Does Foreign Direct Investment Enhance Exports of China’s Wood Products? The Role of Wood Resource Efficiency

Abstract

China is one of the world’s leading producers and exporters of wood-based panels and plays a crucial role in ensuring a stable global supply of wood products. But China’s wood product exports have recently diminished, potentially due to the retraction of foreign investment. This behavior remains unexamined mechanistically in the current literature. This study investigates the impact of FDI on the export performance of China’s wood processing industry and explores the potential for leveraging foreign investment to reverse the downward trend in export growth. Our findings indicate that FDI alleviates export constraints by enhancing wood resource efficiency, which suggests a substantive response to industry challenges rather than a mere strategic adjustment. However, FDI inflows have decreased in recent years, negatively affecting export performance and highlighting the need for policy improvements. We further examine the differential effects of FDI on exports across port and non-port regions, given that the urgency of attracting FDI varies spatially. Our analysis reveals that the export spillover effect of FDI in port areas is approximately 165% higher than in non-port areas, largely due to China’s high dependence on wood product imports. In regions with extensive artificial forests, the impact is lower, possibly due to a stronger focus on domestic markets. In particular, Eastern China, benefiting from early market liberalization and a history of successful foreign collaborations, demonstrates significant improvements in export performance. To mitigate the export pressures on China’s wood processing industry, we recommend targeted industrial policies, particularly for port areas, to attract high-quality FDI that supports global supply chain stability and sustainable development.

1. Introduction

Wood is the only biodegradable, renewable, and environmentally friendly material among the major raw materials, which include steel, cement, and plastic. Globally, promoting wood as a substitute for these high-energy-consuming materials is central to achieving sustainable development goals [1]. For instance, according to Japan’s Forest and Forestry Basic Act, the country aims to utilize wood for carbon storage and to create “second forests” in urban areas. This initiative aligns with capturing new demand for wood in various building types and contributes to global warming prevention. Similarly, the United Kingdom’s Circular Economy Plan emphasizes keeping resources in use for as long as possible, recognizing wood’s role in accelerating decarbonization. The European Union regards the circular economy as an “irreversible global trend”. China advocates replacing plastic and steel with wood and bamboo to enhance carbon sequestration. Thus, the strategic promotion and policy support of wood throughout the world not only highlights its core position as a sustainable material, but also becomes a key path for countries to achieve low-carbon economy and circular development goals.

China is among the foremost producers and exporters of wood-based panels [2]; nonetheless, it has experienced a recent decrease. In 2020, the FAO reported that China accounted for 60% of global plywood production and over 50% of the world’s hardboard and MDF/HDF (Figure 1a). In 2020, China’s plywood exports reached 10.09 million cubic meters, or 35.75% of the global total, thereby securing the top position worldwide. It exceeds that of the second-ranked Russian Federation (3.47) by more than threefold. These goods are distributed to approximately 200 nations and regions (Figure 1b), highlighting the importance of China’s wood processing exports in sustaining the worldwide supply of wood products. The export growth rate of China’s wood processing industry has surpassed 10%. Conversely, the export growth rate of China’s wood processing industry has progressively diminished and has even dipped below zero since 2015, as reported by the China Forestry and Grassland Statistical Yearbook. Consequently, in the context of reconciling domestic timber resource availability with import reliance, enhancing China’s export capability of wood processing products is crucial for sustaining the stability of the global industrial chain.

Foreign direct investment (FDI) acts as an essential connection between domestic and foreign markets, significantly contributing to China’s “dual circulation” economic plan. Foreign direct investment can not only stimulate economic growth and industrial transformation [3] but also exhibit a substantial influence on exports [4]. Current research indicates that foreign direct investment predominantly exerts a beneficial influence on exports [5,6,7]. Nevertheless, owing to the developmental models and trade attributes of various industries, the relationship between FDI and exports is not consistently evident [8,9,10]. In the domain of wood processing, there remains an insufficient amount of empirical study about the specific effects of foreign direct investment. In the present global trade landscape, characterized by the escalation of trade protectionism, the exodus of foreign capital has intensified significantly (see Figure 2) [11]. This alteration may substantially affect the exportation of China’s wood processing industry. Consequently, this study posits the subsequent research inquiries: What is the significance of foreign direct investment in China’s wood processing industry? Does it have a substantial influence on exports? What is the precise mechanism of impact? Examining these topics is essential for optimizing industrial policy and improving export competitiveness.

The research goal is to analyze how FDI affects exports of China’s wood processing industry and assess the feasibility of enhancing the international competitiveness of China’s wood processing industry by attracting foreign investment. This study mainly presents the following marginal contributions: First, although scholars have paid attention to some factors affecting the exports of the wood processing industry, there is a lack of research on the impact of FDI on exports of China’s wood processing industry. However, wood processing products are environmentally friendly materials that enable low-carbon development and circular economy [12]. Therefore, it is crucial to discuss abolishing the dilemma imposed on exports of China’s wood processing industry, for it is vital in not only optimizing the efficiency of foreign investment utilization in the industry, but also providing a Chinese case reference for the sustainable development of global forest products trade. Second, the literature often overlooks the significance of China’s wood processing industry, particularly in terms of wood resource efficiency. This research identifies the mechanisms linking FDI to the global supply of China’s wood products, informing tailored industrial policies. Third, we extend and interpolate missing data and reveal the dilemma of China’s wood processing industry, wherein export growth exhibits stagnation, from the micro-enterprise level.

