Background & Summary

The ocean economy refers to the totality of ocean and ocean-related industrial activities that aim for the development, utilization and protection of the ocean1. The Organization for Economic Co-operation and Development (OECD) has identified the vast potential of the ocean economy and forecasted that by 2030, the value added and employment created by ocean sectors will surpass the global economy2. Consequently, many coastal countries have prioritized the development of their ocean economies as a fundamental strategy, positioning ocean sectors as the convergence zone for both international competition and cooperation. As highlighted in the 2024 China Marine Economic Statistical Bulletin, China has experienced a rapid growth in the scale of national ocean economy in recent years, with the gross ocean product (GOP) nearly reaching 11 trillion yuan, which represents approximately 8% of gross domestic product (GDP)3. The ocean economy is serving as a new engine for China’s economic development, with a particularly prominent impact on the economic growth of Chinese coastal provinces.

Ocean economic accounting can be characterized by a variety of methods, such as ocean economic input-output (IO) model4,5,6, ocean economic computable general equilibrium (CGE) model7,8, System of National Accounts (SNA)9,10, System of Environmental Economic Accounting (SEEA)11,12, Ocean Economy Satellite Accounts (OESA)13,14,15,16,17,18, spatial microsimulation approaches19,20,21, non-market valuation techniques22,23 and other methodologies for specific ocean sectors24. Among these, the ocean economic input-output (OEIO) table can effectively reflect the linkage between ocean and land sectors25,26. The literature has constructed the OEIO tables for several countries such as Ireland26, the United Kingdom27, the United States28, Brazil29, Australia30 and China31,32,33 to evaluate and analyze the economic impacts of their ocean sectors. Yet, there is a notable absence of public IO tables for the ocean economy at the provincial level, which hinders a comprehensive understanding of regional heterogeneity in ocean economy development. To address this gap, this study attempts to develop the provincial OEIO tables.

The stripping coefficient method, characterized by its ability to mitigate potential subjective biases and its strong scalability, is extensively used for compiling IO tables33,34,35,36. The stripping coefficients used in the present study are derived from authoritative economic data and specialized ocean economy statistics within the widely accepted and uniform national economy accounting framework, ensuring a high degree of methodological objectivity37. In addition, the stripping coefficient method allows us to easily extend from constructing provincial OEIO tables to developing global OEIO table, as it supports not only the construction of provincial OEIO tables but also the differentiation of ocean sectors within the global multi-regional input-output (MRIO) table. The constructed OEIO tables not only illustrate the economic value and the interconnections between ocean and land sectors, but also serve as the foundation of various extended IO models to analyze the impact of ocean economic activities on employment, carbon emissions and others38.

Methods

We constructed the OEIO datasets of China’s coastal provinces in 2017, based on the official provincial IO tables published by the National Bureau of Statistics (https://data.stats.gov.cn/)39 and the global MRIO table published by OECD40 that contains Chinese provinces (https://www.oecd.org/en/data/datasets/inter-country-input-output-tables.html). The processes of separating the characteristics of the ocean economy entail seven key steps (Fig. 1): (1) we constructed a bridge matrix linking 13 ocean sectors with 42 sectors listed in the provincial IO tables based on Industrial Classification for Ocean Industries and Their Related Activities (GB/T 20794-2006) and Industrial Classification for National Economic Activities (GB/T 4754-2017). (2) We collected the gross domestic product of ocean economy at the sectoral and provincial level respectively, and adjusted the data to maintain consistency in total values. (3) We estimated the total outputs of ocean sectors in coastal provinces based on the gross domestic product and value-added rates derived from micro-level enterprise data. (4) We estimated the intermediate input matrix of ocean sectors based on the stripping coefficients, which are calculated as the ratio of the output of ocean sectors to the output of corresponding sectors in the official IO tables. (5) We estimated the distribution matrix of ocean sectors in coastal provinces based on the stripping coefficients calculated in the previous step. (6) We adopted the RAS method to balance the constructed provincial OEIO tables. (7) We repeated the above steps to differentiate the Chinese ocean sectors in a global MRIO table that contains Chinese provinces.

Fig. 1
figure 1

Construction process of OEIO tables of coastal provinces in China.

