or exports again. Items 46 and 49–52 represent the added value portion realized through participation in international circulation. The sum of items 43, 46, and 49–52 is denoted as

i v w^{'}

. The third category (items 53–57) represents the added value that flows back to city

r

in the form of intermediate products or final products after processing by province

S

. Among them, item 57 represents the added value returned through participation in international circulation, denoted as

v r w^{'}

. The fourth category (items 58–61) represents the double-counted portion, among which, item 60 represents the double-counted portion of added value realized through participation in international circulation (denoted as

d c w^{'}

). The fifth category (items 62–76) represents the portion of added value from other cities (provinces or countries). Items 62–66 represent the added value portions from other cities within the Greater Bay Area, among which, item 66 represents the added value portion realized through participation in international circulation. Items 67–71 represent the added value portions of other provinces within China, and item 71 represents the added value portion realized through international circulation. Items 72–76 represent the added value from other countries, where all of which feature an added value portion realized through participation in international circulation. The sum of items 66, 71, and 72–76 is denoted as

o v w^{'}

.

Likewise, the trade decomposition model for the exports

E^{r S^{'}}

from the cities in the Guangdong–Hong Kong–Macao Greater Bay Area to other countries and regions in the world can be obtained:

\begin{matrix} E^{r S^{'}} = {(V^{r} B^{r r})}^{T} # {Y^{r S^{'}}}_{77} + {(V^{r} L^{r r})}^{T} # (A^{r S^{'}} B^{S^{'} S^{'}} {Y^{S^{'} S^{'}}}_{78}) + {(V^{r} L^{r r})}^{T} # (A^{r S^{'}} B^{S^{'} S^{'}} {Y^{S^{'} T^{'}}}_{79} + A^{r S^{'}} B^{S^{'} S^{'}} {Y^{S^{'} P}}_{80} + A^{r S^{'}} B^{S^{'} S^{'}} \sum_{i \neq r, i \in C} {Y^{S^{'} i}}_{81} \\ + A^{r S^{'}} B^{S^{'} T^{'}} Y {T^{' W}}_{82} + A^{r S^{'}} B^{S^{'} T'} {Y^{T^{'} P}}_{83} + A^{r S^{'}} B^{S^{'} T^{'}} \sum_{i \neq r, i \in C} {Y^{T^{'} i}}_{84} + A^{r S^{'}} B^{S^{'} S} {Y^{S W}}_{85} + A^{r S^{'}} B^{S^{'} S} {Y^{S P}}_{86} + A^{r S^{'}} B^{S^{'} S} \sum_{i \neq r, i \in C} {Y^{S i}}_{87} \\ + A^{r S^{'}} B^{S^{'} s} {Y^{s W}}_{88} + A^{r S^{'}} B^{S^{'} s} {Y^{s P}}_{89} + A^{r S^{'}} B^{S^{'} s} \sum_{i \neq r, i \in C} {Y^{s i}}_{90}) + {(V^{r} L^{r r})}^{T} # (A^{r S^{'}} B^{S^{'} S^{'}} {Y^{S^{'} r}}_{91} + A^{r S^{'}} B^{S^{'} T^{'}} {Y^{T^{'} r}}_{92} + A^{r S^{'}} B^{S^{'} S} {Y^{S r}}_{93} + A^{r S^{'}} B^{S^{'} s} {Y^{s r}}_{94} \\ + A^{r S^{'}} B^{S^{'} r} {Y^{r r}}_{95}) + {(V^{r} L^{r r})}^{T} # (A^{r S^{'}} B^{S^{'} r} {Y^{r W}}_{96} + A^{r S^{'}} B^{S^{'} r} {Y^{r P}}_{97} + A^{r S^{'}} B^{S^{'} r} \sum_{i \neq r, i \in C} {Y^{r i}}_{98}) + {(V^{r} B^{r r} - V^{r} L^{r r})}^{T} # A^{r S^{'}} {X^{S^{'}}}_{99} \\ + {(\sum_{u \neq r, u \in C} V^{u} B^{u r})}^{T} # {Y^{r S^{'}}}_{100} + {(\sum_{u \neq r, u \in C} V^{u} B^{u r})}^{T} # (A^{r S^{'}} L^{S^{'} S^{'}} {Y^{S^{'} S^{'}}}_{101} + A^{r S^{'}} L^{S^{'} S^{'}} {E^{S^{'} T^{'}}}_{102} + A^{r S^{'}} L^{S^{'} S^{'}} {E^{S^{'} P}}_{103} + A^{r S^{'}} L^{S^{'} S^{'}} {E^{S^{'} C}}_{104}) \\ + {(\sum_{K \in P} V^{K} B^{K r})}^{T} # {Y^{r S^{'}}}_{105} + {(\sum_{K \in P} V^{K} B^{K r})}^{T} # (A^{r S^{'}} L^{S^{'} S^{'}} {Y^{S^{'} S^{'}}}_{106} + A^{r S^{'}} L^{S^{'} S^{'}} {E^{S^{'} T^{'}}}_{107} + A^{r S^{'}} L^{S^{'} S^{'}} {E^{S^{'} P}}_{108} + A^{r S^{'}} L^{S^{'} S^{'}} {E^{S^{'} C}}_{109}) \\ + {(\sum_{A \in W} V^{A} B^{A r})}^{T} # {Y^{r S^{'}}}_{110} + {(\sum_{A \in W} V^{A} B^{A r})}^{T} # (A^{r S^{'}} L^{S^{'} S^{'}} {Y^{S^{'} S^{'}}}_{111} + A^{r S^{'}} L^{S^{'} S^{'}} {E^{S^{'} T^{'}}}_{112} + A^{r S^{'}} L^{S^{'} S^{'}} {E^{S^{'} P}}_{113} + A^{r S^{'}} L^{S^{'} S^{'}} {E^{S^{'} C}}_{114}) \end{matrix}

(4)

By decomposing the outflow

E^{r S^{'}}

from city

r

to country

S^{'}

in the Greater Bay Area, we can obtain the following:

The first category (items 77–78) represents the added value directly exported from city

r

in the Greater Bay Area to country

S^{'}

. The second category (items 79–90) represents the added value indirectly realized by city

r

in the Greater Bay Area through exporting intermediate products to country

S^{'}

. Specifically, items 79–81 represent the added value portion that is generated when country

S^{'}

uses the imported intermediate products to produce final products and then exports or outflows these final products. Items 82–90 represent the added value portion that is generated when country

S^{'}

uses the imported intermediate products to produce intermediate products and then exports or outflows these intermediate products. The added value in items 79–90 is collectively referred to as

i v w^{″}

, which represents the added value realized through participating in international circulation. The third category (items 91–95) is the return part of the added value, which is the added value achieved through participation in international circulation, and is recorded as

v r w^{″}

. The fourth category (items 96–99) is the double counting part, in which items 96–98 are the double counting part caused by participation in international circulation, recorded as

d c w^{″}

. The fifth category (items 100–114) represents value added from outside the city. Items 100–104 represent added value from other cities in the Greater Bay Area, items 105–109 represent added value from other provinces in China, and items 110–114 represent added value from other countries and regions, all of which feature value addition from outside the city through participation in international circulation, which is recorded here as

o v w^{″}

.

Based on the classification and organization, the following is the part of the Guangdong–Hong Kong–Macao Greater Bay Area’s participation in the global value chain as defined in this text (Table 2):

3.2.2. Establishing the Measurement Model: Measurement Model for the Global Value Chain Position of the Guangdong–Hong Kong–Macao Greater Bay Area

The previous section has thoroughly broken down the total outflows/exports of cities within the Guangdong–Hong Kong–Macao Greater Bay Area region based on the origin of value addition, resulting in a total of 114 items. By referencing the classification provided in Table 2 regarding the Greater Bay Area’s involvement in the global value chain and incorporating the methodology proposed by Koopman et al. [29], a model for assessing the position within the global value chain that is suitable for analyzing the Greater Bay Area is formulated.

G V C_P O S = \ln (1 + \frac{D V}{O + E}) - \ln (1 + \frac{F V}{O + E})

(5)

In the provided context,

D V

represents the value addition of outflows (exports) from cities within the Guangdong–Hong Kong–Macao Greater Bay Area, while

F V

denotes the value addition of outflows (exports) from other provinces in China or other countries and regions worldwide. Additionally,

O

signifies the total outflow to the domestic market,

E

signifies the total exports to the world, and

G V C_P O S

denotes the global value chain position. A higher value for the global value chain position indicates that the cities within the Greater Bay Area are positioned relatively upstream in the global value chain, signifying greater exports of intermediate products to other cities, provinces, or countries. Conversely, a lower value suggests a higher dependence on importing intermediate products from other provinces or countries, indicating a focus on processing and assembly activities within the local area.

3.2.3. Application of the Measurement Model: Analysis of the Global Value Chain

Position of the Guangdong–Hong Kong–Macao Greater Bay Area

Based on the three-layer nested city–province–world input–output table outlined in the preceding text and the global value chain position measurement model in Formula (5), specific values for the global value chain position of the 11 cities within the Guangdong–Hong Kong–Macao Greater Bay Area in 2012, 2015, and 2017 can be derived. To comprehensively examine the evolutionary characteristics of the global value chain position within the Greater Bay Area of Guangdong, Hong Kong, and Macao, this study analyzes the situation from three perspectives: the overall state of the global value chain position within the Greater Bay Area, the statuses of the 11 cities, and the situations within the 19 sectors, respectively.

Based on the comprehensive evaluation of the Guangdong–Hong Kong–Macao Greater Bay Area in the years 2012, 2015, and 2017, the calculated numerical values for the global value chain position were −0.2885 in 2012, −0.2824 in 2015, and −0.2637 in 2017. The overall value of GVCS in the Greater Bay Area is negative but on the rise, suggesting that recent years have seen increased emphasis by local authorities on constructing global value chains. Both the transformation and upgrading of industries in various cities have yielded some positive outcomes, enhancing the external competitiveness of the Greater Bay Area. However, the magnitude of improvement is relatively modest, showing no significant shift. Currently, foreign value additions surpass local value additions. This suggests that, during this study, the Greater Bay Area mainly held lower ranks in the industrial chain. It was primarily involved in “processing and manufacturing” within the global value chain. This positioning implies a reliance on foreign intermediary products for value addition, with a substantial proportion of the value addition originating from external sources. Consequently, the ability to create local value added is restricted. While the region’s ability to self-generate value addition remains inadequate, a favorable trend toward improvement is gradually emerging.

The analysis of the 11 cities in the Guangdong–Hong Kong–Macao Greater Bay Area’s participation in the global value chain (Figure 2) reveals that their positioning has remained relatively stagnant throughout the observational period, all falling within the negative value range. Notably, Hong Kong, Macao, and Zhaoqing stand at higher positions within the value chain. This advantage stems from Hong Kong and Macao’s earlier integration into the global value chain, allowing them to establish a strong foundation. On the other hand, Zhaoqing’s primary export of raw materials positions it upstream in the value chain, resulting in significant inherent value addition. In contrast, Shenzhen, Zhuhai, and Dongguan occupy lower positions in the global value chain. Despite their specialization in the electronics and communications equipment industries, these cities are primarily involved in processing and assembly tasks. Consequently, their participation in the global value chain often leads to being “locked in” to low-end positions by other developed countries, thereby limiting their capacity to generate significant inherent value addition.

Based on the analysis of Figure 3, it is evident that the 19 sectors in the Guangdong–Hong Kong–Macao Greater Bay Area have exhibited minimal changes in their global value chain positions over the observational period. Except for C17, the commercial services sector, which rose to 0.0097 in 2017, all other sectors remained in negative territory. This indicates that the sectors in the Greater Bay Area are generally positioned downstream in the global value chain, relying heavily on inputs of intermediate products from other countries and regions. Notably, the service industries (C13–C19) in the Guangdong–Hong Kong–Macao Greater Bay Area occupy relatively higher positions in the global value chain compared to other industries. This is attributed to the economic development of the region, which is primarily driven by service industries. In particular, the service industries in Hong Kong and Macao have significant advantages over other industries. Under the influence of Hong Kong and Macao, the global value chain position of the service industry in the entire Greater Bay Area has been steadily rising. However, the overall low global value chain position of the manufacturing industry suggests that various manufacturing sectors heavily depend on foreign intermediate product inputs and are generally positioned in the lower segment of the value chain. This highlights the need for the Greater Bay Area to focus on innovation, technology upgrades, and value-added services to enhance its competitiveness and move towards higher-end positions in the global value chain.

