The impact of agglomeration in China’s timber industry on the quality of forest products Open Access

The construction of an ecological civilization represents a critical theme in contemporary global discourse. The Chinese government places significant emphasis on this issue, integrating ecological civilization into the “five-in-one” overall framework, thereby initiating a new era in its development. In this context, the Third Plenary Session of the 20th Central Committee of the Communist Party of China articulated a vision prioritizing the establishment of a “beautiful China.” This vision includes accelerating the comprehensive green transformation of economic and social development, enhancing the governance system for ecological environments, promoting development with ecological priorities, resource conservation, and low-carbon initiatives, and fostering a harmonious coexistence between humanity and nature. Within this framework, the forestry sector emerges as a pivotal arena, necessitating a focus on ecological protection and green transformation in its developmental strategies. The sector is tasked with the challenge of contributing to high-quality development within the modern forestry industry. Concurrently, China’s rapid economic growth and urbanization have led to increased concentration in the timber industry, profoundly impacting the forestry sector and the quality of forest products ^[1].

Current research on the timber industry exhibits several limitations: Firstly, there are geographical constraints in research perspectives and insufficient theoretical depth. Existing studies predominantly focus on applied research, demonstrating significant regional disparities. Case studies at municipal or district levels in developed regions like the Yangtze River Delta and Pearl River Delta have formed regional research clusters, limiting the generalizability of conclusions and making it difficult to reflect industrial patterns across underdeveloped regions and at the national level. Secondly, fundamental theoretical research remains weak, lacking systematic exploration of the driving mechanisms behind industrial agglomeration, which constrains the development of theoretical frameworks. Thirdly, a provincial-level forest product quality evaluation system has yet to be established. Current research primarily concentrates on the national macro level, failing to support regionally differentiated development strategies. This gap stems from inadequate understanding of the unique characteristics of forest product quality — its volatility is significantly higher than that of manufacturing industries, making it more vulnerable to natural risks such as climate change and pest outbreaks. However, research on risk early warning systems and adaptive management at the provincial scale is severely lacking, resulting in weak risk resilience in local industries. Fourthly, quality improvement in forest products relies heavily on high-cost technology imports, while localized innovation mechanisms remain underdeveloped. Traditional production transformation requires both technological absorption and adaptation of foreign technologies, characterized by long transition periods and substantial capital requirements. Small and medium enterprises particularly face challenges in technology transformation. More critically, the relationship between timber industry agglomeration effects and product quality remains unclear. There is a lack of empirical analysis demonstrating how agglomeration influences product value-added through pathways like technology spillovers and supply chain coordination, leading to ineffective alignment between industrial cluster policies and quality upgrading objectives.

Recent research has focused on the agglomeration of the timber industry and its impact on the quality of forest products. Wang et al. (2024) identified distinct industrial agglomeration in the processing of imported timber within Heilongjiang Province, revealing a pronounced spatial aggregation of the industry across various scales. In contrast, Tao et al. (2024a) reported a sharp rise in the spatial agglomeration of China’s wood processing industry, followed by a decline post-2004, accompanied by a geographical shift in focus from the northeastern region to the eastern coastal areas. Zhenhuan et al. (2021) observed that from 1988 to 2018, the spatial concentration of China’s forest product manufacturing industry was relatively low, characterized by a notably uneven spatial distribution. Furthermore, Tao et al. (2024b) examined the influence of spatial agglomeration on the efficiency of timber resources, assessing the potential for enhancing efficiency through expanding timber processing clusters. A limited body of literature has proposed various methodologies for evaluating product quality. For instance, Lichun and Baodong (2018) employed the Khandelwal model to estimate the quality of China’s primary imported forest products and analyzed the factors influencing this quality. Smith and Taylor (2019) utilized the export technological sophistication index to measure the quality of exported wooden furniture. Li and Zhang (2024) argued that the complexity of export technology serves as an effective indicator of a country or region’s export quality and its technological standing within the global value chain. Hong and Bei (2020) found that the technological complexity of imported producer services significantly enhances the quality of manufacturing exports. Guanyi (2020) demonstrated that industrial agglomeration plays a crucial role in improving industrial green efficiency at both the national level and across eastern, central, and western regions of China. Chen and Wang (2024) suggested that the quality of export products is typically assessed through the technological complexity of the products and their utility value, noting that digital technology can substantially enhance enterprise trade patterns and improve export product quality.

