Introduction

The blue planet inhabited by human beings is connected by the ocean to a community with a shared future, and people of all countries share its dangers and safety together. With the continuous development of the global economy and intensification of resource consumption, the demand for marine resources is increasing, which makes marine resources a new hot spot for competition among countries (Bennett et al., 2022; Chen et al., 2023; Morrissey, 2014; Morrissey and O’Donoghue, 2013). In this context, as a sustainable emerging economic model, the blue economy has received increasing attention from many countries (Bennett et al., 2019; Ni et al., 2024; United Nations, 2015). The blue economy utilizes marine resources to create economic wealth, employment opportunities, and medical services, and it includes traditional activities such as shipping (Chen et al., 2024) and emerging industries such as seabed exploration and biotechnology (Daffonchio, 2016; Yu, 2024). As a complex system, the blue economy includes not only the relationships within subsystems such as products, firms and provinces but also the relationships between subsystems. These internal and external relationships are dynamic; that is, as time changes, the elements within and between subsystems continue to interact dynamically with the external environment, causing the entire system to gradually evolve from initial chaos and disorder to a final stable and orderly structure, and the concept of the national blue economic system (i.e., NBES) has been developed. The NBES refers to a country’s blue economy as a complex system of sustainable development, which involves a series of multiagent activities that directly or indirectly provide key services for the development of the ocean and its related resources (Qi, 2022a, 2024). The development level of the blue economy in different countries depends on their resource endowments and the relationship structure among their industries, products, firms, provinces, and other agents with different attributes (Qi, 2022a). In the NBES, diversification is an important indicator used to measure the level of blue economic development and international competitiveness of a country, which reflects the richness of the marine industry, resource development and the utilization and diversification and robustness of the economic structure of a country (Qi et al., 2020).

In the description of exports that view diversification as endogenous (Cadot et al., 2011), there are significant differences in economic diversification among countries (Regolo, 2013; Steinberg, 2023). Most studies agree that there is a positive correlation between diversification and export growth, including export diversification as a determinant of economic growth in low-income countries (Eicher and Kuenzel, 2016; Ye et al., 2024). In fact, the growth experience of some developing countries shows that the long-term economic growth of a country depends on how much it exports and what it exports (Albornoz et al., 2023; Hazwan et al., 2023). Related developments in trade theory have also emphasized that the benefits of trade come more from the expansion of new products than from the volume of exports and the increase in U.S. dollar earnings (Broda and Weinstein, 2006; Funke and Ruhwedel, 2001; Hummels and Klenow, 2005). Countries with generally more “complex” export mixes appear to have faster economic growth (Hidalgo et al., 2007; Rodrik, 2006) and tend to perform better in the export value chain (Bahar et al., 2019; Hausmann et al., 2007). The complexity of this export diversification depends on the established production capacity (Brandt and Lim, 2024; Hausmann et al., 2019; Hidalgo, 2021; Tacchella et al., 2013) and the comparative advantage distance among different income levels (Cieślik and Parteka, 2021).

In these discussions, the natural resource sector is considered real estate without product differentiation, innovation, or knowledge externalities (Morrissey et al., 2014). However, unlike other industrial sectors (Wang et al., 2022), the ocean and its related sectors are characterized by high technology and an emphasis on green, sustainable and high value-added blue economic activities (Golden et al., 2017; Morrissey et al., 2011). Therefore, understanding the capabilities associated with the diversification of countries’ exports is essential for the development of the blue economy and the transformation of the industrial structure (Ni et al., 2024; Qi et al., 2020). Many developing countries continue to rely on a narrow range of primary blue products (Qi, 2022b). On average, low-income countries have much less export diversification than high-income countries do (Hidalgo et al., 2007). Moreover, the diversification development level of the blue economy is directly proportional to income, i.e., countries with higher incomes have more diversification in the blue economy and vice versa (Qi, 2022b). Although these assertions drawn from the product level have enriched our basic understanding of the diversification of the blue economy, we must systematically evaluate the diversification of the blue economy among multiple agents from the perspective of the NBES.