In summary, the Guangdong–Hong Kong–Macao Greater Bay Area possesses a modern industrial system with a high level of openness to the global economy. However, its international competitiveness remains relatively weak due to a strong reliance on foreign intermediate product inputs. This reliance has resulted in generally negative global value chain position indices across various sectors within the region. Within the sub-sectors, the manufacturing industry is positioned low in the global value chain, indicating a heavy reliance on foreign intermediate product inputs. Comparatively, the service industry holds a higher position in the global value chain than manufacturing. This suggests that the service sector relies less on foreign intermediate product inputs than manufacturing does, however, its international competitiveness remains somewhat limited. Therefore, to enhance the international competitiveness of the Guangdong–Hong Kong–Macao Greater Bay Area, it is crucial to bolster the production capacity and innovative prowess of intermediate products in each city. Alternatively, seeking improved import substitution within the Greater Bay Area or domestically can reduce reliance on foreign intermediate product inputs.

4. Empirical Model Setting, Variable Selection and Data Description

4.1. Empirical Model Setting

4.1.1. Baseline Model

To validate Hypothesis 1, which explores the impact of digital transformation on the global value chain position of the Guangdong–Hong Kong–Macao Greater Bay Area, this study employed fixed effects testing on panel data and developed a specific model as illustrated in Equation (6).

\ln G V C_P O S_{i k t} = β_{1} + β_{2} \ln D I G_{i k t} + β_{3} \ln X_{i k t} + γ_{t} + μ_{i} + α_{k} + ε_{i t}

(6)

In this context,

i

represents the city,

k

represents the sector,

t

represents the year,

G V C_P O S

stands for the global value chain position of the Guangdong–Hong Kong–Macao Greater Bay Area,

D I G

represents the degree of digital transformation,

X

represents the control variables,

γ

represents the time fixed effect,

μ

represents the city fixed effect,

α

represents the industry fixed effect, and

ε

represents the random disturbance term.

4.1.2. Mediation Model

To rigorously test the previously proposed hypothesis regarding whether digital transformation can influence the global value chain position of the Guangdong–Hong Kong–Macao Greater Bay Area through the mediating effects of technological innovation transformation capabilities and the value added by exports from the Greater Bay Area, this paper constructed a mediation model to examine Hypothesis 2 and Hypothesis 3.

\ln T E C H_{i k t} = β_{4} + β_{5} \ln D I G_{i k t} + β_{6} \ln X_{i k t} + γ_{t} + μ_{i} + α_{k} + ε_{i t}

(7)

\ln G V C_P O S_{i k t} = β_{7} + β_{8} \ln D I G_{i k t} + β_{9} \ln T E C H_{i k t} + β_{10} \ln X_{i k t} + γ_{t} + μ_{i} + α_{k} + ε_{i t}

(8)

\ln V A_{i k t} = β_{11} + β_{12} \ln D I G_{i k t} + β_{13} \ln X_{i k t} + γ_{t} + μ_{i} + α_{k} + ε_{i t}

(9)

\ln G V C_P O S_{i k t} = β_{14} + β_{15} \ln D I G_{i k t} + β_{16} \ln V A_{i k t} + β_{17} \ln X_{i k t} + γ_{t} + μ_{i} + α_{k} + ε_{i t}

(10)

Equations (7) and (8) represent the mediation effect models designed to test the mediating role of technological innovation transformation capability (

T E C H

). Similarly, Equations (9) and (10) represent the mediation effect models designed to test the mediating role of the value added by exports from the Greater Bay Area (

V A

).

4.2. Variable Selection

4.2.1. Explained Variable

The global value chain position (

G V C_P O S

) of the Guangdong–Hong Kong–Macao Greater Bay Area is utilized as the dependent variable in this study.

G V C_P O S

was computed using Equation (5) to capture the region’s involvement in the global value chain position. The level of the global value chain position serves as an indicator of the Greater Bay Area’s relative position in participating in the global value chain.

4.2.2. Core Explanatory Variables

To operationalize the core explanatory variable, we have selected the level of digital transformation (

D I G

), which serves as a metric for gauging the extent of digitization within a given context. According to the idea of Wang et al. [37] regarding building a trade decomposition model, the digital transformation sector (C16, postal and telecommunications) was proposed separately and an index of the degree of digital transformation was constructed. See Equations (11) and (12) for details.

D I G = \overset{Λ}{V_{i}^{s}} B_{i j}^{s r} \overset{Λ}{Y_{j}^{r}}

(11)

C D I G = \sum_{s, r \in C} \overset{Λ}{V_{i}^{s}} B_{i j}^{s r} \overset{Λ}{Y_{j}^{r}}, N D I G = \sum_{S \in P, r \in C} \overset{Λ}{V_{i}^{S}} B_{i j}^{S r} \overset{Λ}{Y_{j}^{r}}, G D I G = \sum_{S^{'} \in W, r \in C} \overset{Λ}{V_{i}^{S^{'}}} B_{i j}^{S^{'} r} \overset{Λ}{Y_{j}^{r}}

(12)

In this empirical study, the digital transformation level (

D I G

) of the Guangdong–Hong Kong–Macao Greater Bay Area was constructed based on three components:

C D I G

,

N D I G

, and

G D I G

.

C D I G

captures the usage of digital transformation within the cities of the Greater Bay Area.

N D I G

represents the usage of digital transformation from other provinces within China. Lastly,

G D I G

quantifies the usage of digital transformation from foreign sources. These three variables,

C D I G

,

N D I G

, and

G D I G

, were combined to form

D I G

, as described in Equation (11), which serves as the core explanatory variable in this study.

D I G

provides a comprehensive measure of the overall digital transformation level within the Greater Bay Area. Equation (12) introduces the variables that are utilized for heterogeneity analysis, where

i

represents the digital sector (C16 postal and telecommunications),

\hat{V}

is the diagonal matrix of value added vectors,

B

is the complete consumption coefficient, and

\hat{Y}

is the diagonal matrix of final demand. A higher level of

D I G

indicates that the Greater Bay Area places greater emphasis on developing the digital economy, reflecting a robust investment in digital technologies, infrastructure, and talent. Conversely, a lower level of

D I G

suggests that the region has less of a focus on digital economy development, potentially indicating limitations in resources, strategies, or awareness.

4.2.3. Control Variables

Based on the influence of other factors on the global value chain position of the Guangdong–Hong Kong–Macao Greater Bay Area, this study selected control variables from three perspectives: the level of economic development, degree of openness to foreign markets, and industry scale. The level of economic development (

G D P

) is measured by the Gross Domestic Product of each city in the Greater Bay Area. The degree of openness to foreign markets is divided into import dependence (

I M

) and export dependence (

E X

). Import dependence is measured by the ratio of trade imports to total trade volume, while export dependence is measured by the ratio of trade exports to total trade volume for each city in the Greater Bay Area. Industry scale (

E M P

) is measured by the number of people employed in each city in the Greater Bay Area.

4.2.4. Mediating Variables

Integrating the theoretical analysis presented in Section 2, the current study endeavors to delve into the mechanisms underlying the impact of digital transformation on the global value chain position of the Guangdong–Hong Kong–Macao Greater Bay Area. In particular, this study scrutinizes the mediating roles played by two critical factors: technological innovation transformation capability (

T E C H

) and the value added by exports from the Greater Bay Area (

V A

).

T E C H

is quantified by the product of patent counts and industry value addition.

V A

captures the value-adding component embedded in the exports stemming from the cities within the Greater Bay Area. By examining these mediating effects, this study aims to provide a comprehensive understanding of how digital transformation influences the global value chain position of the Guangdong–Hong Kong–Macao Greater Bay Area.

4.3. Data Description

The global value chain position of the Guangdong–Hong Kong–Macao Greater Bay Area serves as our explained variable. Digital transformation stands as the core explanatory variable. Our mediating variables include industry value addition, which represents a facet of technological innovation transformation capabilities, and the value added by exports from the Greater Bay Area. All these datapoints originate from the three-tier nested city–province–world input–output table which was previously assembled in this paper. The data for the control variables and the number of patents (a component of the mediating variable for technological innovation transformation capability) were sourced from various official publications, including the “China City Statistical Yearbook”, “China Statistical Yearbook”, “and Guangdong Statistical Yearbook”, as well as the statistical yearbooks of individual cities, the “Macao Statistical Yearbook”, and the Hong Kong government statistics sector. Due to the existence of zero values for the explained variables, core explanatory variables, and mediating variables, this paper uses the natural logarithm treatment after adding 1, while the control variables are treated directly with the natural logarithm.

5. Analysis of Empirical Test Results

5.1. Benchmark Test

Table 3 comprehensively documents the regression outcomes examining the influence of digital transformation on the global value chain position of the Guangdong–Hong Kong–Macao Greater Bay Area. Column (1) offers an initial assessment without incorporating control variables or fixed effects, revealing a positive relationship. Column (2) extends this analysis by introducing time, city, and industry fixed effects, yet excluding control variables, and the positive trend persists. Column (3) incorporates control variables, albeit without fixed effects, further substantiating the significance of digital transformation. Finally, column (4) provides a robust analysis by including both control variables and fixed effects, conclusively demonstrating that digital transformation significantly elevates the global value chain position of the Greater Bay Area, passing the significance test at a 1% level.

5.2. Robustness Test

5.2.1. Replace the Measurement Method of Explained Variables

By adopting the methodology proposed by Wang et al. [37], we revised the estimation model (Equation (5)) by replacing the denominator with the value added by cities within the Guangdong–Hong Kong–Macao Greater Bay Area (

V A

) and total output (

X

), as specified in Equation (13). This modification allowed us to re-evaluate the global value chain position of the region. The regression results, presented in column (1) of Table 4, demonstrate that, after modifying the method of calculating the explained variable, the estimated coefficient for the degree of digital transformation stands at 0.0136. This coefficient passes the 5% significance test, indicating that digital transformation continues to enhance the global value chain position of the Guangdong–Hong Kong–Macao Greater Bay Area. The robustness of these results further confirms the positive impact of digital transformation on the region’s global value chain positioning.

G V C_P O S = \ln (1 + \frac{D V}{v a}) - \ln (1 + \frac{F V}{X})

(13)

5.2.2. Replace the Measurement Method of Core Explanatory Variables

After excluding the digital transformation of the Greater Bay Area cities themselves from the calculation scope from the original calculation method for the degree of digital transformation, while still controlling for time, city, and industry fixed effects, the regression results in column (2) of Table 4 remain consistent. The estimated coefficient for digital transformation maintains its positive direction and statistical significance, indicating that digital transformation, even when excluding the region’s internal transformations, still contributes to improving the global value chain position of the Guangdong–Hong Kong–Macao Greater Bay Area. This further supports the robustness and reliability of our analysis.

5.2.3. Replace the Regression Method

To ensure the robustness of the above conclusions, we have employed the generalized least squares (GLS) method as an alternative to the original regression approach, aiming to mitigate potential issues of autocorrelation and heteroscedasticity. The regression results presented in column (3) of Table 4 reveal a significantly positive regression coefficient of 0.0152 for digital transformation, surpassing the 1% significance threshold. This finding underscores the pivotal role of digital transformation in enhancing the global value chain position of the Guangdong–Hong Kong–Macao Greater Bay Area, thus affirming the robustness of our conclusions.

5.2.4. Tailing Treatment

This paper’s application of tail indentation to handle outliers in the data ensures the reliability of regression results. We replaced values greater than the 99% quantile with the 99% quantile value and values less than the 1% quantile with the 1% quantile value. As reported in column (4) of Table 4, even after applying this tail indentation method, the estimated coefficient for digital transformation remained significant and positive. This suggests that the degree of digital transformation continues to have a positive and statistically significant impact on improving the global value chain position of the Guangdong–Hong Kong–Macao Greater Bay Area.

5.3. Endogeneity Test

5.3.1. High-Order Joint Fixed Effect Model Test

Including higher-order joint fixed effects like “time × city” and “time × industry” in the regression model is beneficial. This approach helps mitigate biases caused by omitted variables linked to both the dependent variable (global value chain position) and the independent variable (digital transformation). This methodology can help mitigate the effects of autocorrelation and heteroscedasticity. The results presented in column (4) of Table 4 indicate that, even under the joint fixed effect of “time × city” and “time × industry”, digital transformation can still significantly promote the improvement of the global value chain position of the Guangdong–Hong Kong–Macao Greater Bay Area at the 1% significance level. This finding suggests that the positive effect of digital transformation on the global value chain position is robust.

5.3.2. Instrumental Variable Method

Instrumental variable 1

This paper constructs a digital input index

B = {(I - A)}^{- 1}

, selects the total number of posts and telecommunications services in 2001 (IV1), constructs a digital input index as the weight, and multiplies the total number of posts and telecommunications services in 2001 as the instrumental variable. The rationale for this choice is sound: the development of the postal and telecommunications sectors is closely related to digital transformation, satisfying the endogeneity requirement. At the same time, it is argued that the development of these sectors is unlikely to directly affect the global value chain position of the Guangdong–Hong Kong–Macao Greater Bay Area, satisfying the exclusivity requirement. The results presented in column (5) of Table 4, using the two-stage least squares method, support this approach. The estimated coefficient and sign for digital transformation are consistent with the baseline regression results and pass the 10% significance test. The statistical tests (Anderson canon. corr. LM and Cragg–Donald Wald F) further confirm the strong correlation between the instrumental variable and the potential endogenous variable, rejecting the hypothesis of insufficient identification. In summary, this paper’s choice of instrumental variable and the analysis using the two-stage least squares method provide robust evidence that digital transformation positively impacts the global value chain position of the Guangdong–Hong Kong–Macao Greater Bay Area.

