Geopolitical Conflict and Resource Trade Flows: A Study on the Impact of the Russia–Ukraine Conflict on China’s Timber Imports from Russia

Abstract

Russia is an important supplier of timber to China, and the outbreak of the Russia–Ukraine conflict has had a significant impact on China’s timber imports. Based on the provincial-level timber import trade data in China from January 2020 to December 2024, three-dimensional panel data of time–province–export country were constructed, and the difference-in-differences model was used to analyze the impact of the Russia–Ukraine conflict on China’s timber imports from Russia. The results show that (1) the outbreak of the Russia–Ukraine conflict significantly reduced the volume of timber imports from Russia across Chinese provinces; (2) the Russia–Ukraine conflict adversely affected China’s timber imports from Russia by increasing price volatility and constraining Russia’s timber supply capacity; (3) based on the heterogeneity test results, the negative impact of the Russia–Ukraine conflict on timber imports from Russia was more pronounced in regions with a high degree of industrialization and greater dependence on imported timber; (4) the Russia–Ukraine conflict raised China’s timber import prices and intensified price volatility. Therefore, it is essential to enhance the overall risk management capacity of the timber industry supply chain by building a diversified timber supply system, strengthening the strategic reserves of timber resources, and improving price risk management instruments, in order to effectively respond to the impact of geopolitical conflicts.

1. Introduction

Since the beginning of the 21st century, China’s forest products industry has developed rapidly, forming a trade pattern characterized by high volumes of both imports and exports [1,2], with China serving as a major global processing hub for timber resources [3,4]. Leveraging its abundant forest resources and geographical proximity to China, Russia has become one of the country’s most important timber suppliers [5,6]. Existing studies highlight the complementary advantages of Russia’s abundant timber resources and China’s strong wood processing capacity, which have facilitated the establishment of a relatively stable bilateral cooperation framework in the timber trade [7,8,9,10]. The long-standing and stable import channel has provided critical support for the development of China’s timber industry and has generated significant economic benefits [11,12,13].

With China’s continued economic growth and the structural upgrading of domestic timber demand, this resource-input trade model has exhibited an accelerating trend of dependency [5,14]. However, such reliance on Russian timber imports—while ensuring a relatively stable supply of raw materials—also entails latent trade risks [15,16]. External variables such as exchange rate volatility and international sanctions may disrupt the steady supply of Russian timber, thereby posing potential threats to the stability of China’s timber market [17,18,19].

Since the outbreak of the Russia–Ukraine conflict, Western countries have introduced a series of economic sanctions against Russia, completely banning its timber products and defining them as “conflict timber” [20]. In the short term, the interaction between sanctions and Russian countermeasures may temporarily increase timber exports to China [21,22,23,24]. Nevertheless, Russian timber enterprises are simultaneously facing production and logistical difficulties, supply chain instability, and rising operational costs, which undermine their market competitiveness and reshape the procurement strategies of Chinese importers [25,26,27,28]. As risk expectations intensify, Chinese companies increasingly diversify their sourcing channels, reducing long-term dependence on Russian timber [29,30,31].

Although the literature on Sino-Russian timber trade provides rich theoretical perspectives, empirical evidence on the specific impacts of the Russia–Ukraine conflict remains limited. Most existing studies adopt a macro-level, national perspective, often overlooking provincial heterogeneity in economic structure, industrial orientation, and policy response. By contrast, provincial-level data can more accurately capture variations in timber imports and assess the uneven severity of external shocks across regions.

Against this backdrop, this paper employs a difference-in-differences (DID) model using provincial-level timber import data from China to evaluate the specific impact of the Russia–Ukraine conflict on China’s imports of Russian timber. Furthermore, it explores the potential transmission mechanisms underlying the conflict’s trade effects. By doing so, this study contributes new insights into how geopolitical crises reshape resource-based trade flows and provides targeted policy recommendations for strengthening China’s timber supply security and promoting high-quality development of provincial import trade.

2. Materials and Methods

2.1. Research Hypothesis

Geopolitical conflicts suppress bilateral trade flows through multiple channels, and timber—due to its fragmented supply chains, dependence on transportation, and sensitivity to environmental certification—exhibits particularly pronounced responses to such conflicts [32,33,34]. Following the outbreak of the Russia–Ukraine conflict, Western sanctions designated Russian timber as “conflict timber” and suspended the endorsement of legality by international forest certification systems such as FSC and PEFC [35]. As a result, Chinese importers face a dual compliance dilemma: they must avoid the risk of secondary sanctions under Western extraterritorial jurisdiction, while also losing access to the European re-export market due to the enhanced supply chain traceability requirements imposed by the EU Deforestation Regulation (EUDR) [34]. Such institutional trade barriers have heightened uncertainty in the trading environment, compelling enterprises to bear additional risk premiums and compliance costs [36]. Meanwhile, maritime disruptions have led to a logistics premium associated with the substitution of China–Europe Railway transport [37,38], which, together with the exchange hedging costs arising from restricted access to the SWIFT payment system [33], has resulted in a rigid increase in the cost structure for importers. On the other hand, military mobilization has led to labor shortages that directly undermine logging efficiency. The withdrawal of international equipment manufacturers from the Russian market has resulted in a shortage of forestry machinery and a decline in processing automation rates [39]. Moreover, the reallocation of Far Eastern Railway capacity toward military logistics has further exacerbated supply chain bottlenecks. The implicit deterioration in the quality dimension on the supply side is also significant. The suspension of international forest certification for Russian timber has led to the loss of its ESG attributes—referring to environmental, social, and governance sustainability standards—thereby hindering its integration into global green supply chains. This progressive transmission chain—characterized by “policy constraints → cost escalation → supply deterioration”—ultimately drives a structural decline in China’s timber imports from Russia through mechanisms such as negatively adjusted market expectations and weakened trade elasticity. Based on the above, the following hypothesis is proposed:

H1: 

The Russia–Ukraine conflict negatively affects China’s imports of Russian timber.