Baodong and Weiming (2006) explored the relationship between industrial agglomeration and the competitiveness of China’s timber industry; however, their findings were limited by outdated data, preventing them from accurately depicting the current characteristics and trends of timber industry agglomeration in China. Lastly, Yang et al. (2020) employed panel data regression analysis to conclude that spatial agglomeration within the timber processing industry significantly enhances product quality. Jingjing et al. (2023) provided a spatial perspective analysis of the industrial agglomeration of China’s non-wood forest sector. Their study revealed that the spatial distribution of non-wood forest industries exhibits significant regional differences, with higher levels of agglomeration in areas characterized by abundant forest resources and favorable policy environments. This spatial agglomeration not only promotes the sharing of resources and information but also drives technological innovation and product quality improvements within the cluster. Their findings complement the existing literature on timber industry agglomeration by highlighting the importance of spatial factors in shaping agglomeration patterns and their subsequent impact on product quality. Recent research has delved into various facets of the timber industry. Notably, Guan and Zhang (2023), in their study titled “The Impact of Log Export Restrictions on China’s Log Import,” offered a fresh perspective by examining how restrictions on log exports affect China’s log import patterns and volumes. This work underscores the potential volatility in timber supply chains and the adaptability required by the industry to navigate such changes, thereby enriching our understanding of the external factors influencing the timber industry and its product quality. This highlights the dynamic nature of the timber industry and the importance of considering external market factors when examining the relationship between industrial agglomeration and product quality.

The primary objective of this study is to systematically uncover the regional heterogeneity in the impact of China’s timber industry agglomeration on the quality of forest products, thereby providing theoretical underpinnings and policy references for advancing high-quality regional timber industry development. To achieve this goal, this study adopts an industrial agglomeration perspective and employs regression models to empirically analyze the causal mechanisms through which timber industry agglomeration influences product quality, utilizing panel data from 27 Chinese provinces between 2012 and 2021.

This research makes three significant contributions: First, it establishes a provincial-level forest product quality assessment framework, filling a critical gap in regional comparative research. Second, it integrates novel methodologies for measuring agglomeration intensity and technical sophistication to elucidate the multifaceted “agglomeration-quality” causal pathways. Third, it offers an empirical foundation for governmental policymaking aimed at promoting timber industry advancement, facilitating the sector’s green and low-carbon transition.

The rest of this paper is organized as follows. Section 2 presents the Theoretical Analysis Framework, while Section 3 details the Research Methodology. Section 4 introduces the Data Sources and Descriptive Statistics, followed by Section 5 which covers the Measurement Results and Empirical Analysis. Finally, Section 6 provides the Research Conclusions along with Countermeasures and Suggestions.

This paper aims to develop a comprehensive and methodologically sound analytical framework (refer to Figure 1) that examines the influence of timber industry agglomeration on the quality of forest products. The framework focuses on assessing the impact of timber industry agglomeration on forest product quality. In particular, this section explores five dimensions of the external economic effects associated with timber industry agglomeration, investigating the specific mechanisms through which knowledge spillover effects, human capital effects, economies of scale effects, competition effects, and specialization and division of labor effects contribute to the quality of forest products. 1318

Crucially, the manifestation and impact intensity of these external economic effects are not uniform but exhibit significant regional heterogeneity within China’s timber industry context. The eastern region, characterized by its advanced economic development and industrial maturity, may approach a saturation point where further agglomeration could yield diminishing returns, potentially rendering its impact on product quality relatively insignificant. This is likely due to intensified competition for resources, congestion effects, and the exhaustion of readily available knowledge spillover opportunities within mature clusters. In contrast, the central region, with its growing economic base and favorable policies, is positioned to derive substantial benefits from agglomeration economies. The confluence of abundant forest resources, increased investment in fixed assets, and a larger pool of forestry practitioners in the central region acts synergistically to amplify the impact of agglomeration on product quality by facilitating the realization of the five external effects. Conversely, the western region faces structural challenges such as weak infrastructure, limited policy support, and a less developed economic base, which collectively constrain the efficient generation and absorption of positive agglomeration externalities, thereby hindering their positive impact on product quality. These pronounced regional differences underscore the critical importance of contextual factors — specifically, regional economic development stages, resource endowments, policy environments, and institutional thickness — in mediating the relationship between industrial agglomeration and product quality. This theoretical foundation, explicitly incorporating regional heterogeneity, provides a robust basis for the subsequent empirical investigation.