On the basis of the diversification characteristics of the NBES (Qi, 2024), this paper designs a nonmonetary index system to quantitatively measure the diversification of blue products, blue firms and blue provinces in the development of a country’s blue economy and uses China as an example for verification. Although this nonmonetary indicator system cannot replace traditional marine resource analysis tools (such as GDP, GVA, and employment), it is a supplementary analytical tool to measure the development of the blue economy. This study expands the analysis field of traditional marine resources and enables countries to analyze their ability to diversify the production and export of blue products, identify the types of products that can be exported by blue firms, and help provinces develop policies for the diversified development of the blue industry. In particular, from a methodological viewpoint, this work provides a more detailed and structured method to measure multiagent diversification in the NBES and enriches the measurement methods used to assess the development of the blue economy in relevant countries. Thus, it provides effective assistance for national, provincial and enterprise decision-makers in the development of the blue industry and its products and provides important theoretical and practical suggestions for the development of blue industries and blue products to continue to invest in and maintain the export basket.

The remainder of this paper is as follows. Section “Theoretical foundation” analyzes the relationship between the NBES and economic diversification theories. Section “Data and methods” presents the data and designs the methodology. Section “Results” uses China as an example to analyze the empirical results on the basis of data from 2010. Section “Discussion” discusses the empirical evolution results in China from 1985 to 2018. Section “Conclusions and limitations” summarizes the study.

Theoretical foundation

As a complex economic system centered on the sustainable development of marine resources, the NBES aims to achieve economic diversification and sustainable development through the collaboration of multiple agents. As shown in Table 1, it has multidimensional correlations with existing economic diversification theories. This is a “blue expansion” of economic diversification theories, and it is also a systematic practice of economic diversification theories in the ocean scenario. It should be made clear that the NBES is not the application of a single theory but rather a diversified framework that integrates theories such as industrial structure transformation, endogenous growth, and regional development. Through the trinity of “economic diversification + ecological sustainability + regional balance”, it collaborates across disciplines and borders to solve complex challenges (such as the disorderly exploitation of marine resources) and achieve the resilient development of a maritime community with a shared future maritime.

Table 1 Relationship between the NBES and economic diversification theories.
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Data and methods

Data

The blue product category and blue firm category in this work used 116 and 324 classification data points proposed by Qi (2024a), respectively. The trade data were from the Altas dataset of the Harvard Growth Lab, which records the import and export trade of 250 countries and regions around the world in detail and subdivides products according to the Standard International Trade Classification (i.e., SITC) into several categories. This dataset is currently the world’s relatively authoritative dataset. In this work, the 4-digit product data in the dataset were filtered, and the product data with no trade value codes were excluded.

Methods

The export diversification of the NBES is a diversified process composed of the internal and interrelated structures of products, firms and provinces (Qi, 2022a).

Blue product diversification

  1. (1)

    Maximum proximity matrix for blue products

    Proximity is an effective indicator for measuring the technological differences between any two products in the export basket of a country/region. Fraccascia et al. (2018) and Qi et al. (2020) argued that even if there is high proximity between products, the average value between products might be low, which is not a correct measure of the production capacity required by a country to develop a product, and the maximum value of proximity between products and products with high revealed comparative advantages (i.e., RCA > 1) can be used to evaluate the production capacity of a country and its firms for a product. Therefore, in year t, the maximum proximity of blue product i exported by blue firm bf in country \(c\) to product j with RCA > 1 is calculated as follows:

    $${\vartheta }_{b{f}_{i}}^{c}(t)=\,{\rm{Max}}{\{{\phi }_{i,j}^{c}(t)\}}_{\forall j\in \prod (bf)}$$
    (1)

    Here, Π(bf) is the product set with RCA > 1 exported by blue firm bf.

    Matrix kbj × tbp is defined as the maximum proximity matrix ϑMax of the blue product, where kbj is the number of blue firms, tbp is the number of blue products exported by a country, and any element in the matrix is the maximum proximity between the blue products and products with RCA > 1 that the blue firm exports.

  2. (2)

    Measurement of blue product diversification

    Product diversification is a source of economic growth (He et al., 2016). Because different countries and regions have different resource endowments, infrastructures and technological capabilities, the blue industry and its product development activities are diverse in the economic development process of each country, especially in developing and developed countries (Qi et al., 2020). To strengthen the development of blue products and upgrade the blue industrial structure, it is first necessary to clarify the blue products that are consistent with the development characteristics of each country and have the greatest growth potential (Qi, 2024). Therefore, blue product diversification (i.e., BPD) is defined on the basis of matrix ϑMax. BPD is the number of blue products in a country’s export basket. A higher BPD value corresponds to more diverse blue product exports of a country. The BPD is calculated as follows:

    $$BPD={t}_{bp}$$
    (2)

    This method aims to extrapolate information about the production capacity and industrial structure of the countries with blue industries and their products by comparing the relative quantities of blue products in the export baskets of countries.