2.: Instrumental variable 2

The digital transformation tool variable (IV2) at the “city industry” level is constructed by multiplying the proportion of digital transformation in various industries of cities in the Greater Bay Area with the number of internet users of cities in the Guangdong–Hong Kong–Macao Greater Bay Area in 2001. The development of the internet is closely related to digital transformation, but it is difficult to have a relationship with the global value chain position which satisfies the endogeneity and exclusivity of instrumental variables. In this paper, the least square method was used for testing, and the results are shown in column (3) of Table 5. Both the results of the first and second stage regression pass the test. The Anderson canon. corr. LM and Cragg–Donald Wald F statistics show that the selection of instrumental variables is reasonable and that the conclusion of this paper is still robust.

5.3.3. Omitted Variable Test

After testing with the Oster [38] method, Table 6 exhibits the δ value required to make the core variable estimation coefficient equal to 0 and that the absolute value of the δ value pair is 5.98. This suggests that an unobserved variable with at least six times the influence of the controlled variable is required to overturn the conclusion of this study. Given that this paper has fully considered and controlled for various factors that may affect the global value chain position of the Greater Bay Area, it is argued that there is not a single missing variable that can have such a large impact. Therefore, this paper firmly believes that the conclusions of this study are robust and unlikely to be affected by omitted variable bias.

5.4. Mechanism Test

5.4.1. Technological Innovation Transformation Capability

The results presented in Table 7 provide valuable insights into how digital transformation affects the global value chain position of the Guangdong–Hong Kong–Macao Greater Bay Area through the mediating effect of technological innovation transformation capability. Column (2) of Table 7 shows that digital transformation has a significant and positive effect on technological innovation transformation capability at the 1% significance level. This indicates that digital transformation effectively enhances the Greater Bay Area’s ability to transform patents and technological advancements into value-added products more quickly. In column (3) of Table 7, we see that both digital transformation and technological innovation transformation capability have positive estimated coefficients. The positive coefficient for technological innovation transformation capability suggests that an improvement in this capability leads to an enhancement in the global value chain position. Additionally, the estimated coefficient of digital transformation in column (3) is slightly lower than the baseline regression result in Table 7, shown in column (1). This decrease indicates that the effect of digital transformation on the global value chain position is partially mediated by the technological innovation transformation capability. The mediating effect of technological innovation transformation capability is estimated to be 5.6%. This finding validates Hypothesis 2, confirming that digital transformation enhances the global value chain position of the Greater Bay Area through the improvement of technological innovation transformation capabilities.

5.4.2. Value Added by Exports from the Greater Bay Area

The results presented in Table 7, regarding the mediating effect of the value added by exports from the Guangdong–Hong Kong–Macao Greater Bay Area on the global value chain position, are highly informative. In column (4) of Table 7, digital transformation is shown to have a significant and positive impact on the value added by exports from the Greater Bay Area at the 1% significance level. This indicates that digital transformation is effectively enhancing the quality and competitiveness of the region’s exports. In column (5) of Table 7, we observe that both the estimated coefficients of digital transformation and value added by exports from the Greater Bay Area are positive. The positive coefficient for value added by exports suggests that an increase in the value added by exports leads to an enhancement in the global value chain position. Importantly, based on the numerical values from the baseline regression (Table 7, column 1), the value added by exports from the Greater Bay Area satisfies the conditions for the mediating effect test. Specifically, the mediating effect of value-added exports is estimated to be 33.75% on the global value chain position of the Greater Bay Area. This finding validates Hypothesis 3, indicating that digital transformation enhances the global value chain position of the Greater Bay Area by promoting the value added by exports from the region. In summary, the results demonstrate that digital transformation not only directly improves the global value chain position of the Greater Bay Area but also does so indirectly by enhancing the value added by its exports.

5.5. Heterogeneity Analysis

5.5.1. Heterogeneity of Sources of Digital Transformation

Regarding the heterogeneity of different sources of digital transformation impacts on the global value chain position of the Greater Bay Area, viewing the results in Table 8, columns (1)–(3), it is evident that intra-Greater Bay Area digital transformation (

C D I G

) has a significant positive effect on the global value chain position at the 1% significance level. This indicates that digital transformation efforts within the Greater Bay Area itself are highly effective in enhancing its position in the global value chain. This could be attributed to improved efficiency, connectivity, and innovation within the region. Digital transformation in other provinces within China (

N D I G

) also positively and significantly contributes to the global value chain position of the Greater Bay Area at a 5% significance level. This suggests that digital transformation efforts in neighboring provinces have spillover effects that benefit the Greater Bay Area, likely through enhanced trade, investment, and knowledge exchange. In contrast, digital transformation from foreign countries (

G D I G

) did not pass the significance test. This may indicate that while foreign digital transformation has the potential to impact the global value chain position, the current level of foreign digital transformation has not yet reached a threshold that can significantly affect the Greater Bay Area. The findings suggest that the Greater Bay Area should continue to prioritize digital transformation efforts within the region, as well as in neighboring provinces. These efforts seem to be more directly relevant and effective in enhancing the region’s global value chain position.

5.5.2. Heterogeneity of Cities

To research how digital transformation affects the global value chain position of different cities within the Greater Bay Area. This paper categorized Hong Kong and Macau, as well as the nine cities in the Pearl River Delta, with virtual variables constructed for Hong Kong and Macau, as well as the nine cities in the Pearl River Delta. Hong Kong and Macau were assigned a virtual value of 1, and the nine cities in the Pearl River Delta were assigned a virtual value of 0. The virtual variables for Hong Kong, Macau, and the nine cities in the Pearl River Delta (

C I T Y

) were introduced in the benchmark regression model, as well as the interaction term between digital transformation and

C I T Y

(

\ln D I G

×

C I T Y

). Based on the regression results in column (4) of Table 8, the interaction term between digital transformation (

\ln D I G

) and the virtual variable (

C I T Y

) is significantly positive at a 5% significance level. This indicates that digital transformation has a greater positive impact on enhancing the global value chain positions of Hong Kong and Macau compared to the nine cities in the Pearl River Delta.

The positive effect of digital transformation on Hong Kong and Macau can be attributed to their greater advantages in talent development, scientific focus, and digital influence. These factors allow them to quickly respond to and capitalize on the opportunities presented by digital transformation to improve their position in the global value chain. In contrast, the nine cities in the Pearl River Delta, especially those other than Guangzhou and Shenzhen, face some challenges in digital marketing, university construction, and talent attraction. These deficiencies have hindered their ability to cultivate a favorable environment for digital trade and fully leverage the benefits of digital transformation. This suggests that while digital transformation is generally beneficial for the Greater Bay Area, there are differences in how different cities within the region can capitalize on this transformation. Hong Kong and Macau seem to have an edge due to their existing advantages, while the other cities in the Pearl River Delta need to address certain deficiencies to better utilize digital transformation for enhancing their global value chain position.

6. Conclusions and Recommendation

6.1. Conclusions

Amid the flourishing growth of the digital economy, the role of digital transformation in elevating the position of the Greater Bay Area within the global value chain has become a critical topic for economic development in this region. Against this backdrop, this paper has compiled a three-tier nested input–output table encompassing the 11 cities of the Greater Bay Area, spanning city, province, and world levels. We have built models designed for analyzing the value-added decomposition of the Greater Bay Area’s value chain. These models assess the area’s global value chain position, aiming to evaluate and understand its place within the global economy. Based on this framework, our empirical testing has explored the impact of digital transformation on the Greater Bay Area’s global value chain position, as well as its underlying mechanisms and effects. The key findings of our research are as follows:

Firstly, from a theoretical point of view, digital transformation plays a significant role in enhancing the global value chain position of the Guangdong–Hong Kong–Macao Greater Bay Area. Digital transformation enhances the global value chain position of the Greater Bay Area by enhancing technological innovation transformation capacities and the value added by exports from the Greater Bay Area. On the one hand, by promoting the transformation capability of technological innovation, digital transformation effectively promotes the enhancement of the innovation capability of the Greater Bay Area, spawning new industries with digital technology as their core and accelerating the commercialization process of digital technology. This process has not only injected strong innovation vitality into the Greater Bay Area, but also won it a higher position in the global value chain. On the other hand, digital transformation has fostered a novel industrial chain of “digitalization + industry” in the Greater Bay Area, further optimizing its industrial structure. Concurrently, this transformation empowers cities within the Greater Bay Area to collect, consolidate, and harness multidimensional information such as market trends, consumer preferences, and product details, transforming them into valuable data assets. By leveraging these data assets, the Greater Bay Area is able to generate more value, expand its value space, and thereby increase the value added by its exports, significantly elevating its position in the global value chain.

Secondly, digital transformation has a significant positive effect on enhancing the global value chain position of the Greater Bay Area. This conclusion remains robust, even after employing alternative methods for calculating explanatory variables, tailing treatments, higher-order joint fixed-effect models, and instrumental variable tests.

Thirdly, the impact of digital transformation on the global value chain position of the Greater Bay Area varies depending on the source of digital transformation and the specific city. This study finds that digital transformation originating within the Greater Bay Area has the most significant impact on enhancing its global value chain position. The next significant impact comes from digital transformation within other provinces in China. However, digital transformation from foreign sources has not yet had a substantial impact on the global value chain position of the Greater Bay Area. Furthermore, digital transformation is more favorable for Hong Kong and Macau’s participation in the global value chain compared to the Pearl River Delta.

6.2. Recommendation

The conclusions drawn from this study provide valuable insights into the Guangdong–Hong Kong–Macao Greater Bay Area and elucidating how to improve the global value chain position, especially in the context of technological revolution and digital transformation. Here are some recommendations:

Firstly, the Greater Bay Area should increase investment in digital transformation and enhance its global value chain position. The key steps to achieve this are given as follows: First, the Greater Bay Area must prioritize the construction of a “digital Bay Area”. This involves strengthening digital infrastructure and establishing a robust and dependable network environment. Investing in upgrades to internet and mobile communication networks and data centers will ensure data transmission is efficient and secure. Additionally, the region should actively adopt cutting-edge technologies like 5G, the Internet of Things (IoT), and artificial intelligence to maintain its leadership in digitalization. Then, cities within the Greater Bay Area must prioritize attracting investment in digital transformation, both locally and from other provinces in China. Focusing on acquiring and mastering core digital technologies, supporting the commercialization of the digital technology industry, and fostering top-tier digital technology enterprises will help prevent future “bottleneck” issues. Moreover, the Pearl River Delta region can learn from the experience of Hong Kong and Macao in terms of digital marketing methods, higher education institution construction, and talent introduction policies, thereby improving the foundation of digital transformation and enhancing the impetus for transformation, thus helping the Guangdong–Hong Kong–Macao Greater Bay Area realize the organic integration of digital transformation and the global value chain, thereby promoting sustainable economic and social development.

Secondly, the Greater Bay Area should improve its technological innovation transformation capabilities to enhance its competitiveness. This can be achieved as follows: First, the Greater Bay Area should focus on effectively transforming advanced patented technologies into actual productivity. This transformation will significantly boost production efficiency, optimize product quality, and strengthen the region’s ability to capture added value in the global value chain. As a result, the Greater Bay Area will further consolidate and enhance its global value chain position. Then, the region should prioritize strengthening its innovation capabilities, particularly in digital services such as the internet and the Internet of Things (IoT). By leveraging digital services to accurately match supply and demand, research and development funds can be allocated efficiently and precisely to patent research and development. This reduces resource waste and improves innovation efficiency, driving urban digital transformation and enhancing the Greater Bay Area’s global competitiveness. Moreover, the Greater Bay Area should prioritize the development of new digital technology business models and actively support the commercial application of digital technologies. Each city should formulate a detailed digital transformation strategy with clear development goals and paths, and the government should provide necessary policy support to provide a strong guarantee for digital transformation. These initiatives will facilitate the Greater Bay Area to form new growth points in the field of digital technology and further consolidate and enhance the global value chain position.

