We used China’s IO tables for 2007, 2012, and 2017 compiled every 5 years by the NBSC (National Bureau of Statistics of China, 2019), to analyze the roles of China’s MI. Furthermore, the essential data, such as value contributed, total output value, and employee remuneration, were from the China Mineral Resources report and the National Plan for Mineral Resources 2016-2020(Ministry of Natural Resources), issued by the Ministry of Natural Resources (Ministry of Natural Resources PRC).

The entire mining sector was not available in China’s IO tables. Therefore, based on the sectoral analysis, the main issue was summing up the sub-sectors of the mining industry in the IO table to obtain data for the entire mining industry. The steps were as follows: first, the Chinese mining industry’s four major sub-sectors were identified (Table 1) based on the industry classification standard and main mining activities. Next, based on statistics, such as total output value and value-added for the four mining sub-sectors, we aggregated the entire mining industry, the sum of all sub-sectors. They are the basis for further evaluation on economic effects of MI on the national economy of China.

IO analysis is the analysis method to quantitatively grasp the interrelation among the industries produced through production activity by using the input-output table covering the whole of national economy. It is possible for this analysis to analyze the interrelation among the industries which does not deal with macro-analysis. It is useful to analyze a concrete economic structure (Ghosh, 1958; Wu and Chen, 1990). Exogenous specification can examine the influence which one variable has on the endogenous economy sector by treating the variable exogenously. By using the exogenous specification, we can know the influence which the output of a specific sector has and the effect that the output causes other industries.

The first researcher to employ an IO matrix depiction of a national or regional economy was Leontief (1966), who employed an inverse matrix to solve the mathematical relationship of inter-industry transaction tables based on the IO table’s general framework. In an IO table, the columns indicate the input values for each sector, while the rows represent the output values. For example, if the IO table is divided into N sectors: sector i’s total input is denoted by X i ; Y i signifies the final demand for items in the industry i; a ij denotes the sector-between direct input coefficients matrix (from sector i to sector j). The sector-between direct output coefficients matrix (from each sector i to each sector j) is denoted by r i j ; the total output value of sector j is denoted by X j ; and the added value of sector j is denoted by Z j . The primary assumption is related to the basic Leontief model and can be used as an indicator to assess the contribution of a specific sector over a given period. The traditional representation of IO equations is as follows (Miller and Blair, 1985): X i = ∑ j = 1 n x ij + Y i = ∑ j = 1 n a ij X j + Y i (1) X j = ∑ i = 1 n x ij + Z j = ∑ i = 1 n r ij X i + Z j (2)

The inducement effects on production, value-added, and wage-inducing can be evaluated in the demand-driven model. These effects mean that how much the production, value-added, and wages of other industries, excepting for the target industry increase, when the production of the target industry which is the industry related to the China’s MI increases with $1. The following equations to evaluate the inducement effects of the production, value-added, and wage-inducing by treating the industry regarding with the China’s MI (hereinafter referred to as “H sector”) as exogenous. The process to induce each equation can refer to a large number of papers on IO analysis (Yoo and Yang, 1999; Oosterhaven, 2019).

The industry linkage model is proposed to measure the backward and forward relationship between one industry and other sectors in order to examine the strengths of the industry linkage within the national economy account. That is, the backward linkage represents the importance of the MI as demanders, whereas the forward linkage represents the importance of the MI as suppliers (Hirschman, 1958).

In terms of dispersion power, the backward linkage reflects a column vector analysis of the Leontief inverse matrix. This model examines the effects on all sectors of production when the MI’s ultimate demand increases by one unit. All sectors have a societal average level of 1. When the MI’s backward linkage is more than one, it implies that the sector’s influence on the national economy surpasses the societal average level of all sectors. The backward linkage effect can be expressed as follows: F i = ∑ j = 1 n b ij / ( ( 1 / n ) ∑ i = 1 n ∑ j = 1 n b i j ) (6) Where F i denotes the backward linkage effect, ∑ j = 1 n b ij is the sum of the Leontief inverse matrix column vector, and ( 1 / n ) ∑ i = 1 n ∑ j = 1 n b i j is the average of the Leontief inverse matrix.