2. Theoretical Analysis

FDI significantly impacts China’s wood processing industry by providing capital, technology, expertise, human capital, management methods, and marketing channels. FDI influences the exports of China’s wood processing industry indirectly by affecting their efficiency in utilizing wood resources.

FDI can enhance wood resource efficiency through technological spillover effects [13,14], increasing exports. There are five primary pathways for technology spillovers, with the competition effect being the initial one. The introduction of advanced technologies and management practices increases competition, compelling local firms to innovate [15]. The second is the demonstration effect. FDI grants wood processing enterprises access to international companies for close networking. It enables them to refine their productivity by imitating and learning from their technology and organizational management expertise [16]. The third is the personnel flow effect. Multinational companies train local personnel to improve their technical level for production demands. The spillover effect occurs when trained personnel flow from multinational companies to domestic wood processing enterprises. High-quality human capital fosters the improvement of wood processing enterprises [17,18]. The fourth is the R&D investment effect [19,20]. The funds brought in by FDI can enhance the R&D investment of wood processing firms, leading to technological advancements within these enterprises [21]. The fifth type is the economy of scale effect. FDI funds can facilitate the modernization of production equipment and the expansion of production scale. This way, businesses can attain economies of scale, enhancing wood resource efficiency.

The improvement of wood resource efficiency would further affect exports [22,23,24]. According to the New Trade Theory, since enterprises need to pay additional fixed costs to enter the export market, such as customs clearance costs, transportation costs, marketing channel construction costs, and expenses related to managing international trade risks, efficient enterprises can better overcome these export costs [25,26,27]. Consequently, these businesses achieve additional export earnings, demonstrating a “self-selection effect” in corporate exports.

Overall, FDI not only fosters partnerships and transnational operations between Chinese and foreign companies, enhancing export opportunities, but also enhances wood resource efficiency through technological spillovers such as competition effect, demonstration effect, personnel flow effect, R&D investment effect, and scale economy effect. The existence of the “self-selection effect” further affects corporate exports. Therefore, we propose the following hypotheses.

H1: 

FDI has a promoting effect on exports of China’s wood processing industry.

H2: 

FDI enhances exports by improving wood resource efficiency.

3. Methodology and Data

3.1. Data

This study utilizes data from the China Industrial Enterprise Database spanning from 1998 to 2015, which includes over 100 indicators detailing enterprises’ fundamental characteristics, financial health, and operational status. The database offers a sizable sample size, comprehensive data, and nuanced indicators and is crucial for empirical analysis at the enterprise aspect [28,29,30]. The comprehensive large sample size of the database is vital for empirical analysis at the enterprise level. However, the database does present challenges, including sample mismatches and irregular indicators. To address these issues, we filtered out duplicate or missing enterprise codes, anomalous business operations, and meaningless indicators, following the protocol by Nie et al. [31].

The dataset also exhibits missing data and inconsistencies. Notably, research utilizing this dataset typically concludes in 2007 due to these gaps. We extended the dataset coverage to 2015 by interpolating missing data, employing multiple imputation methods to enhance data validity and accuracy. Imputation techniques are often separated into two categories: single imputation and multiple imputation [32]. A collection of believable values is substituted for the missing variable using multiple imputation techniques [33]. Although they perform better than single imputation, they also take longer. One of the most popular of these is multivariate imputation by chained equations (MICE) [34]. We therefore chose this method. Specifically, we also updated the gross industrial output and added value for 2004 using data from the 2004 China Economic Census. We followed Liu and Li’s accounting standard estimation method for gross output value [35], Yu’s approach for intermediate inputs [36], and Zhang and Li’s method for added value [37]. Depreciation and fixed assets were supplemented based on findings by Nie et al. [31] and Su et al. [38].

Additionally, to ensure data comparability and continuity, we adapted our industry classification to align with changes in China’s “National Economic Industry Classification” which was revised in 2002 and 2011. We harmonized the industry codes from 1998 to 2015 with the GB/T4754-1994 version, using the method proposed by Wen and Xian [39].

3.2. Methodology

To investigate the possibility of improving the exports of China’s wood processing industry through FDI, we constructed the following model to test H1.

$${\mathrm{e}\mathrm{x}\mathrm{p}\mathrm{o}\mathrm{r}\mathrm{t}}_{\mathrm{i}\mathrm{t}}={\mathrm{\alpha }}_0+{\mathrm{\alpha }}_1{\mathrm{F}\mathrm{D}\mathrm{I}}_{\mathrm{i}\mathrm{t}}+{\mathrm{\alpha }}_2{\mathrm{X}}_{\mathrm{i}\mathrm{t}}+{\mathrm{\theta }}_{\mathrm{i}}+{\mathrm{\theta }}_{\mathrm{t}}+{\mathrm{\epsilon }}_{\mathrm{i}\mathrm{t}}$$

(1)

where i represents the enterprise and t represents the year. The dependent variable is export, indicating the export scale of the wood processing enterprise, while FDI acts as an explanatory variable. X denotes the control variables, which encompass enterprise size (size), return on investment (ROI), government subsidy (subsidy), human capital (wage), and capital intensity (kain). ${\mathrm{\theta }}_{\mathrm{i}}$ represents the fixed effect of the enterprise and describes the enterprise’s characteristics that remain constant over time. ${\mathrm{\theta }}_{\mathrm{t}}$ denotes the year-fixed effects, characterizing variables that change solely with time. The random error terms are ${\mathrm{\epsilon }}_{\mathrm{i}\mathrm{t}}$, ${\mathrm{\mu }}_{\mathrm{i}\mathrm{t}}$, ${\mathrm{\xi }}_{\mathrm{i}\mathrm{t}}$.