Delving deeper into the mechanisms, Jacobs’ framework on externalities posits that industrial agglomeration facilitates the integrated advancement of diverse industries, thereby enhancing knowledge spillover and mutual learning dynamics (Shen and Peng, 2021). Within timber industry clusters, firms often share similar technologies, equipment, and market access, collaboratively investigating methods to optimize production processes, minimize costs, and improve product quality. This environment promotes technological and knowledge exchanges. This environment naturally promotes intensive technological and knowledge exchanges among actors. For example, Audretsch (1998) demonstrated that agglomeration can significantly enhance innovation through knowledge spillovers, a mechanism which is crucial for driving continuous improvement in timber product quality. Simultaneously, the expansion of industrial scale, driven by cost efficiencies, shared resources, and infrastructure, can lead to reduced marginal costs and energy consumption (Mauler et al., 2021). Furthermore, the vertical and horizontal interconnections within the industry enable centralized pollution management, thereby promoting environmentally sustainable development. Economies of scale are fundamental to the concept of agglomeration (Shengqiang and Weiping, 2013). Specifically within the timber industry context, the economies of scale effect translates into a reduction in unit costs as production or operational scale increases within the cluster, freeing up resources that can be reinvested in quality enhancement. Concurrently, agglomeration generates a distinct human capital effect. According to the principles of new economic geography, the concentration of the timber industry in a specific locale enhances the levels of human capital. This occurs because the clustering of similar enterprises within the timber sector creates a natural “magnetic field” that attracts specialized talent, thereby forming a robust “labor pool.” Within such agglomerations, firms benefit collectively from access to this concentrated pool of similar labor resources. This proximity enhances employee skills and experience through learning-by-doing and observation, reduces costs associated with recruitment, training, and management, and ultimately improves production efficiency and competitiveness.

Tao et al. (2025) further elaborated on the role of wood resource efficiency in enhancing the export competitiveness of China’s wood products. Their study highlighted that within timber industry clusters, efficient utilization of wood resources can significantly improve the quality and market performance of forest products. Through optimized resource allocation and advanced processing technologies, firms within these clusters can achieve higher resource efficiency, which in turn drives product quality upgrades and fosters sustainable development. This aligns with the view that the economies of scale and resource-sharing mechanisms within industrial agglomeration can lead to more efficient use of resources and better quality control. Moreover, the human capital effect can stimulate the development of knowledge-based talent, fostering compound human capital (Shengqiang and Weiping, 2013), which further advances the technological capabilities and innovation potential of the timber industry, steering the sector towards high-quality development. For instance, Ellison et al. (2010) showed that agglomeration can significantly enhance the productivity of firms through improved labor matching and skill development, processes which are essential for enabling sustained timber industry quality improvement.

Complementing these effects, Porter’s theory of externalities emphasizes the role of competitive dynamics. This perspective suggests that the coexistence of diverse industries and various sub-industries within a single sector, driven by market dynamics, fosters a competitive atmosphere that stimulates innovation in both products and technologies. Within a timber cluster, heightened competition exerts selective pressure: this competitive landscape tends to eliminate backward, highly polluting, and inefficient firms, while simultaneously compelling surviving firms to improve their environmental management practices and, critically, their product quality to maintain market share. Such competitive pressures thus contribute to the continuous enhancement of timber product quality across the entire agglomeration. Moreover, the inherent tendency towards specialization and division of labor within clusters enriches and diversifies the quality attributes of timber products available within the cluster, while also improving overall efficiency and competitiveness through focused expertise.

In conclusion, timber industry agglomeration exerts its influence on the quality of forest products predominantly through the integrated operation of several interlinked dimensions of external economic effects, including knowledge spillover effects, economies of scale effects, human capital effects, competitive dynamics, and the specialization and division of labor effects. These interrelated effects collectively not only enhance the quality and stability of products but also bolster the market competitiveness of enterprises, thereby fostering the sustainable development of forest products. However, as the regional analysis underscores, the strength and efficacy of these mechanisms are contingent upon localized economic, institutional, and resource conditions.

This study primarily utilizes the methodology established by Hua and Jing (2016) for quantifying the degree of industrial agglomeration by computing the Location Quotient (LQ) index. This methodology enables the evaluation of the LQ across various regions, thus enhancing the analysis of resource endowments, competitive advantages, and resource distribution within each locality. Consequently, it contributes to a more comprehensive understanding of the geographical distribution and advantageous areas within the timber industry. The LQ index, an economic metric, represents the ratio of an industry’s share within a specific region to its share within the national economy. This indicator effectively quantifies the degree of specialization in industrial agglomeration at both national and regional levels. Employing this approach to assess the level of agglomeration in the timber industry provides a nuanced and thorough depiction of specialization across different provinces and cities, viewed from both micro and macro perspectives. This methodology is particularly relevant for investigating timber industry agglomeration in China and enables comparative analyses of agglomeration levels across various provinces or over time. However, this measurement method also has certain limitations, as the Location Quotient (LQ) index may be influenced by the economic scale of different regions. Areas with a larger economic scale may exhibit higher LQ values merely due to their size advantage, rather than genuine agglomeration effects. The specific calculation method is detailed below:

In Equation (1), $LEIij$ represents the level of industrial agglomeration of industry j in region i, $Vij$ represents the output value of industry j in region i, $Vi$ represents the total output value of region i; $Vkj$ represents the total output value of industry j in the national k sector, $Vk$ represents the total output value of industry j within the national sector k.