In terms of dispersion sensitivity, the forward linkage examines the change in one mining sector when the ultimate usage in the remaining sectors increases by one unit. A row vector analysis of the Leontief inverse matrix is reflected in the forward linkage. The following is an expression for the forward linkage effect: E i = ∑ i = 1 n b ij / ( ( 1 / n ) ∑ i = 1 n ∑ j = 1 n b i j ) (7) Where E i denotes the backward linkage effect, ∑ i = 1 n b ij is the sum of the Leontief inverse matrix row vector.

Equation 2 in the IO table can also be expressed as X ′ = V ′ ( I − R ) -1 . The upper mark (′) denotes the transposition of the given matrix, R represents the direct distribution coefficient matrix, and ( I − R e ) -1 denotes the complete distribution matrix for eliminating the row and column of the MI (Giarratani, 1976; Davis and Salkin, 1984). Thus, the supply shortage effect can be expressed as follows: Δ X e ′ = R H e Δ X H ( I − R e ) -1 (8) Where Δ X e ′ denotes the supply shortage effect; In addition, this approach may also be used to calculate the cost of loss in the MI’s supply shortfall.

The price-side model or Leontief price model can be used to assess the impact of price changes in one sector on the price levels of other sectors (Hirschman, 1958). The IO price-side model does not take into account the impact of changes in net wages or production taxes and operating earnings on prices (Yoo and Yoo, 2009). The price-side effect can be modified as follows: Δ P ¯ e = ( I− A e ′ ) -1 A H e ′ Δ P ¯ H (9) where Δ P ¯ e denotes the vector of the price change excluding sector H; Δ P ¯ H presents a scalar of the price change of sector H. Assuming that the cost change in each sector is able to completely transferred, plus the annual sectorial outputs are offered, how the cost change of the MI sector will drive the wholesale price changes on the economic system can be investigated by Eq. 9.

The MI is divided into four sub-sectors in the basic scale IO table: coal, crude petroleum and natural gas, metal ores, and non-metallic mineral mining. The sector categorization utilized in this study is shown in Table 2, comprising 38 large-scale sectors and four MI sub-sectors. Consequently, five analysis findings are presented: one for the whole MI and one for each of the four sub-sectors. In addition, all findings are based on research identifying the MI sector as exogenous rather than endogenous.

The sectoral impacts of mining investment are summarized in Tables 3–5. Table 3 shows the results of the demand-driven model in analyzing the MI’s production-inducing impact. The sum of $1 of production or investment in the other sectors is $0.896–$1.360 (coal), $0.880–$1.214 (crude petroleum and natural gas), $1.185–$1.711 (metal ores), and $1.522–$1.716 (non-metallic mineral mining), respectively. Thus, the overall production-inducing effect of $1 of mining production or investment on other sectors is $0.862–$1.171.

Table 4 evaluates an impact analysis of the MI’s value-added creation on other sectors. Production or investment of $1 in the coal, crude petroleum and natural gas, metal ores, and non-metallic mineral mining sectors, respectively, generates $0.131–$0.426, $0.144–$0.356, $0.179–$0.536, and $0.460–$0.539 of value-added for other sectors. Thus, the effect of $1 production or investment in value-added creation in the entire MI on other sectors is $0.271–$0.333.

Table 5 presents the conclusions of calculating the MI’s wage-inducing effects. Production or investment of $1 in the coal, crude petroleum and natural gas, metal ores, and non-metallic mineral mining sectors, respectively, produces $0.131–$0.145, $0.117–$0.158, $0.149–$0.179, and $0.171–$0.212 of wages in other sectors. Thus, the wage-inducing effect of $1 of MI production or investment on other sectors is $0.106–$0.125.

Table 6 depicts the costs of sectoral mining supply shortages in 2007, 2012, and 2017. Each figure represents how much output loss would be suffered in other sectors in the case of a $1 supply failure in the MI. The total impact of a supply failure in each MI sub-sector on the national economy can be obtained by combining the effects of a supply shortfall in each sector. The supply shortage effects of the coal, crude petroleum and natural gas, metal ores, non-metallic mineral mining sectors, and the entire MI is calculated to be $4.026–$5.280, $5.045–$9.553, $9.300–$13.755, $5.294–$6.557, and $4.383–$5.949, respectively. All of the results are significantly greater than one. In other words, a $1 failure in MI supply could substantially influence the overall national economy, implying that MI products are critical to manufacturing in other industries.