Furthermore, we also constructed models based on the intermediary effects to investigate the relationship between FDI and exports in China’s wood processing industry. The intermediary effect model, consisting of Model 1, Model 2, and Model 3, tests whether FDI can enhance wood resource efficiency, leading to an increase in exports in the wood processing industry.

$${\mathrm{W}\mathrm{R}\mathrm{E}}_{\mathrm{i}\mathrm{t}}={\mathrm{\beta }}_0+{\mathrm{\beta }}_1{\mathrm{F}\mathrm{D}\mathrm{I}}_{\mathrm{i}\mathrm{t}}+{\mathrm{\beta }}_2{\mathrm{X}}_{\mathrm{i}\mathrm{t}}+{\mathrm{\theta }}_{\mathrm{i}}+{\mathrm{\theta }}_{\mathrm{t}}+{\mathrm{\mu }}_{\mathrm{i}\mathrm{t}}$$

(2)

$${\mathrm{e}\mathrm{x}\mathrm{p}\mathrm{o}\mathrm{r}\mathrm{t}}_{\mathrm{i}\mathrm{t}}={\mathrm{\gamma }}_0+{\mathrm{\gamma }}_1{\mathrm{F}\mathrm{D}\mathrm{I}}_{\mathrm{i}\mathrm{t}}+{\mathrm{\gamma }}_2{\mathrm{W}\mathrm{R}\mathrm{E}}_{\mathrm{i}\mathrm{t}}+{\mathrm{\gamma }}_3{\mathrm{X}}_{\mathrm{i}\mathrm{t}}+{\mathrm{\theta }}_{\mathrm{i}}+{\mathrm{\theta }}_{\mathrm{t}}+{\mathrm{\xi }}_{\mathrm{i}\mathrm{t}}$$

(3)

where WRE represents wood resource efficiency of Chinese wood processing enterprises.

3.3. Variables

(1)

Explained variable. We measured exports of wood processing enterprises by using export delivery value (export). The higher the value, the greater the export volume.

(2)

Explanatory variable. Foreign direct investment of wood processing enterprises (FDI) is the primary independent variable, determined by the foreign capital investment of enterprises.

(3)

Mediating variable. Wood resource efficiency (WRE) is identified as a mediator between FDI and export scales. Represented by total factor productivity, WRE indicates the efficient use of wood resources [40]. Total factor productivity is predominantly assessed using micro-data through semi-parametric estimation methods like the OP method [41] and the LP method [42]. However, according to the assumptions of the OP approach, total factor productivity cannot be computed when investment is zero. In actuality, not every company invests annually. Consequently, the OP technique may result in numerous missing data [43]. The LP method utilizes intermediate inputs as proxy variables, thereby overcoming the limitations of the OP method and yielding more precise estimation results. Hence, we adopted the LP method to assess the wood resource efficiency of Chinese wood processing enterprises [40,44,45].

(4)

Control variables. Enterprise size (size). Measured by the logarithm of fixed assets, larger enterprises are shown to significantly influence export behavior due to their ability to leverage economies of scale, thus reducing costs and managing international trade risks effectively [46].

Return on investment (ROI). The return on investment in enterprises’ production is important in the decisions of enterprises’ production activities [47,48]. Wood processing companies that need to input less in unit output value are more likely to overcome export trade barriers. We employed the proportion of total capital investments to the total output value to gauge the return on investment.

Human capital (wage). The international market imposes stricter requirements for product safety, environmental protection, and quality compared to the domestic trade [49]. High human capital is crucial for maintaining the quality standards required by international markets [50]. The higher an enterprise’s wage, the easier it is to attract high-caliber labor. We employed the average salary of employees as a measure of human capital [48].

Government subsidy (subsidy). The impact of government subsidies on exports is ambiguous. While they may enhance export profitability, they also risk inciting retaliatory trade measures [51,52]. The ratio of government subsidy to main business income serves as the metric for this variable.

Capital intensity (kain). According to the New Trade Theory, the factors utilized by a country’s exporting enterprises determine the factor intensity of the country’s exported products [53]. Capital intensity is a determinant of comparative advantage in exports [54]. Enterprises that make substantial capital investments typically maintain an edge with more modern factories, equipment and machinery, enabling them to capitalize on economies of scale. High-capital enterprises usually enjoy greater advantages in the export market. Therefore, we measured the capital intensity of the enterprise by dividing the total number of fixed assets by the number of employees.