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Construction of a bridge matrix between ocean sectors and sectors listed in the IO tables

The ocean industry includes 12 major ocean sectors and marine scientific research, education, management and service (O13)1. The 12 major ocean sectors include marine fishery (O1), offshore oil and gas (O2), marine mining (O3), marine salt (O4), marine shipbuilding (O5), marine chemical (O6), marine biomedicine (O7), marine engineering architecture (O8), marine electric power (O9), seawater utilization (O10), marine communications and transport (O11) and coastal tourism (O12). In previous IO tables, ocean sectors were not distinguished from traditional sectors. To address this issue, we first established the correspondence between the traditional sectors in IO tables and 13 ocean sectors, based on the descriptions of ocean sectors outlined in the GB/T 20794-2006 and GB/T 4754-2017 (Supplementary Information 1). Subsequently, we constructed a bridge matrix W that links the 13 ocean sectors to the 42 sectors listed in the official provincial IO tables (Supplementary Information 2). If the ocean sector i belongs to the sector m in provincial IO tables, Wi,m is equal to 1; otherwise, Wi,m is equal to zero. Notably, the bridge matrix W (13 × 42) reveals that an ocean sector can correspond to one or more sectors listed in the official IO Table (1: n), which implies that products produced by a single ocean sector may be categorized into multiple sectors in the official IO tables framework.

Table 1 Gross domestic product of ocean sectors in coastal provinces (2017, 100 million CNY).
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Adjustment of gross domestic product of 13 ocean sectors in 11 coastal provinces

China Marine Economic Statistical Yearbook 201841 reported the GOP at the provincial level and the gross domestic product at sectoral level, respectively. For each ocean sector, we allocated the gross domestic product at the sectoral level to the 11 coastal provinces based on the collected provincial ocean economic activity data. For instance, the gross domestic product of marine communications and transport is allocated by the volume of ocean transportation of each coastal province. To ensure the sum of gross domestic product of 13 ocean sectors is equal to the GOP at the provincial level, we further adopted the RAS method42,43,44 to balance the matrix of gross domestic product. Table 1 lists the gross domestic product of 13 ocean sectors in 11 coastal provinces in 2017. Further information on how to determine the gross domestic product of 13 ocean sectors can be found in the Supplementary Information 3–15.

Based on Table 1, we could obtain a 1 × 13 value-added (namely mentioned gross domestic product) vector \({{\bf{v}}}_{r}^{O}\) representing the value added of 13 ocean sectors in coastal province r. The gross value-added vector in coastal province r of provincial IO table is \({{\bf{v}}}_{r}\), whose dimension is 1 × 42. We then defined the value-added stripping coefficient vector in coastal province r as \({{\bf{d}}}_{r}\), which is calculated as the ratio by ocean sectors’ gross value added \({{\bf{v}}}_{r}^{O}\) to the sum of gross value added of all corresponding traditional sectors \({{\bf{v}}}_{r}^{t}={({\bf{W}}{({{\bf{v}}}_{r})}^{{\bf{T}}})}^{{\bf{T}}}\). Then,

$${{\bf{d}}}_{r}={{\bf{v}}}_{r}^{O}{(\hat{{{\bf{v}}}_{r}^{t}})}^{-1}$$
(1)

where \(\hat{{{\bf{v}}}_{r}^{t}}\) denotes a diagonal matrix of the vector \({{\bf{v}}}_{r}^{t}\), which is determined via the bridge matrix W matching and aggregating the corresponding traditional sectors for each ocean sector. Finally, the land sectors’ value added in coastal province r is defined as \({{\bf{v}}}_{r}^{L}\), which is the remain of 42 traditional sectors’ gross value added that have not been allocated to the ocean sectors.

$${{\bf{v}}}_{r}^{L}={{\bf{v}}}_{r}-{\bf{s}}\hat{{{\bf{d}}}_{r}}{\bf{W}}\hat{{{\bf{v}}}_{r}}$$
(2)

where s is a 1 × 13 column sum vector with each element equating to 1, employed to sum the columns of the matrix on the right, and where \(\hat{{{\bf{d}}}_{r}}{\bf{W}}\hat{{{\bf{v}}}_{r}}\) is a 13 × 42 matrix with the element representing the value added separated from the one traditional sector to the one ocean sector.