Table 7 shows the effect of a 10% rise in the price of MI products on the price levels of other sectors. For example, in the food and tobacco industry, the MI price-pervasive effect is given as 0.037–0.063, meaning that a 10% rise in the price of MI products increases the price of food and tobacco by 0.037%–0.063%.

When calculating the impact of price changes in the mining sector on the whole economy, the percentage values for each industry are summed and averaged, and the total impact is overestimated. Therefore, the price-pervasive effect on the economy can be calculated by weighted averaging of each sector’s price-pervasive effect on each sector’s output. For example, the weighted averages of the price-pervasive effect of a 10% increase in the price of coal, crude petroleum and natural gas, metal ores, and non-mineral mining sectors, and the entire MI sector, are estimated to be 0.033%–0.055%, 0.041%–0.087%, 0.033%–0.049%, 0.014%–0.018%, and 0.108%–0.171%, respectively.

Table 8 shows the mining sector’s inter-industry forward and backward linkage effects in 2007, 2012, and 2017. Two interesting facts were uncovered. First, except for metal ores (2007), the mining sector’s sensitivity to dispersion was less than one, implying that the MI’s forward linkage impact is less than the total of other industries. In other words, the MI is unaffected by changes in other industries and contributes significant input to the country’s economy. Second, the MI’s dispersion power is greater than one, indicating that mining exerts considerable economic influence on other industries. Therefore, the MI should be classified as a final manufacturer, as it has high backward and low forward linkages (Ivanova, 2014).

This paper investigated the influence of the four MI sectors, including the overall MI sector, on the Chinese national economy from 2007 to 2017 using extensive IO analyses, including the demand-driven, supply-driven, and price-side models, and inter-industry linkage effect. The economic consequences of the IO analysis had three significant implications.

First, we used the demand-driven model to evaluate the MI’s economic consequences. As a result, the production-inducing effects, value-added creation effects, and wage-inducing effects of $1 of MI production or investment on the national economy were calculated to be $0.862–$1.171, $0.271–$0.333, and $0.106–$0.125, respectively. The figures on sectoral impacts of MI investments from the demand-driven model can be interpreted as benefits which ensue from development projects. As a result, policy decisions on whether to conduct a proposed MI project or not could, in principle, be deduced from an examination of costs and benefits associated with the project (Heo et al., 2010).

Since the study also estimated the economic effects for each sector, it is possible to examine the impacts of increased production or investment in the MI on each sector. In particular, the chemical products sector is the most affected by production or investment in the MI. This means that if the MI is activated, the Chemical Products sector will be activated the most. That is, the MI demands output from the Chemical Products sector more than it does from other sectors. However, production and investment in the MI have little influence on the Production and Supply of Tap Water sector.

Second, we used the supply-driven model to investigate the MI impact on supply shortages. Using the supply-driven model in IO analysis has increased in recent years due to the increased frequency of global natural disasters, such as COVID-19 pandemics and earthquakes, as it is helpful in setting economic reliability standards in the MI industry, but also in determining pricing and load management strategies (Howe et al., 1994). The effects of $1 of supply shortages in the Coal, Crude Petroleum and Natural Gas, Metal Ores, Non-Metallic Mineral Mining sectors, and the entire MI on the national economy were calculated to be $4.0262–$5.280, $5.045–$9.553, $9.300–$13.755, $5.294–$6.557, and $4.383–$5.949, respectively.

The results demonstrate that these numbers are much higher than one, indicating that MI production failure would significantly negatively impact the national economy because MI output is a primary input in other sectors’ manufacturing. Therefore, the government should make every effort to guarantee a steady MI supply, as a MI supply deficit could substantially negatively impact the economy (Feng et al., 2019). Supply shortages effects of the Metal Ores sector would be the most serious in the four MI sub-sectors.

Third, we employed a price-side model to analyze the impact of increased MI pricing on other industries. In addition, as the MI is exogenous in classic models, we attempted to consider the MI as an exogenous sector in this research. The impacts of a 10% price increase in the MI sector on the national economy were 0.033%–0.055%, 0.041%–0.087%, 0.033%–0.049%, 0.014%–0.018%, and 0.108%–0.171%, for the Coal, Crude Petroleum and Natural Gas, Metal Ores, Non-Metallic Mineral Mining sectors, and the entire MI, respectively. Thus, the price effects were minor overall.