4. Empirical Findings

To address the dilemma of China’s reduced supply of forest products to the world, we analyzed the impact of FDI on exports of Chinese wood processing enterprises.

4.1. Heteroskedasticity Test and Correlation Test

To ensure accurate research results and mitigate potential issues with heteroskedasticity in the model, this study employs the White test to identify any such problems. The White test yielded a p-value of 0.000, which rejects the null hypothesis of homoskedasticity and indicates the presence of heteroskedasticity in the data. To mitigate the effects of heteroskedasticity, robust standard errors for regression are employed. To prevent potential issues with multicollinearity, we used the variance inflation factor (VIF).

Table 1. Multicollinearity test.

Variables	VIF	1/VIF
FDI	1.46	0.6847
WRE	1.39	0.7214
size	1.19	0.8424
kain	1.63	0.6122
subsidy	1.00	0.9998
ROI	1.00	0.9965
wage	1.63	0.6151
Mean VIF	1.24

displays the test results that there is no multicollinearity problem among explanatory variables, as all VIF values between them are less than 10.

4.2. Benchmark Regression Results

To examine the effect of foreign investment on China’s wood processing industry’s export trade, this article conducts regression analysis on Model 1 using data of Chinese wood processing enterprises between 1998 and 2015. The Hausman test results show p-values of 0.000, rejecting the null hypothesis and selecting the fixed effects model. The regression results are displayed in

Table 2. Benchmark regression results.

Variables	Model 1	FDI Heterogeneity	FDI Heterogeneity
FDI	0.248 ***
	(7.090)
Foreign capital investment		0.090 ***
		(3.300)
Capital investment from Hong Kong, Macao, and Taiwan			0.033
			(1.210)
size	5067.510 ***	4781.656 ***	4708.362 ***
	(15.010)	(11.550)	(11.150)
kain	9.576 ***	12.685 ***	16.178 ***
	(24.770)	(31.370)	(35.860)
subsidy	2.888	0.093	2.358
	(0.020)	(0.000)	(0.020)
ROI	−34.292	−29.481	−614.765 ***
	(−1.150)	(−1.030)	(−4.570)
wage	−25.467 ***	−35.183 ***	−40.257 ***
	(−8.690)	(−11.350)	(−12.080)
C	−34,659 ***	−32,735 ***	−32,220 ***
	(−12.030)	(−9.430)	(−9.120)
Fixed effect	YES	YES	YES

Note: *** indicate significance at the 1% level.

.

We employ the fixed effects model to examine the impact of FDI on exports of China’s wood processing industry. The regression results are presented in the first column of Table 2, indicating that FDI has a significant positive impact on the industry’s export at the 1% significance level. For each unit increase in FDI, the volume of exported goods increases by 0.248 units. It demonstrates that foreign investment promotes the export of China’s wood processing industry, confirming H1. Currently, the export growth of China’s wood processing industry is slow. Empirical evidence indicates that FDI has a positive impact on exports of China’s wood processing industry. Reasonable use of FDI can help alleviate the export pressure faced by the wood processing industry, provide financial support to enterprises, and reduce operating risks for export enterprises. This will help China’s wood processing products maintain their dominant position in the international market. Such measures are of great practical significance in assisting the healthy development of exports. The government should maintain an open attitude towards FDI in the wood processing industry. Enterprises should utilize the funds, technology, human capital, marketing channels, and other resources brought by FDI to promote exports.

Further, we identify the categories of FDI in China’s wood processing industry that has significantly contributed to promoting exports. The impact of each category on export trade is examined separately. The FDI is divided into two categories: capital investment from Hong Kong, Macao, and Taiwan and foreign capital investment from other regions. As shown in the second and third columns of Table 2, FDI has a significant positive impact on the export trade of China’s wood processing industry, while the positive impact of capital investment from Hong Kong, Macao, and Taiwan is not significant. The evidence suggests that FDI can promote exports through capital, technology, personnel, and marketing. However, it is important to note that only foreign capital investment, not capital investment from Hong Kong, Macao, and Taiwan, plays a substantial role in promoting exports.

Enterprise size has a positive and significant impact on exports. It indicates that larger enterprises have a stronger effect on promoting exports. It is due to their ability to form economies of scale, which reduces production costs and increases their export advantage. Large-scale enterprises often benefit from economies of scale, which can reduce production costs and increase their ability to withstand risks such as international trade uncertainty, giving them a competitive advantage in exports. Wood processing enterprises are often small in scale and have limited funds, which can make it challenging to enter the international market and manage risks effectively. Therefore, the government should provide strong support to the wood processing industry. On the one hand, wood industrial parks can be established to bring small businesses together, forming economies of scale and reducing corporate production costs. On the other hand, corporate financing channels can be broadened, and financial support can be provided to help companies expand their production scale.