Determination of the total output of 13 ocean sectors in 11 coastal provinces

A sector’s value-added rate is equivalent to the division of its value added by its total output. For this analysis, we first gathered value-added rates of 13 ocean sectors in 11 coastal provinces during 2017 from micro-level enterprise data. Then, the total outputs of ocean sectors can be calculated by the division between the value added (as shown in Table 1) and the corresponding value-added rate. Table 2 lists the total output of 13 ocean sectors in 11 coastal provinces during 2017.

Table 2 Total output of ocean sectors in coastal provinces (2017, 100 million CNY).
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After obtaining the 1 × 13 total output vector \({{\bf{x}}}_{r}^{O}\) of 13 ocean sectors in coastal province r from Table 2, we further estimated the total output of land sectors by the method used in assessing their value added. The sum of total output of all corresponding traditional sectors in coastal province r is \({{\bf{x}}}_{r}^{t}={({\bf{W}}{({{\bf{x}}}_{r})}^{{\bf{T}}})}^{{\bf{T}}}\), where \({{\bf{x}}}_{r}\) is a 1 × 42 vector of 42 traditional sectors’ total output in provincial IO table. Then we constructed the stripping coefficient vector of the total output \({{\bf{c}}}_{r}\), which is a 1 × 13 vector composed of the ratio by ocean sectors’ total output \({{\bf{x}}}_{r}^{O}\) to all corresponding traditional sectors’ total output \({{\bf{x}}}_{r}^{t}\).

$${{\bf{c}}}_{r}={{\bf{x}}}_{r}^{O}{(\hat{{{\bf{x}}}_{r}^{t}})}^{-1}$$
(3)

By excluding the total output allocated to the ocean sectors from the traditional sectors, we obtained the total output of the land sectors.

$${{\bf{x}}}_{r}^{L}={{\bf{x}}}_{r}-{\bf{s}}\hat{{{\bf{c}}}_{r}}{\bf{W}}\hat{{{\bf{x}}}_{r}}$$
(4)

The element of the vector \({{\bf{x}}}_{r}^{L}\) means the total output of each land sector.

Estimation of intermediate input matrix of ocean sectors in 11 coastal provinces

Based on the stripping coefficient method, the intermediate input of ocean sectors can be separated from the intermediate input of corresponding sectors in the provincial IO tables by using the stripping coefficient vector \({{\bf{c}}}_{r}\). Following this, the intermediate input matrix of ocean sectors in province r is estimated as \({(\hat{{{\bf{c}}}_{r}}{\bf{W}}{({{\bf{Z}}}_{r})}^{{\bf{T}}})}^{{\bf{T}}}\), where \({{\bf{Z}}}_{r}\) is a 42 × 42 intermediate input matrix of traditional sectors in official provincial IO tables. It should be noted that the intermediate inputs of ocean sectors originate from both ocean and land sectors. For the part of ocean sectors’ input, the intermediate input matrix from ocean sectors to ocean sectors \({{\bf{Z}}}_{r}^{OO}\) is a 13 × 13 matrix that reflects the direct input by ocean sectors themselves, which are separated from the intermediate input matrix from traditional sectors to ocean sectors \({(\hat{{{\bf{c}}}_{r}}{\bf{W}}{({{\bf{Z}}}_{r})}^{{\bf{T}}})}^{{\bf{T}}}\) by using the stripping coefficient method.

$${{\bf{Z}}}_{r}^{OO}=\hat{{{\bf{c}}}_{r}}{\bf{W}}{(\hat{{{\bf{c}}}_{r}}{\bf{W}}{({{\bf{Z}}}_{r})}^{{\bf{T}}})}^{{\bf{T}}}$$
(5)

Then, for the part of land sectors’ input, the intermediate input matrix from land sectors to ocean sectors \({{\bf{Z}}}_{r}^{LO}\) is a 42 × 13 matrix, which is calculated by

$${{\bf{Z}}}_{r}^{LO}={(\hat{{{\bf{c}}}_{r}}{\bf{W}}{({{\bf{Z}}}_{r})}^{{\bf{T}}})}^{{\bf{T}}}-\hat{{{\bf{c}}}_{r}{\bf{W}}}{(\hat{{{\bf{c}}}_{r}}{\bf{W}}{({{\bf{Z}}}_{r})}^{{\bf{T}}})}^{{\bf{T}}}$$
(6)