For some reasons, there may be changes in the price of the output of the MI. A rise in raw material prices or labor expenses for the MI, tighter government regulations linked to safety and the environmental protection for the MI, or a decrease in yield due to a natural reduction in mineral deposits may cause an increase in the price of production in the MI (Ilankoon et al., 2018). Therefore, these results can be useful in formulating a MI pricing strategy based on the price-side model. Furthermore, it is possible to predict the sectors with significant or minor influence because mining costs have a high and low impact on different industry sectors. For example, the price effect of the MI is the largest for the Products of the Smelting and Metal Pressing sector.

This study investigated the influence of the MI sector on the Chinese national economy from 2007 to 2017 using comprehensive IO analyses, such as the demand-driven, supply-driven, and price-side models and inter-industry linkage effects. Except for our inter-industry linkage effects analysis, the MI sector was treated as exogenous to evaluate the net effects by changes in investment, supply, or price in each sector.

Based on the analytical results from the demand-driven model, a $1.0 change in the MI sector investment induced $0.862–$1.171 of the output, $0.271–$0.333 of the value-added, and $0.106–$0.125 of the wages in the national economy.

According to the analytical results from the supply-driven model, the supply shortage effect of the MI sector was $4.383–$5.949. In detail, sectors including “Processing of Petroleum, Coking, Processing of Nucleus Fuel”, “Products of Smelting and Pressing of Metals”, and “Repair Services of Metal Products Equipment and Machinery” presented high shortage effect.

According to the analysis based on price-side model, the national economic effect under 10% increase in the MI sector rate was 0.108%–0.171%. High sectoral price impacts were found in “Processing of Petroleum, Coking, Processing of Nucleus Fuel”, “Products of Smelting and Pressing of Metals”, and “Production and Supply of Electric Power, Steam and Hot Water”.

Finally, in the inter-industry linkage effect, the backward linkage effect of the MI sector was found greater than one, as 2.228–2.350, and the forward linkage effect was less than one, as 0.825–0.896. This indicated that the MI sector can be classified as a final manufacturer. This implies that the MI has bigger impacts in terms of investment expenditures on the national economy than other business. That is, the MI has a relatively strong capacity for pulling in other industries.

In conclusion, the temporal analysis shows that MI sectors play an increasingly important role in China from 2007 to 2017. The total contribution of the MI sectors shows a significant increase in this decade, but the growth rate slows down between 2012 and 2017. This may be caused by the lack of new mining economic motivation in the process of China’s mining restructuring. Another reason may be the global concern for environmental protection issues and mining sustainability strategy. The Chinese National 13th 5-year plan (2016–2020) recommends low carbon, clean, safe and modern energy systems (e.g., replace coal power generation with nuclear power) (Xing et al., 2017). However, with the promotion a series of mining regulations, such as the development of Green Mining Construction (GMC), the sustainable mining industry is gradually recovering.

While our findings verified the positive effects of MI on national economy based on the evidence of China, much more work is needed to really integrate MI as an economic system innovation. Enlightened by the experiences of China and the key findings from our analysis, several policy implications were proposed and discussed as follows:

• First, the IO analysis was found to be beneficial in evaluating the economic effects of the Chinese MI, which is in line with prior study findings (Wang et al., 2019; Kim et al., 2020). IO analysis is a useful tool for studying various MI-related policy concerns, despite its inherent constraint of assuming fixed input needs. To modernize the MI and develop a sustainable, low-carbon, safe and efficient energy system, it is necessary for the Chinese government to transform the extensive mining paradigm by adopting new technologies, processes and equipment.

This paper used IO analysis to assess the feasibility of the mining sector’s overall economic contribution in China and produced a set of important indicators that could be utilized to build policy and the MI economy. However, the limitation of this article is that we treat the investments of MI projects as accumulative value rather than on-site survey data, while the NBSC is published every 5 years, hence the data match and timeliness issue might generate some uncertainty on the result.

As a follow-up to this study, future related studies may be carried out in two directions. First, although the article used the national IO table, multi-regional IO analysis can be carried out by employing a multi-regional IO table. This allows quantitative analysis of inter-regional effects as well as intra-regional effects. Second, the aim of the study is to assess the value of both direct and indirect contributions of China’s MI; however, it only considers the benefits of mining economy activities. Therefore, in future research, factors such as environmental pollution and shortage of mining resources should be taken into consideration to examine both the advantageous and disadvantageous impacts of the MI on the national economy.