Blue firm diversification

On the basis of the diversity index of green product development (Fraccascia et al., 2018), to further measure the current and future production and export capacity of blue firms for blue products, blue firm diversification (i.e., BFD) and blue firm diversity development (i.e., BFDD) are defined on the basis of matrix ϑMax. BFD is the number of blue products produced and exported by relevant blue firms in a country (Qi et al., 2020). A higher BFD value corresponds to more diverse blue products exported by blue firms. BFD is calculated as follows:

$$BFD=\mathop{\sum }\limits_{p=1}^{p}{\chi }_{p},\,where\left\{\begin{array}{c}{\chi }_{p}=1,\,if\,{\vartheta }_{tk}^{MAX}=1\\ {\chi }_{p}=0,\,if\,{\vartheta }_{tk}^{MAX} < 1\end{array}\right.$$
(3)

BFDD is the number of blue products with element values of 0.5–1 in the maximum proximity matrix (Qi et al., 2020). BFDD indicates how blue firms can further diversify their existing blue products in the coming years. A larger BFDD value corresponds to more categories of blue products that blue firms can produce and export in the future and more blue products that can be added to a country’s export basket. BFDD is calculated as follows:

$$BFDD=\mathop{\sum }\limits_{p=1}^{p}{\mu }_{p},where\left\{\begin{array}{c}{\mu }_{p}=1,\,if\,0.5\le {\vartheta }_{tk}^{MAX} < 1\\ {\mu }_{p}=0,\,if\,{\vartheta }_{tk}^{MAX} < 0.5\end{array}\right.$$
(4)

These two methods aim to infer the production capacity of relevant blue firms for existing and potential blue products by comparing the number of blue products exported by blue firms.

Blue province diversification

The specialized development of the blue industry in each province of a country can benefit its blue economy, but excessive specialization can make the development of the blue economy in each province extremely dependent on a certain blue industry, which makes the provinces with too single blue industrial structures fall into the “lock-in effect” of industrial development (Martin and Sunley, 2006). This strict locking of the blue industrial structure in each province will lead to unstable growth of the blue economy, where the specialized development mode of the blue industry cannot dynamically adapt to changes in the environment. This phenomenon is particularly serious for developing countries in the economic transition period (Essletzbichler, 2004), but “regional industrial diversification can avoid the impact of economic fluctuations caused by industrial structure changes” (Zhang, 2017) and radiate and drive other related blue provinces through diverse externalities of the blue industrial structure of a province. The diversification of blue industries can play an important role in the growth of the blue economy in various provinces, and this role can be indirectly measured through the diversification of blue firms.

On the basis of BFD, BFDD and enterprise geographical affiliation information, blue province diversification (i.e., BProvD) and blue province diversity development (i.e., BProvDD) are defined. BProvD is the total number of blue products in the blue industry in which a blue province has a competitive advantage, i.e., the BFD values of the same blue province are summed. A higher BProvD value corresponds to more diverse blue products exported by the blue province. BProvD is calculated as follows:

$$BProvD=\mathop{\sum }\limits_{p=1}^{p}{\psi }_{p},\,where \left \{\begin{array}{l}{\psi }_{p}=BF{D}_{p},\,if\,b{f}_{p}\in bprov\\ {\psi }_{p}=0,\,if\,b{f}_{p}\notin bprov\end{array}\right.$$
(5)

BProvDD is the total amount of blue products that can be further diversified in a blue province in the future, i.e., the BFDD values of the same blue province are summed. Thus, blue provinces can further diversify their existing blue products from different blue industries in the coming years. A larger BProvDD value corresponds to more blue industries for which blue provinces can encourage investment and development in the future and more types of blue products that can be added to the province’s export basket in the future. BProvDD is calculated as follows:

$$BProvDD=\mathop{\sum }\limits_{p=1}^{p}{\varpi }_{p},\,where \left \{\begin{array}{l}{\varpi }_{p}=BFD{D}_{p},\,if\,b{f}_{p}\in bprov\\ {\varpi }_{p}=0,\,if\,b{f}_{p}\notin bprov\end{array}\right.$$
(6)

Owing to the diversification of firm exports, i.e., the same product can be produced and exported by multiple blue firms, BProvD and BProvDD may be greater than those of the 116 blue products mentioned in this work. The two methods aim to compare the total number of blue product categories exported by blue provinces to infer the development capacity of relevant blue provinces in the existing and potential blue industries for different blue products.