The conflict has resulted in international financing difficulties for Russia, leading to rising capital costs and increased financial pressure on timber enterprises [40]. This has been further compounded by sharp fluctuations in the ruble exchange rate and soaring energy prices [41]. Under this dual pressure, both the explicit production costs (e.g., transportation and energy inputs) and implicit costs (e.g., exchange rate risk hedging) associated with Russian timber have increased simultaneously [42], causing high-frequency volatility in timber trade prices. This price volatility systematically increases the comprehensive trade costs of China’s imports of Russian timber through three transmission channels. First, economic and trade uncertainties induced by price fluctuations undermine supply chain stability [26,38]. To maintain production continuity, Chinese importers are compelled to adopt precautionary inventory strategies [43], resulting in higher warehousing and capital holding costs. Simultaneously, the widening gap between order-locked prices and actual settlement prices significantly increases exposure to foreign exchange loss risk [37,38]. Secondly, uncertainty in price signals also generates non-economic costs, such as moral opposition and international compliance risks associated with the “conflict timber” label, compelling Chinese importers to incur additional compliance review costs to avoid reputational sanctions [44]. In addition, rising volatility exacerbates market pessimism, prompting Chinese companies to adopt a more cautious stance toward timber sourced from Russia, allocate greater resources to risk assessment and management, and incur higher risk mitigation costs [45,46,47]. The rise in trade costs incentivizes Chinese forestry enterprises to proactively shift sourcing to markets with lower price volatility and more manageable compliance risks, such as New Zealand and Canada. Based on this, Hypothesis 2 is proposed:

H2: 

The Russia–Ukraine conflict, by exacerbating the price volatility of Russian timber imports, prompts China to reduce its imports of timber from Russia.

As a critical node in global forest products trade, Russia’s timber industry is currently confronting a systemic crisis in production factors triggered by the Russia–Ukraine conflict. At the labor input level, military mobilization has not only resulted in the passive transfer of working-age labor to the defense sector but also triggered a chain reaction of voluntary outmigration among industrial workers. Consequently, timber processing enterprises face the dual challenge of disrupted technical knowledge transmission and difficulties in rapidly replenishing the new generation workforce, culminating in a persistent human capital gap that constrains capacity recovery [26,48]. At the capital factor dimension, the freezing of cross-border financing channels disrupted short-term working capital flows for equipment procurement, while the withdrawal of international equipment manufacturers obstructed the technological input pipeline for forestry machinery upgrades [49]. The combined effects of capital liquidity depletion and technological freeze have led to a sustained decline in production efficiency. At the land factor level, military control policies implemented in forest areas along the conflict frontline have designated large forested regions as logging prohibition zones, directly constraining the physical space available for raw material supply [50,51]. The triple squeeze exerted by these three production factors has reduced Russia’s timber supply capacity. This decline in supply capacity triggers a cascading effect across the global forest products trade network, compelling China to implement import substitution and supply chain diversification strategies. Specifically, major demand centers, including China, accelerate their shift toward alternative supply sources such as New Zealand and Canada, thereby driving a reconfiguration of global value chain nodes and power structures. Based on the above analysis, the following hypothesis is proposed:

H3: 

The Russia–Ukraine conflict, by reducing Russia’s timber supply capacity, prompts China to reduce its imports of timber from Russia.

2.2. Methods

The DID model effectively identifies the causal effects of policies or events by capturing the differential changes between the treatment group and the control group before and after an exogenous shock [52]. The primary rationale for selecting the DID method lies in the strictly exogenous nature of the Russia–Ukraine conflict, characterized by its sudden onset (February 2022) and its source of impact (sanctions led by Western countries), both of which are independent of endogenous decision-making processes in Sino-Russian timber trade, thus fulfilling the DID model’s requirement for a quasi-natural experiment. Specifically, (1) the conflict and sanctions are not a result of the imbalance in Sino-Russian bilateral trade or market behavior, but rather a derivative of geopolitical tensions; (2) China, as a third party not directly involved in the conflict, maintains consistent trade policies before and after the conflict; (3) countries in the control group are not directly affected by the conflict, fulfilling the parallel trends assumption. Based on this, the model is constructed as follows:

$$\mathrm{l}\mathrm{n}{Qty}_{ict}={\alpha }_0+{\alpha }_1\left(Trea{t}_c\times Tim{e}_t\right)+\beta \mathrm{l}\mathrm{n}Controls+{\mu }_i+{\gamma }_t+{\lambda }_c+{\epsilon }_{ict}$$

(1)

where Qty_ict denotes the timber import quantity from country c to province i in time t. Treat_c is used to differentiate between the treatment and control groups; Time_t indicates the timing of the conflict. Treat_c × Time_t is the interaction term between the treatment group and the conflict period. ‘Controls’ represents the set of control variables. μ_i, γ_t, and λ_c represent province fixed effects, time fixed effects, and country fixed effects, respectively. ε_ict is the random disturbance term.