This study primarily utilizes the methodology established by Hasmann et al. (2007) for assessing export technological complexity. This methodology is applied to facilitate inter-provincial comparisons regarding the technological complexity of forest product exports, which serving as a metric for evaluating the quality of these products. This approach provides a more objective and quantifiable framework, aiding both enterprises and governmental bodies in comprehending the technological level and competitive position of the timber industry. Furthermore, it offers a more thorough evaluation of the technological advancement and developmental status of the timber sector, thereby enabling the identification of deficiencies and unexplored opportunities within the industry. As a result, targeted strategies can be devised to enhance and promote the overall performance of the sector. However, this measurement method also has certain limitations. The export technological complexity fails to fully cover all dimensions of product quality. Factors such as brand reputation, customer satisfaction, and environmental standards are not directly reflected in the index, which may not fully represent the overall quality of forest products.

Initially, the export technological complexity (ETC) for three categories of timber industries is quantified using the following formula:

In the equation, $EXPYt$ denotes the export technological complexity of the sub-industry t within the broader export industry, $xct$ and $xc$ respectively represent the export value of the sub-industry t within the timber sector of country c, as well as the total export value of all products within the timber industry of country c, $Yc$ represents the per capital gross national product (GNP) of country c. $EXPYt$ the larger the value, the more complex the export technology possessed by the timber industry sub-industry t in that country or region, indicating a higher quality of exported products. Equation (2) enables the calculation of the export technological complexity for the sub-industry t in country c. The technological complexity at the national level can be computed using Equation (3):

In Equation (3), $EXPYc$ represents the export technological complexity of the timber industry in country c., $xc$ represents the total export value of the timber industry in country c. The export technological complexity serves as an indicator of both the technological advancement and the quality of the products manufactured within the timber sector.

Utilizing the previously discussed theoretical analysis framework and drawing upon relevant literature12, this study employs the quality of forest products in China as the dependent variable and timber industry agglomeration as the independent variable to develop a benchmark regression model. The objective of this model is to assess the extent to which timber industry agglomeration may enhance the quality of forest products, as represented in the subsequent equation:

In Model (4), The variable $EXPYcn$ denotes the export technological complexity associated with the timber industry in province c during the year n, serving as an indicator of product quality. $εcn$ represents the stochastic error component.

The core explanatory variable is the level of timber industry agglomeration $LEIcn$⁠, The location quotient index uses 1 as a threshold, The larger the value of $LEIij$⁠, the higher the level of timber industry agglomeration within a certain region, and the smaller the value, the lower the level of industrial agglomeration in that region.

Control variables $covercn$ are represented by the logarithm of forest coverage, which typically indicates the availability of forest resources. Forests are essential to the timber industry clusters, as their coverage is directly correlated with the diversity and quality of timber, which in turn has significant implications for the quality of the final products. In the context of timber industry clusters, a substantial level of forest coverage within the cluster can secure a steady supply of raw materials for forest products, lower production and transportation costs, and ultimately improve the competitiveness of forest products generated through the agglomeration of the timber industry.

$productioncn$ represents the production efficiency of forest products. Typically, organizations within an industrial cluster establish a competitive-cooperative dynamic, which facilitates increased production efficiency and cost reduction through collaborative efforts and competitive practices. An increase in production efficiency frequently results in improved production quality, as more efficient manufacturing processes are typically associated with enhanced quality control and elevated levels of standardization. This study utilizes the timber output from various provinces and municipalities as a metric to assess the technological advancement within the timber industry (Brown and Green, 2022).

$foreigncn$ represents the degree of external dependence. An increased reliance on foreign trade within a region signifies heightened competitive pressures, which subsequently motivates firms to pursue innovation and facilitates industrial advancement. This process ultimately contributes to the improvement of the technological complexity of exports.

$lninvestcn$ represents the logarithm of fixed investment in forestry, reflecting the investment intensity of government departments in the development of the forestry industry. Increased levels of fixed investment in the forestry sector exert a substantial influence on the advancement of the industry. Such investment facilitates the promotion of technological innovation, enhances product quality, lowers costs, improves efficiency, and optimizes production processes. These initiatives are critical for sustaining a company’s competitive edge (Johnson and Williams, 2018).

$personnelcn$ represents the logarithm of the number of people employed in the forestry industry, indicating the degree of labor security in forestry. In the forestry sector, the workforce and its size are paramount considerations for ensuring labor security. It is essential to maintain the number of employees at an optimal level, avoiding both excessive and insufficient staffing. A workforce that is too small may be inadequate to fulfill the developmental requirements of the forestry industry, whereas an excessively large workforce can lead to increased operational costs (Davis and Miller, 2019).

