Theoretical analytical framework

Industrial organization theory conceptualizes international market power as the capacity of a country or industry to exercise comprehensive control in the global marketplace. This concept essentially reflects the ultimate pattern of competitive interactions among global industries in terms of productivity and technological innovation within an open economy system. Historically, discourse in this field has often conflated market power with monopoly power. Although both phenomena involve firms setting prices above marginal costs, they differ fundamentally. Unlike monopoly power, enterprises with market power typically do not achieve profit levels exceeding the average competitive standard, and such market power tends to be short-term, with limited sustainability over the long run. Consequently, firms possessing market power should not be regarded as a primary focus of antimonopoly efforts. Further research has explicitly noted that monopoly power and market power are not inherently symbiotic, and their nonequivalent relationship reaffirms the essential distinctions between them (Bannock 2005).

From a microeconomic perspective, the mechanism underlying international market power is illustrated in Figure 1, which depicts the interaction between the market demand curve (D, downward sloping), the marginal cost curve (MC), the marginal revenue curve (MR), and the average revenue curve (AR, coinciding with D). Within an imperfectly competitive market framework, firms operate according to the profit maximization principle (MR = MC), where the production equilibrium point lies at the intersection of MR and MC, denoted as point E. The corresponding output and price at this equilibrium are Q^m and MC^m, respectively.

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Notably, the D curve lies above the MR curve, so firms can set their product’s transaction price at P^m based on the D curve. When the difference (P^m − MC^m) is positive, it indicates that the firm has price control (i.e., market power occurs). The larger the value of (P^m − MC^m), the stronger the firm’s market power, which is visually reflected by the vertical distance between P^m and MC^m in the figure (Young 2000). This relationship is closely linked to demand elasticity (ε) and can be quantitatively analyzed using the formula (P^m − MC^m)/P^m = −1/ε. The left-hand side of the equation represents the price-cost markup, thereby revealing the intrinsic connection between market power and market demand elasticity.

Data collection

The research sample includes countries with chemical wood pulp import volumes accounting for more than 1 percent of China’s total chemical wood pulp imports between January 2001 and December 2023. These countries include Brazil, Canada, Chile, Finland, Germany, Indonesia, Japan, New Zealand, the Russian Federation, Sweden, Thailand, and the United States.

The specific data sources and calculation methods are as follows. The chemical wood pulp prices from each source country are calculated based on trade value per trade volume. Data on trade value and volume of chemical wood pulp imported from these countries are sourced from the National Research Network Statistical (2024) database. Nominal exchange rate data for these countries are obtained from the Economy Prediction System (2024) database. The real exchange rate is calculated as the nominal exchange rate multiplied by China’s monthly consumer price index (CPI) divided by the source country’s monthly CPI. This method is adopted because there is no specific chemical wood pulp price index and previous studies have indicated that results derived from CPI calculations are less accurate than those from nominal exchange rates (Yumkella et al. 1994, Wang et al. 2017). As a robustness check, the calculation results of the real exchange rate are also presented, with CPI data for the countries sourced from the Bureau Van Dijk EIU Countrydata (2024) database.

PTM model

Krugman (1986) suggested the PTM model theory, which states that fluctuations in bilateral exchange rates between an exporter and multiple importers lead to changes in the price ratios paid by the importers. Building on this theoretical basis, Goldberg and Knetter (1999) developed a representative model for export pricing strategies, which is constructed based on the principle of profit maximization. In this model, the scenario states that in an exporting country supplying goods to N destination countries, the demand in each country is defined as follows: (1) $$q_{it}=f(p_{it}{e}_{it})z_{it}\forall i=1\dots N,\forall t=1\dots T$$