2.3. Variable Definitions

2.3.1. Dependent Variable

The timber import trade volume (Qty_ict) is a key variable in this study. As the primary raw material in the forest products industry, logs are at the core of the entire supply chain. China is one of the largest wood processing and consuming countries in the world. With the rapid development of the economy and urbanization, demand for logs in industries such as construction, furniture, and papermaking has been continuously increasing, resulting in sustained growth in log demand. Therefore, trade data for HS4403 industrial logs were used as a proxy for the dependent variable in this model. In terms of core indicator selection, compared to trade volume data, which is susceptible to fluctuations in international market prices, exchange rates, and inflationary factors, the physical import quantity of logs effectively avoids price signal distortions and more accurately measures the actual scale of trade activity. This indicator demonstrates stronger data robustness and longitudinal comparability, allowing for a more precise assessment of changes in the actual scale of trade activity over time.

2.3.2. Independent Variable

On 24 February 2022, in response to NATO’s ongoing eastward expansion and the prolonged conflict in the Donbas region, Russian President Vladimir Putin announced the initiation of a “special military operation,” marking the official outbreak of the Russia–Ukraine conflict. Given that the conflict began at the end of February, its impact on economic and other indicators for that month was minimal. Furthermore, the sanctions imposed by Western countries on Russia exhibited a transmission lag, particularly in areas such as transportation and energy trade. Supply chain adjustments were not instantaneous. In-depth analysis of macroeconomic data revealed that, starting in March, global energy prices experienced significant volatility, Russia’s external trade structure underwent a dramatic shift, and economic indicators in several countries reliant on Russian resources began to exhibit cascading effects. Accordingly, this study identifies March 2022 as the starting point of the exogenous shock. Regarding the selection of national samples, Russia is defined as the treatment group, as it was directly affected by the conflict and subsequent sanctions. The control group was selected following the principles of market representativeness and data completeness. Based on China’s 2024 import rankings for raw timber (HS4403), and after excluding Russia, six major supplier countries—the United States, Germany, France, Canada, New Zealand, and Japan—were identified as the control group. These countries were not directly involved in the conflict and maintained relatively stable timber trade trends with China prior to its outbreak.

2.3.3. Control Variables

Building on the studies of Tian [53], Li [54], and Cao [55], we selected the following control variables: Exchange rate (rate_ct): The exchange rate directly determines the cost of imported timber. Fluctuations in the exchange rate of the currency of the timber-exporting country cause timber import prices to fluctuate, which in turn affects the price sensitivity and procurement decisions of Chinese provinces regarding timber from different countries. Exchange rate movements also impact the price competitiveness of imported timber in the domestic market. When the currency of an importing country depreciates, its timber becomes more price-competitive, potentially attracting more imports and altering the structure of China’s timber import sources.

Trade distance cost (dist_ct): This is measured as the product of the geographical distance between China and the capital of the sample country and the monthly oil price. In international trade, geographical distance directly influences transportation costs; the closer the distance, the lower the trade cost. Fluctuations in oil prices further affect transportation costs, with rising oil prices significantly increasing trade costs.

Population size of the exporting country (pop_ct): The population size of the exporting country is closely linked to its labor resources, the completeness of its industrial chain, and the stability of its production capacity. These factors, in turn, influence labor input and production efficiency in stages such as timber harvesting and processing. As a result, the population size plays a fundamental role in supporting the export scale of resource-intensive products like timber.

Economic scale (gdp_ct): as a macroeconomic indicator, it reflects the production capacity and overall economic vitality of the exporting country, serving as a key determinant of its timber export potential. A higher GDP typically signifies a more developed industrial structure, stronger trade resilience, and greater capacity to absorb external shocks, thereby enhancing the stability and continuity of timber exports.

Foreign trade scale: The foreign trade scale is reflected by the timber export value of Chinese provinces (fpev_it), trade import and export value of Chinese provinces (trade_it), and the commodity trade export value of the exporting country (export_ct). These variables reflect the demand for timber in Chinese provinces, the scale of the processing industry in these provinces, and the supply capacity of timber in the exporting country. The industrial application of China’s timber imports exhibits significant characteristics of processing trade, with its core value lying in the cross-border integration of industrial chains to achieve value-added growth. The export value of forest products in each province directly reflects the demand for timber and other raw materials by local processing enterprises. The trade import and export value of Chinese provinces can reflect the industrial scale, production and processing capacity, and the degree of connectivity with external markets. A higher trade volume typically indicates a more active processing industry with stronger market adaptability and flexibility. The commodity trade export value of the exporting country serves as a comprehensive indicator of its economic vitality and degree of outward-oriented industrial development. Higher export values indicate well-established trade infrastructure, efficient logistics systems, and mature international trade channels, which facilitate timber exports and influence the scale of timber exports from the country.

2.4. Data Resources

We selected 27 provinces of China from January 2020 to December 2024 as the sample, excluding Guizhou, Ningxia, Yunnan, and Tibet due to severe data deficiencies. Based on trade data from the China Customs Database and the principle of data availability, seven major timber-importing countries (Russia, the United States, Germany, France, Canada, New Zealand, and Japan) were selected as the subjects of the study. The data for this article were primarily collected and organized from the China Customs Database, the National Bureau of Statistics of China, the World Bank Database, the CEPII Database, the Wind Database, and the China Forestry and Grassland Statistical Yearbook. All statistical analyses were performed using Stata 17. Descriptive statistics for the main variables are provided in

Table 1. Summary statistics.