Results

Blue product diversification analysis

On the basis of the maximum proximity matrix \({\vartheta }^{Max}(2010)\) of blue products, BPD is the number of blue products in a country’s export basket. Since China exported all 116 blue products in 2010, the BPD of China in 2010 was 116 according to Eq. (2) (i.e., BPD = 116). The BPD value is sufficiently large to indicate that the blue product structure exported by China is very diverse and covers eight blue industries: fisheries and aquaculture, marine chemical, marine salt, seabed mining, marine (blue) energy, marine agroforestry, shipping and transport, and coastal manufacturing. China can introduce policies to continue guiding the concentration of social capital and technology in these industries with the revealed comparative advantages to enhance its international competitiveness.

On the basis of the 2010 world trade data and China’s trade data, the blue product space is plotted so that all colored circles represent their export shares in terms of world trade and China’s trade in 2010. Figure 1 shows the spatial distribution map of the export structure of the world’s and China’s blue products in 2010. Not all gray nodes were covered in the analysis in this work. Figure 1 clearly shows the importance and distribution of blue products in the product space. In 2010, people focused mainly on marine (blue) energy, shipping and transport, coastal manufacturing and seabed mining in terms of production and consumption, among which most products of coastal manufacturing and some related products of shipping and transport were located at the core of the blue product space, whereas other related products of blue industries were distributed at the periphery of the blue product space. Figure 1 reports that in the context of international blue product market competition, China exported many of the eight major blue industries in 2010. The related products in blue industries, such as coastal manufacturing, shipping and transport, marine chemical, and fisheries and aquaculture, accounted for 77.3%, 11.9%, 5.8%, and 3.5% of the blue export basket, respectively. More than 50 blue products firmly occupied the core of the product space, among which 39 blue products had an RCA > 1, indicating that these products were internationally competitive.

Fig. 1: Blue product space: world and China.
figure 1

Schematic diagram of the blue product space in the world and in China in 2010.

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To further analyze the export situation of these blue products with the revealed comparative advantages, Table 2 shows the blue product information. In the fisheries and aquaculture industry, the highest proportion of exports was “fish fillets, frozen”. In the marine chemical industry, the highest proportion of exports was “metallic salts and peroxysalts of inorganic acids”. In the shipping and transport industry, the export proportion of “ships, boats and other vessels” was 2.02%. In the coastal manufacturing industry, the export of “complete digital data processing machines” accounted for 5.88%, and it was the most competitive, followed by the export proportion of “parts, nes of and accessories for apparatus falling in heading 76”, which accounted for 3.32%. This information illustrates that China has considerable international competitiveness and market share in shipping, transport and coastal manufacturing, and China can continue to invest in these industries to strengthen its market position.

Table 2 Information on blue products with an RCA > 1 in China’s exports in 2010.
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Blue firm diversification analysis

On the basis of Eqs. (3) and (4) and the maximum proximity matrix ϑMax (2010) of China’s blue products in 2010, the BFD and BFDD values of 302 blue firms were calculated. The BFD and BFDD on the coordinate axis in Fig. 2 show that blue firm SH20 exported the most blue products with an RCA > 1 (12 types), whereas SD24, SH33, and ZJ5 exported 10 blue products with an RCA > 1. However, 68 blue firms exported the least blue products with an RCA > 1 (0 types); i.e., the blue products exported by these blue firms were not internationally competitive. Blue firms SD14, SH33, and BJ13 can further expand their blue product categories with comparative advantages in the future (6 types), whereas 138 blue firms will no longer be able to produce blue products with comparative advantages in the future (0 types); i.e., they can only maintain their current category of producing and exporting blue products with an RCA > 1.

Fig. 2: Coordinate distribution of the BFD and BFDD.
figure 2

Distribution of the BFD and BFDD coordinates of 302 blue firms in China in 2010. Note: The red line represents the average BFD and BFDD values.

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Tables 3 and 4 show the classification of blue firms on the basis of the BFD and BFDD values (excluding 0 values), respectively. Both categories show an “inverted pyramid” pattern: the number of blue firms with high diversity and high development is the lowest, the number of blue firms with medium diversity and medium development is high, and the number of blue firms with low diversity and low development is the highest. This model also shows that 214 blue firms in China currently operate with a small number of internationally competitive blue products and that 144 blue firms cannot further develop blue products with comparative advantages in the future due to resource, technological and financial constraints.