In Equation 1, q_it denotes the demand from destination country i in period t; p_it represents the export price set by the exporter for i, expressed in the exporter’s domestic currency in period t; e_it signifies the bilateral exchange rate between the exporter and destination country i in period t, expressed as the number of units of the destination country’s currency exchanged for one unit of the exporter’s currency; and z_it is a random variable capturing shifts in the demand curve. Furthermore, the production costs incurred by exporting country are given by the following: (2) $$C_t=C\left({\displaystyle \sum _{i=1}^N{q}_{it}}\right){\delta }_t\forall i=1\dots N,\forall t=1\dots T$$where C_t represents the total costs associated with all destination countries i, expressed in the exporter’s domestic currency; and δ_t is a random variable that introduces changes in the cost function, such as fluctuations in input prices during period t. Therefore, the profit maximization problem for the exporting country can be expressed as follows: (3) $$\max \pi ={\displaystyle \sum _{i=1}^N[p_{it}f(p_{it}{e}_{it})z_{it}]-C\left\{{\displaystyle \sum _{i=1}^N[f(p_{it}{e}_{it})z_{it}]}\right\}}{\delta }_t$$

In Equation 3, by taking the derivative with respect to p_it and expressing it in terms of elasticity, the first-order condition is derived as follows: (4) $$p_{it}=c_t\left(\frac{{\epsilon }_t^i}{{\epsilon }_t^i-1}\right)\forall i=1\dots N,\forall t=1\dots T$$where c_t represents the common marginal cost faced by the exporting country in period t, and εⁱ_t denotes the absolute value of the price elasticity of demand faced by the exporter in destination country i during period t. Therefore, the price in the exporter’s currency should reflect the price-cost markup, with the markup depending on the demand elasticity in destination market i (Varma and Issar 2016). If the demand elasticity in destination country i is not constant, fluctuation in the exchange rate between the exporting and destination countries will influence the transaction price by affecting either the marginal cost or the demand elasticity. Specifically, changes in marginal cost will affect all destination countries, while variations in demand elasticity will only affect the destination country where the exchange rate change occurs (Dawson et al. 2017). Taking the natural logarithm of Equation 4 and performing a total differential yields the final form of the export PTM model as follows: (5) $$\mathrm{In}{\mathit{P}}_{\mathit{it}}=\alpha +{\beta }_i\mathrm{In}{e}_{it}+{\lambda }_i+{\theta }_t+{\mu }_{it}\forall i=1\dots N,\forall t=1\dots T$$where p_it represents the price in the exporter’s currency for exports from the exporting country to country i in period t; β_i is the parameter to be estimated, reflecting the elasticity of export prices to exchange rate fluctuations and serving as a measure of the exchange rate pass-through effect in destination country i; λ_i captures country-specific effects; θ_t represents the time effect as a monthly dummy variable to control for seasonal influences, thereby eliminating the need for seasonal adjustments to the price and exchange rate series; and μ_it is the error term.

Building on the export pricing strategy model developed by Goldberg and Knetter (1999), Manitra and Shapouri (2001) introduced the import PTM model. The specific formulation of the PTM model is as follows: (6) $$\mathrm{In}{\mathit{r}}_{\mathit{jt}}=\varphi +{\phi }_j\mathrm{In}{e}_{jt}+{\lambda }_j+{\theta }_t+{\mu }_{jt}\forall j=1\dots N,\forall t=1\dots T$$

In Equation 6, r_jt represents the price of chemical wood pulp imported from country j to China in period t, denominated in Renminbi (RMB) with the unit of measurement being yuan per ton; e_jt represents the bilateral exchange rate between China and source country j, expressed as the amount of RMB (China’s lawful currency) per unit of j’s currency; φ_j is the parameter to be estimated, reflecting the elasticity of chemical wood pulp import prices with respect to exchange rate fluctuations and measures the exchange rate pass-through effect for country j; λ_j represents country-specific effects; θ_t accounts for time effects as a monthly dummy variable to control for seasonal variations, thus eliminating the need for seasonal adjustment of the price and exchange rate series; and μ_jt is the error term.