The capital intensity of wood processing enterprises has a positive impact on their exports, which is significant at the 1% level of significance. It suggests that, despite being a labor-intensive industry, companies with higher levels of mechanization are better equipped to overcome trade barriers and are more likely to engage in exporting. The influence of government subsidy on exports appears to be insignificant. As previously mentioned, the impact of government subsidy on corporate exports is uncertain. While subsidy may reduce production costs and promote exports, they may also attract external trading partners. Retaliatory behavior occurs when government subsidies reduce the production costs of enterprises, giving their products a price advantage. However, this low-price sales model can be viewed as anti-dumping and may attract sanctions from trading partners. Chinese wood processing products are typically priced low in the international market. If trading partners impose anti-dumping sanctions on these products, it could hinder the export of related items. In 2003, the United States conducted an anti-dumping investigation against over 130 Chinese wooden furniture companies. The investigation amounted to as much as USD 1 billion. The final result of the investigation was that 82 companies received an anti-dumping tax rate of 10.92%, and 36 companies received a tax rate of 198.08%. Anti-dumping sanctions have increased the export costs of enterprises. The effect of increased government subsidy on the export trade of wood processing enterprises remains uncertain.

The impact of the return on investment on the exports of wood processing enterprises is not significant, which is inconsistent with our expectations. Wood processing enterprises that need to input less in unit output value are more likely to overcome export trade barriers. This gives them an advantage in the export market. However, it is important to note that leading enterprises typically have strong capabilities and input less in unit output, and can achieve economies of scale and establish their brands [55]. This enables them to rapidly capture the domestic and international market. China’s wood processing industry is primarily composed of small and medium-sized enterprises. So, they tend to engage in OEM or ODM production [56] due to the industrial characteristic. The impact of return on investment is not as large as expected.

The effect of human capital promotion on exports of wood processing enterprises is significant at the 1% level due to China’s wood processing industry being labor intensive. Higher employee wages can motivate most employees to actively engage in production, thereby increasing the efficiency of the company and promoting its export trade. High human capital within a company can lead to improved production efficiency, research and development innovation, and product quality. This, in turn, it can enhance the export competitiveness and promote their exportation. Conversely, a low level of human capital can hinder the expansion of a company’s export scale.

4.3. Robustness Test

To prevent changes in results caused by method specificity or sample selection, we test the robustness of the empirical findings by altering the research methods and replacing the research samples. First, we use OLS and GLS to analyze and the results are presented in columns one and two of

Table 3. Results of robustness test.

Variables	Model 1	Model 1	Model 1	Model 1
	Ols	Panel Gls	Replacement Samples	GMM
FDI	0.254 ***	0.216 ***	0.293 ***
	(15.460)	(12.860)	(6.210)
L.FDI				0.231 ***
				(2.690)
size	6741.185 ***	5163.522 ***	3584.192 ***	7320.838 ***
	(28.310)	(23.200)	(8.270)	(7.810)
kain	1.058 ***	0.919 ***	−0.076	8.416
	(5.460)	(5.760)	(−0.100)	(1.070)
subsidy	−106.457	−25.790	−7.737	−115.986 ***
	(−0.560)	(−0.190)	(−0.090)	(−7.970)
ROI	−27.186	−17.101	−347.548 **	−1403.110 *
	(−1.260)	(−1.010)	(−2.460)	(−1.770)
wage	2.491	0.302	−16.371	−8.582
	(0.900)	(0.130)	(−1.300)	(−0.400)
C	−47,319 ***	−35,177 ***	−21,891 ***	−51,743.110 ***
	(−23.050)	(−18.510)	(−6.340)	(−7.210)
Fixed effect	NO	NO	Yes	NO

Note: ***, **, * respectively indicate significance at the 1%, 5%, and 10% levels.

. The OLS results indicate FDI has a significant positive impact on exports of China’s wood processing industry at the 1% significance level. It does not significantly differ from the GLS results and results in Table 2, which supports H1. Second, we use fixed effects model to estimate Model 1 with a sample of 21,920 observations from 1998 to 2007 [57], which is shown in the third column of Table 3. The regression coefficient of FDI on exports is 0.293, which is significant at the 1% level. This suggests a positive impact of FDI on exports, consistent with the research conclusion of this article, indicating robust results.

4.4. Endogeneity Test

The reciprocal influence between explanatory and dependent variables may result in bias in basic regression due to endogeneity concerns. Consequently, we engaged in more discourse around the matter of endogeneity. The rise in foreign direct investment will influence the exports of China’s wood processing sector, while simultaneously, enhanced exports and product competitiveness may draw foreign investment. Therefore, this study considers the impact of endogeneity and adopts the first-lagged values of foreign direct investment (L.FDI) as instrumental variables, using the system Gaussian Mixture Model (GMM) method for regression [58], and the regression results are shown in the last column of Table 3. The findings from the system GMM regression indicate that foreign direct investment continues to have a major effect on the exports of China’s wood processing industry. The coefficients of the control variables remain consistent with the fundamental regression results.

5. Discussion

The results indicate that FDI can increase the supply of China’s wood processing industry to the world. However, further clarification is needed regarding its mechanism and heterogeneity to provide a reference for formulating targeted industrial policies.

5.1. Heterogeneity Analysis

China has a vast territory, and due to geographical differences and early policy tendencies, the economic development of different provinces is not consistent. As illustrated in Figure 3, the regional distribution of export trade in China’s wood processing industry varies significantly. Export companies are primarily located in the eastern coastal areas, particularly in Shandong, Jiangsu, Zhejiang, Guangdong, and Fujian. Export destinations are mainly concentrated in Asia, North America, as well as Europe. The largest trading country is the United States, with an export trade volume of USD 4.44 billion, followed by Japan and the United Kingdom in second and third place, respectively.