Variable	Meaning	Obs	Mean	Std. Dev	Min	Max
lnQtyict	Timber Import Volume (m3)	4903	8.799	2.150	0.000	13.270
did	Difference-in-Differences Variable	4903	0.052	0.223	0.000	1.000
lnratect	Exchange Rate (per 100 RMB)	4903	5.505	1.848	1.553	6.690
lndistct	Trade Distance Cost	4903	13.210	0.536	11.83	14.020
lnpopct	Population of the Exporting Country (Thousands of People)	4903	11.120	1.264	8.535	12.740
lngdpct	GDP of the Exporting Country (Million USD)	4903	14.890	1.355	12.260	17.190
lnexportct	Trade Export Value of the Exporting Country (Million USD)	4903	10.750	1.230	7.973	12.100
lntradeit	Provincial Import and Export Trade Volume (Ten Thousand USD)	4903	15.990	1.361	12.550	18.460
lnfpevit	Provincial Forest Product Export Value (RMB)	4903	20.690	1.541	16.690	22.960

.

3. Results

3.1. Estimation Result of the Basic Model

Based on the DID model constructed above, a stepwise regression was conducted on the sample panel data, with the estimation results presented in

Table 2. Baseline regression results.

	lnQtyict
Variables	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
did	−0.383 **	−0.710 ***	−0.663 ***	−0.390 *	−0.531 ***	−0.545 ***	−0.559 ***	−0.560 ***
	(0.171)	(0.205)	(0.215)	(0.202)	(0.206)	(0.207)	(0.207)	(0.207)
lndistct		−1.853 ***	−1.848 ***	−2.139 ***	−2.387 ***	−2.390 ***	−2.436 ***	−2.459 ***
		(0.612)	(0.623)	(0.538)	(0.522)	(0.521)	(0.524)	(0.524)
lnpopct			3.328	13.540 ***	14.950 ***	14.470 ***	14.610 ***	14.730 ***
			(3.379)	(3.491)	(3.497)	(3.516)	(3.517)	(3.521)
lngdpct				−2.861 ***	−1.865 ***	−1.888 ***	−1.900 ***	−1.906 ***
				(0.476)	(0.552)	(0.582)	(0.580)	(0.580)
lnratect					−1.919 ***	−1.827 ***	−1.844 ***	−1.854 ***
					(0.547)	(0.554)	(0.553)	(0.554)
lnexportct						−0.012	0.034	0.032
						(0.294)	(0.294)	(0.295)
lntradeit							0.831 ***	0.995 ***
							(0.231)	(0.254)
lnfpevit								−0.154
								(0.094)
_cons	8.819 ***	33.310 ***	−3.760	−70.940 *	−87.550 **	−82.220 **	−96.640 **	−96.990 **
	(0.024)	(8.095)	(39.680)	(37.730)	(37.780)	(38.020)	(38.160)	(38.220)
Obs	4903	4903	4903	4903	4903	4903	4903	4903
R2	0.520	0.522	0.522	0.527	0.528	0.529	0.530	0.531
Date FE	YES	YES	YES	YES	YES	YES	YES	YES
Country FE	YES	YES	YES	YES	YES	YES	YES	YES
Province FE	YES	YES	YES	YES	YES	YES	YES	YES

***, **, and * are significant at the 1%, 5%, and 10% levels, respectively; the data in brackets are robust standard errors.

. Column (1) shows the results without control variables, while columns (2) to (8) present the regression results after progressively adding control variables. All regression models control for time fixed effects, provincial fixed effects, and country fixed effects to account for potential confounding factors such as time trends, regional heterogeneity, and country-level disturbances. As shown, after the gradual inclusion of control variables, the coefficient of the key explanatory variable (did) remains significantly negative, indicating that the model results are robust. This suggests that, after controlling for potential confounders, the Russia–Ukraine conflict has had a negative impact on the scale of timber imports from Russia to Chinese provinces, leading to a reduced dependence on Russian timber. This outcome is mainly attributable to the fact that, due to the sanctions triggered by the conflict, Russian timber has lost international certification and market recognition. As a result, Chinese importers, particularly those engaged in processing Russian timber for export, are facing increased compliance risks and rising costs. Meanwhile, declining Russian logging and processing capacity and worsening supply chain bottlenecks have forced Chinese importers to deal with problems such as unstable supply and deteriorating timber quality when sourcing from Russia. Collectively, these factors have led Chinese importers to reduce their imports of Russian timber, which is consistent with expectations and confirms Hypothesis 1.

Examining the regression results with all control variables in column (8) reveals the following: (1) Trade distance cost has a significant negative impact on timber imports by Chinese provinces. This is because greater geographical distance directly increases both the time and cost required for transportation, thereby raising overall import costs as well as transaction risks and uncertainties. (2) The population of the exporting country has a clear positive effect on China’s timber import volume. From the demand side, continued population growth usually means higher demand for housing and infrastructure, which in turn stimulates the timber industry, leading to economies of scale and greater production efficiency. On the supply side, a large population base supports a more complete upstream and downstream industrial chain, which helps reduce unit production costs and enhances product diversity and price competitiveness in international markets, thereby boosting China’s timber import motivation. (3) The GDP of the exporting country has a significant negative impact on China’s timber imports. This phenomenon may be explained by the fact that as a country’s GDP rises, overall economic activity and prosperity increase, leading the country to shift toward exporting higher-value-added products, thus reducing the proportion of basic products such as timber in total exports. (4) Fluctuations in the exchange rate of the exporting country’s currency relative to the RMB have a significant negative impact on timber imports by Chinese provinces. Under the direct RMB pricing method, an appreciation of the exporting country’s currency (i.e., RMB depreciation) leads to higher prices for imported timber, thereby increasing enterprise costs. Rising costs compress profit margins, prompting enterprises to reduce imports in pursuit of profit maximization, and thus, the import volume of timber from that country decreases significantly. (5) The total trade value of each Chinese province has a significant positive effect on timber imports. Provinces with larger trade volumes tend to have more active and robust economies, with industries such as construction and furniture manufacturing showing greater demand for timber. Their industrial and market systems are also better equipped to absorb imported timber, thereby increasing import volume. Moreover, provinces with higher trade volumes typically have closer connections to global markets, better infrastructure, and more developed logistics networks, supporting strong timber processing industries and offering more cost advantages for imports, thus attracting timber inflows.