Estimation of the distribution matrix of ocean sectors in 11 coastal provinces

The distribution matrix of ocean sectors in coastal province r indicates the flows of the products produced by these ocean sectors, which could be further divided into intermediate products (\({{\bf{Z}}}_{r}^{OO}\) and \({{\bf{Z}}}_{r}^{OL}\)) and final products \({{\bf{Y}}}_{r}^{O}\). We also used the stripping coefficient vector \({{\bf{c}}}_{r}\) to separate the distribution matrix of ocean sectors from traditional sectors’ distribution matrix.

$${{\bf{Z}}}_{r}^{OL}=\hat{{{\bf{c}}}_{r}}{{\bf{W}}{\bf{Z}}}_{r}$$
(7)
$${{\bf{Y}}}_{r}^{O}=\hat{{{\bf{c}}}_{r}}{{\bf{W}}{\bf{Y}}}_{r}$$
(8)

\({{\bf{Y}}}_{r}\) is a 42 × 7 final use matrix of traditional sectors. The matrix \({{\bf{Z}}}_{r}^{OL}\) means the intermediate products matrix from 13 ocean sectors to 42 land sectors. The matrix \({{\bf{Y}}}_{r}^{O}\) represents the final products from 13 ocean sectors to 7 sub-categories of final use. Then, the distribution matrix from land sectors to land sectors \({{\bf{Z}}}_{r}^{LL}\) is the remain of traditional sectors’ intermediate input matrix, which has not yet been allocated to the intermediate input matrix and distribution matrix of ocean sectors,

$${{\bf{Z}}}_{r}^{LL}{={\bf{Z}}}_{{\bf{r}}}-{(\hat{{{\bf{c}}}_{r}{\bf{W}}}{({{\bf{Z}}}_{r})}^{{\bf{T}}})}^{{\bf{T}}}-\hat{{{\bf{c}}}_{r}{\bf{W}}}{{\bf{Z}}}_{{\bf{r}}}$$
(9)

and the distribution matrix from land sectors to final use \({{\bf{Y}}}_{r}^{L}\) is

$${{\bf{Y}}}_{r}^{L}{={\bf{Y}}}_{{\bf{r}}}-\hat{{{\bf{c}}}_{r}{\bf{W}}}{{\bf{Y}}}_{{\bf{r}}}$$
(10)

Additionally, the export and import volume of ocean sectors are calculated based on the China customs import and export database (http://stats.customs.gov.cn/).

Balance of the constructed OEIO table

The row balance of an IO table requires that the sum of each row in the intermediate input matrix and the final use matrix must equal the total output of the corresponding sector. Similarly, the column balance stipulates that the sum of each column in the intermediate input matrix and the value-added vector must equal the total input of the corresponding sector. As we differentiated the ocean sectors from the original IO table, the balance of the original IO table was disrupted. To deal with this issue, we adopted the RAS method to adjust the elements of OEIO tables, ensuring that the modified OEIO tables maintain both row and column balances. Figure 2 shows the structure of the constructed provincial OEIO table following the balance adjustment process.

Fig. 2
figure 2

The structure of provincial OEIO table. The superscripts O,L represent the ocean sector and land sector, respectively. The first superscript of the matrix represents the meaning of the matrix’s rows and the last one represents the meaning of the matrix’s columns.

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Construction of the global OEIO table

To clarify the economic interactions among provincial ocean sectors with the international economy, we constructed a global OEIO table that captures the ocean sectors of 11 provinces in China through the same stripping coefficient method described above. Specifically, we separated the ocean sectors in a Chinese provincial MRIO table that has been embedded into the global IO table. The global OEIO table will provide insight into the position of provincial ocean sectors in the global value chains. However, the process for constructing this table differs slightly from that used for provincial OEIO table. First, each region in the global MRIO table used in the present study contains 30 sectors, requiring the construction of a new bridge matrix between 13 ocean sectors and 30 sectors listed in the global MRIO table. Second, to construct the distribution matrix of ocean sectors, we need not only the magnitude of gross international trade of ocean sectors in 11 coastal provinces but also the bilateral trade of ocean products between 11 coastal provinces and specific foreign economies. These data are obtained from the China customs import and export database. The structure of the global OEIO table is shown in Fig. 3. The detailed steps for construction of global OEIO table can be found in the Supplementary Information 16.