Differences in factor endowments result in varying levels of success in attracting and utilizing foreign investment and participating in international trade across China’s eastern, central, and western regions. This study categorizes the significant differences in industrial development and geographical location of China’s wood processing industry based on the eastern, central, and western patterns. It also conducts an in-depth analysis of the relationship between FDI and export trade. The eastern region benefits from its proximity to ports and trade advantages that inland regions lack. This study examines whether FDI can enhance this advantage and promote China’s wood processing industry’s export trade. Similarly, regions with abundant forest resources have relatively mature industrial chains. Our study also investigates whether FDI in these regions can significantly promote export trade.

Table 4. Heterogeneity regression results.

Variables	Model 1	Model 1	Model 1
FDI	0.103 **	0.163 ***	0.544 ***
	(0.049)	(0.019)	(0.026)
FDI*east	0.123 **
	(0.052)
FDI*port		0.269 ***
		(0.041)
FDI*Plantation			−0.539 ***
			(0.033)
Control variables	Yes	Yes	Yes
Fixed effect	Yes	Yes	Yes

Note: ***, ** respectively indicate significance at the 1% and 5% levels.

shows that the estimated coefficient of FDI is 0.103. Additionally, the estimated coefficient of FDI*east is 0.123, which is significant at the 5% level. This indicates that the boosting impact of FDI on exports in the eastern region is 0.226, which is twice that of the inland regions. This phenomenon may be the result of a combination of economic and policy factors. Firstly, the geographical advantages of ports in the eastern region offer significant convenience for foreign trade activities, facilitating the access of enterprises to the international market. Secondly, as the eastern region serves as a demonstration area for reform and opening up, it was the first to introduce FDI and has established a successful model for integrating state-owned, private, and foreign capital investment. This model can be effectively utilized to enhance the management of foreign capital investment and promote exports of the wood processing industry. As a result, the added value of foreign investment in the eastern coastal areas is comparatively high.

Further, we focus on the comparison between the port areas and other regions, since half of China’s wood raw material supply is imported [59,60]. The regions with the highest throughput of imported wood (namely Jingjiang Port, Changshu Port, and Taicang Port in Jiangsu Province, Rizhao Port and Penglai Port in Shandong Province, Zhangzhou Port and Xiuyu Port in Fujian Province, Ningbo Port and Jiuguan Port in Zhejiang Province, Suifenhe River in Heilongjiang Province, and Manzhouli in Inner Mongolia) require further attention. Table 4 displays the results. The research indicates that FDI in areas near major wood ports positively impacts China’s recovery of wood supply to the world. Within the port area, a unit increase in FDI in the wood processing industry leads to a 0.432 increase in exports. This increment exceeds almost 165% of the boost effect of FDI on exports observed in other regions. The study shows that the impact of FDI on exports in the areas surrounding wood ports is significantly higher than that of the eastern region as a whole, almost double that of the central and western regions, and more than four times higher than that of the central and western regions. This suggests that the areas surrounding wood processing ports can effectively utilize the resources and channels brought by FDI, thereby promoting China’s wood products supply to the world. One of the reasons is that the port area has developed international trade networks and superior logistics facilities, which increases the direct role of foreign investment in promoting exports.

To determine the best areas for introducing FDI in China’s wood processing industry, we compare the impact of FDI in areas near ports and areas near plantation forests. The abundance of forest resources may affect the impact of foreign direct investment on exports. Taking into account the peculiarities of China’s forestry policy, we consider the impact of plantation forests on the relationship between them. The artificial afforestation area of the current year is extracted from the “China Forestry and Grassland Statistical Yearbook” and dummy variables representing the top four provinces (Plantation1) in the artificial afforestation area of the year are set up for the regression. The third column of Table 4 shows that the estimated coefficient of FDI is 0.550 and the estimated coefficient of FDI*plantation is −0.546, significant at the 1% level. In provinces with larger areas of artificial forestation, the boosting effect of FDI on exports is merely 0.004. It indicates that in provinces with extensive artificial afforestation, FDI has a negligible pull effect. This is directly linked to the resource structure and market reliance of these regions. According to the China Forestry and Grassland Statistical Yearbook, exports from places with considerable volumes of artificial forest only account for less than 5% of total domestic exports. The sales market is mainly the domestic market, and the value added by the introduction of foreign investment is relatively low. The means for soliciting foreign investment in these locations are comparatively feeble, and policy support is also inadequate [61]. Comparing wood entry ports and plantation forest areas, it was found that in wood entry ports, the ability of the wood processing industry to use FDI to promote export growth was higher than in areas rich in plantation forest resources. Therefore, the urgency of attracting FDI varies from region to region.

5.2. Mechanism Analysis

It is evident that FDI can enhance exports of China’s wood processing industry. Theoretical literature suggests that this can be achieved by improving wood resource efficiency. We estimate whether wood resource efficiency acts as a mediating factor in the impact of FDI on exports of the wood processing industry.

Table 5. Results of the mechanism analysis.