3.2. Parallel Trend Test

Before using the DID model to evaluate the impact of the Russia–Ukraine conflict on China’s imports of Russian timber, it is essential to verify the parallel trends assumption. This assumption requires that, prior to the conflict, China’s timber import trends and volumes from Russia were consistent with those from the six representative control countries. Following the event-study framework, the time dimension was transformed into a relative window centered on March 2022, introducing a series of relative time dummy variables to capture dynamic pre- and post-conflict effects. Here, D_{k, it} denotes the interaction between the relative time dummy and the treatment indicator, taking the value of 1 when period t is k months away from the conflict onset and the observation belongs to the treatment group (Russia), and 0 otherwise. When k < 0, it captures pre-conflict effects; when k > 0, it captures post-conflict effects.

$$\mathrm{l}\mathrm{n}{Qty}_{ict}={\alpha }_0+\sum _{k=-K}^M{\beta }_k{D}_{k,it}+\beta \mathrm{l}\mathrm{n}Controls+{\mu }_i+{\gamma }_t+{\lambda }_c+{\epsilon }_{ict}$$

(2)

If the estimated coefficients β_k for all pre-conflict periods (k < 0) are statistically insignificant, it indicates that the treatment and control groups followed parallel trends prior to the conflict, thereby satisfying the parallel trend assumption. Conversely, if the coefficients β_k for post-conflict periods (k > 0) significantly deviate from zero, this suggests that the Russia–Ukraine conflict exerted a significant impact on the scale of China’s timber imports from Russia. Figure 1 reports the estimated coefficients and 95% confidence intervals. The results show no significant differences in import trends before March 2022, confirming the parallel trend assumption. After the conflict, a sharp decline in Russia’s timber imports relative to the control group demonstrates the negative shock of the conflict.

3.3. Placebo Test

To further test the robustness of the baseline estimation and rule out the influence of unobservable factors, a placebo test was conducted. Specifically, 500 random placebo treatments were generated by assigning the “conflict” occurrence to a random month between January 2020 and December 2024, and DID estimations were performed for each draw. Figure 2 shows the distribution of these placebo coefficients for China’s imports of Russian timber. The kernel density and p-value scatter plots demonstrate that the placebo coefficients are clustered around zero, with magnitudes substantially smaller than the true conflict effect. This result confirms the robustness of the empirical strategy and reduces concerns about spurious regression.

3.4. Robustness Checks

3.4.1. Alternative Measures of the Dependent Variable

To verify the robustness of the research findings and avoid the potential influence of random data fluctuations in the main regression, this study conducts robustness checks by substituting alternative measures for the dependent variable. In addition to using log import volume of logs as the primary dependent variable, the study further tests with alternative indicators, including the share of import volume (pqty_ict), import value (value_ict), and the share of import value (pvalve_ict). The results of these robustness regressions are shown in columns (1) to (3) of

Table 3. Robustness test results.

	(1)	(2)	(3)	(4)
Variables	lnpqtyict	lnvalueict	lnpvalveict	lnQtyict
did	−0.389 **	−0.653 ***	−0.464 **	−0.606 ***
	(0.172)	(0.197)	(0.204)	(0.207)
_cons	−79.417 **	−89.940 ***	−81.542 **	−96.687 **
	(34.618)	(34.454)	(33.376)	(38.290)
Controls	Yes	Yes	Yes	Yes
Obs	4903	4903	4903	4872
R2	0.457	0.515	0.444	0.526
Date FE	Yes	Yes	Yes	Yes
Country FE	Yes	Yes	Yes	Yes
Province FE	Yes	Yes	Yes	Yes

*** and ** are significant at the 1% and 5%, levels, respectively; the data in brackets are robust standard errors.

. The findings indicate that the regression coefficients remain significantly negative after substituting the dependent variables, which is highly consistent with the main regression results, thereby confirming the robustness of the research mechanism.

3.4.2. Sample and Data Processing

First, considering that the timber import volumes in Gansu, Qinghai, Shanxi, Shaanxi, and Xinjiang provinces (including autonomous regions) exhibit incomplete data series and insufficient observational density, these regions were excluded to ensure the comparability of the treatment and control groups in the DID estimation. Regression analysis was then re-conducted with these areas removed. The results, presented in column (4) of Table 3, show that the sign of the coefficient for the core explanatory variable remains consistent with the baseline regression and passes the robustness check at the 5% confidence level.

3.5. Mechanism Test

Based on the theoretical analysis presented earlier, the Russia–Ukraine conflict influences China’s timber imports from Russia through its impact on both the demand side in China and the supply side in Russia. Therefore, this paper aims to examine the mechanism of this influence by using timber price volatility and timber supply capacity as intermediary variables, employing a two-step method for the impact mechanism test [56].