Some scholars have attempted to extend the PTM model by incorporating additional control variables such as gross domestic product. However, results indicate that the original PTM model by Goldberg and Knetter (1999) provides more reliable outcomes. This occurs primarily because from a multilateral trade perspective, the inclusion of individual and time effects in the PTM model already accounts for unobservable heterogeneity. Adding additional control variables may reduce degrees of freedom and affect the accuracy of the model’s results (Griffith and Mullen 2001, Pall et al. 2013, Wang et al. 2017).

Building on the analytical framework, the exchange rate pass-through effect (ERPT*) reflects the effect of exchange rate fluctuations on the price of chemical wood pulp denominated in foreign currency, embodying the market power of chemical wood pulp exporting country. Conversely, the PTM ability (PTM*) captures the effect of exchange rate fluctuations on the price of chemical wood pulp denominated in RMB(φ_j), which signifies China’s market power in chemical wood pulp imports. Note that PTM* = ERPT* + 1. Assuming constant costs, when foreign currencies appreciate, the research findings can be categorized into the following seven scenarios based on the values of ERPT* and PTM* (Table 1; specific explanations provided in the Appendix).

Table 1.Relationship between China’s chemical wood pulp import demand elasticity, ERPT*, PTM*, and market power.^a

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Analysis of key characteristics

With the rapid development of China’s paper industry, domestic demand for high-quality raw materials, such as wood pulp, has surged. Because of China’s relatively limited forest resources and the delayed integration of the forestry and paper industries, domestic wood pulp production remains severely constrained, resulting in a significant structural supply shortage that can be alleviated only through imports (Cheng et al. 2023).

From 2001 to 2023, China’s chemical wood pulp imports grew steadily. Table 2 details the breakdown of China’s chemical wood pulp import sources in 2001 and 2023, revealing a high degree of supplier concentration. This concentration not only weakens China’s bargaining power in import trade but also elevates international trade risks, leaving China’s chemical wood pulp supply highly vulnerable to policy shifts in source countries and potential trade frictions. In 2001, China primarily imported chemical wood pulp from 10 countries, including Indonesia, Russia, and Canada, which collectively accounted for 97.9 percent of total imports. By 2023, Brazil, Indonesia, and Chile had become the main sources, contributing 94.25 percent of the total. Notably, Brazil has emerged as the largest supplier of China’s chemical wood pulp imports, while the positions of Indonesia, Canada, Chile, and the United States have remained stable contributors. In contrast, shares from Russia and New Zealand have declined significantly.

Table 2.Regional structure of China’s chemical wood pulp imports.

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Diagnostic tests for the PTM model

To ensure the scientific validity of the regression results, a series of diagnostic tests were conducted on the PTM model, including stationarity tests, cointegration tests, Hausman tests, two-way fixed effects tests, varying coefficient model tests, Wald heteroskedasticity tests, Wooldridge autocorrelation tests, and multicollinearity tests. Ultimately, it was determined that a varying coefficient PTM model incorporating both individual and time-fixed effects should be adopted. The model was then estimated using feasible generalized least squares regression to address heteroscedasticity issues and ensure the reliability of the estimation results. The specific diagnostic process is as follows.

Stationarity tests.—

To avoid the issue of spurious regression, Fisher-type and Levin-Lin-Chu tests were used to assess the stationarity of the panel data. The Fisher-type test is applicable to data with heterogeneous unit roots, whereas the Levin-Lin-Chu test is suitable for homogeneous unit roots. Panel data can be determined stationary only if both tests confirm stationarity. In the data, rwp represents the price of chemical wood pulp imported by China, and ewp denotes the bilateral exchange rate between China and the source countries of chemical wood pulp imports. Test results show that all series are stationary, confirming that the data constitute a long panel (Table 3).

Table 3.Stationarity tests for each series.

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Cointegration tests.—

The Pedroni Residual and Kao Residual tests were used to examine the cointegration relationship among variables. The Pedroni test is applicable to heterogeneous panels, whereas the Kao test is suited for homogeneous panels. Cointegration is confirmed only when both tests simultaneously reject the null hypothesis. Results indicate a long-term cointegration relationship between lnrwp and lnewp (Table 4).

Table 4.Cointegration tests for variables.