Variables	Model 1	Model 2	Model 3
FDI	0.248 ***	2.27 × 10−6 ***	0.236 ***
	(7.090)	(4.410)	(6.450)
WRE			6193.163 ***
			(15.910)
C	−34,660 ***	4.338 ***	−60,402 ***
	(−12.030)	(104.280)	(−18.020)
Control variables	Yes	Yes	Yes
Fixed effect	Yes	Yes	Yes

Note: *** indicate significance at the 1% levels.

shows that in Model 1, the coefficient for the impact of FDI on the export of the wood processing industry is 0.248, which is significant at the 1% level. This indicates a total effect of 0.248 for FDI on the export of the wood processing industry. The regression results show that in Model 2, the coefficient of FDI on wood resource efficiency is 2.27 × 10⁻⁶, and in Model 3, the coefficient of wood resource efficiency on corporate exports is 6193.163, both of which are significant. Additionally, the coefficient of FDI on exports in Model 3 is also significant at 0.236, indicating a partial mediating effect of 0.014. After calculation, it was determined that the mediating effect accounted for approximately 6% of the total effect. In summary, FDI has both a direct impact on the export of the wood processing industry and an indirect impact through the mediating variable of wood resource efficiency. FDI can effectively enhance the efficiency of wood resources, thereby promoting the export of the wood processing industry, which validates H2. FDI brings capital, technology, human capital, and management experience, enabling wood processing enterprises to achieve technological progress and improve technical efficiency, thus enhancing the efficiency level of wood resources. Efficient enterprises possess comparative advantages, enabling them to overcome trade barriers, respond to international market risks with greater ease, and achieve stable growth in export trade. This is in line with the research findings of Bernard et al. [62]. The results of Model 3 in Table 5 demonstrate that after controlling for the impact of FDI, wood resource efficiency has a significant positive effect on the export of wood processing enterprises (6193.163), with a significance level of 1%. It indicates that wood resource efficiency promotes the export of wood processing industry companies. This result is consistent with the “self-selection effect” of corporate exports, as predicted by the New–New Trade Theory and Heterogeneous Trade Theory [26].

To ensure the reliability of the mechanism test, we conduct robustness testing by changing the regression method, replacing the main explanatory variables, and replacing the sample.

Table 6. Robustness test results of the mechanism analysis.

Variables	Model 1	Model 2	Model 3	Model 1	Model 2	Model 3
FDI	0.216 ***	1.08 × 10−6 ***	0.211 ***
	(12.860)	(3.580)	(12.640)
Foreign capital investment				0.090 ***	1.07 × 10−6 ***	0.062 **
				(3.300)	(2.720)	(2.250)
WRE			7573.837 ***			6736.193 ***
			(23.330)			(14.420)
Control variables	Yes	Yes	Yes	Yes	Yes	Yes
Fixed effect	No	No	No	Yes	Yes	Yes

Note: ***, ** respectively indicate significance at the 1% and 5% levels. Foreign capital investment represents foreign investment excluding Hong Kong, Macao, and Taiwan.

shows that FDI has a positive and significant impact on exports (0.216) and wood resource efficiency (1.08 × 10⁻⁶) by GLS. The direct impact of FDI on exports is 0.211, while the indirect impact is 0.008. We regress Models 1–3 using FDI data from regions other than Hong Kong, Macao, and Taiwan, instead of total FDI. Then, we use samples from 1998 to 2007. The results in Table 6 and

Table A1. Robustness test of replacement samples for mechanism analysis.

Variables	Model 1	Model 2	Model 3
FDI	0.293 ***	3.75 × 10−6 ***	0.274 ***
	(6.210)	(3.830)	(5.840)
WRE			5064.580 ***
			(12.530)
Control variables	Yes	Yes	Yes
Fixed effect	Yes	Yes	Yes

Note: *** indicate significance at the 1% levels.

are consistent with those in Table 5, confirming FDI can impact the exports of wood processing enterprises through both direct and indirect channels. These findings demonstrate the robustness of the results.

A major challenge of this study is to determine the causal effect between FDI wood resource efficiency exports, which is caused by reverse causality and omitted variables. To solve this issue, we employ an instrumental variable method to mitigate the potential endogeneity issue [63]. We use the first-order lag of FDI as an instrumental variable to conduct system GMM regression. The regression results are shown in

Table 7. GMM regression results of the mechanism analysis.

Variables	Model 1	Model 2	Model 3
L.FDI	0.231 ***	1.03 × 10−6 ***	0.219 ***
	(2.690)	(3.570)	(2.550)
WRE			11,847.890 ***
			(10.790)
Control variables	Yes	Yes	Yes
Fixed effect	No	No	No

Note: *** indicate significance at the 1% levels.

. The system GMM regression results suggest that FDI has a significant impact on China’s wood processing industry exports by improving wood resource efficiency, which is consistent with the basic regression results.

5.3. Further Discussion

This study evaluates the influence of FDI on export capabilities of China’s wood processing industry, utilizing data from the China Industrial Enterprises Database for the years from 1998 to 2015. Our research revealed that FDI considerably boosted the export expansion of China’s wood processing industry, particularly in the eastern coastal areas. Although previous research [7,64,65] has shown that FDI has a favorable influence on manufacturing exports, this study presents a more in-depth investigation of regional variances and industrial features. In particular, under the influence of economic structure, foreign investment attraction and resource endowment in different regions, there are significant differences in the impact of FDI on exports of wood processing industry.