3.5.1. Intensification of Timber Price Volatility

To dynamically assess the price volatility (range_ict) of Russian timber imported by Chinese provinces, this study introduces a rolling window method to enhance the dynamic analysis of price volatility [57]. Specifically, the coefficient of variation for monthly import timber prices is calculated on a rolling basis using an 8-month fixed time window for each province. This metric, derived as the ratio of the standard deviation to the mean of monthly import prices within the window, quantifies the degree of dispersion in the price series and is positively correlated with the intensity of price fluctuations. The following mechanism model is established:

$$\mathrm{l}\mathrm{n}rang{e}_{ict}={\alpha }_0+{\alpha }_1\left(Trea{t}_c\times Tim{e}_t\right)+\beta \mathrm{l}\mathrm{n}Controls+{\mu }_i+{\gamma }_t+{\lambda }_c+{\epsilon }_{ict}$$

(3)

As shown in column (1) of

Table 4. Mechanism test regression results.

	(1)	(2)
Variables	lnrangeict	lnsupplyct
did	0.184 **	−2.539 ***
	(0.085)	(0.224)
_cons	−11.294	31.191
	(17.357)	(25.186)
Controls	Yes	Yes
Obs	4725	409
R2	0.275	0.876
Date FE	Yes	Yes
Country FE	Yes	Yes
Province FE	Yes

*** and ** are significant at the 1% and 5%, levels, respectively; the data in brackets are robust standard errors.

, the DID estimation with fixed effects reveals that the Russia–Ukraine conflict significantly increased the volatility of China’s timber imports from Russia. This fluctuation mainly stems from the series of Western sanctions imposed on Russia, which disrupted its timber industry across both production and transportation stages. At the production stage, restrictions on raw material supply and labor allocation constrained output, while blocked logistics routes and surging transportation costs further disturbed market equilibrium. These disruptions jointly triggered dual shocks in the market and logistics systems. Under these adverse conditions, Russian timber exporters, aiming to minimize financial losses, were compelled to frequently adjust export prices. This pricing strategy is directly transmitted to the Chinese market, substantially amplifying fluctuations in import prices. External shocks typically magnify price volatility significantly, and in some cases, can become the primary driver of price fluctuations, posing a potential threat to national industrial security [58]. Specifically, the sharp rise and instability of import prices have increased domestic production costs and inflationary risks. For Chinese enterprises importing intermediate goods, rising input costs and unpredictable price movements have intensified operational pressure [59]. Consequently, many firms have begun diversifying import sources toward markets with lower volatility and more stable supply conditions. These phenomena strongly validate Hypothesis 2, indicating that the Russia–Ukraine conflict exacerbates the price volatility of Chinese timber imports from Russia, leading Chinese enterprises to reduce their reliance on Russian timber imports.

3.5.2. Weakening of Timber Supply Capacity

Within the main regression framework, this study further examines whether a country’s timber supply capacity (supply_ct) mediates the effect of the Russia–Ukraine conflict on China’s timber imports from Russia. Specifically, the logarithm of each country’s log export value is employed as the proxy variable for supply capacity.

On one hand, the export value of logs reflects the share of timber output that can be allocated to international markets—higher export values indicate greater resource availability and stronger export capability. On the other hand, consistent and stable export performance suggests higher overall efficiency in the country’s timber industry, encompassing production, processing, and logistics, thereby ensuring supply stability and continuity. Given that Russia has ceased publishing import–export statistics since April 2022, the study uses trade data on logs imported from Russia by other countries as a proxy. The following mechanism model is established:

$$\mathrm{l}\mathrm{n}suppl{y}_{ct}={\alpha }_0+{\alpha }_1\left(Trea{t}_c\times Tim{e}_t\right)+\beta \mathrm{l}\mathrm{n}Controls+{\gamma }_t+{\lambda }_c+{\epsilon }_{ct}$$

(4)

As shown in column (2) of Table 4, since the outbreak of the Russia–Ukraine conflict, Russia’s timber supply capacity has been significantly negatively impacted. The conflict triggered a series of chain reactions that disrupted Russia’s timber industry. These include difficulties in labor allocation, financing constraints faced by forestry enterprises, and instability in timber-producing regions. Together, these factors led to a contraction in production inputs and a significant decline in timber supply capacity. This weakening has severely undermined the stability of timber supply, resulting in extended delivery cycles and frequent supply fluctuations. Supply chain stability is a key determinant of market supply [60,61]. As Russia’s supply capacity declines, the scale of China’s timber imports from Russia necessarily decreases.

For Chinese forestry enterprises, a stable supply of raw materials is fundamental for continuous operations. Any disruption in input materials risks production delays, potential contract defaults, and reputational losses [33]. To mitigate these risks and ensure operational continuity, Chinese importers are increasingly shifting procurement toward countries with more reliable and sustainable timber supplies. These findings, derived from the DID model controlling for time, province, and country fixed effects, provide strong evidence in support of Hypothesis 3.

3.6. Heterogeneity Analysis

3.6.1. Degree of Forestry Industrialization

The development of China’s forestry sector exhibits pronounced regional disparities. Influenced by differences in resource endowment and policy orientation, southern forest regions have formed a market-driven model of industrialization. These regions feature spatial industrial clustering and a complete industry chain developed through large-scale and intensive operations.

In contrast, non-southern forest regions operate largely under state-owned forest rights. Their development strategies have gradually shifted from traditional timber production toward ecological service provision. As a result, local policies now emphasize forest resource conservation and ecological barrier construction, causing the industrial chain in these areas to remain relatively underdeveloped.

To investigate the differential impact of the Russia–Ukraine conflict on regions with varying degrees of industrialization, this study classifies Hainan, Guangdong, Guangxi, Fujian, Zhejiang, Anhui, Jiangxi, Hubei, Hunan, and Guizhou as southern forest regions and the remaining provinces as non-southern forest regions for group regression analysis. The results are shown in

Table 5. Heterogeneity analysis of regression results.