Thirdly, to enhance the value added by exports from the Guangdong–Hong Kong–Macao Greater Bay Area and broaden the scope for obtaining further added value, several strategic initiatives should be prioritized. Firstly, efficient infrastructure development and robust personnel training must be emphasized, particularly in the fields of digital technology and digital economy. This will provide a solid talent pool to drive digital transformation in the region. Then, digital marketing efforts should be strengthened to effectively promote and market products and services. Leveraging social media, search engine optimization, and big data analysis will boost product exposure and market competitiveness, thus enhancing export value. Meanwhile, the role of the digital government must be maximized. Its guidance and support are crucial in fostering the comprehensive digitalization of the Greater Bay Area, thereby increasing its share of value-added exports. In addition, based on national policies and economic conditions, cities in the Greater Bay Area should promote effective alignment of their respective rules and mechanisms through digitalization, strengthen trade cooperation among cities, achieve win-win cooperation, promote the effect of “1 + 1 > 2”, and enhance their global value chain position.

Author Contributions

Conceptualization, M.T.; methodology, M.T.; software, M.T.; resources, M.T.; data curation, M.T.; writing—original draft preparation, M.T.; writing—review and editing, X.L.; visualization, X.L.; supervision, X.L.; project administration, X.L.; funding acquisition, X.L. All authors have read and agreed to the published version of the manuscript.

Funding

The research was jointly supported by the National Natural Science Foundation of China “Innovation Network Evolution and Policy Effects in Guangdong-Hong Kong-Macao Greater Bay Area: A Perspective of the Flow of Innovation Factors” (No.72173032), the National Natural Science Foundation of China “Research on Innovation Chain Synergy, Innovation Value Chain Division and Industrial Chain Resilience in Guangdong-Hong Kong-Macao Greater Bay Area” (No.72373032), and the Guangdong Provincial Natural Science Foundation “Study on the Interaction Mechanism and Regulatory Countermeasures between the Flow of Innovation Factors and the Evolution of Innovation Network in Guangdong-Hong Kong-Macao Greater Bay Area” (No.2021A1515011958).

Data Availability Statement

Readers may contact the corresponding author to obtain the data used here.

Acknowledgments

We would like to express our gratitude to all the individuals and organizations who have supported this study, and we are very grateful for the valuable advice and assistance received during this study.

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A

The fundamental approach to constructing input–output tables involves embedding the inter-provincial input–output table within the global input–output table, followed by embedding the city-level input–output table into the combined interprovincial–global input–output table. Based on the currently available data, this paper aims to generate city–provincial–global input–output tables for the years 2012, 2015, and 2017.

It is crucial to mention that among the existing input–output databases, only the EORA database offers tables that encompass Hong Kong, Macao, and Taiwan simultaneously (Table A1). However, a significant limitation is that these tables from EORA are in basic prices, whereas domestically published tables are in current prices. Furthermore, the tables from EORA are not balanced and do not adhere to the standard balancing rules of input–output tables. To ensure consistency in units and avoid imbalances, we have opted to utilize the input–output tables from the ADB database as the global input table component in the city–province–world input–output tables we intend to create. However, a challenge arises as the ADB data for Macao are included within China’s figures (Table A2), necessitating a separation process. To address this, we will initially compare the sectoral classifications in the EORA and ADB input–output tables. Subsequently, we will calculate separate input coefficients for Macao and Mainland China, utilizing the EORA database’s tables. These coefficients will then be applied to the intermediate and final input data for China in the ADB tables, enabling us to segregate them into data for Macao and Mainland China. Similarly, the value addition and total input components will be partitioned using the proportional splits derived from the EORA tables. Once this is completed, we will employ the RAS method to balance and generate a comprehensive world input–output table that incorporates Macao (Table A3).

Table A1. Basic structure of EORA input–output tables.

EORA		Intermediate Demand						Final Demand			Total Output
		$Mainland China (L)$		$Macao (M)$		$Other Regions of the World (A)$		$Mainland China (L)$	$Macao (M)$	$Other Regions of the World (A)$
		$S_{i}$	$S_{j}$	$S_{i}$	$S_{j}$	$S_{i}$	$S_{j}$	$Mainland China (L)$	$Macao (M)$	$Other Regions of the World (A)$
$Mainland China (L)$	$S_{i}$	$x_{i i}^{L L}$	$x_{i j}^{L L}$	$x_{i i}^{L M}$	$x_{i j}^{L M}$	$x_{i i}^{L A}$	$x_{i j}^{L A}$	$y_{i}^{L L}$	$y_{i}^{L M}$	$y_{i}^{L A}$	$x_{i}^{L}$
$Mainland China (L)$	$S_{j}$	$x_{j i}^{L L}$	$x_{j j}^{L L}$	$x_{j i}^{L M}$	$x_{j j}^{L M}$	$x_{j i}^{L A}$	$x_{j j}^{L A}$	$y_{j}^{L L}$	$y_{j}^{L M}$	$y_{j}^{L A}$	$x_{j}^{L}$
$Macao (M)$	$S_{i}$	$x_{i i}^{M L}$	$x_{i j}^{M L}$	$x_{i i}^{M M}$	$x_{i j}^{M M}$	$x_{i i}^{M A}$	$x_{i j}^{M A}$	$y_{i}^{M L}$	$y_{i}^{M M}$	$y_{i}^{M A}$	$x_{i}^{M}$
$Macao (M)$	$S_{j}$	$x_{j i}^{M L}$	$x_{j j}^{M L}$	$x_{j i}^{M M}$	$x_{j j}^{M M}$	$x_{j i}^{M A}$	$x_{j j}^{M A}$	$y_{j}^{M L}$	$y_{j}^{M M}$	$y_{j}^{M A}$	$x_{j}^{M}$
$Other Regions of the World (A)$	$S_{i}$	$x_{i i}^{A L}$	$x_{i j}^{A L}$	$x_{i i}^{A M}$	$x_{i j}^{A M}$	$x_{i i}^{A A}$	$x_{i j}^{A A}$	$y_{i}^{A L}$	$y_{i}^{A M}$	$y_{i}^{A A}$	$x_{i}^{A}$
$Other Regions of the World (A)$	$S_{j}$	$x_{j i}^{A L}$	$x_{j j}^{A L}$	$x_{j i}^{A M}$	$x_{j j}^{A M}$	$x_{j i}^{A A}$	$x_{j j}^{A A}$	$y_{j}^{A L}$	$y_{j}^{A M}$	$y_{j}^{A A}$	$x_{j}^{A}$
Value Added		$v_{i}^{L}$	$v_{j}^{L}$	$v_{i}^{M}$	$v_{j}^{M}$	$v_{i}^{A}$	$v_{j}^{A}$
Total Input		$x_{i}^{L}$	$x_{j}^{L}$	$x_{i}^{M}$	$x_{j}^{M}$	$x_{i}^{A}$	$x_{j}^{A}$

Note: The EORA input–output table consists of 26 sectors. For ease of comparison, the EORA input–output table corresponds to the ADB input–output table with 19 sectors. Region A in the rest of the world includes Hong Kong, China.

Table A2. Basic structure of ADB input–output tables.

ADB		Intermediate Demand				Final Demand		Total Output
		$China (C)$		$Other Regions of the World (A)$		$China (C)$	$Other Regions of the World (A)$
		$S_{i}$	$S_{j}$	$S_{i}$	$S_{j}$	$China (C)$	$Other Regions of the World (A)$
$China (C)$	$S_{i}$	$X_{i i}^{C C}$	$X_{i j}^{C C}$	$X_{i i}^{C A}$	$X_{i j}^{C A}$	$Y_{i}^{C C}$	$Y_{i}^{C A}$	$X_{i}^{C}$
$China (C)$	$S_{j}$	$X_{j i}^{C C}$	$X_{j j}^{C C}$	$X_{j i}^{C A}$	$X_{j j}^{C A}$	$Y_{j}^{C C}$	$Y_{j}^{C A}$	$X_{j}^{C}$
$Other Regions of the World (A)$	$S_{i}$	$X_{i i}^{A C}$	$X_{i j}^{A C}$	$X_{i i}^{A A}$	$X_{i j}^{A A}$	$Y_{i}^{A C}$	$Y_{i}^{A A}$	$X_{i}^{A}$
$Other Regions of the World (A)$	$S_{j}$	$X_{j i}^{A C}$	$X_{j j}^{A C}$	$X_{j i}^{A A}$	$X_{j j}^{A A}$	$Y_{j}^{A C}$	$Y_{j}^{A A}$	$X_{j}^{A}$
Value Added		$V_{i}^{C}$	$V_{j}^{C}$	$V_{i}^{A}$	$V_{j}^{A}$
Total Input		$X_{i}^{C}$	$X_{j}^{C}$	$X_{i}^{A}$	$X_{j}^{A}$

Note: The ADB input–output table consists of 35 sectors. For ease of comparison, the ADB input–output table corresponds to the EORA input–output table with 19 sectors. Region A in the rest of the world includes the Hong Kong area.

Table A3. Basic structure of the EORA input–output table after splitting the

A D B

input–output table.

Table A3. Basic structure of the EORA input–output table after splitting the

A D B

input–output table.

EORA-ADB		Intermediate Demand						Final Demand			Total Output
		$Mainland China (L)$		$Macao (M)$		$Other Regions of the World (A)$		$Mainland China (L)$	$Macao (M)$	$Other Regions of the World (A)$
		$S_{i}$	$S_{j}$	$S_{i}$	$S_{j}$	$S_{i}$	$S_{j}$	$Mainland China (L)$	$Macao (M)$	$Other Regions of the World (A)$
Mainland China $(L)$	$S_{i}$	${\tilde{X}}_{i i}^{L L}$	${\tilde{X}}_{i j}^{L L}$	${\tilde{X}}_{i i}^{L M}$	${\tilde{X}}_{i j}^{L M}$	${\tilde{X}}_{i i}^{L A}$	${\tilde{X}}_{i j}^{L A}$	${\tilde{Y}}_{i}^{L L}$	${\tilde{Y}}_{i}^{L M}$	${\tilde{Y}}_{i}^{L A}$	${\tilde{X}}_{i}^{L}$
Mainland China $(L)$	$S_{j}$	${\tilde{X}}_{j i}^{L L}$	${\tilde{X}}_{j j}^{L L}$	${\tilde{X}}_{j i}^{L M}$	${\tilde{X}}_{j j}^{L M}$	${\tilde{X}}_{j i}^{L A}$	${\tilde{X}}_{j j}^{L A}$	${\tilde{Y}}_{j}^{L L}$	${\tilde{Y}}_{j}^{L M}$	${\tilde{Y}}_{j}^{L A}$	${\tilde{X}}_{j}^{L}$
Macao $(M)$	$S_{i}$	${\tilde{X}}_{i i}^{M L}$	${\tilde{X}}_{i j}^{M L}$	${\tilde{X}}_{i i}^{M M}$	${\tilde{X}}_{i j}^{M M}$	${\tilde{X}}_{i i}^{M A}$	${\tilde{X}}_{i j}^{M A}$	${\tilde{Y}}_{i}^{M L}$	${\tilde{Y}}_{i}^{M M}$	${\tilde{Y}}_{i}^{M A}$	${\tilde{X}}_{i}^{M}$
Macao $(M)$	$S_{j}$	${\tilde{X}}_{j i}^{M L}$	${\tilde{X}}_{j j}^{M L}$	${\tilde{X}}_{j i}^{M M}$	${\tilde{X}}_{j j}^{M M}$	${\tilde{X}}_{j i}^{M A}$	${\tilde{X}}_{j j}^{M A}$	${\tilde{Y}}_{j}^{M L}$	${\tilde{Y}}_{j}^{M M}$	${\tilde{Y}}_{j}^{M A}$	${\tilde{X}}_{j}^{M}$
Other Regions of the World $(A)$	$S_{i}$	${\tilde{X}}_{i i}^{A L}$	${\tilde{X}}_{i j}^{A L}$	${\tilde{X}}_{i i}^{A M}$	${\tilde{X}}_{i j}^{A M}$	$X_{i i}^{A A}$	$X_{i j}^{A A}$	${\tilde{Y}}_{i}^{A L}$	${\tilde{Y}}_{i}^{A M}$	$Y_{i}^{A A}$	$X_{i}^{A}$
Other Regions of the World $(A)$	$S_{j}$	${\tilde{X}}_{j i}^{A L}$	${\tilde{X}}_{j j}^{A L}$	${\tilde{X}}_{j i}^{A M}$	${\tilde{X}}_{j j}^{A M}$	$X_{j i}^{A A}$	$X_{j j}^{A A}$	${\tilde{Y}}_{j}^{A L}$	${\tilde{Y}}_{j}^{A M}$	$Y_{j}^{A A}$	$X_{j}^{A}$
Value Added		${\tilde{V}}_{i}^{L}$	${\tilde{V}}_{j}^{L}$	${\tilde{V}}_{i}^{M}$	${\tilde{V}}_{j}^{M}$	$V_{i}^{A}$	$V_{j}^{A}$
Total Input		${\tilde{X}}_{i}^{L}$	${\tilde{X}}_{j}^{L}$	${\tilde{X}}_{i}^{M}$	${\tilde{X}}_{j}^{M}$	$X_{i}^{A}$	$X_{j}^{A}$