Table 3 BFD levels.
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Table 4 BFDD levels.
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The average BFD and BFDD values (\(\overline{BFD}=1.1\) and \(\overline{BFDD}=1.8\)) in Fig. 2 indicate that each blue business currently exports 1.8 blue products and is likely to increase the production of 1.1 blue products in the future. The BFD and BFDD averages divide 302 blue firms into 4 groups: ① The blue firms with high BFD and high BFDD values (50 firms) represent the group characterized by various exports of blue products with comparative advantages and can continue to expand the categories of blue products with comparative advantages in the future. ② The blue firms with high BFD and low BFDD values (76 firms) represent the group with nearly the most exports of blue products with comparative advantages and fewer blue products with comparative advantages that can be developed in the future. ③ The blue firms with low BFD and high BFDD values (41 firms) represent the group with fewer exports of blue products with comparative advantages, and more room to expand to additional blue products with comparative advantages can be expanded in the future, and such firms have greater development potential. ④ The blue firms with low BFD and low BFDD values (135 firms) represent the group that currently is limited to the specialized production and export of several blue products, and the potential to enrich the types of blue products with comparative advantages is small.

Blue province diversification analysis

On the basis of Eqs. (5) and (6) and the BFD values and BFDD values, the BProvD and BProvDD values of 25 blue provinces (autonomous regions and municipalities) in China in 2010 were calculated, and the indicators are plotted on the coordinate axes in Fig. 3. Guangdong (GD) is the province with the highest level of development of the blue industries and their products (119 types) because it has continuously optimized the traditional marine industry, paid attention to strategic emerging marine industries, formed a blue industrial system with strong international market competitiveness and adjusted the proportion of “the first, second, and third marine industries from 23:40:37 in 2005 to 10:42:48 in 2010”Footnote 1 to ensure the sustained and stable growth of the blue economy. Jilin (JL) and Yunnan (YN) are the provinces with the lowest degree of development of blue industries and their products (0 type); i.e., the blue industries developed by these two provinces and the blue products that they export are not internationally competitive. The structure of the blue industry introduces social investment and knowledge and technology to enhance the comparative advantages of related blue industries.

Fig. 3: Coordinate distribution of the BProvD and BProvDD.
figure 3

Distribution of the BProvD and BProvDD coordinates in 25 blue provinces (autonomous regions and municipalities) in China in 2010. Note: The red line represents the average BProvD and BProvDD values.

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The average BProvD and BProvDD values (\(\overline{BProvD}=13\) and \(\overline{BProvDD}=21.6\)) in Fig. 3 indicate that each blue province currently exports 13 blue products and is likely to increase the production of 21.6 blue products in the future. Similarly, the average of the BProvD and BProvDD values divides 25 blue provinces (autonomous regions and municipalities) into 4 groups: ① The blue provinces (6 provinces) with high BProvD and high BProvDD values represent the group with diverse blue industries and high export of their products with comparative advantages and can continue to expand their product basket with comparative advantages in the future. ② The blue provinces (0 provinces) with high BProvD and low BProvDD values represent the group with the near maximum number of blue industries and high export of their products with comparative advantages and fewer blue products with comparative advantages that can be developed in the future. This finding indicates that China currently does not have provinces with optimal levels of blue industrial development, and adjusting the blue industrial structure in the future to continuously optimize the allocation of blue resources is necessary. ③ The blue provinces with low BProvD and high BProvDD values (1 province) represent the group that is characterized by few blue industries and limited export of their products with comparative advantages. In the future, social investment, production technology, and other factors can be introduced to develop blue industries with comparative advantages and increase the number of convertible blue products. Zhejiang (ZJ) has great potential for the transformation of blue industrial achievements in the future. ④ The provinces with low BProvD and low BProvDD values (18 provinces) represent the group that is currently limited to the specialized production and export of several products from blue industries, and the potential for further development of other blue industries and their products in the future is relatively small.

To analyze the development characteristics of the blue economy in each province (autonomous regions and municipalities), Figs. 4 and 5 show the distributions of the BProvD and BProvDD values in 25 blue provinces. Similar to the geographical distribution of marine-related products (Qi et al., 2020), Fig. 4 shows that Guangdong (119 types), Shanghai (101 types), and Shandong (85 types) export the most blue products, followed by Jiangsu (55 types), Beijing (49 types), Liaoning (25 types), Zhejiang (21 types), Anhui (17 types), Tianjin (15 types) and Xinjiang (14 types), which have more diverse blue product categories. Hebei (8 types), Heilongjiang (6 types), Hubei and Hainan (4 types), Shanxi (3 types), Sichuan (3 types), Guangxi (3 types), Shaanxi (2 types), Henan (2 types), Fujian (1 type), Jiangxi (1 type), and Qinghai (1 type) export the least number of blue products. Almost all coastal provinces have a diversified product basket, and this development feature of 25 blue provinces (autonomous regions and municipalities) is consistent with their economic development trend, i.e., the export plans and innovation orientation of Guangdong, Shanghai, Shandong, Jiangsu, and Beijing are more conducive to the performance of the export market (Hughes et al., 2019). However, Hebei, Hubei, Jiangxi, Henan, Guangxi and other provinces often have more labor-intensive processing trade firms, which often have the production capacity of simple products (Dai et al., 2014). They have different export behaviors than do general trade firms in the province (i.e., general trade firms often follow the heterogeneous trade theory of interfirm competition for exports). The uniqueness and influencing factors of the export behavior of processing trade firms in relevant provinces require further analysis via theoretical and empirical research (Li, 2015).