This study examines the agglomeration and quality of forest products across 27 provinces ^[2] in China over the period from 2012 to 2021. This timeframe encompasses the ten years following the gradual economic recovery and stabilization that ensued after the 2008 financial crisis. Utilizing data from this recent decade enables a comprehensive analysis and forecasting of the evolutionary patterns of timber industry agglomeration, as well as the variations in forest product quality across the provinces of China.

Forest products are delineated in accordance with the definitions provided by the Food and Agriculture Organization (FAO). These products encompass logs, various raw materials, sawn timber, wood-based panels, wood pulp, paper, and paper products. Trade data pertaining to the quality of forest products is categorized based on the initial two digits of their Harmonized System (HS) codes, specifically codes 44, 45, and 48. These codes correspond to three distinct categories: wood and wood products, charcoal; cork and cork products; and paper and paperboard, including pulp, paper, or paperboard products.

We use data from the China Statistical Yearbook to assess the level of agglomeration in the timber industry. The quality of forest products is evaluated using data obtained from the databases of China Customs and the National Bureau of Statistics of China. Information regarding forest coverage is extracted from the statistical yearbooks of various provinces as well as the Forestry Statistical Yearbook. Production efficiency data is also sourced from the China Statistical Yearbook. The assessment of foreign trade dependence relies on data from the China Statistical Yearbook on Industrial Economics. Furthermore, data concerning fixed asset investment in forestry is gathered from multiple sources, including the Ministry of Agriculture of China, the State Forestry Administration, the National Bureau of Statistics of China, the Ministry of Water Resources, the China Meteorological Administration, and China Customs. Lastly, information on forestry practitioners is obtained from the China Economic Information Network and the China Statistical Yearbook.

The data sources used in this study are comprehensive and reliable. However, due to changes in the classification of forest products, the measurement method for the variable of forest product quality was adjusted in the statistical standards in 2006, which may lead to minor data discrepancies and affect the cross-year comparability of the data. In addition, some provinces may have missing or incomplete data, which may lead to result bias. To address these data issues, we used the moving average method to handle missing data. At the same time, we cross-referenced multiple sources of data, such as the National Bureau of Statistics and local statistical yearbooks, to verify the accuracy and consistency of the data. This approach helps to reduce potential biases and ensure the robustness of the analysis results.

Table 1 presents the descriptive statistics for the variables, revealing significant variations in the values of the primary variables and underscoring the notable differences among the provinces in China. Given the observed maximum and minimum values for each variable, a thorough re-evaluation of the relevant data from multiple databases was undertaken. The findings confirmed the accuracy of the data, detecting no outliers.

The Location Quotient (LQ) index employs a threshold value of 1, whereby values exceeding this threshold indicate a greater degree of timber industry agglomeration within a designated region, and values below indicate a lesser degree of such agglomeration. The benchmark for assessing the level of industrial agglomeration is established at 1.25, with values surpassing this threshold denoting a substantial level of agglomeration. Table 2 delineates the measurement outcomes pertaining to timber industry agglomeration levels across various provinces in China. The national average agglomeration level is 0.92, below the 1.25 threshold, indicating that the overall level of timber industry agglomeration in the country is relatively low. However, notable regional industrial agglomeration is evident, particularly in Liaoning, Jilin, and Heilongjiang provinces in the northeast, where LQ indices exceed 1.25, reflecting a high level of timber industry agglomeration. Furthermore, the Yangtze River Delta region, which encompasses provinces and municipalities such as Jiangsu and Fujian, also demonstrates a comparatively high level of timber industry agglomeration.

The primary factors contributing to these trends include: The three northeastern provinces are endowed with extensive forest resources, encompassing both natural and cultivated forests, which provide a robust material foundation for the timber industry and facilitate a significant degree of timber industry agglomeration within the region. Moreover, these provinces have historically functioned as key timber processing hubs in China, characterized by well-established industrial chains and a wealth of processing expertise accumulated over decades. This historical context has led to a relatively high level of timber industry concentration, thereby promoting a notable industrial clustering effect. In contrast, the Yangtze River Delta region has experienced rapid development in its timber processing sector, marked by dynamic economic growth and a concentration of both private and foreign-invested enterprises. This region is increasingly favored by foreign companies for establishing manufacturing operations in China. Additionally, as newly established factories and enterprises continue to evolve, there has been a notable shift in product offerings from low-to-mid-range to mid-to-high-end categories.