Fig. 3
figure 3

The structure of global OEIO table. The superscripts C,N,E,O,L represent coastal provinces, inland provinces, other economies, ocean sectors and land sectors, respectively. In China, 11 coastal provinces include 8 provinces (Hebei, Liaoning, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong, and Hainan), 2 municipalities (Tianjin and Shanghai), and 1 autonomous region (Guangxi). 20 inland provinces include 14 provinces, 2 municipalities and 4 autonomous in China. Beyond the aforementioned, there are 66 other economies in the global OEIO table.

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Data Records

The OEIO datasets contain the following three items.

  1. (1)

    The 2017 provincial OEIO tables of 11 coastal provinces: the provincial OEIO tables stored as an Excel document show the economic structure of 55 sectors (13 ocean sectors and 42 land sectors) in each coastal province, the layout of which follows a standard IO table with the following three main sections (Fig. 2). The first quadrant at the upper left is the intermediate input matrix (55 × 55), representing the intermediate monetary flows among the 55 sectors in each coastal province. The second quadrant at the upper right is the final use matrix (55 × 7) composed of 4 final use categories, which represents the final use of 55 sectors in each coastal province. In detail, these 4 categories can be subdivided into 7 sub-categories, including final consumption expenditure (rural resident household consumption expenditure, urban resident household consumption expenditure, government consumption expenditure), gross capital formation (gross fixed capital formation and rising inventories), export and domestic out-of-province outflow. The third quadrant at the lower left is the row vector of value added (1 × 55), which represents the value added of 55 sectors in each coastal province. In addition, import, domestic out-of-province inflow and others are demonstrated as the type of 55 × 1 column vector, respectively. The last column vector and row vector represent the total output (55 × 1) and total input (1 × 55), and the total output column vector is equal to the transpose of the total input row vector. To be consistent with the national IO table, all data were calculated at 2017 producers’ prices with a monetary unit of 10 thousand CNY45.

  2. (2)

    The 2017 global OEIO table that captures ocean sectors of 11 coastal provinces in China: the global OEIO table stored as an Excel document shows the economic structure of 43 sectors (13 ocean sectors and 30 land sectors) in 11 coastal provinces and 30 land sectors in 86 regions. The global OEIO table is organized according to a standard IO table format, which consists of the following three main sections (Fig. 3). The first quadrant at the upper left is the intermediate input matrix (3053 × 3053), representing the intermediate monetary flows among all sectors in 97 regions. The second quadrant at the upper right is the final use matrix (3053 × 388) composed of 4 final use categories, representing the final use of all sectors in 97 regions. The third quadrant at the lower left is the row vector of value added (1 × 3053). Additionally, unlike the provincial IO table, the information on international export and import is presented on the intermediate input matrix and the final use matrix. The last column vector and row vector represent the total output (3053 × 1) and total input (1 × 3053) respectively. All data were calculated at 2017 producers’ prices with monetary values expressed in millions of dollars45.

  3. (3)

    External data: The macro data mainly comes from statistical departments (National Bureau of Statistics, Bureau of Statistics of Coastal Provinces, Ministry of Natural Resources, General Administration of Customs, Ministry of Commerce), including the value added, value-added rate, related ocean economic activity data, and custom import and export data of 13 ocean sectors. We further provided gross domestic product data for ocean sectors in coastal provinces from 2018 to 2023 (Supplementary Information 17), enabling future studies to analyze the ocean economy in recent years by combining them with the 2017 OEIO tables.

Technical Validation

Due to the lack of publicly available provincial-level OEIO tables in China, it is difficult to directly quantify the differences between the OEIO tables in the present study and those in the literature. Here, we compared the value added of the ocean sectors from published official data and those from the constructed OEIO tables in the present study in Fig. 4. The results show that the value added of the ocean sectors in OEIO tables are approximately equivalent to the published official data (R2 values for all coastal province are greater than 0.98) except for the slight underestimations observed in a few ocean sectors. These results imply that the constructed OEIO tables retain the original data structure while achieving a strong alignment of key ocean economic indicators with publicly released data.

Fig. 4
figure 4

Comparisons of value added of ocean sectors in 2017 between the OEIO tables and provincial official data. Each colored dot corresponds to an ocean sector, and the blue dashed line indicates the theoretical perfect consistency (1:1 ratio) between both datasets.

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