Fig. 4: Geographical distribution of the BProvD.
figure 4

BProvD distribution map of 25 blue provinces (autonomous regions and municipalities) in China in 2010.

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Fig. 5: Geographical distribution of the BProvD.
figure 5

BProvDD distribution map of 25 blue provinces (autonomous regions and municipalities) in China in 2010.

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Figure 5 shows that the groups with the highest BProvDD values are Guangdong (73 types), Shanghai (53 types), Beijing (49 types) and Shandong (45 types), Jiangsu (29 types), Zhejiang (15 types), Liaoning (14 types), and Anhui (10 types). The remaining provinces have the fewest blue products that can be exported in the future. In addition to Chongqing, Fujian, Guangxi, Henan, Qinghai, Shaanxi, Shanxi, and Yunnan, these provinces may further develop other blue industries and increase the export variety and quantity of their blue products in the future. Almost all coastal provinces can further diversify their product baskets, including Guangdong, Shandong, Shanghai, Liaoning, Jiangsu, Tianjin, Hebei, Zhejiang, and Hainan, which have specialized blue product baskets. Finally, Chongqing, Fujian, Guangxi, Henan, Qinghai, Shaanxi, Shanxi and Yunnan have highly specialized production capacities and cannot further diversify their blue export baskets; thus, they should focus on developing and enhancing the international competitiveness of the existing blue industries and their products.

Discussion

In terms of time (1985–2018), the evolution trends of the export structures of blue industries, blue products, blue firms and blue provinces in China’s blue economic system were measured. The spatial differences in China’s blue resource development are discussed to help policy-makers and stakeholders intuitively understand the complexity of China’s blue economy.

Evolution of blue product diversification

Figure 6 shows the blue product diversity (BPD) of China from 1985 to 2018, which was calculated using Eq. (2). Since the deadline of the Sixth Five Year Plan (1980–1985), China has gone from “moving toward reform and opening up” to “reform and rectification” in the Seventh Five Year Plan (1986–1990) and to “Xiaoping’s Southern Tour and Reform Tide” in the Eighth Five Year Plan (1991–1995). As a result, a series of reform slogans and measures has strengthened China’s trade with other countries and gradually promoted a series of blue products with competitive advantages. The number of blue products increased from 108 in 1985 to 113 in 1990 and 116 in 1995; then, it gradually stabilized at 115–116.

Fig. 6
figure 6

Evolution of the BPD in China from 1985 to 2018.

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Evolution of blue firm diversification

Figures 7 and 8 are based on the maximum proximity matrix from 1985 to 2018, and we used Eqs. (3) and (4) to calculate the BFD and BFDD values, respectively, i.e., the number of blue products exported by blue firms with an RCA > 1 and 1 > RCA > 0.5. Figure 7 shows that since the reform and opening up, China’s blue firms have exported many labor-intensive blue products with high international competitiveness, i.e., in 1995, a blue firm produced and exported a maximum of 6 products. However, this positive trend gradually weakened until 2005, when the number of highly competitive products exported by blue firms began to rebound, possibly due to the technological spillover effect of import and export trade since China’s accession to the WTO in 2001. In a short period of time, firms with obvious geographical and policy advantages enhanced their ability to develop various potential blue products, and this ability has been continuously strengthened with the in-depth development of trade between China and various countries. In other words, many blue firms can produce and export four or more blue products and are generally located in coastal areas. For example, in 2018, 8 of the top 10 blue firms with the largest number of exports of blue products are located in coastal areas, and 7 are shipbuilding firms: “SIPG Logistics Co., Ltd.” (SH20), “Yihai (Yantai) Cereal & Oil Industry Co., Ltd.“ (SD34), “Shanghai Zhenhua Heavy Industries Co., Ltd.” (SH33), “Qingdao Beihai Shipbuilding Heavy Industry Co., Ltd.” (SD16), “Samsung Heavy Industries (Rongcheng) Co., Ltd.” (SD24), “Samsung Heavy Industries (Ningbo) Co., Ltd.” (ZJ5), “Penglai Jutao Offshore Engineering Heavy Industry Co., Ltd.” (SD14) and “Shanghai Jiangnan-Changxing Shipbuilding Co., Ltd.” (SH25). This result shows that the original export of simple blue products at the periphery of the blue product space has gradually shifted to the production and export of both simple blue products at the periphery and complex blue products at the core.