Figure 2 illustrates the technological sophistication and growth rate of China’s timber industry exports from 2012 to 2021. Technological sophistication is utilized as an indicator for assessing the quality of forest products. The data reveals a general upward trajectory in both technological sophistication and the growth rate of timber industry exports, although there are fluctuations. Specifically, technological sophistication increased from 92.72 in 2012 to 110.78 in 2021, reflecting a total increase of 18.06 and an average annual growth rate of 1.8%, which suggests a relatively modest pace of growth. Notably, the 2012–2016 period saw a more rapid growth rate, culminating in a peak value of 140.75 in 2016, the highest recorded figure. Overall, while there has been an increase in the technological sophistication of China’s timber industry exports, this growth has been characterized by fluctuations, especially between 2016 and 2018. This phenomenon can be attributed to the country’s initiatives to assimilate and localize advanced foreign technologies over the past decade, a process that is inherently time-consuming and potentially constrained by the capabilities related to technology transfer and innovation. Additionally, the timber industry in China has undergone a transition from traditional processing and manufacturing methods to high value-added, technology-intensive sectors. This structural transformation necessitates both technological advancement and the upgrading of enterprises, which require significant time and capital investment.

From a regional standpoint, significant disparities are evident among the eastern, central, and western regions of China, with the central region demonstrating the highest levels of development, while the western region shows the lowest (Figure 3). At the provincial level, Jilin, Hainan, Shandong, and Anhui have consistently occupied leading positions over the past decade. This trend can be largely attributed to their favorable geographical locations and the benefits derived from supportive opening-up policies implemented in these provinces.

This study performs a benchmark linear regression analysis of variables as outlined in Model (4), examining mixed regression, panel random effects regression, and panel fixed effects regression in isolation. The findings from each regression analysis are detailed in Columns (1), (2), and (3) of Table 3. Specifically, Column (3) of Table 3 presents the benchmark regression outcomes assessing the influence of timber industry agglomeration on the quality of forest products.

Column (4) excludes all control variables present in Column (3), thereby concentrating exclusively on the influence of timber industry agglomeration on the quality of forest products. When control variables are incorporated, timber industry agglomeration demonstrates a significant positive effect on the quality of forest products, achieving statistical significance at the 1% level. Following the removal of the control variables, there is a minor alteration in the estimated coefficient of the primary variable; however, the coefficient remains positive and statistically significant at the 1% level. This finding suggests that an increase in timber industry agglomeration correlates with an enhancement in the quality of forest products, thereby corroborating the analysis presented in the theoretical section. This finding indicates that agglomeration economies such as knowledge spillovers and scale effects have promoted the improvement of product quality. From an economic perspective, this positive correlation implies that regions with a higher degree of timber industry agglomeration are more likely to attract advanced technologies and skilled labor, thereby enhancing the quality of forest products.

With respect to the control variables, the observed insignificant positive effect of forest coverage may be attributed to the multifaceted nature of factors influencing the quality of timber products, including tree species, growth conditions, harvesting techniques, storage practices, and processing methods. The productivity variable demonstrates a significant negative effect at the 1% significance level, indicating that enhancements in production efficiency within the timber industry may diminish the concentration of industry activities, thereby impeding advancements in the technological sophistication of forest product exports. The insignificant positive effect of foreign dependence may be explained by the persistent high level of foreign reliance within China’s timber industry, suggesting that an increase in foreign dependence does not effectively facilitate the technological advancement of forest product exports. Furthermore, forestry fixed asset investment exhibits a positive effect on export technological sophistication at the 10% significance level, implying that increased investment in forestry fixed assets positively influences the technological content of forest products, thereby contributing to improvements in product quality. Conversely, there exists a negative correlation between the number of forestry practitioners and the technological sophistication of timber industry exports, primarily due to the disruptive impact of fluctuations in the workforce on forest product production, which adversely affects product quality enhancement.

The temporal scope of this article spans the years 2012 to 2021, constituting a decade of analysis. Notably, the COVID-19 pandemic, which emerged and persisted throughout 2020, precipitated an economic crisis across numerous sectors, leading to substantial deviations in certain data points relative to the years preceding and following this period. Such discrepancies may have influenced the empirical outcomes of the study. Consequently, to ensure the validity of the regression results, data from 2020 was excluded, and the analysis focused on the remaining nine years of timber industry agglomeration and product quality data. As illustrated in Column (2) of Table 4, the significance and direction of the regression models remained largely consistent before and after the exclusion of 2020, thereby underscoring the robustness of the research findings.