The specific process is outlined as follows:

Method of splitting intermediate demand and final demand for Mainland China (

L

) in Mainland China (

L

):

\begin{array}{l} {\tilde{X}}_{i i}^{L L} = \frac{x_{i i}^{L L}}{x_{i}^{L}} * X_{i i}^{C C}, {\tilde{X}}_{i j}^{L L} = \frac{x_{i j}^{L L}}{x_{j}^{L}} * X_{i j}^{C C}, \\ {\tilde{Y}}_{i}^{L L} = \frac{y_{i}^{L L}}{y_{i}^{L L} + y_{j}^{L L} + y_{i}^{M L} + y_{j}^{M L} + y_{i}^{A L} + y_{j}^{A L}} * Y_{i}^{C C}, {\tilde{Y}}_{j}^{L L} = \frac{y_{j}^{L L}}{y_{i}^{L L} + y_{j}^{L L} + y_{i}^{M L} + y_{j}^{M L} + y_{i}^{A L} + y_{j}^{A L}} * Y_{j}^{C C}, \end{array}

Method of splitting intermediate input and final input for Mainland China (

L

) from Macao (

M

):

\begin{array}{l} {\tilde{X}}_{i i}^{M L} = \frac{x_{i i}^{M L}}{x_{i}^{L}} * X_{i i}^{C C}, {\tilde{X}}_{i j}^{M L} = \frac{x_{i j}^{M L}}{x_{j}^{L}} * X_{i j}^{C C}, \\ {\tilde{Y}}_{i}^{M L} = \frac{y_{i}^{M L}}{y_{i}^{L L} + y_{j}^{L L} + y_{i}^{M L} + y_{j}^{M L} + y_{i}^{A L} + y_{j}^{A L}} * Y_{i}^{C C}, {\tilde{Y}}_{j}^{M L} = \frac{y_{j}^{M L}}{y_{i}^{L L} + y_{j}^{L L} + y_{i}^{M L} + y_{j}^{M L} + y_{i}^{A L} + y_{j}^{A L}} * Y_{j}^{C C}, \end{array}

Method of splitting intermediate demand and final demand for Mainland China (

L

) from other regions of the world (

A

):

\begin{array}{l} {\tilde{X}}_{i i}^{A L} = \frac{x_{i i}^{A L}}{x_{i}^{L}} * X_{i i}^{A C}, {\tilde{X}}_{i j}^{A L} = \frac{x_{i j}^{A L}}{x_{j}^{L}} * X_{i j}^{A C}, \\ {\tilde{Y}}_{i}^{A L} = \frac{y_{i}^{A L}}{y_{i}^{L L} + y_{j}^{L L} + y_{i}^{M L} + y_{j}^{M L} + y_{i}^{A L} + y_{j}^{A L}} * Y_{i}^{A C}, {\tilde{Y}}_{j}^{A L} = \frac{y_{j}^{A L}}{y_{i}^{L L} + y_{j}^{L L} + y_{i}^{M L} + y_{j}^{M L} + y_{i}^{A L} + y_{j}^{A L}} * Y_{j}^{A C}, \end{array}

Method of value added and total input (total output) splitting:

\begin{array}{l} {\tilde{V}}_{i}^{L} = \frac{v_{i}^{L}}{v_{i}^{L} + v_{i}^{M}} * V_{i}^{c}, {\tilde{V}}_{j}^{L} = \frac{v_{j}^{L}}{v_{j}^{L} + v_{j}^{M}} * V_{j}^{c}, {\tilde{V}}_{i}^{M} = \frac{v_{i}^{M}}{v_{i}^{L} + v_{i}^{M}} * V_{i}^{c}, {\tilde{V}}_{j}^{M} = \frac{v_{j}^{M}}{v_{j}^{L} + v_{j}^{M}} * V_{j}^{c}, \\ {\tilde{X}}_{i}^{L} = \frac{x_{i}^{L}}{x_{i}^{L} + x_{i}^{M}} * X_{i}^{c}, {\tilde{X}}_{j}^{L} = \frac{x_{j}^{L}}{x_{j}^{L} + x_{j}^{M}} * X_{j}^{c}, {\tilde{X}}_{i}^{M} = \frac{x_{i}^{M}}{x_{i}^{L} + x_{i}^{M}} * X_{i}^{c}, {\tilde{X}}_{j}^{M} = \frac{x_{j}^{M}}{x_{j}^{L} + x_{j}^{M}} * X_{j}^{c} . \end{array}

The splitting methodology for the flows between Mainland China (

L

) and Macao (

M

), Macao (

M

) to itself, other regions of the world (

A

) to Macao (

M

), Mainland China (

L

) to the rest of the world (

A

), and Macao (

M

) to other regions of the world (

A

) will adhere to the previously described approach.

Moving forward, the next step involves embedding the China interprovincial input–output table published by CEADs (as detailed in Appendix A Table A4) into Appendix A Table A3. Subsequently, the RAS method will be employed to balance the table, resulting in Appendix A Table A5. The specific steps for this process are outlined below:

Table A4. Basic structure of China’s interprovincial input–output tables.

Interprovincial in China		Intermediate Demand				Final Demand		Export	Total Output
		$Guangdong (G)$		$Other Province of China (B)$		$Guangdong (G)$	$Other Province of China (B)$
		$S_{i}$	$S_{j}$	$S_{i}$	$S_{j}$	$Guangdong (G)$	$Other Province of China (B)$
$Guangdong (G)$	$S_{i}$	$x_{i i}^{G G}$	$x_{i j}^{G G}$	$x_{i i}^{G B}$	$x_{i j}^{G B}$	$y_{i}^{G G}$	$y_{i}^{G B}$	${e x}_{i}^{G}$	$x_{i}^{G}$
$Guangdong (G)$	$S_{j}$	$x_{j i}^{G G}$	$x_{j j}^{G G}$	$x_{j i}^{G B}$	$x_{j j}^{G B}$	$y_{j}^{G G}$	$y_{j}^{G B}$	${e x}_{j}^{G}$	$x_{j}^{G}$
$Other province of China (B)$	$S_{i}$	$x_{i i}^{B G}$	$x_{i j}^{B G}$	$x_{i i}^{B B}$	$x_{i j}^{B B}$	$y_{i}^{B G}$	$y_{i}^{B B}$	${e x}_{i}^{B}$	$x_{i}^{B}$
$Other province of China (B)$	$S_{j}$	$x_{j i}^{B G}$	$x_{j j}^{B G}$	$x_{j i}^{B B}$	$x_{j j}^{B B}$	$y_{j}^{B G}$	$y_{j}^{B B}$	${e x}_{j}^{B}$	$x_{j}^{B}$
Import		$i m_{i}^{G}$	$i m_{j}^{G}$	$i m_{i}^{B}$	$i m_{j}^{B}$	${i m}^{G}$	${i m}^{B}$
Value Added		$v_{i}^{G}$	$v_{j}^{G}$	$v_{i}^{B}$	$v_{j}^{B}$
Total Input		$x_{i}^{G}$	$x_{j}^{G}$	$x_{i}^{B}$	$x_{j}^{B}$

Table A5. China’s Interprovincial Input–Output Tables Embedded in World Input–Output Tables.

Embedding Interprovincial China into the World		Intermediate Demand								Final Demand				Total Output
		$Guangdong (G)$		$Other Province of China (B)$		$Macao (M)$		$Other Regions of the World (A)$		$Guangdong (G)$	$Other Province of China (B)$	$Macao (M)$	$Other Regions of the World (A)$
		$S_{i}$	$S_{j}$	$S_{i}$	$S_{j}$	$S_{i}$	$S_{j}$	$S_{i}$	$S_{j}$	$Guangdong (G)$	$Other Province of China (B)$	$Macao (M)$	$Other Regions of the World (A)$
$Guangdong (G)$	$S_{i}$	$x_{i i}^{G G}$	$x_{i j}^{G G}$	$x_{i i}^{G B}$	$x_{i j}^{G B}$	${\hat{X}}_{i i}^{G M}$	${\hat{X}}_{i j}^{G M}$	${\hat{X}}_{i i}^{G A}$	${\hat{X}}_{i j}^{G A}$	$y_{i}^{G G}$	$y_{i}^{G B}$	${\hat{Y}}_{i}^{G M}$	${\hat{Y}}_{i}^{G A}$	${\hat{X}}_{i}^{G}$
$Guangdong (G)$	$S_{j}$	$x_{j i}^{G G}$	$x_{j j}^{G G}$	$x_{j i}^{G B}$	$x_{j j}^{G B}$	${\hat{X}}_{j i}^{G M}$	${\hat{X}}_{j j}^{G M}$	${\hat{X}}_{j i}^{G A}$	${\hat{X}}_{j j}^{G A}$	$y_{j}^{G G}$	$y_{j}^{G B}$	${\hat{Y}}_{j}^{G M}$	${\hat{Y}}_{j}^{G A}$	${\hat{X}}_{j}^{G}$
$Other Province of China (B)$	$S_{i}$	$x_{i i}^{B G}$	$x_{i j}^{B G}$	$x_{i i}^{B B}$	$x_{i j}^{B B}$	${\hat{X}}_{i i}^{B M}$	${\hat{X}}_{i j}^{B M}$	${\hat{X}}_{i i}^{B A}$	${\hat{X}}_{i j}^{B A}$	$y_{i}^{B G}$	$y_{i}^{B B}$	${\hat{Y}}_{i}^{B M}$	${\hat{Y}}_{i}^{B A}$	${\hat{X}}_{i}^{B}$
$Other Province of China (B)$	$S_{j}$	$x_{j i}^{B G}$	$x_{j j}^{B G}$	$x_{j i}^{B B}$	$x_{j j}^{B B}$	${\hat{X}}_{j i}^{B M}$	${\hat{X}}_{j j}^{B M}$	${\hat{X}}_{j i}^{B A}$	${\hat{X}}_{j j}^{B A}$	$y_{j}^{B G}$	$y_{j}^{B B}$	${\hat{Y}}_{j}^{B M}$	${\hat{Y}}_{j}^{B A}$	${\hat{X}}_{j}^{B}$
$Macao (M)$	$S_{i}$	${\hat{X}}_{i i}^{M G}$	${\hat{X}}_{i j}^{M G}$	${\hat{X}}_{i i}^{M B}$	${\hat{X}}_{i j}^{M B}$	${\tilde{X}}_{i i}^{M M}$	${\tilde{X}}_{i j}^{M M}$	${\tilde{X}}_{i i}^{M A}$	${\tilde{X}}_{i j}^{M A}$	${\hat{Y}}_{i}^{M G}$	${\hat{Y}}_{i}^{M B}$	${\tilde{Y}}_{i}^{M M}$	${\tilde{Y}}_{i}^{M A}$	${\tilde{X}}_{i}^{M}$
$Macao (M)$	$S_{j}$	${\hat{X}}_{j i}^{M G}$	${\hat{X}}_{j j}^{M G}$	${\hat{X}}_{j i}^{M B}$	${\hat{X}}_{j j}^{M B}$	${\tilde{X}}_{j i}^{M M}$	${\tilde{X}}_{j j}^{M M}$	${\tilde{X}}_{j i}^{M A}$	${\tilde{X}}_{j j}^{M A}$	${\hat{Y}}_{j}^{M G}$	${\hat{Y}}_{j}^{M B}$	${\tilde{Y}}_{j}^{M M}$	${\tilde{Y}}_{j}^{M A}$	${\tilde{X}}_{j}^{M}$
$Other Regions of the World (A)$	$S_{i}$	${\hat{X}}_{i i}^{A G}$	${\hat{X}}_{i j}^{A G}$	${\hat{X}}_{i i}^{A B}$	${\hat{X}}_{i j}^{A B}$	${\tilde{X}}_{i i}^{A M}$	${\tilde{X}}_{i j}^{A M}$	$X_{i i}^{A A}$	$X_{i j}^{A A}$	${\hat{Y}}_{i}^{A G}$	${\hat{Y}}_{i}^{A B}$	${\tilde{Y}}_{i}^{A M}$	$Y_{i}^{A A}$	$X_{i}^{A}$
$Other Regions of the World (A)$	$S_{j}$	${\hat{X}}_{j i}^{A G}$	${\hat{X}}_{j j}^{A G}$	${\hat{X}}_{j i}^{A B}$	${\hat{X}}_{j j}^{A B}$	${\tilde{X}}_{j i}^{A M}$	${\tilde{X}}_{j j}^{A M}$	$X_{j i}^{A A}$	$X_{j j}^{A A}$	${\hat{Y}}_{j}^{A G}$	${\hat{Y}}_{j}^{A B}$	${\tilde{Y}}_{j}^{A M}$	$Y_{j}^{A A}$	$X_{j}^{A}$
Value Added		${\hat{V}}_{i}^{G}$	${\hat{V}}_{j}^{G}$	${\hat{V}}_{i}^{B}$	${\hat{V}}_{j}^{B}$	${\tilde{V}}_{i}^{M}$	${\tilde{V}}_{j}^{M}$	$V_{i}^{A}$	$V_{j}^{A}$
Total Input		${\hat{X}}_{i}^{G}$	${\hat{X}}_{j}^{G}$	${\hat{X}}_{i}^{B}$	${\hat{X}}_{j}^{B}$	${\tilde{X}}_{i}^{M}$	${\tilde{X}}_{j}^{M}$	$X_{i}^{A}$	$X_{j}^{A}$

Note: The rest of the world region (

A

) in the table includes Hong Kong, Taiwan, and other countries worldwide.