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Other tests.—

For the PTM model of China’s chemical wood pulp imports, the Hausman test yields a χ² statistic of 26.56, which significantly rejects the null hypothesis at the 1 percent level. The F tests for individual and time-fixed effects produce values of 24.15 and 19.85, respectively, both of which also significantly reject the null hypothesis at the 1 percent level. Additionally, the χ² statistic for testing parameter stability is 4.5 by 10⁴, which again significantly rejects the null hypothesis at the 1 percent level. Consequently, a varying coefficient PTM model with individual and time-fixed effects should be constructed (Tables 5 to 7).

Table 5.Hausman test for the model.

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Table 6.Individual and time-fixed effect tests for the model.

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Table 7.Varying coefficient test for the model.

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The χ² statistic for Wald heteroscedasticity test is 256.46, which significantly rejects the null hypothesis at the 1 percent level, indicating the presence of heteroscedasticity. The Wooldridge autocorrelation test yields an F statistic of 7.25, also significantly rejecting the null hypothesis at the 1 percent level, suggesting the presence of autocorrelation. The variance inflation factor is 1, indicating the absence of multicollinearity (Table 8). Therefore, to ensure the reliability of the results, the feasible generalized least squares method should be used for estimation.

Table 8.Other three tests for the model.

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Analysis of empirical results for the PTM model

R results of the PTM model are displayed in Table 9. Here, the estimates derived from the nominal exchange rate are treated as the baseline, whereas those from the real exchange rate are used for robustness checks. Through analysis, it is determined that although the specific values of φ_j differ, the overall assessment of market power remains highly consistent. This indicates that inflation exerts a negligible effect on market power in China’s chemical wood pulp import trade and does not lead to any significant changes. This consistency further validates both the reliability of the constructed PTM model and the robustness of its results. Table 9 shows that notable disparities occur in market power among different source countries in China’s chemical wood pulp import trade. Based on the degree of market power, the source countries of China’s chemical wood pulp imports can be classified into the following three categories.

Table 9.Estimation results of market power in China’s chemical wood pulp import market.

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International market level

Research shows that China holds superlative market power in its chemical wood pulp imports from Thailand, Japan, and Russia and strong market power in imports from Indonesia and New Zealand. However, China lacks market power in chemical wood pulp imports from Chile, the United States, Germany, Sweden, Finland, Canada, and Brazil. Analysis further reveals that the likelihood of China having weaker market power in chemical wood pulp imports rises when the source countries exhibit the following conditions. (1) The country holds a small share in both the global and Chinese chemical wood pulp markets, whereas China accounts for the majority of its pulp exports; (2) its wood-processing industry is underdeveloped, resulting in lower-quality chemical wood pulp products (Gordeev 2020); (3) China has established integrated forestry-pulp-paper production bases in the country, allowing for the direct use of local timber resources; (4) the country faces raw material shortages, which raise production costs (Liao and Ning 2023, Hayafune and Tachibana 2024, Kumar and Luo 2024); (5) the country is frequently hit by natural disasters (e.g., earthquakes, tsunamis), leading to poor market stability; and (6) the country lacks coordinated price control efforts with other nations.

In contrast, the likelihood of China wielding stronger market power in chemical wood pulp imports increases when the source countries demonstrate the following traits. (1) The country commands a significant share in both the global and Chinese chemical wood pulp markets, and China is highly dependent on it (Li et al. 2008, Karthikeyan et al. 2013); (2) it boasts advanced wood-processing technologies and equipment, which enables the production of high-quality chemical wood pulp products (Silva et al. 2019); (3) it has an ample supply of raw materials, ensuring convenient production; (4) it exercises strong price control in the international chemical wood pulp market (Silva and Maciel 2022, Cheng et al. 2023 Jiang and Dai 2023, Zou and Xu 2024); and (5) its industry features a high level of concentration, which facilitates coordinated negotiation efforts.