First, FDI boosts the export capacity of the wood processing industry by injecting capital [66], introducing technology, facilitating processing trade [67], and establishing international marketing channels. This approach not only provides development chances for small-scale and capital-strapped wood processing firms, but it also encourages Chinese-foreign cooperation and lowers the “sunk cost” of market development [68]. It is important to note that the impact of FDI is not equally dispersed throughout all regions. Foreign investment has a significantly greater impact in the eastern coastline areas than in the inland parts, which is due to the region’s superior topographical characteristics as well as early reform and opening up. The geographical benefits of eastern regions have resulted in lower logistics and transportation costs, increasing export businesses’ global competitiveness.

Additionally, we also found that FDI boosts wood resource efficiency, thereby improving export performance. This intermediary role of resource efficiency aligns with findings from Cao and Chen [69] and Tuan et al. [70], who noted enhanced local productivity due to FDI. Similarly, Wagner [71] found that enterprises with higher productivity tend to enter the export market independently, indicating a “self-selection effect” in export enterprises. This is in line with the explanation provided by the New Trade Theory. Enterprises with higher productivity are better positioned to manage the fixed costs associated with exporting, such as customs, transportation, and marketing, thus more likely to engage in international trade. Our findings align with the research conclusions of Qin et al. [72] regarding the “self-selection effect” of Chinese wood product export enterprises. Therefore, efficient use of FDI has a positive impact on the export of China’s wood processing enterprises.

We also found that enterprise size positively influences exports of wood processing enterprises. Larger enterprises are better equipped to manage international market risks and leverage economies of scale to reduce costs. These findings are consistent with Zhou et al. [73] and Bonaccorsi [66]. For instance, the establishment of the Zhonglin Wood Processing Park in Yancheng City by Zhonglin Group in 2017 exemplifies how the scale of wood processing enterprise can support wood processing companies in operations and boost exports. Moreover, we discovered that human capital significantly impacts export performance in this labor-intensive industry. Higher wages motivate employees, enhancing productivity and export capability. This finding differs from manufacturing studies [49], highlighting the unique dynamics within the wood processing industry.

6. Conclusions and Implications

In the context of the evolving global landscape, China’s wood processing industry faces challenges such as slowing export growth. Addressing these challenges is essential for stabilizing the global wood products supply chain. Based on China Industrial Enterprises Database, we examine the relationship and impact mechanism of FDI and exports, and access the potential for FDI to counteract the decline in export growth within China’s wood processing industry.

The results show that FDI has a positive effect on the export of China’s wood processing industry. It suggests that FDI can help alleviate the pressure on China’s limited wood product exports and increase China’s supply of wood products to the global market. Therefore, FDI can contribute to safeguarding the global supply of wood products. The promotion of FDI on exports is achieved by improving wood resource efficiency within China’s wood processing industry, indicating that this was a substantive response, which is beneficial to industry development. Notably, the urgency of attracting FDI varies from region to region. The eastern region, especially the port area, can effectively use FDI to promote export growth with its perfect management model and low shipping costs. However, the artificial forest area is affected by market targets, resource endowment, and policy restrictions, and the utility brought by foreign investment is relatively low. Therefore, considering the resource advantages and policy environment of different regions, striving for better regional foreign investment guidance and management policies could be the key to boosting the export of China’s wood processing industry. Furthermore, we also found that although China’s wood processing industry is a labor-intensive industry, larger, more mechanized wood processing firms are better equipped to navigate international trade barriers and benefit from economies of scale, thereby reducing production costs and mitigating trade risks. However, government subsidies appear to have a minimal effect on exports due to the potential for anti-dumping and countervailing measures.

Based on these insights, the following policy implications are proposed: Firstly, improving and optimizing the Catalogue of Industries for Encouraged Foreign Investment. In recent years, due to global economic shifts, FDI in China’s wood processing industry has declined consistently. By updating the Catalogue of Industries for Encouraged Foreign Investment, the attractiveness of China’s wood processing industry to FDI should be maintained. It is also vital to attract and guide FDI into the wood processing industry by enhancing legal frameworks, protecting intellectual property, ensuring fair competition, and fostering a favorable business environment. Secondly, FDI should be introduced appropriately to the specific regions. The impact of FDI on China’s wood processing industry exports varies significantly in different regions. Targeted policies must be diligently executed based on the actual circumstances of each region. For the eastern region, the stock and flow of absorbed FDI are quite significant. Its foreign direct investment has promoted the export of China’s timber processing industry, especially in port areas. Regional special funds may be implemented to motivate foreign investment to contribute to the enhancement of resource utilization efficiency and the implementation of environmental protection technologies. Finally, companies should enhance wood resource efficiency. Our research concludes that efficiency directly determines the ability to export. The government can facilitate enterprises through fiscal and taxation policies to boost investment in research and development, pursue technological advancements, enhance wood resource efficiency and sustainable forestry development, and thereby provide a Chinese case reference for the sustainable development of global forest product trade.