From a vertical embedding approach:

\begin{array}{l} {\hat{X}}_{i i}^{M G} = \frac{{\tilde{X}}_{i i}^{M L}}{{\tilde{X}}_{i i}^{M L} + {\tilde{X}}_{j i}^{M L} + {\tilde{X}}_{i i}^{A L} + {\tilde{X}}_{j i}^{A L}} * i m_{i}^{G}, {\hat{X}}_{j i}^{M G} = \frac{{\tilde{X}}_{j i}^{M L}}{{\tilde{X}}_{i i}^{M L} + {\tilde{X}}_{j i}^{M L} + {\tilde{X}}_{i i}^{A L} + {\tilde{X}}_{j i}^{A L}} * i m_{i}^{G} \dots \dots \\ {\overset{⌢}{Y}}_{i}^{M G} = \frac{{\tilde{Y}}_{i}^{M L}}{{\tilde{Y}}_{i}^{M L} + {\tilde{Y}}_{j}^{M L} + {\tilde{Y}}_{i}^{A L} + {\tilde{Y}}_{j}^{A L}} * i m^{G}, {\hat{Y}}_{j}^{M G} = \frac{{\tilde{Y}}_{j}^{M L}}{{\tilde{Y}}_{i}^{M L} + {\tilde{Y}}_{j}^{M L} + {\tilde{Y}}_{i}^{A L} + {\tilde{Y}}_{j}^{A L}} * i m^{G} \dots \dots \\ {\overset{⌢}{V}}_{i}^{G} = \frac{v_{i}^{G}}{v_{i}^{G} + v_{i}^{B}} * {\tilde{V}}_{i}^{L}, {\overset{⌢}{V}}_{i}^{B} = \frac{v_{i}^{B}}{v_{i}^{G} + v_{i}^{B}} * {\tilde{V}}_{i}^{L}, {\overset{⌢}{V}}_{j}^{G} = \frac{v_{j}^{G}}{v_{j}^{G} + v_{j}^{B}} * {\tilde{V}}_{j}^{L}, {\overset{⌢}{V}}_{j}^{B} = \frac{v_{j}^{B}}{v_{j}^{G} + v_{j}^{B}} * {\tilde{V}}_{j}^{L}, \\ {\hat{X}}_{i}^{G} = \frac{x_{i}^{G}}{x_{i}^{G} + x_{i}^{B}} * {\tilde{X}}_{i}^{L}, {\hat{X}}_{i}^{B} = \frac{x_{i}^{B}}{x_{i}^{G} + x_{i}^{B}} * {\tilde{X}}_{i}^{L}, {\hat{X}}_{j}^{G} = \frac{x_{j}^{G}}{x_{j}^{G} + x_{j}^{B}} * {\tilde{X}}_{j}^{L}, {\hat{X}}_{j}^{B} = \frac{x_{j}^{B}}{x_{j}^{G} + x_{j}^{B}} * {\tilde{X}}_{j}^{L} . \end{array}

From a horizontal embedding approach:

\begin{array}{l} {\hat{X}}_{i i}^{G M} = \frac{{\tilde{X}}_{i i}^{L M}}{{\tilde{X}}_{i i}^{L M} + {\tilde{X}}_{i j}^{L M} + {\tilde{X}}_{i i}^{L A} + {\tilde{X}}_{i j}^{L A}} * e x_{i}^{G}, {\hat{X}}_{i j}^{G M} = \frac{{\tilde{X}}_{j i}^{M L}}{{\tilde{X}}_{i i}^{L M} + {\tilde{X}}_{i j}^{L M} + {\tilde{X}}_{i i}^{L A} + {\tilde{X}}_{i j}^{L A}} * e x_{i}^{G} \dots \dots \\ {\overset{⌢}{Y}}_{i}^{G M} = \frac{{\tilde{Y}}_{i}^{L M}}{{\tilde{Y}}_{i}^{L M} + {\tilde{Y}}_{i}^{L A}} * e x^{G}, {\hat{Y}}_{i}^{G A} = \frac{{\tilde{Y}}_{i}^{L A}}{{\tilde{Y}}_{i}^{L M} + {\tilde{Y}}_{i}^{L A}} * e x^{G} \dots \dots \\ {\overset{⌢}{V}}_{i}^{G} = \frac{v_{i}^{G}}{v_{i}^{G} + v_{i}^{B}} * {\tilde{V}}_{i}^{L}, {\overset{⌢}{V}}_{i}^{B} = \frac{v_{i}^{B}}{v_{i}^{G} + v_{i}^{B}} * {\tilde{V}}_{i}^{L}, {\overset{⌢}{V}}_{j}^{G} = \frac{v_{j}^{G}}{v_{j}^{G} + v_{j}^{B}} * {\tilde{V}}_{j}^{L}, {\overset{⌢}{V}}_{j}^{B} = \frac{v_{j}^{B}}{v_{j}^{G} + v_{j}^{B}} * {\tilde{V}}_{j}^{L}, \\ {\hat{X}}_{i}^{G} = \frac{x_{i}^{G}}{x_{i}^{G} + x_{i}^{B}} * {\tilde{X}}_{i}^{L}, {\hat{X}}_{i}^{B} = \frac{x_{i}^{B}}{x_{i}^{G} + x_{i}^{B}} * {\tilde{X}}_{i}^{L}, {\hat{X}}_{j}^{G} = \frac{x_{j}^{G}}{x_{j}^{G} + x_{j}^{B}} * {\tilde{X}}_{j}^{L}, {\hat{X}}_{j}^{B} = \frac{x_{j}^{B}}{x_{j}^{G} + x_{j}^{B}} * {\tilde{X}}_{j}^{L} . \end{array}

Finally, to embed the input–output table containing the nine cities in the Pearl River Delta (Appendix A Table A6) into Appendix A Table A5 and balance it using the RAS method, you can follow these steps:

Table A6. Basic Structure of Input–Output Table of Guangdong Province.

Guangdong		Intermediate Demand				Final Demand		Outflow	Export	Total Output
		$Pearl River Delta (Z)$		$Other Cities in Guangdong (S)$		$Pearl River Delta (Z)$	$Other Cities in Guangdong (S)$
		$S_{i}$	$S_{j}$	$S_{i}$	$S_{j}$	$Pearl River Delta (Z)$	$Other Cities in Guangdong (S)$
$Pearl River Delta (Z)$	$S_{i}$	$x_{i i}^{Z Z}$	$x_{i j}^{Z Z}$	$x_{i i}^{Z S}$	$x_{i j}^{Z S}$	$y_{i}^{Z Z}$	$y_{i}^{Z S}$	$o_{i}^{Z}$	${e x}_{i}^{Z}$	$x_{i}^{Z}$
$Pearl River Delta (Z)$	$S_{j}$	$x_{j i}^{Z Z}$	$x_{j j}^{Z Z}$	$x_{j i}^{Z S}$	$x_{j j}^{Z S}$	$y_{j}^{Z Z}$	$y_{j}^{Z S}$	$o_{j}^{Z}$	${e x}_{j}^{Z}$	$x_{j}^{Z}$
$Other Cities in Guangdong (S)$	$S_{i}$	$x_{i i}^{S Z}$	$x_{i j}^{S Z}$	$x_{i i}^{S S}$	$x_{i j}^{S S}$	$y_{i}^{S Z}$	$y_{i}^{S S}$	$o_{i}^{S}$	${e x}_{i}^{S}$	$x_{i}^{S}$
$Other Cities in Guangdong (S)$	$S_{j}$	$x_{j i}^{S Z}$	$x_{j j}^{S Z}$	$x_{j i}^{S S}$	$x_{j j}^{S S}$	$y_{j}^{S Z}$	$y_{j}^{S S}$	$o_{j}^{S}$	${e x}_{j}^{S}$	$x_{j}^{S}$
Inflow		$i_{i}^{Z}$	$i_{j}^{Z}$	$i_{i}^{S}$	$i_{j}^{S}$	$i^{Z}$	$i^{S}$
Import		$i m_{i}^{Z}$	$i m_{j}^{Z}$	$i m_{i}^{S}$	$i m_{j}^{S}$	${i m}^{Z}$	${i m}^{S}$
Value Added		$v_{i}^{Z}$	$v_{j}^{Z}$	$v_{i}^{S}$	$v_{j}^{S}$
Total Input		$x_{i}^{Z}$	$x_{j}^{Z}$	$x_{i}^{S}$	$x_{j}^{S}$

Table A7. World input–output tables including Guangdong–Hong Kong–Macao Greater Bay Area.