	(1)	(2)	(3)	(4)
	Degree of Industrialization		Timber Supply Capacity of the Province
Variables	High	Low	High	Low
did	−0.733 **	−0.338	−0.849	−0.436 **
	(0.358)	(0.260)	(0.438)	(0.199)
_cons	−95.957 *	−85.394	−216.149 **	−59.734
	(50.126)	(56.380)	(70.013)	(41.472)
Controls	Yes	Yes	Yes	Yes
Obs	2020	2883	1204	3699
R2	0.612	0.518	0.661	0.520
Date FE	Yes	Yes	Yes	Yes
Country FE	Yes	Yes	Yes	Yes
Province FE	Yes	Yes	Yes	Yes

** and * are significant at the 5% and 10%, levels, respectively; the data in brackets are robust standard errors.

.

The findings indicate that, compared with non-southern forest regions, the negative impact of the Russia–Ukraine conflict on Russian timber imports is more pronounced in the highly industrialized southern forest regions. This finding challenges the conventional view that regions with complete industry chains and stable supply networks are more resilient to external shocks.

The main explanation lies in the “processing trade model” widely used in China’s timber industry. Southern regions import large volumes of resource-based timber from countries such as Russia, process it domestically, and re-export it to developed markets, particularly the European Union. However, Western sanctions have significantly tightened sustainability certification requirements for wood product exports, classifying Russian timber as “conflict timber.” As a result, southern forest regions—being deeply embedded in global value chains—face more severe trade barriers and are compelled to adjust their raw material sourcing strategies, such as reducing imports from Russia or switching to more stable suppliers.

By contrast, non-southern forest regions are more domestically oriented. They rely primarily on local resources, display lower dependence on imported timber, and are therefore less affected by global supply chain disruptions. This production structure enhances their resilience to the trade restructuring triggered by the Russia–Ukraine conflict.

3.6.2. Provincial Timber Supply Capacity

The timber supply capacity of each province in China is closely related to its dependence on imported timber. Provinces rich in forest resources can rely on local timber to reduce demand for external sources. In resource-scarce provinces, the lack of local timber supply capacity necessitates increased imports to bridge the supply–demand gap.

To explore the differential impact of the Russia–Ukraine conflict on provinces with different timber supply capacities, this study classifies Guangxi, Guangdong, Fujian, Hunan, Anhui, Yunnan, and Jiangxi as timber supply-advantaged regions, while the remaining provinces are categorized as timber import-dependent regions, based on their log output rankings. The results are presented in Table 5.

The findings indicate that timber import-dependent regions experienced a significantly stronger negative impact on Russian timber imports compared with timber supply-advantaged regions. This suggests that provinces with higher local supply capacity were less affected by the conflict and exhibited stronger resilience to external shocks. A plausible explanation lies in the role of self-sufficiency: provinces with abundant forest resources can draw more heavily on domestic timber to buffer against price volatility and supply disruptions caused by the conflict. This helps them maintain relatively stable trade volumes, export shares, and product diversity.

Conversely, provinces with limited local resources are highly reliant on imported timber—particularly from Russia—and face greater difficulties in finding short-term substitutes. As a result, they are more exposed to international supply chain disruptions and price instability, amplifying the negative trade shock induced by the conflict.

3.7. Further Analysis

Building upon the systematic analysis of the scale and volatility of timber imports from Russia to Chinese provinces in the context of the Russia–Ukraine conflict, this study further investigates the impact of this geopolitical event on the price levels and volatility of timber imports across different regions of China.

Russia is one of the world’s leading timber producers. The Russia–Ukraine conflict has restricted Russian timber exports, reducing global supply. At the same time, Ukraine itself is also a major wood exporter in Europe, but its timber harvesting and exports have been severely affected by the conflict, resulting in a tightening of supply in the global timber market. Disruptions in the global supply chain, coupled with panic buying, have led to sharp fluctuations in global timber markets. The dual pressures of tightening global supply and rising international shipping costs have further exacerbated the overall volatility in China’s timber import prices.

Against this backdrop, clarifying the changes in timber import prices and their volatility is crucial—not only for timber processing enterprises to accurately calculate costs, but also for distributors to arrange inventories more rationally, thereby effectively reducing the risk of market imbalances. Therefore, this study conducts regressions on the log import price level (PL_it) and price volatility (PV_it) of logs across 31 Chinese provinces.

The results, shown in

Table 6. Further analysis of regression results.

	(1)	(2)
Variables	lnPLit	lnPVit
did	0.085 ***	0.206 ***
	(0.021)	(0.033)
_cons	0.400	1.201 *
	(0.416)	(0.657)
Controls	Yes	Yes
Obs	1374	1340
R2	0.614	0.531
Province FE	Yes	Yes

*** and * are significant at the 1% and 10%, levels, respectively; the data in brackets are robust standard errors.

, indicate that the Russia–Ukraine conflict has increased the volatility of timber import prices and driven up overall timber import prices in China. This is because reduced supply in the global timber market and the restructuring of international supply chains have pushed up the international price benchmark. As the world’s largest timber importer, China must bear the “risk premium” associated with regional conflicts, which is reflected in a narrowing of bargaining space in long-term contract price negotiations.

4. Conclusions and Implications

4.1. Conclusions

Taking the Russia–Ukraine conflict as the research background, this paper investigates its impact on China’s imports of Russian timber. Utilizing monthly log import data from the China Customs Database spanning January 2020 to December 2024, a DID model is constructed based on a three-dimensional panel dataset (year–month–province–country of origin), with a multi-dimensional fixed-effects model employed. Import timber price volatility and timber supply capacity are introduced as mediating variables to analyze the mechanisms of impact. The main research findings are as follows:

First, the outbreak of the Russia–Ukraine conflict has significantly reduced the scale of Russian timber imports across Chinese provinces. This result remains robust after applying the DID framework with high-dimensional fixed effects regressions, parallel trend tests, placebo tests, and other robustness checks.