The disparity in regional economic development can result in variations in several factors, including economic status, talent retention, and infrastructure, subsequently affecting the degree of agglomeration within the timber industry. Furthermore, the distinct characteristics of different regional environments may affect the relationship between industrial agglomeration and the technological advancement of timber products. Thus, investigating is essential for the potential regional heterogeneity. The empirical results reveal significant regional differences in the impact of timber industry agglomeration on product quality. Specifically, the central region exhibits a stronger positive correlation between agglomeration and product quality, while the western region shows a negative correlation. This divergence can be attributed to several factors. The central region benefits from higher forest coverage rates, increased investment in fixed assets, and a larger number of forestry practitioners, all of which collectively enhance the impact of agglomeration on product quality. Additionally, the central region enjoys better infrastructure and more supportive policies compared to the western region, facilitating the agglomeration of timber-related enterprises and improving product quality. In contrast, the western region’s weaker economic base and limited infrastructure make it difficult to achieve the economies of scale necessary for agglomeration to positively impact product quality. The eastern region, despite its advanced economic development, shows an insignificant positive effect, which can be attributed to the region’s focus on tertiary industries and the diminishing returns from further agglomeration in the timber industry.

In accordance with the classification provided by the National Development and Reform Commission, existing provincial data were categorized into three regions — East, Central, and West — based on their economic development levels, and regression analyses were conducted. The findings are detailed in Columns (2), (3), and (4) of Table 5.

Focusing on the eastern region, the Leading Economic Indicator (LEI) coefficient for timber industry agglomeration shows an insignificant positive effect. This can be attributed to several factors: including the region’s highest economic development, the growth rate of the timber industry in the eastern region has gradually diminished. As a result, the impact of timber industry agglomeration on the enhancement of timber product quality has become increasingly negligible over the past decade. A comparative analysis of the benchmark model and the regression outcomes for the central region indicates that the leading variable, the Leading Economic Indicator (LEI), demonstrates a statistically significant positive effect at the 1% significance level. Notably, the coefficient associated with the core variable in the central region is greater than that observed at the national level, suggesting that timber industry agglomeration has a more pronounced positive influence on the quality of forest products in the central region. Despite the central region’s economic development level being marginally lower than that of the eastern region, it benefits from a higher forest coverage rate, increased investment in forestry fixed assets, and a greater number of forestry practitioners. These factors collectively enhance the impact of timber industry agglomeration on product quality.

To further elevate the quality of forest products, additional policy support is imperative for the central region to additional policy support aimed at bolstering the competitiveness of its timber industry agglomeration. Such measures would attract a greater number of timber-related enterprises to the region, thereby better supporting the manufacturing sector. Enhancements in production efficiency and product quality could subsequently stimulate comprehensive development within the central region.

Conversely, the LEI coefficient for timber industry agglomeration in the western region reveals a significant negative effect at the 1% level. This adverse outcome can be attributed to several factors, including the region’s relatively weak economic conditions, insufficient infrastructure, and a lack of supportive economic policies. These challenges have resulted in persistently low efficiency within the timber industry over time. Consequently, the level of agglomeration remains low, preventing the timber industry in the western region from achieving the economies of scale necessary to positively influence the quality of forest products.

In order to address potential endogeneity concerns within the empirical analysis and to enhance the reliability of the findings, this study adopts the methodology proposed by Zhang et al. (2020) using the lagged value of the timber industry agglomeration level (LEI-1) as an instrumental variable. The empirical findings are detailed in Column (2) of Table 6, which align with the primary regression outcomes presented in Column (3), thereby confirming the validity of the instrumental variable employed. The results indicate that timber industry agglomeration continues to significantly enhance the quality of forest products, suggesting that the empirical findings remain robust even after accounting for potential endogeneity issues. This reinforces the conclusion of the study, which asserts that timber industry agglomeration positively influences the quality of forest products in China.

This study commences with a comprehensive review of the relevant literature regarding the agglomeration of the timber industry and the quality of forest products. It theoretically examines the impact of timber industry agglomeration on the quality of forest products. Utilizing data from 27 provinces over the period from 2012 to 2021, this study conducts quantitative measurements and empirical analyses.

Firstly, the evaluations and analyses conducted across the 27 provinces indicate that the overall level of agglomeration within China’s timber industry is relatively low. However, notable regional concentrations of industrial activity are evident, particularly in the three northeastern provinces of Liaoning, Jilin, and Heilongjiang, where the location quotient index exceeds 1.25, signifying a substantial degree of timber industry agglomeration. Additionally, the Yangtze River Delta region, primarily comprising Jiangsu and Fujian provinces, also demonstrates a comparatively high level of timber industry concentration.

Moreover, the technological complexity of timber industry exports exhibits a general upward trajectory, albeit with some fluctuations. Specifically, it rose from a value of 92.72 in 2012 to 110.78 in 2021, indicating a total enhancement of 18.06 and an average annual growth rate of 1.8%, which can be characterized as relatively modest. The period from 2012 to 2016 witnessed a more rapid growth rate, culminating in a peak of 140.75 in 2016. This acceleration can be attributed to the agglomeration of the timber industry during this timeframe, which facilitated the adoption of advanced production technologies and equipment. Such advancements significantly improved the efficiency of timber processing and production, minimized resource wastage and human errors, and incentivized enterprises to augment their research and development investments. Consequently, this environment fostered technological innovation and process enhancements, resulting in the creation of timber products with distinctive attributes and increased value. The improvements in production efficiency and quality, along with product innovation, were further supported by the agglomeration phenomenon. Moreover, this agglomeration contributed to brand development and marketing initiatives, enabling enterprises to cultivate brand identities, broaden market channels, and enhance product visibility and market share, thereby establishing a more robust foundation for the sustainable development of timber products.