China’s Interprovincial Embeddedness in the World		Intermediate Demand										Final Demand					Total Output
		$Pearl River Delta (Z)$		$Other Cities in Guangdong (S)$		$Other Province of China (B)$		$Macao (M)$		$Other Regions of the World (A)$		$Pearl River Delta (Z)$	$Other Cities in Guangdong (S)$	$Other Province of China (B)$	$Macao (M)$	$Other Regions of the World (A)$
		$S_{i}$	$S_{j}$	$S_{i}$	$S_{j}$	$S_{i}$	$S_{j}$	$S_{i}$	$S_{j}$	$S_{i}$	$S_{j}$	$Pearl River Delta (Z)$	$Other Cities in Guangdong (S)$	$Other Province of China (B)$	$Macao (M)$	$Other Regions of the World (A)$
$Pearl River Delta (Z)$	$S_{i}$	$x_{i i}^{Z Z}$	$x_{i j}^{Z Z}$	$x_{i i}^{Z S}$	$x_{i j}^{Z S}$	${\dot{x}}_{i i}^{Z B}$	${\dot{x}}_{i j}^{Z B}$	${\dot{X}}_{i i}^{Z M}$	${\dot{X}}_{i j}^{Z M}$	${\dot{X}}_{i i}^{Z A}$	${\dot{X}}_{i j}^{Z A}$	$y_{i}^{Z Z}$	$y_{i}^{Z S}$	${\dot{y}}_{i}^{Z B}$	${\dot{Y}}_{i}^{Z M}$	${\dot{Y}}_{i}^{Z A}$	${\dot{X}}_{i}^{Z}$
$Pearl River Delta (Z)$	$S_{j}$	$x_{j i}^{Z Z}$	$x_{j j}^{Z Z}$	$x_{j i}^{Z S}$	$x_{j j}^{Z S}$	${\dot{x}}_{j i}^{Z B}$	${\dot{x}}_{j j}^{Z B}$	${\dot{X}}_{j i}^{Z M}$	${\dot{X}}_{j j}^{Z M}$	${\dot{X}}_{j i}^{Z A}$	${\dot{X}}_{j j}^{Z A}$	$y_{j}^{Z Z}$	$y_{j}^{Z S}$	${\dot{y}}_{j}^{Z B}$	${\dot{Y}}_{j}^{Z M}$	${\dot{Y}}_{j}^{Z A}$	${\dot{X}}_{j}^{Z}$
$Other Cities in Guangdong (S)$	$S_{i}$	$x_{i i}^{S Z}$	$x_{i j}^{S Z}$	$x_{i i}^{S S}$	$x_{i j}^{S S}$	${\dot{x}}_{i i}^{S B}$	${\dot{x}}_{i j}^{S B}$	${\dot{X}}_{i i}^{S M}$	${\dot{X}}_{i j}^{S M}$	${\dot{X}}_{i i}^{S A}$	${\dot{X}}_{i j}^{S A}$	$y_{i}^{S Z}$	$y_{i}^{S S}$	${\dot{y}}_{i}^{S B}$	${\dot{Y}}_{i}^{S M}$	${\dot{Y}}_{i}^{S A}$	${\dot{X}}_{i}^{S}$
$Other Cities in Guangdong (S)$	$S_{j}$	$x_{j i}^{S Z}$	$x_{j j}^{S Z}$	$x_{j i}^{S S}$	$x_{j j}^{S S}$	${\dot{x}}_{j i}^{S B}$	${\dot{x}}_{j j}^{S B}$	${\dot{X}}_{j i}^{S M}$	${\dot{X}}_{j j}^{S M}$	${\dot{X}}_{j i}^{S A}$	${\dot{X}}_{j j}^{S A}$	$y_{j}^{S Z}$	$y_{j}^{S S}$	${\dot{y}}_{j}^{S B}$	${\dot{Y}}_{j}^{S M}$	${\dot{Y}}_{j}^{S A}$	${\dot{X}}_{j}^{S}$
$Other Province of China (B)$	$S_{i}$	${\dot{x}}_{i i}^{B Z}$	${\dot{x}}_{i j}^{B Z}$	${\dot{x}}_{i i}^{B S}$	${\dot{x}}_{i j}^{B S}$	$x_{i i}^{B B}$	$x_{i j}^{B B}$	${\hat{X}}_{i i}^{B M}$	${\hat{X}}_{i j}^{B M}$	${\hat{X}}_{i i}^{B A}$	${\hat{X}}_{i j}^{B A}$	${\dot{y}}_{i}^{B Z}$	${\dot{y}}_{i}^{B S}$	$y_{i}^{B B}$	${\hat{Y}}_{i}^{B M}$	${\hat{Y}}_{i}^{B A}$	${\hat{X}}_{i}^{B}$
$Other Province of China (B)$	$S_{j}$	${\dot{x}}_{j i}^{B Z}$	${\dot{x}}_{j j}^{B Z}$	${\dot{x}}_{j i}^{B S}$	${\dot{x}}_{j j}^{B S}$	$x_{j i}^{B B}$	$x_{j j}^{B B}$	${\hat{X}}_{j i}^{B M}$	${\hat{X}}_{j j}^{B M}$	${\hat{X}}_{j i}^{B A}$	${\hat{X}}_{j j}^{B A}$	${\dot{y}}_{j}^{B Z}$	${\dot{y}}_{j}^{B S}$	$y_{j}^{B B}$	${\hat{Y}}_{j}^{B M}$	${\hat{Y}}_{j}^{B A}$	${\hat{X}}_{j}^{B}$
$Macao (M)$	$S_{i}$	${\dot{X}}_{i i}^{M Z}$	${\dot{X}}_{i j}^{M Z}$	${\dot{X}}_{i i}^{M S}$	${\dot{X}}_{i j}^{M S}$	${\hat{X}}_{i i}^{M B}$	${\hat{X}}_{i j}^{M B}$	${\tilde{X}}_{i i}^{M M}$	${\tilde{X}}_{i j}^{M M}$	${\tilde{X}}_{i i}^{M A}$	${\tilde{X}}_{i j}^{M A}$	${\dot{Y}}_{i}^{M Z}$	${\dot{Y}}_{i}^{M S}$	${\hat{Y}}_{i}^{M B}$	${\tilde{Y}}_{i}^{M M}$	${\tilde{Y}}_{i}^{M A}$	${\tilde{X}}_{i}^{M}$
$Macao (M)$	$S_{j}$	${\dot{X}}_{j i}^{M Z}$	${\dot{X}}_{j j}^{M Z}$	${\dot{X}}_{j i}^{M S}$	${\dot{X}}_{j j}^{M S}$	${\hat{X}}_{j i}^{M B}$	${\hat{X}}_{j j}^{M B}$	${\tilde{X}}_{j i}^{M M}$	${\tilde{X}}_{j j}^{M M}$	${\tilde{X}}_{j i}^{M A}$	${\tilde{X}}_{j j}^{M A}$	${\dot{Y}}_{j}^{M Z}$	${\dot{Y}}_{j}^{M S}$	${\hat{Y}}_{j}^{M B}$	${\tilde{Y}}_{j}^{M M}$	${\tilde{Y}}_{j}^{M A}$	${\tilde{X}}_{j}^{M}$
$Other Regions of the World (A)$	$S_{i}$	${\dot{X}}_{i i}^{A Z}$	${\dot{X}}_{i j}^{A Z}$	${\dot{X}}_{i i}^{A S}$	${\dot{X}}_{i j}^{A S}$	${\hat{X}}_{i i}^{A B}$	${\hat{X}}_{i j}^{A B}$	${\tilde{X}}_{i i}^{A M}$	${\tilde{X}}_{i j}^{A M}$	$X_{i i}^{A A}$	$X_{i j}^{A A}$	${\dot{Y}}_{i}^{A Z}$	${\dot{Y}}_{i}^{A S}$	${\hat{Y}}_{i}^{A B}$	${\tilde{Y}}_{i}^{A M}$	$Y_{i}^{A A}$	$X_{i}^{A}$
$Other Regions of the World (A)$	$S_{j}$	${\dot{X}}_{j i}^{A Z}$	${\dot{X}}_{j j}^{A Z}$	${\dot{X}}_{j i}^{A S}$	${\dot{X}}_{j j}^{A S}$	${\hat{X}}_{j i}^{A B}$	${\hat{X}}_{j j}^{A B}$	${\tilde{X}}_{j i}^{A M}$	${\tilde{X}}_{j j}^{A M}$	$X_{j i}^{A A}$	$X_{j j}^{A A}$	${\dot{Y}}_{j}^{A Z}$	${\dot{Y}}_{j}^{A S}$	${\hat{Y}}_{j}^{A B}$	${\tilde{Y}}_{j}^{A M}$	$Y_{j}^{A A}$	$X_{j}^{A}$
Value Added		${\dot{V}}_{i}^{Z}$	${\dot{V}}_{j}^{Z}$	${\dot{V}}_{i}^{S}$	${\dot{V}}_{j}^{S}$	${\hat{V}}_{i}^{B}$	${\hat{V}}_{j}^{B}$	${\tilde{V}}_{i}^{M}$	${\tilde{V}}_{j}^{M}$	$V_{i}^{A}$	$V_{j}^{A}$
Total Input		${\dot{X}}_{i}^{Z}$	${\dot{X}}_{j}^{Z}$	${\dot{X}}_{i}^{S}$	${\dot{X}}_{j}^{S}$	${\hat{X}}_{i}^{B}$	${\hat{X}}_{j}^{B}$	${\tilde{X}}_{i}^{M}$	${\tilde{X}}_{j}^{M}$	$X_{i}^{A}$	$X_{j}^{A}$

Note: Other regions of the world (

A

) include Hong Kong, and the Pearl River Delta (

Z

) includes nine cities: Guangzhou, Shenzhen, Foshan, Zhuhai, Jiangmen, Zhaoqing, Huizhou, Dongguan, and Zhongshan, along with Macao, forming the Greater Bay Area of Guangdong–Hong Kong–Macao.

From a vertical embedding approach:

\begin{array}{l} {\dot{x}}_{i i}^{B Z} = \frac{x_{i i}^{B G}}{x_{i i}^{B G} + x_{j i}^{B G}} * i_{i}^{Z}, {\dot{x}}_{j i}^{B Z} = \frac{x_{j i}^{B G}}{x_{i i}^{B G} + x_{j i}^{B G}} * i_{i}^{Z} \\ {\dot{y}}_{i}^{B Z} = \frac{y_{i}^{B G}}{y_{i}^{B G} + y_{j}^{B G}} * i^{Z}, {\dot{y}}_{j}^{B Z} = \frac{y_{j}^{B G}}{y_{i}^{B G} + y_{j}^{B G}} * i^{Z} \\ {\dot{X}}_{i i}^{M Z} = \frac{{\hat{X}}_{i i}^{M G}}{{\hat{X}}_{i i}^{M G} + {\hat{X}}_{j i}^{M G} + {\hat{X}}_{i i}^{A G} + {\hat{X}}_{j i}^{A G}} * i m_{i}^{Z}, {\dot{X}}_{j i}^{M Z} = \frac{{\hat{X}}_{j i}^{M G}}{{\hat{X}}_{i i}^{M G} + {\hat{X}}_{j i}^{M G} + {\hat{X}}_{i i}^{A G} + {\hat{X}}_{j i}^{A G}} * i m_{i}^{Z} \\ {\dot{Y}}_{i}^{M Z} = \frac{{\hat{Y}}_{i}^{M G}}{{\hat{Y}}_{i}^{M G} + {\hat{Y}}_{j}^{M G} + {\hat{Y}}_{i}^{A G} + {\hat{Y}}_{j}^{A G}} * i m^{Z}, {\dot{Y}}_{j}^{M Z} = \frac{{\hat{Y}}_{j}^{M G}}{{\hat{Y}}_{i}^{M G} + {\hat{Y}}_{j}^{M G} + {\hat{Y}}_{i}^{A G} + {\hat{Y}}_{j}^{A G}} * i m^{Z} \\ {\dot{V}}_{i}^{Z} = \frac{v_{i}^{Z}}{v_{i}^{Z} + v_{i}^{S}} * {\hat{V}}_{i}^{G}, {\dot{V}}_{i}^{S} = \frac{v_{i}^{S}}{v_{i}^{Z} + v_{i}^{S}} * {\hat{V}}_{i}^{G}, {\dot{V}}_{j}^{Z} = \frac{v_{j}^{Z}}{v_{j}^{Z} + v_{j}^{S}} * {\hat{V}}_{j}^{G}, {\dot{V}}_{j}^{S} = \frac{v_{j}^{S}}{v_{j}^{Z} + v_{j}^{S}} * {\hat{V}}_{j}^{G} \\ {\dot{X}}_{i}^{Z} = \frac{x_{i}^{Z}}{x_{i}^{Z} + x_{i}^{S}} * {\hat{X}}_{i}^{G}, {\dot{X}}_{i}^{S} = \frac{x_{i}^{S}}{x_{i}^{Z} + x_{i}^{S}} * {\hat{X}}_{i}^{G}, {\dot{X}}_{j}^{Z} = \frac{x_{j}^{Z}}{x_{j}^{Z} + x_{j}^{S}} * {\hat{X}}_{j}^{G}, {\dot{X}}_{j}^{S} = \frac{x_{j}^{S}}{x_{j}^{Z} + x_{j}^{S}} * {\hat{X}}_{j}^{G} \end{array}

From a horizontal embedding approach:

\begin{array}{l} {\dot{x}}_{i i}^{Z B} = \frac{x_{i i}^{G B}}{x_{i i}^{G B} + x_{i j}^{G B} + y_{i}^{G B}} * o_{i}^{Z}, {\dot{x}}_{i j}^{Z B} = \frac{x_{i j}^{G B}}{x_{i i}^{G B} + x_{i j}^{G B} + y_{i}^{G B}} * o_{i}^{Z}, {\dot{y}}_{i j}^{Z B} = \frac{y_{i}^{G B}}{x_{i i}^{G B} + x_{i j}^{G B} + y_{i}^{G B}} * o_{i}^{Z} \\ {\dot{X}}_{i i}^{Z M} = \frac{{\hat{X}}_{i i}^{G M}}{{\hat{X}}_{i i}^{G M} + {\hat{X}}_{i j}^{G M} + {\hat{X}}_{i i}^{G A} + {\hat{X}}_{i j}^{G A} + {\hat{Y}}_{i}^{G M} + {\hat{Y}}_{i}^{G A}} * e x_{i}^{Z}, {\dot{X}}_{i j}^{Z M} = \frac{{\hat{X}}_{i j}^{G M}}{{\hat{X}}_{i i}^{G M} + {\hat{X}}_{i j}^{G M} + {\hat{X}}_{i i}^{G A} + {\hat{X}}_{i j}^{G A} + {\hat{Y}}_{i}^{G M} + {\hat{Y}}_{i}^{G A}} * e x_{i}^{Z}, {\dot{Y}}_{i i}^{Z M} = \frac{{\hat{Y}}_{i}^{G M}}{{\hat{X}}_{i i}^{G M} + {\hat{X}}_{i j}^{G M} + {\hat{X}}_{i i}^{G A} + {\hat{X}}_{i j}^{G A} + {\hat{Y}}_{i}^{G M} + {\hat{Y}}_{i}^{G A}} * e x_{i}^{Z} \end{array}

Due to space constraints, this paper has provided a brief description of the calculation methods for splitting and embedding input–output tables. The approach for splitting and embedding with similar symbols remains consistent. Using the above splitting and embedding methods, an input–output table containing the Guangdong–Hong Kong–Macao Greater Bay Area that applies to this study of this paper will be produced, which provides the data basis for this paper to measure the Guangdong–Hong Kong–Macao Greater Bay Area’s global value chain position.

Notes

1

China’s 23 major trading partners are the United States, Japan, South Korea, Germany, Australia, Vietnam, Malaysia, Brazil, India, Russia, Thailand, Singapore, the United Kingdom, the Netherlands, Indonesia, France, Canada, the Philippines, Italy, Mexico, Switzerland, Spain and Poland.

2

The 19 sectors are: C1, agriculture; C2, mining; C3, food and tobacco manufacturing; C4, textiles, garments, and leathers and their products; C5, wood processing, paper, and paper products; C6, petroleum, chemical, and non-metallic products; C7, metals and their products, C8, electrical and machinery manufacturing; C9, transportation equipment manufacturing; C10, other manufacturing; C11, electricity, gas, water supply; C12, construction; C13, wholesale and retail trade; C14, accommodation and catering; C15, transportation services; C16 postal and telecommunications; C17, business services; C18, public services; and C19, education, health, and other services.