Second, mechanism analysis reveals a dual transmission path: On one hand, the increased volatility in Russian timber prices, triggered by the conflict, prompts Chinese enterprises to actively adjust procurement strategies and reduce their reliance on Russian timber through cost management mechanisms. On the other hand, the decline in Russia’s timber supply capacity creates supply constraints, forcing importers to passively substitute sources by turning to alternative markets with abundant resources.

Third, the negative shock is more pronounced in regions with specific economic characteristics. In particular, highly industrialized southern forest regions experience stronger negative impacts due to the unique structure of China’s forest product trade, while timber import-dependent regions display greater contractionary trade elasticity due to resource endowment constraints.

Fourth, further analysis shows that the impact of the Russia–Ukraine conflict goes beyond a single market, not only significantly amplifying the volatility of Russian timber import prices but also driving up the overall price center and volatility of China’s timber imports through market linkage effects.

4.2. Policy Recommendations

Based on the above conclusions, the following policy recommendations are proposed:

First, build a diversified timber supply chain system. Given that highly industrialized southern forest regions are deeply integrated into global value chains, it is recommended to expand the range of timber import sources and promote market diversification. By increasing imports and broadening cooperation partners, potential supply shortages stemming from the Russia–Ukraine conflict can be alleviated, for example, by expanding imports from major timber trading countries such as Canada and New Zealand. In the long term, forestry cooperation with countries along the Belt and Road Initiative could be intensified, making them reliable and stable sources of timber imports for China and reducing China’s dependence on any single country.

Second, enhance the strategic reserve capacity of timber resources. Compared with timber supply-advantaged regions, the more significant shock of the conflict on timber import-dependent regions highlights the importance of strategic reserves. For areas with timber supply advantages, it is necessary to develop a systematic and standardized strategic reserve mechanism. This can be achieved by strengthening collaboration between the government and enterprises and jointly expanding the reserve scale. Fiscal subsidies and tax incentives could encourage processing enterprises and trade agencies to establish commercial reserves. Additionally, in regions with concentrated forest resources, the government should plan and construct large-scale timber reserve bases to ensure sufficient emergency supplies during critical periods, effectively responding to supply–demand fluctuations caused by geopolitical conflict, extreme climate events, or other emergencies. For regions with high dependence on imported timber, it is essential to improve the overall planning and dynamic management of resource reserves. By strengthening monitoring mechanisms and reserve allocation systems, a rapid response can be provided in the face of market fluctuations, mitigating potential shocks to the local economy. From a strategic perspective, these regions should gradually promote a transition to a more domestically circulating supply structure and increase the proportion of domestic timber usage, thereby fundamentally enhancing the autonomy and resilience of the timber supply system.

Third, improve price risk management tools. The study finds that the Russia–Ukraine conflict significantly increases Russian timber price volatility and, through market linkage effects, influences overall timber import prices in China. Importing enterprises must therefore strengthen their ability to manage price fluctuations. On one hand, it is recommended to actively use financial derivatives such as futures and options for risk hedging, locking in procurement costs in advance, and reducing the adverse effects of price volatility on profit margins. On the other hand, industry regulators and organizations should establish price monitoring and early warning platforms, regularly releasing key information on market trends and price forecasts, thus providing enterprises with reliable guidance for procurement and inventory decisions and improving their ability to proactively respond to market uncertainty.

4.3. Discussion

Most existing studies in this field have primarily focused on national-level theoretical analyses, whereas this study advances to the provincial level and systematically identifies the trade effects of the Russia–Ukraine conflict using a DID model. The analysis not only reveals the heterogeneous impact of the conflict on the scale of timber imports across Chinese provinces but also empirically verifies two key transmission mechanisms—price volatility and declining supply capacity—thereby providing more fine-grained evidence of the micro-level dynamics underlying the conflict–trade nexus. This innovative attempt enriches the empirical foundations of international trade and conflict research. However, due to limitations in statistical standards and the timeliness of data updates, the acquisition of several important variables remains challenging, preventing their inclusion in the model. While this may constrain the comprehensiveness of the analysis, it does not undermine the robustness of the main findings.

Beyond economic and industrial dimensions, the impact of the Russia–Ukraine conflict on timber trade also carries profound implications for environmental sustainability and governance. Geopolitical disruptions have forced cross-border transportation routes to be rerouted and distances extended, thereby directly increasing the carbon footprint of timber trade flows [62]. Furthermore, trade tensions between Russia and other major economies have reshaped the flows of energy products and exacerbated production structure adjustments driven by regional disparities in resource-use efficiency. Such structural shifts not only heighten regional CO₂ emissions and air pollution levels but also generate additional health risks and environmental pressures, posing obstacles to global low-carbon transitions and sustainable development goals [63,64]. In addition, the direct destruction of forests and land resources in Russia and Ukraine further undermines the stability of regional ecosystems and diminishes carbon sink capacity. Against this backdrop, an even more pressing concern is the risk of “environmental leakage”: price shocks and supply disruptions may redirect demand toward countries and regions with weaker certification, monitoring, and enforcement systems [65]. Such diversion does not reduce global demand for timber but rather shifts the risks of illegal logging and associated carbon emissions to ecologically fragile areas [66,67], thereby intensifying the risks of global forest degradation and biodiversity loss.