Third, the empirical results show that there are differences in the agglomeration level of the timber industry and the quality of forest products among provinces and regions, but the overall agglomeration of the timber industry generates positive externalities, improves the quality of forest products, and further enhances the competitiveness of China’s timber industry. Through comparative analysis, it is found that the conclusions of this study are consistent with the views in the existing literature that emphasize the positive impact of industrial agglomeration on product quality. For example, the studies by Smith and Taylor (2019), as well as Hong and Bei (2020), also show that agglomeration economies can significantly improve the quality of export products. However, this study expands the existing literature by specifically focusing on China’s timber industry and identifying regional differences in the impact of industrial agglomeration on product quality, especially that the agglomeration of the timber industry in the central region has a particularly significant role in improving the quality of forest products. Subsequent robustness tests have verified the validity of these empirical results.

Firstly, the successful transformation of Northeast China from a significant national timber production hub to an ecological barrier for the northern region serves as a valuable case study for enhancing the competitive advantages of forest products in central China and promoting forestry development in the western region. Based on our empirical findings, we recommend the following specific actions: Initially, while the eastern region has redirected its economic development towards tertiary industries, it can still disseminate its historical experiences, particularly those derived from Northeast China, which currently exhibits robust economic development in forest products. This process should include the transfer of technology and information resources to the central and western regions. For example, establishing technology transfer centers and information-sharing platforms can facilitate the diffusion of advanced technologies and management practices. Furthermore, the central region should leverage its local timber resource advantages by adopting revitalization strategies from Northeast China’s timber industry, thereby converting resource endowments into industrial strengths. Specifically, the central region can focus on developing specialized timber processing zones with modern infrastructure and support services to attract more enterprises and enhance agglomeration effects. Finally, the western region should implement customized strategies that align with local resource advantages. Regions abundant in timber resources should actively develop their timber industries, utilizing clustering advantages and best practices to stimulate local economic revitalization. For instance, the western region can prioritize the development of eco-friendly timber products and sustainable forestry practices to meet the growing demand for green products.

Furthermore, it is imperative for the government to enhance the scale and efficiency of timber industry agglomeration through targeted policy initiatives and financial support. By investing in the infrastructure necessary for timber industry clusters, the government can effectively cultivate a conducive environment for development, thereby facilitating both industrial expansion and efficiency improvements. Such measures are likely to enhance the competitiveness and sustainability of the timber sector, ultimately generating increased opportunities for economic growth and job creation. Additionally, by augmenting funding for research and development in forest products, the government can incentivize enterprises to engage in technological research and innovation initiatives. Financial support can further strengthen the agglomeration of the timber industry by advancing the establishment of timber industrial parks, promoting the development and enhancement of infrastructure, and facilitating technological advancements. Through the provision of financial assistance, the government can assist timber industry clusters in achieving greater completeness within their industrial chains and in reducing production costs, thereby enhancing both the scale and efficiency of the timber industry. Hou et al. (2023) explored the mechanism through which industrial agglomeration affects the global value chain position of Chinese wood enterprises and identified the realization path. Their study emphasized that enhancing the technological innovation capacity and management level of enterprises within timber industry clusters is crucial for improving the global value chain position of these enterprises. This can be achieved through policy support, financial incentives, and the establishment of collaborative innovation platforms. Drawing on their insights, policymakers can develop targeted strategies to strengthen the linkages between timber industry clusters and related support industries, improve the technological innovation capacity and management level of enterprises within the clusters, and ultimately boost the quality and competitiveness of forest products.

Thirdly, it is essential for the timber industry to enhance regional cooperation and coordination in order to optimize resource utilization efficiency. Relevant functional departments in both the eastern and western regions should intensify their collaborative efforts, thereby promoting intra-regional partnerships that aim to improve the efficiency of timber resource utilization and elevate the quality of timber products. Concurrently, it is essential to foster synergies between the timber industry and associated sectors to further expand the scale and efficiency of timber industry agglomeration. Collaboration with related industries, such as construction, furniture manufacturing, and pulp production, should be encouraged to facilitate the extension of industrial chains and promote mutual benefits. By cultivating inter-industry cooperation and coordination, governmental entities can enhance the output value and competitiveness of the timber industry, ultimately achieving large-scale production and significant efficiency improvements.