Fig. 7
figure 7

Evolution of the BFD in China from 1985 to 2018.

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Fig. 8
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Evolution of the BFDD in China from 1985 to 2018.

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Figure 8 supports the above speculation; with the deepening of China’s reform and opening up and its accession to the WTO, blue firms have gradually transitioned from original single production to diversified production, and firms with more types of blue products that can be developed are generally located in a few inland areas (Bai et al., 2017). For example, in 2018, 9 of the top 10 blue firms with the greatest variety of developable blue products were located in a few inland areas, namely, “China National Electronic Devices Co., Ltd.” (BJ15), which went from 1 product in 1985 to 12 products in 2018; “SMC (China) Co., Ltd.” (BJ1), which went from 1 product in 1995 to 10 products in 2018; “Siemens (China) Co., Ltd.” (BJ10), which went from 1 product in 2005 to 7 products in 2018; “Anhui BBCA International Freight Co., Ltd.” (AH1), which went from 1 product in 2000 to 6 products in 2018; “China Petroleum Materials Co., Ltd.” (BJ24), which went from 6 products in 1990 to 6 products in 2018; “Huamao Guangtong Supply Chain Management (Beijing) Co., Ltd.” (BJ3), which went from 2 products in 2010 to 6 products in 2018; “Anhui Tianrun Chemical Industry Co., Ltd.” (AH2), which went from 1 product in 2010 to 5 products in 2018; “CNOOC Gas & Power Group Co., Ltd.” (BJ28), which went from 1 product in 2000 to 6 products in 2018; and “China Shipbuilding & Offshore International Co., Ltd.” (BJ13), which went from 1 product in 2005 to 4 products in 2018.

To analyze the development capacity of blue firms, the correlations between the BFD and BFDD values from 1985 to 2018 are compared in Figs. 9 and 10. The product production capacity of blue firms in 2018 ranged from uncorrelated in 1985 and 1990 to significantly correlated in 1995, 2000, 2005, 2010 and 2015, whereas the product developability of blue firms in 2018 was significantly correlated with that from 1985 to 2015. This result indicates that the existing production capacity of a firm largely depends on its past accumulated capabilities and that firm diversification is driven mainly by changes in the variety of nontechnical exports, which is consistent with the judgment of Parteka et al. (2025).

Fig. 9: Correlation of the BFD values in China from 1985 to 2018.
figure 9

The numbers in each scatter plot are slope values, and **p < 0.05.

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Fig. 10: Correlation of the BFDD values in China from 1985 to 2018.
figure 10

The numbers in each scatter plot are slope values, and **p < 0.05.

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Evolution of blue province diversification

Figures 11 and 12 use Eqs. (5) and (6) to calculate the resulting BProvD and BProvDD values, respectively. Figure 11 shows that the seven provinces with the strongest BProvD values are Guangdong (GD), Shanghai (SH), Shandong (SD), Jiangsu (JS), Beijing (BJ), Liaoning (LN), and Zhejiang (ZJ), among which six are coastal provinces. This result indicates that these coastal provinces are currently vigorously developing blue industries on the basis of their geographical advantages, and the unique municipality is Beijing, which is neither close to the sea nor has any marine infrastructure. Why is the diversity so large compared with that of the other provinces? As the capital, Beijing has the most convenient policy conditions and trade channels in China, so Beijing’s blue firms often have export advantages that most firms in other provinces cannot match in terms of their trade transshipment transactions.

Fig. 11
figure 11

Evolution of the BProvD values in China from 1985 to 2018.

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Fig. 12
figure 12

Evolution of the BProvDD values in China from 1985 to 2018.

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Figure 12 shows that the top seven provinces with the strongest BProvDD values are also Guangdong (GD), Shanghai (SH), Shandong (SD), Beijing (BJ), Jiangsu (JS), Zhejiang (ZJ), and Liaoning (LN), which vigorously explore the development potential of blue industries according to their own geographical advantages, so increasingly many blue products can be developed. Provinces with weak BProvDD values are generally inland provinces such as Yunnan (YN), Shanxi (SX), Hubei (HuB), Heilongjiang (HLJ) and Jiangxi (JX).