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Figure 1. Theoretical mechanisms.

Figure 2. Changes in the global value chain positions of 11 cities in the Greater Bay Area.

Figure 3. Changes in the global value chain positions of 19 sectors in the Greater Bay Area.

Table 1. Three levels of nested input–output table-specific structures.

			Intermediate Demand																								Final Demand
			PRD			MO			HK			GD			CP			TWN			W			ROW			PRD			MO			HK			GD			CP			TWN			W			ROW
			$S_{1}$	…	$S_{19}$	$S_{1}$	…	$S_{19}$	$S_{1}$	…	$S_{19}$	$S_{1}$	…	$S_{19}$	$S_{1}$	…	$S_{19}$	$S_{1}$	…	$S_{19}$	$S_{1}$	…	$S_{19}$	$S_{1}$	…	$S_{19}$	$Y_{1}$	…	$Y_{19}$	$Y_{1}$	…	$Y_{19}$	$Y_{1}$	…	$Y_{19}$	$Y_{1}$	…	$Y_{19}$	$Y_{1}$	…	$Y_{19}$	$Y_{1}$	…	$Y_{19}$	$Y_{1}$	…	$Y_{19}$	$Y_{1}$	…	$Y_{19}$
Intermediate Input	PRD	$S_{1}$
		…
		$S_{19}$
	MO	$S_{1}$
		…
		$S_{19}$
	HK	$S_{1}$
		…
		$S_{19}$
	GD	$S_{1}$
		…
		$S_{19}$
	CP	$S_{1}$
		…
		$S_{19}$
	TWN	$S_{1}$
		…
		$S_{19}$
	W	$S_{1}$
		…
		$S_{19}$
	ROW	$S_{1}$
		…
		$S_{19}$
VA
X

Note: PRD stands for the Pearl River Delta, MO stands for Macau, HK stands for Hong Kong, GD stands for other cities in Guangdong Province, CP stands for other provinces in China, TWN stands for Taiwan, and W stands for other countries and regions in the world, ROW stands for rest of the world.

Table 2. Components of the Guangdong–Hong Kong–Macao Greater Bay Area’s Participation in the Global Value Chain.

Sources of Added Value	Code	General Term
Added Value of City $r$ in the Greater Bay Area	$i v w$ , $v r w$ , $i v w^{'}$ , $v r w^{'}$ , $i v w^{″}$ , $v r w^{″}$	$D V$
Inbound Added Value	$o v w$ , $o v w^{'}$ , $o v w^{″}$	$F V$
Double-Counted Portion	$d c w$ , $d c w^{'}$ , $d c w^{″}$	$D C$

Table 3. Benchmark test results.

Variables	(1) lnGVC_POS	(2) lnGVC_POS	(3) lnGVC_POS	(4) lnGVC_POS
lnDIG	0.0276 ***	0.0165 ***	0.0351 ***	0.0160 ***
lnDIG	(0.0043)	(0.0058)	(0.0042)	(0.0057)
lnGDP			−0.0131	0.0461
lnGDP			(0.0180)	(0.0965)
lnIM			−0.0503 ***	0.1934 ***
lnIM			(0.0124)	(0.0513)
lnEX			−0.0183 *	−0.2292 ***
lnEX			(0.0098)	(0.0693)
lnEMP			−0.0158	−0.0192
lnEMP			(0.0191)	(0.0155)
ConstantTerm	−0.3028 ***	−0.2271 ***	−0.0616	−0.4034
ConstantTerm	(0.0113)	(0.0280)	(0.1591)	(0.8680)
Time Fixed Effects	No	Yes	No	Yes
City Fixed Effects	No	Yes	No	Yes
Industry Fixed Effects	No	Yes	No	Yes
N	627	627	627	627
r²	0.0610	0.6926	0.1989	0.7019
F	40.6004	43.2474	30.8299	39.7516

Standard errors in parentheses. * p < 0.10, *** p < 0.001.

Table 4. Robustness test results.

Methods	(1) Replace the Explained Variable	(2) Replace the Core Explanatory Variable	(3) Replace the Regression Method	(4) Tailing Treatment
lnDIG	0.0136 **		0.0152 ***	0.0156 ***
lnDIG	(0.0060)		(0.0057)	(0.0057)
lnDIG_NEW		0.0117 *
lnDIG_NEW		(0.0068)
Anderson Canon. Corr. LM Statistic
Anderson Canon. Corr. LM Statistic
Cragg–Donald Wald F Statistic
Cragg–Donald Wald F Statistic
Constant Term	0.7521	−0.3734	−0.3744	−0.4817
Constant Term	(0.9153)	(0.8723)	(0.8980)	(0.8564)
Time Fixed Effects	Yes	Yes	Yes	Yes
City Fixed Effects	Yes	Yes	Yes	Yes
Industry Fixed Effects	Yes	Yes	Yes	Yes
Time × City Fixed Effects	No	No	No	No
Time × Industry Fixed Effects	No	No	No	No
N	627	627	627	627
r²	0.3270	0.6994	0.7147	0.7045
F	8.2048	39.2911	42.2952	40.2526

Standard errors in parentheses. * p < 0.10, ** p < 0.05, *** p < 0.001.

Table 5. Endogeneity test results.

Methods	High-Order Joint Fixation Effect	Instrumental Variable Method
	High-Order Joint Fixation Effect	First Step	Second Step	First Step	Second Step
	(1)	(2)	(3)	(4)	(5)
IV1		0.0000 ***
IV1		(0.0000)
lnDIG	0.0176 ***		0.0552 *		0.0238 ***
lnDIG	(0.0058)		(0.0321)		(0.0068)
IV2				0.7708 ***
IV2				(0.0227)
Anderson Canon. Corr. LM Statistic			19.963		415.104
Anderson Canon. Corr. LM Statistic			(0.0000)		(0.0000)
Cragg–Donald Wald F Statistic			19.395		1157.772
Cragg–Donald Wald F Statistic			(16.38)		(16.38)
Constant Term	0.4289	1.5998	−0.7331	−7.3215 **	−0.3879
Constant Term	(31.5896)	(5.7666)	(0.8168)	(3.6253)	(0.8441)
Time Fixed Effects	Yes	Yes	Yes	Yes	Yes
City Fixed Effects	Yes	Yes	Yes	Yes	Yes
Industry Fixed Effects	Yes	Yes	Yes	Yes	Yes
Time × City Fixed Effects	Yes	No	No	No	No
Time × Industry Fixed Effects	Yes	No	No	No	No
N	627	513	513	627	627
r²	0.7496	0.8581	0.7301	0.9354	0.7009
F	18.5511	87.7588	39.5495	244.4585	39.7309

Standard errors in parentheses. * p < 0.10, ** p < 0.05, *** p < 0.001.

Table 6. Omitted variable test results.

	Coefficient		R²
	Uncontrolled	Controlled	Uncontrolled	Controlled	δ
GVC_POS	0.02755	0.03506	0.061	0.199	−5.98071

Table 7. Mechanism test results.

Mechanism	Benchmark Test	Technological Innovation Transformation Capability		Value Added by Exports from the Greater Bay Area
Mechanism	(1) lnGVC_POS	(2) lnTECH	(3) lnGVC_POS	(4) lnTECH	(5) lnGVC_POS
lnDIG	0.0160 ***	0.5368 ***	0.0151 ***	0.2053 ***	0.0106 **
lnDIG	(0.0057)	(0.0815)	(0.0057)	(0.0708)	(0.0051)
lnTECH			0.0068 **
lnTECH			(0.0028)
lnVA					0.0337 ***
lnVA					(0.0029)
Constant Term	−0.4034	11.9465	−0.3807	1.1003	−0.3659
Constant Term	(0.8680)	(12.3131)	(0.8326)	(10.7063)	(0.7662)
Time Fixed Effects	Yes	Yes	Yes	Yes	Yes
City Fixed Effects	Yes	Yes	Yes	Yes	Yes
Industry Fixed Effects	Yes	Yes	Yes	Yes	Yes
N	627	623	623	626	626
r²	0.7019	0.8032	0.7259	0.6968	0.7664
F	39.7516	68.4440	43.1166	38.7353	53.6886

Standard errors in parentheses. ** p < 0.05, *** p < 0.001.

Table 8. Heterogeneity analysis results.

Category	Heterogeneity of Sources of Digital Transformation			Heterogeneity of City
Category	(1) lnGVC_POS	(2) lnGVC_POS	(3) lnGVC_POS	(4) lnGVC_POS
lnDIG				0.0117 *
lnDIG				(0.0061)
lnCDIG	0.0216 ***
lnCDIG	(0.0063)
lnNDIG		0.0181 **
lnNDIG		(0.0080)
lnGDIG			0.0125
lnGDIG			(0.0080)
lnDIG × CITY				0.0147 **
lnDIG × CITY				(0.0070)
Constant Term	−0.4504	−0.3917	−0.4139	−0.4142
Constant Term	(0.8652)	(0.8701)	(0.8720)	(0.8655)
Time Fixed Effects	Yes	Yes	Yes	Yes
City Fixed Effects	Yes	Yes	Yes	Yes
Industry Fixed Effects	Yes	Yes	Yes	Yes
N	627	627	627	627
r²	0.7038	0.7005	0.6992	0.7041
F	40.1155	39.5027	39.2429	38.9943

Standard errors in parentheses. * p < 0.10, ** p < 0.05, *** p < 0.001.

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Li, X.; Tan, M. The Impact of Digital Transformation on the Global Value Chain Position of the Guangdong–Hong Kong–Macao Greater Bay Area. Systems 2024, 12, 223. https://doi.org/10.3390/systems12060223

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Li X, Tan M. The Impact of Digital Transformation on the Global Value Chain Position of the Guangdong–Hong Kong–Macao Greater Bay Area. Systems. 2024; 12(6):223. https://doi.org/10.3390/systems12060223

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Li, Xiumin, and Minshan Tan. 2024. "The Impact of Digital Transformation on the Global Value Chain Position of the Guangdong–Hong Kong–Macao Greater Bay Area" Systems 12, no. 6: 223. https://doi.org/10.3390/systems12060223

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The Impact of Digital Transformation on the Global Value Chain Position of the Guangdong–Hong Kong–Macao Greater Bay Area

Abstract

1. Introduction

2. Theoretical Mechanism

2.1. The Impact of Digital Transformation on the Global Value Chain Position of the Guangdong–Hong Kong–Macao Greater Bay Area

2.2. The Mechanism of Digital Transformation on the Global Value Chain Position of the Guangdong–Hong Kong–Macao Greater Bay Area

2.2.1. Technology Innovation Transformation Capability

2.2.2. Value Addition of Exports from the Greater Bay Area

3. Data Preparation, Calculation Model Establishment, and Application

3.1. Data Preparation: Compilation of Three-Layer Nested Input–Output Tables for Guangdong–Hong Kong–Macao Greater Bay Area

3.2. Establishment of the Calculation Model

3.2.1. Prerequisite for Establishing the Calculation Model: Establishment of the Trade Decomposition Model

3.2.2. Establishing the Measurement Model: Measurement Model for the Global Value Chain Position of the Guangdong–Hong Kong–Macao Greater Bay Area

3.2.3. Application of the Measurement Model: Analysis of the Global Value Chain

Position of the Guangdong–Hong Kong–Macao Greater Bay Area

4. Empirical Model Setting, Variable Selection and Data Description

4.1. Empirical Model Setting

4.1.1. Baseline Model

4.1.2. Mediation Model

4.2. Variable Selection

4.2.1. Explained Variable

4.2.2. Core Explanatory Variables

4.2.3. Control Variables

4.2.4. Mediating Variables

4.3. Data Description

5. Analysis of Empirical Test Results

5.1. Benchmark Test

5.2. Robustness Test

5.2.1. Replace the Measurement Method of Explained Variables

5.2.2. Replace the Measurement Method of Core Explanatory Variables

5.2.3. Replace the Regression Method

5.2.4. Tailing Treatment

5.3. Endogeneity Test

5.3.1. High-Order Joint Fixed Effect Model Test

5.3.2. Instrumental Variable Method

5.3.3. Omitted Variable Test

5.4. Mechanism Test

5.4.1. Technological Innovation Transformation Capability

5.4.2. Value Added by Exports from the Greater Bay Area

5.5. Heterogeneity Analysis

5.5.1. Heterogeneity of Sources of Digital Transformation

5.5.2. Heterogeneity of Cities

6. Conclusions and Recommendation

6.1. Conclusions

6.2. Recommendation

Author Contributions

Funding

Data Availability Statement

Acknowledgments

Conflicts of Interest

Appendix A

Notes

References

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