A large market share is one of the conditions for attaining market power, but it does not automatically ensure it; therefore, “market power” should not be conflated with “monopoly power”—a point that reinforces the argument that using market concentration as a measure of market power lacks a robust theoretical foundation. This perspective provides a more nuanced understanding of the true nature of market power. To improve China’s chemical wood pulp import trade conditions, efforts should be focus on the previously outlined factors, and adjustments to the import market structure could be made to increase market share in countries with significant market power.

China imports large quantities of chemical wood pulp and has a high degree of dependence on foreign trade, yet it holds a relatively weak bargaining position in the international market. This study is valuable for improving the stability of China’s chemical wood pulp import trade and ensuring the security of domestic chemical wood pulp supply. Gaining market power commensurate with the scale of its import trade will effectively mitigate the adverse effects of the large country effect, reduce trade losses, and further promote the sustainable development of the domestic paper industry.

Therefore, China should adopt measures concerning its chemical wood pulp imports. China should adjust the import market structure by increasing the import share from source countries with market power, such as Thailand, Japan, Russia, Indonesia, and New Zealand. Meanwhile, China should actively explore new import sources to reduce reliance on countries where it lacks market power, including Chile, the United States, Germany, Sweden, Finland, Canada, and Brazil. This task is feasible because China has been pursuing a diversified import strategy for chemical wood pulp in recent years to mitigate import trade risks. For instance, amid the United States–China trade frictions, China has met domestic demand for chemical wood pulp by increasing imports from trading partners other than the United States. Currently, the focus of chemical wood pulp imports has shifted from North America to the Asia-Pacific region, where countries such as Indonesia have grown in prominence as China’s trading partners.

China should extend the successful experience gained in Thailand by proactively establishing factories and developing forest exploitation bases in regions abundant in forest resource. This approach will help alleviate raw material shortages, enhance the depth and intensity of participation in the global value chain, and effectively guard against the risk of price manipulation in international trade. China could establish long-term cooperative partnerships with countries such as Chile, the United States, Germany, Sweden, Finland, Canada, and Brazil. By signing bilateral or multilateral chemical wood pulp trade agreements, it will be possible to stabilize both supply and transaction prices. Drawing on the chemical wood pulp export practices of countries such as Chile, China could standardize the order of its domestic chemical wood pulp import market, strengthen the role of industry associations, reduce information exchange costs, forge a unified external synergy, and enhance discourse power in negotiations.

Domestic market level

According to Food and Agriculture Organization of the United Nations database, in 2023, China’s chemical wood pulp output reached 24.57 million tons, with imports totaling 32.84 million tons and exports at 0.27 million tons. From these figures, China’s domestic consumption stood at 57.14 million tons. The data on output, imports, and exports clearly indicates that China’s imports of chemical wood pulp are used to meet domestic market demand. To address raw material shortages, in addition to actively implementing measures to stabilize international supply and transaction prices, the following strategies can be considered for the domestic market in China.

• Accelerate the establishment of fast-growing timber reserves dedicated to pulping. This entails integrating seedling cultivation, forest management, and timber harvesting while advancing the informatization of forestland to build a comprehensive forest mapping database. The aim is to extend the forestry industry chain, upgrade the technological level of forestry development, meet diverse needs such as afforestation and logging, and enhance the economic value of the forestry sector.

• Intensify research and development on new papermaking materials and technologies to reduce reliance on chemical wood pulp and effectively overcome the constraints imposed by scarce forest resources. For instance, actively explore the development and use of nonwood fiber raw materials (e.g., crop straw, bamboo, reed) to diversify papermaking material sources (Guan and Zhang 2023).

• Raise the level of domestic integrated forest-pulp-paper cyclical development. While ensuring clean production and energy conservation, China should continuously explore innovative models for comprehensive resource use and industrial cyclical development. This includes building a circular economy ecological chain of resources–products–renewable resources and developing a complete industrial system characterized by supporting forests through paper, promoting paper through forests, and integrating forest and paper industries. The goal is to maximize resource use and facilitate the development of resource-saving, environmentally friendly, and green circular development models (Dai et al. 2023).