Comparison with traditional economic indicators

To further demonstrate the effectiveness of the diversification indicator system in this paper, Fig. 13 compares the BProvD and BProvDD indicators with traditional economic indicators (e.g., GDP and trade balance). The BProvD and BProvDD indicators are positively correlated with the GDP and trade balance indicators, respectively. This paper compares the two types of indicators to ensure that the diversification indicator system can capture unique information that is not present in traditional economic indicators such as the GDP and trade balance.

Fig. 13: Comparison analysis.
figure 13

Comparison of the BProvD and BProvDD indicators with the GDP and trade balance indicators from 1985 to 2018, (a)-(b) GDP correlations with BProvD (a) and BProvDD (b), (c)-(d) trade balance correlations with BProvD (c) and BProvDD (d). Note: The GDP data come from the World Bank, and the trade balance data come from the General Administration of Customs of the People’s Republic of China.

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Notably, on the one hand, the two types of indicators have different focuses: traditional economic indicators are indicators that quantify economic activities in monetary units, reflecting dimensions that can be monetized, such as economic aggregate, trade, and income; the diversification indicator system does not rely on monetary units but comprehensively evaluates the multidimensional development level of the blue economy through the correlation structure between multiple agents. On the other hand, both types of indicators emphasize the use of multidimensional data to reveal the current state of national economic development, promote policy optimization, and ultimately achieve balanced development of the economy, society, and environment. The two are complementary rather than contradictory. For example, the United Nations Sustainable Development Goals can build a more comprehensive evaluation system by integrating the two types of indicators.

Conclusions and limitations

With respect to the NBES, this work examined the diversification of blue products, blue firms, and blue provinces in the system. Moreover, China was taken as an example in applied research on the NBES, and the hierarchical nature of the NBES was verified. Specifically, the contributions of this paper are as follows:

  1. (1)

    In terms of the selection of export agents, this work uses the blue products, blue firms and blue provinces proposed by Qi (2022a) as the research objects. The main reason is that the product chain formed on the basis of product diversification and the product network formed by continuous technological upgrading represent the internal basis of a country’s value creation and international economic and trade circulation. At the firm level, blue firms produce and operate their blue product chains on the basis of existing resource endowments, and the business diversity of blue firms formed on the basis of product diversification is the mesoscopic subject of value creation in a country’s market economy. Conversely, blue firm diversification affects its product diversification structure. At the geospatial level, the blue province network formed on the basis of the trade exchanges of blue firms is a spatial map of the diversification structure of blue products and the spatial carrier of the blue product network and blue firm network (Qi, 2024).

  2. (2)

    This paper quantifies the degree of diversification in the NBES at the “product–firm–province” level. First, the maximum proximity matrix of blue products is defined to measure the proximity between blue products and other products and the categories of blue products that have been developed in a country. Second, based on blue product diversification, the categories and potential categories of blue products that can be exported by blue firms can help blue firms invest limited resources in the development of blue products that can obtain export benefits. Finally, on the basis of blue firm diversification, the main export blue industry categories of the relevant blue provinces and the competitiveness of blue firms are measured to help provincial decision-makers formulate blue industrial development policy guidelines that are consistent with the interests of the province and its firms.

  3. (3)

    This paper depicts the evolution law and development path of diversification in the NBES. In blue economic practice, the diversification index of the export structure of a single-layer sub-network based on the time dimension (1985–2018) is used to measure the evolution trend of the export structure of the blue industries, blue products, blue firms, and blue provinces in the NBES. The results show that although the development trend of China’s blue economy is coordinated between land and sea, there is still a problem that most of the coastal provinces still have strong diversity, while most inland provinces have weak diversity. This problem stems from the differences in the diversification development of blue firms in relevant provinces. Owing to the different degrees of combination of resources, technology, capital and other factors of these firms, their development differences have emerged. This difference is also the key to explaining the differences in diversification development among blue provinces. Therefore, strengthening the flow and agglomeration of production factors such as technology, capital and information among blue firms in different provinces to form a strategic layout of mutual coordination and cooperation is an important issue faced by the relevant blue provinces in the process of development.

There are still two limitations to this study. (i) The role of firm-level factors (e.g., firm size, ownership structure, and innovation capacity) in shaping export diversification is largely overlooked. (ii) How to capture changes in external conditions beyond multiagent elements through this systematic analysis, for example, the impact of the marine environment, may change the export diversity performance of the NBES is another issue worth exploring in future research.